This paper presents a concept for using road wetness information in an all-wheel-drive (AWD) control that distributes drive torques in the longitudinal direction. Driving on wet roads requires special attention. Not only does the road surface friction coefficient decrease, but driving dynamics targets must be adjusted to prevent vehicle instability under wet conditions. As an exemplary application, the otherwise generic control concept is implemented on an AWD vehicle with a torque-on-demand transfer case. Therefore, the AWD topology of a drive train with a torque-on-demand transfer case is analysed in advance in terms of occurring torques and rotational speeds. In the fol-lowing, the vehicle dynamics goals for driving in wet road conditions are described—divided into primary and secondary goals. Starting from a state-of-the art AWD control, an adaptive control strategy is derived by superimposing a wetness coordination unit. With the knowledge of occurring road wetness, this unit adapts newly introduced parameters in order to meet the target driving behaviour under wet conditions. Lastly, the derived AWD control is implemented into a 14-DOF, non-linear vehicle model in Matlab/Simulink, which is used as a virtual plant. The performance of the developed concept is assessed by the driving maneuver “Power On Cornering“ (PON), which means an acceleration out of steady-state circular motion. As its essential benefit, the AWD control enables a maximum spread between driving stability, agility and traction under combined dynamics when using wetness information. The newly introduced wetness coordination unit uses only a few additional and physically interpretable key parameters for this purpose, without significantly increasing the controller complexity. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

For the automated generation of simulation environments in the context of inland waterways navigation, a toolchain for the reconstruction of roadside buildings is used for the first time in this field. It was first implemented and tested for the reconstruction of roadside buildings. The toolchain uses data of a stereo camera to automatically generate models of the surrounding objects. This contribution describes the major changes that have to be made to adapt the toolchain to the changed environment. An unmanned aerial vehicle (UAV) is used to take images of specific objects. Due to the limited space on this UAV, only the supplied camera is used. Thus, the further steps in the toolchain have to be adapted. For the evaluation of the resulting model quality images of two bridges are considered. The implemented programs Metashape and Meshroom are compared with each other in terms of quality and computational effort. It is shown that the resulting model quality is better by using the program Metashape. Regarding the computational effort, the necessary time as well as the CPU and GPU utilization are reviewed. Although the GPU utilization is similar, Metashape outperforms Meshroom in terms of CPU utilization and total processing time. Furthermore, two different image recording methods are compared. On the one hand, models are reconstructed from only the top view. On the other hand, a tilted viewing angle with images from both sides of the bridges is used. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

The high number of devices with limited computational resources as well as limited communication resources are two characteristics of the Industrial Internet of Things (IIoT). With Industry 4.0 emerges a strong demand for data processing in the edge, constrained primarily by the limited available resources. In industry, deep reinforcement learning (DRL) is increasingly used in robotics, job shop scheduling and supply chain. In this work, DRL is applied for intelligent resource allocation for industrial edge devices. An optimal usage of available resources of the IIoT devices should be achieved. Due to the structure of IIoT systems as well as security aspects, multi-agent systems (MASs) are preferred for decentralized decision-making. In our study, we build a network from physical and virtualized representative IIoT devices. The proposed approach is capable of dealing with several dynamic changes of the target system. Three aspects are considered when evaluating the performance of the MASs: overhead due to the MASs, improvement of the resource usage of the devices as well as latency and error rate. In summary, the agents’ resource usage with respect to traffic, computing resources and time is very low. It was confirmed that the agents not only achieve the desired results in training but also that the learned behavior is transferable to a real system. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

The use of virtual sensors in vehicles represents a cost-effective alternative to the installation of physical hardware. In addition to physical models resulting from theoretical modeling, artificial intelligence and machine learning approaches are increasingly used, which incorporate experimental modeling. Due to the resulting black-box characteristics, virtual sensors based on artificial intelligence are not fully reliable, which can have fatal consequences in safety-critical applications. Therefore, a hybrid method is presented that safeguards the reliability of artificial intelligence-based estimations. The application example is the state estimation of the vehicle roll angle. The state estimation is coupled with a central predictive vehicle dynamics control. The implementation and validation is performed by a co-simulation between IPG CarMaker and MATLAB/Simulink. By using the hybrid method, unreliable estimations by the artificial intelligence-based model resulting from erroneous input signals are detected and handled. Thus, a valid and reliable state estimate is available throughout. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

Evaluation of the steering wheel control is important to optimise the posture of the driver. The purpose of this study was to determine the relationship between muscle contraction at the shoulder and anthropometric variables when performing steering wheel task. Participants were recruited to perform multiple steering wheel actions. The surface electromyogram (SEMG) evaluation and anthropometric parameter measurement of individuals were recorded simultaneously during the experiment. For the statistical analysis, the anthropometric parameter was selected as an independent variable, while muscle activity based on SEMG measurement was chosen as the dependent variable. The results reveal that the left deltoid muscle showed the highest contraction at the right turn with high degree of turning. The SEMG and anthropometric data were positively correlated, and the predictive model shows the validity of the proposed model with the R2 value nearly 0.50. This finding recommends that driver’s anthropometric parameter may provide a good reference in a real driving task for controlling the steering wheel. Thus, some of potential utilization from this research is the optimizing in changing the vehicle design for allowing an independent adjustment to the relative distance between the driver seat and the steering wheel. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

This paper aims to assess the fatigue reliability of random loading signals of a suspension coil spring using probabilistic approaches. Strain time histories were acquired while the car was travelling on different road conditions (i.e., in a rural area, in an industrial area, on a university campus, on a highway and on a newly constructed road). Fatigue lives were predicted from the strain histories and fitted into probability density functions. Lognormal distribution was found to be an appropriate way to represent fatigue data. Next, the reliability function and mean-cycles-to-failure (MCTF) were determined. The results indicated that fatigue reliability rapidly deteriorated under rural road conditions, which resulted in a short MCTF of 104 cycles. Meanwhile, the new road signals had the longest MCTF of about 108 cycles. Accordingly, this is due to the rural road having the most surface irregularities, which caused more severe fatigue damage to the coil spring. This study contributed to a greater in-depth understanding of the effect of loading signals on fatigue reliability. This is essential in determining the appropriate service life of the coil spring during its production to ensure vehicle safety and reduce maintenance costs. © 2021, The Korean Society of Mechanical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature.

A growing interest in Electromechanical Brakes (EMBs) is discernible in the automotive industry. Nevertheless, no EMBs have ever been deployed for series production, although countless publications have been made, and patents have been filed. One reason for this is the need for the optimization of functional safety. Due to the missing mechanical/hydraulic link between the driver and the actuator, sophisticated concepts need to be elaborated upon. This paper presents the current state of the art of safety concepts for EMB systems (only publicly available publications are reviewed). An analysis of current regulatory and safety requirements is conducted to provide a base for design options. These design options are explored on the basis of an extensive patent and literature research. The various discovered designs are summarized and analyzed according to their (a) EMB actuators; (b) control topology; (c) energy supply; and (d) communication architecture. This paper concludes by revealing the weak points of the current systems. © 2022 by the authors.

This paper describes the conception and implementation of the research and control center VeLABi for the development of automation functions up to a fully autonomous inland vessel. A high-tech simulator was designed and built as the central component of VeLABi. It allows simulative developments and demonstrations of automation functions. Furthermore, ongoing research activities aim at transforming the simulator into a remote operators' stand to remotely control inland vessels and, in case of an autonomous vessel, to provide corresponding monitoring and emergency intervention functions. © 2022 Walter de Gruyter GmbH, Berlin/Boston.

This paper presents a microscopic vehicle guidance model which adapts to different levels of vehicle automation. Independent of the vehicle, the driver model built is different from the common microscopic simulation models that regard the driver and the vehicle as a unit. The term “Vehicle Guidance Model” covers, here, both the human driver as well as a combination of human driver and driver assistance system up to fully autonomously operated vehicles without a (human) driver. Therefore, the vehicle guidance model can be combined with different kinds of vehicle models. As a result, the combination of different types of driver (human/machine) and different types of vehicle (internal combustion engine/electric) can be simulated. Mainly two parts constitute the vehicle guidance model in this paper: The first part is a traditional microscopic car-following model adjusted according to different degrees of automation level. The adjusted model represents the automation level for the present and the near and the more distant future. The second part is a fuzzy control model that describes how humans adjust the pedal position when they want to reach a target speed with their vehicle. An experiment with 34 subjects was carried out with a driving simulator based on the experimental data and the fuzzy control strategy was determined. Finally, when comparing the simulated model data and actual driving data, it is found that the fuzzy model for the human driver can reproduce the behavior of human participants almost accurately. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Generalized Learning Vector Quantization methods are a powerful and robust approach for classification tasks. They compare incoming samples with representative prototypes for each target class. While prototypes are physically interpretable, they do not account for changes in the environment. We propose a novel framework for the incorporation of context information into prototype generation. We can model dependencies in a modular way ranging from polynomials to neural networks. Evaluations on artificial and real-world datasets show an increase in performance and meaningful prototype adaptations. © 2021 ESANN Intelligence and Machine Learning. All rights reserved.

BACKGROUND: With the development of autonomous driving, the occupants' comfort perception and their activities during the drive are becoming increasingly the focus of research. Especially in one of the first applications, a drive on a motorway, vertical dynamics play a major role. OBJECTIVE: To be able to robustly objectify ride comfort, better models need to be developed. Initial studies have shown, that the current ISO-2631 standard creates good results in the objectification and can be regarded as benchmark. METHODS: To increase the accuracy in objectification, an extended model with the occupants' head as additional measuring point is introduced. Instead of the known frequency filters, weighting (k-factors) is used to differentiate possible excitations. For comparing the model with the ISO-2631, a simulator study with 5 excitations and 50 inattentive subjects is carried out. RESULTS: Evaluating the study with the ISO-2631, 3 out of 5 excitations indicate a significant difference between the occupant's impression and the calculated comfort value. In comparison the extended model has no significant difference. CONCLUSION: The results further show, that inattentive occupants move their heads significantly more. By measuring accelerations of the head, the extended model creates equivalent or more accurate comfort values than the ISO-2631. © 2021 - IOS Press. All rights reserved.

This work shows the application of artificial neural networks for the control task of the roll angle in passenger cars. The training of the artificial neural network is based on the specific actor-critic reinforcement learning training algorithm. It is implemented and trained utilizing the Python API for TensorFlow and set up in a co-simulation with the vehicle simulation realized in IPG CarMaker via MATLAB/Simulink to enable online learning. Subsequently it is validated in different representative driving maneuvers. For showing the practicability of the resulting neural controller it is also validated for different vehicle classes with respect to their corresponding structure, geometries and components. An analytical approach to adjust the resulting controller to various vehicle bodies dependent on physical correlations is presented. © Springer Nature Switzerland AG 2021.

In this article, we propose a method of combining neural networks (NN) with nonlinear state-space models (SSM). Such model parts that are well understood can be integrated into the state space, while the NN can estimate such parts that are uncertain or hard to model. We apply the method to vehicle state estimation on a race track. Therefore, we derive a nonlinear two-track model with a scaled magic formula and adaptively estimate the tire parameters - stiffnesses and maximum friction potential - with the NN. The results show that the NN is able to reach an excellent estimation performance and generalizes over different model parameters, such as tire type, tread depth, surfaces conditions, and maneuvers. The trained model is furthermore integrated into an Extended Kalman Filter (EKF) to estimate the longitudinal speed, lateral speed, and yaw rate of the vehicle. ©

Considering automated driving, vehicle dynamics control systems are also a crucial aspect. Vehicle dynamics control systems serve as an important influence factor on safety and ride comfort. By reducing the driver’s responsibility through partially or fully automated driving functions, the occupants’ perception of safety and ride comfort changes. Both aspects are focused even more and have to be enhanced. In general, research on vehicle dynamics control systems is a field that has already been well researched. With regard to the mentioned aspects, however, a central control structure features sufficient potential by exploiting synergies. Furthermore, a predictive mode of operation can contribute to achieve these objectives, since the vehicle can act in a predictive manner instead of merely reacting. Consequently, this contribution presents a central predictive control system by means of a non-linear model-based predictive control algorithm. In this context, roll, self-steering and pitch behavior are considered as control objectives. The active roll stabilization demonstrates an excellent control quality with a root mean squared error of 7.6953 × 10−3 rad averaged over both validation maneuvers. Compared to a vehicle utilizing a conventional control approach combined with a skyhook damping, pitching movements are reduced by 19.75%. Furthermore, an understeering behavior is maintained, which corresponds to the self-steering behavior of the passive vehicle. In general, the central predictive control, thus, increases both ride comfort and safety in a holistic way. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

This study aims to compute low-frequency energy with Hölder singularities in vibration signals of suspension system to predict the durability of coil spring. High frequencies in vibrations often had minimal contribution towards fatigue damage due to low amplitude range and thus induce errors in energy analysis of vibration signals. Since traditional low-pass method had not only been ineffective in reducing high frequencies, it also resulted in the loss of signal information. This study had therefore proposed characterising low-frequency energy for road excitations using Hölder singularities and power spectral analyses. Singularities and low-frequency energy of road vibration signals would first be identified through Hölder local regularity analysis. This was then followed by fatigue life prediction using the strain-life approaches (i.e. Coffin-Manson, Morrow and Smith–Watson–Topper models). The energy-based fatigue life prediction models had not only shown good fit with R2 values higher than 0.8, but had also demonstrated an accurate prediction of fatigue life with more than 95% of the data being within the acceptance boundary. The Morrow-based model provided the highest accuracy in fatigue life prediction because of its highest R2 value of 0.8625 and 100% data survival in the fatigue life correlation study. This showed that energy-based fatigue life prediction models provide an accurate and effective prediction of the durability performance. This study proposed a more precise energy characterisation method for energy-based durability prediction of suspension coil spring under random loading conditions. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature.

The purpose of this study is to develop an acceleration-strain conversion model that considers torsional strain and spring curvature effects in inducing strain for a suspension coil spring. Measurements of strain–time histories at the coil spring are limited by complex geometry and insufficient workspace. This condition increases the demand for strain signal generation through multibody dynamics (MBD) simulation, reducing the need for real strain measurement of coil spring. Road tests were conducted to obtain the unsprung mass acceleration and strain signals of a coil spring under four road conditions (rural, industrial, highway, and campus road). Quarter-car suspension MBD simulation was modelled to simulate the deflection of a spring excited under unsprung mass acceleration. By using this model, simulated strain data with similar properties as the experimental data were generated for fatigue life prediction. The predicted fatigue life from the generated strain indicated a good correlation with the experimental fatigue life within the boundary and showed very low normalised root-mean-square error (NRMSE) between 4 × 10−6 and 2 × 10−4. Finally, it is suggested that the acceleration-strain conversion model showed an enhanced performance for producing realistic strain signals in accurately predicting the durability of coil spring. This can, therefore, further reduce the need for real strain measurement at the coil spring that can result in an erroneous signal. © 2020

Highly automated vehicles are regarded as the next revolution of the transport system. Automated vehicles include a spectrum from vehicles with driver assistance systems through to highly automated vehicles. These vehicles will only gradually appear in the overall vehicle fleet. Their impact as part of future traffic is of reference value for transport decision-makers. The present paper starts from assumptions for the shares of vehicles with different levels of automation in 2030 and 2050 (representing the near and far-distant future) and compares the effects of these automated vehicles on traffic flow using microscopic traffic simulations. The simulated vehicles include non-assisted vehicles, semi-automated vehicles with driver assistance systems, and fully autonomous vehicles. To obtain a more realistic result, a traffic scenario of the city of Duisburg is used in this thesis. With the support of the city administration, existing data of the origin/target matrix, detector data including induction loops, and cameras were available. Thus, the data of the origin/target matrix are used to generate the real traffic scenario and the detector data to investigate the accuracy of the generated traffic. The result shows that automated vehicles would have a positive impact on traffic, a proportion of automated vehicles can reduce the average travel time. For areas with different traffic conditions, the degree of impact of automated vehicles can be very different. © 2013 IEEE.

A traffic simulation of the Jianghan Zone in Wuhan, China was carried out. In order to simulate genuine traffic flow without traditional hard-to-implement data collection methods, geographic population distribution data were gathered from the public information and traffic flow was generated by ActivityGen in SUMO (Simulation of Urban Mobility). For the sake of discovering the accuracy of the simulated traffic, real-time road condition and traffic prediction based on previous data on same time of each road in this area was compared. The results show that traffic flow generated from geographic population distribution data has referential meanings and with more detailed model classification, simulated traffic data can be closer to real conditions. This may offer a new way to generate traffic flow for researchers working in traffic simulation area. Further improvement of the accuracy in traffic flow generation by geographic population needs to pay more attention on special places like hospital and train stations. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

This article presents the experience of building a simulation scenario of the whole city of Duisburg using real traffic data. The establishment of the simulation scenario is based on road network and traffic volume. In most cases, it is hard to collect all data sources with high precision. Moreover, it is time-consuming to set up a realistic traffic scenario. Even with available data, conversion, calibration, and validation all take a large effort. With the increase of the respective simulation area, the difficulty and workload rise. In this study, a simulation scenario of the whole city of Duisburg with the road network area of 232 km2 and Origin/Destination (OD) matrix area over 800 km2 was established in the software package SUMO. Four cases with different networks and traffic volumes were built and compared with real traffic data collected from induction loops. The percentage of simulated traffic volume in real traffic volume range can be up to 72.22%. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Data-driven models are obtained purely from data without complex theoretical modeling and without explicit model knowledge. This results in black box models whose traceability and reliability constitute a major challenge. This contribution addressed this issue and presents a novel hybrid estimation method, which enhances the reliability of artificial neural networks. Within this method, a simple model based on physical knowledge secures a model based on an artificial neural network. An unscented Kalman filter realizes the interaction of the two individual models. Thereby, a confidence level determines which model is trusted to a greater extent or even entirely. As part of the method for adjusting this confidence level, the input variables of the artificial neural network are related to the data used in training. The more often the artificial neural network has encountered a situation in the training process, the greater the confidence level will be. Finally, the confidence level is used to set the covariances of the unscented Kalman filter. In this contribution, the method is presented using the application of roll angle estimation for passenger cars. By using the hybrid method the reliability of the estimation is increased in comparison to the artificial neural network. For this purpose, sensor malfunctions as well as a sensor failure are simulated. These disturbances are compensated by the introduced method. In addition, the hybrid state estimator increases the estimation quality compared to the individual estimators. The proposed method can be applied to any problem, where knowledge-based models are available to secure data-driven models. IEEE

Software sensors are playing an increasingly important role in current vehicle develop-ment. Such soft sensors can be based on both physical modeling and data‐based modeling. Data-driven modeling is based on building a model purely on captured data which means that no system knowledge is required for the application. At the same time, hyperparameters have a particularly large influence on the quality of the model. These parameters influence the architecture and the training process of the machine learning algorithm. This paper deals with the comparison of different hyperparameter optimization methods for the design of a roll angle estimator based on an artificial neural network. The comparison is drawn based on a pre‐generated simulation data set cre-ated with ISO standard driving maneuvers. Four different optimization methods are used for the comparison. Random Search and Hyperband are two similar methods based purely on randomness, whereas Bayesian Optimization and the genetic algorithm are knowledge‐based methods, i.e., they process information from previous iterations. The objective function for all optimization methods consists of the root mean square error of the training process and the reference data generated in the simulation. To guarantee a meaningful result, k‐fold cross‐validation is integrated for the training process. Finally, all methods are applied to the predefined parameter space. It is shown that the knowledge‐based methods lead to better results. In particular, the Genetic Algorithm leads to prom-ising solutions in this application. © 2021 by the authors. Li-censee MDPI, Basel, Switzerland.

In the industrial Internet of Things (IIoT), a high number of devices with limited resources, like computational power, memory, bandwidth and, in case of wireless sensor networks, also energy, communicate. At the same time, the amount of data as well as the demand for data processing in the edge is rapidly increasing. To enable Industry 4.0 (I4.0) and the IIoT, an intelligent resource allocation is required to make optimal use of the available resources. For this purpose, a multi-agent system (MAS) based on deep reinforcement learning (DRL) is proposed. Multi-agent reinforcement learning (MARL) is already taken into account in different communication networks, e.g. for intelligent routing. Despite its great potential, little attention is paid to these methods in industry so far. In this work, DRL is applied for resource allocation and load balancing for industrial edge computing. An optimal usage of the available resources of the IIoT devices should be achieved. Due to the structure of IIoT systems as well as for security reasons, a MAS is preferred for decentralized decision making. In subsequent steps, it is planned to add and remove devices during runtime, to change the number of tasks to be executed as well as evaluations on single- and multi-policy-approaches. The following aspects will be considered for evaluation: (1) improvement of the resource usage of the devices and (2) overhead due to the MAS. © 2021 IEEE.

This study aims to establish a fatigue life predictive model based on multifractality of road excitations using neuro-fuzzy method to assess the durability of suspension spring. Traditional durability analysis in time domain is complicated and time-consuming due to the needs of large data amount. Thus, it is an idea to adopt an adaptive neuro-fuzzy inference system (ANFIS) for relating the performance of coil spring to the multifractal properties of road excitations, giving a meaningful fatigue life prediction. Different membership function numbers were tested to obtain the optimum membership function number. During the data training process, the checking data was used to test the trained model each Epoch of training for overfitting detection. As a result, the Morrow-based fatigue life prediction model was found to give the most suitable result with three membership functions. The SWT-based model needed five membership functions due to nonlinear properties in the SWT-based fatigue life data. Training process of Morrow-based-ANFIS was stopped at Epoch 8 given its lowest checking root-mean-square-error of 0.6953. SWT-based model recorded a higher error of 0.7940. The neuro-fuzzy models gave accurate fatigue life predictions with 96 % of the data distributed within the acceptance boundary, hence, contributing to an acceptable assessment of coil spring fatigue life based on load multifractality. This study had shown a nonlinear relationship between road multifractality and durability performance of coil spring. Multifractality had been proven an important feature to characterise various road excitations for durability prediction. © 2021, The Korean Society of Mechanical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature.

Due to the big advantages like immutable, decentralized data record and smart contracts (SC), distributed ledger technologies (DLT) gain strongly in importance in a wide variety of areas. With the fourth industrial revolution and the industrial internet of things (IIoT), new business models and technology fields based on data-driven approaches evolve and lead to the demand for trustworthy data and secure data exchange in a decentralized system. While a comprehensive number of possible industrial use cases exists, there is a lack of experimental studies on their applicability on IIoT devices. Available performance tests mostly focus on the performance of the DLT but not on their influence on the resources of the IIoT device. Furthermore, one very important DLT, namely IOTA, which is explicitly designed for application in IoT environments, did not receive much attention yet. This was due to two major drawbacks, namely the need for a centralized instance and the lack of SC functionality in the first IOTA version compared to other DLTs such as Hyperledger. The recently published version IOTA Coordicide improved in both. This paper presents detailed results from two industrial use cases and experiments on a private DLT network based on IOTA in IIoT, focusing on the resource demands for the IIoT devices with different network setups. The results confirm the suitability of IOTA for IIoT devices. Furthermore, an overview of the required resources of the IIoT devices with different transaction rates and networks sizes is given. © 2021 IEEE.

Both data compression and anomaly detection are very deeply studied areas for the last decades and gain significance for the Internet of Things (IoT), especially industrial IoT (IIoT). Due to the advantages like fewer latency and security aspects, edge computing is often preferred to cloud solutions. While the amount of data as well as the demand for edge data processing increases, resources like bandwidth, computational performance, memory and, in case of Wireless Sensor Networks (WSN), also energy are still limited. This leads primarily to a trade-off between maximum data reduction, information extraction and minimal computational effort. Often, both data compression and anomaly detection are required. This paper demonstrates additional benefits if already one is implemented. Although in many cases the algorithms for both are based on the same models, there are almost no studies on their combined use. In this work, a perspective on the efficiency of combined model usage with only different interpretations for anomaly detection and data compression is proposed. Concrete examples for selected models and the detection of different kinds of anomalies are given. Finally, an outlook on the planned future work is given. © 2021 IEEE.

Driving in a simulator might induce stress because of the confrontation with new environments, dealing with new technologies, and experience with symptoms of simulator sickness, which, in turn, may influence individuals’ driving performance. The present study aims to provide a better understanding of the association between simulated environments and humans’ stress level under consideration of age, simulator adaptation, experience with simulator sickness, and driving performance. Data from 164 participants (M = 61.62 years, SD = 12.66 years, ranging from 25 to 89 years, 42 women) were analyzed in the present study. During three measurement times, participants completed an advance first simulator drive (T0), followed by an online survey, assessing experience with simulator sickness (T1), and a second simulator drive (T2) including pre- and post-cortisol measurements. The hypothesized model shows no correlations of driving performance with experience with simulator sickness or stress level before and after a further simulator drive. Beyond the effect of age, previous experience with simulator sickness does further account for stress-level changes following a simulated drive but current driving performance did not. The present study provides relevant findings for future studies in the field of simulated environments. Copyright © 2021 Liebherr, Mueller, Schweig, Maas, Schramm and Brand.

With the development of microscopic traffic simulation and computer performance, traffic simulation is no longer limited to solving problems in the field of transportation. Microscopic traffic simulation can provide massive details, which can provide more possibilities for many studies. For studying traffic emissions, fuel consumptions and how the vehicle dynamic characteristics will affect traffic flow, a series of driver model and vehicle model for traffic simulation were built in this work. A traffic scenario of part of City Duisburg was also built in Simulation of Urban MObility (SUMO), which took advantage of the road network from Open Street Map (OSM) and traffic counter data provided by the municipality of Duisburg. As the vehicle models in SUMO are quite abstract and simplified, more detailed driver model and vehicle models were developed and applied in the simulation. The driver model is a fuzzy logic model based on behavior data of human drivers collected from driving simulator. In the vehicle models the characteristics of powertrains, braking systems, and the changes of driving resistances are all considered. These models make the simulated vehicles more similar to human drivers and realistic vehicles. The average fuel consumptions in this scenario was studied and the results with different models were also compared. © 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

The reliability of electrical connectors is essential for modern electrical on-board systems in cars, especially for automated driving vehicles. Electrical connectors in cars are stressed by harsh automotive application conditions such as vibrations and thermal loadings. These factors may induce fretting corrosion which influence the lifetime of electrical systems. Multivariable lifetime tests are useful tools to validate the performance of electrical systems under mechanical and thermal loadings. This study investigated the failure mode and surface degradation of electrical connectors stressed by accelerated multivariable lifetime tests. In order to gain a better understanding with the failed and non-failed surfaces of stressed electrical connectors, such connectors are analyzed by using various methods. The surfaces were characterized by tools like 3D-Topography-Measurements, Light-Microscopy, Scanning-Electron-Micro-scope and Electron-Microanalysis. © 2021 IEEE.

Intelligent vehicles gradually enter the vehicular fleet with advanced driver-assistance technologies. Their impact on traffic should, therefore, be considered by transportation decision-makers. This paper examines the effect of vehicles with different levels of automation on traffic flow, such as non-assisted vehicles, vehicles with driver assistance systems, and fully autonomous vehicles. The accuracy of the examined traffic scenario is also an important factor in microscopic traffic simulation. In this paper, the central part of the city of Duisburg, Duisburg’s inner ring, is chosen for the traffic scenario. Through the cooperation with local government, official data of Origin/Destination matrices, induction loops, and traffic light plans are provided for this work. Thus, traffic demand from Origin/Destination matrices and induction loops are generated and compared, respectively. Finally, vehicles with different levels of automation are simulated in the Duisburg inner ring scenario. © 2020 by the authors. Li-censee MDPI, Basel, Switzerland.

The aim of this study is to identify the issues that arise when measuring a strain signal in order to characterise fatigue failure. In traditional methods, acquisition of a strain signal is constrained due to the presence of errors, time-consuming process and associated high cost. In this study, a new method for generating strain signals based on computer simulation was proposed. A strain gauge was positioned near the citical area pertaining to an automotive coil spring driven on road surfaces in order to measure strain signals. The strain signals were utilised for the inputs in the simulation. For validation purposes, the actual and simulated strain signals were examined by performing fatigue tests. The actual and simulated urban strain signals, respectively, required 412 and 415 reversals of blocks. For the rural road, the fatigue life was 137 and 139 reversals of blocks, respectively, for the actual and simulated strain signals. These indicated that simulated strain signals were accurately generated, providing a minimum fatigue life deviation, which was lower than 1.5 %. By developing the strain signals of a component through simulation, its integrity and fatigue failure can adequately be determined, thereby, saving the cost associated with operation and maintenance. Thus, the simulation is expected to assist the automotive industries involved with strain signal acquisition. © The Authors 2020

This paper aims to study the identification of bumps in vibrational signals and develop bump-energybased durability predictive models for a suspension coil spring. The bump energy of the loading signal is affected by high frequency noises and can lead to inaccurate results. Therefore, it is necessary to eliminate high frequency noise during bump identification. Local regularity analysis was employed to determine the singular points in road signals. Bump signals were then reconstructed from these singular points. Subsequently, bump-energy-based models were developed by correlating with the fatigue lives estimated using the Coffin-Manson, Morrow and Smith-Watson-Topper strain-life models. The results show that the bump signals extracted from the road excitations had a frequency band within 0-50 Hz, indicating that the high frequency noises had been successfully removed during extraction of the bumps. The bump-energy-based models predicted a fatigue life ranging from 3.98x104 to 4x109 cycles within a 95% confidence interval, where the Coffin-Manson-based model showed the highest fatigue life. This is because the Coffin-Manson model did not consider the mean stress effects. When compared with the experimental results, the Coffin-Manson-based model indicates the highest accuracy, given its highest R2 of 0.948. The bump-energy-based models developed in this study contributed an accurate durability prediction of coil springs. © Universiti Tun Hussein Onn Malaysia Publisher's Office.

This paper presents a regression analysis of uniaxial and multiaxial fatigue life for automobile coil spring under various road excitations. Coil spring is a suspension component with complex geometry and shear loading which is applied during operating conditions. Hence, uniaxial strain measurement for durability assessment of coil spring is insufficient because the loadings are non-proportional. Rosette strain signals of coil spring under five different road conditions were obtained and used as input to uniaxial strain-life and multiaxial critical plane models to predict the spring fatigue life. During the multiaxial fatigue analysis, the strain biaxiality ratio of range 0.3 to 0.5 indicates the loadings as out-of-phase. Through a simple linear regression method, a linear regression model between uniaxial and multiaxial fatigue life were obtained with coefficient of determination value as high as 0.8696. This model provides significant contribution through correlating uniaxial to multiaxial fatigue life. Hence, uniaxial fatigue life predictions could be approximated to multiaxial for more conservative analysis through the application of generated linear models. © 2019, The Society for Experimental Mechanics, Inc.

This study presents the characterisation of multifractality of road excitation time series under different road conditions for prediction of the durability of a suspension coil spring. Road excitation acceleration signals and strain signals were acquired from the suspension system of a vehicle travelling under different road conditions. Multifractal analysis revealed a higher tendency to multifractality in road excitations with more surface irregularities. With the fatigue lives predicted by different strain-life models (Coffin-Manson, Morrow, and Smith-Watson-Topper), fatigue life prediction linear models based on road multifractality were established. It was found that the Morrow-based linear model gave the most accurate estimation of fatigue life, with the highest R2 of 0.8762. In conclusion, the models for the prediction of coil springs’ fatigue life based on road multifractality provide an accurate and faster alternative for durability assessment of coil springs. This can significantly facilitate the design process of coil springs to meet industry requirements. © 2020 Elsevier Ltd

This research recognizes the impact of lower cycle amplitude discharge on fatigue failure. Here, the strain data were measured at a lower arm when the vehicle was driven at a particular speed on different road surfaces. Thereafter, the strain data in a time domain was transferred to the time-requency domain for determining the wavelet-based signal energies. The lower amplitudes on the strain data were recognized and eliminated according to the wavelet energy. The greater amplitudes were removed and put together to create a refined signal. The extraction algorithm reduced the formal signal by over 33% and was able to preserve about 93.2% of fatigue damage. It was noticed that by employing the wavelet-based energy for removing strain data, there was no serious impact on fatigue life. © 2020 Elsevier Ltd

This paper presents an object-oriented modeling method capable of simulating the dynamics including the reaction forces of multibody systems with kinematic loops in hard real-time, called RTOOM. The modeling method describes the system by explicit equations, which can be solved numerically stable with a standard explicit numerical integrator with fixed step size. By knowing the application and the desired accuracy, the model can be adapted to fit the problem. Algebraic loops are resolved with low-pass filters parameterized for the frequency range of the application. Bushings with optimized spring and damping constants are used to avoid iterative methods for solving kinematics loops. For the optimization, a high accurate, non-manipulated and non-real-time multibody model is used. The optimization targets are stability, computing time and accuracy. The double wishbone suspension of the Formula Student racing car A40-02 of the University of Duisburg-Essen is used as an example. It has been successfully proven that a simulation up to 30 Hz with a required step size of 1 ms can be achieved. The simulation results show a very good accuracy up to 15 Hz with a deviation of the force below 4% and the acceleration below 7%. If the parameterization of the bushings remains the same, the accuracy is still acceptable even at higher frequencies. © 2020, Springer Nature Switzerland AG.

In the context of automated driving, the control of vehicle dynamics is one of the important issues. In addition to conventional control strategies, algorithms with predictive working principles are particularly relevant here. Using mathematical models, the future system behavior can be predicted and thus optimally set. The present paper deals with an integrated non-linear model-based predictive vehicle dynamics control, taking into account the roll and pitch behavior of a vehicle. Due to the optimization, such model-based predictive control algorithms usually result in high computation efforts. With respect to this issue, a non-linear model-based predictive control algorithm regarding an integrated vehicle dynamics control is represented by a co-active neuro-fuzzy inference system. The validation of the two vehicle dynamics control algorithms is done with respect to the control quality and the computation effort. © 2020 IEEE.

One of the major research and development efforts in the automotive industry worldwide is the introduction of highly automated driving functions. Due to the high level of complexity, suppliers and manufacturers often wonder whether and to what extent the potential or the effectiveness of newly developed systems can already be estimated a priori in terms of the different requirements in order to make the development process more targeted and thus timelier and also more cost-efficient. In the context of a current research project, that is developing a sensor system for the detection of the road surface condition, it shall be investigated how such a system can be used to improve high-level driving functions. In this paper, as an example to this end, the algorithm of a state-of-the-art adaptive cruise control (ACC) is first extended by a friction coefficient awareness and then tested and examined based on simulations within a complex vehicle model. Subsequently, the system is implemented in a traffic flow simulation and analysed for its impact on road safety via the analysis of corresponding parameters. Copyright © 2020 Inderscience Enterprises Ltd.

Previous studies increasingly report problems with simulator adaptation as well as dropouts. Therefore, the present study aims at better understanding these aspects by considering individual factors, such as age and mental abilities. 414 people were tested with commonly used neuropsychological measures as well as within a driving simulator which consists of a close-to-production vehicle of the compact class. In contrast to previous findings, neither a significant relationship between age and the time of adaptation nor an interaction between age and mental abilities on adaptation time could be identified. However, the time participants spent in the simulator (simulator dropout) significantly correlated with age but not with mental abilities. People who showed no adaptation spent significantly less time in the simulator, because of the occurrence of simulator sickness. Although attention was only mildly associated with the time of simulator adaptation, further research on this linkage is suggested. Practitioner summary: The study at hand clarifies the relevance of considering the process of simulator adaptation within simulator studies. However, the present findings suggest no relation between age and the time of adaptation but with simulator dropouts. Abbreviations: TMT: trail making test; LPS: leistungsprüfsystem; IOP: index of performance; ALFASY: altersgerechte fahrerassistenzsystem (Age-based Driver Assistance Systems). © 2020 Informa UK Limited, trading as Taylor & Francis Group.

This paper presents a supervised machine learning state estimation scheme that is able to estimate the current side-slip angle of a vehicle. It consists of a recurrent neural network with gated recurrent units, an additional input projection and a regression head. This structure has been chosen to limit the computational complexity of the model while preserving the expressiveness of the overall system. It will also be shown how equations of a simplified vehicle model can be incorporated to make use of existing domain knowledge. The results show that the neural network is able to reach an excellent estimation quality while generalizing over different tires, surfaces, and driving situations. Comparisons of different model variants on selected data subsets allow us to draw conclusions on the adaptation to varying parameters and show a quality improvement through the physical model. Evaluations with the mean squared error are complemented by more expressive fit and error plots to give a better understanding of the model behavior. All data for this paper have been collected with a Porsche 911 Turbo (Type 991 II) with a series sensor setup and an additional reference sensor. © 2016 IEEE.

With an increasing number and diversification of powertrain setups, the evaluation of drivability is a major challenge in the vehicle development process. Since comprehensive tests with prototype cars are complex, time-consuming and expensive, using dynamic driving simulators for drivability evaluations is a promising alternative. Currently, driving simulators are not an established tool for drivability evaluations despite offering many advantages. They could enable concept evaluations in early development stages and offer a high degree of reproducibility and controllability regarding the test conditions. One reason for simulators not being utilised for this purpose is the circumstance that certain effects are experienced differently compared to real driving. Therefore, it is important to understand how accelerations are perceived in a simulator and to what extend motion scaling influences the perception. The ability to distinguish between several acceleration profiles resulting from different powertrain setups can be expressed with the just noticeable difference (JND). The JND corresponds to the differential perception threshold and is a suitable measure to classify and assess powertrain modifications. In this paper, the effect of motion scaling on the JND is analysed with a driving simulator study. The aim is to explain the differences in perception between driving simulator studies and real road tests in the context of drivability and to provide guidance for transferring results to real world conditions. © 2019 Elsevier Ltd

This paper presents the compression of strain loading time history of automobile suspension spring by extracting singularities in the signal using Lipschitz regularity analysis. Time histories of suspension spring always contained redundant data that increase the size of the signal and are insignificant to durability analysis. Excessive signal data will cause the analysis to be time consuming and computationally expensive. Hence, elimination of insignificant data is important to improve the efficiency of durability analysis. A strain signal was captured from a suspension spring of a sedan car and analysed with continuous wavelet transform to identify the modulus maxima lines. Holder exponents of each singular point were estimated from the log-log plot of modulus maxima lines. The extracted singularities was compressed and compared against the original signal the determine durability and were compared statistical to determine the characteristics of the signal. A conventional time-domain-based fatigue data editing technique had been performed to compare the effectiveness of Lipschitz-based technique. Results showed that the Lipschitz-based edited signal was only quarter of the original signal length that could retain 100% of fatigue damage of the original signal with less than 5% of difference when compared in terms global statistics. Lipschitz-based technique had outperformed the time-domain-based technique which had shown unacceptable deviations in global statistics. This suggested that the Lipschitz-based singularities extracted using Lipschitz regularity analysis can sufficiently represent a strain loading history without compromising the data behaviours. © Springer Nature Singapore Pte Ltd. 2019.

This paper presents the optimisation of spring fatigue life based on an artificial neural network (ANN) architecture and particle swarm optimisation algorithm (PSO) using ISO 2631 vertical vibration as input. The road-induced vibration of a ground vehicle caused the spring to fail due to fatigue and human discomfort. Hence, there is a need to model the relationship between these two parameters for spring design assistance. Vibration and force signals were extracted from a quarter car model simulation for fatigue life and ISO 2631 vertical vibration estimations. PSO was applied to the datasets for ANN weights and biases adjustments while the mean squared error (MSE) was set as the objective function. For validation purposes, a set of independent datasets was applied to the ANN. The residuals were analysed using Lilliefors normality and error histogram. For prediction accuracy, the predicted fatigue lives were analysed using scatter band approach and compared with traditional trained ANN. The results have shown that most of the PSO-based ANN predicted fatigue lives were in the acceptable region and the root mean square error (RMSE) value of 0.6391 life cycles in natural logarithm was obtained. The PSO-based ANN has shown improved performance compared to the conventional ANN approach in predicting fatigue life. © 2019, KSME & Springer.

The usage of unmanned civil and military drones is steadily rising. Essential components of every multicopter are its electric motors and the electronic speed controllers (ESC). These also determine, besides the flight dynamics and the stability behavior, the flight duration in a crucial way. Therefore in this contribution, an ESC for permanent magnet synchronous motors with a sinusoidal back electromotive force (PMSM) is presented, which was designed, under consideration of all these points. The basis for the precise and dynamic speed control is a high-resolution position sensor in combination with a 72MHz microcontroller. Under these circumstances, the speed control can operate at a high frequency of 20 kHz together with a high resolution. Using a field oriented control in combination with a flatness based control concept theoretically guarantees a maximum torque per ampere and thus a low energy consumption. The required speed signal is gained from the rotor position using a proportional integral observer. An exact asymptotic regulation is achieved implementing a state feedback integral controller. © 2019 EUCA.

This paper discusses the establishment of multiple linear regression (MLR)-based spring durability models for predicting the fatigue life of automotive coil springs based on the vertical vibrations of the vehicle and natural frequencies of the vehicle suspension system. These models were developed in order to simplify the design and development process of vehicle suspension systems, which is both time-intensive and cost-intensive. The simulated force-time histories were processed to obtain the fatigue life of the automotive coil spring based on the strain-life models whereas the acceleration-time histories were weighted according to the ISO-2631-1:1997 standard to determine the vertical vibrations of the vehicle. MLR was used to establish the spring durability models and the goodness of fit, linearity, normality, and homoscedasticity of the models were assessed. The highest coefficient of determination at 0.8820 was obtained for the Morrow MLR-based spring durability model, with the mean square error of 0.5855. The models were validated by comparing the fatigue life values predicted by the models with those predicted from strain measurements. The results show a good agreement between the predicted and experimental values, indicating the suitability of these models in predicting the fatigue life of automotive coil springs. © 2018 Elsevier Ltd

Converging evidence from numerous previous studies highlights the relevance of attention in driving. However, these studies mostly conclude from respective situations or use complex tests that tap into further cognitive processes. Aiming a better understanding of specific attentional domains, we investigated the relation between visual selective attention, auditory selective attention, visual divided attention, switching attentional demands, switching between attributes, switching between rules, vigilance and driving performance in a driving simulator. Furthermore, we tested three-way interaction effects with respective attentional domains, inhibition and working memory. In the present study, 123 participants completed a driving scenario as well as commonly used measures of attention (SwAD-task, Oddball-task, MCST, TMT-B, D2), inhibition (Go/NoGo-task), and working memory (visual digit-span-task). Findings indicate no correlations between the tested attentional domains and driving performance. Furthermore, we found no interaction effects with the attentional domains and the two factors of inhibition and working memory on simulator driving performance. The present findings suggest no possibility to transfer findings from specific attentional domains, as well as the used measures for inhibition, and working memory to peoples' simulator driving performance. Along with previous findings we suggest using rather context-specific tasks than basic neuropsychological measures to quantify specific attentional domains, in order to predict peoples' driving performance. © 2019 The Authors

This paper presents the evaluation of the automobile coil-spring strain-displacement relationship for strain signals generation and fatigue life predictions. The development of a strain and spring vertical displacement relationship is significant because measuring vehicle wheel displacements and forces are complex and costly. Hence, there is a need to estimate the strain data using alternative measurement, such as vibration signals. In this analysis, strain and acceleration data were collected from a vehicle that has travelled on different road conditions. Through the material elastic strain energy and spring potential energy relationship, a coil-spring parameterise strain-displacement relationship has been developed and evaluated using a scatter band and correlation approach. Using this proposed model, the strain time histories were obtained based on acceleration data. For fatigue life analysis, most of the predicted fatigue life was distributed in the acceptable range using the scatter band approach where the data correlated at coefficient of determination value (R 2 ) of 0.8788. With a suitable correlation value, this analysis proposed an alternative strain generation method for suspension coil spring fatigue life prediction, which could significantly shorten the spring development time. © 2019 by the authors. Licensee MDPI, Basel, Switzerland.

This paper presents an evaluation of pruned regression decision tree for spring fatigue life predictions. The inputs for this regression decision tree models are vehicle ISO 2631 vertical vibrations and suspension frequencies. The design process of a coil spring involved many steps which consume many time and efforts. Hence, there is a need to generate a prediction model to assist spring design. Loading time histories were obtained from a quarter car model simulation for spring fatigue life assessment and ISO 2631 vertical vibrations calculations. The obtained force time histories were used to predict fatigue life of spring using strain-life approaches while acceleration time histories were used to obtain ISO 2631 vertical vibration. Together with spring stiffness sensitivities, the spring fatigue life was modelled using regression decision tree and mean squared error of the generated regression decision tree residuals were analysed. Five sets of independent experimental measurement strain time histories were used to validate the fatigue life predictions using a conservative approach. Most of the validation data points have lied beyond the acceptable region. Therefore, these proposed regression tree models are providing a good prediction on spring fatigue life which could shorten the spring design process. © Springer Nature Switzerland AG 2019.

This article presents a hybrid state estimation using vehicle dynamics as an application. The knowledge about the dynamic states are essential in the vehicle. Ultimately, the built-in control algorithms are using these states to exploit safety, comfort, and performance. In most cases, the states of the vehicle are measured directly. Nevertheless, direct measurement is not profitable or difficult to implement for all states of vehicle dynamics. In this case, state estimators are used. In the past, classical approaches such as modelling of the physical systems have been used for estimation. Due to the continuous developments in the field of computing hardware, methods of machine learning can now also be used in this context. The presented article includes artificial neural networks. With this method, a transfer behavior can be mapped without having knowledge about the system to be estimated. A major problem of such artificial neural networks, however, is the traceability as well as checking the robustness for universal use. Therefore, the artificial neural network is coupled with physical knowledge. This results in a hybrid state estimator based on a Kalman filter. This novel hybrid approach is presented using the example of estimating the roll angle of a vehicle. © 2019 IEEE.

The introduction of highly automated driving functions is one of the main research and development efforts in the automotive industry worldwide. In the early stages of the development process, suppliers and manufacturers often wonder whether and to what extend the potential of the systems under development can be estimated in a cheap and timely manner. In the context of a current research project, a sensor system for the detection of the road surface condition is to be developed and it is to be investigated how such a system can be used to improve higher level driving functions. This paper presents how road surface conditions are introduced in various elements of the microscopic traffic simulation such as the actual network, the network editor, a device for detection, and an adaptation of the standard Krauß car following model. It is also shown how the adaptations can subsequently affect traffic scenarios. Furthermore, a summary is given how this preliminary work integrates into the larger scope of using SUMO as a tool in the process of analyzing the effectiveness of a road surface condition sensor. © 2019, EasyChair. All rights reserved.

Today the automotive industry faces a robust trend toward assisted and automated driving. The technology to accomplish this ambition has evolved rapidly over the last few years, and yet there are still a lot of algorithmical challenges left to make an automation of the driving task a safe and comfortable experience. One of the main remaining challenges is the comprehension of the current traffic situation and the anticipation of all traffic participants' future driving behavior, which is needed for the technical system to obtain situation awareness: An indispensable foundation for successful decision-making. In this paper, a prediction framework is presented that is able to infer a driver's maneuver intention. This is achieved via a hybrid Bayesian network whose hidden layers represent a driver's lane contentedness. A pre-training of the network's parameters with simulated data provides for human interpretable parameters even after running the expectation maximization algorithm based on data gathered on German highways. Moreover, the future driving path of any traffic participant is predicted by solving an optimal control problem, whereby the parameters of the optimal control formulation are found via inverse reinforcement learning. © 2019 IEEE.

The use of multivariate time series generation in industrial settings such as the automotive industry continues to increase. The complexity of data analysis requirements in such industries has led to an urgent need to develop effective methods for extracting structural information from data based on the clustering of system behavior time series. Because there are complex interactions between vehicle data variables, the time series clustering of single variables can lead to insufficient results. To the best of our knowledge, only univariate dynamic time warping (DTW) approaches have thus far been applied in an automotive context. To close this research gap, this paper presents a review of generic approaches in multivariate dynamic time warping (MDTW) to determine the most promising approaches for use in the automotive domain. Four approaches are found to be particularly useful for tasks such as the objective assessment of subjective driving perceptions. © 2019 - IOS Press and the authors. All rights reserved.

This article deals with the integration of a neural state estimator into a control environment. A pre-trained recurrent neural roll angle estimator is analyzed in a closed loop environment of a model predictive control. The roll estimator consists of long short-term memory cells and predicts the angle based on the lateral acceleration, the steering wheel angle and the outputs of the control system, which influence active stabilizers and semi-active dampers. The analysis is done in a simulation environment. The artificial neural network that was trained on standard driving maneuvers is tested with independent data, i.e. a drive on a racetrack. To estimate the quality of the neural estimator, it is compared to a classical non-linear roll model that was also used as estimator in the control system when constructing the training data for the network. The results show that the neural estimator can interact with the controller. Especially by comparing the neural and the classical estimator, it can be seen that using the recurrent network to predict the roll angle leads to better control results than using the non-linear roll estimator. © 2019 IEEE.

This article presents a non-linear model-based control approach that specifically influences the vehicle dynamics, in particular the roll and pitch behavior of a passenger car. The aim of such a driving dynamics control is to increase safety in critical areas of vehicle dynamics, and generally to increase the comfort while driving. The roll behavior is defined as a rotation about the vehicle-fixed longitudinal axis. A rotation about the lateral vehicle-fixed axis is described by the pitch behavior. Based on a non-linear predictive control algorithm for active roll stabilization, the pitch behavior is additionally reduced in the control approach presented here. Through the modeled vehicle, equipped with active stabilizers and semi-active shock absorbers, chassis movements and thereby also pitching movements can be reduced especially using the shock absorbers. By considering the pitching behavior in the model-based predictive controller, driving comfort is thus further enhanced. © 2019, Springer-Verlag GmbH Deutschland, ein Teil von Springer Nature.

For efficient suspension development, objective performance indicators are needed which quantify the system with regard to ride comfort. This paper aims to investigate the capabilities of objectification methods from literature and presents a new method for automated and autonomous driving situations. Two studies were conducted in order to investigate objectification methods for ride comfort. In the first study, different objectification methods from literature were tested and compared. Sixteen subjects drove on a country road with a BMW 650i (F06). The subjects had to rate different damper settings which were tested at the same road section. The sensor data was evaluated according to ISO-2631:1997 (equal to VDI-2057:2017), BS 6841:1987, as well as according to the methods of Rericha, Cucuz, Klingner and Hennecke. As subjective ratings imply ordinally scaled data sets, the correlation between the objective values and the subjective ratings is tested with the rank-correlation coefficient Kendalls-Tau and exploratory statistical methods. In the second study, forty participants drove and were driven around country roads in a BMW 135i (F20). The inattentive occupants had to perform an activity on a tablet which distracted them from the current driving situation. The subjects had to rate the ride comfort on four different road sections. Measurements have been conducted with a self-developed body measurement system and a seat pad sensor according to ISO-10326. The measurements and subjective ratings were correlated and statistically analyzed. The results of the first Study show that the ISO-2631, VDI-2057 and BS 6841:1987 perform best under the given test conditions. The method of Klingner shows the best results of the non-standardized methods. The results of the second study show a significant difference in comfort perception between attentive and inattentive occupants. It can also be seen that inattentive occupants show higher values of RMS accelerations measured at the head, which is also visible in the vehicle to head transmissibility. © 2019, Springer-Verlag GmbH Deutschland, ein Teil von Springer Nature.

In this study, hybrid multi-layer perceptron artificial neural network (HMLP ANN) models were developed to predict the fatigue life of automotive coil springs with high accuracy based on the vertical vibrations of the vehicle and natural frequencies of the vehicle suspension system. The design and development of vehicle suspension systems involve numerous steps from conceptual design to prototyping and testing, including fatigue life evaluation and vehicle ride analysis. Optimizing HMLP ANN models will significantly simplify the design and development process, which forms the motivation of this study. Simulations were conducted on a quarter car model to extract the loading signals using the measured acceleration signals and artificial road profiles as inputs. The fatigue life was predicted based on the Coffin-Manson, Morrow, and Smith-Watson-Topper strain-life models whereas the comfort ride index was assessed according to the ISO 2631-1:1997 standard. Various HMLP ANN models were trained using the Levenberg-Marquardt backpropagation algorithm to determine the optimum architectures. The lowest mean square error (0.0117) is obtained for the Morrow HMLP ANN model with three hidden layers. The coefficient of determination values are more than 0.9559, indicating that there is good fit between the training/testing datasets and the data predicted by the optimum HMLP ANN models. These models were validated using the conservative correlation approach and there is good agreement between the targeted and predicted fatigue life values. It can be concluded that the optimum HMLP ANN models are capable of predicting the fatigue life of automotive coil springs with acceptable accuracy. © 2018 Elsevier Ltd

Objective: The present study aims to investigate whether drivers’ age and their experience with driving simulators could explain differences between a subjective estimation of system adaptation and a respective objective systematic measurement. Background: Assessing valid measurements in driving simulators causes concern because driving simulators are not yet as realistic as real on-road driving scenarios. Common methods like pre-defined training sessions and self-appraisals of simulator adaptation might therefore be insufficient to ensure actual valid data. Hence, influential variables on this discrepancy are investigated. Method: In total, N = 203 drivers participated in a training session and a subsequent testing session in a close-to-production driving simulator. Subjective adaptation was estimated by the drivers and an objective adaptation value was gathered on the basis of driving accuracy. The discrepancy between these two measures was calculated and related to age, self-reported driving experience and occurrence of previous adaptation. Results: Subjective adaptation was significantly faster than objective adaptation but neither drivers’ age, experience, nor previous adaptation could explain this discrepancy. Discussion: Results indicate that younger and older drivers likewise underestimate the time needed for adaptation. Measuring a subjective point of adaptation seems to be an insufficient measure to ensure simulator validity when assessing both older and younger drivers. © 2019 Elsevier Ltd

Future automotive technologies become more and more autonomous and connected. This trend requires a rethinking of validation processes due to the amount of test kilometers needed. To be able to test automated and connected functions in many different traffic scenarios, virtual and mixed real-virtual prototypes will be used. Moreover, due to the complexity of such systems, cross-company cooperation is increasing and demands for common prototypes. Spatially distributed prototypes simplify and enhance cross-company collaboration due to faster provisioning of models and better IP protection. However, the setup of such prototypes is very time consuming due to the high integration effort. Here it is shown that the integration effort of spatially distributed prototypes can be massively reduced by using the Distributed Co-Simulation Protocol (DCP). A demonstrator consisting of a small scale test bed located in Graz and a co-simulation containing Bosch driving functions located in Renningen is presented. The demonstrated integration workflow as well as an analysis of the communication challenges of the coupling can be transferred to any other coupling of this kind. © 2019 IEEE.

Modeling a cable model in multibody dynamics simulation tool which dynamically varies in length, mass and stiffness is a challenging task. Simulation of cable-driven parallel robots (CDPR) for instance requires a cable model that can dynamically change in length for every desired pose of the platform. Thus, in this paper, a detailed procedure for modeling and simulation of a dynamic cable model in Dymola is proposed. The approach is also applicable for other types of Modelica simulation environments. The cable is modeled using standard mechanical elements like mass, spring, damper and joint. The parameters of the cable model are based on the factsheet of the manufacturer and experimental results. Its dynamic ability is tested by applying it on a complete planar CDPR model in which the parameters are based on a prototype named CABLAR, which is developed in Chair of Mechatronics, University of Duisburg-Essen. The prototype has been developed to demonstrate an application of CDPR as a goods storage and retrieval machine. The performance of the cable model during the simulation is analyzed and discussed. © Published under licence by IOP Publishing Ltd.

This paper presents the establishment of a relationship between coil spring fatigue life and automotive vertical vibration using neural network. During an automotive suspension design process, the suspension components are designed with the consideration of structure strength and fatigue life as well as the effects toward automotive ride. Hence, it is important to have a functional mathematical model to predict the fatigue life and automotive life simultaneously. To build the mathematical model, a multibody kinematic quarter model of suspension system was constructed to simulate force and acceleration time histories from the suspension system and the sprung mass of the vehicle model. The force time histories were used to predict the fatigue life of the coil spring while the acceleration time histories were converted into ISO vertical vibration index. A neural network model was created and used to fit the spring fatigue life and vehicle vertical vibration into a mathematical function. The neural network with 1 hidden layer and 2 neurons has shown a good fitting of the data with coefficient of determination as high as 0.88, 0.98, 0.96 for training, validation and testing, respectively. This constructed neural network serves to predict the vehicle vertical vibration using the spring fatigue life and suspension natural frequencies as input, and hence reduce the automotive suspension design process. © 2018 Authors.

This paper presents the evaluation of frequency-based approach predicted spring using acceleration signals that were collected from various road conditions. Random loadings in the forms of acceleration are nominal and more flexible for vehicle components fatigue assessment. In this analysis, the strain time history of the spring and acceleration signals of the suspension strut was measured from three different road conditions. The acceleration signals were then transformed into power spectra density (PSD). PSD cycle counter, like Lalanne, Dirlik, and narrow band approach, was applied to obtain equivalent load cycles. The stress response was obtained through having the equivalent load cycles with a spring modal frequency response function (FRF) and different stress criterion, like absolute maximum principal and critical plane approaches. Then, the stress response was used to predict the spring fatigue life using stress-life (S-N) approach. The results revealed that the harshest road condition was the rural road where the spring with fatigue life of 4.47 × 107 blocks to failure was obtained. The strain predicted fatigue life was used to validate the frequency-based predictions using a conservative approach. It was found that the Dirlik approach has shown the closest results to the strain life approach, which suggested that the Dirlik approach could be used for spring fatigue life prediction with the acceptable accuracy. © 2018 by the authors. Licensee MDPI, Basel, Switzerland.

Driver evaluation and performance monitoring is crucial for safety and risk assessment. This paper proposes a novel algorithm for evaluating driver performance using vehicle state data. This indirect method of evaluation has the advantage of being unobtrusive and cheap as it uses only the easily accessible real time vehicle data. The proposed algorithm can eliminate subjectivity and automate the evaluation process. The driver being evaluated is asked to maintain lane center and drive smoothly while steering input, pedal position and acceleration data is collected. Steering activity is evaluated by defining an optimal input for lane keeping and comparing driver's input. Wheel slip and cornering conditions are considered for pedal activity evaluation. Vehicle acceleration data is used for estimating passenger comfort. An instantaneous and average index of performance is generated in real time as a final evaluation metric combining the three evaluation criteria. The Algorithm was validated using a vehicle simulator and the results are discussed. © 2017 IEEE.

Requirements to mobility are changing worldwide due to a focus on environment and resource protection and a simultaneous increase of mobility needs. Accordingly, the focus in automotive development shifts to the use of alternative energies for vehicle propulsions in order to reduce emissions. The amount of emission reduction by using alternative drive systems depends on marginal conditions, such as the energy mix structure, climate conditions and the vehicle's usage profile. The individual usage profile contains characteristic values such as the number of trips, trip duration and length, as well as the velocity distribution. These characteristic data are individual for each user and differ from the values derived through test cycles such as e.g. the NEDC or the JC08. Accordingly, for the current market analysis and future trends prediction, different user profiles have to be considered and analysed. Within this paper, real world driving data recorded within the German project RuhrautoE will be analysed. The focus lies on evaluation criteria that differentiate between countries due to local framework conditions. In this context, the costs and emissions of different propulsion systems are compared between Germany and China. Afterwards an outlook on Malaysia is given for these criteria. Hitherto, such an approach is rarely considered for Malaysia. Conventional and alternative fuel vehicles as well as electrified vehicles (EV) are compared within this paper. All car models considered are based on real vehicle series to ensure a valid common basis. As expected, the consumptions of all analysed propulsion systems are higher for real driving data than for the NEDC. Then the consumption costs of the electric vehicle are the lowest in Germany and China, but not in Malaysia, where a gas driven vehicle (LPG) achieves the best results. Though, while electric cars can reduce well-to-wheel emissions by 82 % in Germany, there is no reduction possible in China as long as the fossil proportion during the generation of electricity is not reduced. However, EV can reduce the local emission in high traffic areas. According to our estimation, EV are not profitable in all considered countries due to the Total Cost of Ownership. © Universiti Malaysia Pahang Publishing.

This study presents the mission profiling of three road conditions to assess the fatigue life of vehicle coil spring. Identifying all the service conditions of vehicle components, which indicate different possible situations and events, is crucial for the design of such components. Analyzing loading profiles prevents the over design or under design of components, which may lead to wastage or unexpected failure. Every road condition is different because of surface roughness. Acceleration signals were collected and processed using the shock response spectrum and extreme response spectrum methods for transient and stationary events, respectively. Mission profiling was performed to synthesize the equivalent power spectrum density (PSD) load profile for different uses of roads. The resulting PSD indicated that the greater involvement of the rural road reduced the lifetime of coil spring. The synthesized PSD was validated through the mean value of the responses. Fatigue life of the spring was compared to a concatenated strain life measurement using a conservative approach. This research revealed the generation of accurate road loading profile with measurement of acceleration from different roads for spring fatigue studies. © 2017 Elsevier Ltd

In this paper challenges to face in “taking over control from highly automated driving mode” are derived from human driving patterns and a technological analysis of the vehicle state. On the same basis, an automated driving model (driver model) is generated and used for studies in a driving simulator. Finally, strategies, which support the driver in taking over control from highly automated driving are designed in three different levels, implemented and tested in a driving simulator. © 2017, Springer Nature Singapore Pte Ltd.

This study aims to introduce a novel method for accelerating fatigue tests. Strain signals measured at automotive suspension components were extracted based on the Morlet wavelet producing damaging segments. Furthermore, the segments were clustered using the Fuzzy C-Means to remove the segments having lower energy. The process was able to shorten the strain signals up to 41.4% and it was able to retain at least 90% of the fatigue damage. It reduced the testing time by more than 33%, with equivalent fatigue life. Indirectly, the use of modified strain signals could reduce device operating costs. © 2016 Elsevier Ltd

This study aims to accelerate fatigue tests using simulated strain signals. Strain signals were acquired from a coil spring involving car movements. Using a mathematical expression, the measured strain signals yielded acceleration signals, and were considered as disturbances on generating strain signals. The simulated strain signals gave the testing time deviation by only 1.5%. The wavelet-based data editing was applied to shorten the strain signals time up to 36.7% and reduced the testing time up to 33.9%. In conclusion, the simulated strain signals were able to maintain the majority of fatigue damage and decreased the testing time. © 2017 Elsevier Ltd

This paper presents the study of a relationship between objective vertical vibration and coil spring fatigue life under different road excitation to shorten suspension design process. Current development processes of vehicle suspension systems consist of many different stages of analysis and time consuming. Through this vertical vibration and durability characterisation, the vehicle ISO weighted vertical accelerations were used to describe fatigue life of coil spring. Strain signals from various roads were measured using a data acquisition and then converted into acceleration signal. The acceleration signals were then used as input to multibody suspension model for forces time history on spring and acceleration signal of sprung mass extraction. The acceleration signals were then processed for ISO weighted indexes while the force time history was used for coil spring fatigue life prediction respectively. It has been found that the rural road contributed the lowest fatigue life and the highest weighted vertical vibration index when compared to other road conditions. The measured strain predicted fatigue life were also possessed acceptable range when compared to the simulated force fatigue life using a conservative comparison method. The vertical weighted accelerations were plotted against the measured strain and simulated force fatigue life with a coefficient correlations more than 0.99. This model provides immediate prediction between vertical weighted acceleration and fatigue of spring to shorten automotive suspension development time frame. Copyright © 2017 SAE International.

Driving simulators are currently not a widely used tool in powertrain development although they offer numerous advantages especially for this use case. Simulators allow subjective evaluations in early phases of the development process and enable reproducible and controlled experiments with the exact same conditions for test drivers. To investigate on this subject, a study with 31 participants was conducted at the Porsche dynamic driving simulator. The research focusses on the systematic evaluation of the minimum perceptible difference in longitudinal acceleration profiles in a dynamic driving simulator with the goal to establish the simulator as a design tool for powertrain modifications. The just noticeable difference (JND) of the absolute acceleration level and the acceleration gradient is an important indicator, whether a mid-size driving simulator can be used to evaluate the impact of different engine configurations on the drivability of passenger cars. The results were analyzed statistically and show that the participants could detect differences of 4.25% in the absolute level and 13.89% in the gradient. This demonstrates that driving simulators should be integrated in the development process to assess powertrain concepts before using real prototype cars in order to decrease cost and time. © 2017 Elsevier Ltd.

The current approach to determine the platform position of the Cable Driven Parallel Robot (CDPR) is by using camera system or forward kinematics. However both has their drawbacks. Alternatively, a laser scanner is introduced. Three popular algorithms to extract the straight line is applied to estimate two among three component of platform position vector. Then, the proposed method is applied on the physical prototype. Two estimated coordinates of the position vector are compared with the desired trajectory in order to evaluate the platform position accuracy. The comparison shows that the platform has deviation from the desired position. (C) 2016 The Authors. Published by Elsevier Ltd.

To assure a safe, comfortable and especially a cooperative driving experience while driving semi, highly or even fully automated, anticipation of the driving behavior of other traffic participants is needed. Because of the amount of different traffic situations and influence factors on the task of driving, due to uncertainties in environmental sensor measurements, and as a consequence of variable and individual driving styles, probabilistic models in combination with machine learning techniques are often applied to learn driving behavior from data. In this paper, with the help of a simulator study, the driving behavior of a subject group is examined regarding their intention to change lane on highways. The simulator is set up as a partially automated driving system that takes discrete maneuver wishes as input (lane change left or lane change right). If the traffic situation permits it, the requested maneuvers is executed automatically by the system. This generates ground truth labels that are being used to train a lane change intention classifier. The results show that the approach is able to predict upcoming lane changes at an average of more than 3.5 seconds in advance. © 2016 IEEE.

Fatigue is a gradual process related with an effort to keep awake, eventually resulting in declination of human performance. It is one of the well-known risk factors for traffic accidents. The objective of this study is to understand the psychophysiological aspects of driver fatigue by using driving simulator. This study had focused on heart rate (HR) measures to determine drivers’ performance as this method can be measured in a less intrusive manner. Hence, in this study, 17 relevant studies were discussed, chosen from electronic databases. This study encompasses a range of subject areas, including concepts and theories of fatigue, driver fatigue, and psychophysiological indicators and countermeasures of driver fatigue. A variety of psychophysiological measures and parameters have been used in past research as indicators of fatigue. Based on this review, HR can assists researchers to determine performance according to a task demand, condition and its complexity. The review highlighted gaps in the literature and opportunities for future studies. © 2016 Penerbit UTM Press. All rights reserved.

Performance impairment may occur if the driver feels fatigue while driving. This study investigated the drivers’ condition while performing one hour driving simulation in a controlled environment. The aim of this study was to evaluate whether heart rate measures can be used to detect impaired driver performance as well as reduced alertness. There are two different experiments conducted among the subjects; (i) without vibration and (ii) with vibration. A monotonous driving simulation scenario with low demand of traffic flow was utilized to detect drivers’ performance impairment. Heart rate (HR) was recorded over the entire experiment; (i) 30 minutes before driving, (ii) one hour during driving and (iii) 30 minutes after driving in the morning before lunch break. The baseline measurement was recorded when the subject has performed his daily routine in the same hours of experiment, which is about three hours. HR measures were derived and correlated to variation of lane deviation (VLD), a driving performance measure, and to the driver's state, which was estimated by the Karolinska Sleepiness Scale (KSS). Experimental result shows all subjects’ HR data were lower at the end of the driving task, particularly when driving in the simulator without vibration. Based on KSS evaluation, subjects tend to feel sleepy during driving and become less sleepy when they reach the destination. In term of VLD, all subjects tend xto cross the lane, which means they were not focused to the task. In conclusion, HR can be used as a tool to detect drivers’ performance and it is a useful indicator of physiological adaptation and intensity of effort. © 2015 Penerbit UTM Press. All rights reserved.

Concept design of both axles and tires is difficult, because they simultaneously affect many different objective quantities in vehicle dynamics related to, e.g., self-steering behavior, transient behavior, maximum lateral acceleration, etc. Typically, the tire performance is evaluated using a specified axle design (or vice versa) and then optimized. This way, the optimal tire will depend on the axle that was chosen. If the axle design changes during the development process, the vehicle performance will suffer. Similarly, the optimal tire or axle for one vehicle may not be sufficient for another vehicle from the same platform or vehicle architecture. This paper proposes a new method that computes permissible ranges of tire and axle properties, so-called solution spaces. Within these solution spaces, all possible combinations of tires and axles are guaranteed to deliver the required vehicle performance in all relevant disciplines. During concept design, solution spaces are maximized for flexibility in the development process. Large solution spaces can relax conflicts of goals when they are large enough to allow for optimization with respect to additional requirements, for example the optimization of tire properties with respect to fuel consumption and CO2 emissions. Solution spaces of many vehicles may be overlaid in order to identify parameter settings for modules or vehicle platforms. Developing axles and tires to lie in the center of solution spaces provides maximum robustness with respect to unintended variations or uncertainties. The method is illustrated using examples from the vehicle development process at BMW. Fast computing simplified vehicle and tire models are linked with appropriate numerical methods for solution space analysis. Requirements on tires and axles are derived and expressed as permissible ranges for their functional properties, such as the cornering stiffness. Examples of tires and axles are provided that may be realized and will make the vehicle reach the performance goals.

The current study aimed to develop the mechanism for generating strain signal utilising computer-based simulation. The strain data, caused by the acceleration, were undertaken from a fatigue data acquisition involving car movements. Using a mathematical model, the measured strain signals yielded to acceleration data used to describe the bumpiness of road surfaces. The acceleration signals were considered as an external disturbance on generating strain signals. Based on this comparison, both the actual and simulated strain data have similar pattern. The results are expected to provide new knowledge to generate a strain signal via a simulation. © 2015 Elsevier Ltd. All rights reserved.

This paper presents the ability of the wavelet transforms for compressing automobile strain data. The wavelet transforms identified and extracted higher amplitude segments and produced shorter edited signals. Based on the comparison of the edited signals resulted, it was found that the Morlet wavelet gave the shortest signals. It was able to summarize strain signals up to 77% and maintain more than 90% of the statistical parameters and the fatigue damage. Meanwhile the continuous and discrete Daubechies wavelet transforms summarized the signals below 60%. It proved that the Morlet wavelet was the best technique for fatigue data editing, especially for the automotive applications. © (2014) Trans Tech Publications, Switzerland.

This contribution investigates algorithms for egomotion estimation from environmental features. Various formulations for solving the underlying procrustes problem exist. It is analytically shown that in the 2-D case this can be performed more efficiently compared to common implementations based on matrix decompositions. Furthermore, analytic error propagation is performed to second order which reveals a multiplicative estimator bias. A novel bias-corrected solution is proposed and evaluated in Monte Carlo simulations. Propagation of the derived error model to a representation used in the recursive trajectory reconstruction is presented and verified. © 2014 IEEE.

This paper explores the feasibility of cable-driven actuators in combination with the lightweight skeleton structure of the humanoid robot HUMECH. At the beginning the setup of the robot prototype is described in detail followed by a Dymola® simulation model of the cable-driven actuators. However, the load and dynamic motion behavior of the Dyneema® cable-fibers are examined in order to obtain an evaluation of the developed model and its dynamic behavior. Finally the simulation results being presented and discussed in accordance to the goal of following a human motion trajectory of the right shoulder. © 2014 IEEE.

This contribution presents a method for a virtual development process using a driving simulator. Driving simulators are widely used as development tools in the automotive industry. Amongst others the development of advanced driver assistance systems or the gain of detailed data for the energy efficiency of different powertrain concepts based on driving data are possible fields of application. Here the different needs according to the goal of the intended simulator studies and the thereby required stimuli of the simulator for real driver behavior in the virtual environment are discussed. Afterwards the dynamic driving simulator used at the University of Duisburg-Essen and its various fields of usage are shown with example studies. As examples studies the interaction with a Hardware-in-the-Loop test bed for an electric powertrain and a study on the driver's intention identification to enhance the comfort performance of a modern driver assistance system are used. For both of the studies the driver and its interactions with the respective vehicular system are set as the point of interest. The used simulator setup is then judged upon the degree of immersion which is reached due to different sensory channels that are addressed in the setup. © 2014 IEEE.

The authors examine in detail the fundamentals and mathematical descriptions of the dynamics of automobiles. In this context different levels of complexity will be presented, starting with basic single-track models up to complex three-dimensional multi-body models. A particular focus is on the process of establishing mathematical models on the basis of real cars and the validation of simulation results. The methods presented are explained in detail by means of selected application scenarios. © Springer-Verlag Berlin Heidelberg 2014. All rights are reserved.

The study presents the development of a wavelet-based segmentation algorithm for fatigue life assessment. Strain data was extracted using the Morlet family. The extraction process identified damaging segments, and it was able to shorten the original signal by 74.3%, with less than 10% difference with statistical parameters. The extraction algorithm was able to retain at least 97.9% of fatigue damage. The damaging segments drawn were clustered using the k-means method to provide three groups of segments, i.e., lower, moderate, and higher groups representing statistical values. The approach was suggested as an alternative method for evaluating and clustering fatigue strain signals. © (2014) Trans Tech Publications, Switzerland.

To provide reliable assistance to drivers at intersections, a priori information has to be supplied to so-called Cooperative Intersection Collision Avoidance Systems (CICAS), such as lane geometry, admissible turning maneuvers, and the areas where the paths of traffic participants typically cross. In the implementation of the CICAS of the project simTD, this information is stored in the intersection topology and transmitted via a wireless network to vehicles approaching an intersection. To achieve high coverage, timeliness and precision of the stored data at the same time, an approach to automatic generation of intersection topologies from noisy measurements is applied, which is based on B-spline curve approximation. © 2013 IEEE.

In this paper a centralized non-linear model predictive control (NMPC) for redundantly actuated Parallel Kinematic Machines (PKM) is proposed. The controller has the structure of an augmented PD controller with variable gains. These gains are intended to minimize the future tracking error. With this approach the computation error is kept low. To emphasize the robustness of the method, experiments with a planar 2DOF redundantly actuated PKM with industrial torque motors are presented. © Springer Science+Business Media Dordrecht 2013.

Currently electric vehicles are introduced in e.g. public transport and individual traffic in order to reduce i.a. The green house gas emissions. The main disadvantage of electric vehicles compared to vehicles with conventional drive is the shorter operating distance. In contrast this disadvantage is partially negligible in urban usage scenarios, like e.g. taxi or delivery services. This paper focuses on the simulation of electric vehicle propulsion systems using a Hardware in the Loop (HiL) model. The model consisting of components used in actual electric vehicles is scaled using Buckingham's Pi-Theorem in order to analyze the impact of different electric traction systems on the vehicle's energy consumption. Thus the available operating distance of such vehicles can be optimized in urban traffic. © 2013 IEEE.

According to the analysis of car accidents many casualties occur at intersections. As ongoing research demonstrates, Advanced Driver Assistance Systems that aim at preventing this type of accident, need to reliably predict the turning maneuver of all relevant participants in the scene. In this work an approach is introduced, which models human drivers as the optimizer of an optimal control problem with an unknown terminal state. Tracking the cost-to-go gradient to the terminal state of each driving option leads to the most plausible hypothesis. The optimal control problem itself is formulated with costs that minimize jerk, time and steering effort with good resemblance to typical human driving behavior. In combination with a simplified vehicle model this leads to a nonlinear constrained dynamic optimization problem, which is solved numerically. The performance of the proposed approach is evaluated on data obtained in a field test with promising results. © 2013 IEEE.

In the field of passenger transportation a reduction of the consumption of fossil fuels has to be achieved by any measures. Advanced designs of internal combustion engine have the potential to reduce CO2 emissions, but still suffer from low efficiencies in the range from 33% to 44%. Recuperation of waste heat can be achieved with thermoelectric generators (TEGs) that convert heat directly into electric energy, thus offering a less complicated setup as compared with thermodynamic cycle processes. During a specific driving cycle of a car, the heat currents and temperature levels of the exhaust gas are dynamic quantities. To optimize a thermoelectric recuperation system fully, various parameters have to be tested, for example, the electric and thermal conductivities of the TEG and consequently the heat absorbed and rejected from the system, the generated electrical power and the system efficiency. A Simulink model consisting of a package for dynamic calculation of energy management in a vehicle, coupled with a model of the thermoelectric generator system placed on the exhaust system, determines the drive-cycle-dependent efficiency of the heat recovery system, thus calculating the efficiency gain of the vehicle. The simulation also shows the temperature drop at the heat exchanger along the direction of the exhaust flow and hence the variation of the voltage drop of consecutively arranged TEG modules. The connection between the temperature distribution and the optimal electrical circuitry of the TEG modules constituting the entire thermoelectric recuperation system can then be examined. The simulation results are compared with data obtained from laboratory experiments. We discuss error bars and the accuracy of the simulation results for practical thermoelectric systems embedded in cars. © 2013 TMS.

Wire-based Stewart-Gough platforms are known to allow fastmovements of the end-effector. But as for every robotic system, their performance and energy efficiency can be optimized by the generation of end-effector trajectories suited for that particular robot type. In this contribution the optimal control strategy is applied on an innovative wire-based storage-retrievalmachine in order to design time, power and energy optimal trajectories. © Springer Science+Business Media Dordrecht 2013.

The objective of this paper is the modelling of an unbounded environment of a human-driven car that may contain multilevel structures such as bridges or parking decks. Such a model might be used by a driver assistant system (DAS) where one drives through an urban environment, requests for an assistance and the DAS should immediatly be able to give the user the required support. E.g. it can guide through a narrow passage or a turn. For such an assistance an environment model is needed that runs in real-time. But to keep the system at low cost the required memory should be as small as possible. Hence the algorithm should be optimized with respect to computational power and memory consumption. A new approach is proposed that models the environment by incrementally adding small tiles at places where obstacles created measurements. Each tile contains an occupancy grid and some neighbourhood relations. By modelling the world this way, a compact representation of the environment is created that is aligned at the user-given trajectory and the allocation of obstacles. By using a grid based algorithm efficient and hence fast techniques can be used to work on the world representation. Also an extension is introduced to restrict the required memory to a given limit and concurrently map the local obstacles but avoids any transformation of historic data. A comparision with state of the art algorithms was made and the capability of the proposed algorithm is demonstrated with some experimental results. © 2012 IEEE.

This paper describes the simulation of an electric vehicle drive train via a hardware in the loop (HiL) setup in combination with an interactive driving simulator based on MATLAB/Simulink. This setup provides the possibility to test the suitability of electric vehicles in different usage scenarios using a laboratory environment. Another aspect is that by a comprehensive analysis of the measurement data, system components of the drive train can be tested under realistic conditions and further developed in order to improve the efficiency of such vehicles. © 2012 IEEE.

A mathematical modelling approach of a multi-body wheel suspension is presented. The wheel suspension is modelled in a modular manner so that different types of vehicles can be simulated. The inter-changeability of the wheel suspensions is achieved by calculating the translational and rotational JACOBIAN matrix and its partial time derivatives for the wheel carrier and the wheel. The results of modelling the kinematics of a McPherson wheel suspension are shown. © (2012) Trans Tech Publications.

The knowledge of the wheel forces is fundamental to the development of Advanced Driver Assistance Systems (ADAS) such as Electronic Stability Control (ESC) and Anti-Roll Control (ARC). However the direct measurement of the wheel forces has been very difficult. To overcome this drawback, it has been a common practice in the industry to estimate the forces at the tire-road contact using mathematical vehicle models and estimators, especially the Extended Kalman Filter (EKF). In this contribution, the performance of the EKF is evaluated using a complete spatial model with 14 degrees of freedom. © (2012) Trans Tech Publications.

This contribution proposes a mathematical model for an amplification mechanism (scissors mechanism) intended to be used as an actuating platform in the setup of a driving simulator. The main goal is reproducing different types of road profiles. For this purpose, hydraulic actuation has been chosen by means of high forces and powers that need to be developed. An optimization method is presented (GA and hybrid functions) for the geometrical parameters of the mechanism and the effectiveness of the method is validated by the simulation results in MATLAB/Simulink. © 2011 IEEE.

This paper focuses on the effect of using an actuated driving simulator to increase the realistic perception on a human driver. Understanding the implication of decision making while driving a vehicle, remains a challenging task. The driving task was often approached in the literature as a visual guided task, nevertheless latest research claim that driving skills are considerably affected by the vestibular system of a human's body. The main idea is to test how the human vestibular system responds to the movement reproduced with an actuated driving simulator.

The modelling via transmission elements, which was originally introduced to model the kinematics and dynamics of multi-body systems, is applied to modelling electric drive systems with switch circuits in this article. This approach is implemented in C++ in the simulation software Drive&Control D&C Engine which is designed to model and simulate mechatronic systems in the drive engineering by the department of advanced engineering of the Bosch Rexroth AG. The switch elements, used in the power converters of electric drive systems, are treated as ideal switches and are described in an efficient, stable way. Discontinuities caused by the ideal switch models are dealt with an event-handling algorithm. The application example is a complete permanent-magnet-synchronous-motor (PMSM) drive system. A benchmark simulation is carried out to demonstrate this approach's performance in terms of computational time and accuracy. © 2011 Taylor & Francis.

Nowadays, engineers still search for more efficient methods in order to decrease simulation times. However, most simulation environments do not use the full power provided by modern PCs. Even though every modern computer is equipped with a multicore processor, only very few simulation environments use more than one core for simulations. There are various possibilities to parallelize simulations. One approach is to partition the model into several submodels. Using adequate solvers for each submodel can result in lower computation times, especially if there is a significant difference in the time constants of the submodels. Other approaches are based on parallelization of the ODE solver. For example, it is possible to parallelize the linear algebra methods inside the solver. Parallelization of the solver itself is another way to use the multicore architecture. From the modeling and simulation point of view, the latter approach is more interesting. Consequently, the question is whether it is beneficial to partition the model or to use a parallelized solver. In this paper this question is answered at least for an example system. However, the more efficient approach may not be the better approach for the usage inside a simulation environment. Therefore, it is discussed which approach can be automated and integrated easier into a simulation environment. Copyright © 2011 by ASME.

Due to the increased computing power in the last decade, more and more complex vehicle models were developed. Nowadays even complex multibody models can be generated via graphical user interfaces in object-oriented simulation tools like Dymola or SimulationX. On the other hand, the available computing power in electronic control units is still limited, mostly by the cost pressure in the automotive industry. Hence, it is not possible to generate a complex model by drag and drop via a graphical user interface and run it in real-time within a desired time cycle on an ECU inside the vehicle. The same holds for HIL-testbeds and driving simulators, where the model must run in real-time as well. Thus, generally the model is adjusted in an iterative process until the model can be integrated in real-time on the particular ECU. In other words, a model has to be generated that is on the one hand complex enough to reproduce the desired physical effects and on the other hand simple enough to fulfill the real-time requirements. As it is easy to generate a complex model nowadays, an algorithm for the automated reduction of the model is required. Equation-based reduction techniques are a tool for the automated reduction of a given DAE-system for a defined error bound. This approach was already adopted and extended to generate vehicle models with an adjustable accuracy. In this contribution, equation-based reduction techniques are extended to generate models, which are guaranteed to run in real-time on a given real-time target within a given real-time cycle. © Springer Science + Business Media B.V. 2011.

Wind tunnels are a standard tool to evaluate the air flow properties of aerodynamical vehicles in model scale. This is widely used to optimize the design of aircrafts and aircraft components. Additionally, the hydrodynamic properties of marine components like ship hulls or propulsion systems can be predicted. It is desirable to guide the models along defined trajectories during the tests to vary the angle of attack. Parallel wire robots were successfully used to perform airplane maneuvers in wind tunnels due to their good aerodynamical and mechanical properties. Compared to aircraft design, marine models are very heavy (up to 500kg). Thus, the positioning system must be very stiff to avoid vibrations. Additionally, fast maneuvers require powerful drives. Nevertheless, the positioning system should not influence the air flow. In this contribution, a novel design is presented. Additionally, a new realtime capable force distribution calculation method for parallel tensed systems is presented. Copyright © 2009 by ASME.

This paper focuses on the actuation strategy of an active driving simulator and its validation using experimental driving data. The simulator combines the simulation of both the road characteristics and the vehicle dynamics into a single architecture. The goal is to combine actual road excitation signals with imposed vehicle movements to create a realistic driving experience. © (2010) Trans Tech Publications.

Wind tunnels are an experimental tool to evaluate the air flow properties of vehicles in model scale and to optimize the design of aircrafts and aircraft components. Also the hydrodynamic properties of marine components like ship hulls or propulsion systems can be examined. For advanced optimization, it is necessary to guide the models along defined trajectories during the tests to vary the angle of attack. Due to their good aerodynamical properties, parallel wire robots were successfully used to perform these maneuvers in wind tunnels. Compared to aircraft hulls, marine models may be very heavy-weight (up to 150 kg). Thus, the suspension system must be very stiff to avoid vibrations. Additionally, fast maneuvers require powerful drives. On the other hand, the positioning system should not influence the air flow to ensure unaltered experimental results. In this paper, different designs are presented and discussed. © Springer Science+Business Media B.V. 2010.

This paper considers the application of simulators or demonstrators in the development of mechatronic products. It is shown at what step of the mechatronic design process a simulator or demonstrator can be used to significantly improve a products quality and thus identify possible errors and provide potential workarounds. Cost reduction is achieved by the use of simulators or demonstrators in the early design stage and less real product tests have to be carried out which also could be hazardous for the test person. © (2010) Trans Tech Publications.

Wire robots consist of a movable end-effector which is connected to the machine frame by motor driven wires. Since wires can transmit only tension, positive wire forces have to be ensured. During workspace analysis, the wires forces need to be calculated. Discrete methods do not produce satisfying results, since intermediate points on the discrete calculation grids are neglected. Using intervals instead of points leads to reliable results. Formulating the analysis problem as a Constraint-Satisfaction-Problem (CSP) allows convenient transition to the synthesis problem, i.e. to find suitable designs for practical applications. In this paper, two synthesis approaches are employed: Design-to-Workspace (i.e. calculation of an optimal robot layout for a given workspace) and an extension called Design-to-Task (i.e. calculation of the optimal robot for a specific task). To solve these optimization problems, the paper presents approaches to combine the reliability and robustness of interval-based computations with the effectiveness of available optimizer implementations. Copyright © 2009 by ASME.

A conventional way to enhance the steering features (hence optimize the vehicle dynamics) and steering feeling has been realized with EPS (Electric Power Steering) Systems. Another milestone of steering systems is the steer-by-wire steering system, which has no mechanical connection between the steering wheel and tires. Steer-by-wire systems offer many additional desirable steering characteristics. In this paper, a steer-by-wire implementation with new functions will be introduced. Furthermore first results with real-time experimental examples will be elucidated in detail. © 2010 IFAC.

Tendon based parallel manipulators are capable of realizing movement of high speed and acceleration. In order to perform tasks that require direct contact with the environment control schemes are needed that adapt both operational space variables and tendon forces. By use of an inverse dynamics approach a motion control scheme in operational space is presented. In redundant systems the forces that act along the tendons can be divided into internal forces, the sum of which is zero, and external forces, that produce the driving force for the endeffector. Based on that property an additional and decoupled force control scheme is presented. © (2010) Trans Tech Publications.

To further improve passengers' safety, novel driver assistance systems are able to avoid a collision by triggering an active braking. However, at high relative speeds between the ego vehicle and the potential opponent vehicle, the collision-avoiding emergency braking has to be initiated at a point of time where the driver is still able to resolve the situation by overtaking or swerving. This causes a trade-off between collision avoidance and collision mitigation when specifying the system's parameters. By integrating a reaction time model and hence the driver into the situation's analysis, this trade-off can be reduced. For that purpose, an appropriate method using data from additional lateral and rear sensors is presented within this paper. In conclusion, its performance is evaluated and discussed using test data. © 2010 IFAC.