TY - CHAP A1 - Bauder, Maximilian A1 - Kubjatko, Tibor A1 - Schweiger, Hans-Georg T1 - Development of an Accident Reconstruction Method Using Cooperative Awareness Message Data T2 - Proceedings of the 32nd Annual Congress of the EVU KW - Accident Reconstruction KW - Vehicle-2-X Communication KW - Cooperative Awareness Message Y1 - 2024 SP - 376 EP - 390 PB - EVU CY - Berlin ER - TY - JOUR A1 - Paula, Daniel A1 - Bauder, Maximilian A1 - Pfeilschifter, Claus A1 - Petermeier, Franziska A1 - Kubjatko, Tibor A1 - Böhm, Klaus A1 - Riener, Andreas A1 - Schweiger, Hans-Georg T1 - Impact of Partially Automated Driving Functions on Forensic Accident Reconstruction: A Simulator Study on Driver Reaction Behavior in the Event of a Malfunctioning System Behavior JF - Sensors N2 - Partially automated driving functions (SAE Level 2) can control a vehicle’s longitudinal and lateral movements. However, taking over the driving task involves automation risks that the driver must manage. In severe accidents, the driver’s ability to avoid a collision must be assessed, considering their expected reaction behavior. The primary goal of this study is to generate essential data on driver reaction behavior in case of malfunctions in partially automated driving functions for use in legal affairs. A simulator study with two scenarios involving 32 subjects was conducted for this purpose. The first scenario investigated driver reactions to system limitations during cornering. The results show that none of the subjects could avoid leaving their lane and moving into the oncoming lane and, therefore, could not control the situation safely. Due to partial automation, we could also identify a new part of the reaction time, the hands-on time, which leads to increased steering reaction times of 1.18 to 1.74 s. The second scenario examined driver responses to phantom braking caused by AEBS. We found that 25 of the 32 subjects could not override the phantom braking by pressing the accelerator pedal, although 16 subjects were informed about the system analog to the actual vehicle manuals. Overall, the study suggests that the current legal perspective on vehicle control and the expected driver reaction behavior for accident avoidance should be reconsidered. UR - https://doi.org/10.3390/s23249785 Y1 - 2023 UR - https://doi.org/10.3390/s23249785 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-42766 SN - 1424-8220 VL - 23 IS - 24 PB - MDPI CY - Basel ER - TY - JOUR A1 - Bauder, Maximilian A1 - Langer, Robin A1 - Kubjatko, Tibor A1 - Schweiger, Hans-Georg T1 - Post-Processing Kalman Filter Application for Improving Cooperative Awareness Messages’ Position Data Accuracy JF - Sensors N2 - Cooperative intelligent transportation systems continuously send self-referenced data about their current status in the Cooperative Awareness Message (CAM). Each CAM contains the current position of the vehicle based on GPS accuracy, which can have inaccuracies in the meter range. However, a high accuracy of the position data is crucial for many applications, such as electronic toll collection or the reconstruction of traffic accidents. Kalman filters are already frequently used today to increase the accuracy of position data. The problem with applying the Kalman filter to the position data within the Cooperative Awareness Message is the low temporal resolution (max. 10 Hz) and the non-equidistant time steps between the messages. In addition, the filter can only be applied to the data retrospectively. To solve these problems, an Extended Kalman Filter and an Unscented Kalman Filter were designed and investigated in this work. The Kalman filters were implemented with two kinematic models. Subsequently, driving tests were conducted with two V2X vehicles to investigate and compare the influence on the accuracy of the position data. To address the problem of non-equidistant time steps, an iterative adjustment of the Process Noise Covariance Matrix Qand the introduction of additional interpolation points to equidistance the received messages were investigated. The results show that without one of these approaches, it is impossible to design a generally valid filter to improve the position accuracy of the CAM position data retrospectively. The introduction of interpolation points did not lead to a significant improvement in the results. With the Qmatrix adaptation, an Unscented Kalman Filter could be created that improves the longitudinal position accuracy of the two vehicles under investigation by up to 80% (0.54 m) and the lateral position accuracy by up to 72% (0.18 m). The work thus contributes to improving the positioning accuracy of CAM data for applications that receive only these data retrospectively. UR - https://doi.org/10.3390/s24247892 Y1 - 2024 UR - https://doi.org/10.3390/s24247892 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-55492 SN - 1424-8220 VL - 24 IS - 24 PB - MDPI CY - Basel ER - TY - JOUR A1 - Langer, Robin A1 - Bauder, Maximilian A1 - Moghariya, Ghanshyam Tukarambhai A1 - Eckert, Michael Clemens Georg A1 - Kubjatko, Tibor A1 - Schweiger, Hans-Georg T1 - Testing and Validation of the Vehicle Front Camera Verification Method Using External Stimulation JF - Sensors N2 - The perception of the vehicle’s environment is crucial for automated vehicles. Therefore, environmental sensors’ reliability and correct functioning are becoming increasingly important. Current vehicle inspections and self-diagnostics must be adapted to ensure the correct functioning of environmental sensors throughout the vehicle’s lifetime. There are several promising approaches for developing new test methods for vehicle environmental sensors, one of which has already been developed in our previous work. A method for testing vehicle front cameras was developed. In this work, the method is improved and applied again. Various test vehicles, including the Tesla Model 3, Volkswagen ID.3, and Volkswagen T-Cross, are stimulated by simulating driving scenarios. The stimulation is carried out via a tablet positioned before the camera. The high beam assist is used to evaluate the vehicle’s reaction. It was observed whether the vehicle switched from high to low beam as expected in response to the stimulation. Although no general statement can be made, the principle of stimulation works. A vehicle reaction can be successfully induced using this method. In further test series, the influence of display brightness is examined for the first time in this work. The results show that the display brightness significantly influences the test procedure. In addition, the method is validated by stimulation with colored images. It is shown that no complex traffic simulation is necessary to trigger a vehicle reaction. In the following validation approach, the CAN data of the Tesla Model 3 is analyzed during the tests. Here, too, the assumption that the vehicle reaction is based solely on the detected brightness instead of identifying road users is confirmed. The final validation approach examines the method’s applicability to other vehicles and high beam assist technologies. Although the method could not be used on the Volkswagen T-Cross due to a fault detected by the vehicle’s self-diagnosis, it worked well on the Volkswagen ID.3. This vehicle has a dynamic light assist in which individual segments of the high beam are dimmed during stimulation. Although the method developed to stimulate vehicle front cameras is promising, the specific factors that trigger the vehicle responses remain to be seen. This uncertainty suggests that further research is needed better to understand the interaction of stimulation and sensor detection. UR - https://doi.org/10.3390/s24248166 Y1 - 2024 UR - https://doi.org/10.3390/s24248166 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-56097 SN - 1424-8220 VL - 24 IS - 24 PB - MDPI CY - Basel ER - TY - JOUR A1 - Geisbauer, Christian A1 - Wöhrl, Katharina A1 - Lott, Susanne A1 - Nebl, Christoph A1 - Schweiger, Hans-Georg A1 - Goertz, Roland A1 - Kubjatko, Tibor T1 - Scenarios involving accident-damaged electric vehicles JF - Transportation Research Procedia N2 - Electrically propelled cars will play a major role on the streets, not only in the far, but also in the near future. In this work, an overview on the possible risks arising from electric vehicles is given that involves electrical, chemical and thermal hazards. Additionally, an analysis on different scenarios involving damaged electric vehicles, the involved parties, as well as their risk assessment in terms of hazard rating is presented. Furthermore, two scenarios are explained and addressed with more detail. With the derived risks of the battery and with possible crash scenarios presented, the still existing uncertainties for handling damaged electric vehicles are addressed, followed by a discussion on ways to overcome this problem. UR - https://doi.org/10.1016/j.trpro.2021.07.136 KW - electromobility KW - batteries KW - lithium ion KW - electric vehicle KW - accident KW - rescue service KW - recycling Y1 - 2021 UR - https://doi.org/10.1016/j.trpro.2021.07.136 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-13130 SN - 2352-1465 VL - 2021 IS - 55 SP - 1484 EP - 1489 PB - Elsevier CY - Amsterdam ER - TY - CHAP A1 - Paula, Daniel A1 - Bauder, Maximilian A1 - König, Thomas A1 - Dengler, Yannick A1 - Böhm, Klaus A1 - Kubjatko, Tibor A1 - Schweiger, Hans-Georg T1 - Impact of vehicle electrification on fundamental accident reconstruction parameters T2 - Proceedings of the 30th Annual Congress of the EVU Y1 - 2022 SP - 34 EP - 41 PB - EVU CY - Berlin ER - TY - CHAP A1 - Paula, Daniel A1 - Bauder, Maximilian A1 - König, Thomas A1 - Kubjatko, Tibor A1 - Schweiger, Hans-Georg T1 - Systematic investigation of influences on advanced driver assistance systems for accident reconstruction T2 - Proceedings of the 31st Annual Congress of the EVU Y1 - 2023 SP - 253 EP - 261 PB - EVU CY - Berlin ER - TY - CHAP A1 - Bauder, Maximilian A1 - Paula, Daniel A1 - Böhm, Klaus A1 - Kubjatko, Tibor A1 - Wech, Lothar A1 - Schweiger, Hans-Georg T1 - Opportunities and challenges of cooperative intelligent transportation systems on accident analysis T2 - Proceedings of the 30th Annual Congress of the EVU Y1 - 2022 PB - EVU CY - Berlin ER - TY - JOUR A1 - Bauder, Maximilian A1 - Paula, Daniel A1 - Kubjatko, Tibor A1 - Schweiger, Hans-Georg T1 - Evaluation of the vehicle behaviour when not responding to the take-over request of Tesla Autopilot and Volkswagen Travel Assist JF - Transportation Research Procedia N2 - In this work, driving tests were carried out with a Tesla Model 3 and VW ID.4. During the tests, the vehicles were controlled by an SAE Level 2 system (Tesla Autopilot and VW Travel Assist). The study aimed to investigate the behaviour of the vehicles when the driver does not react to the take-over request of the vehicle while it is in a non-critical driving situation. The aim was to clarify whether this can lead to accidents from a purely technical point of view and whether there is still room for improvement. As a result, it could be determined that a non-reaction of the driver to the take-over request leads to system-initiated braking decelerations, which can lead to a rear-end collision by the following traffic. Furthermore, secondary accidents are possible if the driver fails to act as a fallback. This could be reduced by improving the applicable UNECE standard R79 by specifying deceleration values for this case. In addition, the risk of an accident could be reduced by decelerating earlier and warning the environment. For this purpose, a driver monitoring system would also be recommended to allow situational decisions by the system. The implementation of the measures proposed could result in a significant increase in road safety while using these systems. UR - https://doi.org/10.1016/j.trpro.2023.11.167 Y1 - 2024 UR - https://doi.org/10.1016/j.trpro.2023.11.167 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-63210 SN - 2352-1465 VL - 2023 IS - 74 SP - 450 EP - 457 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Langer, Robin A1 - Bauder, Maximilian A1 - Paula, Daniel A1 - Kubjatko, Tibor A1 - Schweiger, Hans-Georg T1 - Development of a model environment for autonomous driving JF - Transportation Research Procedia N2 - Developing automated driving functions can be elaborate and cost-intensive. Simulation helps to decrease both, the effort and the costs. With physical model environments of a smaller scale, automotive research can be improved even further. This work presents a method with five steps for building a scaled model environment to answer research questions regarding autonomous driving functions in the automotive domain. For this purpose, the five steps of the method are first introduced and explained. Each step comes with indications that can be further extended. Then, the method is performed by creating a model environment. The model environment build in this work is a recreation of an urban intersection in Ingolstadt, Germany, in the scale of 1:10. In addition to two model vehicles, the model environment consists of pedestrians and traffic control elements such as signs, signals, various lanes and road markings. The advantages of the model environment are low costs and high reproducibility. On the other hand it comes with limitations. For example, the materials used for construction (polylactic acid) are different from the materials in reality. The aim of this work was achieved by providing a method for building a model environment for automotive domain. The limitations of the built model environment and actual testing of automated driving functions will be performed in the future. UR - https://doi.org/10.1016/j.trpro.2023.11.122 Y1 - 2024 UR - https://doi.org/10.1016/j.trpro.2023.11.122 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-63201 SN - 2352-1465 VL - 2023 IS - 74 SP - 133 EP - 140 PB - Elsevier CY - Amsterdam ER - TY - CHAP A1 - Paula, Daniel A1 - König, Thomas A1 - Bauder, Maximilian A1 - Petermeier, Franziska A1 - Kubjatko, Tibor A1 - Schweiger, Hans-Georg T1 - Performance Tests of the Tesla Autopilot and VW Travel Assist on a Rural Road T2 - Transport Means 2022: Proceedings of the 26th International Scientific Conference - Part II UR - https://doi.org/10.5755/e01.2351-7034.2022.P2 KW - Accidents Analysis KW - SAE Level 2 Systems KW - VW Travel Assist KW - Tesla Autopilot KW - Performance test Y1 - 2022 UR - https://doi.org/10.5755/e01.2351-7034.2022.P2 SN - 2351-7034 SN - 1822-296X SP - 498 EP - 508 PB - Kaunas University of Technology CY - Kaunas ER -