TY - CHAP A1 - Gerner, Jeremias A1 - Bogenberger, Klaus A1 - Schmidtner, Stefanie T1 - Temporal Enhanced Floating Car Observers T2 - 2024 IEEE Intelligent Vehicles Symposium (IV) UR - https://doi.org/10.1109/IV55156.2024.10588538 Y1 - 2024 UR - https://doi.org/10.1109/IV55156.2024.10588538 SN - 979-8-3503-4881-1 SP - 1035 EP - 1040 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Zhang, Yunfei A1 - Gerner, Jeremias A1 - Ilic, Mario A1 - Schmidtner, Stefanie A1 - Bogenberger, Klaus T1 - Autonomous Vehicles as Sensors: Traffic State Estimation T2 - 104th Annual Meeting of the Transportation Research Board (TRB) Y1 - 2025 PB - National Academy of Sciences CY - Washington ER - TY - INPR A1 - Rößle, Dominik A1 - Gerner, Jeremias A1 - Bogenberger, Klaus A1 - Cremers, Daniel A1 - Schmidtner, Stefanie A1 - Schön, Torsten T1 - Unlocking Past Information: Temporal Embeddings in Cooperative Bird’s Eye View Prediction N2 - Accurate and comprehensive semantic segmentation of Bird's Eye View (BEV) is essential for ensuring safe and proactive navigation in autonomous driving. Although cooperative perception has exceeded the detection capabilities of single-agent systems, prevalent camera-based algorithms in cooperative perception neglect valuable information derived from historical observations. This limitation becomes critical during sensor failures or communication issues as cooperative perception reverts to single-agent perception, leading to degraded performance and incomplete BEV segmentation maps. This paper introduces TempCoBEV, a temporal module designed to incorporate historical cues into current observations, thereby improving the quality and reliability of BEV map segmentations. We propose an importance-guided attention architecture to effectively integrate temporal information that prioritizes relevant properties for BEV map segmentation. TempCoBEV is an independent temporal module that seamlessly integrates into state-of-the-art camera-based cooperative perception models. We demonstrate through extensive experiments on the OPV2V dataset that TempCoBEV performs better than non-temporal models in predicting current and future BEV map segmentations, particularly in scenarios involving communication failures. We show the efficacy of TempCoBEV and its capability to integrate historical cues into the current BEV map, improving predictions under optimal communication conditions by up to 2% and under communication failures by up to 19%. The code will be published on GitHub. UR - https://doi.org/10.48550/arXiv.2401.14325 Y1 - 2024 UR - https://doi.org/10.48550/arXiv.2401.14325 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-46123 PB - arXiv CY - Ithaca ER - TY - CHAP A1 - Schlamp, Anna-Lena A1 - Gerner, Jeremias A1 - Bogenberger, Klaus A1 - Schmidtner, Stefanie T1 - User-Centric Green Light Optimized Speed Advisory with Reinforcement Learning T2 - 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC) UR - https://doi.org/10.1109/ITSC57777.2023.10422501 Y1 - 2024 UR - https://doi.org/10.1109/ITSC57777.2023.10422501 SN - 979-8-3503-9946-2 SP - 3463 EP - 3470 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Gerner, Jeremias A1 - Rössle, Dominik A1 - Cremers, Daniel A1 - Bogenberger, Klaus A1 - Schön, Torsten A1 - Schmidtner, Stefanie T1 - Enhancing Realistic Floating Car Observers in Microscopic Traffic Simulation T2 - 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC) UR - https://doi.org/10.1109/ITSC57777.2023.10422398 Y1 - 2024 UR - https://doi.org/10.1109/ITSC57777.2023.10422398 SN - 979-8-3503-9946-2 SP - 2396 EP - 2403 PB - IEEE CY - Piscataway ER - TY - JOUR A1 - Meess, Henri A1 - Gerner, Jeremias A1 - Hein, Daniel A1 - Schmidtner, Stefanie A1 - Elger, Gordon A1 - Bogenberger, Klaus T1 - First steps towards real-world traffic signal control optimisation by reinforcement learning JF - Journal of Simulation N2 - Enhancing traffic signal optimisation has the potential to improve urban traffic flow without the need for expensive infrastructure modifications. While reinforcement learning (RL) techniques have demonstrated their effectiveness in simulations, their real-world implementation is still a challenge. Real-world systems need to be developed that guarantee a deployable action definition for real traffic systems while prioritising safety constraints and robust policies. This paper introduces a method to overcome this challenge by introducing a novel action definition that optimises parameter-level control programmes designed by traffic engineers. The complete proposed framework consists of a traffic situation estimation, a feature extractor, and a system that enables training on estimates of real-world traffic situations. Further multimodal optimisation, scalability, and continuous training after deployment could be achieved. The first simulative tests using this action definition show an average improvement of more than 20% in traffic flow compared to the baseline – the corresponding pre-optimised real-world control. UR - https://doi.org/10.1080/17477778.2024.2364715 Y1 - 2024 UR - https://doi.org/10.1080/17477778.2024.2364715 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-48585 SN - 1747-7778 VL - 18 IS - 6 SP - 957 EP - 972 PB - Taylor & Francis CY - London ER - TY - CHAP A1 - Gerner, Jeremias A1 - Bogenberger, Klaus A1 - Schmidtner, Stefanie T1 - Investigating Floating Car Observers for Traffic State Estimation through Co-Simulation T2 - MFTS: The 5th Symposium on Management of Future Motorway and Urban Traffic System 2024 Y1 - 2024 PB - Technion CY - Haifa ER - TY - JOUR A1 - Denk, Florian A1 - Fröhling, Felix A1 - Brunner, Pascal A1 - Huber, Werner A1 - Margreiter, Martin A1 - Bogenberger, Klaus A1 - Kates, Ronald T1 - Influence of gaze strategies and cognitive load on safeguarding performance of motorists in right-turning scenarios involving potential conflicts with vulnerable road users JF - Transportation Research Part F: Traffic Psychology and Behaviour N2 - In urban traffic, while the fraction of collisions involving Vulnerable Road Users (VRU) is low, their importance is high due to the higher injury risk for VRU. Their infrequent occurrence on average (compared with far more common individual perceptual and behavioral errors by both drivers and VRUs) reflects an underlying fault tolerance in traffic processes. However, the degree of fault tolerance varies among traffic situations. The underlying perceptual and cognitive processes involved are complex and can require a high level of attention and concentration, particularly in situations with intersecting trajectories. These processes can occasionally fail, leading to collision risk. The situation of right-turning motorists (in right-hand-drive countries) encountering cyclists moving straight on a bike lane (with right of way) has a particularly low error tolerance, since motorists must actively scan for cyclists approaching from behind. In order to develop, test and assess solutions that mitigate collision risk in this situation, the behavior-related causation mechanisms need investigation. This is the focus of this article. We conducted a trial on our closed test track with n = 35 subjects. The experiment was designed as a within-subject design with three independent factors: maneuver, target velocity, and cognitive load in an n-back task. The trial included observations of participants' gaze control. A primary research focus was the quality and efficiency of the safeguarding gaze behavior of participants in order to draw conclusions on the causation mechanisms of collisions in this situation. For this purpose we define metrics in order to quantify the quality and efficiency of a specific gaze behavior. Furthermore, we studied the effect of factors cognitive load and target velocity on safety and secondary (n-back) task performance. Remarkably, only four out of 35 participants reached a collision risk of 0% relating to the defined quality metric. Furthermore, we identified four distinct gaze strategy groups through hierarchical clustering, where one group performed particularly few glances overall. This group showed significant differences with respect to the defined quality metric whereas the other groups showed only slight differences to each other. The results have implications on subsequent crash causation model development. UR - https://doi.org/10.1016/j.trf.2024.11.012 Y1 - 2024 UR - https://doi.org/10.1016/j.trf.2024.11.012 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-63967 SN - 1873-5517 VL - 2025 IS - 109 SP - 32 EP - 49 PB - Elsevier CY - Amsterdam ER - TY - CHAP A1 - Lindner, Johannes A1 - Keler, Andreas A1 - Grigoropoulos, Georgios A1 - Malcolm, Patrick A1 - Denk, Florian A1 - Brunner, Pascal A1 - Bogenberger, Klaus T1 - A coupled driving simulator to investigate the interaction between bicycles and automated vehicles T2 - 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC) UR - https://doi.org/10.1109/ITSC55140.2022.9922400 KW - Visualization KW - Roads KW - Urban areas KW - Virtual environments KW - Bicycles KW - Mobile handsets KW - Software Y1 - 2022 UR - https://doi.org/10.1109/ITSC55140.2022.9922400 SN - 978-1-6654-6880-0 SP - 1335 EP - 1341 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Denk, Florian A1 - Fröhling, Felix A1 - Brunner, Pascal A1 - Huber, Werner A1 - Margreiter, Martin A1 - Bogenberger, Klaus A1 - Kates, Ronald T1 - Design of an Experiment to Pinpoint Cognitive Failure Processes in the Interaction of Motorists and Vulnerable Road Users T2 - IEEE IV 2023: Symposium Proceedings UR - https://doi.org/10.1109/IV55152.2023.10186550 KW - Traffic analysis KW - human factors KW - cognitive models KW - experimental design Y1 - 2023 UR - https://doi.org/10.1109/IV55152.2023.10186550 SN - 979-8-3503-4691-6 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Lindner, Johannes A1 - Grigoropoulos, Georgios A1 - Keler, Andreas A1 - Malcolm, Patrick A1 - Denk, Florian A1 - Brunner, Pascal A1 - Bogenberger, Klaus T1 - A mobile application for resolving bicyclist and automated vehicle interactions at intersections T2 - 2022 IEEE Intelligent Vehicles Symposium (IV) UR - https://doi.org/10.1109/IV51971.2022.9827439 KW - Visualization KW - Navigation KW - Roads KW - Simulation KW - Color KW - Mobile handsets KW - Regulation Y1 - 2022 UR - https://doi.org/10.1109/IV51971.2022.9827439 SP - 785 EP - 791 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Keler, Andreas A1 - Denk, Florian A1 - Brunner, Pascal A1 - Grigoropoulos, Georgios A1 - Malcom, Patrick A1 - Bogenberger, Klaus T1 - Varying Bicycle Infrastructures BT - an Interconnected Simulator Study for Inspecting Motorist-Cyclist Conflicts N2 - In this research, we estimate the influence of different bicycle infrastructure on the severity of motorist-cyclist conflicts via an interconnected simulator study. Our focus is a specific conflict type: motorist is turning at an intersection and the cyclist is going straight, crossing the intersection based on previous evaluations from literature. Additionally, we reason on previously-conducted bicycle simulator studies and adapt specific methodological components of investigation area depiction in VR and scenario definition. In the end, we present four scenarios of a case study based on a signalized intersection in Ingolstadt, Germany, where we inspect motorist-cyclist conflicts in an interconnected simulator environment. After every simulator run both test subjects will be teleported to specific starting positions. UR - https://doi.org/10.5281/zenodo.5513664 KW - Interconnected Simulator Studies KW - Bicycle Simulator KW - Traffic Safety KW - Motorist-Cyclist Conflicts KW - Visibility Fields Y1 - 2021 UR - https://doi.org/10.5281/zenodo.5513664 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-12899 PB - Zenodo CY - Genf ER - TY - CHAP A1 - Denk, Florian A1 - Brunner, Pascal A1 - Huber, Werner A1 - Margreiter, Martin A1 - Bogenberger, Klaus A1 - Kates, Ronald T1 - Assessment of traffic safety interventions using virtual randomized controlled trials: potential of connected and automated driving including V2X for collision reduction at urban intersections T2 - 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC) UR - https://doi.org/10.1109/ITSC55140.2022.9921764 KW - Laser radar KW - Simulation KW - Roads KW - Redundancy KW - Stochastic processes KW - Data integration KW - Predictive models Y1 - 2022 UR - https://doi.org/10.1109/ITSC55140.2022.9921764 SN - 978-1-6654-6880-0 SP - 1183 EP - 1190 PB - IEEE CY - Piscataway ER - TY - INPR A1 - Schlamp, Anna-Lena A1 - Gerner, Jeremias A1 - Bogenberger, Klaus A1 - Huber, Werner A1 - Schmidtner, Stefanie T1 - ROSA: Roundabout Optimized Speed Advisory with Multi-Agent Trajectory Prediction in Multimodal Traffic UR - https://doi.org/10.48550/arXiv.2602.14780 Y1 - 2026 UR - https://doi.org/10.48550/arXiv.2602.14780 PB - arXiv CY - Ithaca ER -