@inproceedings{GernerBogenbergerSchmidtner2024, author = {Gerner, Jeremias and Bogenberger, Klaus and Schmidtner, Stefanie}, title = {Temporal Enhanced Floating Car Observers}, booktitle = {2024 IEEE Intelligent Vehicles Symposium (IV)}, publisher = {IEEE}, address = {Piscataway}, isbn = {979-8-3503-4881-1}, doi = {https://doi.org/10.1109/IV55156.2024.10588538}, pages = {1035 -- 1040}, year = {2024}, language = {en} } @inproceedings{ZhangGernerIlicetal.2025, author = {Zhang, Yunfei and Gerner, Jeremias and Ilic, Mario and Schmidtner, Stefanie and Bogenberger, Klaus}, title = {Autonomous Vehicles as Sensors: Traffic State Estimation}, pages = {TRBAM-25-06186}, booktitle = {104th Annual Meeting of the Transportation Research Board (TRB)}, publisher = {National Academy of Sciences}, address = {Washington}, year = {2025}, language = {en} } @unpublished{RoessleGernerBogenbergeretal.2024, author = {R{\"o}ßle, Dominik and Gerner, Jeremias and Bogenberger, Klaus and Cremers, Daniel and Schmidtner, Stefanie and Sch{\"o}n, Torsten}, title = {Unlocking Past Information: Temporal Embeddings in Cooperative Bird's Eye View Prediction}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.2401.14325}, year = {2024}, abstract = {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.}, language = {en} } @inproceedings{SchlampGernerBogenbergeretal.2024, author = {Schlamp, Anna-Lena and Gerner, Jeremias and Bogenberger, Klaus and Schmidtner, Stefanie}, title = {User-Centric Green Light Optimized Speed Advisory with Reinforcement Learning}, booktitle = {2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)}, publisher = {IEEE}, address = {Piscataway}, isbn = {979-8-3503-9946-2}, doi = {https://doi.org/10.1109/ITSC57777.2023.10422501}, pages = {3463 -- 3470}, year = {2024}, language = {en} } @inproceedings{GernerRoessleCremersetal.2024, author = {Gerner, Jeremias and R{\"o}ssle, Dominik and Cremers, Daniel and Bogenberger, Klaus and Sch{\"o}n, Torsten and Schmidtner, Stefanie}, title = {Enhancing Realistic Floating Car Observers in Microscopic Traffic Simulation}, booktitle = {2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)}, publisher = {IEEE}, address = {Piscataway}, isbn = {979-8-3503-9946-2}, doi = {https://doi.org/10.1109/ITSC57777.2023.10422398}, pages = {2396 -- 2403}, year = {2024}, language = {en} } @article{MeessGernerHeinetal.2024, author = {Meess, Henri and Gerner, Jeremias and Hein, Daniel and Schmidtner, Stefanie and Elger, Gordon and Bogenberger, Klaus}, title = {First steps towards real-world traffic signal control optimisation by reinforcement learning}, volume = {18}, journal = {Journal of Simulation}, number = {6}, publisher = {Taylor \& Francis}, address = {London}, issn = {1747-7778}, doi = {https://doi.org/10.1080/17477778.2024.2364715}, pages = {957 -- 972}, year = {2024}, abstract = {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.}, language = {en} } @inproceedings{GernerBogenbergerSchmidtner2024, author = {Gerner, Jeremias and Bogenberger, Klaus and Schmidtner, Stefanie}, title = {Investigating Floating Car Observers for Traffic State Estimation through Co-Simulation}, booktitle = {MFTS: The 5th Symposium on Management of Future Motorway and Urban Traffic System 2024}, publisher = {Technion}, address = {Haifa}, year = {2024}, language = {en} } @article{DenkFroehlingBrunneretal.2024, author = {Denk, Florian and Fr{\"o}hling, Felix and Brunner, Pascal and Huber, Werner and Margreiter, Martin and Bogenberger, Klaus and Kates, Ronald}, title = {Influence of gaze strategies and cognitive load on safeguarding performance of motorists in right-turning scenarios involving potential conflicts with vulnerable road users}, volume = {2025}, journal = {Transportation Research Part F: Traffic Psychology and Behaviour}, number = {109}, publisher = {Elsevier}, address = {Amsterdam}, issn = {1873-5517}, doi = {https://doi.org/10.1016/j.trf.2024.11.012}, pages = {32 -- 49}, year = {2024}, abstract = {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.}, language = {en} } @inproceedings{LindnerKelerGrigoropoulosetal.2022, author = {Lindner, Johannes and Keler, Andreas and Grigoropoulos, Georgios and Malcolm, Patrick and Denk, Florian and Brunner, Pascal and Bogenberger, Klaus}, title = {A coupled driving simulator to investigate the interaction between bicycles and automated vehicles}, booktitle = {2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-6654-6880-0}, doi = {https://doi.org/10.1109/ITSC55140.2022.9922400}, pages = {1335 -- 1341}, year = {2022}, language = {en} } @inproceedings{DenkFroehlingBrunneretal.2023, author = {Denk, Florian and Fr{\"o}hling, Felix and Brunner, Pascal and Huber, Werner and Margreiter, Martin and Bogenberger, Klaus and Kates, Ronald}, title = {Design of an Experiment to Pinpoint Cognitive Failure Processes in the Interaction of Motorists and Vulnerable Road Users}, booktitle = {IEEE IV 2023: Symposium Proceedings}, publisher = {IEEE}, address = {Piscataway}, isbn = {979-8-3503-4691-6}, doi = {https://doi.org/10.1109/IV55152.2023.10186550}, year = {2023}, language = {en} } @inproceedings{LindnerGrigoropoulosKeleretal.2022, author = {Lindner, Johannes and Grigoropoulos, Georgios and Keler, Andreas and Malcolm, Patrick and Denk, Florian and Brunner, Pascal and Bogenberger, Klaus}, title = {A mobile application for resolving bicyclist and automated vehicle interactions at intersections}, booktitle = {2022 IEEE Intelligent Vehicles Symposium (IV)}, publisher = {IEEE}, address = {Piscataway}, doi = {https://doi.org/10.1109/IV51971.2022.9827439}, pages = {785 -- 791}, year = {2022}, language = {en} } @inproceedings{KelerDenkBrunneretal.2021, author = {Keler, Andreas and Denk, Florian and Brunner, Pascal and Grigoropoulos, Georgios and Malcom, Patrick and Bogenberger, Klaus}, title = {Varying Bicycle Infrastructures}, subtitle = {an Interconnected Simulator Study for Inspecting Motorist-Cyclist Conflicts}, publisher = {Zenodo}, address = {Genf}, doi = {https://doi.org/10.5281/zenodo.5513664}, year = {2021}, abstract = {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.}, language = {en} } @inproceedings{DenkBrunnerHuberetal.2022, author = {Denk, Florian and Brunner, Pascal and Huber, Werner and Margreiter, Martin and Bogenberger, Klaus and Kates, Ronald}, title = {Assessment of traffic safety interventions using virtual randomized controlled trials: potential of connected and automated driving including V2X for collision reduction at urban intersections}, booktitle = {2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-6654-6880-0}, doi = {https://doi.org/10.1109/ITSC55140.2022.9921764}, pages = {1183 -- 1190}, year = {2022}, language = {en} } @unpublished{SchlampGernerBogenbergeretal.2026, author = {Schlamp, Anna-Lena and Gerner, Jeremias and Bogenberger, Klaus and Huber, Werner and Schmidtner, Stefanie}, title = {ROSA: Roundabout Optimized Speed Advisory with Multi-Agent Trajectory Prediction in Multimodal Traffic}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.2602.14780}, year = {2026}, language = {en} }