@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doi = {https://doi.org/10.1109/ITSC57777.2023.10422398}, pages = {2396 -- 2403}, year = {2024}, language = {en} } @unpublished{RoessleGernerBogenbergeretal.2024, author = {R{\"o}ßle, Dominik and Gerner, Jeremias and Bogenberger, Klaus and Cremers, Daniel and Schmidtner, Stefanie and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} } @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}, journal = {Journal of Simulation}, publisher = {Taylor \& Francis}, address = {London}, issn = {1747-7778}, doi = {https://doi.org/10.1080/17477778.2024.2364715}, 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{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} } @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{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} }