VeVuSafety

Project description

Traffic safety is the fundamental criterion for vehicular environments and many artificial intelligence-based systems like autonomous vehicles. There are places, e.g., intersections and shared spaces, in urban environments with high risks where vehicles and VRUs directly interact with each other. By advancing state-of-the-art artificial intelligence methodologies, this project VeVuSafety aims to build an end-to-end deep learning framework to learn road users’ behavior in various mixed traffic situations for the safety of vehicles and VRUs.

Project profile and work packages

VeVuSafety aims to learn road users’ behavior automatically and effectively in various mixed traffic situations for the safety of vehicles and VRUs by using state-of-the-art AI-based methodologies.
Heterogeneous traffic data captured by, e.g., camera and laser sensors will be leveraged to train deep generative models complying with data protection requirements.

Publications

Cheng, H., Liu, M., Chen, L., Broszio, H., Sester, M., & Yang, M. Y. (2023). Gatraj: A graph-and attention-based multi-agent trajectory prediction model. ISPRS Journal of Photogrammetry and Remote Sensing, 205, 163-175.

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Liu, M., Cheng, H., & Yang, M. Y. (2023). Tracing the Influence of Predecessors on Trajectory Prediction. arXiv preprint arXiv:2308.05634. Accepted at ICCVW 2023.

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Cheng, H., & Chen, L. (2023). An End-to-End Framework of Road User Detection, Tracking, and Prediction from Monocular Images. arXiv preprint arXiv:2308.05026. ITSC 2023.

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Yunshuang Yuan, Hao Cheng, Michael Ying Yang, Monika Sester, Generating evidential BEV maps in continuous driving space, ISPRS Journal of Photogrammetry and Remote Sensing, Volume 204, 2023, Pages 27-41, ISSN 0924-2716, https://doi.org/10.1016/j.isprsjprs.2023.08.013.

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W. Zhang, H. Cheng, F. T. Johora and M. Sester, "ForceFormer: Exploring Social Force and Transformer for Pedestrian Trajectory Prediction," 2023 IEEE Intelligent Vehicles Symposium (IV), Anchorage, AK, USA, 2023, pp. 1-7, doi: 10.1109/IV55152.2023.10186643.

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Li, Y., Cheng, H., Zeng, Z., Deml, B., & Liu, H. (2023). An AV-MV negotiation method based on synchronous prompt information on a multi-vehicle bottleneck road. Transportation Research Interdisciplinary Perspectives, 20, 100845.

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