Description: Traffic safety is the fundamental criterion for vehicular environments and many artificial intelligence-based systems like self-driving cars. There are places, e.g., intersections and shared spaces, in the urban environment with high risks where vehicles and vulnerable road users (VRUs) such as pedestrians and cyclists directly interact with each other. By advancing state-of-the-art artificial intelligence methodologies, this project aims to build a privacy-aware deep learning framework to learn road users’ behaviour in various mixed traffic situations for the safety of vehicles and VRUs.
VeVuSafety proposes a 3D environment model based on a 3D point cloud for privacy protection — private information like license plates and faces is anonymized. Then, within this environment model, an end-to-end deep learning framework using camera data will be built for multimodal trajectory prediction, anomaly detection, and potential risk classification based on deep generative models such as the Variational Auto-Encoder. Besides road user safety and privacy, VeVuSafety can help traffic engineers and city planners to better estimate the design of traffic facilities in order to achieve a road-user-friendly urban traffic environment.
Fig. 1 project profile and work packages
Partners: Leibniz University Hannover, Nara Institute of Science and Technology (NAIST), VISCODA GmbH
Sponsor: Marie Sklodowska-Curie Actions Postdoctoral Fellowshipes (MSCA-PF)
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