Traffic safety is the fundamental criterion for vehicular environments and many artificial intelligence-based systems like self-driving cars. There are places in the urban environment with high risks where vehicles and pedestrians and cyclists directly interact with each other. By advancing state-of-the-art artificial intelligence methodologies, the project aims to build a privacy-aware deep learning framework to learn road users’ behaviour in various mixed traffic situations for the safety between vehicles and vulnerable road users.
This project is called VeVuSafety and is led by dr. Hao Cheng. He was awarded the prestigious European Marie Skłodowska-Curie Postdoctoral Fellowship for VeVuSafety. The project is supervised by dr. Michael Ying Yang and prof. George Vosselman. Starting from October 1st, Hao Cheng will carry out the two-year project at ITC’s EOS department of the University of Twente. Cheng currently holds a research position at Leibniz University Hannover.
VeVuSafety proposes a 3D environment model based on 3D point cloud for privacy protection. 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. 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.
If you want to learn more about this project, get in touch with Michael Ying Yang or George Vosselman.