Jiang, Junkai (Tsinghua University), Han, Zeyu (Tsinghua University), Wang, Yuning (Tsinghua University), Cai, Mengchi (Tsinghua University), Qingwen, Meng (Tsinghua university), Xu, Qing (Tsinghua University), Wang, Jianqiang (Tsinghua University)

EDRF: Enhanced Driving Risk Field Based on Multimodal Trajectory Prediction and Its Applications

Scheduled for presentation during the Invited Session "Towards Human-Inspired Interactive Autonomous Driving II" (ThBT2), Thursday, September 26, 2024, 15:30−15:50, Salon 5

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on December 26, 2024

Abstract

Driving risk assessment is crucial for both autonomous vehicles and human-driven vehicles. The driving risk can be quantified as the product of the probability that an event (such as collision) will occur and the consequence of that event. However, the probability of events occurring is often difficult to predict due to the uncertainty of drivers’ or vehicles’ behavior. Traditional methods generally employ kinematic-based approaches to predict the future trajectories of entities, which often yield unrealistic prediction results. In this paper, the Enhanced Driving Risk Field (EDRF) model is proposed, integrating deep learning-based multimodal trajectory prediction results with Gaussian distribution models to quantitatively capture the uncertainty of traffic entities’ behavior. The applications of the EDRF are also proposed. It is applied across various tasks (traffic risk monitoring, ego-vehicle risk analysis, and motion and trajectory planning) through the defined concept Interaction Risk (IR). Adequate example scenarios are provided for each application to illustrate the effectiveness of the model.