Wang, Caojun (Tongji University), Yang, Shuo (Tongji University), Huang, Yanjun (Tongji University)

A Data-Driven Risk Assessment Method for Autonomous Vehicles without Expert Rule Design

Scheduled for presentation during the Regular Session "Advanced Vehicle Safety Systems I" (WeBT8), Wednesday, September 25, 2024, 14:50−15:10, Salon 16

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 October 7, 2024

Abstract

Autonomous vehicles (AVs) enhance driving efficiency and reduce accidents but require robust risk assessment methods due to dense traffic and uncertainties. Existing methods rely on predefined rules, which lack generalization. This paper presents a novel risk quantification method without expert rules, leveraging reinforcement learning and adversarial agents. The proposed model uses Gated Transformer Networks for multivariate time series regression, analyzing historical traffic data to generate continuous risk assessments. Simulation experiments validate the method's efficacy, demonstrating its precision and robustness.