Fan, Jialin (Tongji University), Ni, Ying (Tongji University), Chen, Yuhang (Tongji University), Li, Siying (Tongji University), Sun, Jie (University of Queensland), Sun, Jian (Tongji University)

Towards Solvable Safety-Critical Traffic Generation: Constrained-Adversarial Policy Optimization Based on Continual Learning

Scheduled for presentation during the Video Session "On-Demand Video Presentations" (VP-VP), Saturday, November 22, 2025, 08:00−18:00, On-Demand Platform

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on April 2, 2026

Abstract

Safety-critical scenarios are of significant value for testing and validating autonomous vehicles (AVs). However, their long-tailed distributions in real-world environments make efficient data collection challenging. Data-driven methods for targeted generation of challenging testing scenarios offer a promising solution, but adversarial approaches that solely focus on maximizing adversarial interactions often lead to inevitable collisions, leaving no space for the AV to make decisions. To address this challenge, we propose Constrained-Adversarial Policy Optimization (CAPO), an interactive scenario generation method that incorporates adversarial rationality. CAPO is built on a two-phase continual learning framework. In Phase I, multi-agent reinforcement learning (MARL) with safety-constraint function (SCF) is utilized to train agents to interact safely and accomplish driving tasks. In the Phase II, an AV expert is introduced to scenarios which are generated by adversarial agents considering the AV’s minimum safety constraint. These generated scenarios are adversarial yet solvable for the AV expert. Through open-loop and closed-loop tests, CAPO demonstrates its ability to produce more solvable and safety-critical scenarios, while significantly reducing unrealistic adversarial cases and unavoidable collisions.