PCASim: Promptable Closed-loop Adversarial Simulation for Urban Traffic Environment

TL;DR

PCASim introduces a novel framework combining adversarial scenario generation, large language models, and reinforcement learning to enhance urban traffic simulation for autonomous driving testing.

Contribution

It presents an integrated approach using LLMs and reinforcement learning for generating diverse, realistic, and safety-critical traffic scenarios in closed-loop urban traffic environments.

Findings

Improved language generation accuracy by 12%

Scenario transformation success rate increased by 8%

Obstacle-avoidance capability improved by 30%

Abstract

Real-world autonomous driving, particularly in urban environments with numerous corner cases, requires rigorous testing to ensure product safety and robustness. However, few studies have explored integrating adversarial scenario generation with the training of safety agents in closed-loop testing, enabling efficient co-evolution and mutual enhancement of both. To address this challenge, an adversarial behavior knowledge repository is constructed by applying rule-based filtering to an open-source dataset, combined with knowledge retrieval modules tailored for simulation environments. A large language model (LLM) is employed to integrate knowledge-, data-, and adversarial-driven approaches, generating safety-critical traffic scenarios customized to user needs. Additionally, while evaluating the generated scenarios, we employ reinforcement learning models to train the behaviors of different types of vehicles, thereby enriching scenario diversity beyond existing datasets while preserving realism. Experimental results demonstrate that the proposed framework improves the accuracy of domain-specific language generation by 12\%. Moreover, the success rate of newly generated scenario transformations increases by 8\%, while obstacle-avoidance capability is enhanced by 30\%. For the complete manuscript, please refer to: https://zhenhaooo.github.io/PCASim.github.io/