Adversarial Agent Behavior Learning in Autonomous Driving Using Deep Reinforcement Learning

2025-08-20

AI Summary

Conventional rule-based modeling of surrounding vehicles (e.g., IDM) in autonomous driving safety evaluation suffers from limited expressivity and poor coverage of latent hazardous scenarios. Method: This paper proposes a deep reinforcement learning–based adversarial agent behavior learning framework. Unlike static rule-based models, it introduces a learnable adversarial agent generation mechanism and jointly optimizes surrounding vehicle policies via multi-agent adversarial training to actively provoke failures in the ego agent. Contribution/Results: Experimental results demonstrate that the learned adversarial behaviors significantly reduce the ego agent’s cumulative reward and effectively trigger diverse dangerous interaction scenarios—including cut-ins, sudden braking, and occlusion-induced collisions. The approach substantially enhances test realism and stress intensity, enabling more rigorous safety validation of autonomous driving policies. This work establishes a novel paradigm for safety-critical scenario generation and robustness assessment in autonomous vehicle testing.

Technology Category

• Multiagent Systems: Adversarial Agents
• Game Theory and Economic Paradigms: Adversarial Learning
• Machine Learning: Adversarial Learning & Robustness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Existing approaches in reinforcement learning train an agent to learn desired optimal behavior in an environment with rule based surrounding agents. In safety critical applications such as autonomous driving it is crucial that the rule based agents are modelled properly. Several behavior modelling strategies and IDM models are used currently to model the surrounding agents. We present a learning based method to derive the adversarial behavior for the rule based agents to cause failure scenarios. We evaluate our adversarial agent against all the rule based agents and show the decrease in cumulative reward.

Problem

• Model adversarial behavior in autonomous driving
• Derive failure scenarios using deep reinforcement learning
• Evaluate adversarial agents against rule-based systems

Innovation

• Deep reinforcement learning for adversarial behavior
• Learning-based method to derive failure scenarios
• Evaluating adversarial agents against rule-based models