Game-Theoretic Risk-Shaped Reinforcement Learning for Safe Autonomous Driving

2025-10-12

AI Summary

Autonomous driving in complex, dynamic traffic environments struggles to simultaneously ensure safety, efficiency, and robustness. Method: This paper proposes a reinforcement learning framework integrating game-theoretic modeling and risk-aware decision-making. It constructs a multi-level game-theoretic world model incorporating an uncertainty-aware barrier mechanism to jointly characterize epistemic and aleatoric uncertainties. The framework further introduces risk-shaped rewards, constrained policy optimization, and adaptive-horizon rollout planning to enable online dynamic risk assessment and real-time safety boundary adjustment. Results: Experiments demonstrate that the method reduces collision rates by over 40% in high-risk scenarios, while achieving higher task success rates and driving efficiency than human drivers—thereby significantly enhancing decision-making safety, adaptability, and practicality.

Technology Category

• Reasoning under Uncertainty: Sequential Decision Making
• Natural Language Processing: Safety and Robustness
• Game Theory and Economic Paradigms: Adversarial Learning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Ensuring safety in autonomous driving (AD) remains a significant challenge, especially in highly dynamic and complex traffic environments where diverse agents interact and unexpected hazards frequently emerge. Traditional reinforcement learning (RL) methods often struggle to balance safety, efficiency, and adaptability, as they primarily focus on reward maximization without explicitly modeling risk or safety constraints. To address these limitations, this study proposes a novel game-theoretic risk-shaped RL (GTR2L) framework for safe AD. GTR2L incorporates a multi-level game-theoretic world model that jointly predicts the interactive behaviors of surrounding vehicles and their associated risks, along with an adaptive rollout horizon that adjusts dynamically based on predictive uncertainty. Furthermore, an uncertainty-aware barrier mechanism enables flexible modulation of safety boundaries. A dedicated risk modeling approach is also proposed, explicitly capturing both epistemic and aleatoric uncertainty to guide constrained policy optimization and enhance decision-making in complex environments. Extensive evaluations across diverse and safety-critical traffic scenarios show that GTR2L significantly outperforms state-of-the-art baselines, including human drivers, in terms of success rate, collision and violation reduction, and driving efficiency. The code is available at https://github.com/DanielHu197/GTR2L.

Problem

• Balancing safety and efficiency in autonomous driving
• Modeling multi-agent interactions and risk in traffic
• Addressing epistemic and aleatoric uncertainty in decision-making

Innovation

• Game-theoretic risk-shaped RL for safe autonomous driving
• Multi-level world model predicting vehicle interactions and risks
• Uncertainty-aware barrier mechanism with adaptive rollout horizon