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This paper introduces GameAD, a risk-aware game planning framework for end-to-end autonomous driving that prioritizes interactions with high-risk agents. GameAD uses risk-aware topology anchoring, a strategic payload adapter, minimax risk-aware sparse attention, and risk-consistent equilibrium stabilization to improve game-theoretic decision making. Experiments on nuScenes and Bench2Drive show GameAD significantly outperforms state-of-the-art methods, especially in trajectory safety.
Autonomous vehicles can now better avoid collisions by focusing on the riskiest actors in traffic, rather than treating all agents equally.
End-to-end autonomous driving resides not in the integration of perception and planning, but rather in the dynamic multi-agent game within a unified representation space. Most existing end-to-end models treat all agents equally, hindering the decoupling of real collision threats from complex backgrounds. To address this issue, We introduce the concept of Risk-Prioritized Game Planning, and propose GameAD, a novel framework that models end-to-end autonomous driving as a risk-aware game problem. The GameAD integrates Risk-Aware Topology Anchoring, Strategic Payload Adapter, Minimax Risk-Aware Sparse Attention, and Risk Consistent Equilibrium Stabilization to enable game theoretic decision making with risk prioritized interactions. We also present the Planning Risk Exposure metric, which quantifies the cumulative risk intensity of planned trajectories over a long horizon for safe autonomous driving. Extensive experiments on the nuScenes and Bench2Drive datasets show that our approach significantly outperforms state-of-the-art methods, especially in terms of trajectory safety.
Tsinghua AI