让自动驾驶更懂危险：只关注关键车辆，提升安全决策能力
Not All Agents Matter: From Global Attention Dilution to Risk-Prioritized Game Planning
- 区分风险高低，只对高危车辆做重点交互
- 在nuScenes和Bench2Drive上轨迹安全性大幅提升
- 适合追求高安全性的自动驾驶系统研发者
端到端自动驾驶的关键不在于感知与规划的集成，而在于统一表示空间中的动态多智能体博弈。现有模型普遍平等地对待所有交通参与者，难以区分真实碰撞威胁与复杂背景。为此，本文提出风险优先博弈规划（Risk-Prioritized Game Planning）框架GameAD，将端到端自动驾驶建模为风险感知的博弈问题。GameAD融合风险感知拓扑锚定、策略适配器、极小极大风险稀疏注意力与风险一致性均衡稳定机制，实现以风险优先的博弈决策。我们还提出规划风险暴露（Planning Risk Exposure）指标，量化长时程规划轨迹的累积风险强度，以保障安全。在nuScenes和Bench2Drive数据集上的大量实验表明，该方法显著优于现有先进方法，尤其在轨迹安全性方面表现突出。
原文摘要 · Abstract (English)
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.
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