驾驶是一场游戏：结合规划与预测的贝叶斯迭代最佳回应
Driving is a Game: Combining Planning and Prediction with Bayesian Iterative Best Response
AI总结：
BIBeR通过结合运动预测与博弈论规划，实现自动驾驶中的交互感知和双向适应，提升复杂交通场景下的规划性能。
摘要：
自动驾驶规划系统在常规场景中使用轻量规则方法几乎完美，但在密集城市交通中仍面临挑战，特别是在车道变更和汇入时需要预测和影响其他车辆。现代运动预测器提供高度准确的预测，但其与规划的整合大多处于初级阶段：丢弃不安全的计划。端到端模型提供了一种单向整合，避免了在不确定性下联合预测和规划建模的挑战。相比之下，博弈论方法提供了一种原则性的替代方案，但在自动驾驶中应用有限。我们提出了贝叶斯迭代最佳回应（BIBeR），一种将运动预测和博弈论规划统一到单一交互感知过程的框架。BIBeR是首个将最先进的预测器整合到迭代最佳回应（IBR）循环中的，反复优化主车和周围车辆的策略。这种反复的最佳回应过程近似纳什均衡，使主车既能反应又能塑造他人的行为。此外，我们提出的贝叶斯置信度估计量化了预测的可靠性并调节更新强度，在低置信度时更加保守，在高置信度时更加果断。BIBeR兼容现代预测器和规划器，结合了结构化规划的透明性和学习模型的灵活性。实验表明，BIBeR在高度互动的interPlan车道变更场景中比最先进的规划器提高了11%，同时在标准nuPlan基准上也优于现有方法。
英文摘要：
Autonomous driving planning systems perform nearly perfectly in routine scenarios using lightweight, rule-based methods but still struggle in dense urban traffic, where lane changes and merges require anticipating and influencing other agents. Modern motion predictors offer highly accurate forecasts, yet their integration into planning is mostly rudimental: discarding unsafe plans. Similarly, end-to-end models offer a one-way integration that avoids the challenges of joint prediction and planning modeling under uncertainty. In contrast, game-theoretic formulations offer a principled alternative but have seen limited adoption in autonomous driving. We present Bayesian Iterative Best Response (BIBeR), a framework that unifies motion prediction and game-theoretic planning into a single interaction-aware process. BIBeR is the first to integrate a state-of-the-art predictor into an Iterative Best Response (IBR) loop, repeatedly refining the strategies of the ego vehicle and surrounding agents. This repeated best-response process approximates a Nash equilibrium, enabling bidirectional adaptation where the ego both reacts to and shapes the behavior of others. In addition, our proposed Bayesian confidence estimation quantifies prediction reliability and modulates update strength, more conservative under low confidence and more decisive under high confidence. BIBeR is compatible with modern predictors and planners, combining the transparency of structured planning with the flexibility of learned models. Experiments show that BIBeR achieves an 11% improvement over state-of-the-art planners on highly interactive interPlan lane-change scenarios, while also outperforming existing approaches on standard nuPlan benchmarks.
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