让自动驾驶车主动预判周围车辆反应，提升安全与效率。
Active Interaction-Aware Model Predictive Path Integral via Ego-Conditioned Generative Predictions
- 用生成模型预测其他车辆对自身动作的多种可能反应
- 通过嵌套采样计算不同动作下的碰撞风险与成本期望
- 适合需要实时交互决策的自动驾驶系统
密集交通中，自车与周边车辆持续相互影响，因此“若如此会怎样”的推理对安全高效驾驶至关重要。我们提出一种规划框架，将自车条件化的生成式自回归预测模型集成到模型预测路径积分（MPPI）控制中。该预测模型输出在自车考虑的不同未来动作下，周边车辆的随机多模态预测结果。通过嵌套采样方案，可高效评估在诱导分布下的期望成本与碰撞风险。该方法使自车能主动探查不同候选动作如何塑造交互结果，并识别出能降低不确定交互模糊性的动作。闭环仿真表明，相比传统先预测后规划及被动交互感知方法，本方法在安全性和效率上均有提升。
原文摘要 · Abstract (English)
Dense traffic is inherently interactive. The ego vehicle and surrounding agents continuously influence each other's reactions, making "what-if" reasoning essential for safe and efficient driving. To enable such an active interaction-aware behavior, we propose a planning framework that integrates an ego-conditioned generative autoregressive prediction model within Model Predictive Path Integral (MPPI) control. The generative prediction model outputs stochastic, multi-modal predictions of surrounding agents conditioned on each of the ego's considered future actions. A nested sampling scheme enables tractable evaluation of expected cost and collision risk under the induced distribution. This formulation allows the ego to actively probe how different candidate actions shape the interaction outcomes and to identify actions that reduce ambiguity in uncertain interactions. Closed-loop simulations demonstrate improved safety and efficiency compared to conventional predict-then-plan and passive interaction-aware approaches.
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