基于自车条件生成预测的主动交互感知模型预测路径积分方法
Active Interaction-Aware Model Predictive Path Integral via Ego-Conditioned Generative Predictions
- TU Delft（代尔夫特理工大学）
- Aumovio SE（奥莫维奥股份公司）
- Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
本文提出将自车条件生成自回归预测模型集成到MPPI控制的规划框架，通过嵌套采样评估代价与风险，使自车主动探测交互、降低歧义，仿真显示其安全效率优于传统方法。
中文摘要 AI 辅助
密集交通本质上具有交互性，自车与周围智能体的反应会持续相互影响，因此“假设分析”对安全高效驾驶至关重要。为实现这种主动交互感知行为，本文提出一种规划框架，将自车条件生成自回归预测模型集成到模型预测路径积分（MPPI）控制中。该生成预测模型会输出以自车每个候选未来动作为条件的、针对周围智能体的随机多模态预测。嵌套采样方案可对诱导分布下的期望代价与碰撞风险进行可处理评估。该公式允许自车主动探究不同候选动作如何影响交互结果，并识别能降低不确定交互中歧义的动作。闭环仿真表明，与传统的先预测再规划及被动交互感知方法相比，该方法的安全性与效率均得到提升。
英文摘要
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.
