Parameter tuning is a powerful approach to enhance adaptability in model predictive control (MPC) motion planners. However, existing methods typically operate in a myopic fashion that only evaluates executed actions, leading to inefficient parameter updates due to the sparsity of failure events (e.g., obstacle nearness or collision). To cope with this issue, we propose to extend evaluation from executed to non-executed actions, yielding a hierarchical proactive tuning (HPTune) framework that combines both a fast-level tuning and a slow-level tuning. The fast one adopts risk indicators of predictive closing speed and predictive proximity distance, and the slow one leverages an extended evaluation loss for closed-loop backpropagation. Additionally, we integrate HPTune with the Doppler LiDAR that provides obstacle velocities apart from position-only measurements for enhanced motion predictions, thus facilitating the implementation of HPTune. Extensive experiments on high-fidelity simulator demonstrate that HPTune achieves efficient MPC tuning and outperforms various baseline schemes in complex environments. It is found that HPTune enables situation-tailored motion planning by formulating a safe, agile collision avoidance strategy.

翻译：参数调优是增强模型预测控制（MPC）运动规划器适应性的有效方法。然而，现有方法通常以短视方式运行，仅评估已执行的动作，由于失败事件（例如障碍物接近或碰撞）的稀疏性，导致参数更新效率低下。为解决这一问题，我们提出将评估范围从已执行动作扩展到未执行动作，从而构建一个结合快速层调优与慢速层调优的分层主动调参（HPTune）框架。快速层采用预测接近速度与预测接近距离的风险指标，慢速层则利用扩展评估损失进行闭环反向传播。此外，我们将HPTune与多普勒激光雷达集成，该雷达除仅位置测量外还能提供障碍物速度信息，从而增强运动预测能力，促进HPTune的实际应用。在高保真仿真器上的大量实验表明，HPTune实现了高效的MPC参数调优，并在复杂环境中优于多种基线方案。研究发现，HPTune通过构建安全、敏捷的避碰策略，能够实现情境自适应的运动规划。