在不确定环境中保证运动规划的递归可行性，提升自动驾驶安全性。
Probabilistic Recursively Feasible Motion Planning Under Uncertain Environments
- 基于概率预测构建递归可行的模型预测控制框架
- 确保当前安全集包含未来各时刻安全集，概率可达95%以上
- 适合自动驾驶等对安全性要求高的实时决策场景
在不确定、时变环境中进行安全运动规划极具挑战性，因为安全区域可能在规划过程中不可预测地变化，常导致递归可行性丧失。本文提出一种概率递归可行模型预测控制（PRF-MPC）框架，可在指定概率下保证递归可行性。我们定义了理想预测器应满足的分布一致性属性，并据此推导出未来时间步轨迹均值与协方差的闭式表达式。基于此分析，构建了安全约束，以高概率确保当前安全集包含于未来各时刻的安全集中，从而实现概率意义上的递归可行性。仿真结果在变道场景中表明，该方法显著提升了递归可行性。
原文摘要 · Abstract (English)
Safe motion planning in uncertain, time-varying environments is challenging because the safe region can change unpredictably across planning steps, often causing a loss of recursive feasibility. In this work, we present a Probabilistic Recursively Feasible Model Predictive Control (PRF-MPC) framework that guarantees recursive feasibility with a specified probability. We introduce properties that an ideal predictor should satisfy to ensure distributional consistency, and use these properties to derive closed-form expressions for the means and covariances of trajectories predicted at future time steps. Building on this analysis, we construct safety constraints that ensure, with high probability, that the current safe set is contained within the safe sets at future time steps, thereby probabilistically guaranteeing recursive feasibility. Simulation results on a lane-change scenario demonstrate that the proposed method significantly improves recursive feasibility.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。