标题： 基于混合共形预测的风险感知模型预测规划在密集不确定环境中的应用 显示英文标题

标题： Hybrid Conformal Prediction-based Risk-Aware Model Predictive Planning in Dense, Uncertain Environments

摘要： 在密集且不确定的环境中进行实时路径规划仍然是一个具有挑战性的问题，因为预测众多动态障碍物的未来运动在计算上负担沉重且不现实。 为了解决这个问题，我们引入了基于预测的风险感知路径规划框架HyPRAP，该框架使用模型的混合组合来预测局部障碍物的运动。 HyPRAP使用一种新颖的基于预测的碰撞风险指数（P-CRI）来评估每个障碍物带来的风险，从而根据智能体是否优先考虑高预测准确性或低计算预测开销来选择性地使用预测器。 这种选择性路由使智能体能够专注于高风险障碍物，同时忽略或简化低风险障碍物，使其适用于障碍物数量众多的环境。 此外，HyPRAP通过混合共形预测来量化不确定性，通过不同模型的多个预测同时获得置信区间。 理论分析表明，HyPRAP通过利用预测模型的多样性，有效平衡了安全性和计算效率。 大量仿真验证了这些见解在更一般情况下的有效性，证实与单一预测器方法相比，HyPRAP表现更好，P-CRI在风险评估方面优于简单的邻近度方法。 显示英文摘要

摘要： Real-time path planning in dense, uncertain environments remains a challenging problem, as predicting the future motions of numerous dynamic obstacles is computationally burdensome and unrealistic. To address this, we introduce Hybrid Prediction-based Risk-Aware Planning (HyPRAP), a prediction-based risk-aware path-planning framework which uses a hybrid combination of models to predict local obstacle movement. HyPRAP uses a novel Prediction-based Collision Risk Index (P-CRI) to evaluate the risk posed by each obstacle, enabling the selective use of predictors based on whether the agent prioritizes high predictive accuracy or low computational prediction overhead. This selective routing enables the agent to focus on high-risk obstacles while ignoring or simplifying low-risk ones, making it suitable for environments with a large number of obstacles. Moreover, HyPRAP incorporates uncertainty quantification through hybrid conformal prediction by deriving confidence bounds simultaneously achieved by multiple predictions across different models. Theoretical analysis demonstrates that HyPRAP effectively balances safety and computational efficiency by leveraging the diversity of prediction models. Extensive simulations validate these insights for more general settings, confirming that HyPRAP performs better compared to single predictor methods, and P-CRI performs better over naive proximity-based risk assessment.