智能网联汽车的预测风险分析与安全轨迹规划
Predictive Risk Analysis and Safe Trajectory Planning for Intelligent and Connected Vehicles
AI总结：
本文提出一种智能网联汽车的预测风险分析与安全轨迹规划框架，通过局部风险感知算法预测未来轨迹并进行时空离散化风险分析以生成安全轨迹，仿真与实验验证了其有效性与实时性。
摘要：
智能网联汽车的安全轨迹规划是自动驾驶技术的关键组成部分。通过场对环境风险信息进行建模是一种有前景且有效的安全轨迹规划方法。然而，现有的风险评估理论仅通过当前信息分析风险，忽略了未来预测。本文提出了一种针对智能网联汽车的预测风险分析与安全轨迹规划框架。该框架首先通过局部风险感知算法预测物体的未来轨迹，随后利用预测结果进行时空离散化的预测风险分析。接着基于该预测风险分析生成安全轨迹。最后，仿真与车辆实验证实了我们方法的有效性与实时实用性。
英文摘要：
The safe trajectory planning of intelligent and connected vehicles is a key component in autonomous driving technology. Modeling the environment risk information by field is a promising and effective approach for safe trajectory planning. However, existing risk assessment theories only analyze the risk by current information, ignoring future prediction. This paper proposes a predictive risk analysis and safe trajectory planning framework for intelligent and connected vehicles. This framework first predicts future trajectories of objects by a local risk-aware algorithm, following with a spatiotemporal-discretised predictive risk analysis using the prediction results. Then the safe trajectory is generated based on the predictive risk analysis. Finally, simulation and vehicle experiments confirm the efficacy and real-time practicability of our approach.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
