预测未来风险，让智能网联车提前规划安全路线
Predictive Risk Analysis and Safe Trajectory Planning for Intelligent and Connected Vehicles
- 用局部感知算法预测前方目标轨迹
- 基于时空离散化分析未来风险，提升预判能力
- 实测验证方案有效且实时，适合自动驾驶系统
智能网联车辆的安全轨迹规划是自动驾驶技术的关键。通过场域建模环境风险信息是一种有前景且有效的方法。然而，现有风险评估理论仅依赖当前信息，忽视未来预测。本文提出一种面向智能网联车辆的预测性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目标未来轨迹，随后利用预测结果进行时空离散化的预测性风险分析，最后基于风险分析生成安全轨迹。仿真与实车实验验证了本方法的有效性与实时可行性。
原文摘要 · Abstract (English)
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.
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