融合激光雷达与雷达数据，实现高速赛车的精准目标跟踪。
Target Tracking via LiDAR-RADAR Sensor Fusion for Autonomous Racing
- 基于延迟感知的扩展卡尔曼滤波，融合激光雷达与雷达数据。
- 在275 km/h下完成全自动超车，支持乱序测量重处理。
- 适用于高速自动驾驶场景，尤其适合竞速类复杂动态环境。
高速多车自主竞速将提升公路自动驾驶车辆的安全性与性能。从移动平台精确检测车辆并估计动态参数，是规划与执行复杂自主超车动作的关键。为此，我们开发了一种基于延迟感知扩展卡尔曼滤波（EKF）的多目标跟踪算法，融合激光雷达（LiDAR）与雷达（RADAR）测量数据。该算法通过在EKF观测函数中显式引入径向速度（Range Rate），并利用赛道先验知识进行状态预测，充分挖掘两类传感器的互补特性。通过双状态与测量缓冲区实现乱序测量重处理，确保传感器延迟补偿且无信息丢失。该算法已部署于团队PoliMOVE的自动驾驶赛车上，并在实验中成功完成多个全自主超车动作，最高速度达275 km/h。
原文摘要 · Abstract (English)
High Speed multi-vehicle Autonomous Racing will increase the safety and performance of road-going Autonomous Vehicles. Precise vehicle detection and dynamics estimation from a moving platform is a key requirement for planning and executing complex autonomous overtaking maneuvers. To address this requirement, we have developed a Latency-Aware EKF-based Multi Target Tracking algorithm fusing LiDAR and RADAR measurements. The algorithm explots the different sensor characteristics by explicitly integrating the Range Rate in the EKF Measurement Function, as well as a-priori knowledge of the racetrack during state prediction. It can handle Out-Of-Sequence Measurements via Reprocessing using a double State and Measurement Buffer, ensuring sensor delay compensation with no information loss. This algorithm has been implemented on Team PoliMOVE's autonomous racecar, and was proved experimentally by completing a number of fully autonomous overtaking maneuvers at speeds up to 275 km/h.
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