基于激光雷达的车辆检测与追踪算法，支持超高速自动驾驶超车。
LiDAR-Based Vehicle Detection and Tracking for Autonomous Racing
- 采用快速点云分割与车辆姿态估计结合的方法，实现高精度感知。
- 在275公里/小时以上速度下完成全自动驾驶超车，延迟极低。
- 适合高速竞赛场景，对实时性与鲁棒性要求极高的应用。
自动驾驶赛车为测试车辆在极限性能下的软硬件提供了可控环境。然而多辆自动驾驶赛车之间的竞争互动带来了极具挑战性且潜在危险的情况。准确一致的车辆检测与跟踪对于超车操作至关重要，而低延迟传感器处理则是快速应对危险状况的关键。本文介绍了团队PoliMOVE在自主赛车中部署的基于激光雷达的感知算法，该算法在印第安纳自主挑战赛系列中多次夺冠。我们的车辆检测与追踪流水线包含一种新型快速点云分割技术、特定的车辆姿态估计方法，以及可变步长多目标追踪算法。实验结果表明，该算法在性能、鲁棒性、计算效率方面表现优异，适用于自动驾驶赛车应用，能够在超过275公里/小时的速度下实现完全自主超车。
原文摘要 · Abstract (English)
Autonomous racing provides a controlled environment for testing the software and hardware of autonomous vehicles operating at their performance limits. Competitive interactions between multiple autonomous racecars however introduce challenging and potentially dangerous scenarios. Accurate and consistent vehicle detection and tracking is crucial for overtaking maneuvers, and low-latency sensor processing is essential to respond quickly to hazardous situations. This paper presents the LiDAR-based perception algorithms deployed on Team PoliMOVE's autonomous racecar, which won multiple competitions in the Indy Autonomous Challenge series. Our Vehicle Detection and Tracking pipeline is composed of a novel fast Point Cloud Segmentation technique and a specific Vehicle Pose Estimation methodology, together with a variable-step Multi-Target Tracking algorithm. Experimental results demonstrate the algorithm's performance, robustness, computational efficiency, and suitability for autonomous racing applications, enabling fully autonomous overtaking maneuvers at velocities exceeding 275 km/h.
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