Target Tracking via LiDAR-RADAR Fusion for Autonomous Racing

Abstract

High-speed multi-vehicle autonomous racing increases the safety and performance of road-going Autonomous Vehicles. Precise Vehicle Detection and Tracking 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 exploits 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 validated experimentally by completing a number of fully autonomous overtaking maneuvers at speeds up to 275 km/h.