Target Tracking via LiDAR-RADAR Sensor Fusion for Autonomous Racing

2025-05-26

AI Summary

To address low detection accuracy and delayed dynamic estimation—leading to failed overtaking maneuvers—in high-speed autonomous multi-vehicle racing under motion-induced platform disturbances, this paper proposes a latency-aware heterogeneous sensor fusion tracking framework. Methodologically, we pioneer the incorporation of radar radial velocity into the Extended Kalman Filter (EKF) measurement model; integrate track geometric priors to refine state prediction; and design a dual-buffer mechanism enabling out-of-order measurement reprocessing and zero-loss latency compensation. The approach leverages LiDAR–radar fusion, an enhanced EKF, and constrained modeling. Real-world experiments on the PoliMOVE platform demonstrate 100% autonomous overtaking success at up to 275 km/h; average tracking latency is reduced by 42%, and lateral/longitudinal positioning errors decrease by 31%. Our core contribution is the first latency-robust multi-object tracking system explicitly designed for high-speed racing, uniquely balancing real-time performance and tracking precision.

Technology Category

• Computer Vision: Motion & Tracking
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Planning, Routing, and Scheduling: Activity and Plan Recognition

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Security and Privacy: Tracking, profiling, and countermeasures against them
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

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.

Problem

• Develop LiDAR-RADAR fusion for high-speed autonomous racing
• Enable precise vehicle detection and dynamics estimation
• Handle out-of-sequence measurements with delay compensation

Innovation

• Latency-Aware EKF-based Multi Target Tracking
• Fuses LiDAR and RADAR measurements
• Handles Out-Of-Sequence Measurements via Reprocessing