LiDAR-Based Vehicle Detection and Tracking for Autonomous Racing

This work has been submitted to the IEEE for possible publication. Abstract--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-Figure 1. Team PoliMOVE's Dallara AV21 "MinerVa" defending from an Dallara AV21 "MinerVa" which won first place in all three In this work, we build an online algorithm for reliable I. UTONOMOUS RACING allows for safe testing of an autonomous vehicle's full software and hardware stack fully observing the target's 2D pose, tracking its motion at the limits of its performance in a controlled environment. Point Cloud segmentation algorithm capable of processing in Providing this kind of testing environment is one of the main parallel the three LiDAR sensors mounted on the vehicle, a goals of the Indy Autonomous Challenge (IAC), the first multivehicle multi-hypothesis L-shape fitting technique for a racing vehicle competition series for level 4 autonomous racecars.