RACECAR - The Dataset for High-Speed Autonomous Racing

摘要

This paper describes RACECAR, the first open dataset for full-scale and high-speed autonomous racing. Multi-modal sensor data was collected from fully autonomous Indy race cars operating at speeds of up to 170 mph (273 kph). Six teams who raced in the Indy Autonomous Challenge (2021–2022) have contributed to this dataset. The dataset spans 11 racing scenarios across two race tracks which include solo laps, multi-agent laps, overtaking situations, high-accelerations, banked tracks, obstacle avoidance, pit entry and exit at different speeds. The dataset contains 27 racing sessions across 11 scenarios with over 6.5 hours of autonomous racing. The data has been released in both ROS 2 and nuScenes format. We have also developed the ROSbag2nuScenes conversion library to achieve this. The RACECAR data is unique because of the high-speed environment of autonomous racing. We present several benchmark problems on localization, object detection and tracking (LiDAR, Radar, and Camera), and mapping to explore issues that arise at the limits of operation of the vehicle. RACECAR data can be accessed at https://github.com/linklab-uva/RACECAR_DATA.