  This paper describes the first open dataset for full-scale and high-speed
autonomous racing. Multi-modal sensor data has been 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 have contributed to this
dataset. The dataset spans 11 interesting 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 data from 27 racing sessions across the
11 scenarios with over 6.5 hours of sensor data recorded from the track. The
data is organized and released in both ROS2 and nuScenes format. We have also
developed the ROS2-to-nuScenes 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 using the RACECAR data to
explore issues that arise at the limits of operation of the vehicle.
