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.

翻译：本文介绍了首个用于全尺寸高速自动驾驶赛车的开源数据集。多模态传感器数据采集自全自主印地赛车，其运行速度高达170英里/小时（273公里/小时）。参与印地自动驾驶挑战赛的六支车队为本次数据集提供了数据贡献。该数据集涵盖两条赛道上的11种有趣赛车场景，包括单圈行驶、多智能体圈赛、超车情况、高速加速、倾斜赛道、避障以及不同速度下的进站和出站。数据集包含11个场景下27场赛车会话的数据，记录了来自赛道的超过6.5小时的传感器数据。数据以ROS2和nuScenes两种格式组织并发布。我们还开发了ROS2到nuScenes的转换库以实现这一目标。由于自动驾驶赛车的高速环境，RACECAR数据具有独特性。我们利用RACECAR数据提出了多个基准问题，涵盖定位、目标检测与跟踪（激光雷达、雷达和摄像头）以及地图构建，以探索车辆运行极限状态下出现的问题。