A2RL V\textsubscript{max}: The A2RL autonomous racing dataset for long-range, high-speed perception and multi-vehicle interaction
2026-07-20 • Robotics
Robotics
AI summaryⓘ
The authors created a new dataset called A2RL Vmax for teaching and testing self-driving cars that race at high speeds. This dataset includes lots of detailed LiDAR and RADAR data recorded during an actual racing event with multiple cars. They also tested some common 3D detection and tracking tools on the data, finding that while these tools work okay, new methods are needed to handle the fast and complex racing environment. The dataset aims to help others develop better perception systems for fast autonomous driving.
Autonomous DrivingLiDARRADAR3D Detection3D TrackingDatasetMultimodal PerceptionAutonomous RacingDeep LearningPoint Clouds
Authors
Marvin Klemp, Dominic Ebner, Cornelius Schröder, Davide Malvezzi, László Turányi, Riccardo Donati, Ilia Schminik, Xia Ning, Yanxin Zhou, Matthew Flagg, Christoph Stiller, Markus Lienkamp, Marko Bertogna, Gergely Bári, Andreas Birk, Ren Jin, Chen Lv, Johannes Betz
Abstract
In autonomous driving development, a perception dataset is crucial, as it provides fundamental data for training, testing, and validating algorithms for an autonomous vehicle's multimodal perception systems. So far, most research has concentrated on providing datasets for well-structured urban environments. This work introduces the A2RL V\textsubscript{max} open-source dataset, specifically designed for perception tasks in high-speed autonomous driving and multi-vehicle interaction. The dataset was captured during the 2024 Abu Dhabi Autonomous Racing League (A2RL), held at the Yas Marina F1 Circuit, with participation from all competing teams. It contains diverse scenarios, including single-vehicle data at varying speeds, multi-vehicle sessions, and the full final four-vehicle race. The dataset contains almost 30,000 professionally annotated LiDAR point clouds, along with RADAR point clouds. In particular, it is the first large-scale dataset in autonomous racing to feature professionally annotated LiDAR point clouds, enabling deep learning-based perception research. The data is provided in a developer-friendly format, enabling easy implementation and evaluation in future research. We provide implementation and evaluation for off-the-shelf 3D detection and tracking methods. Although baseline methods show promising results for both 3D detection and tracking, specialized methods are required to address the unique challenges of high-speed autonomous driving. For a detailed description of the dataset, please visit the \href{https://tum-avs.github.io/A2RL_Dataset_website/}{A2RL V\textsubscript{max} Dataset Website}