AARK: An Open Toolkit for Autonomous Racing Research
Not on the map. Why?
Abstract (arXiv)
Autonomous racing demands safe control of vehicles at their physical limits for extended periods of time, providing insights into advanced vehicle safety systems which increasingly rely on intervention provided by vehicle autonomy. Participation in this field carries with it a high barrier to entry. Physical platforms and their associated sensor suites require large capital outlays before any demonstrable progress can be made. Simulators allow researches to develop soft autonomous systems without purchasing a platform. However, currently available simulators lack visual and dynamic fidelity, can still be expensive to buy, lack customisation, and are difficult to use. AARK provides three packages, ACI, ACDG, and ACMPC. These packages enable research into autonomous control systems in the demanding environment of racing to bring more people into the field and improve reproducibility: ACI provides researchers with a computer vision-friendly interface to Assetto Corsa for convenient comparison and evaluation of autonomous control solutions; ACDG enables generation of depth, normal and semantic segmentation data for training computer vision models to use in perception systems; and ACMPC gives newcomers to the field a modular full-stack autonomous control solution, capable of controlling vehicles to build from. AARK aims to unify and democratise research into a field critical to providing safer roads and trusted autonomous systems.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 12 of its 22 references that are in the map. First posted on arXiv 1 Oct 2024.
Builds on
12 of 22 references.
References · 12
- 2017CARLA: An Open Urban Driving Simulatorcited by 2,431
- 2016OpenAI Gymcited by 1,684
- 2022Autonomous Vehicles on the Edge: A Survey on Autonomous Vehicle Racingcited by 56
- 2021Autonomous Overtaking in Gran Turismo Sport Using Curriculum Reinforcement Learningcited by 41
- 2018End-to-End Race Driving with Deep Reinforcement Learningcited by 38
- 2021Learn-to-Race: A Multimodal Control Environment for Autonomous Racingcited by 23
- 2021Formula RL: Deep Reinforcement Learning for Autonomous Racing using Telemetry Datacited by 10
- 2022TUM autonomous motorsport: An autonomous racing software for the Indy Autonomous Challengecited by 9
- 2021Game-Theoretic Model Predictive Control with Data-Driven Identification of Vehicle Model for Head-to-Head Autonomous Racingcited by 7
- 2023er.autopilot 1.0: The Full Autonomous Stack for Oval Racing at High Speedscited by 2
- 2024A Simulation Benchmark for Autonomous Racing with Large-Scale Human Datacited by 2
- 2022The Edge of Disaster: A Battle Between Autonomous Racing and Safetycited by 1
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Citing papers per year: 2025: 2.
2 papers.