# AARK: An Open Toolkit for Autonomous Racing Research

arXiv: https://arxiv.org/abs/2410.00358 · HTML: https://mlsanity.com/paper/2410.00358/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 22 references that are in the map.
- First posted on arXiv 1 Oct 2024.

## Builds on (most cited first, 12 of 12)

- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Autonomous Vehicles on the Edge: A Survey on Autonomous Vehicle Racing](https://mlsanity.com/paper/2202.07008/) (2022)
- [Autonomous Overtaking in Gran Turismo Sport Using Curriculum Reinforcement Learning](https://mlsanity.com/paper/2103.14666/) (2021)
- [End-to-End Race Driving with Deep Reinforcement Learning](https://mlsanity.com/paper/1807.02371/) (2018)
- [Learn-to-Race: A Multimodal Control Environment for Autonomous Racing](https://mlsanity.com/paper/2103.11575/) (2021)
- [TUM autonomous motorsport: An autonomous racing software for the Indy Autonomous Challenge](https://mlsanity.com/paper/2205.15979/) (2022)
- [Formula RL: Deep Reinforcement Learning for Autonomous Racing using Telemetry Data](https://mlsanity.com/paper/2104.11106/) (2021)
- [Game-Theoretic Model Predictive Control with Data-Driven Identification of Vehicle Model for Head-to-Head Autonomous Racing](https://mlsanity.com/paper/2106.04094/) (2021)
- [A Simulation Benchmark for Autonomous Racing with Large-Scale Human Data](https://mlsanity.com/paper/2407.16680/) (2024)
- [er.autopilot 1.0: The Full Autonomous Stack for Oval Racing at High Speeds](https://mlsanity.com/paper/2310.18112/) (2023)
- [The Edge of Disaster: A Battle Between Autonomous Racing and Safety](https://mlsanity.com/paper/2206.15012/) (2022)

## Built on by (most cited first, 2 of 2)

- [LimSim Series: An Autonomous Driving Simulation Platform for Validation and Enhancement](https://mlsanity.com/paper/2502.09170/) (2025)
- [Self driving algorithm for an active four wheel drive racecar](https://mlsanity.com/paper/2506.06077/) (2025)

## 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.