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AARK: An Open Toolkit for Autonomous Racing Research

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arxiv 2410.00358 v2 pith:CUYNEBNG submitted 2024-10-01 cs.RO cs.LGcs.SYeess.SY

classification cs.ROcs.LGcs.SYeess.SY
keywords autonomoussystemscontrolfieldaarkracingresearchacdg
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Self driving algorithm for an active four wheel drive racecar

    cs.RO 2025-06 conditional novelty 6.0 of 10

    A PPO agent learns end-to-end control of steering and four independent wheel torques in TORCS, implicitly discovering torque-vectoring and traction-stability behaviors.

  2. LimSim Series: An Autonomous Driving Simulation Platform for Validation and Enhancement

    cs.RO 2025-02 conditional novelty 4.0 of 10

    The LimSim Series is an open-source closed-loop simulation platform that integrates multiple driving-system pipelines, an Area-of-Interest efficiency mechanism, and a multi-metric evaluation suite.

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