2024/10/01 by James Bockman, Bockman, James, Matthew D. Howe +5 · 1 citation
Computer Science · Engineering · #Autonomous Vehicle Technology and Safety #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Reinforcement Learning in Robotics #Robotics (cs.RO) #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2410.00358
openalex publication_date 2024/10/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.