Abstract
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.
Abstract (translated)
自动驾驶赛车对车辆在物理极限范围内的安全控制提出了要求，为人们深入了解自动驾驶系统提供了对高级车辆安全系统的深入了解，这些系统 increasingly依赖于车辆自主性的干预。参与这个领域需要很高的门槛。物理平台及其相关传感器套件需要巨额资金投入，在实现任何可见的进步之前，都需要投入大量资源。仿真器允许研究人员在不购买平台的情况下开发软自主系统。然而，目前可用的仿真器缺乏视觉和动态精度，仍然很难购买，缺乏定制化，而且很难使用。AARK提供了三个软件包，ACI，ACDG和ACMPC。这些软件包使研究人员能够在赛车领域对自动驾驶系统进行研究，为更多人进入这个领域并提高可重复性做出了贡献：ACI为研究人员提供了一个计算机视觉友好的界面，以便比较和评估自动驾驶解决方案；ACDG能够生成深度、法和语义分割数据，用于训练计算机视觉模型用于感知系统；而ACMPC为新手研究人员提供了一个模块化的全栈自动驾驶解决方案，可用于控制车辆构建。AARK的目标是将和民主化该领域的研究，以提供更安全的道路和值得信赖的自动驾驶系统。
URL
https://arxiv.org/abs/2410.00358
