The advent of autonomous vehicle technologies has significantly impacted various sectors, including motorsport, where Formula Student and Formula: Society of Automotive Engineers introduced autonomous racing classes. These offer new challenges to aspiring engineers, including the team at QUT Motorsport, but also raise the entry barrier due to the complexity of high-speed navigation and control. This paper presents an open-source solution using the Robot Operating System 2, specifically its open-source navigation stack, to address these challenges in autonomous Formula Student race cars. We compare off-the-shelf navigation libraries that this stack comprises of against traditional custom-made programs developed by QUT Motorsport to evaluate their applicability in autonomous racing scenarios and integrate them onto an autonomous race car. Our contributions include quantitative and qualitative comparisons of these packages against traditional navigation solutions, aiming to lower the entry barrier for autonomous racing. This paper also serves as a comprehensive tutorial for teams participating in similar racing disciplines and other autonomous mobile robot applications.

翻译：自动驾驶车辆技术的出现显著影响了包括赛车运动在内的多个领域，其中大学生方程式赛车与汽车工程师学会引入了自动驾驶竞赛类别。这不仅为包括QUT Motorsport团队在内的有志工程师带来了新挑战，同时也因高速导航与控制的复杂性提高了参与门槛。本文提出了一种基于机器人操作系统2的开源解决方案，特别是其开源导航栈，以应对自动驾驶大学生方程式赛车中的这些挑战。我们对比了该导航栈所包含的现成导航库与QUT Motorsport团队开发的传统定制程序，评估其在自动驾驶赛车场景中的适用性，并将其集成到自动驾驶赛车上。我们的贡献包括对这些软件包与传统导航方案进行的定量与定性比较，旨在降低自动驾驶赛车的参与门槛。本文同时可作为参与同类赛车赛事及其他自主移动机器人应用的团队的综合性教程。