论文标题
强大的建模和控制赛车的控制
Robust Modeling and Controls for Racing on the Edge
论文作者
论文摘要
在远高于200mph的动态场景中，赛车通常会被驱动到其处理极限的边缘。在自主赛车中也提出了类似的挑战，在自动赛车上，软件堆栈而不是人类驾驶员在多机构环境中进行交互。对于自动赛车（ARV），在处理极限的边缘运行并在这些动态环境中安全起作用仍然是一个未解决的问题。在本文中，我们提出了一个基线控件，用于可安全操作高达140mph的ARV。此外，讨论了当前方法中的局限性，以强调需要改进动态建模和学习的需求。
Race cars are routinely driven to the edge of their handling limits in dynamic scenarios well above 200mph. Similar challenges are posed in autonomous racing, where a software stack, instead of a human driver, interacts within a multi-agent environment. For an Autonomous Racing Vehicle (ARV), operating at the edge of handling limits and acting safely in these dynamic environments is still an unsolved problem. In this paper, we present a baseline controls stack for an ARV capable of operating safely up to 140mph. Additionally, limitations in the current approach are discussed to highlight the need for improved dynamics modeling and learning.
