Existing research on autonomous driving primarily focuses on urban driving, which is insufficient for characterising the complex driving behaviour underlying high-speed racing. At the same time, existing racing simulation frameworks struggle in capturing realism, with respect to visual rendering, vehicular dynamics, and task objectives, inhibiting the transfer of learning agents to real-world contexts. We introduce a new environment, where agents Learn-to-Race (L2R) in simulated competition-style racing, using multimodal information--from virtual cameras to a comprehensive array of inertial measurement sensors. Our environment, which includes a simulator and an interfacing training framework, accurately models vehicle dynamics and racing conditions. In this paper, we release the Arrival simulator for autonomous racing. Next, we propose the L2R task with challenging metrics, inspired by learning-to-drive challenges, Formula-style racing, and multimodal trajectory prediction for autonomous driving. Additionally, we provide the L2R framework suite, facilitating simulated racing on high-precision models of real-world tracks. Finally, we provide an official L2R task dataset of expert demonstrations, as well as a series of baseline experiments and reference implementations. We make all code available: https://github.com/learn-to-race/l2r.

翻译：关于自主驾驶的现有研究主要侧重于城市驾驶,这不足以说明高速赛背后的复杂驾驶行为。与此同时,现有的赛跑模拟框架在捕捉现实主义、视觉成像、车辆动态和任务目标方面挣扎,禁止将学习代理商转移到现实世界环境中。我们引入了一个新的环境,在模拟竞争式赛事中,代理商L2R(L2R)学习到Race(L2R),使用从虚拟相机到一系列全面的惯性测量传感器的多式信息。我们的环境包括模拟器和互动培训框架、准确的车辆模型动态和赛跑条件。我们在此文件中发布了自动赛车的 " 抵达模拟器 " 。接下来,我们提出L2R任务与具有挑战性的指标相结合,这是由学习到驱动的挑战、公式式赛事和自动驾驶的多式联运轨迹预测所启发的。此外,我们提供了L2R框架套,便利在现实世界轨道的高精度模型上进行模拟赛程。最后,我们提供了正式的L2R任务数据集,用于自动赛车。我们提出了专家演示的基线/运行。