基于大规模人类数据的自主赛车模拟基准测试

A Simulation Benchmark for Autonomous Racing with Large-Scale Human Data

July 23, 2024

作者: Adrian Remonda, Nicklas Hansen, Ayoub Raji, Nicola Musiu, Marko Bertogna, Eduardo Veas, Xiaolong Wang

cs.AI

摘要

尽管国际奖金竞赛、按比例缩小的车辆和模拟环境等资源已经可用，但关于自主赛车和控制运行接近极限的运动汽车的研究受到车辆采购和管理成本高昂，以及开源模拟器物理精度有限的限制。本文提出了基于模拟器Assetto Corsa的赛车模拟平台，用于测试、验证和基准自主驾驶算法，包括强化学习（RL）和经典模型预测控制（MPC），在现实和具有挑战性的场景中。我们的贡献包括开发这一模拟平台、几种适用于赛车环境的最新算法，以及从人类驾驶员收集的全面数据集。此外，我们在离线RL设置中评估算法。所有必要的代码（包括环境和基准）、工作示例、数据集和视频均已公开发布，可在以下网址找到：https://assetto-corsa-gym.github.io。

English

Despite the availability of international prize-money competitions, scaled vehicles, and simulation environments, research on autonomous racing and the control of sports cars operating close to the limit of handling has been limited by the high costs of vehicle acquisition and management, as well as the limited physics accuracy of open-source simulators. In this paper, we propose a racing simulation platform based on the simulator Assetto Corsa to test, validate, and benchmark autonomous driving algorithms, including reinforcement learning (RL) and classical Model Predictive Control (MPC), in realistic and challenging scenarios. Our contributions include the development of this simulation platform, several state-of-the-art algorithms tailored to the racing environment, and a comprehensive dataset collected from human drivers. Additionally, we evaluate algorithms in the offline RL setting. All the necessary code (including environment and benchmarks), working examples, datasets, and videos are publicly released and can be found at: https://assetto-corsa-gym.github.io.