A Simulation Benchmark for Autonomous Racing with Large-Scale Human Data
Authors: name: Adrian Remonda, name: Nicklas Hansen, name: Ayoub Raji, name: Nicola Musiu, name: Marko Bertogna, name: Eduardo Veas, name: Xiaolong Wang

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.