title: A Simulation Benchmark for Autonomous Racing with Large-Scale Human Data
summary: 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.
paper_authors._id: 66a0857102b0d28104696a81
paper_authors.name: Adrian Remonda
paper_authors._id: 66a0857102b0d28104696a82
paper_authors.name: Nicklas Hansen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-07-24T08:28:09.845Z
paper_authors._id: 62d8ab94bf50eca8389bbc01
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/62d8ab94bf50eca8389bbc01/sXJupPzRO5ZV-BiQRdFeV.png
paper_authors.fullname: Nicklas Hansen
paper_authors.type: user
paper_authors.user: nicklashansen
paper_authors._id: 66a0857102b0d28104696a83
paper_authors.name: Ayoub Raji
paper_authors._id: 66a0857102b0d28104696a84
paper_authors.name: Nicola Musiu
paper_authors._id: 66a0857102b0d28104696a85
paper_authors.name: Marko Bertogna
paper_authors._id: 66a0857102b0d28104696a86
paper_authors.name: Eduardo Veas
paper_authors._id: 66a0857102b0d28104696a87
paper_authors.name: Xiaolong Wang
paper_title: A Simulation Benchmark for Autonomous Racing with Large-Scale Human Data
paper_summary: 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.
paper_ai_summary: A simulation platform for autonomous racing using Assetto Corsa tests reinforcement learning and MPC algorithms, providing a realistic benchmarking environment.
paper_ai_keywords: reinforcement learning Model Predictive Control offline RL