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