Robust Modeling and Controls for Racing on the Edge

05/22/2022
by   Joshua Spisak, et al.
0

Race cars are routinely driven to the edge of their handling limits in dynamic scenarios well above 200mph. Similar challenges are posed in autonomous racing, where a software stack, instead of a human driver, interacts within a multi-agent environment. For an Autonomous Racing Vehicle (ARV), operating at the edge of handling limits and acting safely in these dynamic environments is still an unsolved problem. In this paper, we present a baseline controls stack for an ARV capable of operating safely up to 140mph. Additionally, limitations in the current approach are discussed to highlight the need for improved dynamics modeling and learning.

READ FULL TEXT
research
02/08/2022

Indy Autonomous Challenge – Autonomous Race Cars at the Handling Limits

Motorsport has always been an enabler for technological advancement, and...
research
05/20/2020

Benchmarking of a software stack for autonomous racing against a professional human race driver

The way to full autonomy of public road vehicles requires the step-by-st...
research
03/14/2023

Adaptive Planning and Control with Time-Varying Tire Models for Autonomous Racing Using Extreme Learning Machine

Autonomous racing is a challenging problem, as the vehicle needs to oper...
research
10/06/2020

The Autonomous Racing Software Stack of the KIT19d

Formula Student Driverless challenges engineering students to develop au...
research
02/14/2022

Autonomous Vehicles on the Edge: A Survey on Autonomous Vehicle Racing

The rising popularity of self-driving cars has led to the emergence of a...
research
06/05/2023

RACECAR – The Dataset for High-Speed Autonomous Racing

This paper describes the first open dataset for full-scale and high-spee...
research
03/26/2021

Autonomous Overtaking in Gran Turismo Sport Using Curriculum Reinforcement Learning

Professional race car drivers can execute extreme overtaking maneuvers. ...

Please sign up or login with your details

Forgot password? Click here to reset