Fast and Modular Autonomy Software for Autonomous Racing Vehicles

TL;DR

This paper presents a modular, high-speed autonomy software stack for autonomous racing vehicles, tested in the Indy Autonomous Challenge, emphasizing rapid deployment, real-world performance, and lessons learned for future improvements.

Contribution

The paper introduces a fast, modular autonomy software framework specifically designed for high-speed racing scenarios, with detailed analysis and deployment insights from the IAC competition.

Findings

Effective agent detection and motion planning at high speeds

Identified challenges in multi-agent scenarios and real-world deployment

Lessons learned for improving autonomous racing software

Abstract

Autonomous motorsports aim to replicate the human racecar driver with software and sensors. As in traditional motorsports, Autonomous Racing Vehicles (ARVs) are pushed to their handling limits in multi-agent scenarios at extremely high ($\geq 150mph$) speeds. This Operational Design Domain (ODD) presents unique challenges across the autonomy stack. The Indy Autonomous Challenge (IAC) is an international competition aiming to advance autonomous vehicle development through ARV competitions. While far from challenging what a human racecar driver can do, the IAC is pushing the state of the art by facilitating full-sized ARV competitions. This paper details the MIT-Pitt-RW Team's approach to autonomous racing in the IAC. In this work, we present our modular and fast approach to agent detection, motion planning and controls to create an autonomy stack. We also provide analysis of the performance of the software stack in single and multi-agent scenarios for rapid deployment in a fast-paced competition environment. We also cover what did and did not work when deployed on a physical system the Dallara AV-21 platform and potential improvements to address these shortcomings. Finally, we convey lessons learned and discuss limitations and future directions for improvement.