Winning the 3rd Japan Automotive AI Challenge -- Autonomous Racing with the Autoware.Auto Open Source Software Stack

TL;DR

This paper presents the winning strategy for autonomous racing in the Japan Automotive AI Challenge, utilizing the Autoware.Auto open source platform with innovative lane-switching, raceline optimization, and perception-control integration.

Contribution

It introduces a comprehensive autonomous racing approach combining rule-based strategies and Autoware.Auto tools, providing insights and benchmarks for high-speed autonomous driving.

Findings

Successful implementation of a lane-switching overtaking strategy

Effective global raceline optimization for racing performance

Insights into using Autoware.Auto modules for high-speed autonomous racing

Abstract

The 3rd Japan Automotive AI Challenge was an international online autonomous racing challenge where 164 teams competed in December 2021. This paper outlines the winning strategy to this competition, and the advantages and challenges of using the Autoware.Auto open source autonomous driving platform for multi-agent racing. Our winning approach includes a lane-switching opponent overtaking strategy, a global raceline optimization, and the integration of various tools from Autoware.Auto including a Model-Predictive Controller. We describe the use of perception, planning and control modules for high-speed racing applications and provide experience-based insights on working with Autoware.Auto. While our approach is a rule-based strategy that is suitable for non-interactive opponents, it provides a good reference and benchmark for learning-enabled approaches.