Autoware.Autoで挑む自動運転レース：第3回日本自動車AIチャレンジ優勝記
Winning the 3rd Japan Automotive AI Challenge -- Autonomous Racing with the Autoware.Auto Open Source Software Stack
自動運転OSS「Autoware.Auto」を用いて、レーン変更による追い抜き戦略や走行ライン最適化、モデル予測制御を組み合わせ、国際オンラインレースで優勝した手法を解説した論文。
著者: Zirui Zang, Renukanandan Tumu, Johannes Betz, Hongrui Zheng, Rahul Mangharam
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
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.
