自律レース車両のための高速でモジュール式な自律走行ソフトウェア
Fast and Modular Autonomy Software for Autonomous Racing Vehicles
Indy Autonomous Challenge 向けに、エージェント検出・運動計画・制御をモジュール化した高速な自律走行スタックを開発し、単一・複数エージェント環境での性能を評価した。
著者: Andrew Saba, Aderotimi Adetunji, Adam Johnson, Aadi Kothari, Matthew Sivaprakasam, Joshua Spisak, Prem Bharatia, Arjun Chauhan, Brendan Duff, Noah Gasparro, Charles King, Ryan Larkin, Brian Mao, Micah Nye, Anjali Parashar, Joseph Attias, Aurimas Balciunas, Austin Brown, Chris Chang, Ming Gao, Cindy Heredia, Andrew Keats, Jose Lavariega, William Muckelroy, Andre Slavescu, Nickolas Stathas, Nayana Suvarna, Chuan Tian Zhang, Sebastian Scherer, Deva Ramanan
分類: cs.RO, cs.AI, cs.SE
原文アブストラクト
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.
