Fast and Modular Autonomy Software for Autonomous Racing Vehicles
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) p...
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| creator | Saba, Andrew Adetunji, Aderotimi Johnson, Adam Kothari, Aadi Sivaprakasam, Matthew Spisak, Joshua Bharatia, Prem Chauhan, Arjun Duff, Brendan Gasparro, Noah King, Charles Larkin, Ryan Mao, Brian Nye, Micah Parashar, Anjali Attias, Joseph Balciunas, Aurimas Brown, Austin Chang, Chris Gao, Ming Heredia, Cindy Keats, Andrew Lavariega, Jose Muckelroy, William Slavescu, Andre Stathas, Nickolas Suvarna, Nayana Chuan Tian Zhang Scherer, Sebastian Ramanan, Deva |
| description | 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. |
| doi_str_mv | 10.48550/arxiv.2408.15425 |
| format | Article |
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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.</description><identifier>EISSN: 2331-8422</identifier><identifier>DOI: 10.48550/arxiv.2408.15425</identifier><language>eng</language><publisher>Ithaca: Cornell University Library, arXiv.org</publisher><subject>Automobile racing ; Autonomy ; Computer Science - Artificial Intelligence ; Computer Science - Robotics ; Computer Science - Software Engineering ; Motion perception ; Motion planning ; Multiagent systems ; Race cars ; Racing ; Software</subject><ispartof>arXiv.org, 2024-08</ispartof><rights>2024. This work is published under http://arxiv.org/licenses/nonexclusive-distrib/1.0/ (the “License”). 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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. 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| subjects | Automobile racing Autonomy Computer Science - Artificial Intelligence Computer Science - Robotics Computer Science - Software Engineering Motion perception Motion planning Multiagent systems Race cars Racing Software |
| title | Fast and Modular Autonomy Software for Autonomous Racing Vehicles |
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