The ROBORACE contest

Rieber, Jochen M.; Wehlan, H.; Allgöwer, Frank

doi:10.1109/mcs.2004.1337859

Cited by 12 publications

(5 citation statements)

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“…Since then, there have been several autonomous racing competitions, such as Formula Student (Kabzan et al, 2019), (Vázquez et al, 2020), Roborace (Rieber et al, 2004), (Betz et al, 2019), and now the Indy Autonomous Challenge. Moreover, companies such as Argo AI, Motional, Waymo, and many more have been publicizing and realizing AV development around the globe.…”

Section: Related Workmentioning

confidence: 99%

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2024

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Autonomous motorsports aim to replicate the human race-car driver with software and sensors. As in traditional motorsports, Autonomous Racing Vehicles (ARVs) are pushed to their handling limits in multiagent scenarios at extremely high (≥150 mph) 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 race-car 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 multiagent 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.

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Section: Related Workmentioning

confidence: 99%

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2024

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Section: Related Workmentioning

confidence: 99%

Fast and Modular Autonomy Software for Autonomous Racing Vehicles

Saba,

Adetunji,

Johnson

et al. 2024

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Autonomous motorsports aim to replicate the human race-car driver with software and sensors. As in traditional motorsports, Autonomous Racing Vehicles (ARVs) are pushed to their handling limits in multiagent scenarios at extremely high ( 150 mph) 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 race-car 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 multiagent 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.

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“…It has become increasingly popular in the past few years, since it provides a suitable scenario for evaluation of both safety (no crash) and progress (high speed). Race cars of different scales are widely used for research and competition, such as Indy Autonomous Challenge [25], Roborace [26], Formula Student Driverless [27], F1TENTH [28], and so on.…”

Section: B Autonomous Racingmentioning

confidence: 99%

MEGA-DAgger: Imitation Learning with Multiple Imperfect Experts

Sun¹,

Yang²,

Mangharam³

2023

Preprint

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Imitation learning has been widely applied to various autonomous systems thanks to recent development in interactive algorithms that address covariate shift and compounding errors induced by traditional approaches like behavior cloning. However, existing interactive imitation learning methods assume access to one perfect expert. Whereas in reality, it is more likely to have multiple imperfect experts instead. In this paper, we propose MEGA-DAgger, a new DAgger variant that is suitable for interactive learning with multiple imperfect experts. First, unsafe demonstrations are filtered while aggregating the training data, so the imperfect demonstrations have little influence when training the novice policy. Next, experts are evaluated and compared on scenarios-specific metrics to resolve the conflicted labels among experts. Through experiments in autonomous racing scenarios, we demonstrate that policy learned using MEGA-DAgger can outperform both experts and policies learned using the state-of-the-art interactive imitation learning algorithm. The supplementary video can be found at https://youtu.be/pYQiPSHk6dU.

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The ROBORACE contest

Cited by 12 publications

References 1 publication

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Fast and Modular Autonomy Software for Autonomous Racing Vehicles

MEGA-DAgger: Imitation Learning with Multiple Imperfect Experts

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