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Competitive Driving of Autonomous Vehicles

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arxiv 2109.05455 v2 pith:KC5GKGYK submitted 2021-09-12 cs.RO

classification cs.RO
keywords racevehiclesautonomouscontrolleropponentracingalongmaneuver
verification ladder T0 review T1 audit T2 compute T3 formal
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This paper describes Ariel Team's autonomous racing controller for the Indy Autonomous Challenge (IAC) simulation race. IAC is the first multi-vehicle autonomous head-to-head competition, reaching speeds of 300 km/h along an oval track, modeled after the Indianapolis Motor Speedway (IMS). Our racing controller attempts to maximize progress along the track while avoiding collisions with opponent vehicles and obeying the race rules. To this end, the racing controller first computes a race line offline. Then, it repeatedly computes online a small set of dynamically feasible maneuver candidates, each tested for collision with the opponent vehicles. Finally, it selects the maneuver that maximizes progress along the track, taking into account the race line. The maneuver candidates, as well as the predicted trajectories of the opponent vehicles, are approximated using a point mass model. Despite the simplicity of this racing controller, it managed to drive competitively and with no collision with any of the opponent vehicles in the IAC final simulation race.

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Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. DKMGP: A Gaussian Process Approach to Multi-Task and Multi-Step Vehicle Dynamics Modeling in Autonomous Racing

    cs.RO 2024-11 conditional novelty 5.0 of 10

    A multi-task deep-kernel Gaussian process with an adaptive correction horizon predicts racecar state residuals with one model, making real-time dynamics correction feasible.

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