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Autonomous Racing with AutoRally Vehicles and Differential Games

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arxiv 1707.04540 v1 pith:S47ZFLR3 submitted 2017-07-14 cs.RO

Grady Williams , Brian Goldfain , Paul Drews , James M. Rehg , Evangelos A. Theodorou This is my paper · ORCID
classification cs.RO
keywords autonomousracingautorallybr-mppicontroldifferentialmppireact
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Safe autonomous vehicles must be able to predict and react to the drivers around them. Previous control methods rely heavily on pre-computation and are unable to react to dynamic events as they unfold in real-time. In this paper, we extend Model Predictive Path Integral Control (MPPI) using differential game theory and introduce Best-Response MPPI (BR-MPPI) for real-time multi-vehicle interactions. Experimental results are presented using two AutoRally platforms in a racing format with BR-MPPI competing against a skilled human driver at the Georgia Tech Autonomous Racing Facility.

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Cited by 2 Pith papers

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

  1. Strategizing at Speed: A Learned Model Predictive Game for Multi-Agent Drone Racing

    cs.RO 2026-02 conditional novelty 6.0 of 10

    A learned model predictive game trained offline wins head-to-head drone races against both game-theoretic and non-interactive planners while cutting solve latency from roughly 60 ms to 3.5 ms.

  2. {\alpha}-RACER: Real-Time Algorithm for Game-Theoretic Motion Planning and Control in Autonomous Racing using Near-Potential Function

    cs.RO 2024-12 conditional novelty 6.0 of 10

    α-RACER learns an approximate α-potential function offline from simulated races and maximizes it online to obtain approximate Nash equilibrium strategies for multi-car autonomous racing.

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