Autonomous Racing with AutoRally Vehicles and Differential Games
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Abstract (arXiv)
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.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Jul 2017.
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Citing papers per year: 2021: 3; 2022: 3.
6 papers.
- 2022Autonomous Vehicles on the Edge: A Survey on Autonomous Vehicle Racingcited by 56
- 2021Deep Latent Competition: Learning to Race Using Visual Control Policies in Latent Spacecited by 20
- 2021Learning Interactive Driving Policies via Data-driven Simulationcited by 17
- 2022Motion Planning and Control for Multi Vehicle Autonomous Racing at High Speedscited by 8
- 2021Modeling human intention inference in continuous 3D domains by inverse planning and body kinematicscited by 1
- 2022Game-theoretic Objective Space Planningcited by 0
- 2022Game-theoretic Objective Space Planning16 Sep 2022
- 2022Motion Planning and Control for Multi Vehicle Autonomous Racing at High Speeds22 Jul 2022
- 2022Autonomous Vehicles on the Edge: A Survey on Autonomous Vehicle Racing14 Feb 2022
- 2021Modeling human intention inference in continuous 3D domains by inverse planning and body kinematics2 Dec 2021
- 2021Learning Interactive Driving Policies via Data-driven Simulation23 Nov 2021
- 2021Deep Latent Competition: Learning to Race Using Visual Control Policies in Latent Space19 Feb 2021