Ensemble Gaussian Processes for Adaptive Autonomous Driving on Multi-friction Surfaces

Tomáš Nagy, Ahmad Amine, Truong X. Nghiem, Ugo Rosolia, Zirui Zang, Rahul Mangharam

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Driving under varying road conditions is challenging, especially for autonomous vehicles that must adapt in real-time to changes in the environment, e.g., rain, snow, etc. It is difficult to apply offline learning-based methods in these time-varying settings, as the controller should be trained on datasets representing all conditions it might encounter in the future. While online learning may adapt a model from real-time data, its convergence is often too slow for fast varying road conditions. We study this problem in autonomous racing, where driving at the limits of handling under varying road conditions is required for winning races. We propose a computationally-efficient approach that leverages an ensemble of Gaussian processes (GPs) to generalize and adapt pre-trained GPs to unseen conditions. Each GP is trained on driving data with a different road surface friction. A time-varying convex combination of these GPs is used within a model predictive control (MPC) framework, where the model weights are adapted online to the current road condition based on real-time data. Extensive simulations of a full-scale autonomous car demonstrated the effectiveness of our proposed EGP-MPC method for providing good tracking performance in varying road conditions and the ability to generalize to unknown maps.

Original languageEnglish (US)
Title of host publicationIFAC-PapersOnLine
EditorsHideaki Ishii, Yoshio Ebihara, Jun-ichi Imura, Masaki Yamakita
PublisherElsevier B.V.
Number of pages7
ISBN (Electronic)9781713872344
StatePublished - Jul 1 2023
Externally publishedYes
Event22nd IFAC World Congress - Yokohama, Japan
Duration: Jul 9 2023Jul 14 2023

Publication series

ISSN (Electronic)2405-8963


Conference22nd IFAC World Congress


  • Bayesian Methods
  • Convex optimization
  • Data-driven control
  • Data-driven optimal control
  • Learning for control
  • Nonlinear predictive control

ASJC Scopus subject areas

  • Control and Systems Engineering


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