TR2020-104

Online Bayesian Tire-Friction Learning by Gaussian-Process State-Space Models


    •  Berntorp, K., "Online Bayesian Tire-Friction Learning by Gaussian-Process State-Space Models", World Congress of the International Federation of Automatic Control (IFAC), July 2020.
      BibTeX TR2020-104 PDF
      • @inproceedings{Berntorp2020jul,
      • author = {Berntorp, Karl},
      • title = {Online Bayesian Tire-Friction Learning by Gaussian-Process State-Space Models},
      • booktitle = {World Congress of the International Federation of Automatic Control (IFAC)},
      • year = 2020,
      • month = jul,
      • url = {https://www.merl.com/publications/TR2020-104}
      • }
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  • Research Areas:

    Control, Machine Learning, Signal Processing

The friction dependence between tire and road is highly nonlinear and varies heavily between different surfaces. The tire friction is important for real-time vehicle control, but difficult to learn with automotive-grade sensors as they only provide indirect measurements based on sensing parts of the vehicle state. In this paper we leverage recent advances in particle filtering and Gaussian Processes (GPs), to provide an online method for jointly estimating the vehicle state and subsequently identifying the tire friction as a function of the wheel slip. The unknown function mapping the wheel slip to tire friction is modeled as a GP that is included in a dynamic vehicle model relating the GP to the vehicle state. We illustrate the efficacy of the method using synthetic data on a snow-covered road.