TR2019-145
Bayesian Learning of Tire Friction with Automotive-Grade Sensors by Gaussian-Process State-Space Models
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- "Bayesian Learning of Tire Friction with Automotive-Grade Sensors by Gaussian-Process State-Space Models", IEEE Conference on Decision and Control (CDC), DOI: 10.1109/CDC40024.2019.9029732, December 2019, pp. 6681-6686.BibTeX TR2019-145 PDF
- @inproceedings{Berntorp2019dec,
- author = {Berntorp, Karl and Hiroaki, Kitano},
- title = {Bayesian Learning of Tire Friction with Automotive-Grade Sensors by Gaussian-Process State-Space Models},
- booktitle = {IEEE Conference on Decision and Control (CDC)},
- year = 2019,
- pages = {6681--6686},
- month = dec,
- doi = {10.1109/CDC40024.2019.9029732},
- url = {https://www.merl.com/publications/TR2019-145}
- }
,
- "Bayesian Learning of Tire Friction with Automotive-Grade Sensors by Gaussian-Process State-Space Models", IEEE Conference on Decision and Control (CDC), DOI: 10.1109/CDC40024.2019.9029732, December 2019, pp. 6681-6686.
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Research Areas:
Abstract:
The friction dependence between tire and road is highly nonlinear and varies heavily between different surfaces. Knowledge of the tire friction is important for real-time vehicle control, but difficult to estimate with automotive-grade sensors. Based on recent advances in particle filtering and Markov chain Monte-Carlo methods, we propose a batch method for 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 Gaussian process (GP) that is included in a dynamic vehicle model relating the GP to the vehicle state. The method is able to efficiently learn the tire friction using only wheel-speed, steering-wheel angle, and inertial automotivegrade sensors. We illustrate the efficacy of the method using several experimental data sets obtained on a snow-covered road.
Related News & Events
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NEWS MERL researchers presented 8 papers at Conference on Decision and Control (CDC) Date: December 11, 2019 - December 13, 2019
Where: Nice, France
MERL Contacts: Mouhacine Benosman; Scott A. Bortoff; Ankush Chakrabarty; Stefano Di Cairano
Research Areas: Control, Machine Learning, OptimizationBrief- At the Conference on Decision and Control, MERL presented 8 papers on subjects including estimation for thermal-fluid models and transportation networks, analysis of HVAC systems, extremum seeking for multi-agent systems, reinforcement learning for vehicle platoons, and learning with applications to autonomous vehicles.