Amortized Variational Inference for Road Friction Estimation
Paper i proceeding, 2020

Road friction estimation concerns inference of the coefficient between the tire and road surface to facilitate active safety features. Current state-of-the-art methods lack generalization capability to cope with different tire characteristics and models are restricted when using Bayesian inference in estimation while recent supervised learning methods lack uncertainty prediction on estimates. This paper introduces variational inference to approximate intractable posterior of friction estimates and learns an amortized variational inference model from tire measurement data to facilitate probabilistic estimation while sustaining the flexibility of tire models. As a by-product, a probabilistic tire model can be learned jointly with friction estimator model. Experiments on simulated and field test data show that the learned friction estimator provides accurate estimates with robust uncertainty measures in a wide range of tire excitation levels. Meanwhile, the learned tire model reflects well-studied tire characteristics from field test data.


Shuangshuang Chen

Volvo Cars

Kungliga Tekniska Högskolan (KTH)

Sihao Ding

Volvo Cars

L. Srikar Muppirisetty

Volvo Cars

Yiannis Karayiannidis

Chalmers, Elektroteknik, System- och reglerteknik

Marten Bjorkman

Kungliga Tekniska Högskolan (KTH)

IEEE Intelligent Vehicles Symposium, Proceedings

1777-1784 9304712

31st IEEE Intelligent Vehicles Symposium, IV 2020
Virtual, Las Vegas, USA,


Bioinformatik (beräkningsbiologi)

Annan samhällsbyggnadsteknik

Sannolikhetsteori och statistik



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