author = "Ivan Tanev and Hiroshige Hasegawa and Manaka Tokiwai",
title = "Evolving the Models of Friction Coefficients of
Automobile Tires via Genetic Programming",
booktitle = "Proceedings of the 31st International Symposium on
Artificial Life and Robotics, and 11th International
Symposium on BioComplexity (AROB-ISBC 2026)",
year = "2026",
editor = "Fumitoshi Matsuno",
address = "Beppu, Oita, Japan",
month = "21--23",
keywords = "genetic algorithms, genetic programming, maximum
friction coefficient, sensing",
size = "5 pages",
abstract = "Our research is motivated by the apparent discrepancy
between (i) the importance of the real time
(continuous) estimation of the maximum value of the
friction coefficient (MFC) max between the automobile
tires and the road surface and (ii) the lack of both a
practically feasible and robust methods of such
estimation. Our objective is investigating the
possibility to develop the models of MFC as algebraic
functions of various (real time) parameters, pertinent
to the interaction of the tires and the road surface in
a normal, steady state (rather than accelerating-,
braking-, or turning-) driving in the four main dry,
wet, snowy, and icy road conditions. Due to the
anticipated complexity of the models of MFC, and the
amount of the training set of data (timestamped values
of parameters obtained during a brief driving session)
it approximates, we assumed that the models cannot be
handcrafted by a human expert. To develop such models,
we applied genetic programming (GP) a heuristic,
automated problem-solving approach inspired by the
evolution in nature. The experimentally obtained
best-evolved models were acceptably accurate (with a
difference between the estimated MFC and the actual one
lower than 0.05) for slippery road conditions (wet,
snowy, and icy).",
