Tire Road Friction Coefficient Estimation: Review and Research Perspectives

Yan Wang,Yongjun Yan,Jingyu Hu,Yanjun Ren,Fa’An Wang,Haoxuan Dong,Chaobin Zhou,Guodong Yin

doi:10.1186/s10033-021-00675-z

Abstract

Many surveys on vehicle traffic safety have shown that the tire road friction coefficient (TRFC) is correlated with the probability of an accident. The probability of road accidents increases sharply on slippery road surfaces. Therefore, accurate knowledge of TRFC contributes to the optimization of driver maneuvers for further improving the safety of intelligent vehicles. A large number of researchers have employed different tools and proposed different algorithms to obtain TRFC. This work investigates these different methods that have been widely utilized to estimate TRFC. These methods are divided into three main categories: off-board sensors-based, vehicle dynamics-based, and data-driven-based methods. This review provides a comparative analysis of these methods and describes their strengths and weaknesses. Moreover, some future research directions regarding TRFC estimation are presented.

Highlights

Traffic accidents are one of the leading causes of injuries and death in China and abroad
According to the National Bureau of Statistics of China, in 2019, there were 247646 traffic accidents, which resulted in 62763 fatalities, 256101 injuries, and a direct economic loss of 1346.18 million yuan
This article systematically reviews the recent developments on tire road friction coefficient (TRFC) estimation from different research directions

Summary

Introduction

Traffic accidents are one of the leading causes of injuries and death in China and abroad. According to the National Bureau of Statistics of China, in 2019, there were 247646 traffic accidents, which resulted in 62763 fatalities, 256101 injuries, and a direct economic loss of 1346.18 million yuan. Both industry and academia have made great efforts to develop new technologies to reduce or even avoid traffic accidents. This article systematically reviews the recent developments on TRFC estimation from different research directions. It contains a comparative analysis of existing methods and describes their strengths and weaknesses.

Off‐board Sensors‐based Methods

Vehicle Dynamics‐based Approaches

Longitudinal Dynamics‐based Methods

Lateral Dynamics‐based Methods

Coupled Dynamics‐based Methods

Data Driven‐based Approaches