Multi-sensor Fusion Road Friction Coefficient Estimation During Steering with Lyapunov Method

Abstract

The road friction coefficient is a key parameter for autonomous vehicles and vehicle dynamic control. With the development of autonomous vehicles, increasingly, more environmental perception sensors are being installed on vehicles, which means that more information can be used to estimate the road friction coefficient. In this paper, a nonlinear observer aided by vehicle lateral displacement information for estimating the road friction coefficient is proposed. First, the tire brush model is modified to describe the tire characteristics more precisely in high friction conditions using tire test data. Then, on the basis of vehicle dynamics and a kinematic model, a nonlinear observer is designed, and the self-aligning torque of the wheel, lateral acceleration, and vehicle lateral displacement are used to estimate the road friction coefficient during steering. Finally, slalom tests and DLC (Double Line Change) tests in high friction conditions are conducted to verify the proposed estimation algorithm. Test results showed that the proposed method performs well during steering and the estimated road friction coefficient converges to the reference value rapidly.

Estimation for Runway Friction Coefficient Based on Multi-Sensor Information Fusion and Model Correlation

“…The related literature [ 8 , 10 , 11 , 15 ] showed that the contribution of the caused-based method to measure the friction coefficient is not outstanding. It obtains the parameters related to friction, not the friction itself.…”

“…Apart from these, different methods have been applied to estimate the tire–runway friction coefficient. These methods can be divided into two categories: the cause-based method and the effect-based method [ 8 ]. The cause-based method, also known as the experimental method, uses sensors to measure friction-related parameters and attempts to correlate these parameters with the tire–road friction coefficient.…”

Sensors and Sensing for Intelligent Vehicles

“… Path planning (1): [ 36 ] Smart regenerative braking systems for electric vehicles (2): [ 37 , 38 ]. Physical intelligence in sensors and sensing (3): [ 39 , 40 , 41 ] Driver assistance systems and automatic vehicle operation (3) Advanced driver assistance systems (1): [ 42 ]. Automatic parking of road vehicles (1): [ 43 ].…”

“…Gao et al propose in [ 40 ] a nonlinear observer aided by vehicle lateral displacement information for estimating the road friction coefficient, which is a key parameter for autonomous vehicles and vehicle dynamic control. A modified version of the tire brush model is proposed to describe the tire characteristics more precisely in high friction conditions using tire test data.…”

Vision‐aided intelligent vehicle sideslip angle estimation based on a dynamic model

“…Vehicle dynamic control has always been an important branch of research in the field of vehicles [1–3], such as electronic stability control [4–6] and electric differential power steering control [7, 8]. In the process of designing the vehicle control algorithm, how to estimate states of the vehicle and identify essential parameters with high accuracy is particularly critical, such as road friction coefficient [9, 10], vehicle longitudinal velocity [11], tyre force estimation [12], and sideslip angle [13]. Also, the vehicle sideslip is especially an essential element for vehicle lateral dynamic control.…”