GPS-Based Real-Time Identification of Tire-Road Friction Coefficient

Abstract

Vehicle control systems such as collision avoidance, adaptive cruise control and automated lane-keeping systems as well as ABS and stability control systems can benefit significantly from being made “road-adaptive”. The estimation of tire-road friction coefficient at the wheels allows the control algorithm in such systems to adapt to external driving conditions. This paper develops a new tire-road friction coefficient estimation algorithm based on measurements related to the lateral dynamics of the vehicle. A lateral tire force model parameterized as a function of slip angle, friction coefficient, normal force and cornering stiffness is used. A real-time parameter identification algorithm that utilizes measurements from a differential GPS system and a gyroscope is used to identify the tire-road friction coefficient and cornering stiffness parameters of the tire. The advantage of the developed algorithm is that it does not require large longitudinal slip in order to provide reliable friction estimates. Simulation studies indicate that a parameter convergence rate of one second can be obtained. Experiments conducted on both dry and slippery road indicate that the algorithm can work very effectively in identifying a slippery road. Two other new approaches to real-time tire road friction identification system are also discussed in the paper.

Stability measurement of high-speed vehicles

“…In this section simulation results are presented to validate the accuracy of the indirect expression of the SM given in Equation (22). In Section 4.1, the vehicle model and simulation environment are described.…”

“…Force estimation based on measurement of suspension configuration (or state) can be used instead of direct force measurement [22][23][24], though this requires knowledge of suspension design parameters, which may be uncertain and time varying. Techniques for direct estimation of load transfer on flat ground with measurement of lateral acceleration [25,26], roll angle [7], or both [6] have been proposed that require minimal knowledge of suspension design parameters, though none of these methods have been evaluated in off-road scenarios.…”

Road Friction Virtual Sensing: A Review of Estimation Techniques with Emphasis on Low Excitation Approaches

“…Simulation results are presented in Carsim, and an accurate tracking of the friction potential and tyre parameters is achieved in a road formed by µ max = 0.9, µ max = 0.3, µ max = 0.6 under a continuous lateral excitation level of approximately 4 m/s 2 . The Brush tyre model has been also employed by Hahn et al [34,43]. In this case, the authors proposed an adaptive law to estimate the tyre cornering stiffness and friction potential using a differential Global Positioning System (DGPS) setup.…”

“…A comprehensive discussion on pure lateral and combined (lateral and longitudinal) dynamics is presented in this subsection. The majority of the solutions proposed in the literature are predominantly model-based approaches [8,23,34,43,[98][99][100][101][102][103][104][105][106][107][108][109][110][111], and differ depending on the technical solution adopted to infer the friction potential from the parameterised friction or tyre force model (e.g., Unscented Kalman filter (UKF) [8] or Bayesian Hypothesis selection [43,111]). Apart from this dominant group, additional solutions have been proposed using data-based [3,112], rule-based [20], self-excitation [113], or wheel acceleration-based approaches [114].…”

“…A first classification provided in [7] establishes a distinction between cause-based and effect-based approaches. While cause-based approaches focus on estimating the road grip potential from aspects such as the lubricant present at the road surface [32,33], effect-based approaches infer the road grip from the tyre responses (e.g., slip [34,35]). Despite the fact that the former approaches can provide an accurate estimation of the grip potential during free-rolling driving conditions, an important handicap of these is that a database and extensive training is required in order to achieve an accurate correlation between the monitored road property and the road grip potential [7].…”