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Tire/road friction coefficient estimation applied to road safety

TLDR
In this paper, a new method for the estimation of the maximum tire/road friction coefficient is proposed to automatically detect possible state of loss of friction which result in an abrupt change on the road state.
Abstract
Recent statistics show that a large number of traffic accidents occur due to a loss of control on vehicle by the driver. This is mainly due to a loss of friction between tire and road. Many of these accidents could be avoided by introducing ADAS (Advanced Driver Assistance Systems) based on the detection of loss of tire/road friction. Friction (more specifically the maximum coefficient of friction) which is a parameter of tire/road interaction, mainly depends on the state of the road (dry, wet, snow, ice) and is closely related to the efforts at the tire level. In this paper, we propose, a new method for the estimation of the maximum tire/road friction coefficient, to automatically detect possible state of loss of friction which result in an abrupt change on the road state. This method is based on an iterative quadratic minimization of the error between the developed lateral force and the model of tire/road interaction. Results validate the application of the method.

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Citations
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Journal ArticleDOI

A technical survey on tire-road friction estimation

TL;DR: This literature survey introduces different approaches, which have been widely used to estimate the friction or other related parameters, and covers the recent literature that contains these methodologies.
Journal ArticleDOI

Novel Tire Force Estimation Strategy for Real-Time Implementation on Vehicle Applications

TL;DR: The novelty in the proposed approach lies in the independence of the estimates from the vehicle tire model, thereby making the structure robust against variations in vehicle mass, tire parameters due to tire wear, and, most importantly, road surface conditions.
Journal ArticleDOI

Literature review and fundamental approaches for vehicle and tire state estimation

TL;DR: In this paper, real-time estimates of the vehicle dynamic states and tire-road contact parameters are provided for automotive chassis control systems, where feedback control structures employ a feedback control structure.
Journal ArticleDOI

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

TL;DR: A review on road friction virtual sensing approaches is provided in this paper, where the authors attempt to address whether the road grip potential can be estimated accurately under regular driving conditions in which the vehicle responses remain within low longitudinal and lateral excitation levels.
Journal ArticleDOI

Tire Condition Monitoring and Intelligent Tires Using Nanogenerators Based on Piezoelectric, Electromagnetic, and Triboelectric Effects

TL;DR: In this paper, a comprehensive review on intelligent tire and tire condition monitoring systems is presented, including estimation techniques, sensing, and energy harvesting approaches for TCMS based on piezoelectric, electromagnetic, and TENGs.
References
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Journal ArticleDOI

Dynamic friction models for road/tire longitudinal interaction

TL;DR: In this paper, a new dynamic friction force model for the longitudinal road/tire interaction for wheeled ground vehicles is derived, based on a dynamic friction model developed previously for contact-point friction problems, called the LuGre model.
Journal ArticleDOI

Estimation of the Maximum Tire-Road Friction Coefficient

TL;DR: In this article, a slip-based method was proposed to estimate the maximum available tire-road friction during braking using data from short braking maneuvers with peak accelerations of 3.9 m/s 2.
Journal ArticleDOI

Road Friction Coefficient Estimation For Vehicle Path Prediction

TL;DR: In this article, a lane departure warning system is proposed to project vehicle trajectory, and compare with the perceived road geometry to calculate a performance metric termed "time to lane crossing" (TLC), when the calculated TLC is less than a threshold value.
Journal ArticleDOI

Lateral Load Transfer and Normal Forces Estimation for Vehicle Safety: Experimental Test

TL;DR: In this paper, the estimation process is separated into three blocks: the first block identifies the vehicle's mass, the second block contains a linear observer whose main role is to estimate the roll angle and the one-side lateral transfer load, while in the third block compared linear and nonlinear models for the estimation of four wheel vertical forces.
Related Papers (5)
Frequently Asked Questions (9)
Q1. What are the contributions in "Tire/road friction coefficient estimation applied to road safety" ?

In this paper, the authors propose, a new method for the estimation of the maximum tire/road friction coefficient, to automatically detect possible state of loss of friction which result in an abrupt change on the road state. 

Following this work, the authors plan to validate the method embedded on a real vehicle, and to integrate the multi-model approach to estimate the maximum lateral friction coefficient. 

The main objective of the first block is to provide the vehicle mass, the load transfer and vertical forces applied at the tire/road level, and the corrected lateral acceleration relative to vehicle roll, denoted ay . 

Because of the extremely low friction, and the speed of 60km/h, the dynamics of interaction exceeds the saturation limit for the lateral forces. 

The estimation process consists of two blocks, and its role is to estimate side slip angle, normal and lateral forces at each tire/road contact point, which provides the input of the process of estimating the maximum friction described in Section IV. 

Where ax and ay are respectively the longitudinaland lateral accelerations, ψ̇ is the yaw rate, θ̇ is the roll rate, ∆i j (i represents the front(1) or the rear(2) and j represents the left(1) or the right (2)) is the suspension deflection, wi j is the wheel velocity, Fzi j and Fyi j are respectively the normal and lateral tire-road forces, αi j is the side slip angle at the center of gravity (cog). 

The simulator CALLAS provide parameters of vehicle dynamics that constitute the inputs of the block “ Observer (Data)” in Figure 3, which are essentially: suspension deflections, longitudinal and lateral accelerations , yaw rate, wheel speeds and steering angle (inputs for their forces observers in Figure 2). 

Hypothesis 2: If Hypothesis 1 is verified, the authors assume that at every time k of the trajectory, it is possible to calculate an error ei j = Fyi j −Fyi j where Fyi j is the lateral force calculated with the Dugoff model and Fyi j the lateral force estimated by the observer. 

Fy11 denotes the lateral force estimated by their forces observers with the parameters provided by CALLAS; Fydugo f f11 denotes the lateral force estimated by the Dugoff model for a given µmax , and e11 the error function.