A technical survey on tire-road friction estimation

Khaleghian, Seyedmeysam; Emami, Anahita; Taheri, Saied

doi:10.1007/s40544-017-0151-0

Cited by 150 publications

(73 citation statements)

References 88 publications

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“…Most prior works on RFE detection implement experimentbased or model-based approaches to find a correlation between the vehicle dynamics sensor data and road-tire friction related parameters [10]. The recent work in [11] used 6 vehicle sensors as inputs to a neural network (NN) model for prediction of slippery/non-slippery RSC, which controlled the driver maneuvering actions for left or right lane departures.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning Models for Road Surface and Friction Estimation using Front-Camera Images

Roychowdhury

Zhao

Wallin

et al. 2018

2018 International Joint Conference on Neural Networks (IJCNN)

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Automotive active safety systems can significantly benefit from real-time road friction estimates (RFE) by adapting driving styles, specific to the road conditions. This work presents a 2-stage approach for indirect RFE estimation using front-view camera images captured from vehicles. In stage-1, convolutional neural network model architectures are implemented to learn region-specific features for road surface condition (RSC) classification. Texture-based features from the drivable surface, sky and surroundings are found to be separate regions of interest for dry, wet/water, slush and snow/ice RSC classification. In stage-2, a rule-based model that relies on domain-specific guidelines is implemented to segment the ego-lane drivable surface into [5x3] patches, followed by patch classification and quantization to separate images with high, medium and low RFE. The proposed method achieves average accuracy of 97% for RSC classification in stage-1 and 89% for RFE classification in stage-2, respectively. The 2-stage models are trained using publicly available data sets to enable benchmarking for future methodologies in the autonomous driving domain.

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Section: Related Workmentioning

confidence: 99%

Machine Learning Models for Road Surface and Friction Estimation using Front-Camera Images

Roychowdhury

Zhao

Wallin

et al. 2018

2018 International Joint Conference on Neural Networks (IJCNN)

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“…To study power parameters of interaction of the wheel with seating various methods based on the known analytical models [3][4][5][6] are applied. An important factor when using this or that model is the choice of the suitable law of distribution of specific normal reaction on the length of the contact area of the tire.…”

Section: Methodsmentioning

confidence: 99%

Determination of specific power parameters and coefficient of slipping of tractor wheel

Romanchenko

Pastukhov

2019

Engineering for Rural Development

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The technique of determination of weight ratio longitudinal force, weight ratio traction moment, coefficient of slipping of the wheel and their analysis is given at variation of the central angle of the contact area of the tire. Longitudinal force consists of two components − on the site of slipping and on the site of rest of the contact area of the tire. The first component is defined by normal reaction of seating on the site of slipping and dynamic coefficient of traction of the tire, which is expressed by the function from relative normal reaction on the site of slipping and is in interval of two boundary values. The maximum and minimum values are equal according to the tire static friction coefficient without slipping of the wheel and to tire sliding friction coefficient at full slipping of the wheel. The second component is defined by normal reaction of seating on the site of rest and static coefficient of traction of the tire, which is expressed by the function from relative normal reaction on the site of rest and is limited to two limits -zero value and value of the coefficient of static friction of the tire. The traction moment on the wheel is defined by the work of longitudinal force on the radius of its rolling. The optimum value of the coefficient of slipping of the wheel is defined by functions of the weight ratio traction moment on the wheel to the maximum. Settlement assessment for the wheel with the Goodyear OPTITRAC DT812 tire of standard size 520/70R38 of the BELARUS 82.1 tractor is executed. The central angle of the contact area of the tire is equal to 0.881 radian. The maximum values of the weight ratio longitudinal force and the weight ratio traction moment are respectively to 0.722 and 0.634. Coefficients of slipping of the wheel make at the same time respectively 0.15 and 0.107. These values do not exceed the slipping coefficient size limited under operating conditions to values 0.18 and 0.15 for tractors according to one and two leading bridges.

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“…Therefore, if the lower cutoff angular frequency is taken into account, the actual power spectral density [12] could be expressed as follows:…”

Section: Filtered White Noise Generation For Stochastic Road Surfacementioning

confidence: 99%

“…From all perspectives of research it is found that inadequate skid resistance of pavement is a major cause of traffic accident and the road surface topography is significantly associated with vehicle driving performance [12]. The reconstruction of real and accurate road surface roughness has been discussed both in the fields of automotive engineering and road engineering.…”

Section: Introductionmentioning

confidence: 99%

Construction of Three‐Dimensional Road Surface and Application on Interaction between Vehicle and Road

Wenqing

Zhang

2018

Shock and Vibration

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The quantitative description is given to three-dimensional micro and macro self-similar characteristics of road surface from the perspective of fractal geometry using FBM stochastic midpoint displacement and diamond-square algorithm in conjunction with fractal characteristics and statistical characteristics of standard pavement determined by estimation method of box-counting dimension. The comparative analysis between reconstructed three-dimensional road surface spectrum and theoretical road surface spectrum and correlation coefficient demonstrate the high reconstruction accuracy of fractal reconstructed road spectrum. Furthermore, the bump zone is taken as an example to reconstruct a more arbitrary 3D road model through isomorphism of special road surface with stochastic road surface model. Measurement is taken to assume the tire footprint on road surface to be a rectangle, where the pressure distribution is expressed with mean stiffness, while the contact points in the contact area are replaced with a number of springs. Two-DOF vehicle is used as an example to analyze the difference between three-dimensional multipoint-andplane contact and traditional point contact model. Three-dimensional road surface spectrum provides a more accurate description of the impact effect of tire on road surface, thereby laying a theoretical basis for studies on the dynamical process of interaction of vehicle-road surface and the road friendliness.

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A technical survey on tire-road friction estimation

Cited by 150 publications

References 88 publications

Machine Learning Models for Road Surface and Friction Estimation using Front-Camera Images

Machine Learning Models for Road Surface and Friction Estimation using Front-Camera Images

Determination of specific power parameters and coefficient of slipping of tractor wheel

Construction of Three‐Dimensional Road Surface and Application on Interaction between Vehicle and Road

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