Tire-road friction coefficient estimation based on longitudinal measurements

Hu, Juqi; Rakheja, Subhash; Zhang, Youmin

doi:10.1109/icamechs.2017.8316533

Cited by 6 publications

(2 citation statements)

References 20 publications

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“…The software is an industry standard, being used by car manufacturers (General Motors, Toyota, Honda, Ford, etc. ), suppliers, and research laboratories and universities [9,54,[56][57][58][59][60][61][62][63][64][65][66]. This software contains the main effects that determine how the tire contacts the road and how the forces are transferred in the tire-road interaction through the suspension to the chassis.…”

Section: Tire Parameters Estimatormentioning

confidence: 99%

A Strain-Based Intelligent Tire to Detect Contact Patch Features for Complex Maneuvers

Mendoza-Petit

García-Pozuelo

Díaz

et al. 2020

Sensors

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Tires are essential components of vehicles and are able to transmit traction and braking forces to the contact patch, contribute to directional stability, and also help to absorb shocks. If these components can provide information related to the tire–road interaction, vehicle safety can be increased. This research is focused on developing the tire as an active sensor capable to provide its functional parameters. Therefore, in this work, we studied strain-based measurements on the contact patch to develop an algorithm to compute the wheel velocity at the contact point, the effective rolling radius and the contact length on dynamic situations. These parameters directly influence the dynamics of wheel behavior which nowadays is not clearly defined. Herein, hypotheses have been assumed based on previous studies to develop the algorithm. The results expose to view an experimental test regarding influence of the tire operational condition (slip angle, vertical load, and rolling velocity) onto the computed parameters. This information is used to feed a fuzzy logic system capable of estimating the effective radius and contact length. Furthermore, a verification process has been carried out using CarSim simulation software to get the inputs for the fuzzy logic system at complex maneuvers.

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Section: Tire Parameters Estimatormentioning

confidence: 99%

A Strain-Based Intelligent Tire to Detect Contact Patch Features for Complex Maneuvers

Mendoza-Petit

García-Pozuelo

Díaz

et al. 2020

Sensors

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“…The design of maximum TRFC observer or estimator usually depends on how the vehicle behavior is modeled. If the longitudinal dynamics model is used, the friction dependency in the longitudinal slip ratio can be fitted with a parametric model and be used to calculate the maximum TRFC straightforwardly ( 18 – 20 ). While if the lateral dynamics are modeled, the relationships between the TRFC and side slip angle or self-align torque of the tire are the two most studied ways to obtain the maximum TRFC ( 21 , 22 ).…”

Section: Introductionmentioning

confidence: 99%

Pavement Friction Evaluation Based on Vehicle Dynamics and Vision Data Using a Multi-Feature Fusion Network

Skar

Pettinari³

et al. 2023

Transportation Research Record: Journal of the Transportation R

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The tire–road friction coefficient is a critical evaluation index of the service performance of roads: it governs the stopping distance, traction control, and stability of vehicles. Moreover, friction information is also needed in many function units of modern vehicles. This paper proposes a novel data-driven approach for inference of the maximum tire–road friction coefficient using a combination of vehicle dynamics signal and machine vision data. The approach is aimed at robust road condition perception that can provide frequent measurements over large areas across all weather conditions. Two different neural network architectures were adopted to extract in-depth features behind vehicle dynamics signals and road surface images. Features from these two types of data were then fused in two different levels, namely feature level and decision level, forming a multi-feature fusion neural network. The proposed network performs better than models based only on dynamic signals or vision data. The method proposed was applied to real data obtained from an electric car in a highway driving scenario. For classification of the maximum tire–road friction coefficient, the proposed network can yield F1-score increments of 0.09 and 0.18 from dynamics-based and vision-based sub-models, respectively. For the maximum tire–road friction coefficient value regression, the proposed model also achieves the highest R-square score of 0.71. Of these two types of data collected under highway driving scenarios, the vision data contribute more to the overall performance of the proposed model. Nevertheless, the dynamics data possess excellent potential in poor lighting conditions. With these fused features, the proposed multi-feature fusion network can not only improve the accuracy of maximum tire–road friction coefficient estimation but also is deemed workable for a broader range of scenarios.

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A comparative study of the recursive least squares and fuzzy logic estimation methods for the measurement of road adhesion coefficient

Fan

2021

Australian Journal of Mechanical Engineering

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Tire-road friction coefficient estimation based on longitudinal measurements

Cited by 6 publications

References 20 publications

A Strain-Based Intelligent Tire to Detect Contact Patch Features for Complex Maneuvers

A Strain-Based Intelligent Tire to Detect Contact Patch Features for Complex Maneuvers

Pavement Friction Evaluation Based on Vehicle Dynamics and Vision Data Using a Multi-Feature Fusion Network

A comparative study of the recursive least squares and fuzzy logic estimation methods for the measurement of road adhesion coefficient

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