An intelligent tyre system for road condition perception

Yang, Shichun; Wang, Rui; Shi, Runwu; Chen, Yu-Yi; Lu, Jiayi; Li, Zhuoyang; Cao, Yaoguang

doi:10.1080/10298436.2022.2096882

Cited by 3 publications

(2 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The measured IRI values were comparable with those obtained using a laser profiler. Differently, Yang et al [139] employed machine learning algorithms to classify road roughness levels based on data collected using an intelligent tire system with a piezoelectric cable. The layout of the piezoelectric cable was optimized using a finite element model.…”

Section: Tire Pressure Sensorsmentioning

confidence: 99%

Industry- and Academic-Based Trends in Pavement Roughness Inspection Technologies over the Past Five Decades: A Critical Review

Fares

Zayed

2023

Remote Sensing

View full text Add to dashboard Cite

Roughness is widely used as a primary measure of pavement condition. It is also the key indicator of the riding quality and serviceability of roads. The high demand for roughness data has bolstered the evolution of roughness measurement techniques. This study systematically investigated the various trends in pavement roughness measurement techniques within the industry and research community in the past five decades. In this study, the Scopus and TRID databases were utilized. In industry, it was revealed that laser inertial profilers prevailed over response-type methods that were popular until the 1990s. Three-dimensional triangulation is increasingly used in the automated systems developed and used by major vendors in the USA, Canada, and Australia. Among the research community, a boom of research focusing on roughness measurement has been evident in the past few years. The increasing interest in exploring new measurement methods has been fueled by crowdsourcing, the effort to develop cheaper techniques, and the growing demand for collecting roughness data by new industries. The use of crowdsourcing tools, unmanned aerial vehicles (UAVs), and synthetic aperture radar (SAR) images is expected to receive increasing attention from the research community. However, the use of 3D systems is likely to continue gaining momentum in the industry.

show abstract

Section: Tire Pressure Sensorsmentioning

confidence: 99%

Industry- and Academic-Based Trends in Pavement Roughness Inspection Technologies over the Past Five Decades: A Critical Review

Fares

Zayed

2023

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Te efect-based method can identify the road adhesion coefcient based on tire noise, tire deformation, longitudinal, and lateral dynamic response of the vehicle. Yang et al used the wavelet analysis method to analyze vibration signals under diferent roads to identify the road surface adhesion coefcient, which has strong robustness and poor practicability [8]. Choi et al used the least square method combined with vehicle sensor and GPS data to estimate road adhesion coefcient through a vehicle longitudinal model, but they needed enough data points, poor real-time performance, and high accuracy of data points [9].…”

Section: Introductionmentioning

confidence: 99%

Road Adhesion Coefficient Estimation Based on Second‐Order Linear Extended State Observer

Jian

Yang

et al. 2023

Mathematical Problems in Engineering

View full text Add to dashboard Cite

In order to estimate different road adhesion coefficients, a new method based on the second-order linear extended state observer is proposed to improve the active safety and driving stability of vehicles. The vehicle single-wheel driving dynamics model and single-wheel braking dynamics model are built with analyzing the driving and braking processes of the vehicle. The controllers of ASR and ABS are designed by using sliding mode structure control, and the chattering phenomenon of sliding mode control is eliminated by the saturation function. The road adhesion coefficient can be estimated by the second-order linear extended state observer in real time with the wheel speeds, and driving torque and braking torque are used as input variables. The simulation results show that the road adhesion coefficient estimation method based on the second-order linear extended state observer can accurately identify the adhesion coefficient of different roads in the presence of external interference and has strong robustness. At the same time, it also provides a reference for the design of the stability control system of unmanned vehicles.

show abstract

Enhancing road surface recognition via optimal transport and metric learning in task-agnostic intelligent driving environments

Chen,

Yang,

Wang

et al. 2025

Expert Systems with Applications

View full text Add to dashboard Cite

An intelligent tyre system for road condition perception

Cited by 3 publications

References 32 publications

Industry- and Academic-Based Trends in Pavement Roughness Inspection Technologies over the Past Five Decades: A Critical Review

Industry- and Academic-Based Trends in Pavement Roughness Inspection Technologies over the Past Five Decades: A Critical Review

Road Adhesion Coefficient Estimation Based on Second‐Order Linear Extended State Observer

Enhancing road surface recognition via optimal transport and metric learning in task-agnostic intelligent driving environments

Contact Info

Product

Resources

About