TY - JOUR
T1 - Road surface status recognition and estimation
T2 - State-of-the-art review and research perspectives
AU - Yan, Yongjun
AU - Du, Chao
AU - Wang, Weihua
AU - Ma, Renjie
AU - Wang, Yan
AU - Wang, Hongliang
AU - Pi, Dawei
AU - Chen, Ye Hwa
N1 - Publisher Copyright:
© 2026 Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY-NC-ND license. http://creativecommons.org/licenses/by-nc-nd/4.0/
PY - 2026/12
Y1 - 2026/12
N2 - Accurate recognition and estimation of road state in real-time is an important prerequisite for vehicle driving safety, transient handling stability, ride comfort, and other performance. In order to grasp the main development trend in this field systematically and in a timely manner, in this paper, the latest research progress of the identification and estimation methods of two key road state parameters, road roughness and road surface adhesion coefficient, is comprehensively reviewed. Firstly, according to the difference in basic theory and implementation principle, the research progress of road roughness recognition and estimation methods is reviewed from three aspects: section class method, system response-based method, and machine learning-based method. Then, the research progress of road surface adhesion coefficient recognition and estimation methods is summarized from four aspects: cause-based method, effect-based method, data-driven method, and multi-method fusion method. Finally, the advantages and disadvantages, technical challenges, and future development trends of different road recognition and estimation methods are analyzed.
AB - Accurate recognition and estimation of road state in real-time is an important prerequisite for vehicle driving safety, transient handling stability, ride comfort, and other performance. In order to grasp the main development trend in this field systematically and in a timely manner, in this paper, the latest research progress of the identification and estimation methods of two key road state parameters, road roughness and road surface adhesion coefficient, is comprehensively reviewed. Firstly, according to the difference in basic theory and implementation principle, the research progress of road roughness recognition and estimation methods is reviewed from three aspects: section class method, system response-based method, and machine learning-based method. Then, the research progress of road surface adhesion coefficient recognition and estimation methods is summarized from four aspects: cause-based method, effect-based method, data-driven method, and multi-method fusion method. Finally, the advantages and disadvantages, technical challenges, and future development trends of different road recognition and estimation methods are analyzed.
KW - Kalman filtering
KW - Machine learning
KW - Road adhesion coefficient
KW - Road condition
KW - Road roughness
UR - https://www.scopus.com/pages/publications/105036806136
U2 - 10.1016/j.cjme.2025.100170
DO - 10.1016/j.cjme.2025.100170
M3 - 文章
AN - SCOPUS:105036806136
SN - 1000-9345
VL - 39
JO - Chinese Journal of Mechanical Engineering (English Edition)
JF - Chinese Journal of Mechanical Engineering (English Edition)
M1 - 100170
ER -