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This article reviews the key physical parameters that need to be estimated and identified during vehicle operation, focusing on two key areas: vehicle state estimation and road condition identification. In the vehicle state estimation section, parameters such as longitudinal vehicle speed, sideslip angle, and roll angle are discussed, which are critical for accurately monitoring road conditions and implementing advanced vehicle control systems. On the other hand, the road condition identification section focuses on methods for estimating the tire–road friction coefficient (TRFC), road roughness, and road gradient. The article first reviews a variety of methods for estimating TRFC, ranging from direct sensor measurements to complex models based on vehicle dynamics. Regarding road roughness estimation, the article analyzes traditional methods and emerging data-driven approaches, focusing on their impact on vehicle performance and passenger comfort. In the section on road gradient estimation, details are given on how to measure the grade and bank angles of a road, and their role in enhancing vehicle stability under extreme driving conditions is emphasized. The article also provides an in-depth overview of different vehicle state estimation techniques, including model-based, observer-based, and techniques using neural networks for estimation. Finally, the article summarizes the challenges facing current research and suggests potential directions for further research. The article emphasizes the importance of combining vehicle state estimation with road condition recognition and suggests that this combination has the potential to provide a more robust framework for adaptive vehicle control systems in variable and complex driving environments.
Chen et al. (Wed,) studied this question.