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.
