Abstract
In order to improve handling stability performance and active safety of a ground vehicle, a large number of advanced vehicle dynamics control systems—such as the direct yaw control system and active front steering system, and in particular the advanced driver assistance systems—towards connected and automated driving vehicles have recently been developed and applied. However, the practical effects and potential performance of vehicle active safety dynamics control systems heavily depend on real-time knowledge of fundamental vehicle state information, which is difficult to measure directly in a standard car because of both technical and economic reasons. This paper presents a comprehensive technical survey of the development and recent research advances in vehicle system dynamic state estimation. Different aspects of estimation strategies and methodologies in recent literature are classified into two main categories—the model-based estimation approach and the data-driven-based estimation approach. Each category is further divided into several sub-categories from the perspectives of estimation-oriented vehicle models, estimations, sensor configurations, and involved estimation techniques. The principal features of the most popular methodologies are summarized, and the pros and cons of these methodologies are also highlighted and discussed. Finally, future research directions in this field are provided.
Highlights
The work [79] proposed nonlinear constrained MHE to estimate mainly the vehicle position Pp and vehicle sideslip angle for future autonomous vehicles; the delayed measurements from the global navigation satellite system (GNSS) and road boundary constraints can be directly incorporated into MHE, and real-world experiments show that the proposed MHE possesses improved estimation performance in comparison to the extended Kalman filter (EKF)
The higher-order sliding mode observer (HOSMO) presented in [109] was used to estimate vehicle sideslip angle and longitudinal force of IWM electric vehicles, and it was reconstructed by model decoupling and the electric driving wheel model, whereas vertical forces that can calculate the load transfer ratio (LTR) of heavy-vehicles for rollover risk prediction was observed by the HOSMO, and its performance was validated experimentally within many scenarios [110]
Different aspects of estimation strategies and methodologies in the most recent literature are reviewed and classified into two main categories consisting of the model-based estimation approach and data-driven-based estimation approach
Summary
To improve ground vehicle handling stability and passenger safety, a large number of advanced vehicle-active safety dynamic control systems, such as the direct yaw control system (DYC) [1,2,3,4], anti-lock braking systems (ABS) [5,6], four-wheel steering system (4WS) [7,8], active front steering system (AFS) [2,9], active suspension system (ASS) [9,10], adaptive cruise control (ACC) [11], collision avoidance system (CAS) [12], and other advanced driver assistance systems (ADAS) towards a connected and automated driving vehicle have been developed and brought into the market in recent years [13,14,15]. Vehicle whereas thewhereas second category is the data-driven-based estimation approach. Based estimation approach and theapproach vehicle dynamic model-based estimation approach. With the rapid development of artificial intelligence, the datathe data-driven-based artificial neural network (ANN)-based in driven-based estimationestimation approach, approach, artificial neural network (ANN)-based estimations estimations in particular, particular, have shown promising perspectives in vehicle state estimation applications. Thereby, this paper presents a technical survey for development and recent research progress of fundamental vehicle system dynamic state estimation terms of vehicle models, estimations, sensor. The remainder of the paper is organized as fundamental vehicle system dynamic state estimation in terms of vehicle models, estimations, sensor follows: In Section. Paper is organized data-driven-based estimation approaches for vehicle dynamic state are introduced and discussed. Data-driven-based estimation for vehicle dynamic state are introduced and discussed.
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