ReviewSensors (Basel, Switzerland)2019
Advanced Estimation Techniques for Vehicle System Dynamic State: A Survey.
Review in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Predicting road adhesion coefficient with a fusion strategy of SHAP dynamic parameters.Scientific reports · 2025Article
- Sensitivity Analysis of Long Short-Term Memory-Based Neural Network Model for Vehicle Yaw Rate Prediction.Sensors (Basel, Switzerland) · 2025Article
- Review of Integrated Chassis Control Techniques for Automated Ground Vehicles.Sensors (Basel, Switzerland) · 2024Review
- An Adaptive Unscented Kalman Filter for the Estimation of the Vehicle Velocity Components, Slip Angles, and Slip Ratios in Extreme Driving Manoeuvres.Sensors (Basel, Switzerland) · 2024Article
- Robust Virtual Sensing of the Vehicle Sideslip Angle through the Cross-Combination of Multiple Filters Using a Decision Tree Algorithm.Sensors (Basel, Switzerland) · 2023Article
- Prediction for Future Yaw Rate Values of Vehicles Using Long Short-Term Memory Network.Sensors (Basel, Switzerland) · 2023Article
- Estimation of Vehicle Dynamic Parameters Based on the Two-Stage Estimation Method.Sensors (Basel, Switzerland) · 2021Article
- The New Trend of State Estimation: From Model-Driven to Hybrid-Driven Methods.Sensors (Basel, Switzerland) · 2021Review
- Estimation of Vehicle Attitude, Acceleration, and Angular Velocity Using Convolutional Neural Network and Dual Extended Kalman Filter.Sensors (Basel, Switzerland) · 2021Article
- Feature Point Registration Model of Farmland Surface and Its Application Based on a Monocular Camera.Sensors (Basel, Switzerland) · 2020Article
- Simultaneous Estimation of Vehicle Roll and Sideslip Angles through a Deep Learning Approach.Sensors (Basel, Switzerland) · 2020Article
- An Acquisition Method of Agricultural Equipment Roll Angle Based on Multi-Source Information Fusion.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
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.
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Registered trials
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