Review of State Estimation Methods for Autonomous Ground Vehicles: Perspectives on Estimation Objects, Vehicle Characteristics, and Key Algorithms
This paper reviews research on vehicle driving state estimation research. Based on the discussion of the importance, development history, and application fields of this topic of research, it focuses on analyzing vehicle state estimation techniques from different perspectives, namely (1) from the per...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-06-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/13/3927 |
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| author | Xiaoyu Wang Te Chen Renzhong Wang Jiankang Lu Guowei Dou |
| author_facet | Xiaoyu Wang Te Chen Renzhong Wang Jiankang Lu Guowei Dou |
| author_sort | Xiaoyu Wang |
| collection | DOAJ |
| description | This paper reviews research on vehicle driving state estimation research. Based on the discussion of the importance, development history, and application fields of this topic of research, it focuses on analyzing vehicle state estimation techniques from different perspectives, namely (1) from the perspective of the estimation objects, including vehicle attitude and driving state estimations, chassis component key dynamic parameter estimations, and vehicle driving environment state estimations; (2) from the perspective of vehicle characteristics, including vehicle dynamics coupling characteristics, vehicle multi-source information redundancy characteristics, and vehicle state transition characteristics; (3) from the perspective of key estimation algorithms, including model-based Kalman filtering algorithms, data-driven machine learning algorithms, and optimization estimation algorithms combining mechanism-based and data-driven approaches. This manuscript helps interested readers to comprehensively understand the research progress, technical features, and future trends of vehicle state estimation technology from the perspective of overall architecture and subdomains. |
| format | Article |
| id | doaj-art-e916044ebffe4aa7a1734ab390178df5 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-e916044ebffe4aa7a1734ab390178df52025-08-20T03:28:59ZengMDPI AGSensors1424-82202025-06-012513392710.3390/s25133927Review of State Estimation Methods for Autonomous Ground Vehicles: Perspectives on Estimation Objects, Vehicle Characteristics, and Key AlgorithmsXiaoyu Wang0Te Chen1Renzhong Wang2Jiankang Lu3Guowei Dou4School of Mechanical and Electrical Engineering, Suzhou Vocational University, Suzhou 215000, ChinaAutomotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, ChinaSchool of Mechanical and Electrical Engineering, Suzhou Vocational University, Suzhou 215000, ChinaSchool of Mechanical and Electrical Engineering, Suzhou Vocational University, Suzhou 215000, ChinaSchool of Automotive and Transportation Engineering, Jiangsu University, Zhenjiang 212013, ChinaThis paper reviews research on vehicle driving state estimation research. Based on the discussion of the importance, development history, and application fields of this topic of research, it focuses on analyzing vehicle state estimation techniques from different perspectives, namely (1) from the perspective of the estimation objects, including vehicle attitude and driving state estimations, chassis component key dynamic parameter estimations, and vehicle driving environment state estimations; (2) from the perspective of vehicle characteristics, including vehicle dynamics coupling characteristics, vehicle multi-source information redundancy characteristics, and vehicle state transition characteristics; (3) from the perspective of key estimation algorithms, including model-based Kalman filtering algorithms, data-driven machine learning algorithms, and optimization estimation algorithms combining mechanism-based and data-driven approaches. This manuscript helps interested readers to comprehensively understand the research progress, technical features, and future trends of vehicle state estimation technology from the perspective of overall architecture and subdomains.https://www.mdpi.com/1424-8220/25/13/3927vehicle observerstate estimationvehicle characteristicsdata drivenfiltering algorithm |
| spellingShingle | Xiaoyu Wang Te Chen Renzhong Wang Jiankang Lu Guowei Dou Review of State Estimation Methods for Autonomous Ground Vehicles: Perspectives on Estimation Objects, Vehicle Characteristics, and Key Algorithms Sensors vehicle observer state estimation vehicle characteristics data driven filtering algorithm |
| title | Review of State Estimation Methods for Autonomous Ground Vehicles: Perspectives on Estimation Objects, Vehicle Characteristics, and Key Algorithms |
| title_full | Review of State Estimation Methods for Autonomous Ground Vehicles: Perspectives on Estimation Objects, Vehicle Characteristics, and Key Algorithms |
| title_fullStr | Review of State Estimation Methods for Autonomous Ground Vehicles: Perspectives on Estimation Objects, Vehicle Characteristics, and Key Algorithms |
| title_full_unstemmed | Review of State Estimation Methods for Autonomous Ground Vehicles: Perspectives on Estimation Objects, Vehicle Characteristics, and Key Algorithms |
| title_short | Review of State Estimation Methods for Autonomous Ground Vehicles: Perspectives on Estimation Objects, Vehicle Characteristics, and Key Algorithms |
| title_sort | review of state estimation methods for autonomous ground vehicles perspectives on estimation objects vehicle characteristics and key algorithms |
| topic | vehicle observer state estimation vehicle characteristics data driven filtering algorithm |
| url | https://www.mdpi.com/1424-8220/25/13/3927 |
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