Trajectory Tracking Control of Autonomous Vehicles Combining ACT-R Cognitive Framework and Preview Tracking Theory
By improving the ability of Trajectory Tracking Control (TTC) algorithms to mimic the manipulation behaviors of real drivers, which is of great significance in improving the personalized driving experience of autonomous vehicles. In this paper, we propose a TTC method that combines the Adaptive Cont...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10341244/ |
| _version_ | 1797393050741243904 |
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| author | Xuequan Tang Yunbing Yan Baohua Wang |
| author_facet | Xuequan Tang Yunbing Yan Baohua Wang |
| author_sort | Xuequan Tang |
| collection | DOAJ |
| description | By improving the ability of Trajectory Tracking Control (TTC) algorithms to mimic the manipulation behaviors of real drivers, which is of great significance in improving the personalized driving experience of autonomous vehicles. In this paper, we propose a TTC method that combines the Adaptive Control of Thought-Rational (ACT-R) cognitive theory framework with the Preview Tracking (PT) theory. Firstly, by analyzing and describing the ACT-R cognitive framework and the PT theory, a TTC framework that combines the two theories is proposed. Secondly, a virtual driving simulator was built to collect driving data from 30 drivers and construct a driving memory database. Thirdly, a central production system for driver trajectory tracking is designed, which consists of: three control modes, production rules, a timing generator, and filtering methods for driving memory segments. Fourthly, the TTC method based on PT theory is designed to adapt to different control modes. Finally, statistical and comparative analyses of the human-like trajectory tracking results of the proposed method were carried out through co-simulation experiments, and it was verified that the human-like performance of the proposed new method had a high degree of similarity with the manipulation behaviors and the vehicle motion state of the real driver. The real-vehicle experiments are carried out, which verifies the consistency of the proposed method with the results of the simulation experiments. |
| first_indexed | 2024-03-08T23:57:32Z |
| format | Article |
| id | doaj.art-31979c89087b4ddba04ae5a1dcd5dd1c |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-08T23:57:32Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-31979c89087b4ddba04ae5a1dcd5dd1c2023-12-13T00:01:09ZengIEEEIEEE Access2169-35362023-01-011113706713708210.1109/ACCESS.2023.333915610341244Trajectory Tracking Control of Autonomous Vehicles Combining ACT-R Cognitive Framework and Preview Tracking TheoryXuequan Tang0https://orcid.org/0000-0002-0283-6712Yunbing Yan1Baohua Wang2https://orcid.org/0000-0001-6327-2626Department of Automotive and Traffic Engineering, Wuhan University of Science and Technology, Hongshan, Wuhan, ChinaDepartment of Automotive and Traffic Engineering, Wuhan University of Science and Technology, Hongshan, Wuhan, ChinaDepartment of Automotive Engineering, Hubei University of Automotive Technology, Shiyan, ChinaBy improving the ability of Trajectory Tracking Control (TTC) algorithms to mimic the manipulation behaviors of real drivers, which is of great significance in improving the personalized driving experience of autonomous vehicles. In this paper, we propose a TTC method that combines the Adaptive Control of Thought-Rational (ACT-R) cognitive theory framework with the Preview Tracking (PT) theory. Firstly, by analyzing and describing the ACT-R cognitive framework and the PT theory, a TTC framework that combines the two theories is proposed. Secondly, a virtual driving simulator was built to collect driving data from 30 drivers and construct a driving memory database. Thirdly, a central production system for driver trajectory tracking is designed, which consists of: three control modes, production rules, a timing generator, and filtering methods for driving memory segments. Fourthly, the TTC method based on PT theory is designed to adapt to different control modes. Finally, statistical and comparative analyses of the human-like trajectory tracking results of the proposed method were carried out through co-simulation experiments, and it was verified that the human-like performance of the proposed new method had a high degree of similarity with the manipulation behaviors and the vehicle motion state of the real driver. The real-vehicle experiments are carried out, which verifies the consistency of the proposed method with the results of the simulation experiments.https://ieeexplore.ieee.org/document/10341244/Trajectory tracking controlhuman-like drivingautonomous vehiclesACT-R cognitive frameworkpreview tracking theory |
| spellingShingle | Xuequan Tang Yunbing Yan Baohua Wang Trajectory Tracking Control of Autonomous Vehicles Combining ACT-R Cognitive Framework and Preview Tracking Theory IEEE Access Trajectory tracking control human-like driving autonomous vehicles ACT-R cognitive framework preview tracking theory |
| title | Trajectory Tracking Control of Autonomous Vehicles Combining ACT-R Cognitive Framework and Preview Tracking Theory |
| title_full | Trajectory Tracking Control of Autonomous Vehicles Combining ACT-R Cognitive Framework and Preview Tracking Theory |
| title_fullStr | Trajectory Tracking Control of Autonomous Vehicles Combining ACT-R Cognitive Framework and Preview Tracking Theory |
| title_full_unstemmed | Trajectory Tracking Control of Autonomous Vehicles Combining ACT-R Cognitive Framework and Preview Tracking Theory |
| title_short | Trajectory Tracking Control of Autonomous Vehicles Combining ACT-R Cognitive Framework and Preview Tracking Theory |
| title_sort | trajectory tracking control of autonomous vehicles combining act r cognitive framework and preview tracking theory |
| topic | Trajectory tracking control human-like driving autonomous vehicles ACT-R cognitive framework preview tracking theory |
| url | https://ieeexplore.ieee.org/document/10341244/ |
| work_keys_str_mv | AT xuequantang trajectorytrackingcontrolofautonomousvehiclescombiningactrcognitiveframeworkandpreviewtrackingtheory AT yunbingyan trajectorytrackingcontrolofautonomousvehiclescombiningactrcognitiveframeworkandpreviewtrackingtheory AT baohuawang trajectorytrackingcontrolofautonomousvehiclescombiningactrcognitiveframeworkandpreviewtrackingtheory |