RSS Tracking Control for AVs Under Bayesian-Network-Based Intelligent Learning Scheme
In complex real-world traffic environments, the task of automatic lane changing becomes extremely challenging for vehicle control systems. Traditional control methods often lack the flexibility and intelligence to accurately capture and respond to dynamic changes in traffic flow. Therefore, developi...
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| Format: | Article |
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MDPI AG
2025-01-01
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| Series: | Actuators |
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| Online Access: | https://www.mdpi.com/2076-0825/14/1/37 |
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| author | Kun Zhang Kezhen Han Nanbin Zhao |
| author_facet | Kun Zhang Kezhen Han Nanbin Zhao |
| author_sort | Kun Zhang |
| collection | DOAJ |
| description | In complex real-world traffic environments, the task of automatic lane changing becomes extremely challenging for vehicle control systems. Traditional control methods often lack the flexibility and intelligence to accurately capture and respond to dynamic changes in traffic flow. Therefore, developing intelligent control strategies that can accurately predict the behavior of surrounding vehicles and make corresponding adjustments is crucial. This paper presents an intelligent driving control scheme for autonomous vehicles (AVs) based on a responsibility-sensitive safety (RSS) tracking control mechanism within a Bayesian network intelligent learning framework. Initially, the Bayesian evidence construction method for vehicle lane changing scenarios is studied. Using this method, prior probability tables for lane-hanging vehicles are constructed, and the Bayesian formula is applied to predict the lane changing probabilities of surrounding vehicles. Subsequently, an optimal control method is employed to integrate Bayesian lane changing probabilities into the design of performance indices and auxiliary systems, transforming tracking and safety avoidance tasks into an optimization control problem. Additionally, a critic learning optimal control algorithm is developed to determine the control law. Finally, the proposed tracking control scheme is validated through simulations, demonstrating its reliability and effectiveness. |
| format | Article |
| id | doaj-art-437b404183c34b019e98bd742306e9e3 |
| institution | Kabale University |
| issn | 2076-0825 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Actuators |
| spelling | doaj-art-437b404183c34b019e98bd742306e9e32025-01-24T13:15:15ZengMDPI AGActuators2076-08252025-01-011413710.3390/act14010037RSS Tracking Control for AVs Under Bayesian-Network-Based Intelligent Learning SchemeKun Zhang0Kezhen Han1Nanbin Zhao2School of Astronautics, Beihang University, Beijing 100191, ChinaSchool of Electrical Engineering, University of Jinan, Jinan 250022, ChinaSchool of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, SingaporeIn complex real-world traffic environments, the task of automatic lane changing becomes extremely challenging for vehicle control systems. Traditional control methods often lack the flexibility and intelligence to accurately capture and respond to dynamic changes in traffic flow. Therefore, developing intelligent control strategies that can accurately predict the behavior of surrounding vehicles and make corresponding adjustments is crucial. This paper presents an intelligent driving control scheme for autonomous vehicles (AVs) based on a responsibility-sensitive safety (RSS) tracking control mechanism within a Bayesian network intelligent learning framework. Initially, the Bayesian evidence construction method for vehicle lane changing scenarios is studied. Using this method, prior probability tables for lane-hanging vehicles are constructed, and the Bayesian formula is applied to predict the lane changing probabilities of surrounding vehicles. Subsequently, an optimal control method is employed to integrate Bayesian lane changing probabilities into the design of performance indices and auxiliary systems, transforming tracking and safety avoidance tasks into an optimization control problem. Additionally, a critic learning optimal control algorithm is developed to determine the control law. Finally, the proposed tracking control scheme is validated through simulations, demonstrating its reliability and effectiveness.https://www.mdpi.com/2076-0825/14/1/37tracking controllane change intention recognitiondynamic programmingoptimal controllearning control |
| spellingShingle | Kun Zhang Kezhen Han Nanbin Zhao RSS Tracking Control for AVs Under Bayesian-Network-Based Intelligent Learning Scheme Actuators tracking control lane change intention recognition dynamic programming optimal control learning control |
| title | RSS Tracking Control for AVs Under Bayesian-Network-Based Intelligent Learning Scheme |
| title_full | RSS Tracking Control for AVs Under Bayesian-Network-Based Intelligent Learning Scheme |
| title_fullStr | RSS Tracking Control for AVs Under Bayesian-Network-Based Intelligent Learning Scheme |
| title_full_unstemmed | RSS Tracking Control for AVs Under Bayesian-Network-Based Intelligent Learning Scheme |
| title_short | RSS Tracking Control for AVs Under Bayesian-Network-Based Intelligent Learning Scheme |
| title_sort | rss tracking control for avs under bayesian network based intelligent learning scheme |
| topic | tracking control lane change intention recognition dynamic programming optimal control learning control |
| url | https://www.mdpi.com/2076-0825/14/1/37 |
| work_keys_str_mv | AT kunzhang rsstrackingcontrolforavsunderbayesiannetworkbasedintelligentlearningscheme AT kezhenhan rsstrackingcontrolforavsunderbayesiannetworkbasedintelligentlearningscheme AT nanbinzhao rsstrackingcontrolforavsunderbayesiannetworkbasedintelligentlearningscheme |