Application of intelligent search algorithm in LKS of autonomous vehicles.
Saved in:
| Title: | Application of intelligent search algorithm in LKS of autonomous vehicles. |
|---|---|
| Authors: | Zhao, P.1 yashifc@126.com, Deng, L.1, Tan, L.1 |
| Source: | Advances in Transportation Studies. Nov2025, Vol. 67, p219-234. 16p. |
| Subjects: | Autonomous vehicles, Search algorithms, Adaptive control systems, Decision making, Reinforcement learning, Road interchanges & intersections |
| Abstract: | As autonomous driving technology becomes increasingly integrated into intelligent transportation systems, enhancing the resilience and swift responsiveness of Lane Keeping Systems (LKS) under challenging driving conditions has emerged as a pivotal area of investigation. In response, a sophisticated dual-layer LKS control approach has been devised, harmonizing input scene refinement with output control optimization through learning. This methodology introduces a novel modeling framework for continuous variable testing scenarios, leveraging intelligent search algorithms, alongside an advanced deep reinforcement learning control model. The latter incorporates priority experience replay and soft update mechanisms to precisely emulate decisionmaking processes within continuous action domains. In typical urban intersections, the trajectory deviation of the proposed method is controlled within ± 0.5 m, and the deviation of the circular ramp is about ± 0.4 m. During the disturbance phase, the lane acceleration remains within ± 0.02 g, the lane offset is 0.0031 m, and the steering wheel angle is -6.9°. The research results indicate that this method has good key variable mining ability and control strategy adaptability in typical extreme scenarios, and can effectively improve the response robustness and decision stability of LKS systems to sudden environments. The research aims to provide a reference path for intelligent algorithm driven integrated testing and control systems for autonomous driving. [ABSTRACT FROM AUTHOR] |
| Copyright of Advances in Transportation Studies is the property of Advances in Transportation Studies and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
|---|---|
| Header | DbId: egs DbLabel: Engineering Source An: 188094121 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Application of intelligent search algorithm in LKS of autonomous vehicles. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Zhao%2C+P%2E%22">Zhao, P.</searchLink><relatesTo>1</relatesTo><i> yashifc@126.com</i><br /><searchLink fieldCode="AR" term="%22Deng%2C+L%2E%22">Deng, L.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Tan%2C+L%2E%22">Tan, L.</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Advances+in+Transportation+Studies%22">Advances in Transportation Studies</searchLink>. Nov2025, Vol. 67, p219-234. 16p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Autonomous+vehicles%22">Autonomous vehicles</searchLink><br /><searchLink fieldCode="DE" term="%22Search+algorithms%22">Search algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Adaptive+control+systems%22">Adaptive control systems</searchLink><br /><searchLink fieldCode="DE" term="%22Decision+making%22">Decision making</searchLink><br /><searchLink fieldCode="DE" term="%22Reinforcement+learning%22">Reinforcement learning</searchLink><br /><searchLink fieldCode="DE" term="%22Road+interchanges+%26+intersections%22">Road interchanges & intersections</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: As autonomous driving technology becomes increasingly integrated into intelligent transportation systems, enhancing the resilience and swift responsiveness of Lane Keeping Systems (LKS) under challenging driving conditions has emerged as a pivotal area of investigation. In response, a sophisticated dual-layer LKS control approach has been devised, harmonizing input scene refinement with output control optimization through learning. This methodology introduces a novel modeling framework for continuous variable testing scenarios, leveraging intelligent search algorithms, alongside an advanced deep reinforcement learning control model. The latter incorporates priority experience replay and soft update mechanisms to precisely emulate decisionmaking processes within continuous action domains. In typical urban intersections, the trajectory deviation of the proposed method is controlled within ± 0.5 m, and the deviation of the circular ramp is about ± 0.4 m. During the disturbance phase, the lane acceleration remains within ± 0.02 g, the lane offset is 0.0031 m, and the steering wheel angle is -6.9°. The research results indicate that this method has good key variable mining ability and control strategy adaptability in typical extreme scenarios, and can effectively improve the response robustness and decision stability of LKS systems to sudden environments. The research aims to provide a reference path for intelligent algorithm driven integrated testing and control systems for autonomous driving. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Advances in Transportation Studies is the property of Advances in Transportation Studies and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=188094121 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.53136/979122182194914 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 16 StartPage: 219 Subjects: – SubjectFull: Autonomous vehicles Type: general – SubjectFull: Search algorithms Type: general – SubjectFull: Adaptive control systems Type: general – SubjectFull: Decision making Type: general – SubjectFull: Reinforcement learning Type: general – SubjectFull: Road interchanges & intersections Type: general Titles: – TitleFull: Application of intelligent search algorithm in LKS of autonomous vehicles. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zhao, P. – PersonEntity: Name: NameFull: Deng, L. – PersonEntity: Name: NameFull: Tan, L. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 11 Text: Nov2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 18245463 Numbering: – Type: volume Value: 67 Titles: – TitleFull: Advances in Transportation Studies Type: main |
| ResultId | 1 |
