Academic Journal
Improving intelligent perception and decision optimization of pedestrian crossing scenarios in autonomous driving environments through large visual language models.
| Τίτλος: | Improving intelligent perception and decision optimization of pedestrian crossing scenarios in autonomous driving environments through large visual language models. |
|---|---|
| Συγγραφείς: | Teng, Xiao, Huang, Lin, Shen, Zhenjiang, Li, Wankai |
| Πηγή: | Scientific Reports; 8/25/2025, Vol. 15 Issue 1, p1-18, 18p |
| Θεματικοί όροι: | Autonomous vehicles, Intelligent transportation systems, Decision support systems, Pedestrian crosswalks, Data structures, Traffic safety, Information processing, Dynamic models |
| Περίληψη: | This study leverages large Visual Language Models (VLM) to develop an intelligent pedestrian crossing scenario system within autonomous driving environments. By establishing standardized checklists and prompts, the system minimizes the risks of misjudgment and omission through multimodal data processing. It offers data-driven decision-making support, presenting an innovative approach to integrating autonomous driving technology with intelligent transportation systems. The study begins by classifying pedestrian crossing scenarios based on international autonomous driving standards, distinguishing between pedestrian crossings and autonomous vehicle crossings, as well as dynamic and static entities. Next, standardized prompts derived from these standards are fed into the VLM, generating structured scenario checklists of dynamic and static entities, outputted in JSON format. This systematic identification and processing of entities—such as pedestrians, vehicles, and traffic facilities—enables the construction of structured data representations for complex traffic scenarios. Building on this foundation, the VLM analyzes scenario data to predict collision risks by modeling the behaviors of both pedestrians and vehicles, supporting real-time decision-making for autonomous vehicles and road users. Furthermore, the VLM processes scene data to anticipate potential conflicts and provide actionable safety recommendations, enhancing the overall security of all traffic participants. The system achieved a perception accuracy of 93.05%, with risk prediction consistency and decision-making rule consistency rates of 85.91% and 87.72% respectively. By constructing a VLM-based intelligent pedestrian crossing perception system, this study offers a novel technical framework for improving perception, prediction, and decision-making in autonomous driving. Unlike traditional rule-based and deep learning approaches, which struggle with complex pedestrian behaviors and dynamic environments, our method integrates visual perception with reasoning capabilities, enabling structured, standardized, and explainable decision-making in pedestrian crossing scenarios. [ABSTRACT FROM AUTHOR] |
| Copyright of Scientific Reports is the property of Springer Nature 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.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Links: – Type: other Text: Availability: 0 CustomLinks: – Url: https://dx.doi.org/doi:10.1038/s41598-025-14827-x Name: EDS - Springer Nature Journals (s7799221) Category: fullText Text: View record at Springer |
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| Items | – Name: Title Label: Title Group: Ti Data: Improving intelligent perception and decision optimization of pedestrian crossing scenarios in autonomous driving environments through large visual language models. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Teng%2C+Xiao%22">Teng, Xiao</searchLink><br /><searchLink fieldCode="AR" term="%22Huang%2C+Lin%22">Huang, Lin</searchLink><br /><searchLink fieldCode="AR" term="%22Shen%2C+Zhenjiang%22">Shen, Zhenjiang</searchLink><br /><searchLink fieldCode="AR" term="%22Li%2C+Wankai%22">Li, Wankai</searchLink> – Name: TitleSource Label: Source Group: Src Data: Scientific Reports; 8/25/2025, Vol. 15 Issue 1, p1-18, 18p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Autonomous+vehicles%22">Autonomous vehicles</searchLink><br /><searchLink fieldCode="DE" term="%22Intelligent+transportation+systems%22">Intelligent transportation systems</searchLink><br /><searchLink fieldCode="DE" term="%22Decision+support+systems%22">Decision support systems</searchLink><br /><searchLink fieldCode="DE" term="%22Pedestrian+crosswalks%22">Pedestrian crosswalks</searchLink><br /><searchLink fieldCode="DE" term="%22Data+structures%22">Data structures</searchLink><br /><searchLink fieldCode="DE" term="%22Traffic+safety%22">Traffic safety</searchLink><br /><searchLink fieldCode="DE" term="%22Information+processing%22">Information processing</searchLink><br /><searchLink fieldCode="DE" term="%22Dynamic+models%22">Dynamic models</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This study leverages large Visual Language Models (VLM) to develop an intelligent pedestrian crossing scenario system within autonomous driving environments. By establishing standardized checklists and prompts, the system minimizes the risks of misjudgment and omission through multimodal data processing. It offers data-driven decision-making support, presenting an innovative approach to integrating autonomous driving technology with intelligent transportation systems. The study begins by classifying pedestrian crossing scenarios based on international autonomous driving standards, distinguishing between pedestrian crossings and autonomous vehicle crossings, as well as dynamic and static entities. Next, standardized prompts derived from these standards are fed into the VLM, generating structured scenario checklists of dynamic and static entities, outputted in JSON format. This systematic identification and processing of entities—such as pedestrians, vehicles, and traffic facilities—enables the construction of structured data representations for complex traffic scenarios. Building on this foundation, the VLM analyzes scenario data to predict collision risks by modeling the behaviors of both pedestrians and vehicles, supporting real-time decision-making for autonomous vehicles and road users. Furthermore, the VLM processes scene data to anticipate potential conflicts and provide actionable safety recommendations, enhancing the overall security of all traffic participants. The system achieved a perception accuracy of 93.05%, with risk prediction consistency and decision-making rule consistency rates of 85.91% and 87.72% respectively. By constructing a VLM-based intelligent pedestrian crossing perception system, this study offers a novel technical framework for improving perception, prediction, and decision-making in autonomous driving. Unlike traditional rule-based and deep learning approaches, which struggle with complex pedestrian behaviors and dynamic environments, our method integrates visual perception with reasoning capabilities, enabling structured, standardized, and explainable decision-making in pedestrian crossing scenarios. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Scientific Reports is the property of Springer Nature 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.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1038/s41598-025-14827-x Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 18 StartPage: 1 Subjects: – SubjectFull: Autonomous vehicles Type: general – SubjectFull: Intelligent transportation systems Type: general – SubjectFull: Decision support systems Type: general – SubjectFull: Pedestrian crosswalks Type: general – SubjectFull: Data structures Type: general – SubjectFull: Traffic safety Type: general – SubjectFull: Information processing Type: general – SubjectFull: Dynamic models Type: general Titles: – TitleFull: Improving intelligent perception and decision optimization of pedestrian crossing scenarios in autonomous driving environments through large visual language models. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Teng, Xiao – PersonEntity: Name: NameFull: Huang, Lin – PersonEntity: Name: NameFull: Shen, Zhenjiang – PersonEntity: Name: NameFull: Li, Wankai IsPartOfRelationships: – BibEntity: Dates: – D: 25 M: 08 Text: 8/25/2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 20452322 Numbering: – Type: volume Value: 15 – Type: issue Value: 1 Titles: – TitleFull: Scientific Reports Type: main |
| ResultId | 1 |