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Adaptive forward vehicle collision warning based on driving behavior.

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Title: Adaptive forward vehicle collision warning based on driving behavior.
Authors: Yuan, Yuan1 (AUTHOR), Lu, Yuwei1 (AUTHOR), Wang, Qi1 (AUTHOR) crabwq_elsevier@163.com
Source: Neurocomputing. Sep2020, Vol. 408, p64-71. 8p.
Subjects: Driver assistance systems, Camera calibration, Warnings
Abstract: Forward Vehicle Collision Warning (FCW) is one of the most important functions for the Advanced Driver Assistance System (ADAS). In this procedure, vehicle detection and distance measurement are core components, requiring accurate localization and estimation. In this paper, we propose a simple but efficient forward vehicle collision warning framework by aggregating monocular distance measurement and precise vehicle detection. In order to obtain forward vehicle distance, a quick camera calibration method which only needs three physical points to calibrate related camera parameters is utilized. As for the forward vehicle detection, a multi-scale detection algorithm that regards the result of calibration as distance prior is proposed to improve the precision. What's more, traditional deterministic FCW approaches cannot be personalized for different drivers, which will lead to false warnings when drivers are in diverse driving status. Therefore, abnormal driver behaviors are introduced to make FCW adaptive. Specifically, the proposed adaptive FCW generates warnings by considering the different behaviors of the driver. Intensive experiments are conducted in our established real scene dataset and the results have demonstrated the effectiveness of the proposed framework. [ABSTRACT FROM AUTHOR]
Copyright of Neurocomputing is the property of Elsevier B.V. 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
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Header DbId: egs
DbLabel: Engineering Source
An: 145296731
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
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  Label: Title
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  Data: Adaptive forward vehicle collision warning based on driving behavior.
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  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Yuan%2C+Yuan%22">Yuan, Yuan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lu%2C+Yuwei%22">Lu, Yuwei</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Qi%22">Wang, Qi</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> crabwq_elsevier@163.com</i>
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  Data: <searchLink fieldCode="JN" term="%22Neurocomputing%22">Neurocomputing</searchLink>. Sep2020, Vol. 408, p64-71. 8p.
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  Data: <searchLink fieldCode="DE" term="%22Driver+assistance+systems%22">Driver assistance systems</searchLink><br /><searchLink fieldCode="DE" term="%22Camera+calibration%22">Camera calibration</searchLink><br /><searchLink fieldCode="DE" term="%22Warnings%22">Warnings</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: Forward Vehicle Collision Warning (FCW) is one of the most important functions for the Advanced Driver Assistance System (ADAS). In this procedure, vehicle detection and distance measurement are core components, requiring accurate localization and estimation. In this paper, we propose a simple but efficient forward vehicle collision warning framework by aggregating monocular distance measurement and precise vehicle detection. In order to obtain forward vehicle distance, a quick camera calibration method which only needs three physical points to calibrate related camera parameters is utilized. As for the forward vehicle detection, a multi-scale detection algorithm that regards the result of calibration as distance prior is proposed to improve the precision. What's more, traditional deterministic FCW approaches cannot be personalized for different drivers, which will lead to false warnings when drivers are in diverse driving status. Therefore, abnormal driver behaviors are introduced to make FCW adaptive. Specifically, the proposed adaptive FCW generates warnings by considering the different behaviors of the driver. Intensive experiments are conducted in our established real scene dataset and the results have demonstrated the effectiveness of the proposed framework. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Neurocomputing is the property of Elsevier B.V. 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: 10.1016/j.neucom.2019.11.024
    Languages:
      – Code: eng
        Text: English
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      Pagination:
        PageCount: 8
        StartPage: 64
    Subjects:
      – SubjectFull: Driver assistance systems
        Type: general
      – SubjectFull: Camera calibration
        Type: general
      – SubjectFull: Warnings
        Type: general
    Titles:
      – TitleFull: Adaptive forward vehicle collision warning based on driving behavior.
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          Name:
            NameFull: Yuan, Yuan
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            NameFull: Lu, Yuwei
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            NameFull: Wang, Qi
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            – D: 30
              M: 09
              Text: Sep2020
              Type: published
              Y: 2020
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              Value: 408
          Titles:
            – TitleFull: Neurocomputing
              Type: main
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