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Research on effectiveness of active braking strategy of autonomous vehicles for VRUs in mixed conditions.

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Title: Research on effectiveness of active braking strategy of autonomous vehicles for VRUs in mixed conditions.
Authors: Hong, Liang1 (AUTHOR) hongliang@ujs.edu.cn, Chen, Zhihao1 (AUTHOR), Li, Liang1 (AUTHOR)
Source: International Journal of Automotive Technology. May2025, Vol. 26 Issue 3, p753-770. 18p.
Subjects: Road users, Brake systems, Autonomous vehicles, Electric vehicles, Prediction models
Abstract: Pedestrians and two-wheeled cyclists are referred to as vulnerable road users (VRUs). The active braking system can prevent collisions between vehicles and VRUs. Currently, the research on the active braking strategy mainly focuses on the safety and comfort when VRUs laterally cross the straight road with uniform motion. However, considering the variety of roads and the diverse motion states of VRUs, it is essential to explore the effectiveness of the active braking strategy in mixed conditions where VRUs diagonally and laterally cross the curved road with different motion trajectories and speeds. Firstly, the location relationships between the vehicles and VRUs are determined to establish the prediction model of VRUs' motion state and the safety evaluation model. Secondly, based on the linear quadratic regulator and supervised Hebb learning rule, the collision avoidance controller is devised. Finally, the proposed active braking strategy is verified through the joint simulation platform and hardware-in-loop tests. The results show all crashes between vehicles and electric bicycles can be avoided. The braking strengths range from 0.35 to 0.71, the braking durations range from 2.34 s to 3.97 s, and the peak braking pressures are less than 75 bar, which can guarantee the comfort of occupants. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Automotive Technology 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.)
Database: Engineering Source
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Items – Name: Title
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  Data: Research on effectiveness of active braking strategy of autonomous vehicles for VRUs in mixed conditions.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Hong%2C+Liang%22">Hong, Liang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> hongliang@ujs.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Chen%2C+Zhihao%22">Chen, Zhihao</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Liang%22">Li, Liang</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Automotive+Technology%22">International Journal of Automotive Technology</searchLink>. May2025, Vol. 26 Issue 3, p753-770. 18p.
– Name: Subject
  Label: Subjects
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  Data: <searchLink fieldCode="DE" term="%22Road+users%22">Road users</searchLink><br /><searchLink fieldCode="DE" term="%22Brake+systems%22">Brake systems</searchLink><br /><searchLink fieldCode="DE" term="%22Autonomous+vehicles%22">Autonomous vehicles</searchLink><br /><searchLink fieldCode="DE" term="%22Electric+vehicles%22">Electric vehicles</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Pedestrians and two-wheeled cyclists are referred to as vulnerable road users (VRUs). The active braking system can prevent collisions between vehicles and VRUs. Currently, the research on the active braking strategy mainly focuses on the safety and comfort when VRUs laterally cross the straight road with uniform motion. However, considering the variety of roads and the diverse motion states of VRUs, it is essential to explore the effectiveness of the active braking strategy in mixed conditions where VRUs diagonally and laterally cross the curved road with different motion trajectories and speeds. Firstly, the location relationships between the vehicles and VRUs are determined to establish the prediction model of VRUs' motion state and the safety evaluation model. Secondly, based on the linear quadratic regulator and supervised Hebb learning rule, the collision avoidance controller is devised. Finally, the proposed active braking strategy is verified through the joint simulation platform and hardware-in-loop tests. The results show all crashes between vehicles and electric bicycles can be avoided. The braking strengths range from 0.35 to 0.71, the braking durations range from 2.34 s to 3.97 s, and the peak braking pressures are less than 75 bar, which can guarantee the comfort of occupants. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Automotive Technology 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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      – Type: doi
        Value: 10.1007/s12239-024-00173-w
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      – Code: eng
        Text: English
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        PageCount: 18
        StartPage: 753
    Subjects:
      – SubjectFull: Road users
        Type: general
      – SubjectFull: Brake systems
        Type: general
      – SubjectFull: Autonomous vehicles
        Type: general
      – SubjectFull: Electric vehicles
        Type: general
      – SubjectFull: Prediction models
        Type: general
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      – TitleFull: Research on effectiveness of active braking strategy of autonomous vehicles for VRUs in mixed conditions.
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            NameFull: Hong, Liang
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            NameFull: Chen, Zhihao
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            – D: 01
              M: 05
              Text: May2025
              Type: published
              Y: 2025
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              Value: 26
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