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Enhancing intersection safety in autonomous traffic: A grid-based approach with risk quantification.

Wei Wu1, Siyu Chen2, Mengfei Xiong2

  • 1Chongqing Key Laboratory of Intelligent Integrated and Multidimensional Transportation System, Chongqing Jiaotong University, 66 Xuefu Avenue, Nanan District, Chongqing 400074, China; Department of Traffic and Transportation Engineering, Changsha University of Science & Technology, 960 Wanjiali South Road, Changsha, Hunan 410114, China.

Accident; Analysis and Prevention
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Summary
This summary is machine-generated.

This study introduces a new method to assess autonomous intersection safety by calculating collision risks and severity. The proposed model enhances traffic safety and efficiency at intersections.

Keywords:
Autonomous intersectionConflict riskRisk quantificationTraffic safety

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Area of Science:

  • Intelligent Transportation Systems
  • Traffic Safety Engineering
  • Autonomous Vehicle Technology

Background:

  • Current autonomous intersection management (AIM) models prioritize traffic efficiency over safety.
  • Existing methods inadequately assess traffic conflicts and collision risks.
  • Simplified conflict separation in AIM overlooks critical safety aspects.

Purpose of the Study:

  • To develop a comprehensive safety assessment for autonomous intersections.
  • To introduce a novel Grid-Based Conflict Index (GBCI) for quantifying collision risks.
  • To propose an AIM model optimizing both traffic efficiency and safety.

Main Methods:

  • Developed a grid-based Post Encroachment Time (PET) calculation for improved collision probability estimation.
  • Utilized total kinetic energy change as a metric for collision severity.
  • Established the Grid-Based Conflict Index (GBCI) to quantify intersection collision risks.
  • Proposed a safety-based AIM model minimizing weighted delay and conflict risk.

Main Results:

  • The GBCI effectively assesses and quantifies collision risks at autonomous intersections.
  • The proposed AIM model significantly reduces vehicle-to-vehicle conflict risks.
  • The developed model enhances overall traffic safety at intersections.
  • Intersection efficiency is maintained while prioritizing safety.

Conclusions:

  • The GBCI provides a robust measure for intersection safety assessment.
  • The novel AIM model successfully balances traffic efficiency with enhanced safety.
  • This research contributes to safer autonomous intersection management systems.