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Proactive safety at CVIS-enabled intersections: a framework based on high-fidelity trajectory reconstruction and

Yunxuan Li1, Shihao Wang2, Lishengsa Yue3

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Summary
This summary is machine-generated.

This study introduces an edge-computing framework for accurate vehicle trajectory reconstruction and risk assessment at intersections. The novel approach enhances proactive safety by improving conflict detection accuracy and reducing latency for cooperative vehicle-infrastructure systems.

Keywords:
Confilct detectionCooperative vehicle infrastructure systemEdge computingIntersectionTrajectory reconstruction

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Area of Science:

  • Intelligent Transportation Systems
  • Computer Vision
  • Robotics

Background:

  • Traditional trajectory reconstruction methods struggle with complex vehicle dynamics at intersections.
  • Deep learning models are computationally expensive for real-time edge deployment.
  • Accurate trajectory data is crucial for proactive intersection safety.

Purpose of the Study:

  • To develop an efficient, edge-computing-enhanced framework for high-fidelity vehicle trajectory reconstruction.
  • To improve dynamic risk assessment and conflict detection at intersections.
  • To enable real-time proactive safety interventions in Cooperative Vehicle-Infrastructure Systems (CVIS).

Main Methods:

  • A two-stage framework combining physics-informed constraints, adaptive wavelet transforms, and hybrid thresholding for trajectory reconstruction.
  • A Vehicle Outline-based Conflict Algorithm (VOCA) for sensitive, timely conflict detection using spatial overlap analysis.
  • Implementation and validation on an NVIDIA Jetson edge device for real-world performance assessment.

Main Results:

  • Reduced acceleration fluctuations by 98.66% through effective noise suppression.
  • VOCA detected 77.47% more conflicts than traditional center-point methods.
  • Achieved real-time performance with processing delays under 100 ms per frame per vehicle.

Conclusions:

  • The proposed framework provides an efficient solution for accurate, low-latency trajectory reconstruction and conflict warnings.
  • Edge-computing enhancement enables practical, real-time application of CVIS for proactive intersection safety.
  • Outline-based conflict detection significantly outperforms point-based methods in complex urban environments.