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Physical and Digital Infrastructure Readiness Index for Connected and Automated Vehicles.

Boris Cucor1, Tibor Petrov2, Patrik Kamencay1

  • 1Faculty of Electrical Engineering and Information Technology, University of Zilina, 010 26 Zilina, Slovakia.

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Summary

This study introduces an assessment framework for infrastructure readiness for Connected and Automated Vehicles (CAVs). It identifies communication latency and localization inaccuracies as key challenges in urban and suburban environments.

Keywords:
connectivity dataconvolutional neural networkcooperative, connected and automated mobilityinfrastructure readiness assessmentpositioning data

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Area of Science:

  • Infrastructure assessment
  • Connected and Automated Vehicles (CAVs)
  • Intelligent Transportation Systems (ITS)

Background:

  • The deployment of Connected and Automated Vehicles (CAVs) requires robust physical and digital infrastructure.
  • Existing infrastructure may not meet the stringent demands for reliable connectivity and precise localization necessary for CAV operation.
  • Automated assessment methods are needed to efficiently evaluate infrastructure readiness.

Purpose of the Study:

  • To present a novel assessment framework for scoring infrastructure segments for CAV deployment.
  • To detail the methodology and equipment for automated data collection and analysis.
  • To demonstrate the framework's application using real-world data and identify infrastructure limitations.

Main Methods:

  • Development of an assessment framework for physical and digital infrastructure.
  • Automated data collection using specialized equipment.
  • Analysis of connectivity, positioning, and image data.
  • Application of Convolutional Neural Networks (CNNs) for road signage detection.

Main Results:

  • The framework successfully scored infrastructure segments along a public transport route in Zilina, Slovakia.
  • Identified highly underserved areas with significant communication latency, particularly at intersections and points of interest.
  • Observed low localization accuracy in dense urban areas due to signal obstruction.
  • Achieved 99.7% precision in automated traffic sign recognition using a CNN.

Conclusions:

  • The proposed framework provides a scalable method for evaluating infrastructure readiness for CAVs.
  • Latency and localization accuracy are critical infrastructure challenges that need addressing for widespread CAV adoption.
  • Automated traffic sign recognition using CNNs is a viable solution for enhancing safety and efficiency.