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TAKEN: A Traffic Knowledge-Based Navigation System for Connected and Autonomous Vehicles.

Nikhil Kamath B1, Roshan Fernandes1, Anisha P Rodrigues1

  • 1Department of Computer Science and Engineering, NMAM Institute of Technology, NITTE (Deemed to be University), Nitte 574110, India.

Sensors (Basel, Switzerland)
|January 21, 2023
PubMed
Summary
This summary is machine-generated.

Connected and autonomous vehicles (CAVs) face navigation challenges in urban areas. TAKEN, a traffic knowledge-based navigation system, improves precise path planning for CAVs using sensor data and shared knowledge.

Keywords:
connected and autonomous vehiclesdeep learningnavigationtraffic knowledge sharing

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Area of Science:

  • Computer Science
  • Robotics
  • Transportation Engineering

Background:

  • Connected and autonomous vehicles (CAVs) rely on navigation systems for path planning.
  • Urban environments with complex infrastructure pose connectivity challenges for existing CAV navigation systems.

Purpose of the Study:

  • To present TAKEN (Traffic Knowledge-based Navigation), a novel system for precise CAV navigation in urban settings.
  • To address the limitations of current navigation systems in environments with unreliable connectivity.

Main Methods:

  • Development of a traffic analysis model to mine sensor-oriented traffic data.
  • Implementation of a knowledge-sharing method for collecting and generating traffic knowledge from on-road vehicles.
  • Execution of CAV navigation using integrated traffic knowledge and analysis.

Main Results:

  • The proposed traffic analysis model effectively mines sensor data for precise path generation.
  • The knowledge-sharing method successfully collects and utilizes real-time traffic insights.
  • TAKEN demonstrates significant improvements in the precision of CAV navigation within urban environments.

Conclusions:

  • TAKEN enhances the reliability and precision of navigation for CAVs in challenging urban traffic conditions.
  • The integration of traffic knowledge and analysis is crucial for robust autonomous vehicle navigation.