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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
Summary
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.
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.
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