The study aims to create a Visible-Light Communication (VLC) system for secure vehicle management at intersections. This involves enabling communication between vehicles and infrastructure (V2V, V2I, and I2V) using headlights, streetlights, and traffic signals. Mobile optical receivers gather data, determine their location, and read transmitted information through joint transmission. An intersection manager coordinates traffic and communicates with vehicles through embedded Driver Agents. The system utilizes a "mesh/cellular" hybrid network configuration and encodes data into light signals emitted by transmitters. Optical sensors and filtering properties enable reception and decoding. The study demonstrates bidirectional communication, employing queue/request/response mechanisms and relative pose concepts for safe vehicle passage. A deep reinforcement learning model controls traffic light cycles, validated via simulation in a Simulation of Urban Mobility simulator. Results show that this adaptive traffic control system effectively collects detailed vehicle data and ensures secure communication within the short-range mesh network.
Adaptive Traffic Control Using Cooperative Communication Through Visible Light
SN COMPUT. SCI.
SN Computer Science ; 5 , 1
2024-01-06
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Vehicular communication , Traffic control , Light controlled intersection , Queue distance , “Mesh/cellular” hybrid network , Deep reinforcement learning model Computer Science , Computer Science, general , Computer Systems Organization and Communication Networks , Software Engineering/Programming and Operating Systems , Data Structures and Information Theory , Information Systems and Communication Service , Computer Imaging, Vision, Pattern Recognition and Graphics
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