Enhancing Urban Intersection Efficiency: Visible Light Communication and Learning-Based Control for Traffic Signal Optimization and Vehicle Management

被引:13
作者
Vieira, Manuel Augusto [1 ,2 ,3 ]
Galvao, Goncalo [1 ]
Vieira, Manuela [1 ,2 ,3 ,4 ]
Louro, Paula [1 ,2 ,3 ]
Vestias, Mario [1 ,5 ]
Vieira, Pedro [1 ,6 ]
机构
[1] DEETC ISEL, IPL, R Conselheiro Emidio Navarro, P-1949014 Lisbon, Portugal
[2] UNINOVA-CTS, P-2829516 Caparica, Portugal
[3] LASI, P-2829516 Caparica, Portugal
[4] NOVA Sch Sci & Technol, P-2829516 Caparica, Portugal
[5] Univ Lisbon, INESC ID, Inst Super Tecn, P-1000029 Lisbon, Portugal
[6] Inst Super Tecn, Inst Telecomun, P-1049001 Lisbon, Portugal
来源
SYMMETRY-BASEL | 2024年 / 16卷 / 02期
关键词
vehicular communication: traffic control; light controlled intersection; queue distance; white LEDs transmitters; SiC photodetectors; OOK modulation scheme; pedestrian density; reinforcement learning model; MODEL;
D O I
10.3390/sym16020240
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
This paper introduces a novel approach, Visible Light Communication (VLC), to optimize urban intersections by integrating VLC localization services with learning-based traffic signal control. The system enhances communication between connected vehicles and infrastructure using headlights, streetlights, and traffic signals to transmit information. Through Vehicle-to-Vehicle (V2V) and Infrastructure-to-Vehicle (I2V) interactions, joint data transmission and collection occur via mobile optical receivers. The goal is to reduce waiting times for pedestrians and vehicles, enhancing overall traffic safety by employing flexible and adaptive measures accommodating diverse traffic movements. VLC cooperative mechanisms, transmission range, relative pose concepts, and queue/request/response interactions help balance traffic flow and improve road network performance. Evaluation in the SUMO urban mobility simulator demonstrates advantages, reducing waiting and travel times for both vehicles and pedestrians. The system employs a reinforcement learning scheme for effective traffic signal scheduling, utilizing VLC-ready vehicles to communicate positions, destinations, and routes. Agents at intersections calculate optimal strategies, communicating to optimize overall traffic flow. The proposed decentralized and scalable approach, especially suitable for multi-intersection scenarios, showcases the feasibility of applying reinforcement learning in real-world traffic scenarios.
引用
收藏
页数:25
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