Decoding Algorithms for Urban Traffic Management System Supported by Visible Light Communication

This work explores the use of Visible Light Communication (VLC) technology to support traffic management systems. By employing RGBV LEDs, VLC can be deployed in urban environments to collect real-time data such as vehicle position, speed, queue length, and waiting time. VLC transmitters embedded in streetlights, traffic signals, and vehicle headlights enable multiple communication modes: vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), infrastructure-to-vehicle (I2V), and lamp-to-vehicle (L2V). The system is simulated using the SUMO traffic simulator, replicating realistic vehicle dynamics and VLC-based data exchange. Communication channels operate using On-Off Keying (OOK) modulation, with optical signals transmitted using RGBV LEDs and received by photodetectors. Each link transmits a 64-bit frame structured according to a defined VLC protocol, carrying essential traffic information. A key contribution of this work is the development of signal pre-processing and decoding algorithms at the receiver module. These algorithms demultiplex the composite optical signal received by the photodetector, enabling identification of the emitting sources and reliable data extraction. Different signal processing techniques were used, such as baseline correction using asymmetric least squares smoothing, correction of the capacitive effect, and transient noise filtering based on signal slope analysis. This approach aims to enhance traffic monitoring, reduce congestion, and support the future integration of VLC into smart transportation infrastructure.