Sunlight Enabled Vehicle Detection by LED Street Lights

TL;DR

This paper presents a novel vehicle detection system that leverages LED street lights' responses to sunlight, using machine learning to classify vehicle presence based on photoelectric signals.

Contribution

It introduces a new traffic sensing method utilizing existing LED street lights and develops an SVM-based algorithm for vehicle detection.

Findings

Effective vehicle detection demonstrated in simulation

Discriminates sunlight responses for accurate classification

Utilizes existing infrastructure for traffic sensing

Abstract

We propose and demonstrate a preliminary traffic sensing system based on the widely distributed LED street lights. The system utilizes and discriminates the photoelectric responses of the LEDs to sunlight when a vehicle moves through the LEDs' field of view aiming at the road. A data vector is constructed from the consecutively collected time samples of a moving observation window, and a support vector machine (SVM) based learning algorithm is subsequently developed to classify the presence of a vehicle. Finally, we build a simulated platform to experimentally evaluate the performance of the vehicle detection algorithm.