Optimized sensor placement for dependable roadside infrastructures

TL;DR

This paper introduces a genetic algorithm-based multi-stage optimization method for deploying roadside sensors to ensure full traffic coverage despite obstacles, enhancing the economic feasibility of traffic surveillance infrastructure.

Contribution

It proposes a novel multi-stage genetic optimization approach for sensor placement that accounts for obstacles and dynamic occlusions in traffic surveillance.

Findings

Efficient sensor placement achieves full road coverage.

Method is effective on realistic road sections.

Enhances economic feasibility of sensor networks.

Abstract

We present a multi-stage optimization method for efficient sensor deployment in traffic surveillance scenarios. Based on a genetic optimization scheme, our algorithm places an optimal number of roadside sensors to obtain full road coverage in the presence of obstacles and dynamic occlusions. The efficiency of the procedure is demonstrated for selected, realistic road sections. Our analysis helps to leverage the economic feasibility of distributed infrastructure sensor networks with high perception quality.