Placement Design of Various Roadside Sensors for Stochastic Position Prediction of Traffic Participants on Community Roads

Abstract

In Japan, traffic accidents on community roads where visibility is limited at non-signalized intersections are one of the social issues. Although cooperative systems using roadside sensors are one of the promising solutions to this issue, cost constraints require strategic approaches to the placement of limited number of sensors. For the above reasons, this paper proposes a framework for exploring cost-efficient placement of Roadside Units (RSUs). To be more precise, this study focuses on the investigation of placement and combination of three types of sensors, which include cameras, radars, and LiDARs, in a 3 × 3 square grid town with different length of road segments. Then, we utilize Monte Carlo simulations to evaluate performance of position prediction of traffic participants approaching to intersections. After that, we explore placements by Non-dominated Sorting Genetic Algorithm II (NSGA-II). Through the proposed framework, we obtain diverse Pareto-optimal solutions which show a trade-off between the lowness of the total sensor costs and the performance of the position prediction. In addition, we obtained cost-efficient placement strategies for different road networks by analysis of Pareto-optimal solutions. This study offers a cost-efficient and adaptable framework for the placement of RSUs, promising enhanced cooperative systems and improved road safety.