Elevated LiDAR Placement under Energy and Throughput Capacity Constraints

TL;DR

This paper presents a planning framework using Particle Swarm Optimization to optimize elevated LiDAR placement for urban road coverage, balancing energy and throughput constraints to enhance autonomous vehicle deployment.

Contribution

It introduces a mixed-integer nonlinear optimization model for LiDAR placement and demonstrates PSO's effectiveness in solving it for realistic scenarios.

Findings

PSO effectively solves the placement optimization problem.

The model achieves desired coverage while respecting energy and throughput limits.

Sensitivity analysis confirms the model's robustness.

Abstract

Elevated LiDAR (ELiD) has the potential to hasten the deployment of Autonomous Vehicles (AV), as ELiD can reduce energy expenditures associated with AVs, and can also be utilized for other intelligent Transportation Systems applications such as urban 3D mapping. In this paper, we address the need for a planning framework in order for ITS operators to have an effective tool for determining what resources are required to achieve a desired level of coverage of urban roadways. To this end, we develop a mixed-integer nonlinear constrained optimization problem, with the aim of maximizing effective area coverage of a roadway, while satisfying energy and throughput capacity constraints. Due to the non-linearity of the problem, we utilize Particle Swarm Optimization (PSO) to solve the problem. After demonstrating its effectiveness in finding a solution for a realistic scenario, we perform a sensitivity analysis to test the model's general ability.