A Dynamic Blind Zone Simulation and Analysis Model for Roadside Light Detection and Ranging Sensor Deployment Considering the Full Roadway Terrain and Vehicle Dynamics

Abstract

Blind zones in light detection and ranging (LiDAR) sensors arise from their limited physical field of view and obstructions caused by static infrastructure or moving objects. Although originally intended for vehicle-based applications, LiDAR sensors are now increasingly deployed in roadside infrastructure for traffic monitoring and connected and automated vehicle safety and mobility applications. However, there is a dearth of robust tools for analyzing their detection range, resolution, and other characteristics in such settings. This study introduces a three-dimensional (3D) blind zone simulation model for analyzing the detection characteristics of roadside LiDAR sensor deployment. The model replicates the impact of static infrastructure conditions and dynamic blind zones during live traffic. Initially, a real-world digital surface model (DSM) captures 3D data of road surfaces and obstructing infrastructure objects. Optical geometry models then assess blind zone severity across various roadway areas. Subsequent 3D vehicle shape and dynamic simulations evaluate blind zone distributions under typical traffic conditions. The model’s effectiveness is validated using field 3D point cloud data and vehicle detection data collected from a roadside LiDAR site on Route 18 in New Brunswick, NJ. Evaluation results demonstrate the model’s capability in analyzing complex static and dynamic blind zone distributions, offering insights for optimizing LiDAR sensor location, height, tilting angle, and manufacturer configuration parameters to minimize sensing blind zones. For code availability, see https://github.com/rutgerstslab/LiDAR-Coverage-Analysis.