Traffic Volume Detection Using Infrastructure-Based LiDAR under Different Levels of Service Conditions

Abstract

Light detection and ranging (LiDAR) technology is a key component of an autonomous vehicle’s sensing system. It also has the potential to be used at the roadside as a major infrastructure-based detection for connected and autonomous traffic infrastructure systems, as well as for the general purpose of traffic data collection and performance evaluation. Lane and movement-based traffic volume data collection is a basic function of roadside traffic sensing systems. The accuracy of volume detection is mainly impacted by occlusion for most of the advanced traffic sensing technologies, such as LiDAR, video, and radar. This paper presents research results to quantify the influence of occlusion on LiDAR systems’ traffic volume detection in different traffic demand scenarios. A method for automatic identification and classification of LiDAR specific occlusion was first developed based on the inherent characteristics of LiDAR sensors, which can report occlusion ratios of roadside LiDAR data. Then, the study was extended to accommodate all traffic demand scenarios, traffic levels of service (LOS A to E), and different truck compositions (5% to 30%) by integrating the developed method and traffic simulation. Lastly, a comprehensive case study first verified the accuracy of the simulation results using field data collected from two testbeds, and then at the third testbed, a lane and movement-based traffic volume study was demonstrated. The practical significance of this paper is to help traffic engineers making informed decisions when considering LiDAR as their choice of sensing technology in the field from two aspects: (1) the quantitative relationship between expected occlusion rate and resulted detection accuracy under various traffic conditions; (2) lessons learned from the pilot field implementation on LiDAR, installation strategy, data storage, and communication.