Investigation of LiDAR for Traffic Monitoring with emphasis on Heavy Duty Trucks

Traffic Monitoring is at the center of any Intelligent Transport System and the current traffic monitoring devices are challenged to deliver in the evolving landscape of connected, autonomous and alternative fuel transportation systems. LiDAR sensor, an emerging traffic monitoring device is being used to derive the core data elements provided by existing traffic monitoring systems such as vehicle count, physical attributes of individual vehicles, their microscopic trajectories, and speed. Along with the traditional data elements, the futuristic data elements required for connected and autonomous vehicles such as real-world relative positions of vehicles on the road and lateral positions within a lane. The high-resolution traffic data elements derived from this work can act as input for microscopic road emission models, road safety assessment models to aid in key decision making.
The sensor is deployed at a dense urban corridor and the estimate of vehicle counts across lanes is within 87% to 110% of another calibrated sensor’s vehicle counts. The microscopic trajectories for vehicles are derived at 0.1 second resolution from a sensor deployed at a rural highway and are assessed for any anomalies. Also, precise lateral positions of heavy-duty vehicles are derived for the urban corridor to enable future safety assessment of autonomous trucks.