Project Summary
LiDAR is an emerging technology that can provide detailed point-cloud measurements for accurate detection and characterization of objects. The cost of this technology has seen significant reduction with emerging wide-ranging applications such as autonomous vehicles, infrastructure inventory and topographic mapping, to name a few. Within the field of infrastructure-based traffic monitoring, recent studies have investigated the use of this sensor for advanced truck classification applications in side-fire orientation (by the ITS-Irvine research team), as well as for motorized vehicles, bicycle and pedestrian detection at traffic intersections. Because of its high range resolution, traffic surveillance models developed for this sensor technology have the potential to outperform competing technologies such as inductive loops and microwave radar in traffic stream measurements, as well as vehicle count and classification accuracies for traffic monitoring and census applications. This potentially applies to both permanent freeway locations and temporary work zone locations. In particular, our research to date suggests that the potential for LiDAR to be a cost-effective substitute for inductive loop sensors at permanent traffic surveillance and monitoring sites with available overhead mounting infrastructure, is high. This study will therefore investigate the installation of LiDAR sensors at several locations along existing freeway corridors in both side-fire and overhead configurations. Models will be developed from the obtained data to derive conventional (volume, speed and occupancy) as well as novel traffic stream parameters used in Caltrans traffic operations and compared with existing traffic sensors such as inductive loop detectors. Concerns typically associated with side-fire sensors such as occlusions will be investigated and addressed by harnessing the wide field-of-view characteristics of LiDAR.
