LiDAR Matrix applies LiDAR (Laser-Light Detection and Ranging) and patented AI technologies to provide spatiotemporal details of all-road-user movements and geolocated road feature datasets.
Advanced traffic systems and traffic analysis require behavior-level traffic information with high accuracy movement details which LiDAR Matrix provides in the form of trajectories of road users. Given that existing traffic sensors do not provide the required trajectory-level data, 3D light detection and ranging (LiDAR) sensors on the roadside are an excellent solution because they detect surrounding objects with high-accuracy spatial measurement (spatial cloud points) and are not influenced by light conditions. LiDAR Matrix’s core technology and engineering solutions collect, process, and analyze roadside LiDAR data to meet various traffic requirements in transportation safety, mobility, and energy efficiency.
Mobile Survey LiDAR data (data collected by LiDAR sensors on data collection vehicles and geolocated by post processing) is a promising solution to the existing road feature data requirements for LiDAR cloud points’ high accuracy and extended coverage. However, because of the lack of an automatic measuring tool, road features are currently read by data operators and measured manually in the cloud points. Manually extracting data from LiDAR data requires significant time and labor, especially considering repeat work for recurring LiDAR data collection along roads: LiDAR data on a road segment is re-collected approximately every three years and road feature data always needs to be updated.