Extraction of Mobile Vehicle Traffic Flow Parameters From Sectional Laser Scanning Data

Abstract:

The relationships between vehicles, such as position, velocity and distance, are the main cantonments of microtraffic flow parameters. These parameters are important to unmanned driving, intelligent traffic, etc. A novel method was proposed for microtraffic flow extraction from mobile laser scanning data. Based on the mobile sectional laser scanning data, a threshold was selected to segment and classify the original point cloud into different vehicles. Then, the quadratic polynomial weighting method was used to extract the feature point from vehicle’s point cloud. The distance and velocity parameters were then computed from adjacent vehicles or adjacent sections. Finally, an experiment was conducted in Shanghai Yan’an elevated road to verify the traffic flow parameter extraction method from mobile laser scanning data. The results show that such parameters could be easily and accurately calculated. The average distance error of directly front or behind car is about 0.058 m and its average velocity error is about 1.62 km?h-1. The average distance error of sideward car is merely 0.100 m, and its average velocity error is about 1.29 km?h-1.

Key words:vehicle identification; laser scanning; point cloud; traffic flow parameters; feature point extraction