An Improved Data Mapping Method and Sensitivity Analysis for Roadside LiDAR

LiDAR technology is gaining significance in the transportation sector because of its ability to gather detailed 3D traffic data. Unlike conventional sensors, LiDAR provides highresolution micro-traffic information, independent of lighting conditions. However, the absence of an effective mapping technique has hindered its broader adoption. This study introduces a novel data mapping approach for roadside LiDAR sensors, leveraging GIS-related software. The process involves collecting reference points using both GIS tools and the LiDAR sensor, followed by three key steps: matching reference points, calculating the transformation matrix, and performing data mapping. Post-mapping, a sensitivity analysis was conducted to optimize the number and placement of reference points. Realworld data validation revealed a 0.138m discrepancy between the GPS-measured and calculated points. Additionally, a comparative analysis with another mapping method showed an average deviation of 2.21m per frame of data, which contained 37,832 LiDAR points.