A Software Tool for Automated Extraction and Classification of Road Surface Markings from Mobile LiDAR Point Clouds

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The highly detailed, up-to-date and accurate road maps are required to enable driverless cars to workautonomous. A SUV-borne mobile laser scanning (or LiDAR) system can capture over 600,000 points persecond together with 360 degree panoramic images while the SUV moves along the road. Today a commercialmobile LiDAR system can accumulate 100 gigabytes or more of 3D point cloud data in just one day's driving.All these point clouds allow to build up a high-definition road map that can help position a self-driving car onthe road to an accuracy of within 10-20 cm. Given a huge amount of mobile LiDAR data, the automated androbust software tool that can handle the mobile LiDAR point clouds to support the high-definition road mapmaking and updating in an effective and efficient fashion is urgently needed. The objective of this project is todevelop a cost-effective, point-based road surface marking extraction technology using 3D mobile LiDARpoint clouds. The software tool to be developed consists of novel algorithms for point cloud preprocessing,automated extraction and classification of pavement markings. The extracted road marking information canthen be used to make and update the road databases. The outcomes of this project will provide a novel,cost-effective and time-saving method for making high-definition road maps to support driverless cars on theCanadian roads. Such a new tool can be supplementary or replaceable for current practices of making roadmaps using either conventional surveys or image-based approaches.