Subventions et des contributions :

Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier (2017-2018 à 2018-2019).

The highly detailed, up-to-date and accurate road maps are required to enable driverless cars to workx000D
autonomous. A SUV-borne mobile laser scanning (or LiDAR) system can capture over 600,000 points perx000D
second together with 360 degree panoramic images while the SUV moves along the road. Today a commercialx000D
mobile LiDAR system can accumulate 100 gigabytes or more of 3D point cloud data in just one day's driving.x000D
All these point clouds allow to build up a high-definition road map that can help position a self-driving car onx000D
the road to an accuracy of within 10-20 cm. Given a huge amount of mobile LiDAR data, the automated andx000D
robust software tool that can handle the mobile LiDAR point clouds to support the high-definition road mapx000D
making and updating in an effective and efficient fashion is urgently needed. The objective of this project is tox000D
develop a cost-effective, point-based road surface marking extraction technology using 3D mobile LiDARx000D
point clouds. The software tool to be developed consists of novel algorithms for point cloud preprocessing,x000D
automated extraction and classification of pavement markings. The extracted road marking information canx000D
then be used to make and update the road databases. The outcomes of this project will provide a novel,x000D
cost-effective and time-saving method for making high-definition road maps to support driverless cars on thex000D
Canadian roads. Such a new tool can be supplementary or replaceable for current practices of making roadx000D
maps using either conventional surveys or image-based approaches.