3D high-definition roadmaps promotes autonomous driving
A fully autonomous vehicle (AV), which aims to achieve the Level-4 full self-driving automation, has the capability to determine the best navigation routes, drive vehicle themselves on the most challenging road networks, and avoid collisions with other traffic participants without human interactions. AVs are increasingly developed to enhance quality and performance of modern transportation services with reduced costs and resources consumption. Moreover, in order to provide effective and precise localization services, the development of AV desiderates tremendous advances in 3D high-definition (HD) roadmaps. Such roadmaps are capable of providing 3D positioning information with cm-level accuracy. Therefore, establishing detailed and valuable 3D high-definition roadmaps has been regarded as a fundamental perspective of achieving autonomous driving.
Accordingly, mobile laser scanning (MLS) systems, consisting of advanced navigation and data acquisition devices, can be applied to acquire highly accurate, geo-referenced point clouds with highly sensitive information about road environments for the generation of 3D high-definition roadmaps.
My current research mainly focuses on autonomous driving, intelligent processing of point clouds, and 3D road scene modelling to promote the development of AVs and HD maps. In addition, combined with advanced computer vision techniques, I am looking forward that MLS technique can improve and achieve a wide application in the process of augmented reality.
- MSc, Geography, University of Waterloo, 2017
- BES, Geomatics, University of Waterloo, 2015
- BSc, Geomatics, China University of Geosciences (Beijing), 2015
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- Ma L, Zhao H, *Li J, 2016. Examining urban expansion using multi-temporal Landsat imagery: a case study of the Montreal Census Metropolitan area from 1975 to 2015, Canada. ISPRS 2016, Prague, Czech Republic, pp. 965-972.