Multi-sensory data fusion for 3D vehicle detection
Autonomous cars aim to ensure safe driving and reduce energy use. Among the four key functions of a self-driving car: environment perception, car navigation, path planning, and the car control, environment perception system is the basis of other systems and the prerequisite for the realization of autonomous driving. One of the most important tasks of environment perception is to detect other traffic participants,especially vehicles in 3D space. Because the relative position, speed and heading information of other vehiclesis very important to guide the cars to avoid collisions and generate interpretable motion planning.
My current research mainly focuses on environment perception techniques to support autonomous driving, including the development of the algorithms of deep learning-based LiDAR point cloud processing, multi-sensory data fusion, and 3D vehicle detection.
• PhD Candidate, Mechanical Engineering, Beijing University of Science and Technology, 2016-present
• M.Sc., Vehicle Engineering, Beijing University of Science and Technology, 2016
• B.Sc., Transportation, Lu Dong University, Yantai, 2012
• Zhao K, Liu L, *Meng Y, 2019. 3D detection for occluded vehicles from point clouds, IEEE Access,under review.
• Zhao K, Liu L, *Meng Y, 2019. Traffic signs detection and recognition in low light images, Chinese Journal of Engineering, under review.
• Meng Y, Xiao X, Zhao K, 2018. An underground localization algorithm and topology optimization based on ultra-wideband, Chinese Journal of Engineering, 40(6), 743-753.
• Meng Y, Zhao K, Gu Q, 2015. Data fusion system for accurate localization of mine vehicles, Chinese Journal of Engineering, 37(2), 59-65.