Bing Li, Ph.D.

Bing Li, Ph.D.

Assistant Professor

Clemson University

I am an Assistant Professor of Automotive Engineering at Clemson University International Center for Automotive Research (CU-ICAR) since 2018, directing AutoAI Lab research group.

My team is focusing on Autonomous AI research especially robotic Perception & Intelligence in interactive, dynamic, and uncertain environments, including topics such as sensing, visual perception/mapping, deep/machine learning, and artificial intelligence (AI) for robotics. We are also developing assistive and assistance technologies of navigation and safety aid to helping people with special needs.

Prior to joining Clemson, I earned a Ph.D. degree in Electrical Engineering at The City College (CCNY), The City University of New York (CUNY). I also had industrial R&D experiences at China Academy of Telecommunications Technology, IBM and HERE North America LLC that builds maps and location platform enabling self-driving vehicles.

My group is looking for motivated students! Please don’t hesitate to email me to apply if you are interested in.

Selected Publications

View All Publications at: All Publications, or Google Scholar.

Class-Level Confidence Based 3D Semi-Supervised Learning

IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2023, PDF

Disentangling Object Motion and Occlusion for Unsupervised Multi-frame Monocular Depth

European Conference on Computer Vision (ECCV), 2022, DOI: 10.1007/978-3-031-19824-3_14, Code

Automated Wall-Climbing Robot for Concrete Construction Inspection

Journal of Field Robotics (JFR), 2022, DOI: 10.1002/rob.22119

Advancing Self-Supervised Monocular Depth Learning with Sparse LiDAR

Conference on Robot Learning (CoRL), 2021, PDF, Code

Multimodal Semi-Supervised Learning for 3D Objects

British Machine Vision Conference (BMVC), 2021, PDF, Code


AuE 8930 Computing and Simulation for Autonomy

This course is designed to provide knowledge in the design and implementation of real-time parallel and high-performance computing (HPC), GPU computing, AI and edge-AI computing, autonomy stacks, and simulation technologies for autonomous robots and vehicle software systems. The students will achieve these learning objectives through extensive examples, homework, case and paper studies, and project design.

  • Programming and real-time computing;
  • Parallel/HPC and GPU computing;
  • AI and edge-AI computing for autonomous driving;
  • ROS, autonomous driving stack, and simulation.

AuE 8200 Machine Perception and Intelligence

This course will introduce the fundamental technologies for autonomous vehicle sensors, perception, and machine learning, from electromagnetic spectrum characteristics and signal acquisition, vehicle extrospective sensor data analysis, perspective geometry models, image and point cloud processing, to machine/deep learning approaches. We will also have hands-on programming experience in vehicle perception problems through homework and class projects.

  • Electromagnetic spectrum characteristics and 1D Radar signal processing;
  • Visual perception using 2D image processing and machine learning recognition;
  • 3D LiDAR and point cloud data representation and processing;
  • Visual/LiDAR/IMU for vehicle simultaneous localization and mapping (SLAM);
  • Deep learning for vehicle perceptual sensor data processing.