Chunyang Wang
Papers
4
Total Citations
35
H-Index
3
About
Chunyang Wang is a researcher specializing in robotics perception, autonomous driving, and 3D computer vision. His work bridges deep learning and point cloud processing to enable intelligent navigation and environment understanding. Wang’s most cited paper (26 citations) introduces a CNN-based end-to-end learning model for mobile robot obstacle avoidance, using raw visual input to navigate indoor environments—a foundational contribution to vision-based robotics. He has since advanced LiDAR perception with a novel point cloud object recognition method based on histograms of dual deviation angle features (2023), enhancing 3D object classification for autonomous driving and remote sensing. In 2024, Wang proposed an intra-frame graph structure and inter-frame bipartite graph matching framework for point cloud multi-object tracking, incorporating ReID-based occlusion resilience to maintain identity consistency across sequences. His latest work tackles trajectory prediction in complex traffic scenes with a heterogeneous multi-agent risk-aware graph encoder and continuous parameterized decoder, addressing collision risks at intersections. With over 35 total citations and a clear trajectory from mobile robotics to autonomous driving, Wang’s research is shaping safer, more perceptive autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1CNN-Based Vision Model for Obstacle Avoidance of Mobile Robot26 citations · 2017
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