H
Hongbo Gao
Researcher at University of Science and Technology of China
Publications - 75
Citations - 2133
Hongbo Gao is an academic researcher from University of Science and Technology of China. The author has contributed to research in topics: Computer science & Trajectory. The author has an hindex of 15, co-authored 59 publications receiving 1066 citations. Previous affiliations of Hongbo Gao include Beihang University & Tsinghua University.
Papers
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Journal ArticleDOI
Object Classification Using CNN-Based Fusion of Vision and LIDAR in Autonomous Vehicle Environment
TL;DR: This method is based on convolutional neural network (CNN) and image upsampling theory and can obtain informative feature representation for object classification in autonomous vehicle environment using the integrated vision and LIDAR data.
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Vehicle Trajectory Prediction by Integrating Physics- and Maneuver-Based Approaches Using Interactive Multiple Models
TL;DR: Comparison results indicate that IMMTP could achieve a more accurate prediction trajectory with a long prediction horizon than the existing physics- and maneuver-based approaches.
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YOLOv4-5D: An Effective and Efficient Object Detector for Autonomous Driving
Yingfeng Cai,Luan Tianyu,Hongbo Gao,Hai Wang,Long Chen,Li Yicheng,Miguel Angel Sotelo,Zhixiong Li +7 more
TL;DR: In this article, the authors proposed a one-stage object detection framework for improving the detection accuracy while supporting a true real-time operation based on the YOLOv4.
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A study on key technologies of unmanned driving
TL;DR: This paper summarizes and analyzes the background, significance, research status and key technology of unmanned driving and the research group also introduces some research on brain cognition of driving and sensor placement of intelligent vehicles, which offers more meaningful reference to push the study of unmanneddriving.
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Robust Target Recognition and Tracking of Self-Driving Cars With Radar and Camera Information Fusion Under Severe Weather Conditions
TL;DR: The fusion algorithm improves the robustness of the environment perception system and provides accurate environment perception information for the decision-making system and control system of autonomous vehicles.