Huy Khanh Hua
Papers
2
Total Citations
36
H-Index
2
About
Huy Khanh Hua is a researcher at the forefront of autonomous vehicle systems, specializing in deep learning-based perception and control. His work focuses on enhancing the reliability of self-driving cars through optimized computer vision algorithms, particularly for traffic light detection and end-to-end steering control. Hua’s major contributions include pioneering the integration of improved YOLO (You Only Look Once) architectures with the Robot Operating System 2 (ROS2) and Gazebo simulation environments, achieving real-time, high-accuracy recognition under varied lighting and weather conditions. His most-cited paper, “Optimizing YOLO Performance for Traffic Light Detection and End-to-End Steering Control for Autonomous Vehicles in Gazebo-ROS2” (2023, 21 citations), demonstrates a holistic approach that bridges perception and control, while his follow-up work on “Traffic Lights Detection and Recognition Method using Deep Learning with Improved YOLOv5 for Autonomous Vehicle in ROS2” (2023, 15 citations) refines detection robustness. Together, these studies have garnered significant attention, underscoring their impact on practical autonomous driving solutions. Hua’s research not only advances algorithmic efficiency but also provides a scalable framework for testing in simulated environments, making him a notable contributor to the next generation of intelligent transportation systems.
Research Focus
Key Achievements
Top Papers
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