Luyl-Da Quach
Papers
6
Total Citations
129
H-Index
5
About
Luyl-Da Quach is a leading researcher at the intersection of autonomous systems and explainable artificial intelligence (XAI), with a particular focus on smart agriculture and autonomous driving. His most impactful work, "Explainable Deep Learning Models With Gradient-Weighted Class Activation Mapping for Smart Agriculture" (65 citations), pioneers the application of XAI techniques to agricultural image classification, addressing the critical need for transparency in deep learning models used in precision farming. Quach has made substantial contributions to autonomous vehicle technology, particularly in traffic light detection and lane-keeping control. His research on optimizing YOLO performance for traffic light detection and end-to-end steering control in Gazebo-ROS2 (21 citations) and his improved YOLOv5-based traffic light recognition system (15 citations) have advanced real-time perception capabilities for self-driving cars. He has also developed innovative control systems, including an adaptive lane-keeping assist using fuzzy-PID control (14 citations) and an integrated CNN-LSTM approach for improved lane-keeping (3 citations). His comparative evaluation of SLAM methods with human detection integration (11 citations) further demonstrates his versatility in robotics. Quach's work bridges the gap between theoretical deep learning advances and practical deployment in safety-critical autonomous systems.
Research Focus
Key Achievements
Top Papers
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