Ding‐Hsiang Huang
China Academy of Launch Vehicle Technology, Tunghai University
Papers
2
Total Citations
76
H-Index
1
About
Ding-Hsiang Huang is a researcher at the forefront of autonomous vehicle intelligence and edge AI robotics. His primary research areas span deep reinforcement learning for automated driving, real-time sensor fusion, and embedded artificial intelligence. Huang’s most impactful contribution is his pioneering work on driving decision and control for automated lane change behavior using deep reinforcement learning (2019, 75 citations). This study demonstrated that deep RL can outperform classical rule-based methods in complex, uncertain driving environments, offering a scalable framework for high-level vehicle automation. More recently, Huang has advanced edge AI technology by integrating YOLOv8 object detection with MQTT communication and MQ-5 gas sensors for real-time fire and smoke detection (2025). This work exemplifies his commitment to deploying intelligent systems on resource-constrained devices for safety-critical applications. With a growing citation record and a focus on bridging simulation and real-world deployment, Huang’s research continues to influence both autonomous driving and intelligent sensing systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2