Jae-Young Shin
Papers
1
Total Citations
10
H-Index
1
About
Dr. Jae-Young Shin is pioneering the intersection of radar perception and deep reinforcement learning for autonomous navigation. His most-cited work introduces a robust framework that integrates radar-based dynamic obstacle detection with a Bidirectional Gated Recurrent Unit (BiGRU) deep reinforcement learning architecture, enabling wheeled robots to navigate complex, unpredictable environments with enhanced safety and adaptability. By employing advanced filtering and tracking algorithms to cluster radar object points, Dr. Shin’s approach overcomes the limitations of vision-based systems in adverse conditions, achieving reliable real-time obstacle avoidance. This research, published in 2024 and already garnering 10 citations, underscores his impact on the field of intelligent robotics. Dr. Shin’s contributions are particularly notable for bridging sensor fusion and decision-making, offering a scalable solution for autonomous systems in logistics, service robotics, and autonomous driving. His work continues to shape how robots perceive and interact with dynamic surroundings, making him a rising authority in robust, sensor-driven autonomy.
Research Focus
Key Achievements
Top Papers
- 1