Su-Kit Tang

Papers

Total Citations

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About

Driven by a vision of safer, more efficient transportation and smarter assistive technologies, Su-Kit Tang is a rising researcher at the intersection of autonomous driving and the Internet of Things (IoT). His work centers on two critical challenges: enabling robust machine perception for self-driving vehicles and developing precise localization systems to support vulnerable populations. Tang’s most influential contribution, "Enabling deep reinforcement learning autonomous driving by 3D-LiDAR point clouds" (2022, 8 citations), pioneers a novel approach that fuses deep reinforcement learning with 3D LiDAR data, allowing autonomous agents to navigate complex environments by directly interpreting raw point clouds. This work addresses a fundamental bottleneck in autonomous driving—reliable perception without heavy reliance on pre-mapped data. More recently, Tang has advanced indoor tracking for IoT devices with "Automatic Tracking Based on Weighted Fusion Back Propagation in UWB for IoT Devices" (2024, 6 citations), introducing a weighted fusion back-propagation algorithm that dramatically improves ultra-wideband (UWB) positioning accuracy. This innovation holds particular promise for elderly care and disability support, enabling real-time, non-intrusive monitoring. Though early in his career, Tang’s dual focus on autonomous navigation and human-centric IoT demonstrates a clear commitment to technologies that enhance both mobility and quality of life.