Phuc Phan Hong

About

Phuc Phan Hong is a rising researcher in autonomous vehicle control systems, with a primary focus on deep learning-based lane-keeping and navigation. His most cited work, "An Improved Lane-Keeping Controller for Autonomous Vehicles Leveraging an Integrated CNN-LSTM Approach" (2023), addresses the limitations of traditional rule-based algorithms by training neural networks on front-facing camera data paired with steering commands. This innovative fusion of Convolutional Neural Networks (CNNs) for spatial feature extraction and Long Short-Term Memory (LSTM) networks for temporal sequence learning enables more robust, real-time lane-keeping in dynamic traffic environments. Although early in his career, with 3 citations to date, his work represents a meaningful step toward end-to-end autonomous driving, reducing reliance on hand-coded heuristics. Hong’s research sits at the intersection of computer vision, deep learning, and robotics, contributing to safer, more adaptive self-driving technologies. As autonomous systems continue to evolve, his integrated CNN-LSTM framework offers a promising pathway for scalable, data-driven vehicle control.

Research Focus

Key Achievements

Top Papers

1. 1
   An Improved Lane-Keeping Controller for Autonomous Vehicles Leveraging an Integrated CNN-LSTM Approach

   3 citations · 2023

Key Collaborators

Contact & Links