Hongjie Su

Beihang University, Hunan Institute of Engineering

Papers

Total Citations

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About

Dr. Hongjie Su is a pioneering researcher in autonomous vehicle navigation and intelligent path planning, with a focus on integrating reinforcement learning and classical algorithms for real-world robotics. His most influential work, "Obstacle Avoidance for Self-Driving Vehicle with Reinforcement Learning" (2017, 24 citations), introduced a novel framework enabling vehicles to navigate from arbitrary start to target positions while dynamically avoiding both static and moving obstacles of any shape—a foundational contribution to safe autonomous driving. Building on this, his recent "Improved A-star Method for Collision Avoidance and Path Smoothing" (2023, 9 citations) addresses a critical limitation of conventional A-star algorithms by incorporating robot morphology and path smoothness, moving beyond mere shortest-path optimization. This work bridges the gap between theoretical path planning and practical robotic constraints. Dr. Su’s research uniquely combines learning-based and geometric approaches, demonstrating how reinforcement learning can enhance classical methods for complex environments. With a growing citation impact, his contributions are shaping next-generation self-driving systems and mobile robotics, offering robust solutions for obstacle-dense scenarios. His work is essential reading for students and engineers advancing autonomous navigation.