Long Han

Papers

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About

Long Han is a pioneering researcher in autonomous vehicle navigation, specializing in dynamic path planning and obstacle avoidance in complex, unstructured environments. His major contributions center on developing safe, real-time algorithms that enable car-like robots and autonomous vehicles to navigate among multiple moving obstacles. Han’s work integrates probabilistic methods, such as particle filters, with geometric tools like Bézier curves and machine learning techniques, including support vector machines (SVMs). His most cited paper, "Dynamic and Safe Path Planning Based on Support Vector Machine among Multi Moving Obstacles for Autonomous Vehicles" (2013, 25 citations), introduces a hybrid local-global planning framework that detects obstacles online and generates collision-free trajectories. An earlier foundational work, "Safe path planning among multi obstacles" (2011, 13 citations), laid the groundwork by combining particle filters and Bézier curves for semi-structured environments. Han’s research bridges theoretical robustness and practical deployment, addressing critical challenges in autonomous driving safety. His achievements include advancing SVM-based decision-making for real-time navigation, a notable contribution to the field of intelligent transportation systems.