Guizhen Yu

Papers

Total Citations

H-Index

About

Dr. Guizhen Yu is a leading researcher in autonomous vehicle control and intelligent transportation systems, with a particular focus on reinforcement learning-based navigation. Her most-cited work, "Obstacle Avoidance for Self-Driving Vehicle with Reinforcement Learning" (2017, 24 citations), introduces a novel framework that enables self-driving vehicles to navigate from arbitrary start positions to targets while dynamically avoiding both static and moving obstacles of arbitrary shape. This contribution addresses a critical challenge in autonomous driving: ensuring safe, real-time path planning in unpredictable environments. Dr. Yu's approach leverages reinforcement learning to enable vehicles to learn optimal obstacle avoidance strategies without requiring explicit environmental models, marking a significant advancement over traditional rule-based methods. Her research bridges the gap between theoretical machine learning and practical vehicle control, with implications for safer autonomous navigation in complex urban settings. As a researcher at the forefront of intelligent vehicle systems, Dr. Yu continues to explore how adaptive algorithms can enhance the reliability and efficiency of self-driving technologies, making her work essential reading for students and engineers in robotics, control systems, and artificial intelligence.