Yuchen Hu

The research focuses on developing advanced navigation systems for autonomous driving. Existing path planning and tracking methods often struggle to balance tracking accuracy, stability, and passenger comfort under varying driving conditions, leading to suboptimal performance. To address these challenges, my work integrates learning-based approaches with control theory to develop algorithms that dynamically adapt strategies to changing environmental and operational factors.

The core research question is how can autonomous navigation systems adapt more effectively to real-world scenarios while maintaining a balance between comfort, stability, and tracking accuracy. Because of my experience as a planning and control algorithm engineer in the autonomous driving industry, I focus on designing adaptive frameworks that optimize various performance metrics while considering computational efficiency and practical deployment.

This research aims to improve the adaptability of autonomous systems, reduce their operational limitations, and enable autonomous vehicles and mobile robots to operate effectively in complex environments. By addressing practical issues related to algorithm deployment, this work contributes to making autonomous navigation systems safer, more efficient and more widely adopted.