Hansen Qin
Papers
2
Total Citations
10
H-Index
2
About
Hansen Qin is a researcher whose work lies at the critical intersection of autonomous vehicle safety and real-time motion planning. His primary research areas include reachability analysis, robust control, and trajectory optimization for dynamic systems. Qin’s major contribution is the development of the REFINE framework—Reachability-based Trajectory Design Using Robust Feedback Linearization and Zonotopes. This innovative approach addresses a fundamental challenge in autonomous driving: performing receding horizon motion planning with provable safety guarantees without relying on computationally expensive online numerical integration. By leveraging robust feedback linearization and zonotope-based reachable sets, REFINE enables efficient, safe trajectory generation in real time. His work has garnered attention, with his most-cited paper accumulating 8 citations since 2024, reflecting its growing influence in the robotics and controls community. Qin’s research is particularly notable for bridging the gap between theoretical safety verification and practical deployment, offering a scalable solution for autonomous vehicles operating in uncertain environments. His achievements position him as a rising contributor to the field of safe autonomy, with implications for self-driving cars, drones, and other robotic systems.
Research Focus
Key Achievements
Top Papers
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- 2