Nan Ye

About

Nan Ye is a leading researcher in autonomous systems, robotics, and intelligent decision-making, with a particular focus on safe and efficient navigation in complex environments. His most influential work, the 2015 paper on "Intention-aware online POMDP planning for autonomous driving in a crowd," has garnered over 330 citations and stands as a foundational contribution to the field. In this work, Ye pioneered a framework that enables autonomous vehicles to estimate and hedge against the uncertainty of pedestrian intentions, allowing for safe, smooth, and efficient driving in crowded spaces. This approach has become a benchmark for intention-aware planning in robotics and autonomous driving. Beyond this, Ye has advanced reliability analysis for industrial robots, developing hybrid learning algorithms for radial basis function networks to ensure precision in machining applications. His research also spans practical robotic system design, including Ackerman mobile robots integrated with ROS and LiDAR, and innovative 3D measurement systems using linear profile sensors. With a growing portfolio that addresses both theoretical challenges and real-world deployment, Nan Ye’s work continues to shape the future of autonomous navigation and robotic reliability.

Research Focus

Key Achievements

Top Papers

1. 1
   Intention-aware online POMDP planning for autonomous driving in a crowd

   331 citations · 2015
2. 2
   Hybrid Learning Algorithm of Radial Basis Function Networks for Reliability Analysis

   162 citations · 2020
3. 3
   Design of Ackerman Mobile Robot System Based on ROS and Lidar

   6 citations · 2021
4. 4
   A density-controllable 3D measurement system based on linear profile sensor and universal manipulator

   3 citations · 2024
5. 5
   Cooperative capture trajectory optimization of multi-space robots using an improved multi-objective fruit fly algorithm

   1 citations · 2022

Key Collaborators

Contact & Links