Yu‐Fan Liu
Papers
1
Total Citations
4
H-Index
1
About
Yu-Fan Liu is a researcher specializing in autonomous vehicle navigation and intelligent control systems, with a particular focus on dynamic obstacle avoidance for self-driving utility vehicles. Their most notable contribution is the design of a dynamic obstacle avoidance control system for self-driving sweepers, published in 2021, which has garnered 4 citations. This work integrates the lattice-planner algorithm within the Robot Operating System (ROS) to address the unique driving characteristics of sweepers, offering a tailored solution that improves upon traditional manual operation. By proposing an obstacle avoidance method that aligns with sweeper-specific dynamics, Liu has advanced the practical application of autonomous navigation in low-speed, urban cleaning environments. Their research bridges the gap between theoretical path planning and real-world deployment, demonstrating how lattice-based planning can enhance safety and efficiency in autonomous ground vehicles. Liu’s work contributes to the growing field of intelligent transportation systems, particularly in niche applications where standard autonomous driving solutions may fall short.
Research Focus
Key Achievements
Top Papers
- 1