Papers
9
Total Citations
265
H-Index
7
About
Li Li is a versatile researcher whose work spans autonomous vehicles, robotics motion planning, and agricultural automation. Her most influential contribution, "Harmonious Lane Changing via Deep Reinforcement Learning" (2021, 118 citations), demonstrates her expertise in applying multi-agent reinforcement learning to enable safer, more natural autonomous vehicle behavior without reliance on vehicle-to-everything communication infrastructure. This work has become a notable reference in the autonomous driving community. Beyond autonomous vehicles, Li has made significant strides in trajectory planning and collision avoidance. Her "Embodied Footprints" framework (2023, 34 citations) addresses critical safety gaps in optimization-based planners, while her successive linearization algorithm (2021, 34 citations) tackles the challenging problem of motion planning in unstructured, low-speed environments — foundational challenges for mobile robotics broadly. More recently, Li has expanded into precision agriculture, developing deep learning-based detection and pose estimation systems for citrus harvesting robots (2024), reflecting her adaptability across domains. Her work on ceiling painting robot trajectory planning further underscores her breadth in applied robotics. With over 260 cumulative citations and a consistently growing research portfolio, Li Li represents a dynamic force bridging intelligent transportation, optimization-based planning, and agricultural robotics.
Research Focus
Key Achievements
Top Papers
- 1Harmonious Lane Changing via Deep Reinforcement Learning118 citations · 2021
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- 5A Gridmap-Path Reshaping Algorithm for Path Planning18 citations · 2019
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- 9Formation Control and Collision Avoidance of Nonholonomic Mobile Robots2 citations · 2021