Papers
9
Total Citations
135
H-Index
7
About
Rongrong Ni is a leading researcher at the intersection of autonomous systems, trajectory prediction, and human-robot interaction. Her work primarily focuses on developing intelligent algorithms that enable self-driving vehicles, social robots, and monitoring systems to better understand and anticipate human behavior. Ni’s most significant contributions lie in addressing critical challenges in pedestrian trajectory prediction, including long-tail data distributions and data privacy. Her 2022 paper on continual learning-based trajectory prediction with memory augmented networks has garnered 39 citations, while her 2024 work on dynamic subclass-balancing contrastive learning tackles the long-tail distribution problem in pedestrian data. Ni has also pioneered federated learning approaches for trajectory prediction, ensuring data privacy across scattered scenes. Beyond trajectory prediction, she has made notable advances in affective robotics, developing attention-enhanced facial expression recognition networks and methods for humanoid robots to exhibit human-like facial expressions—work that holds promise for home care applications. Her research on vision-based pedestrian crossing intention recognition further contributes to safer urban autonomous navigation. With a growing citation record and publications spanning from 2019 to 2025, Ni continues to push boundaries in making autonomous systems more perceptive, privacy-conscious, and socially intelligent.
Research Focus
Key Achievements
Top Papers
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- 4Multi-granularity scenarios understanding network for trajectory prediction17 citations · 2022
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