Panagiotis Angeloudis
Papers
3
Total Citations
96
H-Index
2
About
Panagiotis Angeloudis is a leading researcher at the intersection of artificial intelligence, autonomous systems, and urban logistics. His work primarily focuses on developing intelligent coordination and prediction frameworks for autonomous driving, pedestrian behavior modeling, and last-mile delivery systems. Angeloudis made significant contributions to pedestrian trajectory prediction by introducing dual-branch spatio-temporal graph neural networks, a novel approach that captures complex social interactions in dynamic environments—a paper that has garnered 56 citations since 2023. In autonomous driving, he advanced multi-agent reinforcement learning by pioneering sim-to-real transfer policies, enabling effective coordination among multiple vehicles, with his 2022 work accumulating 38 citations. His innovative application of congestion-game models to analyze customer behavior for coordinated delivery services using mobile parcel lockers and autonomous robots demonstrates his ability to bridge theoretical models with practical urban challenges. Angeloudis’s research is characterized by its interdisciplinary nature, combining graph neural networks, reinforcement learning, and game theory to solve real-world problems in transportation and robotics. His work is highly cited and influential, shaping how autonomous systems perceive, interact, and collaborate in complex environments.
Research Focus
Key Achievements
Top Papers
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