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A mobile robot that understands pedestrian spatial behaviors

In human society, there are many invisible social rules or spatial effects existing in our environments. The robot that does not comprehend these spatial effects might harm people or itself. This paper presents a spatial behavior cognition model (SBCM) to describe the spatial effects existing between people and people, people and environments. By understanding the spatial effects in human-lived environments, the robot not only predicts pedestrian intentions and trajectories but also behaves socially acceptable motions. Moreover, the concept of pedestrian ego-graph (PEG) is proposed to efficiently query pedestrian-like paths for trajectory prediction. Model evaluation and experiments are shown to verify the proposed idea in this paper.