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Trajectory prediction is a fundamental and challenging task for numerousapplications, such as autonomous driving and intelligent robots. Currently,most of existing work treat the pedestrian trajectory as a series of fixedtwo-dimensional coordinates. However, in real scenarios, the trajectory oftenexhibits randomness, and has its own probability distribution. Inspired by thisobserved fact, also considering other movement characteristics of pedestrians,we propose one simple and intuitive movement description, probabilitytrajectory, which maps the coordinate points of pedestrian trajectory intotwo-dimensional Gaussian distribution in images. Based on this uniquedescription, we develop one novel trajectory prediction method, called socialprobability. The method combines the new probability trajectory and powerfulconvolution recurrent neural networks together. Both the input and output ofour method are probability trajectories, which provide the recurrent neuralnetwork with sufficient spatial and random information of moving pedestrians.And the social probability extracts spatio-temporal features directly on thenew movement description to generate robust and accurate predicted results. Theexperiments on public benchmark datasets show the effectiveness of the proposedmethod.

Probability Trajectory: One New Movement Description for Trajectory Prediction