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In this paper we propose a deep learning model, which we call step attention, for pedestrian trajectory prediction. The proposed model has a special architecture which consists of recurrent neural networks, convolutional neural networks, and an augmented attention mechanism. Rather than developing architectures to model factors that may affect the walking behavior, the proposed model learns trajectory patterns directly from input sequences. We evaluate the performance of the step attention model using TrajNet–a publicly available benchmark dataset collected from diverse real-world crowded scenarios. We compare the performance of step attention with three existing state-of-the-art algorithms, including social LSTM, social GAN, and occupancy LSTM on the TrajNet benchmark dataset. Our experiments show that the average displacement error (ADE) of step attention for a 4.8-seconds-long prediction horizon …