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This paper reports on a data-driven, interaction-aware motion predictionapproach for pedestrians in environments cluttered with static obstacles. Whennavigating in such workspaces shared with humans, robots need accurate motionpredictions of the surrounding pedestrians. Human navigation behavior is mostlyinfluenced by their surrounding pedestrians and by the static obstacles intheir vicinity. In this paper we introduce a new model based on Long-Short TermMemory (LSTM) neural networks, which is able to learn human motion behaviorfrom demonstrated data. To the best of our knowledge, this is the firstapproach using LSTMs, that incorporates both static obstacles and surroundingpedestrians for trajectory forecasting. As part of the model, we introduce anew way of encoding surrounding pedestrians based on a 1d-grid in polar anglespace. We evaluate the benefit of interaction-aware motion prediction and theadded value of incorporating static obstacles on both simulation and real-worlddatasets by comparing with state-of-the-art approaches. The results show, thatour new approach outperforms the other approaches while being verycomputationally efficient and that taking into account static obstacles formotion predictions significantly improves the prediction accuracy, especiallyin cluttered environments.

A Data-driven Model for Interaction-aware Pedestrian Motion Prediction in Object Cluttered Environments