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

TL;DR

This paper presents a novel LSTM-based model for pedestrian motion prediction that incorporates static obstacles and surrounding pedestrians, significantly improving accuracy in cluttered environments.

Contribution

Introduces the first LSTM model that combines static obstacles and pedestrian interactions for trajectory forecasting in cluttered spaces.

Findings

Outperforms state-of-the-art methods in accuracy.

Incorporating static obstacles improves prediction in cluttered environments.

Model is computationally efficient.

Abstract

This paper reports on a data-driven, interaction-aware motion prediction approach for pedestrians in environments cluttered with static obstacles. When navigating in such workspaces shared with humans, robots need accurate motion predictions of the surrounding pedestrians. Human navigation behavior is mostly influenced by their surrounding pedestrians and by the static obstacles in their vicinity. In this paper we introduce a new model based on Long-Short Term Memory (LSTM) neural networks, which is able to learn human motion behavior from demonstrated data. To the best of our knowledge, this is the first approach using LSTMs, that incorporates both static obstacles and surrounding pedestrians for trajectory forecasting. As part of the model, we introduce a new way of encoding surrounding pedestrians based on a 1d-grid in polar angle space. We evaluate the benefit of interaction-aware motion prediction and the added value of incorporating static obstacles on both simulation and real-world datasets by comparing with state-of-the-art approaches. The results show, that our new approach outperforms the other approaches while being very computationally efficient and that taking into account static obstacles for motion predictions significantly improves the prediction accuracy, especially in cluttered environments.