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

Abstract

This paper reports on a data-driven, interactionaware 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.

Social Ways: Learning Multi-Modal Distributions of Pedestrian Trajectories With GANs

“…Now, this anticipation may not be always very easy, because of the uncertainties in the neighbors future motion and intentions. In most recent NN-based motion prediction systems [20,22,14], the input is taken as the set of most recent observations of the surrounding pedestrians. Hence, the mappings from observations to predicted trajectories built through the networks do not consider explicitly the uncertain and multimodal nature of the neighbors future trajectories, and, in a way, the network is expected to learn it too, which may be too much to expect.…”

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“…Bartoli et al [26] consider environmental context by providing an LSTM with distances of the target pedestrian to static objects in space, as well as a human-to-human context in form of a grid map, or alternatively the neighbors' hidden encodings. Pfeiffer et al [27] propose an LSTM that receives static obstacles as an occupancy grid and surrounding pedestrians as an angular grid. Aside from neural networks, also set-based methods [28], Gaussian Processes [29] and Reinforcement Learning algorithms [30] have been used for predictions that take into account the pedestrians' environment.…”

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“…al [24], which produces a path proposal with uncertainty. A similar technique is proposed by Pfeiffer et al [25], however they only use the RNNs to encode the history information, and choose to use a fully connected layer to output a set of velocities that represent the predicted path. Recently this technique has been extended to predicting the trajectory of vehicles on US highway 101 and interstate 80 in the NGSIM dataset.…”