Probabilistic Crowd GAN: Multimodal Pedestrian Trajectory Prediction using a Graph Vehicle-Pedestrian Attention Network

• URL: http://arxiv.org/abs/2006.12906v2
• Date: Sun, 12 Jul 2020 23:33:28 GMT
• Title: Probabilistic Crowd GAN: Multimodal Pedestrian Trajectory Prediction using a Graph Vehicle-Pedestrian Attention Network
• Authors: Stuart Eiffert, Kunming Li, Mao Shan, Stewart Worrall, Salah Sukkarieh and Eduardo Nebot
• Abstract summary: We show how Probabilistic Crowd GAN can output probabilistic multimodal predictions. We also propose the use of Graph Vehicle-Pedestrian Attention Network (GVAT), which models social interactions. We demonstrate improvements on the existing state of the art methods for trajectory prediction and illustrate how the true multimodal and uncertain nature of crowd interactions can be directly modelled.
• Score: 12.070251470948772
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Understanding and predicting the intention of pedestrians is essential to enable autonomous vehicles and mobile robots to navigate crowds. This problem becomes increasingly complex when we consider the uncertainty and multimodality of pedestrian motion, as well as the implicit interactions between members of a crowd, including any response to a vehicle. Our approach, Probabilistic Crowd GAN, extends recent work in trajectory prediction, combining Recurrent Neural Networks (RNNs) with Mixture Density Networks (MDNs) to output probabilistic multimodal predictions, from which likely modal paths are found and used for adversarial training. We also propose the use of Graph Vehicle-Pedestrian Attention Network (GVAT), which models social interactions and allows input of a shared vehicle feature, showing that inclusion of this module leads to improved trajectory prediction both with and without the presence of a vehicle. Through evaluation on various datasets, we demonstrate improvements on the existing state of the art methods for trajectory prediction and illustrate how the true multimodal and uncertain nature of crowd interactions can be directly modelled.

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