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AI, Engineering

Discrete Residual Flow for Probabilistic Pedestrian Behavior Prediction

October 17, 2019 / Global

Abstract

Self-driving vehicles plan around both static and dynamic objects, applying predictive models of behavior to estimate future locations of the objects in the environment. However, future behavior is inherently uncertain, and models of motion that produce deterministic outputs are limited to short timescales. Particularly difficult is the prediction of human behavior. In this work, we propose the discrete residual flow network (DRF-Net), a convolutional neural network for human motion prediction that captures the uncertainty inherent in long-range motion forecasting. In particular, our learned network effectively captures multimodal posteriors over future human motion by predicting and updating a discretized distribution over spatial locations. We compare our model against several strong competitors and show that our model outperforms all baselines.

Authors

Ajay Jain*, Sergio Casas*, Renjie Liao*, Yuwen Xiong*, Song Feng, Sean Segal, Raquel Urtasun

Conference

NeurIPS 2019

Full Paper

‘Discrete Residual Flow for Probabilistic Pedestrian Behavior Prediction’ (PDF)

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