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arXiv · 1612.01079

End-to-end Learning of Driving Models from Large-scale Video Datasets

Huazhe Xu·Yang Gao·Fisher Yu·Trevor Darrell
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Abstract

Robust perception-action models should be learned from training data with diverse visual appearances and realistic behaviors, yet current approaches to deep visuomotor policy learning have been generally limited to in-situ models learned from a single vehicle or a simulation environment. We advocate learning a generic vehicle motion model from large scale crowd-sourced video data, and develop an end-to-end trainable architecture for learning to predict a distribution over future vehicle egomotion from instantaneous monocular camera observations and previous vehicle state. Our model incorporates a novel FCN-LSTM architecture, which can be learned from large-scale crowd-sourced vehicle action data, and leverages available scene segmentation side tasks to improve performance under a privileged learning paradigm.

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Huazhe Xu, Yang Gao, Fisher Yu, Trevor Darrell. 2016-12-04. End-to-end Learning of Driving Models from Large-scale Video Datasets. https://arxiv.org/abs/1612.01079

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