End-to-End Learning of Driving Models from Large-Scale Video Datasets
Citations Over TimeTop 1% of 2017 papers
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 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. We provide a novel large-scale dataset of crowd-sourced driving behavior suitable for training our model, and report results predicting the driver action on held out sequences across diverse conditions.
Related Papers
- Binocular vision tested with visual evoked potentials in children and infants.(1978)
- → Integration of Intermittent Visual Samples Over Time and Between the Eyes(2006)14 cited
- → Deep fake Detection Through Deep Learning(2023)4 cited
- → Influences of monocular image degradation on the monocular components of fixation disparity(1996)9 cited
- Why & When Deep Learning Works: Looking Inside Deep Learnings.(2017)