Deep Steering: Learning End-to-End Driving Model from Spatial and Temporal Visual Cues

12 Aug 2017 Lu Chi Yadong Mu

In recent years, autonomous driving algorithms using low-cost vehicle-mounted cameras have attracted increasing endeavors from both academia and industry. There are multiple fronts to these endeavors, including object detection on roads, 3-D reconstruction etc., but in this work we focus on a vision-based model that directly maps raw input images to steering angles using deep networks... (read more)

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Methods used in the Paper


METHOD TYPE
Interpretability
Image Models
Sigmoid Activation
Activation Functions
Tanh Activation
Activation Functions
LSTM
Recurrent Neural Networks