George Hotz, Eder Santana - 2016
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Comma.ai's approach to Artificial Intelligence for self-driving cars is based on an agent that learns to clone driver behaviors and plans maneuvers by simulating future events in the road. This paper illustrates one of our research approaches for driving simulation. One where we learn to simulate. Here we investigate variational autoencoders with classical and learned cost functions using generative adversarial networks for embedding road frames. Afterwards, we learn a transition model in the embedded space using action conditioned Recurrent Neural Networks. We show that our approach can keep predicting realistic looking video for several frames despite the transition model being optimized without a cost function in the pixel space.
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This is a compilation of resources, tutorials, side projects, and other useful materials that I am learning and covering as I get ready for Udacity's Self-Driving Car Engineer Nanodegree program
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MIT 6.S094 Tesla
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End-to-End Learning from Human and Autopilot Driving
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copy of comma.ai/research project
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Train a car how to drive using human driving behavior. it uses tensorflow, keras, and image augmentation.
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dataset and code for 2016 paper "Learning a Driving Simulator"
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Deep Learning Project to Teach a Car to Drive Autonomously Using Only Camera Images.
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A convolutional neural network for end-to-end driving in virtual track, using TensorFlow and Keras.
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MIT 6.S094 Tesla()
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Udacity MLND P6
