I can understand why it probably isn't on the list yet (not as many citations, since it is fairly new) - but NVidia's "End to End Learning for Self-Driving Cars

I can understand why it probably isn't on the list yet (not as many citations, since it is fairly new) - but NVidia's "End to End Learning for Self-Driving Cars" needs to be mentioned, I think: https://arxiv.org/abs/1604.07316 https://arxiv.org/abs/1604.07316 https://images.nvidia.com/content/tegra/automotive/images/2016/solutions/pdf/end-to-end-dl-using-px.pdf https://images.nvidia.com/content/tegra/automotive/images/20... I implemented a slight variation on this CNN using Keras and TensorFlow for the third project in term 1 of Udacity's Self-Driving Car Engineer nanodegree course (not special in that regard - it was a commonly used implementation, as it works). Give it a shot yourself - take this paper, install TensorFlow, Keras, and Python, download a copy of Udacity's Unity3D car simulator (it was recently released on GitHub) - and have a shot at it! Note: For training purposes, I highly recommend building a training/validation set using a steering wheel controller, and you'll want a labeled set of about 40K samples (though I have heard you can get by with much fewer, even unaugmented - my sample set actually used augmentation of about 8k real samples to boost it up to around 40k). You'll also want to use GPU and/or a generator or some other batch processing for training (otherwise, you'll run out of memory post-haste).