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Steering a car through traffic is a complex task that is difficult to castinto algorithms. Therefore, researchers turn to training artificial neuralnetworks from front-facing camera data stream along with the associatedsteering angles. Nevertheless, most existing solutions consider only the visualcamera frames as input, thus ignoring the temporal relationship between frames.In this work, we propose a Convolutional Long Short-Term Memory RecurrentNeural Network (C-LSTM), that is end-to-end trainable, to learn both visual anddynamic temporal dependencies of driving. Additionally, We introduce posing thesteering angle regression problem as classification while imposing a spatialrelationship between the output layer neurons. Such method is based on learninga sinusoidal function that encodes steering angles. To train and validate ourproposed methods, we used the publicly available Comma.ai dataset. Our solutionimproved steering root mean square error by 35% over recent methods, and led toa more stable steering by 87%.

End-to-End Deep Learning for Steering Autonomous Vehicles Considering Temporal Dependencies