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