In this paper, we investigate the two most popular families of deep neural architectures (i.e., ResNets and InceptionNets) for the autonomous driving task of steering angle prediction. This work provides preliminary evidence that Inception architectures can perform as well or sometimes better than ResNet architectures with less complexity for the autonomous driving task. Our focus is on the compact end of the complexity spectrum. Compact neural network architectures produce less carbon emissions and are thus more environmentally friendly. We look at various sizes of compact ResNet and InceptionNet models to compare results. Our derived models can achieve state-of-the-art results in terms of steering angle MSE. In addition, we also explore the attention mechanism and investigate its influence on steering angle prediction.

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