In this paper, we investigate the two most popular families of deep neural
architectures (i.e., ResNets and Inception nets) for the autonomous driving
task of steering angle prediction. This work provides preliminary evidence that
Inception architectures can perform as well or better than ResNet architectures
with less complexity for the autonomous driving task. Primary motivation
includes support for further research in smaller, more efficient neural network
architectures such that can not only accomplish complex tasks, such as steering
angle predictions, but also produce less carbon emissions, or, more succinctly,
neural networks that are more environmentally friendly. We look at various
sizes of ResNet and InceptionNet models to compare results. Our derived models
can achieve state-of-the-art results in terms of steering angle MSE