  With the growing phase of artificial intelligence and autonomous learning,
the self-driving car is one of the promising area of research and emerging as a
center of focus for automobile industries. Behavioral cloning is the process of
replicating human behavior via visuomotor policies by means of machine learning
algorithms. In recent years, several deep learning-based behavioral cloning
approaches have been developed in the context of self-driving cars specifically
based on the concept of transfer learning. Concerning the same, the present
paper proposes a transfer learning approach using VGG16 architecture, which is
fine tuned by retraining the last block while keeping other blocks as
non-trainable. The performance of proposed architecture is further compared
with existing NVIDIA architecture and its pruned variants (pruned by 22.2% and
33.85% using 1x1 filter to decrease the total number of parameters).
Experimental results show that the VGG16 with transfer learning architecture
has outperformed other discussed approaches with faster convergence.
