  With a great amount of research going on in the field of autonomous vehicles
or self-driving cars, there has been considerable progress in road detection
and tracking algorithms. Most of these algorithms use GPS to handle road
junctions and its subsequent decisions. However, there are places in the urban
environment where it becomes difficult to get GPS fixes which render the
junction decision handling erroneous or possibly risky. Vision-based junction
detection, however, does not have such problems. This paper proposes a novel
deep convolutional neural network architecture for disambiguation of junctions
from roads with a high degree of accuracy. This network is benchmarked against
other well known classifying network architectures like AlexNet and VGGnet.
Further, we discuss a potential road navigation methodology which uses the
proposed network model. We conclude by performing an experimental validation of
the trained network and the navigational method on the roads of the Indian
Institute of Science (IISc).
