  Lane detection algorithms have been the key enablers for a fully-assistive
and autonomous navigation systems. In this paper, a novel and pragmatic
approach for lane detection is proposed using a convolutional neural network
(CNN) model based on SegNet encoder-decoder architecture. The encoder block
renders low-resolution feature maps of the input and the decoder block provides
pixel-wise classification from the feature maps. The proposed model has been
trained over 2000 image data-set and tested against their corresponding
ground-truth provided in the data-set for evaluation. To enable real-time
navigation, we extend our model's predictions interfacing it with the existing
Google APIs evaluating the metrics of the model tuning the hyper-parameters.
The novelty of this approach lies in the integration of existing segNet
architecture with google APIs. This interface makes it handy for assistive
robotic systems. The observed results show that the proposed method is robust
under challenging occlusion conditions due to pre-processing involved and gives
superior performance when compared to the existing methods.
