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A Unified Convolutional Neural Network Framework for Intelligent Decision Making Related to Navigation

Prevalent intellective systems assist the driver in the navigation errands by taking some simple and intricate decisions based upon available input information. Such decisions should be astute and prompt to provide realistic behaviour in real-life navigation scenarios decisions. Proposed unified Convolutional Neural Network framework presents a novel approach for better performance of navigation related autonomous systems.The suggested work is an extension to authors previous work in which best input features for decision making neural network were obtained by shortlisting eleven texture metrics out of thirty-six metrics using state of art algorithmsUnification of Best Feature Selection Method (our previous work) and Principal Component Analysis Technique was done to find out the most pertinent neural network inputs. CNN framework has been used here to provide a simple decision that is either to move or stop at a particular instant based on finalized inputs. It was found that the collective inputs of unified model yield better results in conjunction with CNN framework. The model outperforms our previous model and PCA Furthermore, it was observed that CNN framework can perform equally well with minimal training in comparison to high order training. It implies CNN’s has remarkable ability to learn from imprecise data gives it an edge over others. In nutshell, the proposed model was able to decide promptly and astutely the next move of vehicle.