Double Helical Ensemble Multi-Dimensional 4-D Structured Neural Network to Analyze the Driving Pattern, Driver DNA and Generate License Score using Smartphone Sensor Data - Dr. S. Karthikeyan, Dr. S. Gopikrishnan, Dhruv Batta, TathagatBanerjee (Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2021)
In this paper, we delineate a way to assess the licensed score of a person. License score is a term coined here in reference with a credit score which is used to identify the credibility of an individual on a financial basis. This research leverages the accelerometer and gyroscope sensors present in the smartphones. These sensors help us realize our goal of classifying driving events and detecting distracted driving due to the use of smartphones. We use sensors rich user micro movements and irregularities that can be detected by sensors in the phone during driving. Our system distinguishes driving events based on reference signals which are matched through our Pattern Matching Algorithm which uses dynamic time warping. To validate our approach, we conducted extensive experiments with several users on various vehicles and smartphones. A neural network model has been developed for the purpose of classifying signals in the future which leverages our collected data. The model has a double helical stranded multidimensional 4-d neural network that uses layers of Convolutional Neural Networks and Artificial Neural Networks. It uses long short-term memory (LSTM) layers to classify and score the driving behaviours. The double-helical stranded neural network simulates a situation similar to the input and output of different senses of the human brain. This helps us classifying driving events.