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Development of machine learning model for mobile advanced driver assistance (ADA)

Abstract:

Every day around 3500 people die on the roads and tens of millions suffer injuries or disabilities each year; vehicle manufacturers spend millions of dollars annually on research for the prevention of traffic accidents in development of Advanced Driving Assistance Systems (ADAS). To solve this problem, the development of a model based on decision trees and supervised neural networks is proposed; it is focusing on the four main risk factors of traffic accidents: a) drive inattentive to traffic conditions, b) not respecting the regulatory traffic signals, c) driving in a state of drowsiness or poor physical condition, d) drive vehicle over the Maximum Speed Limits. The Machine Learning system requires three models: a) neural network for object recognition; b) algorithm for sleepiness detection; and, c) decision tree to issue factor-related alerts: driver status (sleepiness or distraction), speed and traffic signal alert. Finally, the model is put into production in a low cost ADAS mobile application, and it was tested in the laboratory using recordings obtained in real driving tests (n = 200, success>=90%) in ideal luminosity environments.