Multi-Objective Genetic Algorithm for Optimizing an ELM-Based Driver Distraction Detection System

Driver Assistance Systems (DAS) have been progressively incorporated into commercial vehicles in recent years. All these systems are paving the way for the forthcoming autonomous vehicle which will become a reality in the near future. Existing systems are based on numerous electronic systems with advanced skills, high performances, and high degrees of adaptability and intelligence. As is to be expected, these cutting-edge features require, in most cases, the use of powerful computing platforms. However, the deployment of such platforms is not an easy task, since they have to be integrated in the vehicle where there exist important restrictions regarding size, power consumption and cost. In this sense, every smart proposal aimed at reducing the complexity of these systems without degrading performance, is always a valuable contribution in the field. In this work, we propose a methodology to reduce the dimensionality of a driver distraction recognition system. The methodology is based on a multi-objective genetic algorithm that looks for the minimum set of useful features collected during the driving task and also for the simplest recognition system. The recognition algorithm is an Extreme Learning Machine (ELM) whose simplicity and fast learning procedure make it especially suitable to be used by a Genetic Algorithm which needs to evaluate thousands of candidate solutions. The proposed methodology has been tested with a real-world database collected from different drivers performing an itinerary with an instrumented car. The results obtained validate the proposal as a method to reduce the complexity of a driver distraction recognition system.