Cognitive Infotainment Systems for Intelligent Vehicles

Lately, the automotive world is witnessing a trend related to the deployment and extensive use of Intelligent Transportation Systems (ITS) inside vehicles. Such systems generate large volumes of real-time data that need to be managed, communicated, interpreted, aggregated, and analyzed in order to support real-time decision-making capabilities. Furthermore, automotive industries have introduced In-Vehicle-Infotainment (IVI) systems in supporting drivers/passengers with a varying set of functions. The main purpose of this study is to present an IVI cognitive functionality that automatically and dynamically proposes the optimum music genre to the drivers /users when they want to travel with their vehicles. The proposed system utilizes (i) driver/user's profile data and his/her current situation, (ii) driver/user's personal preferences, (iii) external environment information obtained from sensor measurements, and (iv) previous knowledge, turned into experience, in an automated manner. Knowledge is obtained through the exploitation of a machine learning technique based on the Bayesian networking principles. Indicative simulation results showcase the behavior of the aforementioned IVI cognitive functionality in proactively identifying the optimal music genre and accordingly notifying the driver/user.