Context Prediction Architectures in Next Generation of Intelligent Cars

Abstract

The growing number of intelligent components inside a car leads to a considerable increase in amount of the produced data. Context aware paradigm plays a major role in managing this data and offering a numerous number of prospects and advantages for existing and new intelligent applications inside the car. Following that, enabling context prediction promises reliable solutions in terms of enhancing the comfort of the occupants and vehicle dynamics. Moreover, this would be a great step toward facilitating highly automated and autonomous driving. However, due to the complex nature of the data resources in an intelligent car and also the lack of comprehensive studies on different aspects of this concept in automotive, defining a functional architecture for context prediction requires broad knowledge and better understanding of multiple domains which are involved and have impacts. In this paper, we investigate the most effective elements and factors in each one of the related domains which help to enable context prediction architectures inside the intelligent cars and analyze the feasible dimensions in detail, cover their advantages, and address the challenges ahead. We elucidate the possibility and validity of our considerations with the help of two use cases of adaptive HVAC and ACC systems.