AI-Based Systems in Enhancing In-Car User Experience

Abstract

The modern automotive industry is witnessing significant transformation in its traditional operations due to the integration of intelligence. The current generation of vehicles comes with on-board intelligence systems, which enable them to perform a variety of functionalities. Many of these functionalities already exist in some form in consumer vehicles. A number of technologies are combined to deliver these functionalities, such as machine learning, data mining, ontologies, optimization, fuzzy logic, among others. The global automotive AI market is expected to reach USD 10.73 billion by 2027. Since 1995, when General Motors first introduced OnStar as an in-car communication system, the automotive industry has been continuously rolling out new features focused on in-car user experience. Nowadays, the increasing interest in autonomous vehicles has augmented the rush for artificial intelligence within the automotive environment.

The applications of AI in vehicles are multifaceted. The traditional view has been that smart cars and AI would improve the overall passenger experience. Intelligent vehicles reduce human errors, enhance safety, reduce the level of human intervention, increase energy efficiency by managing and optimizing driving behaviors, and can help with vehicle personalization in such a way that the vehicle, understanding its occupant preferences through machine learning, can adjust heating/cooling, music, and provided routes, among others. AI-based vehicles provide level four autonomy, presenting the capacity to drive themselves with full autonomy under predetermined conditions. On the one hand, given these features, autonomous vehicles present a unique and unknown cocoon of in-car space in addition to a revolution in overall urban mobility. Moreover, in order to judge whether autonomous vehicles will take off and be made affordable in the future, it is essential for further research to evaluate user acceptance towards the vehicle in-car experience.