In this paper, we propose MAConAuto, a general framework to design and develop automotive applications as human-centric applications. MAConAuto aims at improving the human experience by integrating the perception and reactions of the human into the interventions made by the automotive applications through monitoring the human response. Modeling the human in a way that captures the change in perception and reaction is an open challenging research question. Borrowing up from the psychology literature, the behavior of the changes in the human decision historically was modeled through the expected utility theorem (EUT) which is based on an axiomatic framework defined as completeness, transitivity, independence, and continuity. Human is said to be rational if these four axioms hold. However, the models that rely on the EUT have shown that these axioms are unrealistic and that human decisions tend to deviate from the axioms of the EUT. In this paper, we dissect these axioms to design a human model for human-in-the-loop automotive applications using Multisample Reinforcement Learning.