Prediction of attitudes towards human-centred cognitive vehicles aware of their users' routines and preferences

Abstract

Advances in the automotive industry are changing the relationship between cars and drivers. Advanced driver assistant systems, such as navigation systems, advanced cruise control, collision avoidance systems, and other safety systems, are now common and assist the driver in controlling the car. Smart key fobs have made getting in and starting the car almost effortless: the fob can be left in the pocket and the doors will unlock when a driver/user approaches the car and simply touches the door handle. This is a level of personalization and convenience that is almost standard today. The research presented here brings a new perspective on personalization and driver assistance systems. An online survey was conducted, which aimed to gather public opinion on the usefulness of endowing future (semi-)autonomous cars with social and cognitive behavior, such as the ability to learn drivers' routines and preferences in order to make decisions and perform actions in preparation for the next trip and to manage comfort within the cockpit without being commanded to do so. After filtering, the study included 657 respondents from 93 nations. The results demonstrate a favorable opinion towards such human-centered cognitive vehicles and could be helpful for designers in the automotive industry and other related stakeholders in the development of future cognitive vehicles.

A Data Recording Mobile Application to Create Datasets of Vehicle Users’ Routines

“…As an outcome, assistance systems may become more acceptable, trustworthy, and personal [4]. Consistent with this level of personalization, a recent survey [5] found a strong positive public interest in the idea of having a vehicle with cognitive and social behavior, capable of learning and assisting users on their everyday traveling habits. More precisely, provide the vehicle the ability to learn drivers' routines and preferences (such as the usual destination, routine timestamps, regular driver and passengers, and their items), so that it can, autonomously, manage comfort within the cockpit and assist its occupants by issuing suggestions/alerts.…”

“…In light of this, further research on the subject is promising. It may focus on the development of prototypes with such cognitive aspects, simulator-based studies, and on-road experiments [5]. For such, and even for the entire Intelligent Transportation Systems (ITS), datadriven approaches are typically adopted, entailing the use of datasets at various stages of development (e.g., for training, testing, simulating, and tuning) [4], [6], [7].…”