Journal of Software Engineering for Autonomous Systems

Autonomous driving is a growing research field, that still has many challenges. The main challenges are related with decision-making algorithms, human-machine interaction and acceptance in the technology. Also, the absence of human drivers in autonomous vehicles creates a gap between users and pedestrians interacting with the vehicle. This article aims to define vehicle awareness, that eases the collaboration with users to improve safety and have a more human-like driving to increase the technology acceptance. In addition, our approach can be extended to express vehicle social awareness towards the pedestrians and road users. Our approach is based on affective computing. Affective computing is a tool to grant computers to genuinely become intelligent and interact better with humans. Moreover, one of its components is the generation of emotions, of which two of the most important elements are cognitive emotions and primary emotions. The article’s objective is to design the model of a primary emotion component, based on safety and that can be personalized depending on the driving style of the user. This component is called the stress factor. The stress factor is correlated with the probability of an accident. The vehicle stress factors contain parameters that can be personalized as a function of a driving style. The stress factor is then attached to an existing cognitive emotion system (CarE) in the automotive domain which we called CarEs. The results of the system behavior showed promising results. The stress factor showed to be useful as a safety indicator. Also, the stress factor can be personalized with the vehicle operation state component. In conclusion, the new system known as CarEs generates vehicle awareness, by improving the vehicle’s collaboration with the driver. The collaboration has a positive impact on the vehicle’s safety and comfort, and people’s reliance on automated vehicles.