To Örebro University

Background: Incorporating knowledge about human behaviour and the effect of the environment is a major goal for the design and engineering of human-centred autonomous vehicles. Systems that aim to establish a common interaction ground with humans require systematic modelling of empirically established behavioural norms customised to specific contexts.

Aims: Focusing on aspects pertaining to visual attention in driving, we develop a cognitive model of visuospatial complexity for naturalistic driving scenes and explore its effect on visual attention tasks (e.g., involving visual search) during everyday driving.

Methods: By analyzing dynamic naturalistic scenes, we define a scale of visuospatial complexity based on a taxonomy of quantitative, structural, and dynamic attributes. We re-create real-world instances in virtual reality (VR) in four levels of visuospatial complexity. The human-centred basis of the model lies in its behavioural evaluation with human subjects with respect to psychophysical measures (e.g. eye-tracking) pertaining to embodied visuospatial attention.

Results: Empirical results show the levels of visuospatial complexity of the scene correlate with visual search performance parameters, however different categories of attributes contribute differently to the overall effect. We report work-in-progress on the development of a (sample) dataset with the central emphasis on the evaluation of the visuospatial complexity levels on driving stimuli within VR.

Conclusion: The presented cognitive model of visuospatial complexity in everyday driving situations can be used as a basis to design, and evaluate visuospatial sensemaking capabilities of autonomous vehicles. We posit that our methodology encapsulates key cognitive principles founded on empirically established behavioural patterns under naturalistic conditions.