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The design of self-driving vehicles requires an understanding of the social interactions between drivers in resolving vague encounters, such as at un-signalized intersections. In this paper, we make the case for social situation awareness as a model for understanding everyday driving interaction. Using a dual-participant VR driving simulator, we collected data from driving encounter scenarios to understand how (N=170) participant drivers behave with respect to one another. Using a social situation awareness questionnaire we developed, we assessed the participants’ social awareness of other driver’s direction of approach to the intersection, and also logged signaling, speed and speed change, and heading of the vehi- cle. Drawing upon the statistically significant relationships in the variables in the study data, we propose a Social Situation Awareness model based on the approach, speed, change of speed, heading and explicit signaling from drivers. 

The characterization of driver interactions is im- portant for a variety of problems associated with the design of autonomy for vehicles. We consider the role of cultural context in driver interactions, by evaluating the differences in driving interactions in simulated driving experiments conducted in New York City, New York, USA, and in Haifa, Israel. The same experiment was conducted in both locations, and focused on naturalistic driving interactions at unsigned intersections, in which interaction with another vehicle was required for safe navigation through the intersection. We employ conditional dis- tribution embeddings, a nonparametric machine learning tech- nique, to empirically characterize differences in the distribution of trajectories that characterize driver interactions, across both locations. We show that cultural variability outweighs individual variability in intersections that require turning ma- neuvers, and that clear distinctions amongst driving strategies are evident between populations. Our approach facilities a data-driven analysis that is amenable to rigorous statistical testing, in a manner that minimizes filtering, pre-processing, and other manipulations that could inadvertently bias the data and obscure important findings. 

Deceptive, manipulative, and coercive practices are deeply embedded in our digital experiences, impacting our ability to make informed choices and undermining our agency and autonomy. These design practices—collectively known as “dark patterns” or “deceptive patterns”—are increasingly under legal scrutiny and sanctions, largely due to the efforts of human-computer interaction scholars that have conducted pioneering research relating to dark patterns types, definitions, and harms. In this workshop, we continue building this scholarly community with a focus on organizing for action. Our aims include: (i) building capacity around specific research questions relating to methodologies for detection; (ii) characterization of harms; and (iii) creating effective countermeasures. Through the outcomes of the workshop, we will connect our scholarship to the legal, design, and regulatory communities to inform further legislative and legal action.