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Criticality perception in dynamic traffic scenarios: an ACT-R model

Noémi Földes-Cappellotto
Ulm University
Moritz Held
University of Groningen ~ Artificial Intelligence
Martin Baumann
Ulm University
Ms. Tanja Stoll
Ulm University ~ Human Factors

Evaluating criticality in driving is of utmost importance, especially in dynamic driving scenarios such as lane changing. Current theories assume that drivers' evaluation process is based on perception of time-to-collision (TTC). We argue that determining whether a situation is critical or not is guided by retrieving memories containing the perceived situation elements. This memory retrieval helps drivers build up situation awareness and it takes place regardless of whether participants possess a memory which is a perfect match to the scenario at hand, or only a partially matching one, including some of the situation’s elements. Therefore, memory retrieval leads to a more or less reliable situation model (SM) and anticipation about how the scenario is going to develop. Furthermore, we assume that drivers’ SM also includes the SM of a potentially relevant road user (i.e., one that might interfere with the driver) to some extent as well. We are creating an ACT-R model in order to explore perception and memory retrieval which result in a perceptual decision participants make about the situation’s criticality in a highly dynamic lane-changing scenario.



situation awareness
criticality perception
dynamic traffic scenario

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Cite this as:

Földes-Cappellotto, N., Held, M., Baumann, M., & Stoll, T. (2021, July). Criticality perception in dynamic traffic scenarios: an ACT-R model. Paper presented at Virtual MathPsych/ICCM 2021. Via