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The current level of road safety for cyclists is estimated mainly based on police reports and self-reports collected during surveys. However, the former focuses on post-accident situations, while the latter is based on subjective perception and focuses only on road sections. This work builds the foundation to automatically assess perceived cyclists’ safety by analyzing their head movements. In an indoor experiment (N = 12) using a Virtual Reality bicycle simulator, we discovered that perceived safety correlates with head rotation frequency and duration but not with head rotation angles. Based on this, we implemented a novel and minimalistic approach to detect head movements based on sensor data from Apple AirPods and an iPhone and conducted an outdoor experiment (N = 8). Our results indicate that perceived safety correlates with head rotation frequency and duration only at uncontrolled intersections when turning left and does not necessarily apply to all situations.
Today’s level of cyclists’ road safety is primarily estimated using accident reports and self-reported measures. However, the former is focused on post-accident situations and the latter relies on subjective input. In our work, we aim to extend the landscape of cyclists’ safety assessment methods via a two-dimensional taxonomy, which covers data source (internal/external) and type of measurement (objective/subjective). Based on this taxonomy, we classify existing methods and present a mobile sensing concept for quantified cycling safety that fills the identified methodological gap by collecting data about body movements and physiological data. Finally, we outline a list of use cases and future research directions within the scope of the proposed taxonomy and sensing concept.
