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Autonomous vehicles (AVs) can prevent collisions by understanding pedestrianintention. We conducted a virtual reality experiment with 39 participants andmeasured crossing times (seconds) and head orientation (yaw degrees). Wemanipulated AV yielding behavior (no-yield, slow-yield, and fast-yield) and theAV size (small, medium, and large). Using machine learning approach, weclassified head orientation change of pedestrians by time into 6 clusters ofpatterns. Results indicate that pedestrian head orientation change wasinfluenced by AV yielding behavior as well as the size of the AV. Participantsfixated on the front most of the time even when the car approached near.Participants changed head orientation most frequently when a large size AV didnot yield (no-yield). In post-experiment interviews, participants reported thatyielding behavior and size affected their decision to cross and perceivedsafety. For autonomous vehicles to be perceived more safe and trustful,vehicle-specific factors such as size and yielding behavior should beconsidered in the designing process.

Gentlemen on the Road: Understanding How Pedestrians Interpret Yielding Behavior of Autonomous Vehicles using Machine Learning