Summary: Autonomous vehicles will have to coordinate their behavior with human road users such as drivers and pedestrians. The majority of recently proposed solutions for autonomous vehicle-to-human communication consist of introducing additional visual cues (such as lights, text and pictograms) on either the car's exterior or as projections on the road. We argue that potential shortcomings in the visibility (due to light conditions, placement on the vehicle) and immediate understandability (learned, directive) of many of these cues make them alone insufficient in mediating multi-party interactions in the busy intersections of day-to-day traffic. Our observations of real-world human road user behavior in urban intersections indicate that movement in context is a central method of communication for coordination among drivers and pedestrians. The observed movement patterns gain meaning when seen within the context of road geometry, current road activity, and culture. While all movement communicates the intention of the driver, we highlight the use of movement as gesture, done for the specific purpose of communicating to other road users and give examples of how these influence traffic interactions. An awareness and understanding of the effect and importance of movement gestures in day-to-day traffic interactions is needed for developers of autonomous vehicles to design forms of human-vehicle communication that are effective and scalable in multi-party interactions.
Similar results from REF shows that vehicle movement is sufficient for indicating the intention of drivers and present some motion patterns of road users such as advancing, slowing early and stopping short.
Human-Vehicle Interfaces: The Power of Vehicle Movement Gestures in Human Road User Coordination
Proceedings of the 9th International Driving Symposium on Human Factors in Driver Assessment, Training, and Vehicle Design: driving assessment 2017
Proceedings of the 9th International Driving Symposium on Human Factors in Driver Assessment, Training, and Vehicle Design: driving assessment 2017
Computer Science