Baby, You Can Ride My Bike: Exploring Maneuver Indications of Self-Driving Bicycles Using a Tandem Simulator

Matviienko, Andrii and Mehmedovic, Damir and Müller, Florian and Mühlhäuser, Max

Abstract: We envision a future where self-driving bicycles can take us to our destinations. This allows cyclists to use their time on the bike efficiently for work or relaxation without having to focus their attention on traffic. In the related field of self-driving cars, research has shown that communicating the planned route to passengers plays an important role in building trust in automation and situational awareness. For self-driving bicycles, this information transfer will be even more important, as riders will need to actively compensate for the movement of a self-driving bicycle to maintain balance. In this paper, we investigate maneuver indications for self-driving bicycles: (1) ambient light in a helmet, (2) head-up display indications, (3) speech feedback, (4) vibration on the handlebar, and (5) no assistance. To evaluate these indications, we conducted an outdoor experiment (N = 25) in a proposed tandem simulator consisting of a tandem bicycle with a steering and braking control on the back seat and a rider in full control of it. Our results indicate that riders respond faster to visual cues and focus comparably on the reading task while riding with and without maneuver indications. Additionally, we found that the tandem simulator is realistic, safe, and creates an awareness of a human cyclist controlling the tandem.


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