Toshihiro Hiraoka

A. Koegel, C. Furet, T. Suzuki et al.

The Thinking Wave is an ongoing development of visualization concepts showing the real-time effort and confidence of semi-autonomous vehicle (AV) systems. Offering drivers access to this information can inform their decision making, and enable them to handle the situation accordingly and takeover when necessary. Two different visualizations have been designed, Concept one, Tidal, demonstrates the AV systems effort through intensified activity of a simple graphic which fluctuates in speed and frequency. Concept two, Tandem, displays the effort of the AV system as well as the handling dynamic and shared responsibility between the driver and the vehicle system. Working collaboratively with mobility research teams at the University of Tokyo, we are prototyping and refining the Thinking Wave and its embodiments as we work towards building a testable version integrated into a driving simulator. The development of the thinking wave aims to calibrate trust by increasing the drivers knowledge and understanding of vehicle handling capacity. By enabling transparent communication of the AV systems capacity, we hope to empower AV-skeptic drivers and keep over-trusting drivers on alert in the case of an emergency takeover situation, in order to create a safer autonomous driving experience.

Yuri Klebanov, Romi Mikulinsky, Tom Reznikov et al.

Trust is a multilayered concept with critical relevance when it comes to introducing new technologies. Understanding how humans will interact with complex vehicle systems and preparing for the functional, societal and psychological aspects of autonomous vehicles' entry into our cities is a pressing concern. Design tools can help calibrate the adequate and affordable level of trust needed for a safe and positive experience. This study focuses on passenger interactions capable of enhancing the system trustworthiness and data accuracy in future shared public transportation.

Hailong Liu, Toshihiro Hiraoka, Seiya Tanaka

As various driving automation system (DAS) are commonly used in the vehicle, the over-trust in the DAS may put the driver in the risk. In order to prevent the over-trust while driving, the trust state of the driver should be recognized. However, description variables of the trust state are not distinct. This paper assumed that the outward expressions of a driver can represent the trust state of him/her-self. The explicit behaviors when driving with DAS is seen as those outward expressions. In the experiment, a driving simulator with a driver monitoring system was used for simulating a vehicle with the adaptive cruise control (ACC) and observing the motion information of the driver. Results show that if the driver completely trusted in the ACC, then 1) the participants were likely to put their feet far away from the pedals; 2) the operational intervention of the driver will delay in dangerous situations. In the future, a machine learning model will be tried to predict the trust state by using the motion information of the driver.

Hailong Liu, Toshihiro Hiraoka

Levels one to three of driving automation systems~(DAS) are spreading fast. However, as the DAS functions become more and more sophisticated, not only the driver's driving skills will reduce, but also the problem of over-trust will become serious. If a driver has over-trust in the DAS, he/she will become not aware of hazards in time. To prevent the driver's over-trust in the DAS, this paper discusses the followings: 1) the definition of over-trust in the DAS, 2) a hypothesis of occurrence condition and occurrence process of over-trust in the DAS, and 3) a driving behavior model based on the trust in the DAS, the risk homeostasis theory, and the over-trust prevention human-machine interface.