Andreas Riener

Towards a Personalized Trust Model for Highly Automated Driving

User acceptance of automated vehicles (and dependent dimensions such as road safety, frequency of use or level of recommendation) is said to be highly dependent on the operator’s individual trust in this technology. As a consequence, the development of driving functions and future driver-vehicle interfaces should allow for appropriate trust calibration. To better understand trust and the effect of mis-calibration on the way to a personalized trust model, we propose a set of trust-related research questions derived from related work and our own user studies. Based on preliminary investigation, we recommend examining 1) differences in users and subgroups of users, 2) different levels of trust based on situation or context, 3) methods for quantifying trust in naturalistic driving studies, and 4) definitions for an established/approved trust model and the individual calibration of the model with regard to driving behavior and automotive user interfaces. The final outcome should be a multidimensional trust model that fits the individual passenger/driver by dynamically adapting driving mode and UI representation/feedback.

Automotive User Interfaces in the Age of Automation (Dagstuhl Seminar 16262)

The next big change in the automotive domain will be the move towards automated and semi-automated driving. We can expect an increasing level of autonomous driving in the coming years, resulting in new opportunities for the car as an infotainment platform when standard driving tasks will be automated. This change also comes with a number of challenges to automotive user interfaces. Core challenges for the assistance system and the user interface will be distributing tasks between the assistance system and the driver, the re-engagement of drivers in semi-automated driving back to the driving task, and collaborative driving in which cars collectively work together (e.g., platoons). Overall, in the coming years we will need to design interfaces and applications that make driving safe while enabling communication, work, and play in human-operated vehicles. This Dagstuhl seminar brought together researchers from human computer interaction, cognitive psychology, human factors psychology and also from automotive industry and OEMs to discuss the new interface paradigms for (semi-)automated driving.

ARV 2018: 2nd Workshop on Augmented Reality for Intelligent Vehicles

It is forecast that augmented reality (AR automotive applications will increase road safety, bring intuitive activities to driving, and finally enhance driving experience. AR technology may also help on the transition towards automated driving. However, many technological challenges need to be addressed before AR applications will hit the mainstream market. In this workshop, we will discuss the potential and constraints as well as impact, role, and adequacy of AR in driving applications. The overarching goal is to define a research agenda for the general use of AR in intelligent vehicles within the next 3 to5 years.

Investigating User Preferences for Windshield Displays in Automated Vehicles

Windshield displays are a promising technology for automotive applications. They easily allow to augment the capabilities of drivers or display content relevant for non-driving related activities. However, little information is available on how potential users would utilize these displays. In this paper we present the results of a user study (N=31) addressing user preferences for windshield displays in automated driving. Our goal was to find out how potential users would like applications to appear in terms of size, position, and content type. Participants could freely choose preferred position and size of multiple windows on an artificial ideal windshield display. We further distinguished between two levels of automation (conditional and full). Initial results of our experiment (presented in form of heatmaps) indicate differences considering the level of vehicle automation and reveal the most important areas for various content types. This can help designers and developers of automotive applications to enhance user experience.

Man vs. Machine: Comparing a Fully Automated Bus Shuttle with a Manually Driven Group Taxi in a Field Study

Automated driving functions are traditionally tested in on-road studies, however, mainly focusing on technological aspects (sensor accuracy, etc.). Field studies addressing users individual needs and expectations are still rare. As a consequence, it is still unclear whether or not automated driving systems will reach a comprehensive market penetration. To address this issue, we set-up a user study and compared users acceptance (utilizing TAM) as a passenger (N=12) of a traditional group taxi vs. an automated bus shuttle both driving in regular traffic. Results show that participants questioned the usefulness of the automated bus shuttle, mainly due to the reduced speed, but, on the other hand, rated their perceived ease of use and their attitude towards using the ADS more positive than expected. Thus, we conclude that with further development of the technology and by including a user-centered design approach, high user acceptance of ADSs can finally be achieved.

Automated Driving: Acceptance and Chances for Elderly People

The increasing life expectancy has led to new models of aging, which empower people to have a fulfilling life. Especially mobility becomes a critical issue and automated vehicles seam to offer many possibilities concerning safety and independence. To examine the user needs of the elderly, we executed a first study based on semi-structured interviews and a role play. The aim was to identify the overall acceptance of automated vehicles and identify arising opportunities for the aging society. Initial results indicate that 75% of the elderly people (n=12.) are not willing to ride with automated vehicles due to uncertainty and distrust (fear of technical failures). However, 25% would like to try out automated driving systems. They are curious and enthusiastic of the technology. Interestingly, in place of high tech and entertainment in the vehicle, they just like to enjoy the environment passing by (and dont want to take a nap).

Workshop on User-Centered Design for Automated Driving Systems

Automated driving systems (ADS) are mainly regarded from an innovation and technology-centered perspective. In academia, as well as in industry, there is a concentration on technical issues to maintain competitiveness while aspects like acceptance, trust and user experience are widely under-researched. However, the human factor is critical for a comprehensive establishment of ADS technology on the market. We believe that there is a need to focus on a user-centered design (UCD) perspective to bring ADS innovation to a next level and to achieve a wide acceptance in society. In this workshop we want to discuss special requirements of UCD applied to ADS, to address challenges and opportunities and to reveal new research fields for future work.

Interacting with Autonomous Vehicles: Learning from other Domains

The rise of evermore autonomy in vehicles and the expected introduction of self-driving cars have led to a focus on human interactions with such systems from an HCI perspective over the last years. Automotive User Interface researchers have been investigating issues such as transition control procedures, shared control, (over)trust, and overall user experience in automated vehicles. Now, it is time to open the research field of automated driving to other CHI research fields, such as Human-Robot-Interaction (HRI), aeronautics and space, conversational agents, or smart devices. These communities have been dealing with the interplay between humans and automated systems for more than 30 years. In this workshop, we aim to provide a forum to discuss what can be learnt from other domains for the design of autonomous vehicles. Interaction design problems that occur in these domains, such as transition control procedures, how to build trust in the system, and ethics will be discussed.

Beyond Transportation: How to Keep Users Attached When They Are Neither Driving nor Owning Automated Cars?

The way drivers relate to cars is likely bound to change with the rise of automated vehicles and new ownership models. However, personal relationships towards products are an important part of buying decisions. Car manufacturers thus need to provide novel bonding experiences for their future customers in order to stay competitive. We introduce a vehicle attachment model based on related work from other domains. In interviews with 16 car owners we verify the approach as promising and derive four attachment types by applying the model: interviewees personal attachments were grounded on either self-empowering reasons, memories with the car, increased status, or a loving friendship towards their car. We propose how to address the needs of these four attachment types as a first step towards emotionally irreplaceable automated and shared vehicles.

Man vs. Machine: A Documentary About Automated Driving In 2018 Somewhere In Bavaria

The shift from manual to automated driving has been one of the most focused research topics in the AutomotiveUI community in recent years. User acceptance and experience was extensively discussed in surveys and simulator studies, however, naturalistic driving studies are still rare. Now, in 2018, we had the chance to test the first authorized automated vehicle (shuttle bus service) in Germany driving in regular traffic. Our video aims to be suggestive of our study setup, in which we compared users acceptance and experience as a passenger in an automated driving versus a manual driven vehicle. In this video paper, we provide first insights into our results.