Human-centered User Interfaces for Automated Driving – (Un-)exploited Potentials

Designing user interfaces for (highly) automated driving is a complex task since users vary considerably regarding their needs and preferences. Therefore, a one-size-fits-all approach will not be sufficient for designing these interfaces. Thus, in this paper we aim to identify unexploited potentials in this area. We do so by performing a systematic literature review. Our contributions are 1) a systematization of human-centered user interface design for automated driving in four key aspects, 2) the research intensity per aspect, 3) the unexploited potential within each aspect and 4) the potentials of the relations between them. Concretely, current research lacks frameworks supporting the customization of the named interfaces based on user characteristics. Among others, personalization of displayed information shows unexploited potentials for acceptance and usability. Thus, we recommend future research to focus on human-centricity accounting for individual needs instead of the interface itself

Adaptation and Personalization in Driver Assistance Systems

Driver-related factors (e.g., driver inattention) are a cause of majority of traffic accidents. To reduce the number of accidents and improve traffic safety a variety of driver assistance systems have been proposed. Today, many of these systems do not adapt recommendations and warning to the particular driver (having his-/her own driving style, reaction time etc.). However, in many cases utilization of personal characteristics and preferences may improve the quality of the driver assistance, besides if a driver's expectations about the functionality provided by the assistance system are not met, it may decrease the trust to the system and lead to turning it off, therefore ignoring its potential utility and influence on increasing the safety. In this paper we review scientific publications in the area of driver assistance systems and a) identify most widely used directions of personalization and adaptation in driver assistance systems, b) identify and describe the most widely used models and methods leveraged for personalization and adaptation, c) identify existing research gaps. The paper may serve as mapping study as well as a reference and a toolset of how to deal with driver variability in driver assistance systems

From Manual Driving to Automated Driving: A Review of 10 Years of AutoUI

This paper gives an overview of the ten-year devel- opment of the papers presented at the International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutoUI) from 2009 to 2018. We categorize the topics into two main groups, namely, manual driving-related research and automated driving-related re- search. Within manual driving, we mainly focus on studies on user interfaces (UIs), driver states, augmented reality and head-up displays, and methodology; Within automated driv- ing, we discuss topics, such as takeover, acceptance and trust, interacting with road users, UIs, and methodology. We also discuss the main challenges and future directions for AutoUI and offer a roadmap for the research in this area.https://deepblue.lib.umich.edu/bitstream/2027.42/153959/1/From Manual Driving to Automated Driving: A Review of 10 Years of AutoUI.pdfDescription of From Manual Driving to Automated Driving: A Review of 10 Years of AutoUI.pdf : Main articl