Refine
Document Type
- Conference Proceeding (14) (remove)
Is part of the Bibliography
- no (14)
Keywords
- Automobiles (1)
- Automotive (1)
- Automotive HMI (1)
- AutomotiveHMI (1)
- Autonomous automobiles (1)
- Data visualization (1)
- HCI (1)
- Human Factors (1)
- Image color analysis (1)
- Inclusion (1)
Institute
For highly automated vehicles (AVs), new interaction concepts need to be developed. Even in AVs, the driver might want to intervene and override the automation from time to time. To create the possibility of control, we explore vehicle control through maneuver-based interventions (MBI). Thereby, we focus on explicit, contact-less interaction, which could be beneficial in future AV designs, where the driver is not necessarily bound to classical controls. We propose a set of freehand gestures and keywords for voice control derived in a user-centered design process. Further, we discuss properties, applicability and user impressions of both interaction modalities. Voice control seems to be an efficient way to select a maneuver and free-hand gestures could be used, if voice channel is blocked, e.g., through conversation with passengers.
Durch den technischen Fortschritt in der Spracherkennung und -verarbeitung wird Sprache als Interaktionsform auch in Fahrzeugen, z.B. zur Bedienung von Infotainmentsystem, immer populärer. Die Steuerung von teilautomatisierten Fahrzeugen über Sprache ist bisher wenig erforscht. Ziel dieser Arbeit ist es unter der grundsätzlichen Annahme der Eignung von Sprachsteuerung für teilautonome Fahrzeuge, Nutzererwartungen und spezielle Anforderungen an eine Sprachsteuerung für die grundlegenden Fahrmanöver zu identifizieren. Aus den Ergebnissen eines Expertenworkshops und einer explorativen Videostudie werden Anforderungen und Sprachkommandos abgeleitet.
Bei Großschadensereignissen kann es durch die Vielzahl der Alarme dazu kommen, dass die verfügbaren Rettungskräfte nicht mehr ausreichen, um die anfallenden Aufgaben zu bewältigen oder Hilfsfristen einzuhalten. Die vorliegende Arbeit beschreibt einen Ansatz, sich zusätzlicher Hilfe aus der Bevölkerung zu bedienen, die über einen Disponenten aus der vorhandenen Leitstelle koordiniert wird. Dabei stehen nicht spontan organisierte Helfer im Vordergrund, sondern Personen, die sich vorab mit einem klaren Fertigkeitsprofil und ggf. auch Ausstattung im System registriert haben. Besondere Anforderungen entstehen bei den Disponenten der Leitstelle, deren Mehrbelastung durch das neue System gering zu halten ist, als auch bei den freiwilligen Helfern, die über eine App auf dem Mobiltelefon alarmiert werden und auch darüber die Kommunikation führen sollen. Die Anforderungen beeinflussen sowohl die System-Infrastruktur als auch die Benutzerschnittstelle.
Self-driving cars will relief the human from the driving task. Nevertheless, the human might want to intervene in the driving process and thus needs the possibility to control the car. Switching back to fully manual controls is uncomfortable once being passive and engaging in non-driving-related activities. A more comfortable way is controlling the car with elemental maneuvers (e.g., "turn left" or "stop"). Whereas touch interaction concepts exist, contactless interaction through voice and mid-air gestures has not yet been explored for maneuver-based car control. In this paper, we, therefore, compare the general eligibility of voice and mid-air gesture with touch interaction as the primary maneuver selection mechanism in a driving simulator study. Our results show high usability for all modalities. Contactless interaction leads to a more positive emotional perception of the interaction, yet mid-air gestures lead to higher task load. Overall, voice and touch control are preferred over mid-air gestures by most users.
The uprising levels of autonomous vehicles allow the drivers to shift their attention to non-driving tasks while driving (ie, texting, reading, or watching movies). However, these systems are prone to failure and, thus, depending on human intervention becomes crucial in critical situations. In this work, we propose using human actuation as a new mean of communicating take-over requests (TOR) through proprioception. We conducted a user study via a driving simulation in the presence of a complex working memory span task. We communicated TORs through four different modalities, namely, vibrotactile, audio, visual, and proprioception. Our results show that the vibrotactile condition yielded the fastest reaction time followed by proprioception. Additionally, proprioceptive cues resulted in the second best performance of the non-driving task following auditory cues.
In catastrophic events, the potential of help has grown through new technologies. Voluntary help has many forms. Within this paper different categories of voluntary help are suggested. Those categories are based on properties like organizational structures, helping process, kind of prosocial behavior and many more. A focus is clearly on the organizational structure and motivational aspects of helper groups. Examples are given for each category. The categorization’s aim is to give a brief overview of possible properties a group of system users could have.
Public transportation will become highly automated in the future, and at some point, human drivers are no longer necessary. Today many people are skeptical about such scenarios of autonomous public transport (abbr.: APT). In this paper, we assess users’ subjective priority of different factors that lead to personal acceptance or rejection of APT using an adapted online version of the Q-Methodology with 44 participants. We found four prototypical attitudes to which subgroups of participants relate: 1) technical enthusiasts, 2) social skeptics, 3) service-oriented non-enthusiasts, and 4) technology-oriented non-enthusiasts. We provide an unconventional perspective on APT acceptance that helps practitioners prioritize design requirements and communicate, targeting users’ specific attitudes.
Anonymity-preserving Methods for Client-side Filtering in Position-based Collaboration Approaches
(2017)
Understanding user needs and behavior in automated vehicles (AVs) while traveling is essential for future in-vehicle interface and service design. Since AVs are not yet market-ready, current knowledge about AV use and perception is based on observations in other transportation modes, interviews, or surveys about the hypothetical situation. In this paper, we close this gap by presenting real-world insights into the attitude towards highly automated driving and non-driving-related activities (NDRAs). Using a Wizard of Oz AV, we conducted a real-world driving study (N= 12) with six rides per participant during multiple days. We provide insights into the users’ perceptions and behavior. We found that (1) the users’ trust a human driver more than a system,(2) safety is the main acceptance factor, and (3) the most popular NDRAs were being idle and the use of the smartphone.