Institut für Informationssysteme (iisys)
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This book summarizes studies conducted by the Empirical Research and User Experience (ERUX) research group at the Institute for Information Systems (iisys) at Hof University of Applied Sciences over the period 2019 to 2022. The main topic focuses on “The Human-Machine-Interface in Advanced Driver Assistance Systems towards Autonomous Driving”, investigated using the example of automatic parking assistant in cars. A broad spectrum of methods was used to ensure comprehensive analyses and valid results.
Starting situation for the studies has been the industry's realization that the demand for parking assistants had fallen short of expectations. Moreover, they concluded that a host of driving assistant system owners´ do not use them. This indicates an unsatisfactory acceptance of new technologies on the part of car drivers. The aim of the studies was therefore to determine the reasons for this restraint, as well as to derive measures to increase acceptance of this systems among end customers.
The first study focused on operators and the restraint of German drivers towards driver assistance systems. The results of this quantitative study displayed that although the modernity of these systems has been identified, the functional enhancements they offer are insufficient. In order to overcome this discrepancy, manufacturers are advised to emphasize the functional advantages more strongly when communication with the end customer. Study also suggested that direct experience can significantly increase customers' willingness to purchase such a system. Therefore, it is of pivotal importance to introduce customers to the new technologies in a targeted manner.
Using conjoint measurement, a second study illustrates driver assistance systems already representing a significant benefit when purchasing a car, even though the trust in fully autonomous vehicles is still restricted. Drivers favor established car manufacturer in terms of the development of autonomous vehicles.
The third study utilizes eye-tracking to examine operating patterns of parking assistance systems. Moreover, it derives potential for improvement in terms of intuitive design and user experience.
Fourthly, alternative scenarios for the design of parking assistants are evaluated in a concept test. This indicated the vital importance of simple and acquainted operating options.
Across all methods, the customer actually being able to experience driving assistance systems is a decisive matter for the acceptance of these new technologies. Therefore, manufacturers should emphasize their focus on getting customers in touch with these systems, as well as offering them targeted usage options. In addition, the integration of customer feedback into development efforts is essential to produce intuitive, user-friendly user interfaces and thus promote the diffusion process of new technologies.
Dieses Buch rekapituliert Studien der Forschungsgruppe "Empirical Research and User Experience (ERUX)" am Institut für Informationssysteme (iisys) der Hochschule Hof über den Zeitraum 2019 bis 2022. Das zentrale Thema umfasst die Mensch- Maschine-Schnittstelle bei Fahrerassistenzsystemen auf dem Weg zum autonomen Fahren, untersucht am Beispiel des automatischen Einparkassistenten im Automobil. Zum Einsatz kam dabei ein breites Methodenspektrum, um eine umfassende Analyse und valide Ergebnisse zu gewährleisten.
Ausgangslage der Studien ist die Erkenntnis der Industrie, dass die Nachfrage nach Parkassistenten hinter den Erwartungen zurückgeblieben ist und viele Besitzer solcher Systeme diese nicht nutzen. Dies deutet auf eine unbefriedigende Akzeptanz neuer Technologien seitens der Autofahrer hin. Ziel der dargestellten Studien war es daher, die Gründe für diese Zurückhaltung zu ermitteln und Maßnahmen zur Steigerung der Akzeptanz bei den Endkunden abzuleiten.
Die erste Studie fokussiert sich auf Treiber und Vorbehalte deutscher Autofahrer gegenüber Fahrerassistenzsystemen. Im Ergebnis dieser quantitativen Studie zeigt sich, dass die Modernität dieser Systeme zwar wahrgenommen wird, die von ihnen ausgehenden funktionalen Verbesserungen jedoch unzureichend. Um diese Diskrepanz zu überwinden, empfiehlt sich eine stärkere Betonung funktionaler Vorteile in der Kommunikation seitens der Hersteller. Zudem zeigt sich, dass direkte Erfahrungen die Kaufbereitschaft der Kunden signifikant steigern können. Essentiell ist daher eine gezielte Heranführung der Kunden an die neuen Technologien.
Mittels Conjoint Measurement wird in einer zweiten Studie herausgearbeitet, dass Fahrerassistenzsysteme bereits einen erheblichen Nutzenbeitrag beim Autokauf darstellen, allerdings ist das Vertrauen in vollkommen autonome Fahrzeuge noch begrenzt. Bei der Entwicklung autonomer Fahrzeuge präferieren Autofahrer etablierte Automobilhersteller.
Die dritte Studie analysiert mittels Eye-Tracking Bedienmuster von Parkassistenz-systemen und leitet daraus Verbesserungspotenziale in Bezug auf intuitives Design und Nutzererfahrung ab.
Zum Vierten werden in einem Konzepttest alternative Szenarien für die Gestaltung von Parkassistenten evaluiert. Hierbei zeigt sich die Wichtigkeit einfacher und vertrauter Bedienungsmöglichkeiten.
Methodenübergreifend ist die entscheidende Rolle realer Erfahrungen für die kundenseitige Akzeptanz neuer Technologien zu konstatieren. Hersteller sollten daher verstärkt auf das Erlebnis ihrer Kunden setzen und diesen gezielte Nutzungsmöglich-keiten offerieren. Zudem ist die Integration von Kundenfeedback in die Entwicklungsbemühungen essentiell, um intuitive, nutzerfreundliche User Interfaces zu produzieren und auf diese Weise den Diffusionsprozess der neuen Technologien zu fördern.
Map matching is about finding the best route of a given track in a road network. This can be useful for many statistical analyses on mobility. With increasing spread of modern cars and mobile devices many tracks are available to match. The difficulty in map matching lies in the geospatial differences between tracks and road networks. Current technologies resolve such differences with Hidden Markov Models and Viterbi algorithm. They majorly vary concerning the used metrics for the probabilities in the models. In this research we improve map matching technology by refining the underlying algorithms, models and metrics. We will introduce Markov Decision Processes with Value Iteration and Q-Learning to the map matching domain and we will compare them to Hidden Markov Models with Viterbi algorithm. Markov Decision Processes allow to use active decisions and rewards, which are not available in previous methods. Combined with improvements concerning the preparation of tracks and the road network, and various technologies for improved processing speed, we will show on a publicly available map matching data set that our approach has a higher overall performance compared to previous map matching technology. We will eventually discuss more possibilities we enable with our approach.
Spatial Hypertext represents associations between chunks of information by spatial or visual attributes (such as proximity, color, shape etc.). This allows expressing information structures implicitly and in an intuitive way. However, automatic recognition of such informal, implicitly encoded structures by a machine (a so-called spatial parser) is still a challenge. One reason is, that conventional (non-adaptive) parsers are conceptually restricted by their underlying source of information (i. e., the spatial hypertext). Due to this limitation there are several types of structures that cannot be recognized properly. This inevitably limits both quality of parser output and parser performance. We claim that considering temporal aspects in addition to spatial and visual properties in spatial parser design will lead to significant increase in parsing accuracy, detection of richer structures and thus higher parser performance.
For the purpose of providing evidence, parsers for recognizing spatial, visual and temporal object relations have been implemented and tested in a series of user surveys. It turned out, that in none of the test cases pure spatial or visual parser could outperform the spatio-temporal parser. Instead, the spatiotemporal parser was able to compensate limitations of conventional parsers. Furthermore, differences in parsing accuracy were successfully tested for statistical significance. The results indicate a non-trivial effect that is recognizable by humans. We have shown that the addition of a temporal parser shifts machine detected structures significantly closer to what knowledge workers intend to express.