Eye tracking use in researching driver distraction: A scientometric and qualitative literature review approach

Many factors affect road safety, but research constantly shows that drivers are the major cause of critical situations that could potentially lead to a traffic accident in road traffic. Visual information is a crucial part of input information into the driving process; therefore, distractions of overt visual attention can potentially have a large impact on driving safety. Modern eye tracking technology enables researchers to gain precise insight into the direction and movement of a driver’s gaze during various distractions. As this is an evolving and currently very relevant field of road safety research, the present paper sets out to analyse the current state of the research field and the most relevant publications that use eye tracking for research of distractions to a driver’s visual attention. With the use of scientometrics and a qualitative review of the 139 identified publications that fit the inclusion criteria, the results revealed a currently expanding research field. The narrow research field is interdisciplinary in its core, as evidenced by the dispersion of publication sources and research variables. The main research gaps identified were performing research in real conditions, including a wider array of distractions, a larger number of participants, and increasing interdisciplinarity of the field with more author cooperation outside of their primary co-authorship networks

Real-time Human Workload Estimation and Its Application in Adaptive Haptic Shared Control

Automated vehicles (AVs) are promising to have the potential to reduce driving-related injuries and deaths. However, autonomous driving technology is currently limited in its scope and reliability, giving rise to the semi-autonomous driving model, where the autonomy and the human share the control of the vehicle. Workload, despite being an important human factor, has not yet been considered when designing adaptive shared control. Recently, researchers have started to apply machine learning techniques to classify mental workload into different levels. However, most of these studies have adopted either a single-model-single-feature approach or a single-model-all-features approach. However different machine learning models are suitable for different features, how to leverage different models for different features is critical. To address these shortcomings and research gaps, the goals of this dissertation were to (1) examine whether and to what extent haptic shared control performance can be improved by incorporating operators' workload; (2) develop a computational model for workload estimation, and the model should be able to leverage different machine learning models that work best for different features; and (3) investigate the generalizability of the workload estimation model. To address these research goals, this dissertation was composed of four research phases with two pilot studies and four human subject experiments. (1) Collaborating with Yifan Weng, Dr. Tulga Ersal, and Prof. Jeffrey Stein from the Department of Mechanical Engineering at the University of Michigan, we developed a teleoperated dual-task shared control simulation platform where the human shared control of a ground vehicle with autonomy while performing a surveillance task simultaneously. In addition, we developed a real-time eye-tracking system based on Tobii Pro Glasses 2 to measure the human gaze points in a world frame and pupil sizes. (2) We proposed a workload-adaptive haptic shared control scheme together with our collaborators. We conducted two human subject experiments during this phase. The results indicated that the proposed workload-adaptive haptic shared control scheme can reduce human workload, increase human trust in the system, increase driving performance, and reduce human effort without sacrificing surveillance task performance. (3) We proposed a Bayesian inference model for workload estimation that can leverage the different machine learning models that work best for different features. Specifically, we used support-vector machines (SVMs) for pupil size change, the Hidden Markov Model (HMM) for gaze trajectory, SVMs for fixation feature, and Gaussian Mixture Models (GMMs) for fixation trajectory. The empirical results indicated that our proposed model achieved a 0.82 F1 score for workload imposed by varying surveillance task urgency. (4) We investigated the generalizability of our proposed Bayesian inference model for workload estimation by conducting two human subject experiments with 24 participants and using different factors to impose human workload, i.e., obstacle headway and driving speed. The results indicated that our proposed model achieved a 0.68 F1 score for the workload imposed by obstacle avoidance and the personalized version of our proposed model can distinguish the workload imposed by different driving speeds under high surveillance task urgency.PHDRoboticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169623/1/ruikunl_1.pd

Automotive user interfaces for the support of non-driving-related activities

Driving a car has changed a lot since the first car was invented. Today, drivers do not only maneuver the car to their destination but also perform a multitude of additional activities in the car. This includes for instance activities related to assistive functions that are meant to increase driving safety and reduce the driver’s workload. However, since drivers spend a considerable amount of time in the car, they often want to perform non-driving-related activities as well. In particular, these activities are related to entertainment, communication, and productivity. The driver’s need for such activities has vastly increased, particularly due to the success of smart phones and other mobile devices. As long as the driver is in charge of performing the actual driving task, such activities can distract the driver and may result in severe accidents. Due to these special requirements of the driving environment, the driver ideally performs such activities by using appropriately designed in-vehicle systems. The challenge for such systems is to enable flexible and easily usable non-driving-related activities while maintaining and increasing driving safety at the same time. The main contribution of this thesis is a set of guidelines and exemplary concepts for automotive user interfaces that offer safe, diverse, and easy-to-use means to perform non-driving-related activities besides the regular driving tasks. Using empirical methods that are commonly used in human-computer interaction, we investigate various aspects of automotive user interfaces with the goal to support the design and development of future interfaces that facilitate non-driving-related activities. The first aspect is related to using physiological data in order to infer information about the driver’s workload. As a second aspect, we propose a multimodal interaction style to facilitate the interaction with multiple activities in the car. In addition, we introduce two concepts for the support of commonly used and demanded non-driving-related activities: For communication with the outside world, we investigate the driver’s needs with regard to sharing ride details with remote persons in order to increase driving safety. Finally, we present a concept of time-adjusted activities (e.g., entertainment and productivity) which enable the driver to make use of times where only little attention is required. Starting with manual, non-automated driving, we also consider the rise of automated driving modes.When cars were invented, they allowed the driver and potential passengers to get to a distant location. The only activities the driver was able and supposed to perform were related to maneuvering the vehicle, i.e., accelerate, decelerate, and steer the car. Today drivers perform many activities that go beyond these driving tasks. This includes for example activities related to driving assistance, location-based information and navigation, entertainment, communication, and productivity. To perform these activities, drivers use functions that are provided by in-vehicle information systems in the car. Many of these functions are meant to increase driving safety or to make the ride more enjoyable. The latter is important since people spend a considerable amount of time in their cars and want to perform similar activities like those to which they are accustomed to from using mobile devices. However, as long as the driver is responsible for driving, these activities can be distracting and pose driver, passengers, and the environment at risk. One goal for the development of automotive user interfaces is therefore to enable an easy and appropriate operation of in-vehicle systems such that driving tasks and non-driving-related activities can be performed easily and safely. The main contribution of this thesis is a set of guidelines and exemplary concepts for automotive user interfaces that offer safe, diverse, and easy-to-use means to perform also non-driving-related activities while driving. Using empirical methods that are commonly used in human-computer interaction, we approach various aspects of automotive user interfaces in order to support the design and development of future interfaces that also enable non-driving-related activities. Starting with manual, non-automated driving, we also consider the transition towards automated driving modes. As a first part, we look at the prerequisites that enable non-driving-related activities in the car. We propose guidelines for the design and development of automotive user interfaces that also support non-driving-related activities. This includes for instance rules on how to adapt or interrupt activities when the level of automation changes. To enable activities in the car, we propose a novel interaction concept that facilitates multimodal interaction in the car by combining speech interaction and touch gestures. Moreover, we reveal aspects on how to infer information about the driver's state (especially mental workload) by using physiological data. We conducted a real-world driving study to extract a data set with physiological and context data. This can help to better understand the driver state, to adapt interfaces to the driver and driving situations, and to adapt the route selection process. Second, we propose two concepts for supporting non-driving-related activities that are frequently used and demanded in the car. For telecommunication, we propose a concept to increase driving safety when communicating with the outside world. This concept enables the driver to share different types of information with remote parties. Thereby, the driver can choose between different levels of details ranging from abstract information such as ``Alice is driving right now'' up to sharing a video of the driving scene. We investigated the drivers' needs on the go and derived guidelines for the design of communication-related functions in the car through an online survey and in-depth interviews. As a second aspect, we present an approach to offer time-adjusted entertainment and productivity tasks to the driver. The idea is to allow time-adjusted tasks during periods where the demand for the driver's attention is low, for instance at traffic lights or during a highly automated ride. Findings from a web survey and a case study demonstrate the feasibility of this approach. With the findings of this thesis we envision to provide a basis for future research and development in the domain of automotive user interfaces and non-driving-related activities in the transition from manual driving to highly and fully automated driving.Als das Auto erfunden wurde, ermöglichte es den Insassen hauptsächlich, entfernte Orte zu erreichen. Die einzigen Tätigkeiten, die Fahrerinnen und Fahrer während der Fahrt erledigen konnten und sollten, bezogen sich auf die Steuerung des Fahrzeugs. Heute erledigen die Fahrerinnen und Fahrer diverse Tätigkeiten, die über die ursprünglichen Aufgaben hinausgehen und sich nicht unbedingt auf die eigentliche Fahraufgabe beziehen. Dies umfasst unter anderem die Bereiche Fahrerassistenz, standortbezogene Informationen und Navigation, Unterhaltung, Kommunikation und Produktivität. Informationssysteme im Fahrzeug stellen den Fahrerinnen und Fahrern Funktionen bereit, um diese Aufgaben auch während der Fahrt zu erledigen. Viele dieser Funktionen verbessern die Fahrsicherheit oder dienen dazu, die Fahrt angenehm zu gestalten. Letzteres wird immer wichtiger, da man inzwischen eine beträchtliche Zeit im Auto verbringt und dabei nicht mehr auf die Aktivitäten und Funktionen verzichten möchte, die man beispielsweise durch die Benutzung von Smartphone und Tablet gewöhnt ist. Solange der Fahrer selbst fahren muss, können solche Aktivitäten von der Fahrtätigkeit ablenken und eine Gefährdung für die Insassen oder die Umgebung darstellen. Ein Ziel bei der Entwicklung automobiler Benutzungsschnittstellen ist daher eine einfache, adäquate Bedienung solcher Systeme, damit Fahraufgabe und Nebentätigkeiten gut und vor allem sicher durchgeführt werden können. Der Hauptbeitrag dieser Arbeit umfasst einen Leitfaden und beispielhafte Konzepte für automobile Benutzungsschnittstellen, die eine sichere, abwechslungsreiche und einfache Durchführung von Tätigkeiten jenseits der eigentlichen Fahraufgabe ermöglichen. Basierend auf empirischen Methoden der Mensch-Computer-Interaktion stellen wir verschiedene Lösungen vor, die die Entwicklung und Gestaltung solcher Benutzungsschnittstellen unterstützen. Ausgehend von der heute üblichen nicht automatisierten Fahrt betrachten wir dabei auch Aspekte des automatisierten Fahrens. Zunächst betrachten wir die notwendigen Voraussetzungen, um Tätigkeiten jenseits der Fahraufgabe zu ermöglichen. Wir stellen dazu einen Leitfaden vor, der die Gestaltung und Entwicklung von automobilen Benutzungsschnittstellen unterstützt, die das Durchführen von Nebenaufgaben erlauben. Dies umfasst zum Beispiel Hinweise, wie Aktivitäten angepasst oder unterbrochen werden können, wenn sich der Automatisierungsgrad während der Fahrt ändert. Um Aktivitäten im Auto zu unterstützen, stellen wir ein neuartiges Interaktionskonzept vor, das eine multimodale Interaktion im Fahrzeug mit Sprachbefehlen und Touch-Gesten ermöglicht. Für automatisierte Fahrzeugsysteme und zur Anpassung der Interaktionsmöglichkeiten an die Fahrsituation stellt der Fahrerzustand (insbesondere die mentale Belastung) eine wichtige Information dar. Durch eine Fahrstudie im realen Straßenverkehr haben wir einen Datensatz generiert, der physiologische Daten und Kontextinformationen umfasst und damit Rückschlüsse auf den Fahrerzustand ermöglicht. Mit diesen Informationen über Fahrerinnen und Fahrer wird es möglich, den Fahrerzustand besser zu verstehen, Benutzungsschnittstellen an die aktuelle Fahrsituation anzupassen und die Routenwahl anzupassen. Außerdem stellen wir zwei konkrete Konzepte zur Unterstützung von Nebentätigkeiten vor, die schon heute regelmäßig bei der Fahrt getätigt oder verlangt werden. Im Bereich der Telekommunikation stellen wir dazu ein Konzept vor, das die Fahrsicherheit beim Kommunizieren mit Personen außerhalb des Autos erhöht. Das Konzept erlaubt es dem Fahrer, unterschiedliche Arten von Kontextinformationen mit Kommunikationspartnern zu teilen. Dies reicht von der abstrakten Information, dass man derzeit im Auto unterwegs ist bis hin zum Teilen eines Live-Videos der aktuellen Fahrsituation. Diesbezüglich haben wir über eine Web-Umfrage und detaillierte Interviews die Bedürfnisse der Nutzer(innen) erhoben und ausgewertet. Zudem stellen wir ein prototypisches Konzept sowie Richtlinien vor, wie künftige Kommunikationsaufgaben im Fahrzeug gestaltet werden sollen. Als ein zweites Konzept betrachten wir zeitbeschränkte Aufgaben zur Unterhaltung und Produktivität im Fahrzeug. Die Idee ist hier, zeitlich begrenzte Aufgaben in Zeiten niedriger Belastung zuzulassen, wie zum Beispiel beim Warten an einer Ampel oder während einer hochautomatisierten (Teil-) Fahrt. Ergebnisse aus einer Web-Umfrage und einer Fallstudie zeigen die Machbarkeit dieses Ansatzes auf. Mit den Ergebnissen dieser Arbeit soll eine Basis für künftige Forschung und Entwicklung gelegt werden, um im Bereich automobiler Benutzungsschnittstellen insbesondere nicht-fahr-bezogene Aufgaben im Übergang zwischen manuellem Fahren und einer hochautomatisierten Autofahrt zu unterstützen

Proceedings experiencing light 2009 : international conference on the effects of light on welbeing

no abstrac

Proceedings experiencing light 2009 : international conference on the effects of light on welbeing

no abstrac

Engineering data compendium. Human perception and performance. User's guide

The concept underlying the Engineering Data Compendium was the product of a research and development program (Integrated Perceptual Information for Designers project) aimed at facilitating the application of basic research findings in human performance to the design and military crew systems. The principal objective was to develop a workable strategy for: (1) identifying and distilling information of potential value to system design from the existing research literature, and (2) presenting this technical information in a way that would aid its accessibility, interpretability, and applicability by systems designers. The present four volumes of the Engineering Data Compendium represent the first implementation of this strategy. This is the first volume, the User's Guide, containing a description of the program and instructions for its use

Change blindness: eradication of gestalt strategies

Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task