Evaluation of a User-adaptive Light-based Interior Concept for Supporting Mobile Office Work during Highly Automated Driving

Automated driving promises that users can devote their travel time to activities like relaxing or mobile office (MO) work. We present an interior light concept for supporting MO work and evaluate it in a driving simulator study with participants. A vehicle mock-up was equipped as MO including light elements for focus and ambient illumination. Based on these, an adaptive (i.e. adapting to user activities) and an adaptable (i.e. could be changed by user according to preference) light set-up were created and compared to a baseline version. Regarding user experience, the adaptive variant was rated best on hedonic aspects, while the adaptable variant scored highest on pragmatic facets. In addition, the adaptable set-up was ranked best on preference before adaptive and baseline. This suggest that adaption of the interior light to non-driving related activities improves user experience. Future studies should evaluate combinations of the adaptive and the adaptive variants tested here

Digitizing Travel Experience: Assessing, Modeling and Visualizing the Experiences of Travelers in Shared Mobility Services

During shared travel, humans regularly have negative experiences resulting from unmet needs in terms of safety, comfort, accessibility, efficiency, reliability or information. Frequent negative travel experiences motivate travelers to use private motorized transport instead of more sustainable, shared mobility services. It is difficult for shared transport providers to react to such negative experiences, as these mostly depend on individual needs and situational factors and can therefore rarely be counteracted with static one-size-fits-all solutions. Additionally, (real-time) information about a traveler’s experience is not (digitally) available to providers and thus a situation-adapted reaction is often not possible. Therefore, methods to assess travel experience and make travel experience digitally available are highly important for enabling means to render shared transport more attractive. Here, we present initial research on digitizing travel experience exemplified by an envisioned automated shuttle line

Towards User-Focused Automated Vehicles Supporting Mobile Office Work

Advances in the development of vehicle automation promise that humans may soon be relieved from the burden of manual driving at least during certain phases. For instance, humans in future vehicles may use the cockpit as mobile office when highway automation is activated, but be the driver in rural areas, when full automation is not available. Since mobile office workers have different needs than drivers, this imposes specific requirements on in-vehicle software and hardware. One option to meet these requirements is the development of user-focused automation that puts human needs into the centre of system design. Systems with user-focused automation derive the current needs of the occupants by combining user and context monitoring using various sensors in real time. Based on this, the system behaviour could be adapted by adjusting the interior lighting or the information on a human-machine interface. Here, we present the current status of the development of a driving-simulator-based demonstrator for the interior of an automated vehicle supporting mobile office work through user-focused automation. In a first driving simulator study, we developed a real-time capable classifier to estimate the user’s current activity (driving, relaxing or working) and stress level. Next, we evaluated how different concepts of interior lighting including spectrally-adaptable ambient lights and focused spot lights as well as changes in the navigation system can support the different activities. In a final study, the different components will be integrated and evaluated to demonstrate the potential of user-focused automation to support varying needs of users in automated vehicles

Activity Recognition and Stress Detection for Manual and Automated Driving

This work aims to explore methods to model physical and mental user states in an automated driving scenario. For this aim, a driving simulator study is conducted in which subjects are tasked to drive themselves or work on a secondary tasks while the car was driving automatically. On the basis of the findings from this study, a statistical classifier is constructed which aims to capture the passengers action state and level of stress. In this work, we use the full body model obtained from using openpose on our visual data as a baseline to asses the drivers’ joint locations. We further use a hierarchical approach to first derive semantically motivated primitive features, namely the position of the subjects’ hands and the subjects’ head rotation. These primitives are then used to classify poses yielding information on the passengers action state. Secondly, we explore strategies to model the subjects’ stress level using heart rate data. We present both a baseline approach depending solely on the passengers’ age and gender and a second approach, where we train a mixture model on previously gathered subject data. We find that using a hierarchical approach, we are able to reach classification accuracy of on average 75%-85% depending on the classifier used. We find that both heart rate approaches yield the same, explainable pattern. The topic of this work is part of a bigger project dealing with improving human-machine understanding and communication in automated driving. It aims at creating a framework for detecting and quantifying both the passengers physical and mental state. When possible and needed, adaptation strategies are then suggested based on the user state, the person’s user profile and the current state of the world, in order to provide a positive impact on the person’s current state

Facilitating participatory design for automotive interfaces by modeling user experience in real time.

With the ongoing integration of advanced driver assistance systems in vehicles, the requirements on efficient dashboard designs are changing rapidly. As such, participatory design strategies promise to be an important tool to rapidly generate and test prototypes by directly integrating end-users in the design process. An open problem of this approach is that the process of designing complex systems' interfaces may require many iterations thereby running the risk of being too time consuming and cognitively demanding for end-users. One potential solution for this is to facilitate iterative design testing by providing smart recommendations on design improvements throughout an interaction. Therefore, this PhD project aims to develop and evaluate an automatic user experience modeling paradigm for participatory interface design which aims to support end-users by providing suggestions for design improvements based on past interactions and support design researchers by generating insights from users' cognitive state and the users' design preferences

Activity and Stress Estimation Based on OpenPose and Electrocardiogram for User-Focused Level-4-Vehicles

Increasing vehicle automation changes the role of humans in the car, which imposes new requirements on the design of in-vehicle software and hardware for flexible interior concepts. An option to meet these requirements is the development of user-focused automation based on combined user and context monitoring in real time. The system behavior may be dynamically adapted by adjusting the driving style or the interior lighting. Here, we present a hierarchical approach on the basis of semantically motivated low-level features for activity and stress recognition based on OpenPose and electrocardiogram data. A driving simulator study with 29 participants was conducted to determine the potential of the approach. Participants had to accomplish different tasks: manual driving (MD); mobile office work with varying task load levels (high task load: MO-HT, low task load: MO-LT); and relaxing (REL) during automated driving. The validation revealed that our model is able to correctly distinguish between different activities using only a set of primitive features (average precision: driving: 76% and mobile office work: 93%, relaxing: 86%). Furthermore, we evaluated a person-independent and a person-specific approach for stress detection and found that both strategies show similar trends in accordance with our predictions (person-independent: stress detected in MO-HT: 22%, MO-LT: 18%, MD: 18%, REL: 15%; person-specific: stress detected in MO-HT: 79%, MO-LT: 72%, MD: 65%, and REL: 50%). These results demonstrate the efficacy of using a lightweight semantic approach for activity recognition and stress detection as basis for user-focused vehicle automation

Human-Centered Design and Evaluation of a Workplace for the Remote Assistance of Highly Automated Vehicles

Remotely operating vehicles utilizes the benefits of vehicle automation when fully automated driving is not yet possible. A human operator ensures safety and availability from afar and supports the vehicle automation when its capabilities are exceeded. The remote operator, conceptualized as remote assistant, fulfils the legal requirements in Germany as a Technical Supervisor to operate highly automated vehicles at SAE 4. To integrate the remote operator into the automated driving system, a novel user-centered human-machine interface (HMI) for a remote assistant’s workplace was developed and initially evaluated. The insights gained in this process were incorporated into the design of a workplace prototype for remote assistance. This prototype was tested in the study reported here by 34 participants meeting the professional background criteria for the role of Technical Supervisor according to the German law. Typical scenarios that may occur in highly automated driving and require remote assistance were created in a simulation environment. Even under elevated cognitive load induced by simultaneously engaging in a secondary task, participants were able to obtain sufficient situation awareness and quickly resolve the scenarios. The HMI also yielded favorable usability and acceptance ratings. The results of the study inform the iterative workplace development and further research on the remote assistance of highly automated vehicles

Thresholding functional connectomes by means of mixture modeling

Contains fulltext : 190103.pdf (publisher's version ) (Open Access

Modellierung der Ausprägung und Auswirkungen mentaler Belastungszustände von Fernassistenten für hochautomatisierte Fahrzeuge

Wechselnde mentale Belastung bei Mitarbeitenden der technischen Aufsicht für automatisierte Fahrzeuge kann zu Performanzeinbußen führen. Eine Erfassung der Arbeitslast während der Aufgabenbearbeitung auf Basis physiologischer Methoden kann helfen, Phasen von Überlast und Unterlast zu erkennen und kritischen Situationen entgegenzuwirken. Zur Untersuchung mentaler Last in der Leitstelle führten wir eine Simulatorstudie durch, in der die Teilnehmenden (insgesamt 41, 35 männlich, 6 weiblich, Durchschnittsalter: 25,1 Jahre) die Aufgabe einer Fernassistenz ("Remote Assistance") erfüllen mussten. Währenddessen wurden verschiedene Stufen mentaler Arbeitsbelastung (niedrig vs. hoch) durch eine auditive n-back Sekundäraufgabe induziert. Während des Experiments wurden physiologische und leistungsbezogene Indikatoren der mentalen Arbeitsbelastung erfasst. Die Ergebnisse zeigen, dass eine hohe Arbeitsbelastung die Leistung der Assistenten tatsächlich beeinträchtigt. Darüber hinaus konnten wir signifikante physiologische Effekte in Bezug auf die Hautleitfähigkeit, die Herzfrequenz und die Pupillengröße zwischen den Arbeitsbelastungsbedingungen feststellen. Mithilfe eines Gradient-Boosting-Modells und einer wiederholten Kreuzvalidierung (leave-one-subject-out) konnten wir den Grad der Arbeitsbelastung mit einer durchschnittlichen Area-under-the-curve von 0,67 über alle Teilnehmer klassifizieren. Zusammengenommen tragen diese Ergebnisse dazu bei, die Auswirkungen mentaler Arbeitsbelastung auf die Leistung von Fernassistenten zu verstehen und ebnen den Weg zur Entwicklung von Methoden zur Echtzeiterfassung von Arbeitsbelastung als Grundlage für adaptive Unterstützungssysteme