Objective: This dissertation approaches the improvement of human-automation collaboration for the transition in highly automated driving. The objective is to identify suitable measurements and methods to improve the collaboration between human and automation. It aims at answering the question how the collaboration between human and automation during the transition in highly automated driving can be improved. The focus lies on cognitive processes to build up situation awareness during a takeover from automated to manual control. Factors that have to be considered when developing assistance systems are identified to develop assistance systems that consider the cognitive state of the driver and the situation on hand. Furthermore, this dissertation aims at providing an answer to how cognitive modeling can be combined with eye movements and the dynamic influence of the situation to understand the cognitive state of the individual driver at every point of the takeover. Background: The field of highly automated driving is developing rapidly and plays a central role in current automotive research. Especially the takeover from automated to manual control represents a crucial and safety relevant process. Different approaches towards investigating the takeover in highly automated driving exist. However, these are not comparable as they differ in their setup and do not understand the underlying cognitive processes. Up to now, research and industry aim at developing adaptive assistance systems for highly automated driving, but the cognitive state of the driver is not included into current approaches. In order to increase safety and comfort during takeover situations, adaptive assistance systems have to be developed that include the current cognitive state of the driver. Method: To approach the above described research questions, a cognitive model is developed that represents individual differences in dynamic traffic situations of different complexities. The developed cognitive model is based on results of a real traffic study. The real traffic study provides information about the behavior of different drivers during takeover situations in real traffic. To investigate the observed factors that are relevant during a takeover, a simulator study is performed. This study provides an insight about factors that have to be regarded when developing assistance systems. Furthermore, the cognitive model is validated using results of the simulator study. Results: Results show that the factors objective complexity, subjective complexity and familiarity with a situation have to be considered when developing assistance systems for highly automated driving. These factors have a significant impact on the time to make an action decision and the takeover quality. The underlying cognitive dynamics of such takeover situations are represented in the cognitive model of this dissertation. The cognitive model is able to represent different driver types in different traffic situations. As the visual perception is the most important sense in driving, the model focuses on representing the visual perception to build up situation awareness accurately. The model is able to display eye movement patterns in takeover situations that are similar to those measured in the simulator study. The different cognitive and visual steps to build up situation awareness in takeover situations can be predicted by the model in situations of different traffic complexity. Conclusion: In the development of future assistance systems for highly automated driving, it is highly important to regard the in this dissertation investigated factors. Furthermore, it is important to integrate the new understanding of underlying cognitive factors during takeover situations into such assistance systems. The designed cognitive model of this dissertation provides this understanding of cognitive processes during the takeover, including the relevant parameters complexity and familiarity with a situation. Application: Based on the identified factors and the developed cognitive model, assistance systems for highly automated driving can be adapted individually. By combining eye movements and the dynamic influence of the situation with predictions of the cognitive model, the cognitive state of the driver can be understood at every point of the takeover. Such an understanding of cognitive processes to build up situation awareness in takeover situations enables a more detailed development of assistance systems at every point of the takeover process. This enhances the collaboration between human and automation in takeover situations and increases safety and comfort in highly automated driving.


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    Titel :

    A cognitive model for the takeover in highly automated driving representing individual differences based on complexity and familiarity.
    empirisch validiert mit Augenbewegungen und Entscheidungszeiten


    Untertitel :

    empirically validated using eye-movement and decision time


    Weitere Titelangaben:

    Ein kognitives Modell für die Übernahme im hochautomatisierten Fahren, das individuelle Unterschiede basierend auf Komplexität und Vertrautheit repräsentiert


    Beteiligte:

    Erscheinungsdatum :

    2021



    Medientyp :

    Sonstige


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Klassifikation :

    DDC:    629



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