Kooperative, manöverbasierte Automation und Arbitrierung als Bausteine für hochautomatisiertes Fahren

Bei der Entwicklung von hochautomatisierten Fahrzeugen ist eine intuitive Bedienbarkeit für den Fahrer von entscheidender Bedeutung. Die kooperative Kontrolle („Cooperative Control“) stellt ein viel versprechendes Konzept dar, wie eine Automation in einem hochautomatisierten Fahrzeug gestaltet werden kann, so dass eine gewinnbringende Zusammenarbeit zwischen Automation und Fahrer möglich wird. Nach dem Aufzeigen von Anforderungen wird eine konkrete Ausgestaltung einer solchen kooperativen Automation vorgestellt. Die Arbitrierung bietet eine Strategie zur Abstimmung über auszuführende Handlungen zwischen Fahrer und kooperativer Automation. Die Umsetzbarkeit der Konzepte wird am Beispiel eines prototypischen Systems zum hochautomatisierten Fahren mit integrierter Längs- und Querführung gezeigt

The theater-system technique: agile designing and testing of system behavior and interaction, applied to highly automated vehicles

In this paper, the theater-system technique, a method for agile designing and testing of system behavior and interaction concepts is described. The technique is based on the Wizard-of-Oz approach, originally used for emulating automated speech recognition, and is extended towards an interactive, user-centered design technique. The paper describes the design process using the theater-system technique, the technical build-up of the theater-system, and an application of the technique: the design of a haptic-multimodal interaction strategy for highly automated vehicles. The use of the theater-system in the design process is manifold: It is used for the concrete design work of the design team, for the assessment of user expectations as well as for early usability assessments, extending the principles of user-centered design towards a dynamically balanced design

Knowledge Augmented Machine Learning with Applications in Autonomous Driving: A Survey

The existence of representative datasets is a prerequisite of many successful artificial intelligence and machine learning models. However, the subsequent application of these models often involves scenarios that are inadequately represented in the data used for training. The reasons for this are manifold and range from time and cost constraints to ethical considerations. As a consequence, the reliable use of these models, especially in safety-critical applications, is a huge challenge. Leveraging additional, already existing sources of knowledge is key to overcome the limitations of purely data-driven approaches, and eventually to increase the generalization capability of these models. Furthermore, predictions that conform with knowledge are crucial for making trustworthy and safe decisions even in underrepresented scenarios. This work provides an overview of existing techniques and methods in the literature that combine data-based models with existing knowledge. The identified approaches are structured according to the categories integration, extraction and conformity. Special attention is given to applications in the field of autonomous driving

Zur Entscheidungskonvergenz in kognitiven Systemen

Das Ziel dieses Beitrags ist, eine Methode für die Analyse der Entscheidungsvorgänge innerhalb kognitiver Systeme vorzustellen. Diese Methode ist ein Teil eines Frameworks für die ganzheitliche Gestaltung dynamischer kognitiver Systeme aus dem Bereich hochautomatisierter Fahrzeugführung. Sie basiert auf der Hypothese, dass Entscheidungen in einem kognitiven System kein Ergebnis einer expliziten Berechnung in einem speziell dafür ausgewiesenen Modul sind, sondern in einem Prozess der Selbstorganisation der Systemelemente generiert werden können. Zur Unterstützung dieser Hypothese ist in diesem Beitrag ein skalierbares Modell der kognitiven Systeme präsentiert, das sich mithilfe der Multiagentenund der Graphentheorie mathematisieren und analysieren lässt. Am generischen Beispiel eines Entscheidungsvorgangs beim Führen eines hochautomatisierten Fahrzeuges ist dieses Modell erläutert. Das Modell und die o.g. Methode sind auf ihre grundsätzliche Anwendbarkeit für die Gestaltung und Analyse kognitiver Systeme mittels einiger bekannter Ansätze aus der Gestaltung der geteilten Fahrzeugführung evaluiert

Arbitration between Driver and Automation:why overriding is just the tip of the iceberg

In the field of driver-automation interaction, there is an imperative of overriding strategies. Every driver shall at all times be able to control his vehicle (Vienna Convention 1968). This presentation sketches the principles and the methods for the cooperative human-machine system design starting from the overriding strategies going over arbitration concept to design patterns for human-machine arbitration. It deals with the notation of system behavior and interaction in parallel, with behavior influence strategies for arbitration and with the necessary semantics and kinds of communication between human and machine. At least it deals with empiric studies where some arbitration stratigies were testes in simulator driving studies as well as in a test vehicle

Automation spectrum, inner / outer compatibility and other potentially useful human factors concepts for assistance and automation

Enabled by scientific, technological and societal progress, and pulled by human demands, more and more aspects of our life can be assisted or automated. One example is the transportation domain, where in the sky commercial aircraft are highly automated, and on the roads a gradual revolution takes place towards assisted, highly automated or fully automated cars and trucks. Assistance and automation can have benefits such as higher safety, lower workload, or a fascination of use. Assistance and automation can also come with downsides, especially regarding the interplay between human and technology (e.g., Bainbridge, 1983; Billings, 1997; Norman, 1990; Sarter and Woods, 1995a). In parallel to the technological progress, the science of human factors has to be continuously developed such that it can help to handle the technological complexity without adding new complexity (e.g., Hollnagel, 2007). In this overview article, some fundamental human factors issues for assistance and automation that the authors found useful in their daily work are briefly sketched. Some examples are described how those concepts could be used in the development of assistance and automation systems. While the article deals especially with assistance and automation in vehicles, the underlying concepts might also be useful in other domains

Driver Monitor and Feedback Dispatcher in SPARC

In context of the EU-project SPARC [1], a comprehensive driver support concept was developed. At first, the actual vehicle behaviour is compared with reference vehicle behaviour, generated by the virtual co-pilot [2]. In case of any deviation the driver will be supported depended on his current condition. With a ‘Driver Monitor’ the system determines to what extent the driver is involved into the actual vehicle guidance. This support is generated by a software module ‘Feedback Dispatcher’ and transmitted as multimodal feedback to the driver

Use Case Design for AdaptIVe

AdaptIVe is a large scale European project on vehicle automation and the pertaining human-machine interaction. The use case design process is a crucial part of the system design process and a part of the human-vehicle integration subproject. This paper explains the methodology for describing use cases in AdaptIVe. They are primarily based on sequence diagrams with main and alternative flows