Eugen Altendorf

Uncanny and Unsafe Valley of Assistance and Automation: First Sketch and Application to Vehicle Automation

Progress in sensors, computer power and increasing connectivity allow to build and operate more and more powerful assistance and automation systems, e.g. in aviation, cars and manufacturing. Besides many benefits, new problems occur e.g. in human-machine-interaction. In the field of automation, e.g. vehicle automation, a comparable, metaphorical design correlation is implied, an unsafe valley e.g. between partially- and highly-automated automation levels, in which due to misperceptions a loss of safety could occur. This contribution sketches the concept of the (uncanny and) unsafe valley of automation, summarizes early affirmative studies, gives first hints towards an explanation of the valley, outlines the design space how to secure the borders of the valley, and how to bridge the valley.

A New Method and Results for Analyzing Decision-Making Processes in Automated Driving on Highways

While automated driving and advanced drivers’ assistant systems (ADAS) become increasingly widespread, the human machine interaction for these technologies gains in importance. In today’s traffic, some vehicles are capable of driving partially, conditionally or highly automated, at least in certain traffic situations, such as driving on developed highways. Nevertheless, these technologically advanced systems are not the only participants in traffic. With the interplay of more or less technologically advanced vehicles and humans on bikes and on foot, complex situations can arise that exceed the capabilities of an automated system and requires human cognition as a part of the solution. Although ADAS and automation solutions take this into account and try to compensate for the resulting effects, encounters with ambiguous situations can emerge. Furthermore, automation systems heavily rely on sensors and are therefore vulnerable to ambient conditions and situations that might limit the performance of the used sensor technology. For this reason, the (human) driver is still required for supervising the situation and often also as a fallback level in the case the technical system reaches or exceeds its performance restrictions. Guiding a vehicle, such as a car with partial or conditional automation, entails a different kind of driver vehicle interaction and cooperation between driver and automation as the one that is needed in the case of manual driving. For analyzing the decision making process of a human-machine-system with such an advanced automation during a typical driving situation like an takeover situation on a highway, a study addressing partially and conditionally/highly automated driving was conducted. The experiment with 30 participants consisted of three rounds with varying conditions in the driving simulator. During and after each round, participants were asked to answer several questions. For this purpose, a questionnaire has been developed to measure the relevant dimensions of the investigated driving situation. These were perceived utility, perceived time consumption, perceived safety, user satisfaction, perceived usability, and perceived dominance (control over the vehicle guidance). The evaluation of the driving experiment shows that the level of automation as well as the volume of traffic have a significant effect on the decision-making behavior and on the individual perception when driving on a highway. This means that during automated driving, humans perceive and judge the driving situation differently. As a consequence, they tend to use the remaining decision authority for other purposes than when driving manually.

Joint Decision Making and Cooperative Driver-Vehicle Interaction during Critical Driving Situations

Abstract In automated driving, the human driver and an automation form a joint human-machine system. In this system, each partner has her own individual cognitive as well as perceptual processes, which enable them to perform the complex task of driving. On different layers of the driving task, both, drivers and automation systems, assess the situation and derive action decisions. Although the processes can be divided between human and machine, and are sometimes very elaborate, the outcome should be a joint one because it affects the entire driver-vehicle system. In this paper, the individual processes for decision-making are defined and a framework for joint decision-making is proposed. Joint decision-making relies on common goals and norms of the two subsystems, human and automation, and evolves with experience.

Derivation of a Model of Safety Critical Transitions between Driver and Vehicle in Automated Driving

In automated driving, there is the risk that users must take over the vehicle guidance despite a potential lack of involvement in the driving task. This publication presents an initial model of control distribution between users and the automated system. In this model, the elements of the control distribution in automated driving are addressed together with possible and safe transitions between different driving modes. Furthermore, the approach is initially empirically validated. In a driving study, in which participants operated both driving and a non-driving related task, objective driving data as well as eye-tracking parameters are used to estimate the model’s accuracy. Such an explanatory model can serve as a first approach to describe potential concepts of cooperation between users and automated vehicles. In this way, prospective road traffic concepts could be improved by preventing safety critical transitions between the driver and the vehicle.

Learning from the Best – Naturalistic Arbitration for Cooperative Driving

In cooperative automated driving, the task of lateral and longitudinal vehicle control can be shared by driver and automation. However, conflicting action intentions of the two partners could arise, which need to be resolved within limited time. This can be achieved through structured multimodal negotiation, called arbitration. In order to explore intuitive interaction patterns for arbitration situations, insights from human-human interaction might be transferred. Accordingly, in a field study, couples holding hands or walking arm in arm were videotaped and interviewed when a conflict concerning motion control has been observed. The analysis of the data shows that conflict situations concerning velocity and/or direction of movement occur in natural human-human interaction and that these types of conflict can be dependent on each other. Furthermore, partners use different interaction resources to successfully solve these situations. Results are transferred to cooperative automated driving and an example of an interaction pattern is presented.

Safety-Enhancing Locating Wearables on Passenger Ships: Privacy and Security Perceptions by the Elderly

Wearables are intimate solutions for a variety of purposes and could enhance safety on large passenger ships in cases of evacuations. Today’s cruise ships offer capacities of up to 8000 passengers. From a technological point of view, wearables offer support for electronic mustering and more efficient possibilities to search for passengers. However, privacy and security perceptions of wearables have so far remained unclear for safety-critical areas. Moreover, the population on large passenger ships is characterized by a relatively high average age. Therefore, we investigated the results of a survey with 2085 passengers for the relationships between demographic data and privacy and security perceptions. Additionally, we explored potential influences of personal attitudes. Evidence was found that privacy concern and perceived security risk are influenced by age but not by gender. Interestingly, the effect of age on both variables is negative and stronger for security than for privacy perceptions. The individual need for safety contributes to explain both variables significantly. In conclusion, privacy concern and perceived security risk decrease with increasing age and need for safety.

Cooperation and the Role of Autonomy in Automated Driving

While automation in human–machine systems can increase safety and comfort, the 2016 lethal crash of an automated vehicle demonstrates that automation is not without its risk. Indeed, accidents from the air traffic domain also demonstrate that cooperation between human and machine is crucial and interaction design must be devoted great care. Therefore, the present paper aims at developing design recommendations to reduce the risks of lethal crashes with automated vehicles. To this end, the concepts of cooperation and autonomy are closely investigated. These two terms are central to research on human machine cooperation; however, the present definition of cooperation and the role of autonomy might be further specified for the domain of automated driving. Therefore, selected perspectives from different scientific fields (e.g., sociology and psychology) will be presented in order to develop a differentially inspired working definition of cooperation, which is tailored to the automated driving domain. Another goal of this approach is to investigate different views on the concept of autonomy, which is often entailed in work on cooperation. This can help clarify the role of autonomy in automated driving in particular. Moreover, insights from the presented theories and findings on cooperation can be transferred to the interaction design of automated vehicles. Accordingly, recommendations for interaction design will be presented. Finally, an example for the implementation of the working definition and the design recommendations will be presented by describing a prototype for automated driving—the H-mode prototype.

Gesture-Based Vehicle Control in Partially and Highly Automated Driving for Impaired and Non-impaired Vehicle Operators: A Pilot Study.

A concept for shared and cooperative guidance and control based on the H-Metaphor is developed, implemented and presented in this paper. In addition, a pilot study with a small user group conducted in a static driving simulator is discussed. The concept enables communication between an automated vehicle and the driver, who is requested to take over driving in a conditional automated driving mode. The request is communicated to the driver by tactile feedback in a sidestick, which is used for control of the automated vehicle. Two different ways of take over request are investigated and later compared in a survey for “Perceived Utility”, “Perceived Safety”, “User Satisfaction” and “Perceived Usability”. The study is a pilot study for investigating interaction paradigms that are suitable in automated vehicles used by impaired people, which frequently are operated by joysticks. The outcomes of the study are used as a basis for further research.

A Study on the Human and the Automation in Automated Driving: Getting to Know Each Other

In recent years, advanced driver assistant systems (ADAS) and solutions for automated driving have been introduced by several automotive original equipment manufacturers (OEMs) and suppliers. Currently, these types of automation are designed for partially automated driving, but the step towards higher levels of automation can be expected to be made soon. One of the most commonly addressed use cases is driving on a highway such as the German Autobahn. In this paper, we propose an approach for adapting the automation’s behavior to the human’s driving preferences, providing a cognitive automation system with a machine-learning algorithm. This system has been implemented in a simulator for automated driving and has been used in a study addressing conditional automation. Within the presented experiment, typical situations for automated driving under varying conditions have been tested in the driving simulator. During cooperative human-machine driving, the automation can learn the human’s preferences regarding relevant driving states.

Das (unheimliche und) unsichere Tal der Assistenz und Automation - Beschreibung und Absicherungsmöglichkeiten

Zusammenfassung Fortschritte in der Automatisierungstechnik ermöglichen es technischen Systemen immer mehr Aufgaben zu übernehmen. Für die Gestaltung der Interaktion und Kooperation von Mensch und Maschine ist die Einschätzung des Menschen bzgl. der Fähigkeiten der Maschine sicherheitsrelevant. Das Uncanny Valley der Robotik beschreibt, wie Roboter mit hoher, aber nicht perfekter Menschenähnlichkeit als unheimlich wahrgenommen werden. Bestehende Studien, z. B. für Luft- und Bodenfahrzeuge, deuten an, dass es einen ähnlichen Zusammenhang zwischen Automationsgrad und Sicherheit geben könnte, da sich zwischen gut funktionierenden teil- und hochautomatisierten Modi ein unsicheres Tal bildet, in welcher die Sicherheit des Systems stark reduziert ist. Es werden Gestaltungsoptionen beschrieben, um diesen Bereich abzusichern.