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While full automation of road vehicles remains a future goal, shared-control and semi-autonomous driving – involving transitions of control between the human and the machine – are more feasible objectives in the near term. These alternative driving modes will benefit from new research towards novel steering control devices, more suitable where the machine intelligence controls only partially the vehicle. In this paper it is proposed that when the human shares the control of a vehicle with an autonomous or semi-autonomous system, a force control or non-displacement steering wheel (i.e., a steering wheel which does not rotate but detects the applied torque by the human driver) can be advantageous under certain schemes: tight rein or loose rein modes according to the Hmetaphor. We support this proposition with the first experiments, to the best of our knowledge, in which human participants drove in a simulated road scene with a force control steering wheel. The experiments exhibited that humans can adapt promptly to force control steering and are able to control the vehicle smoothly. Different transfer functions are tested, which translate the applied torque at the force control steering wheel to the steering angle at the wheels of the vehicle; it is shown that fractional order transfer functions increment steering stability and control accuracy when using a force control device. Transition of control experiments are also performed with both, a conventional and a force control steering wheel. This prototypical steering system can be realized via steer-by-wire controls, which are already incorporated in commercially available vehicles.

The eight papers in this special issue focus on agent and system transparency in human-machine systems. The concept of transparency is investigated in a variety of contexts of human agent interaction—from a single robot to multiple heterogeneous agents and swarms. Two studies examine the effects of individual and cultural differences and, based on the compelling results, provide design recommendations related to transparent interfaces. One of the papers provides a thorough review on theoretical aspects of agent transparency and empirical findings on operator performance, situation awareness, trust and workload, among other outcomes. These measures are also among some of the most common metrics reported in studies in this special issue.

Automated driving puts severe challenges on the design and testing of automotive user interfaces. In partially automated driving, the driver is still responsible for the vehicle control, but is strongly supported by technology. In highly automated driving, the driver can give control to the automation for a certain time and can get control back e.g., when the automation encounters limits. Despite great technological progress, a truly intuitive way to interact with these automated driving modes is still under research. Project Vorreiter is addressing this by using the inspiration of a rider and a horse to provide intuitive steering gestures on the steering wheel or an alternative device, which initiate maneuvers executed by the automation. These can be supervised, influenced or interrupted by the driver. The gestures are built up in a universal design approach, which helps all drivers, including beginners and drivers with disabilities. After an introduction into the overall philosophy and concept, the contribution focuses on a final step in the project, an overall evaluation of the concept in a driving simulator and presents new data especially on the comparison of swiping gestures and pushing gestures regarding false or true positive or negative detected gestures. Finally, a brief outlook sketches next steps with a new Wizard-of-Oz / theater vehicle.

Human factors in transportation : proceedings of 13th AHFE International Conference on Human Factors in Transportation, New York, USA, July 24-28, 2022 / Editors: Katie Plant, Gesa Praetorius 13. AHFE International Conference on Human Factors in Transportation, New York, NY, USA, 24 Jul 2022 - 28 Jul 2022 13. International Conference on Applied Human Factors and Ergonomics, AHFE 2022, New York, USA, 24 Jul 2022 - 28 Jul 2022; New York, United States of America : AHFE International, AHFE international 60, Advances in transportation 501-507 (2022). doi:10.54941/ahfe1002483, Published by AHFE International, New York, United States of America

New technologies offer new potentials to tackle everyday challenges, but this has only a chance to become true if technology is intelligently integrated with humans, organizations and environment. A good balance between different stakeholders and tension fields is one of the most challenging research questions of Human Systems Integration. Especially in the field of energy, where huge pulls from en-vironmental and societal demands into different directions meet technological pushes from new energy sources and methods. First, a short introduction to the electricity market in Europe is given. Afterwards, the relations between different systems are modelled into a system of systems, fo-cusing on the electricity offering and electricity demanding systems. Relevant sys-tem qualities are analysed. The paper continues developing the system dynamics model and brings the subsystems into relation with each other. It concludes by testing the system dynamics model with alternative approaches in the transfor-mation of the European energy system.

Technological progress in form of machine intelligence increases the capabilities of machines to think and act autonomously on the world. This might only result in human and societal progress if these technical systems are intelligently integrated with humans, organizations and the environment, i.e. with intelligent Human Systems Integration.One key paradigm to enable this is human machine cooperation: The ability of agents to work together (e.g. Hoc & Lemoine 1997ff). An example for this is the relationship of a rider and a horse, which has been successfully translated to technical systems in form of shared and cooperative control (e.g. Flemisch et al. 2003ff), and as partially and highly automated, cooperatively interacting systems e.g. in the car domain (e.g. Stiller et al. 20019ff). Another paradigm is that of human machine symbiosis, in which two different species, here humans and machines, have a close and long term relationship, usually for the mutual benefit. This paradigm has been exploited e.g. for symbiotic driving schemes in the car domain (Abbink et al.2010ff). An extreme example of symbiosis that of a centaur, a creature from Greek mythology with a horse body and a human head. Applied to technology this metaphor is discussed for human-computer teams e.g. in chess competitions (e.g. Huffpost 2014) and in NATO for human-machine teaming (SCI 2020). Rider-horse and centaur stand for two different kinds of symbiotic relationships: A non-mandatory, reversible symbiosis like the rider and horse. Applied to humans and machines these are systems, where the humans can still do tasks without the support of the machines. A centaur is a non-reversable, mandatory symbiosis, where none of the symbionts can live and act without the other symbiont. The article will present that state of the art of cooperation, and of symbiotic human machine systems. It will structure and cluster symbiosis especially from a system and functions perspective, and make the differences visible with striking examples from the world of smart phones, automated cars and highly automated defense systems. Finally, the contribution will discuss what the desired kind of symbiotic relationship we as humans want and should strive for in intelligent human systems integration.

It appears that autonomous systems are replacing human in the driving task. However, autonomous driving abilities do not mean that vehicles should not interact with their drivers/passengers or their environment anymore. There are still many scenarios where either the automated system cannot handle the driving very well, or human wants to spontaneously influence the behavior of the system to meet their preferences. Thus, beyond the hype of autonomous driving, a large space opens for human-vehicle cooperation at a different level of automated driving. As this topic draws more attention both by academia and industry, we organize this workshop to in-depth identify potential research opportunities of it under the latest technology of automated driving. In this workshop, participants will discuss the motivations of driver/passenger’s intervention, generate the use cases of cooperative driving, and explore means of cooperation and interaction that human and vehicle would exchange intent smoothly. It is expected that the workshop will consolidate existing knowledge of human-vehicle-environment cooperation and provide insight for future works.

Currently, car manufacturers, suppliers, and IT companies are surpassing each other with ambitious plans regarding their driving automation technology. However, even the most optimistic announcements grant that, for a certain time, a human driver cannot be replaced in all driving situations. Hence, human drivers will still be a part of future traffic by working together with automation systems. Analyzing the joint decision-making process of such a human–machine system in automated driving provides a better understanding of the resulting traffic system. In this paper, a driving simulator study with 33 participants focusing on the utility of cooperative driver–vehicle systems with the use case of highway driving is presented. Based on the study’s results, a model that explains the linkage between subjective measures such as the perceived utility and objective driving data is derived. Moreover, on an individual level, models are parameterized by using driving states as predictors and the individual utility perceived in a driving situation as response. This individual utility can be used for predicting driving actions such as the initiation of overtaking maneuvers.

Cognition, technology & work : CTW 21(4), 555-568 (2019). doi:10.1007/s10111-019-00576-1 special issue: "Special Issue: Shared and Cooperative Control", Published by Springer, London

Humans sometimes struggle when making decisions, because what they want to do in a specific moment can differ from what they feel they should do in general. This phenomenon can also be found in situations of human–machine interaction. In order to support humans in making decisions about their behavior, a new form of support is proposed, which is especially suitable for human–machine interaction: self-determined decision-making with nudging methods (or shortly: self-determined nudging). In this concept, firstly the aspirations of the human are assessed and then supporting mechanisms are offered to guide humans towards their self-set goals. With this procedure, machines can for example support humans in driving safely or economically, help them refraining from scheduling other appointments in their gym-timeslots or push them towards going to bed on time. While originally nudging is based on libertarian paternalism, the concept of self-determined nudging enables the person to decide which goals to get nudged towards. By different examples, it is shown that nudging ideas are already present in numerous technical applications. Then, it is demonstrated how the aspect of self-determination can enrich these approaches. Moreover, already existing as well as potential new implementations of self-determined nudging in the automotive domain are described. As an outlook, the set-up of a study on automated driving is presented.

Current technological achievements and trends show that in a not too far future vehicles will become able to drive with highest levels of automation in different environments all over the world. Until then still many questions need to be solved. Such questions are how to get the driver back into the loop when problematic situations happen that the automation is unable to solve. Others ask the question how to involve drivers in the driving tasks if they want to drive partially automated. Such questions require answers in how cooperation between a machine in terms of a highly automated technical system and a human works and how tasks can be shared. This paper presents an approach how to improve the cooperation between two actors in different domains using interaction patterns. Interaction patterns can be applied for several use cases of cooperative movement, e.g. parent–child, teammates in sports and in highly automated driving. This paper presents an approach how interaction problems sourced in different use cases can be solved with interaction patterns. Inspired by linguistics and psychology, image schemas are used for technical design to structure and improve intuitiveness of these interaction patterns. The concept combines the approaches of design patterns and image schemas to create interactions that are grounded in our bodily experiences and can be applied to target-specific meaning necessary for human machine cooperation, e.g. in cooperative guidance and control of highly automated vehicles.

Robotic assistants will play an important role in future space exploration activities. In order to provide an optimal human-robot cooperation and interaction, the interface between user and machine requires an intuitive design. In this paper an interface evaluation methodology is presented as the synthesis of existing approaches in space projects and general recommendations by Human Factors Engineering. The MARVIN project of the European Space Agency (ESA) serves as a test case for this methodology. As a subsequent step to the evaluation results, the relevance of design improvements is derived from specific guidelines for robotics and from experiences in Earth exploration systems with humans involved.

In 2019, autonomy and autonomous cars are still heavily debated and investigated, but might be at the peak point of public attention, which might ramp down in the next years without diminishing the need to develop safe autonomous capabilities. Thinking beyond autonomy, the research and development paradigm of human autonomy teaming, here cooperativity and cooperatively interacting vehicles makes good use of autonomous capabilities and functions and puts these into the service of a cooperative system of systems. In this system, automobiles are cooperating with each other, with other traffic participants and with drivers and passengers on board. This article focuses on one aspect of system cooperation, on the cooperation between the vehicle and inhabitants in normal operations, at the limits of the automation, and at system failure. Modes, transitions, and especially the take-over ability are identified as crucial elements of a system architecture. A model to determine the take-over ability online and an interaction design to couple this model with the automation and the driver is presented and enriched with first results. An outlook sketches the next steps towards a confidence horizon as an interaction element to bridge the dangerous valleys of automation and cooperation with autonomous functions.

With increasingly automated vehicles, the cooperation between the automation and the human becomes crucial. In cooperative guidance and control (CGC) of highly automated vehicles the questions arise how authority, responsibility and control are distributed between driver and automation, how this distribution is obtained and how different states like abilities, availabilities and modes are communicated between driver and automation. If two independently thinking entities start planning and acting together, the emergence of conflicts is inevitable. These conflicts can be solved by applying the method of arbitration. Arbitration works with different modalities as well as on different planning levels. To handle the complex interdependencies in human-automation interaction in the guidance and control of highly automated vehicles, an “interaction mediator” was designed to incorporate a framework of modules, which in turn are designed to be easily extendable. These modules are the “Mode Selection and Arbitration Unit”, enabling a proper distribution of responsibility depending on present abilities and availabilities, the “Manoeuvre Selection and Arbitration Unit”, the “Trajectory Adaption and Arbitration Unit”, and the “Coupling Valve” where control commands depending on the distribution of responsibility and control are coupled to one common control command for the entire ego-vehicle.