Special issue on shared and cooperative control

Effect of risk-predictive haptic guidance in one-pedal driving mode Abstract The research presented in this article focuses on the design of a driver support system for risk-predictive driving under a potentially hazardous situation for a pedestrian who crosses a road from the driver’s blind spots. Our aim is to develop a system that would cooperate with the driver in leading the normative speed calculated by the co-driver function. The design philosophy of haptic guidance is to communicate to the drivers the potentially hazardous situation through tactile cues from the active gas pedal and to assist drivers to in preparing for possible road surprises. We intended to combine the algorithm of the haptic feedback loop with the functionality of the one-pedal driving mode interface. Three design issues for the haptic guidance system can be distinguished: the design of a one-pedal driving mode based on a one-pedal operation; the modeling of risk-predictive driving behavior; and the haptic feedback algorithm with active gas pedal. We tested our system in human-in-the-loop experiments in a driving simulator to investigate (1) the effect of the one-pedal driving mode interface on the driver behavior and (2) the effect of haptic guidance support on the driver behavior. From the results of our experiments, we confirmed that haptic guidance can improve the risk-predictive driving performance for a slowdown task via the one-pedal driving mode.

Shared control architectures for vehicle steering Abstract Various schemes for sharing control between a human driver and automation system have been proposed, each with the aim of freeing attention while supporting smooth transitions of authority when the control challenge exceeds driver or automation capabilities. When sharing control of a vehicle, it can be expected that the driver develops internal models of the automation and its capabilities and assumes or assigns intent to its actions. In this paper, we develop system models of input mixing and haptic shared control to describe the communication channels open to the driver for monitoring automation behavior and determining automation intent. We pay particular attention to haptic (torque) feedback in the axis of steering that functions both to carry information and to couple the dynamics of the driver’s backdrivable arms and hands with the dynamics of the automation system and vehicle. We assess the various feedback loops present for their promise to reduce cognitive load while maintaining situation awareness. An interpretation of the backdrivable biomechanics and automation impedance in terms of potential wells produces insight into the structured information available through haptic feedback and the internal models that the driver uses to predict automation behavior. The constructed models make explicit the additional communication channels open between human and automation in haptic shared control relative to input mixing.

Action prediction with the Jordan model of human intention: a contribution to cooperative control Abstract Human intentions are internal processes that can be deduced by observation of their resulting actions. Hence, an observation-based model of human intention is needed. The Jordan model of human intention in traffic is presented and applied on empirical data for two different intentions: driver’s braking intention and pedestrian’s crossing intention. The analysis shows that the behavior postulated within the theoretically developed Jordan model is observable in both sets of empirical data of drivers and of pedestrians while they perform intended actions. It can be assumed, that the Jordan model is universally applicable on very distinct intentions, not only limited to traffic scenarios. The Jordan model enhances the development of cooperative control among humans and machines. On the way towards a design of cooperative behavior among humans and vehicles in traffic, the Jordan model contributes with a systematic sequence and description of human behavior that is reflecting intention. Consequently an integration of the Jordan model of human intention with the model of cooperative and shared control Flemisch et al. (Ergonomics 57(3):343–360, 2014) is proposed. This integration facilitates the cooperation between human and system on strategic, tactical, and operational level.

Self-determined nudging: a system concept for human–machine interaction Abstract 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.

Driver–vehicle cooperation: a hierarchical cooperative control architecture for automated driving systems Abstract The concept of automated driving changes the way humans interact with their cars. However, how humans should interact with automated driving systems remains an open question. Cooperation between a driver and an automated driving system—they exert control jointly to facilitate a common driving task for each other—is expected to be a promising interaction paradigm that can address human factors issues caused by driving automation. Nevertheless, the complex nature of automated driving functions makes it very challenging to apply the state-of-the-art frameworks of driver–vehicle cooperation to automated driving systems. To meet this challenge, we propose a hierarchical cooperative control architecture which is derived from the existing architectures of automated driving systems. Throughout this architecture, we discuss how to adapt system functions to realize different forms of cooperation in the framework of driver–vehicle cooperation. We also provide a case study to illustrate the use of this architecture in the design of a cooperative control system for automated driving. By examining the concepts behind this architecture, we highlight that the correspondence between several concepts of planning and control originated from the fields of robotics and automation and the ergonomic frameworks of human cognition and control offers a new opportunity for designing driver–vehicle cooperation.

Simultaneous achievement of driver assistance and skill development in shared and cooperative controls Abstract Advanced driver-assistance systems have successfully reduced drivers’ workloads and increased safety. On the other hand, the excessive use of such systems can impede the development of driving skills. However, there exist collaborative driver-assistance systems, including shared and cooperative controls, which can promote effective collaboration between an assistance system and a human operator under appropriate system settings. Given an effective collaboration setup, we address the goal of simultaneously developing or maintaining driving skills while reducing workload. As there has been a paucity of research on such systems and their methodologies, we discuss a methodology applying shared and cooperative controls by considering related concepts in the skill-training field. Reverse parking assisted by haptic shared control is presented as a means of increasing performance during assistance, while skill improvement following assistance is used to demonstrate the possibility of simultaneous achievement of driver assistance through the reduction of workload and skill improvement.

Towards an interaction pattern language for human machine cooperation and cooperative movement Abstract 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.

Layers of shared and cooperative control, assistance, and automation Abstract Over the last centuries, we have experienced scientific, technological, and societal progress that enabled the creation of intelligent-assisted and automated machines with increasing abilities and that require a conscious distribution of roles and control between humans and machines. Machines can be more than either fully automated or manually controlled, but can work together with the human on different levels of assistance and automation in a hopefully beneficial cooperation. One way of cooperation is that the automation and the human have a shared control over a situation, e.g., a vehicle in an environment. Another way of cooperation is that they trade control. Cooperation can include shared and traded control. The objective of this paper is to give an overview on the development towards a common meta-model of shared and cooperative assistance and automation. The meta-models based on insight from the h(orse)–metaphor and Human–Machine Cooperation principles are presented and combined to propose a framework and criteria to design safe, efficient, ecological, and attractive systems. Cooperation is presented from different points of view such as levels of activity (operational, tactical and strategic levels) as well as the type of function shared between human and machine (information gathering, information analysis, decision selection, and action implementation). Examples will be provided in the aviation domain, in the automotive domain with the automation of driving, as well as in robotics and in manufacturing systems highlighting the usefulness of new automated function but also the increase of systems complexity.

Utility assessment in automated driving for cooperative human–machine systems Abstract 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.