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Understanding behavior of human drivers in interactions with automated vehicles (AV) can aid the development of future AVs. Existing investigations of such behavior have predominantly focused on situations in which an AV a priori needs to take action because the human has the right of way. However, future AVs might need to proactively manage interactions even if they have the right of way over humans, e.g., a human driver taking a left turn in front of the approaching AV. Yet it remains unclear how AVs could behave in such interactions and how humans would react to them. To address this issue, here we investigated behavior of human drivers (N = 19) when interacting with an oncoming AV during unprotected left turns in a driving simulator experiment. We measured the outcomes (Go or Stay) and timing of participants’ decisions when interacting with an AV which performed subtle longitudinal nudging maneuvers, e.g. briefly decelerating and then accelerating back to its original speed. We found that participants’ behavior was sensitive to deceleration nudges but not acceleration nudges. We compared the obtained data to predictions of several variants of a drift-diffusion model of human decision making. The most parsimonious model that captured the data hypothesized noisy integration of dynamic information on time-to-arrival and distance to a fixed decision boundary, with an initial accumulation bias towards the Go decision. Our model not only accounts for the observed behavior but can also flexibly generate predictions of human responses to arbitrary longitudinal AV maneuvers, and can be used for both informing future studies of human behavior and incorporating insights from such studies into computational frameworks for AV interaction planning., Human-Robot Interaction, Human-Centred Artificial Intelligence, Interactive Intelligence

Traffic interactions between merging and highway vehicles are a major topic of research, yielding many empirical studies and models of driver behaviour. Most of these studies on merging use naturalistic data. Although this provides insight into human gap acceptance and traffic flow effects, it obscures the operational inputs of interacting drivers. Besides that, researchers have no control over the vehicle kinematics (i.e., positions and velocities) at the start of the interactions. Therefore the relationship between initial kinematics and the outcome of the interaction is difficult to investigate. To address these gaps, we conducted an experiment in a coupled driving simulator with a simplified, top-down view, merging scenario with two vehicles. We found that kinematics can explain the outcome (i.e., which driver merges first) and the duration of the merging conflict. Furthermore, our results show that drivers use key decision moments combined with constant acceleration inputs (intermittent piecewise-constant control) during merging. This indicates that they do not continuously optimise their expected utility. Therefore, these results advocate the development of interaction models based on intermittent piecewise-constant control. We hope our work can contribute to this development and to the fundamental knowledge of interactive driver behaviour., Human-Robot Interaction, Human-Centred Artificial Intelligence

This study presents a general optimal trajectory planning (GOTP) framework for autonomous vehicles (AVs) that can effectively avoid obstacles and guide AVs to complete driving tasks safely and efficiently. Firstly, we employ the fifth-order Bezier curve to generate and smooth the reference path along the road centerline. Cartesian coordinates are then transformed to achieve the curvature continuity of the generated curve. Considering the road constraints and vehicle dynamics, limited polynomial candidate trajectories are generated and smoothed in a curvilinear coordinate system. Furthermore, in selecting the optimal trajectory, we develop a unified and auto-tune objective function based on the principle of least action by employing AVs to simulate drivers’ behavior and summarizing their manipulation characteristics of “seeking benefits and avoiding losses.” Finally, by integrating the idea of receding-horizon optimization, the proposed framework is achieved by considering dynamic multi-performance objectives and selecting trajectories that satisfy feasibility, optimality, and adaptability. Extensive simulations and experiments are performed, and the results demonstrate the framework's feasibility and effectiveness, which avoids both dynamic and static obstacles and applies to various scenarios with multi-source interactive traffic participants. Moreover, we prove that the proposed method can guarantee real-time planning and safety requirements compared to drivers’ manipulation., Human-Robot Interaction, Algorithmics, Human-Centred Artificial Intelligence

Daily household tasks involve manipulation in cluttered and unpredictable environments and service robots require complex skills and adaptability to perform such tasks. To this end, we developed a teleoperated online learning approach with a novel skill refinement method, where the operator can make refinements to the initially trained skill by a haptic device. After a refined trajectory is formed, it is used to update a probabilistic trajectory model conditioned to the environment state. Therefore, the initial model can be adapted when unknown variations occur and the method is able to deal with different object positions and initial robot poses. This enables human operators to remotely correct or teach complex robotic manipulation skills. Such an approach can help to alleviate shortages of caretakers in elderly care and reduce travel time between homes of different elderly to reprogram the service robots whenever they get stuck. We performed a human factors experiment on 18 participants teaching a service robot how to empty a dishwasher, which is a common daily household task performed by caregivers. We compared the developed method against three other methods. The results show that the proposed method performs better in terms of how much time it takes to successfully adapt a model and in terms of the perceived workload., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Human-Robot Interaction

Robotic assistance for work processes in the hospitality industry is receiving increased attention both in the hospitality industry and in academic research. Unfortunately, academic literature about hospitality robotics is currently disjointed, making it hard for hospitality professionals to decide which processes to target, and which available robotic systems would result in benefits (or limitations) for the organisation, employees or guests. Similarly, it is hard to understand what robotic functionalities need to be developed for particular processes, or what future functionalities to expect from ongoing developments in the robotics field. Researching hospitality robotics requires expertise in both fields. With this chapter, we try to give an insight in the things that should be considered when designing and evaluating the effectiveness of robots in a hospitality work environment. We do this by illustrating how robots can influence many interactions in a work environment and we present a conceptual framework for mapping these different interactions., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Human-Robot Interaction

Modelling causal responsibility in multi-agent spatial interactions is crucial for safety and efficiency of interactions of humans with autonomous agents. However, current formal metrics and models of responsibility either lack grounding in ethical and philosophical concepts of responsibility, or cannot be applied to spatial interactions. In this work we propose a metric of causal responsibility which is tailored to multi-agent spatial interactions, for instance interactions in traffic. In such interactions, a given agent can, by reducing another agent's feasible action space, influence the latter. Therefore, we propose feasible action space reduction (FeAR) as a metric of causal responsibility among agents. Specifically, we look at ex-post causal responsibility for simultaneous actions. We propose the use of Moves de Rigueur (MdR) - a consistent set of prescribed actions for agents - to model the effect of norms on responsibility allocation. We apply the metric in a grid world simulation for spatial interactions and show how the actions, contexts, and norms affect the causal responsibility ascribed to agents. Finally, we demonstrate the application of this metric in complex multi-agent interactions. We argue that the FeAR metric is a step towards an interdisciplinary framework for quantifying responsibility that is needed to ensure safety and meaningful human control in human-AI systems., Human-Robot Interaction, Interactive Intelligence

This paper studies non-physical feedback mechanisms to guide human workers toward ergonomic body postures. Specifically, the focus is to solve the tasks that involve no direct physical interaction between the human and the robotic system, therefore tactile guidance by the robot body is not feasible. We propose a multi-modal ergonomic posture guidance system that comprises visual feedback and speech-based audio feedback. We hypothesise that the proposed multi-modal system leads to better performance compared to uni-modal feedback systems when trying to guide users from one pose to another. To test the hypothesis we conducted an experiment that compared conditions with only audio feedback, only visual feedback and multi-modal feedback. In addition, we examined speech-based audio guidance in joint space and in endpoint space. The results showed that the speech-based feedback in joint space came out as the preferred audio feedback due to its ability to allow users to carry out efficient and coordinated inter-joint movements, especially in cases of high redundancy. Furthermore, the proposed multi-modal feedback system was superior compared to the other feedback modalities both in terms of objective measures and subjective measures., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Human-Robot Interaction

When humans and AI-agents collaborate, they need to continuously learn about each other and the task. We propose a Team Design Pattern that utilizes adaptivity in the behavior of human and agent team partners, causing new Collaboration Patterns to emerge. Human-AI Co-Learning takes place when partners can formalize recognized patterns of collaboration in a commonly shared language, and can communicate with each other about these patterns. For this, we developed an ontology of Collaboration Patterns. An accompanying Graphical User Interface (GUI) enables partners to formalize and refine Collaboration Patterns, which can then be communicated to the partner. The ontology was evaluated empirically with human participants who viewed video recordings of joint human-agent activities. Participants were requested to identify Collaboration Patterns in the footage, and to formalize patterns by using the ontology’s GUI. Results show that the ontology supports humans to recognize and define Collaboration Patterns successfully. To improve the ontology, it is suggested to include pre- and post-conditions of tasks, as well as parallel actions of team members., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., BUS/TNO STAFF, Interactive Intelligence, Human-Robot Interaction

A major challenge for autonomous vehicles is handling interactions with human-driven vehicles—for example, in highway merging. A better understanding and computational modelling of human interactive behaviour could help address this challenge. However, existing modelling approaches predominantly neglect communication between drivers and assume that one modelled driver in the interaction responds to the other, but does not actively influence their behaviour. Here, we argue that addressing these two limitations is crucial for the accurate modelling of interactions. We propose a new computational framework addressing these limitations. Similar to game-theoretic approaches, we model a joint interactive system rather than an isolated driver who only responds to their environment. Contrary to game theory, our framework explicitly incorporates communication between two drivers and bounded rationality in each driver’s behaviours. We demonstrate our model’s potential in a simplified merging scenario of two vehicles, illustrating that it generates plausible interactive behaviour (e.g. aggressive and conservative merging). Furthermore, human-like gap-keeping behaviour emerged in a car-following scenario directly from risk perception without the explicit implementation of time or distance gaps in the model’s decision-making. These results suggest that our framework is a promising approach to interaction modelling that can support the development of interaction-aware autonomous vehicles., Human-Robot Interaction

In this paper, we problematize popular narratives of driving automation. Whether positive or negative, these propagate simplistic assumptions about human abilities and reinforce technocratic approaches to mobility innovation. We build on narrative approaches to participatory research and adversarial design, to explore how design-led confrontation can create opportunities for reflection on implicit assumptions and narratives that stakeholders may refer to when discussing and making decisions about automated driving technologies. Specifically, we discuss the results of four focus groups where we used contestational artifacts to promote critical discussions and confront taken-for-granted beliefs among stakeholders. We reflect on the results to distill methodological insight and design recommendations for conducting adversarial participatory design research as a way towards confronting dominant narratives. Together with the methodological approach, the main contribution of this work, we also provide a set of narrative tensions that can be used to question common beliefs surrounding automated driving futures., Design Aesthetics, Interactive Intelligence, Human-Robot Interaction

Overtaking on two-lane roads can lead to increased collision risks due to drivers' errors in evaluating whether or not to accept the gap to the vehicle in the opposite lane. Understanding these gap acceptance decisions can help mitigate the risks associated with overtaking. Previous research on overtaking has focused on the factors influencing gap acceptance decisions. However, the cognitive processes underlying gap acceptance decisions remain poorly understood. Previous studies have shown that response time (i.e. the time it takes the driver to evaluate the gap and make a decision) can provide valuable insights into the cognitive processes during gap acceptance decisions, in particular in pedestrian crossing and left turn decisions. However, the more complex nature of the overtaking maneuver renders it difficult to measure response times in overtaking. As a result, response times in overtaking have not been investigated, thereby limiting our understanding of overtaking behavior. To address this gap, in this paper we propose a method to measure response time in drivers' overtaking decisions and demonstrate this method in a driving simulator experiment (N=25). Specifically, we analyzed the effect of distance to the oncoming vehicle and speed of the ego vehicle on response time in accepted and rejected gaps. We found that response times for rejected gaps were on average longer than for accepted gaps. The response times increased with the distance gap and decreased with the initial velocity of the ego vehicle. We conclude that using the proposed method for measuring response time can give insight in the way drivers make gap acceptance decisions during overtaking. These results provide basis for cognitive process models that can help further understand overtaking decisions., Cognitive Robotics, Transport and Planning

Increasing online retail has resulted in increased automation in order picking systems, leading to new challenges and opportunities in task scheduling. The job-shop scheduling problem is an optimization problem essential in such systems, but existing JSP literature often overlooks workplace fatigue, which harms employees’ well-being and costs U.S. employers up to €127 billion annually. In this work, we propose fatigue consideration in the job-shop scheduling problem in a cobotic order picking system to mitigate its negative effects. We present a new bi-objective mixed integer nonlinear programming problem formulation that considers worker fatigue and productivity during schedule optimisation. To put the results of simulated optimisation in perspective, we experimentally validate the fatigue model and scheduling results in a real operation. The mathematical model finds solutions that conventional single-objective optimisation cannot, allowing fractional fatigue distribution improvements more than 4x larger than the decrease in productivity they require in 53% of the considered virtual cases. The experiments show that our predictive fatigue model has an average RMSE of 2.20 kcal/min in estimating energy expenditure rates compared to heart rate measurements. It also shows a low correlation, meaning it is unfit for application. On the other hand, fatigue-conscious schedules show no clear benefit regarding measured and perceived fatigue. However, the scheduling model could also use heart rate measurements that do not show these inaccuracies. Our study highlights the need to further develop and validate the mathematical formulation and fatigue model and extend to other human factors and indirect fatigue effects., Human-Centred Artificial Intelligence, Human-Robot Interaction, Transport Engineering and Logistics

How can humans remain in control of artificial intelligence (AI)-based systems designed to perform tasks autonomously? Such systems are increasingly ubiquitous, creating benefits - but also undesirable situations where moral responsibility for their actions cannot be properly attributed to any particular person or group. The concept of meaningful human control has been proposed to address responsibility gaps and mitigate them by establishing conditions that enable a proper attribution of responsibility for humans; however, clear requirements for researchers, designers, and engineers are yet inexistent, making the development of AI-based systems that remain under meaningful human control challenging. In this paper, we address the gap between philosophical theory and engineering practice by identifying, through an iterative process of abductive thinking, four actionable properties for AI-based systems under meaningful human control, which we discuss making use of two applications scenarios: automated vehicles and AI-based hiring. First, a system in which humans and AI algorithms interact should have an explicitly defined domain of morally loaded situations within which the system ought to operate. Second, humans and AI agents within the system should have appropriate and mutually compatible representations. Third, responsibility attributed to a human should be commensurate with that human’s ability and authority to control the system. Fourth, there should be explicit links between the actions of the AI agents and actions of humans who are aware of their moral responsibility. We argue that these four properties will support practically minded professionals to take concrete steps toward designing and engineering for AI systems that facilitate meaningful human control., Interactive Intelligence, Design Aesthetics, Cyber Security, Human-Robot Interaction, Ethics & Philosophy of Technology, Human Information Communication Design, Web Information Systems

Human highway-merging behavior is an important aspect when developing autonomous vehicles (AVs) that can safely and successfully interact with other road users. To design safe and acceptable human-AV interactions, the underlying mechanisms in human-human interactive behavior need to be understood. Exposing and understanding these mechanisms can be done using controlled driving simulator experiments. However, until now, such human-factors merging experiments have focused on aspects of the behavior of a single driver (e.g., gap acceptance) instead of on the dynamics of the interaction. Furthermore, existing experimental scenarios and data analysis tools (i.e., concepts like time-to-collision) are insufficient to analyze human-human interactive merging behavior. To help facilitate human-factors research on merging interactions, we propose an experimental framework consisting of a general simplified merging scenario and a set of three analysis tools: (1) a visual representation that captures the combined behavior of two participants and the safety margins they maintain in a single plot; (2) a signal (over time) that describes the level of conflict; and (3) a metric that describes the amount of time that was required to solve the merging conflict, called the conflict resolution time. In a case study with 18 participants, we used the proposed framework and analysis tools in a top-down view driving simulator where two human participants can interact. The results show that the proposed scenario can expose diverse behaviors for different conditions. We demonstrate that our novel visual representation, conflict resolution time, and conflict signal are valuable tools when comparing human behavior between conditions. Therefore, with its simplified merging scenario and analysis tools, the proposed experimental framework can be a valuable asset when developing driver models that describe interactive merging behavior and when designing AVs that interact with humans., Human-Robot Interaction

Tele-impedance increases interaction performance between a robotic tool and unstructured/unpredictable en-vironments during teleoperation. However, the existing tele-impedance interfaces have several ongoing issues, such as long calibration times and various obstructions for the human operator. In addition, they are all designed to be controlled by the operator's arms, which can cause difficulties when both arms are used, as in bi-manual teleoperation. To resolve these issues, we designed a novel foot-based tele-impedance control method inspired by the human limb stiffness ellipse modulation. The proposed mechanical interface design includes a disc and a foot pressure sensor that controls the orientation and size/shape of the stiffness ellipse, respectively. We evaluated the disc interface control method in an experimental study with 12 participants, who performed a complex drilling task in a virtual environment. The results show the ability of the operator to use the proposed interface in order to dynamically adapt to different phases of the task and changes in the environment. In addition, a comparison with low and high uniform impedance modes demonstrates a superior interaction performance of the proposed method., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Biomechatronics & Human-Machine Control, Human-Robot Interaction

We present a method for arbitration between human and robot involvement in a collaborative physical task execution based on ergonomic metrics. The existing methods for ergonomic control of physical human-robot collaboration perform the real-Time arbitration primarily based on a single type of ergonomic metric. The novelty of our approach is twofold. First, the system enables real-Time arbitration based on combining two types of ergonomic metrics: preventive and reactive. Second, we use a preventive metric to prevent worker fatigue and discomfort due to overexertion in the future and a reactive metric to avoid immediate fatigue and discomfort. To this end, we considered two metrics respectively: human arm manipulability and muscle fatigue. The developed multi-metric arbitration method translates the human multi-metric state to a robot control level over a collaborative task execution using a finite state machine. We demonstrate the proposed method on a Kuka LWR iiwa robotic arm in a collaborative human-robot polishing task that requires a specific force production., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Human-Robot Interaction

This chapter summarizes the presentations of speakers addressing such issues during the Automated Vehicles Symposium 2020 (AVS20) held virtually on July 27–30, 2020. These speakers participated in the break-out session titled “Artificial Intelligence for Automated Vehicle Control and Traffic Operations: Challenges and Opportunities”. The corresponding discussion and recommendations are presented in terms of the lessons learned and the future research directions to be adopted to benefit from AI in order to develop safer and more efficient connected and automated vehicles (CAV). This session was organized by the Transportation Research Board (TRB) Committee on Traffic Flow Theory and Characteristics (ACP50) and the TRB Committee on Artificial Intelligence and Advanced Computing Applications (AED50)., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Human-Robot Interaction

Objective: We aim to bridge the gap between naturalistic studies of driver behavior and modern cognitive and neuroscientific accounts of decision making by modeling the cognitive processes underlying left-turn gap acceptance by human drivers. Background: Understanding decisions of human drivers is essential for the development of safe and efficient transportation systems. Current models of decision making in drivers provide little insight into the underlying cognitive processes. On the other hand, laboratory studies of abstract, highly controlled tasks point towards noisy evidence accumulation as a key mechanism governing decision making. However, it is unclear whether the cognitive processes implicated in these tasks are as paramount to decisions that are ingrained in more complex behaviors, such as driving. Results: The drivers’ probability of accepting the available gap increased with the size of the gap; importantly, response time increased with time gap but not distance gap. The generalized drift-diffusion model explained the observed decision outcomes and response time distributions, as well as substantial individual differences in those. Through cross-validation, we demonstrate that the model not only explains the data, but also generalizes to out-of-sample conditions. Conclusion: Our results suggest that dynamic evidence accumulation is an essential mechanism underlying left-turn gap acceptance decisions in human drivers, and exemplify how simple cognitive process models can help to understand human behavior in complex real-world tasks. Application: Potential applications of our results include real-time prediction of human behavior by automated vehicles and simulating realistic human-like behaviors in virtual environments for automated vehicles., Human-Robot Interaction

A major question in human-automation interaction is whether tasks should be traded or shared between human and automation. This work presents reflections—which have evolved through classroom debates between the authors over the past 10 years—on these two forms of human-automation interaction, with a focus on the automated driving domain. As in the lectures, we start with a historically informed survey of six pitfalls of automation: (1) Loss of situation and mode awareness, (2) Deskilling, (3) Unbalanced mental workload, (4) Behavioural adaptation, (5) Misuse, and (6) Disuse. Next, one of the authors explains why he believes that haptic shared control may remedy the pitfalls. Next, another author rebuts these arguments, arguing that traded control is the most promising way to improve road safety. This article ends with a common ground, explaining that shared and traded control outperform each other at medium and low environmental complexity, respectively. Practitioner summary: Designers of automation systems will have to consider whether humans and automation should perform tasks alternately or simultaneously. The present article provides an in-depth reflection on this dilemma, which may prove insightful and help guide design. Abbreviations: ACC: Adaptive Cruise Control: A system that can automatically maintain a safe distance from the vehicle in front; AEB: Advanced Emergency Braking (also known as Autonomous Emergency Braking): A system that automatically brakes to a full stop in an emergency situation; AES: Automated Evasive Steering: A system that automatically steers the car back into safety in an emergency situation; ISA: Intelligent Speed Adaptation: A system that can limit engine power automatically so that the driving speed does not exceed a safe or allowed speed., Human-Robot Interaction

A major challenge for autonomous vehicles is interacting with other traffic participants safely and smoothly. A promising approach to handle such traffic interactions is equipping autonomous vehicles with interaction-aware controllers (IACs). These controllers predict how surrounding human drivers will respond to the autonomous vehicle’s actions, based on a driver model. However, the predictive validity of driver models used in IACs is rarely validated, which can limit the interactive capabilities of IACs outside the simple simulated environments in which they are demonstrated. In this paper, we argue that besides evaluating the interactive capabilities of IACs, their underlying driver models should be validated on natural human driving behavior. We propose a workflow for this validation that includes scenario-based data extraction and a two-stage (tactical/operational) evaluation procedure based on human factors literature. We demonstrate this workflow in a case study on an inverse-reinforcement-learning-based driver model replicated from an existing IAC. This model only showed the correct tactical behavior in 40% of the predictions. The model’s operational behavior was inconsistent with observed human behavior. The case study illustrates that a principled evaluation workflow is useful and needed. We believe that our workflow will support the development of appropriate driver models for future automated vehicles., Human-Robot Interaction

Introduction: Cars are increasingly computerized, and vehicle settings such as steering gain (SG) can now be altered during driving. However, it is unknown whether transitions in SG should be adaptable (i.e., triggered by driver input) or adaptive (i.e., triggered automatically). We examined this question for road segments expected to require different SG. Objective: This paper aimed to investigate whether SG mode changes should be made by the driver or automatically. Methods: Twenty-four participants drove under four conditions in a simulator: fixed low gain (FL), fixed high gain (FH), a machine-initiated steering system, which switched between the two SG levels at predetermined locations (MI), and a driver-initiated steering system, in which the SG level could be changed by pressing a button on the steering wheel (DI). Results: Participants showed poorer lane-keeping and reported higher effort for FH compared to FL on straights, while the opposite held true on curved roads. On curved roads, the MI condition yielded better lane-keeping and lower subjective effort than the DI condition. However, a substantial portion of the drivers gave low preference rankings to the MI system. Conclusion: Drivers prefer and benefit from a steering system with a variable rather than fixed gain. Furthermore, although automatic SG transitions reduce effort, some drivers reject this concept. Application: As the state of technology advances, MI transitions are becoming increasingly feasible, but whether drivers would want to delegate their decision-making authority to a machine remains a moot point., Human-Robot Interaction

A key question in transportation research is whether drivers show behavioral adaptation, that is, slower or faster driving, when new technology is introduced into the vehicle. This study investigates behavioral adaptation in response to the sport mode, a technology that alters the vehicle's auditory, throttle-mapping, power-steering, and chassis settings. Based on the literature, it can be hypothesized that the sport mode increases perceived sportiness and encourages faster driving. Oppositely, the sport mode may increase drivers’ perceived danger, homeostatically causing them to drive more slowly. These hypotheses were tested using an instrumented vehicle on a test track. Thirty-one drivers were asked to drive as they normally would with different sport mode settings: Baseline, Modified Throttle Mapping (MTM), Artificial Engine Sound enhancement (AESe), MTM and AESe combined (MTM-AESe), and MTM, AESe combined with four-wheel steering, increased damping, and decreased power steering (MTM-AESe-4WS). Post-trial questionnaires showed increased perceived sportiness but no differences in perceived danger for the three MTM conditions compared to Baseline. Furthermore, compared to Baseline, MTM led to higher vehicle accelerations and, with a smaller effect size, a higher time-percentage of driving above the 110 km/h speed limit, but not higher cornering speeds. The AESe condition did not significantly affect perceived sportiness, perceived danger, and driving speed compared to Baseline. These findings suggest that behavioral adaptation is a functional and opportunistic phenomenon rather than mediated by perceived sportiness or perceived danger., Human-Robot Interaction

People seem to hold the human driver to be primarily responsible when their partially automated vehicle crashes, yet is this reasonable? While the driver is often required to immediately take over from the automation when it fails, placing such high expectations on the driver to remain vigilant in partially automated driving is unreasonable. Drivers show difficulties in taking over control when needed immediately, potentially resulting in dangerous situations. From a normative perspective, it would be reasonable to consider the impact of automation on the driver’s ability to take over control when attributing responsibility for a crash. We, therefore, analyzed whether the public indeed considers driver ability when attributing responsibility to the driver, the vehicle, and its manufacturer. Participants blamed the driver primarily, even though they recognized the driver’s decreased ability to avoid the crash. These results portend undesirable situations in which users of partially driving automation are the ones held responsible, which may be unreasonable due to the detrimental impact of driving automation on human drivers. Lastly, the outcome signals that public awareness of such human-factors issues with automated driving should be improved., Human-Robot Interaction, Interactive Intelligence

How can humans remain in control of artificial intelligence (AI)-based systems designed to perform tasks autonomously? Such systems are increasingly ubiquitous, creating benefits - but also undesirable situations where moral responsibility for their actions cannot be properly attributed to any particular person or group. The concept of meaningful human control has been proposed to address responsibility gaps and mitigate them by establishing conditions that enable a proper attribution of responsibility for humans; however, clear requirements for researchers, designers, and engineers are yet inexistent, making the development of AI-based systems that remain under meaningful human control challenging. In this paper, we address the gap between philosophical theory and engineering practice by identifying, through an iterative process of abductive thinking, four actionable properties for AI-based systems under meaningful human control, which we discuss making use of two applications scenarios: automated vehicles and AI-based hiring. First, a system in which humans and AI algorithms interact should have an explicitly defined domain of morally loaded situations within which the system ought to operate. Second, humans and AI agents within the system should have appropriate and mutually compatible representations. Third, responsibility attributed to a human should be commensurate with that human’s ability and authority to control the system. Fourth, there should be explicit links between the actions of the AI agents and actions of humans who are aware of their moral responsibility. We argue that these four properties will support practically minded professionals to take concrete steps toward designing and engineering for AI systems that facilitate meaningful human control., Interactive Intelligence, Design Aesthetics, Cyber Security, Human-Robot Interaction, Ethics & Philosophy of Technology, Human Information Communication Design, Web Information Systems

Human highway-merging behavior is an important aspect when developing autonomous vehicles (AVs) that can safely and successfully interact with other road users. To design safe and acceptable human-AV interactions, the underlying mechanisms in human-human interactive behavior need to be understood. Exposing and understanding these mechanisms can be done using controlled driving simulator experiments. However, until now, such human-factors merging experiments have focused on aspects of the behavior of a single driver (e.g., gap acceptance) instead of on the dynamics of the interaction. Furthermore, existing experimental scenarios and data analysis tools (i.e., concepts like time-to-collision) are insufficient to analyze human-human interactive merging behavior. To help facilitate human-factors research on merging interactions, we propose an experimental framework consisting of a general simplified merging scenario and a set of three analysis tools: (1) a visual representation that captures the combined behavior of two participants and the safety margins they maintain in a single plot; (2) a signal (over time) that describes the level of conflict; and (3) a metric that describes the amount of time that was required to solve the merging conflict, called the conflict resolution time. In a case study with 18 participants, we used the proposed framework and analysis tools in a top-down view driving simulator where two human participants can interact. The results show that the proposed scenario can expose diverse behaviors for different conditions. We demonstrate that our novel visual representation, conflict resolution time, and conflict signal are valuable tools when comparing human behavior between conditions. Therefore, with its simplified merging scenario and analysis tools, the proposed experimental framework can be a valuable asset when developing driver models that describe interactive merging behavior and when designing AVs that interact with humans., Human-Robot Interaction

Unlike many traditional stiff position-controlled robots, new collaborative robots interact with humans and operate in an environment that is often unpredictable and unknown. For safe and effective executions of manipulation tasks within such an environment, the robot requires to modulate its compliance. Therefore, the human operator must have a system that enables an intuitive demonstration of compliance skills to the robot. Ideally, this should also be possible through teleoperation in order to have the ability to demonstrate skills at a distance, such as in remote home care applications or any other scenario where the skilled operators are not physically present all the time. Existing state-of-the-art methods for remote demonstration of impedance skills only enable limited modulation of the stiffness matrix, or they are too complex and cumbersome for practical applications. This research tries to overcome these limitations and proposes a teleoperated stiffness commanding method that enables a complete modulation of the stiffness matrix in 3 degrees of freedom. The method uses the same haptic device hardware as used for controlling the robot manipulator motion, hence it does not require extra specialised equipment for stiffness commands. By wiggling the endpoint of the haptic device, the stiffness is commanded to the robot and also fed back to the operator through haptic and visual feedback. To evaluate the performance and acceptance of the system, we performed a user study where the participants had to demonstrate various interaction behaviour to the remote robot. The results show how varying system parameters (i.e., degrees of freedom, orientation, and size of the stiffness commands) influence the performance of the system and user acceptance., Accepted Author Manuscript, Human-Robot Interaction

In tele-impedance the human can control the impedance of the remote robot through various interfaces, in addition to controlling the motion. While this can improve the performance of the remote robot in unpredictable and unstructured environments, it can add more workload to the human operator compared to the classic teleoperation. This paper presents a novel method for a semi-autonomous tele-impedance, where the controller exploits the robot vision to detect the environment and selects the appropriate impedance. For example, if vision detects a fragile object like glass, the controller autonomously lowers the impedance to increase the safety, while the human is commanding the motion to initiate and perform the interaction. If the vision algorithm is not confident in its detection, we developed an additional verbal communication interface that enables the human to confirm or correct the autonomous decision. Therefore, the method has four modalities: (i) perturbation rejection mode, (ii) object property detection mode, (iii) verbal confirmation mode, (iv) voice control mode. We conducted proof-of-concept experiments on a teleoperation setup, where the human operator performed position tracking and contact establishing tasks., Accepted Author Manuscript, Human-Robot Interaction

The existing state-of-the-art interfaces for commanding a remote robot's endpoint stiffness ellipsoid in tele-impedance lack the ability to independently control its size, shape and orientation or they are not easily to implement due to the use of physiological signals, such as electromyography, to control the endpoint stiffness. We propose a novel method that can command size, shape and orientation independently and simultaneously through a virtual stiffness ellipsoid generated on a touchscreen device. The human operator controls size, shape and orientation of the virtual ellipsoid using his/her index and thumb fingers of one hand. This virtual ellipsoid is then mapped to the Cartesian stiffness ellipsoid of a remote robot endpoint in real-time. The other hand holds the haptic device to control the pose of the remote robotic arm. Compared to the state-of-the-art methods to control the robot stiffness in tele-impedance, the main advantages of the proposed method are its relatively simple implementation and ability of independent control over various aspects of the robot endpoint stiffness ellipsoid. To provide a proof-of-concept and demonstrate the main features of the proposed approach, we performed several experiments on a tele-impedance setup with a Kuka LBR iiwa robotic arm and a Force Dimension Sigma7 haptic device. We examined two principal types of tasks, in which changing stiffness parameters of the remote robot is important for successful task execution: counteracting external perturbations and establishing contact with unknown objects. The results indicate that our proposed approach can successfully deal with these tasks. A human subject study showed that the touchscreen interface is faster in commanding the desired stiffness compared to another state-of-the-art input method, while showing similar workload ratings., Accepted Author Manuscript, Human-Robot Interaction

Tele-impedance augments classic teleoperation by enabling the human operator to actively command remote robot stiffness in real-time, which is an essential ability to successfully interact with the unstructured and unpredictable environment. However, the literature is missing a study on benefits and drawbacks of different types of stiffness command interfaces used in bilateral tele-impedance. In this article, we introduce a term called coupling effect, which pertains to the coupling between human-commanded stiffness going to the remote robot and force feedback coming from the remote robot. We hypothesize that, whenever the operator's commanded stiffness and force feedback are subject to coupling effect (e.g., muscle activity based stiffness command interfaces), force feedback can invoke involuntary changes in the commanded stiffness due to human reflexes. Although the coupling effect takes away some degree of the operator's control over the commanded stiffness, these involuntary changes can be either beneficial (e.g., during position tracking) or detrimental (e.g., during force tracking) to the task performance on the remote robot side. We examined the coupling effect in an experimental study with $\mathbf {16}$ participants, who performed position and force tracking tasks by using both coupled type (muscle activity based) and decoupled type (external device based) of interface. The results demonstrate a benefit of the coupling effect when the remote robot is operating in presence of unexpected force perturbations, where lower absolute error in position tracking task was observed. On the other hand, the decoupled type of interface is beneficial for force tracking tasks on the remote robot side, such as establishing or maintaining a stable contact with objects. However, the coupling effect negatively influences the commanding of reference stiffness to the remote robot in both position and, Accepted Author Manuscript, Human-Robot Interaction

Modern aircraft can be equipped with a flight envelope protection system: automation which modifies pilot control inputs to ensure that the aircraft remains within the allowable limits. Overruling the pilot inputs may lead to mode confusion, even when visual or auditory feedback is provided to alert pilots. We advocate using active control devices to make the flight envelope protection system tangible to the pilot. This paper presents the main findings of an evaluation of three haptic feedback designs for flight envelope protection. The first concept used both force feedback and vibro-tactile alerts, producing promising, yet inconclusive, results. The second concept used asymmetric vibrations to give directional alerting cues, which did not result in improved performance on initial use, but which did yield improved learning rate for the task. The third system employed force feedback to physically guide the pilot away from flight envelope limits, which yielded safety improvements from the first use, but created dependence: pilot performance degraded immediately after the force feedback was removed. From this, we advise to use asymmetric vibrations during training for flight envelope excursions, to leverage active control interfaces for providing force feedback during operation, and reevaluate a combination of both to combine their advantages for single-pilot operations., Control & Simulation, Human-Robot Interaction

Current predictors of fuel consumption are typically based on computer simulations or data collections in real traffic, where the route and vehicle type are not under the researcher's control. Here, we predicted fuel consumption using test track data, an approach that allowed for location-specific predictions. Ninety-one drivers drove a total of 4617 laps, in two vehicles (Renault Mégane, Renault Clio), on two routes (highway and mountain), and with two eco-driving instructions (normal and eco). A multivariate analysis at the level of laps showed a strong predictive value for metrics related to speed, RPM, and throttle position, but with a considerable amount of variance attributable to route and vehicle type. A subsequent location-specific analysis showed that the predictive correlation of driving speed and throttle position fluctuated strongly during the lap and at some locations even became negative. We conclude that there is considerable potential in instantaneous location-specific prediction of fuel consumption., Human-Robot Interaction

Modern aircraft use a variety of fly-by-wire control devices and combine these with a flight envelope protection system to limit pilot control inputs when approaching the aircraft limits. The current research project aims to increase pilot awareness of such a protection system through the use of force feedback on the control device, i.e., haptics. A previous design used asymmetric vibrations to cue the pilot on the flight envelope. The evaluation showed no improvement in metrics at the first emergency encounter, yet did show a potential training benefit. Therefore, a new haptic feedback concept was designed with the specific aim to guide the pilot when approaching a limit and provide support from the first time use. This paper evaluates these haptic feedback designs with 36 active PPL/LAPL pilots who flew a challenging vertical profile and encountered a windshear in a fixed-base simulator. The pilots were divided in three groups who received either cueing, guidance, or no haptic feedback. It was hypothesized that: (i) cueing haptic feedback provides a faster learning rate compared to no-haptics, and (ii) guidance haptic feedback results in best performance from the first run yet worse metrics when no feedback is provided. Comparing the results of the cueing and no-haptic feedback groups confirmed the first hypothesis. Results also showed that the guidance haptic feedback resulted in improved metrics at the first run, and the worsening of metrics when no longer provided., Virtual/online event due to COVID-19, Control & Simulation, Human-Robot Interaction

The existing state-of-the-art interfaces for commanding a remote robot's endpoint stiffness ellipsoid in tele-impedance lack the ability to independently control its size, shape and orientation or they are not easily to implement due to the use of physiological signals, such as electromyography, to control the endpoint stiffness. We propose a novel method that can command size, shape and orientation independently and simultaneously through a virtual stiffness ellipsoid generated on a touchscreen device. The human operator controls size, shape and orientation of the virtual ellipsoid using his/her index and thumb fingers of one hand. This virtual ellipsoid is then mapped to the Cartesian stiffness ellipsoid of a remote robot endpoint in real-time. The other hand holds the haptic device to control the pose of the remote robotic arm. Compared to the state-of-the-art methods to control the robot stiffness in tele-impedance, the main advantages of the proposed method are its relatively simple implementation and ability of independent control over various aspects of the robot endpoint stiffness ellipsoid. To provide a proof-of-concept and demonstrate the main features of the proposed approach, we performed several experiments on a tele-impedance setup with a Kuka LBR iiwa robotic arm and a Force Dimension Sigma7 haptic device. We examined two principal types of tasks, in which changing stiffness parameters of the remote robot is important for successful task execution: counteracting external perturbations and establishing contact with unknown objects. The results indicate that our proposed approach can successfully deal with these tasks. A human subject study showed that the touchscreen interface is faster in commanding the desired stiffness compared to another state-of-the-art input method, while showing similar workload ratings., Accepted Author Manuscript, Human-Robot Interaction

Unlike many traditional stiff position-controlled robots, new collaborative robots interact with humans and operate in an environment that is often unpredictable and unknown. For safe and effective executions of manipulation tasks within such an environment, the robot requires to modulate its compliance. Therefore, the human operator must have a system that enables an intuitive demonstration of compliance skills to the robot. Ideally, this should also be possible through teleoperation in order to have the ability to demonstrate skills at a distance, such as in remote home care applications or any other scenario where the skilled operators are not physically present all the time. Existing state-of-the-art methods for remote demonstration of impedance skills only enable limited modulation of the stiffness matrix, or they are too complex and cumbersome for practical applications. This research tries to overcome these limitations and proposes a teleoperated stiffness commanding method that enables a complete modulation of the stiffness matrix in 3 degrees of freedom. The method uses the same haptic device hardware as used for controlling the robot manipulator motion, hence it does not require extra specialised equipment for stiffness commands. By wiggling the endpoint of the haptic device, the stiffness is commanded to the robot and also fed back to the operator through haptic and visual feedback. To evaluate the performance and acceptance of the system, we performed a user study where the participants had to demonstrate various interaction behaviour to the remote robot. The results show how varying system parameters (i.e., degrees of freedom, orientation, and size of the stiffness commands) influence the performance of the system and user acceptance., Accepted Author Manuscript, Human-Robot Interaction

In tele-impedance the human can control the impedance of the remote robot through various interfaces, in addition to controlling the motion. While this can improve the performance of the remote robot in unpredictable and unstructured environments, it can add more workload to the human operator compared to the classic teleoperation. This paper presents a novel method for a semi-autonomous tele-impedance, where the controller exploits the robot vision to detect the environment and selects the appropriate impedance. For example, if vision detects a fragile object like glass, the controller autonomously lowers the impedance to increase the safety, while the human is commanding the motion to initiate and perform the interaction. If the vision algorithm is not confident in its detection, we developed an additional verbal communication interface that enables the human to confirm or correct the autonomous decision. Therefore, the method has four modalities: (i) perturbation rejection mode, (ii) object property detection mode, (iii) verbal confirmation mode, (iv) voice control mode. We conducted proof-of-concept experiments on a teleoperation setup, where the human operator performed position tracking and contact establishing tasks., Accepted Author Manuscript, Human-Robot Interaction

Tele-impedance augments classic teleoperation by enabling the human operator to actively command remote robot stiffness in real-time, which is an essential ability to successfully interact with the unstructured and unpredictable environment. However, the literature is missing a study on benefits and drawbacks of different types of stiffness command interfaces used in bilateral tele-impedance. In this article, we introduce a term called coupling effect, which pertains to the coupling between human-commanded stiffness going to the remote robot and force feedback coming from the remote robot. We hypothesize that, whenever the operator's commanded stiffness and force feedback are subject to coupling effect (e.g., muscle activity based stiffness command interfaces), force feedback can invoke involuntary changes in the commanded stiffness due to human reflexes. Although the coupling effect takes away some degree of the operator's control over the commanded stiffness, these involuntary changes can be either beneficial (e.g., during position tracking) or detrimental (e.g., during force tracking) to the task performance on the remote robot side. We examined the coupling effect in an experimental study with $\mathbf {16}$ participants, who performed position and force tracking tasks by using both coupled type (muscle activity based) and decoupled type (external device based) of interface. The results demonstrate a benefit of the coupling effect when the remote robot is operating in presence of unexpected force perturbations, where lower absolute error in position tracking task was observed. On the other hand, the decoupled type of interface is beneficial for force tracking tasks on the remote robot side, such as establishing or maintaining a stable contact with objects. However, the coupling effect negatively influences the commanding of reference stiffness to the remote robot in both position and, Accepted Author Manuscript, Human-Robot Interaction

Modern aircraft can be equipped with a flight envelope protection system: automation which modifies pilot control inputs to ensure that the aircraft remains within the allowable limits. Overruling the pilot inputs may lead to mode confusion, even when visual or auditory feedback is provided to alert pilots. We advocate using active control devices to make the flight envelope protection system tangible to the pilot. This paper presents the main findings of an evaluation of three haptic feedback designs for flight envelope protection. The first concept used both force feedback and vibro-tactile alerts, producing promising, yet inconclusive, results. The second concept used asymmetric vibrations to give directional alerting cues, which did not result in improved performance on initial use, but which did yield improved learning rate for the task. The third system employed force feedback to physically guide the pilot away from flight envelope limits, which yielded safety improvements from the first use, but created dependence: pilot performance degraded immediately after the force feedback was removed. From this, we advise to use asymmetric vibrations during training for flight envelope excursions, to leverage active control interfaces for providing force feedback during operation, and reevaluate a combination of both to combine their advantages for single-pilot operations., Control & Simulation, Human-Robot Interaction

Modern aircraft use a variety of fly-by-wire control devices and combine these with a flight envelope protection system to limit pilot control inputs when approaching the aircraft limits. The current research project aims to increase pilot awareness of such a protection system through the use of force feedback on the control device, i.e., haptics. A previous design used asymmetric vibrations to cue the pilot on the flight envelope. The evaluation showed no improvement in metrics at the first emergency encounter, yet did show a potential training benefit. Therefore, a new haptic feedback concept was designed with the specific aim to guide the pilot when approaching a limit and provide support from the first time use. This paper evaluates these haptic feedback designs with 36 active PPL/LAPL pilots who flew a challenging vertical profile and encountered a windshear in a fixed-base simulator. The pilots were divided in three groups who received either cueing, guidance, or no haptic feedback. It was hypothesized that: (i) cueing haptic feedback provides a faster learning rate compared to no-haptics, and (ii) guidance haptic feedback results in best performance from the first run yet worse metrics when no feedback is provided. Comparing the results of the cueing and no-haptic feedback groups confirmed the first hypothesis. Results also showed that the guidance haptic feedback resulted in improved metrics at the first run, and the worsening of metrics when no longer provided., Virtual/online event due to COVID-19, Control & Simulation, Human-Robot Interaction

Current predictors of fuel consumption are typically based on computer simulations or data collections in real traffic, where the route and vehicle type are not under the researcher's control. Here, we predicted fuel consumption using test track data, an approach that allowed for location-specific predictions. Ninety-one drivers drove a total of 4617 laps, in two vehicles (Renault Mégane, Renault Clio), on two routes (highway and mountain), and with two eco-driving instructions (normal and eco). A multivariate analysis at the level of laps showed a strong predictive value for metrics related to speed, RPM, and throttle position, but with a considerable amount of variance attributable to route and vehicle type. A subsequent location-specific analysis showed that the predictive correlation of driving speed and throttle position fluctuated strongly during the lap and at some locations even became negative. We conclude that there is considerable potential in instantaneous location-specific prediction of fuel consumption., Human-Robot Interaction

In the context of human-agent interaction, we see the emergence of computational artefacts that display hybridity because they can be experienced as tools and agents. In this paper we propose a tool-agent spectrum as an analytical lens that uses ‘intention’ as a central concept. This spectrum aims to clarify how a computational object can change from being conducive to the intentions of others (‘tool’) to appearing to have intentions of its own (‘agent’), or vice versa. We have applied this analytical lens to unravel people’s experiences in two hybrid cases; guide dogs as a living mobility aid for the visually impaired and an experimental wearable object named “BagSight” as a rudimentary artificial counterpart. We compared both cases through the lens of a toolagent spectrum and elaborate on these results by discussing some of the principles by which computational artefacts can shift across the spectrum. We conclude by discussing the limitations of this study and provide suggestions for future work., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Human Information Communication Design, Applied Ergonomics and Design, Human-Robot Interaction, Interactive Intelligence

Gibson and Crooks (1938) argued that a ‘field of safe travel’ could qualitatively explain drivers' steering behavior on straights, curved roads, and while avoiding obstacles. This study aims to quantitatively explain driver behavior while avoiding obstacles on a straight road, and quantify the ‘Driver's Risk Field’ (DRF). In a fixed-based driving simulator, 77 (7 longitudinal and 11 lateral) positions of the obstacles were used to quantify the subjectively perceived and objectively (maximum absolute steering angle) measured DRF for eight participants. The subjective response was a numerical answer to the question “How much steering do you think you need at this moment in time?” The results show that the propagation of the width of the DRF, along the longitudinal distance, resembled an hourglass shape, and all participants responded to obstacles that were placed beyond the width of the car. This implies that the Driver's Risk Field is wider than the car width., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Human-Robot Interaction

Advances in exoskeleton technology now enable interacting with rigid objects in a virtual or remote environment using one's hand and fingertips. However, interaction with non-solid materials - such as liquids, sediments and regolith - alongside solids, can greatly extend the versatility of this technology. Rendering rigid objects adequately requires a control loop with high update rates, whereas fluid dynamics equations are computationally expensive. To accommodate this, the fluid dynamics can be simplified - particularly for fluids with high viscosity - resulting in a fast-to-calculate model to enabling haptic rendering of viscous fluids and rigid bodies simultaneously using DLR's Exodex Adam hand exoskeleton. Viscosity as a proprioceptive cue of fluids can be presented to the human through force feedback at multiple points on the human hand - fingers and palm - letting the user interact with a virtual environment in a more natural way and making the experience more immersive. We carry out two user studies to investigate the human perception abilities of virtual fluids rendered with simplified dynamics, and the discernability of different viscosity in virtual fluids compared real fluids. Results show that virtual media can give the user the perception of interacting with a fluid, even with simplified models, at a high update frequency. Furthermore, the material discernibility corresponds well to actual interaction with real viscous fluids. This shows great promise forward for haptic in-hand interaction in fluid and mixed media environments., Accepted Author Manuscript, Human-Robot Interaction

When drivers encounter a road narrowing two potential adaptation strategies come into play that may increase safety margins: decreasing speed and increasing neuromuscular stiffness of the arms. These two adaption strategies have so far been studied in isolation. We expect that there is a trade-off between these two strategies, and that risk duration would impact a driver's selection of the trade-off. Specifically, we hypothesized that for a short risk duration, drivers will favour increased neuromuscular stiffness over speed reduction; and vice versa for longer risk durations. Twenty-six participants drove in a driving simulator and encountered different risk durations; realized by road narrowings (from 3.6 m to 2.2 m) of varying lengths (10 m, 100 m, 250 m, and 500 m). The neuromuscular stiffness was quantified by measuring the grip force exerted by both hands. The results show that all road narrowing conditions successfully induced driver adaptations, as a significant reduction in speed and increase in grip force was observed. However, the tested drivers did not consistently select the hypothesized different trade-offs for increasing duration of road narrowing: a low correlation was found between speed and grip force adaptations. Interestingly, individual trade-off were consistent: the within-subject variability in speed-grip force adaptations was low across the tested risk durations. Future research should further elucidate the underlying motivations for these individual adaptation strategies., Accepted Author Manuscript, Human-Robot Interaction

In this paper, we introduce and explore a concept called coupling effect, which pertains to the influence of force feedback on the commanded stiffness that is voluntarily controlled by the operator through the stiffness interface during bilateral tele-impedance. The degree of coupling effect depends on the type of interface used to control the impedance of the remote robot. In case of muscle activity based stiffness command interfaces, the force feedback can invoke involuntary changes in the commanded stiffness due to human reflexes. These involuntary changes can be either beneficial (e.g., during position tracking) or detrimental (e.g., during force tracking) to the task performance on the remote robot side. To investigate the coupling effect in different types of stiffness command interfaces (i.e., coupled and decoupled), we conduct an experimental study in which participants are asked to perform position and force tracking tasks. The results show that in both position and force tracking tasks a lower tracking error of the reference stiffness is obtained with a decoupled interface (p<0.001). However, the unexpected force perturbation yields lower absolute position error when using a coupled interface (p=0.0091), which indicates a specific benefit of the coupling effect. Finally, a lower absolute force error is found in the force tracking task by using the decoupled interface (p<0.001), which indicates a specific downside of the coupling effect., Accepted Author Manuscript, Human-Robot Interaction

Current driving behaviour models are designed for specific scenarios, such as curve driving, obstacle avoidance, car-following, or overtaking. However, humans can drive in diverse scenarios. Can we find an underlying principle from which driving behaviour in different scenarios emerges? We propose the Driver’s Risk Field (DRF), a two-dimensional field that represents the driver’s belief about the probability of an event occurring. The DRF, when multiplied with the consequence of the event, provides an estimate of the driver’s perceived risk. Through human-in-the-loop and computer simulations, we show that human-like driving behaviour emerges when the DRF is coupled to a controller that maintains the perceived risk below a threshold-level. The DRF model predictions concur with driving behaviour reported in literature for seven different scenarios (curve radii, lane widths, obstacle avoidance, roadside furniture, car-following, overtaking, oncoming traffic). We conclude that our generalizable DRF model is scientifically satisfying and has applications in automated vehicles., Human-Robot Interaction

Subjective experience of human control over remote, artificial, or virtual limbs has traditionally been investigated from two separate angles: presence research originates from teleoperation, aiming to capture to what extent the user feels like actually being in the remote or virtual environment. Embodiment captures to what extent a virtual or artificial limb is perceived as one's own limb. Unfortunately, the two research fields have not interacted much. This survey intends to provide a coherent overview of the literature at the intersection of these two fields to further that interaction. Two rounds of systematic research in topic-related data bases resulted in 414 related articles, 14 of which satisfy the deliberately strict inclusion criteria: 2 theoretical frameworks that highlighted intersections and 12 experimental studies that evaluated subjective measures for both concepts. Considering the surrounding literature as well, theoretical and experimental potential of embodiment and presence are discussed and suggestions to apply them in teleoperation research are derived. While increased publication activity is observed between 2016 and 2018, potentially caused by affordable virtual reality technologies, various open questions remain. To tackle them, human-in-the-loop experiments and three guiding principles for teleoperation system design (mechanical fidelity, spatial bodily awareness, and self-identification) are suggested., Human-Robot Interaction

Objective: This study aims to compare the effectiveness and subjective acceptance of three designs for haptic lane-keeping assistance in truck driving. Background: Haptic lane-keeping assistance provides steering torques toward a reference trajectory, either continuously or only when exceeding a bandwidth. These approaches have been previously investigated in driving simulators, but it is unclear how these generalize toward real-life truck driving. Method: Three haptic lane-keeping algorithms to assist truck drivers were evaluated on a 6.3-km-long oval-shaped test track: (1) a single-bandwidth (SB) algorithm, which activated assistance torques when the predicted lateral deviation from lane center exceeded 0.4 m; (2) a double-bandwidth (DB) algorithm, which activated as SB, but deactivated after returning within 0.15 m lateral deviation; and (3) an algorithm providing assistance torques continuously (Cont) toward the lane center. Fifteen participants drove four trials each, one trial without and one for each haptic assistance design. Furthermore, participants drove with and without a concurrent visually distracting task. Results: Compared to unsupported driving, all three assistance systems provided similar safety benefits in terms of decreased absolute lateral position and number of lane departures. Participants reported higher satisfaction and usability for Cont compared to SB. Conclusion: The continuous assistance was better accepted than bandwidth assistance, a finding consistent with prior driving simulator research. Research is still needed to investigate the long-term effects of haptic assistance on reliance and after-effects. Application: The present results are useful for designers of haptic lane-keeping assistance, as driver acceptance and performance are determinants of reliance and safety, respectively., Human-Robot Interaction

Modern aircraft use a variety of fly-by-wire control devices and combine these with a flight envelope protection system to limit pilot control inputs when approaching the aircraft limits. The current research project aims to increase pilot awareness of such a protection system through the use of force feedback on the control device, i.e., haptics. This paper describes a new iteration of a design with the specific aim to warn the pilot when approaching a limit and provide a clear direction of suggested control input. This is achieved by using vibrations asymmetric in both amplitude, i.e. the mean of the signal is non-zero, and time, i.e. a cue which has a rise time different from the fall time. An evaluation is performed where 24 active PPL/LAPL pilots flew a challenging vertical profile and encountered a windshear. The pilots are divided in two groups: one group performing four flights with haptic feedback, followed by four without, the other groups has a reversed order. Although acceptance ratings slightly improved when providing haptic feedback, the other metrics are unchanged when switching between haptic feedback conditions, due to a large training effect during the first four runs. The results do show that enabling the haptic feedback does seem to improve the learning rate over the first runs, and no after effects are present when feedback is removed. As such, next to the fact that most pilots indicated that they expect an improved safety, this experiment shows a potential training benefit of haptic feedback., Control & Simulation, Human-Robot Interaction

Current driving behaviour models are designed for specific scenarios, such as curve driving, obstacle avoidance, car-following, or overtaking. However, humans can drive in diverse scenarios. Can we find an underlying principle from which driving behaviour in different scenarios emerges? We propose the Driver’s Risk Field (DRF), a two-dimensional field that represents the driver’s belief about the probability of an event occurring. The DRF, when multiplied with the consequence of the event, provides an estimate of the driver’s perceived risk. Through human-in-the-loop and computer simulations, we show that human-like driving behaviour emerges when the DRF is coupled to a controller that maintains the perceived risk below a threshold-level. The DRF model predictions concur with driving behaviour reported in literature for seven different scenarios (curve radii, lane widths, obstacle avoidance, roadside furniture, car-following, overtaking, oncoming traffic). We conclude that our generalizable DRF model is scientifically satisfying and has applications in automated vehicles., Human-Robot Interaction

In the context of human-agent interaction, we see the emergence of computational artefacts that display hybridity because they can be experienced as tools and agents. In this paper we propose a tool-agent spectrum as an analytical lens that uses ‘intention’ as a central concept. This spectrum aims to clarify how a computational object can change from being conducive to the intentions of others (‘tool’) to appearing to have intentions of its own (‘agent’), or vice versa. We have applied this analytical lens to unravel people’s experiences in two hybrid cases; guide dogs as a living mobility aid for the visually impaired and an experimental wearable object named “BagSight” as a rudimentary artificial counterpart. We compared both cases through the lens of a toolagent spectrum and elaborate on these results by discussing some of the principles by which computational artefacts can shift across the spectrum. We conclude by discussing the limitations of this study and provide suggestions for future work., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Human Information Communication Design, Applied Ergonomics and Design, Human-Robot Interaction, Interactive Intelligence