Robotic technologies are often proposed to relieve dull, dirty, or dangerous work, but may cause work to instead be experienced as boring, or dehumanized. Understanding the impact of robotics on the workfloor is complicated by the entanglement between emerging robotic capabilities, social dynamics, and organizational issues—which we call Worker–Robot Relations. Consequently, the impact of robotics on work is often studied in hindsight. Speculative design methodologies can facilitate alignment of robotic developments with a meaningful future of work, by creating boundary objects for communicating about current and future work practices. Making use of an unfolding artistic collaboration, we propose an experiential approach for speculating about future Worker–Robot Relations. We enabled speculative encounters between participants and robotic creatures that embody seven meta-behaviors. We abstracted these from observed behaviors in a current work context in baggage handling. We present the findings from focus groups responding to these encounters, including implications for HRI and speculative design.

This full-day workshop is dedicated to mapping and building a community toward shaping sustainable futures of robot-assisted work, with and for workers. The program encompasses lightning keynotes from HRI experts, talks from selected participants bringing case studies, and a hands-on mapping activity for understanding the landscape of robotics innovation at work - in order to foster knowledge exchange and learning. The workshop culminates in a facilitated conversation with workers and managers from a particular case study, providing real-world insights into the challenges, complexities and opportunities for our HRI community in shaping the future of work. With this rich set of activities, the program aims to bridge the gap between academics and practitioners, and to promote a holistic understanding of our roles in shaping the future of work, with particular attention to systemic challenges and value-driven approaches. The workshop will conclude with a synthesis of key takeaways and potential directions for future research and community building.

Teleoperation is a crucial technology enabling human operators to control robots remotely to perform tasks in hazardous and difficult-to-access environments. Tasks in such environments often involve complex physical interactions with tools and objects of various softness. To this end, teleimpedance enables the operators to adjust the robot impedance in real-time to simplify such interactions. While the existing teleimpedance approaches provide several interfaces to command the robot impedance, there are no interfaces to visualize both the commanded impedance and that of the objects to be interacted with. This paper presents a novel interface to provide visual feedback on the impedance of remote robots and objects. To do so, we use virtual stiffness ellipsoids and different modes that display the individual impedance of the robot and objects as well as combined post-contact impedance. The key advantage of visual feedback on the impedance compared to force feedback is that the operator can see the interaction characteristics before the contact occurs. This enables the operator to act proactively before contact rather than just reactively after the contact. This paper also proposes a new intuitive way to command the robot impedance using mixed reality, interacting with these ellipsoids and modifying them as needed. To demonstrate the key functionalities of the developed interface, we performed proof-of-concept experiments on teleoperated tasks.

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.

Traffic jams occurring on highways cause increased travel time as well as increased fuel consumption and collisions. So-calledphantom traffic jamsare traffic jams that do not have a clear cause, such as a merging on-ramp or an accident. Phantom traffic jams make up 50% of all traffic jams and result from instabilities in the traffic flow that are caused by human driving behavior. Automating the longitudinal vehicle motion of only 5% of all cars in the flow can dissipate phantom traffic jams. However, driving automation introduces safety issues when human drivers need to take over the control from the automation. We investigated whether phantom traffic jams can be dissolved using haptic shared control. This keeps humans in the loop and thus bypasses the problem of humans' limited capacity to take over control, while benefiting from most advantages of automation. In an experiment with 24 participants in a driving simulator, we tested the effect of haptic shared control on the dynamics of traffic flow and compared it with manual control and full automation. We also investigated the effect of two control types on participants' behavior during simulated silent automation failures. Results show that haptic shared control can help dissipating phantom traffic jams better than fully manual control but worse than full automation. We also found that haptic shared control reduces the occurrence of unsafe situations caused by silent automation failures compared to full automation. Our results suggest that haptic shared control can dissipate phantom traffic jams while preventing safety risks associated with full automation.

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.

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.

Robotic teleoperation is used in various applications, including the nuclear industry, where the experience and intelligence of a human operator are necessary for making complex decisions that are beyond the autonomy of robots. Human-robot interfaces that help strengthen an operators situational awareness without inducing excessive cognitive load are crucial to the success of teleoperation. This paper presents a novel visual interface that allows operators to simultaneously control a 6-DoF camera platform and a robotic manipulator whilst experiencing the remote environment through a virtual reality head-mounted display (HMD). The proposed system, Orbital Head-Mounted Display (OHMD), utilizes head rotation tracking to command camera movement in azimuth and elevation directions around a fixation point located at a robot's end-effector. A human factor study was conducted to compare the interface acceptance, perceived workload, and task performance of OHMD with a conventional interface utilizing multiple fixed cameras (Array) and a standard head-mounted display implementation (HMD). Results show that both the OHMD and HMD interfaces significantly improve task performance, reduce perceived workload and increase interface acceptance compared to the Array interface. Participants reported they preferred OHMD due to the increased assistance and freedom in viewpoint selection. Whilst OHMD excelled in usefulness, the standard HMD interface allowed operators to perform robotic welding tasks significantly faster.

Recently, multiple naturalistic traffic datasets of human-driven trajectories have been published (e.g., highD, NGSim, and pNEUMA). These datasets have been used in studies that investigate variability in human driving behavior, for example for scenario-based validation of autonomous vehicle (AV) behavior, modeling driver behavior, or validating driver models. Thus far, these studies focused on the variability on an operational level (e.g., velocity profiles during a lane change), not on a tactical level (i.e., to change lanes or not). Investigating the variability on both levels is necessary to develop driver models and AV s that include multiple tactical behaviors. To expose multi-level variability, the human responses to the same traffic scene could be investigated. However, no method exists to automatically extract similar scenes from datasets. Here, we present a four-step extraction method that uses the Hausdorff distance, a mathematical distance metric for sets. We performed a case study on the highD dataset that showed that the method is practically applicable. The human responses to the selected scenes exposed the variability on both the tactical and operational levels. With this new method, the variability in operational and tactical human behavior can be investigated, without the need for costly and time-consuming driving-simulator experiments.

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.

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 article, 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.

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.

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 with16participants, 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 force tracking tasks for the coupled type of interface, compared to the decoupled type of interface, which is not affected.

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 tool-agent 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.

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.

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.

Humans can rapidly change their low-frequency arm dynamics to resist forces or give way to them. Quantifying driver time-varying arm dynamics is important to develop steer-by-wire and haptic support systems. Conventional linear time-invariant (LTI) identification, and even time-varying techniques such as wavelets, fail to capture fast changing dynamics. Moreover, such techniques require perturbation signals on the steering wheel (SW), which may affect steering feel and control behavior. We propose a novel two-step method to estimate time-varying driver admittance, using unobtrusive grip-force measurements of the hands on the wheel to schedule a linear parameter-varying (LPV) model that captures the full admittance range. A total of 18 subjects participated in two experiments in a simulator with an actuated SW. In a sensorimotor control experiment, we first establish the grip force and admittance relationship, requiring subjects to perform a boundary tracking task where perturbations on the wheel enabled local LTI identification. Six boundary widths is used to evoke admittance changes, after which a global LPV model is obtained through interpolation between the local models. Results show an inverse relationship between grip force and admittance and that the LPV model accurately captures the admittance settings (fit percentage > 90%). Second, a driving experiment is followed that aims to evoke differences in grip force and admittance in response to varying road widths, offering more realistic data to evaluate the LPV model predictions. Results show that the LPV model accurately describes adaptations in admittance to road width. Our method allows for online estimation of time-varying admittance during driving, without applying force perturbations.

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.

Did you know that most drivers swing left before taking a right curve? In fact, this is a given for all race car drivers and a rule for efficient curve negotiation. This distinct way of approaching a curve is called prepositioning. In a recent study it is found that incorporating knowledge of this prepositioning phase is crucial for the reliability and acceptance of some trajectory-guiding advanced-driver-assistance-systems. Unfortunately, our understanding of prepositioning behaviour is still limited, with most driver models unable to account for this phenomenon. In an attempt to improve our understanding, the effects of changing velocity and road radius on prepositioning behaviour are studied experimentally in a fixed-base driving simulator. Twenty-four participants drove four conditions comprising two different fixed-speed velocities (50 and 80 km/h) and two different curve radii (204 and 350 m). The results show that the drivers' maximum prepositioning position significantly increases with increasing velocity and significantly decreases with increasing radius. With 88% of the runs exhibiting a significant displacement, i.e. larger than 0.05 m relative to a constant road bias. The findings suggest that drivers adjust their prepositioning behaviour to the Time-to-Line-Crossing (TLC) of the road environment, in an attempt to maximise TLC. Incorporating these findings in future driver modelling will bridge the gap between straight road and in-curve driving behaviour, thereby bolstering the descriptive capacity of these models.

When taking a curve, drivers follow their own unique trajectory. Most driver style classifiers in literature are based on inertial inputs, denoting whether a given driver is aggressive or calm. However, this does not give any indication of a drivers trajectory style, i.e. whether a driver is curve cutting. To fill this void, this paper introduces a novel rule based classifier that categorises seven different trajectory styles. The classifier is applied to data from a fixed-base driving simulator study in which 45 subjects drove on three roads, comprising three different velocities: 25, 50 and 80 km/h, with three corresponding radii: 20, 80 and 204 m. The results show that some classes are more prevalent than others, with biased outer curve negotiation performed by a majority of the subjects and with no drivers classified as centerline drivers. The proposed trajectory classifier is shown to exhibit high levels of consistency, with 93% of drivers exhibiting consistent trajectory classes for at least 66% of the right curves driven and 84% exhibits consistent trajectory classes for atleast 66% of the left curves driven. Where this consistency indicates a potential for generalising the classification results to other curves. Additionally, this classifier can be used to adapt trajectory-driven advanced driver assistance systems, thereby serving as an alternative to driver modelling.

Time delays in haptic teleoperation affect the ability of human operators to assess mechanical properties (damping, mass, and stiffness) of the remote environment. To address this, we propose a unified framework for human haptic perception of the mechanical properties of environments with delayed force feedback. In a first experiment, we found that the delay in the force feedback led our subjects to underestimate all the three mechanical properties. Moreover, subjects perceived additional damping or stiffness properties that the environment did not possess. It was found that the extents of these changes in the perception depend on both time-delay magnitude and the frequency of the movement with which subjects interacted with the environment. This was due to the fact that subjects were not able to distinguish the delay-caused phase shift in the movement-force relation from changes in the three mechanical properties. Based on this, we proposed a framework that allowed for a prediction of the change associated with delayed force in perception of mass-spring-damper environments. The framework was corroborated by a second experiment, in which a combined mass-damper environment was tested. Our hypotheses that the delay would cause subjects to underestimate the mass but overestimate the damping and that the extents of the under- A nd overestimation would differ between individual subjects due to the difference in the interaction frequency were confirmed.

Understanding speed control in driving is important for analysis of road geometry and for the development of driver support assistance devices. Current models for speed selection are primarily based on the relation between road geometry and observed speeds. This study proposes a more detailed model that relates individual speed control to accelerator and brake pedal, based on perception of the visual scene as captured by the Extended Tangent Point (ETP). We investigated the potential of the the Time to ETP (TETP) as input for accelerator and brake pedal control. Based on observations from driving studies, we propose a model, and tuning rules to adjust the model parameters to observed behavior. A simulator experiment showed that, after individualization of the thresholds using a binary classification method, the model is capable of accurately capturing individual speed adaptation of 15 drivers on single lane roads with multiple curves.

When drivers have opposing intentions to a haptic shared controller which, like the driver, can continuously control the vehicle through torques on the steering wheel, the driver has to fight against the controller torque to reach their goal. This phenomenon is called haptic shared control (steering) conflicts and are a reason for drivers to reject such automation. This study is the first to realise an implementation of the novel "Four-Design-Choice-Architecture" design philosophy for shared control, hypothesized to reduce conflicts through its inherent control structure. The implemented haptic shared controller decouples reference trajectory from independent feedback and feedforward haptic control. The implemented Four-Design-Choice haptic shared controller is compared to the baseline (predecessor) Meshed haptic shared controller through a simulator experiment. The results show that the new shared controller significantly reduces occurrence of conflicts by a factor 2.3 and significantly reduces driver torque by a factor of 3.2. Analysis shows that the novel feed-forward haptic torque and a reference trajectory supporting the drivers future (curve-entry) intentions are the dominant players in conflict reduction. The findings show that the Four-Design-Choice-Architecture is proven very effective and has large potential to further reduce conflicts with different design settings.

Shared control is an increasingly popular approach to facilitate control and communication between humans and intelligent machines. However, there is little consensus in guidelines for design and evaluation of shared control, or even in a definition of what constitutes shared control. This lack of consensus complicates cross fertilization of shared control research between different application domains. This paper provides a definition for shared control in context with previous definitions, and a set of general axioms for design and evaluation of shared control solutions. The utility of the definition and axioms are demonstrated by applying them to four application domains: automotive, robot-assisted surgery, brain-machine interfaces, and learning. Literature is discussed for each of these four domains in light of the proposed definition and axioms. Finally, to facilitate design choices for other applications, we propose a hierarchical framework for shared control that links the shared control literature with traded control, co-operative control, and other human-automation interaction methods. Future work should reveal the generalizability and utility of the proposed shared control framework in designing useful, safe, and comfortable interaction between humans and intelligent machines.

The purpose of this study is to develop and validate a human-like steering model that can capture, not only the mean, but also the intradriver variability (IDV) of steering behavior, in both routine and emergency scenarios. The IDV model proposed in this study is based on the assumption that steering behavior, in both scenarios, is governed by the same principles as performing point-to-point reaching tasks. The optimal feedback control framework that models the reaching tasks, and the presence of signal-dependent noise in motor commands and sensory feedback, are the mainstays of the proposed model. The driver is assumed to have acquired an internal model of system (muscles, arms, and vehicle) dynamics, and has a preview of the upcoming road. The model is validated using simulator-based data from both routine (curve negotiation) and emergency (obstacle avoidance) scenarios. The IDV model could capture mean steering torque behavior in both routine (variance accounted for (VAF) = 92%) and emergency (VAF = 74%) scenarios, but more prominently, it could capture the standard deviation of the steering torque as well, in both routine (VAF = 83%) and emergency (VAF = 65%) scenarios. The promising results show that including signal-dependent noise and modeling steering as a reaching task are steps in the right direction in the field of driver modeling. The model, however, poorly captured the lateral deviation behavior, primarily suspected due to the satisficing behavior exhibited by humans. Developing a nonlinear-iterative version of the IDV model could address the limitations.

Manual control cybernetics aims to understand and describe how humans control vehicles and devices using mathematical models of human control dynamics. This “cybernetic approach” enables objective and quantitative comparisons of human behavior, and allows a systematic optimization of human control interfaces and training associated with manual control. Current cybernetics theory is primarily based on technology and analysis methods formalized in the 1960s and has shown to be limited in its capability to capture the full breadth of human cognition and control. This paper reviews the current state-of-the-art in our knowledge of human manual control, points out the main fundamental limitations in cybernetics, and proposes a possible roadmap to advance the theory and its applications. Central in this roadmap will be a shift from the current linear time-invariant modeling approach that is only truly valid for human behavior under tightly controlled and stationary conditions, to methods that facilitate the analysis of adaptive, and possibly time-varying, human behavior in realistic control tasks. Examples of key current developments in the field of cybernetics-human use of preview, predictable discrete maneuvering, skill acquisition and training, time-varying human modeling, and neuromuscular system modeling-that contribute to this shift are presented in this paper. The new foundations for cybernetics that will emerge from these efforts will impact all domains that involve humans in manual and semiautomatic control.

Emerging technologies in the field of automatization meanwhile enable partially and highly automated vehicles in the aviation and automotive domains, where the human operators are assisted or (partially/temporarily) replaced in their tasks by advanced automation systems. Nevertheless due to technological limitations and ethical reasons, full autonomous vehicles in both domains might not be realizable in the near future. Instead system designs, which enable shared and cooperative guidance and control of vehicles by human operators and automation systems could be more feasible solutions. This paper sketches a common framework of shared and cooperative control that describes the two concepts not as different but as coinciding concepts for the shared intentionality, control and cooperation between humans and machines. A brief overview off developed shared and cooperative control designs in the aviation and ground vehicle domain is given.

The interaction between a human driver and an automated driving system may improve when the automation is designed in such a way that it behaves in a human-like manner. This paper introduces a human-like steering model, in which the driver adapts to the risk due to uncertainty in the environment. Current steering models take a risk-neutral approach, while the fields of economics and sensorimotor control suggest that humans exhibit risk-sensitive behavior. The proposed model uses a risk-sensitive optimal feedback control structure to predict steering behavior. The paper studies the effect of the risk-sensitivity parameter and compares the prediction of the risk-neutral and risk-sensitive controllers in a simulated abstraction of two scenarios: (a) driving while being subjected to lateral wind gusts and (b) overtaking an unpredictably swerving car. The simulation results show that the risk-sensitive model adapts to the uncertainty in the environment. Experimental data will be needed to validate the predictions of our model.

Biodynamic feedthrough (BDFT) refers to the feedthrough of vehicle accelerations through the human body, leading to involuntary control device inputs. BDFT impairs control performance in a large range of vehicles under various circumstances. Research shows that BDFT strongly depends on adaptations in the neuromuscular admittance dynamics of the human body. This paper proposes a model-based approach of BDFT mitigation that accounts for these neuromuscular adaptations. The method was tested, as proof-of-concept, in an experiment where participants inside a motion simulator controlled a simulated vehicle through a virtual tunnel. Through evaluating tracking performance and control effort with and without motion disturbance active and with and without cancellation active, the effectiveness of the cancellation was evaluated. Results show that the cancellation approach is successful: the detrimental effects of BDFT were largely removed.

Haptic guidance is a promising way to support unmanned aerial vehicle (UAV) operators, but the design of haptic guidance forces is often heuristic. This paper describes the design and experimental validation of a systematic neuromuscular analysis-based tuning procedure for haptic guidance, here applied to haptic collision avoidance system for UAV teleoperation. This tuning procedure is hypothesized to reduce operator workload as compared with current heuristic tuning methods. The proposed procedure takes into consideration the estimated mechanical response of the neuromuscular system (NMS) to haptic cues. A “relax-task” setting of the NMS, for which reflexive and muscular activation is minimal, is chosen as the design point for tuning the haptic support, as this setting is expected to yield minimal physical workload. The paper first presents a neuromuscular identification experiment, performed to estimate the “relax task” admittance of an operator's arm. The averaged admittance of a group of subjects (n=10) was then used for tuning the haptic shared controller, which was subsequently evaluated in its ability to support different operators (n=12) in a simulated unmanned aerial vehicle surveillance task. Results show that our novel tuning procedure indeed reduces operator workload and also improves situation awareness compared with haptic settings that ignore the NMS. In fact, it is shown that overtuning, which frequently occurs for these heuristically tuned systems, leads to even lower user acceptance scores than interfaces without any haptic support.

Remote control of vehicles is a difficult task for operators. Support systems that present additional task information may assist operators, but their usefulness is expected to depend on several factors such as 1) the nature of conveyed information, 2) what modality it is conveyed through, and 3) the task difficulty. In an exploratory experiment, these three factors were manipulated to quantify their effects on operator behavior. Subjects ( n = 15 ) used a haptic manipulator to steer a virtual nonholonomic vehicle through abstract environments, in which obstacles needed to be avoided. Both a simple support conveying near-future predictions of the trajectory of the vehicle and a more elaborate support that continuously suggests the path to be taken were designed (factor 1). These types of information were offered either with visual or haptic cues (factor 2). These four support systems were tested in four different abstracted environments with decreasing amount of allowed variability in realized trajectories (factor 3). The results show improvements for the simple support only when this information was presented visually, but not when offered haptically. For the elaborate support, equally large improvements for both modalities were found. This suggests that the elaborate support is better: additional information is key in improving performance in nonholonomic steering tasks.

We conducted an experiment in our fixed-base driving simulator to determine whether participants experienced arm muscle fatigue during three, one-hour driving sessions. Participants experienced different rotational vibrations on the steering wheel, each representative of a different kind of steering system. Our main hypothesis was that a 'Conventional' steering system would result in more muscle fatigue during prolonged driving than a 'Steer By Wire' steering system. The results of our experiment confirmed the hypothesis for the subjectively experienced muscle fatigue of the arms. We conclude that the subjective results could be related to the larger pool of additional motor units available for recruitment as time progresses during the driving task. Hence, the lower forces and the smaller vibrations of the 'Steer By Wire' system allowed for longer exposure to the steering vibrations compared to a 'Conventional' steering system before 'saturation' of the motor unit recruitment set in and objective muscle fatigue could be observed.

Hap tic shared control systems combine control inputs of driver and intelligent vehicle by means of forces at the steering wheel, establishing a physical link combining the strengths of each agent. The majority of hap tic shared control systems generates forces in response to the driving environment in a 'one size-fits-all' manner, without adapting the feedback to the requirements and preferences of individual drivers. This results in conflicts that could be avoided by allowing the hap tics shared to adapt to the environments in a human-like way. Drivers can easily adapt their neuromuscular system to changing steering dynamics, but little is known how drivers adapt it to changes in the driving environment. To enable individualized shared control we need to extend our understanding of human adaptation to environmental factors during driving. This paper describes a fixed-base driving simulator experiment that shows increased speed and reduced road width both decrease neuromuscular admittance of the driver.

Shared control is an exciting up-and-coming engineering field that is blending the boundaries of control, where humans interact with robots or vehicles that are partly automated. The main challenges for robotics and automation today are posed by the less structured, unpredictable environments, in which humans operate naturally, especially when they need to interact with other humans. As a result, many semi-automated systems today need to be supervised by human operators, but as fast as the levels of automation of many systems are increasing, we also need to speed up how we think about what this implies for human-machine interaction. The widely applied paradigm of human-centered automation has been popular and useful for the past two decades, but it requires an update with the current trend in automation becoming more ubiquitous in human environments and less dependent on explicit input from the human operator. In contrast to the supervisory control paradigm---where control is traded between human and machine---the shared control paradigm implicitly assumes the interaction between two or more independent agents that together perform a task to achieve a common goal. This implies that the design of the shared control system is not necessarily only human centered. In shared control systems, all the acting agents need to be aware of the others' capabilities, weaknesses, and authority. Hence, reciprocal communication of each agent's operational boundaries, whether human or machine, is essential.

Haptic shared control systems aim to guide drivers during steering using guidance torques. Many such systems generate torques using a simplified and constant lane-keeping model based on a look-ahead controller, without accounting for individual differences. Literature on haptic steering support shows beneficial effects (reduced control activity and increased performance) under experimental conditions, but also report increased steering torques. In this study, we hypothesized that increased interacting torques are the results of small conflicts between human and a constant haptic support system and that these conflicts may be mitigated by adapting the parameters of the look-ahead controller to best match each individual driver, essentially providing individualized guidance torques. Results showed that this approach provides a better match in terms of desired steering wheel angles, however this did not lead to a relevant reduction in steering torques, as discrepancies in timing and lateral error continued to exist. Future adaptations to the haptic shared control algorithm should take more realistic driver control models into consideration.

During operation of slow dynamic systems such as heavy machinery, users must account for inherent lag in the system dynamics, often via the less intuitive rate control mode. The slow response of these systems requires predictive control based on an understanding of the input-output relationship of system dynamics. In practical applications, such as learning to control an excavator, training can be a long and therefore costly process. This paper investigates the use of haptic shared control (HSC) to support learning of a system with slow dynamics. Previous work has failed to reach a consensus on the effectiveness of training with HSC, although a few recent studies have demonstrated improvements in tasks with time-critical components. Here, subjects learned to perform a pursuit task while controlling a linear system with slow dynamics using a 1-DOF haptic manipulator, either with or without HSC during training. To prevent reliance on the guidance forces, HSC was only present on intermittent trials and decreased in strength over time. Both groups quickly learned the task and showed similar performance after training, regardless of whether or not they trained with HSC.

We predominantly rely on our (intermittent) visual perception of the environment to perform a driving task safely, robustly, and with sufficient experience seemingly effortless and comfortably. Research has shown that haptic shared control can improve car-following performance, curve negotiation, evasive manoeuvring, eco-driving and even adhering to speed limits, while reducing driver effort to do so. While these results show the versatility of the application of haptic guidance the improvements in performance were usually significant but relatively small compared to the visual only conditions. The goal of the research presented in this article was to gather empirical data to determine the information content contained in haptic feedback to present essential steering control information to drivers during curve negotiation. We hypothesized that the absence of essential visual information from the environment critical to lateral control of the vehicle - either near or far visual information - could adequately be compensated for by haptic guidance on the steering wheel. The results of our fixed-base driving simulator experiment show that our haptic guidance system is most effective in compensating for loss of near visual information, which guides the compensatory control process.

Biodynamic feedthrough (BDFT) is the feedthrough of vehicle accelerations through the human body, leading to involuntary control device inputs. BDFT is a relevant problem as it reduces control performance in a large range of vehicles under various circumstances. This paper proposes an approach to mitigate BDFT. What differentiates this method from other mitigation approaches is that it accounts for adaptations in the neuromuscular dynamics of the human body. It is known that BDFT is strongly dependent on these dynamics. The approach was tested, as proof-of-concept, in an experiment in a motion simulator where participants were asked to fly a simulated vehicle through a virtual tunnel. By evaluating the performance with and without motion disturbance active and with and without cancellation active, the performance of the cancellation approach was evaluated. Results showed that the cancellation approach was successful. The detrimental effects of BDFT, such as a decrease in control performance and increase in control effort, were largely removed.

For many applications such as steer-by-wire or telemanipulation, it remains unclear what tasks benef t most from accurate rendering of feedback forces. The objective of this study is to understand the extent to which haptic feedback of system dynamics helps performance of free-space telemanipulation tasks. In a human factors study, subjects performed a multisine pursuit task using two virtual slave systems, one with fast and one with slow dynamics. The dynamics of the controlled system were fed back fully, scaled or not at all. It was expected that task execution would improve when the boundaries of the controlled system were made tangible to the human operator by means of force feedback, but only for a slow dynamic system. Results show that, for a slow dynamic system, both full and scaled feedback of system dynamics reduce tracking error and excessive control input, at the expense of increased physical control effort. It is concluded that physically displaying the boundaries of the controlled system contribute to the human operator's understanding of the system, allowing to compensate for the limitations of the controlled dynamics, even if only a fraction of the full feedback is provided.

A biodynamic feedthrough (BDFT) model is proposed that describes how vehicle accelerations feed through the human body, causing involuntary limb motions and so involuntary control inputs. BDFT dynamics strongly depend on limb dynamics, which can vary between persons (between-subject variability), but also within one person over time, e.g., due to the control task performed (within-subject variability). The proposed BDFT model is based on physical neuromuscular principles and is derived from an established admittance model-describing limb dynamics-which was extended to include control device dynamics and account for acceleration effects. The resulting BDFT model serves primarily the purpose of increasing the understanding of the relationship between neuromuscular admittance and biodynamic feedthrough. An added advantage of the proposed model is that its parameters can be estimated using a two-stage approach, making the parameter estimation more robust, as the procedure is largely based on the well documented procedure required for the admittance model. To estimate the parameter values of the BDFT model, data are used from an experiment in which both neuromuscular admittance and biodynamic feedthrough are measured. The quality of the BDFT model is evaluated in the frequency and time domain. Results provide strong evidence that the BDFT model and the proposed method of parameter estimation put forward in this paper allows for accurate BDFT modeling across different subjects (accounting for between-subject variability) and across control tasks (accounting for within-subject variability).

Biodynamic feedthrough (BDFT) occurs when vehicle accelerations feed through the human body and cause involuntary control inputs. This paper proposes a model to quantitatively predict this effect in rotorcraft. This mathematical BDFT model aims to fill the gap between the currently existing black box BDFT models and physical BDFT models. The model structure was systematically constructed using asymptote modeling, a procedure described in detail in this paper. The resulting model can easily be implemented in many typical rotorcraft BDFT studies, using the provided model parameters. The model's performance was validated in both the frequency and time domain. Furthermore, it was compared with several recent BDFT models. The results show that the proposed mathematical model performs better than typical black box models and is easier to parameterize and implement than a recent physical model.

Biodynamic feedthrough (BDFT) is a complex phenomenon, which has been studied for several decades. However, there is little consensus on how to approach the BDFT problem in terms of definitions, nomenclature, and mathematical descriptions. In this paper, a framework for biodynamic feedthrough analysis is presented. The goal of this framework is two-fold. First, it provides some common ground between the seemingly large range of different approaches existing in the BDFT literature. Second, the framework itself allows for gaining new insights into BDFT phenomena. It will be shown how relevant signals can be obtained from measurement, how different BDFT dynamics can be derived from them, and how these different dynamics are related. Using the framework, BDFT can be dissected into several dynamical relationships, each relevant in understanding BDFT phenomena in more detail. The presentation of the BDFT framework is divided into two parts. This paper, Part I, addresses the theoretical foundations of the framework. Part II, which is also published in this issue, addresses the validation of the framework. The work is presented in two separate papers to allow for a detailed discussion of both the framework's theoretical background and its validation.

Biodynamic feedthrough (BDFT) is a complex phenomenon, that has been studied for several decades. However, there is little consensus on how to approach the BDFT problem in terms of definitions, nomenclature, and mathematical descriptions. In this paper, the framework for BDFT analysis, as presented in Part I of this dual publication, is validated and applied. The goal of this framework is twofold. First of all, it provides some common ground between the seemingly large range of different approaches existing in BDFT literature. Secondly, the framework itself allows for gaining new insights into BDFT phenomena. Using recently obtained measurement data, parts of the framework that were not already addressed elsewhere, are validated. As an example of a practical application of the framework, it will be demonstrated how the effects of control device dynamics on BDFT can be understood and accurately predicted. Other ways of employing the framework are illustrated by interpreting the results of three selected studies from the literature using the BDFT framework. The presentation of the BDFT framework is divided into two parts. This paper, Part II, addresses the validation and application of the framework. Part I, which is also published in this journal issue, addresses the theoretical foundations of the framework. The work is presented in two separate papers to allow for a detailed discussion of both the framework's theoretical background and its validation.

We describe initial steps in the design of a haptic shared control system for obstacle avoidance in unmanned aerial vehicle (UAV) teleoperation. In the considered scenario, the operator is supported by force feedback on the control interface, by which a seamless integration of his commands and the assistance from an automatic collision avoidance system is created. To find proper tuning for this haptic shared control system, analysis of the operator's neuromuscular system is used as a guideline. Relevant properties of human NMS are discussed together with corresponding choices for tuning of the haptic shared control system. Tuning the system for the intrinsic stiffness is selected here, to ensure that during teleoperation only the minimal physical workload of the operator is required. A new way to determine the intrinsic stiffness, even when the operator needs to exert forces on the control inceptor, is described here. Proposed method uses wide bandwidth perturbation signal in the NMS identification. In this way natural reflexive feedback paths are suppressed which allows to determine the intrinsic stiffens of the operator arm. The method is discussed in details and experimental results are shown. The paper then presents results of a NMS identification experiment with 10 participants. The procedure resulted in a set of representative frequency responses functions for the NMS stiffness, on the basis of which the haptic feedback for the UAV can be tuned.

Haptic transparency is an extensively studied subject in teleoperation. However, the exact role of transparency in human-in-the-loop task execution is only partially understood. In this study, a human factors experiment was performed with the goal to assess the effect of transparency on the rate and generalizability of motor learning. Subjects performed a reach adaptation task under the effect of a viscous curl force field, while two levels of transparency were provided, namely (near) natural transparency and reduced transparency based on a bilateral position-error controller. In the `familiarization' stage subjects performed an eight cm planar movement in a straight line without any external dynamics. In the `learning' stage, subjects performed the same movement, but now under the effect of a viscous curl force field. Finally, subjects were instructed to `generalize' their learning of the force field for a comparable movement in a different position and orientation. The results show that, while the rate of learning and steady-state performance of a task may not benefit from the highest level of transparency, the ability to generalize beyond a set of pre-experienced motions increases when haptic transparency is (close to) natural. It is concluded that haptic transparency may allow for more rapid and more accurate behaviour in situations that have not yet been encountered.

Deep-sea mining is an envisioned solution to cope with the fast increasing demand for rare-earth metals and decreasing supplies from conventional mines. It could involve a hydraulically actuated suspended grab to excavate metal-rich minerals from the seabed. Due to environmental uncertainties such an operation cannot be automated and should therefore be controlled by teleoperation, which traditionally suffers from sub optimal performance and limited situation awareness. The current study proposes two methods of hap tic feedback, natural force feedback and hap tic shared control, to improve the control of a grab in deep-sea mining. Natural force feedback is offered to improve the transparency of the system, which is hypothesized to improve situation awareness of the operation. Secondly it is hypothesized to reduce control effort by guiding the operator when offering hap tic shared control. Besides the individual effect, combining both hap tic feedback methods should also improve the overall task performance of the operation. A deep-sea mining simulation experiment is conducted to investigate the effect of these two hap tic feedback methods and their combination on operator control behaviour. The results show improvement of situation awareness (i.e. control errors) when offering natural force feedback and a reduction of control effort (i.e. control inputs) when offering hap tic shared control. However the results do not show an increase of task performance (i.e. excavated rock production) for either method. Although reduction of control error and effort will result eventually in long-term performance benefits. Combining both methods is therefore the best hap tic feedback method for improving a deep-sea mining teleoperation using a grab.

Haptic technology has become a viable way to support operators in vehicular control. This paper investigates two different design philosophies for continuous haptic feedback to support drivers with curve negotiation. The first system, 'direct haptic assistance', is designed to yield best results when the driver gives way to the guidance forces on the steering wheel. The second, 'indirect haptic assistance', is designed to yield best results when the driver counter-acts the forces. The two designs were compared in a driving simulator experiment in which 27 subjects participated. Results show that both systems are helpful in case of low visibility, where the driver lacks sufficient preview of the curves. With normal visibility no improvements on performance were found, for either system. Further experiments are required to investigate the difference between the two approaches.

When performing a manual control task, vehicle accelerations can cause involuntary limb motions, which can result in unintentional control inputs. This phenomenon is called biodynamic feedthrough (BDFT). In the past decades, many studies into BDFT have been performed, but its fundamentals are still only poorly understood. What has become clear, though, is that BDFT is a highly complex process, and its occurrence is influenced by many different factors. A particularly challenging topic in BDFT research is the role of the human operator, which is not only a very complex but also a highly adaptive system. In literature, two different ways of measuring and analyzing BDFT are reported. One considers the transfer of accelerations to involuntary forces applied to the control device (CD); the other considers the transfer of accelerations to involuntary CD deflections or positions. The goal of this paper is to describe an approach to unify these two methods. It will be shown how the results of the two methods relate and how this knowledge may aid in understanding BDFT better as a whole. The approach presented is based on the notion that BDFT dynamics can be described by the combination of two transfer dynamics: 1) the transfer dynamics from body accelerations to involuntary forces and 2) the transfer dynamics from forces to CD deflections. The approach was validated using experimental results.

Haptic shared control is a powerful way of combining the best of humans and intelligent vehicles, keeping humans in the loop while avoiding many automation issues. Literature has shown that haptic shared control can support drivers to increase performance at reduced control effort, but also points out that even then, subtle conflicts occur between driver and shared controller. This paper hypothesizes that at least part of that disagreement lies at the neuromuscular level, and that mismatches in expected torques will result in decreased performance and increased effort. The goal of this paper is to provide experimental evidence that shows the importance of tuning guidance torques with the correct expectations about neuromuscular response. An abstract steering experiment was performed without visual cues, where drivers were guided by haptic shared control torques to perform a lane-change maneuver. The torques were tuned with three different expectations about driver's neuromuscular behavior, and drivers were also instructed to perform three different neuromuscular tasks. The results show that when the tuning of the torques did not match the real neuromuscular behavior, guidance torques were either too high or too low, and performance was reduced. It is concluded that a good understanding of neuromuscular response of drivers is essential to avoid subtle conflicts between driver and shared controller.

Steer-by-Wire (SbW) systems currently under development by the automotive industry offer interesting new approaches to designing driver-steering wheel interactions. The traditional, emerging dynamics in mechanically linked steering systems can be re-designed with SbW to improve or even extend the steering `feel'. In this article we manipulated the steering wheel dynamics such that each design was expected to yield the best driving performance with the least amount of driver control effort for a particular driving task. We tested three designs during three different driving tasks in a fixed-base driving simulator. The results of the experiment showed that steering wheel dynamics should be stiff and sluggish for driving on straight roads and slack and light for curve negotiation. Future experiments will investigate the implications for drivers on a neuromuscular level.

Biodynamic feedthrough (BDFT) refers to a phenomenon where vehicle accelerations feed through the human body, causing involuntary limb motions, which may cause involuntary control inputs. Many studies have been devoted to mitigating BDFT effects. In the current paper, the effectiveness of a simple, cheap and widely-used hardware component is studied: the armrest. An experiment was conducted in which the BDFT dynamics were measured with and without armrest for different levels of neuromuscular admittance (i.e., different settings of the limb dynamics). The results show that the effect of the armrest on BDFT dynamics varies, both with frequency and neuromuscular admittance.

In tele-operation, haptic feedback from the remote environment to the human is often limited, which has been shown to negatively influence the performance and required time of tasks. The conventional research focus is on improving the quality of the haptic feedback (transparency), which may have led to significant improvement, but is still imperfect, with many unresolved issues. The present study presents an alternative approach to improve tele-operated tasks: by offering haptic shared control in which both operator and support system apply the required forces at the input (master) device. It is hypothesized that virtual forces from well-designed shared control will improve required time and accuracy, with less control effort, and that these benefits exist for perfect transparency but even more so for imperfect transparency. In an experimental study haptic shared control was designed to aid operators (n=9) with performing a simple bolt-spanner task using a planar (2D, 3DOF) tele-operator setup. The experimental results provided evidence for the hypotheses, showing that the tested tele-manipulation task benefits from haptic shared control, for three different levels of transparency. Essentially, the presence of haptic shared control allows for a worse transparency without compromising required time, and can even improve required time during perfect transparency.

Knowing the neuromuscular admittance of drivers helps understanding how drivers adapt to different steering wheel configurations. System identification allows, through force perturbations on the steering wheel, for identification of endpoint admittance. Design of the forcing function can greatly influence the obtained results. We conducted an experiment to investigate the effects of frequency content and amplitude on estimated admittance. We also measured electromyography (EMG) activity in order to get an understanding of how the identified endpoint admittance strategies were realized at the level of individual muscle groups. The experiment took place in a fixed base driving simulator with an electrically actuated steering wheel. The results showed that when drivers were asked to be passive in response to force perturbations, amplitude and frequency content had little influence on endpoint admittance and EMG activity. However, resisting the perturbations yielded significantly lower admittance when the perturbations had more frequency content. EMG activity was not much influenced. Hence, we tentatively conclude that too much frequency content above 0.7 Hz suppresses reflexive muscle activity. To prevent this force perturbations for admittance measurements should contain enough power above 0.7 Hz to allow identification but as little as possible so as not to suppress reflexive muscle activity.

With shared control both driver and support system have control authority and as such exert control actions to maneuver the car. In haptic shared control, the support system acts through guiding forces on the same control interface with which the driver interacts with the vehicle. Previous research showed beneficial results when drivers are supported during lane keeping and curve negotiation. Haptic shared control can be used as an intermediary support system in between the opposites of manual and automatic control. In a fixed-base driving simulator experiment we tested a haptic shared control system during evasive maneuvers. Besides the expected benefits with a perfectly functioning system we also tested the capabilities of drivers to override the system in case of a malfunction. Results of the experiment show that for a time-to-contact (TTC) of 1.4s, haptic shared control reduced the hit rate with obstacles from 21.2% to 15.2%. Even though failure of the haptic support system at TTC=1.4s increased the hit rate to 64.7% (compared to 100% in case of full automation) time spent in the opposite lane was not significantly changed by the failure.

Driver control behaviour is highly time variant. When studying the neuromuscular system of drivers in interaction with the steering wheel, the common Fourier system identification techniques are only applicable when time-invariant behaviour is assumed. This paper describes how wavelets can be used to identify time-variant neuromuscular admittance. Using the Morlet wavelet transformation, time domain signals are transformed to a time-frequency representation. A non-parametric, time-variant frequency response function can be estimated using the transformed signals. A model of the neuromuscular system of a driver controlling a steering wheel was used to generate time-variant data. This paper shows that the Morlet wavelet transformation is a valid tool for estimating accurate time-variant frequency responses of neuromuscular arm dynamics. The results of this article give us confidence that wavelet analysis can be used on experimental data, with lower signal-to-noise ratio, too. This will allow us to identify how drivers adjust their neuromuscular system during driving.

Continuous haptic feedback can improve manual control task performance and application methods on steering wheel (haptic steering guidance) have been studied. However, the current haptic steering guidance systems assume there is an average, constant driver's response to force (which can be quantified as the admittance). To improve the performance of haptic steering guidance, the guidance system should be adaptive to the inter and intra-driver variability in admittance. To achieve this we need to be able to get accurate, non-evasive estimates of driver neuromuscular admittance. We believe the gripping force with which drivers control the steering wheel might be a good parameter to estimate admittance. The goal of this paper is to investigate the correlation between grip strength and human admittance of a driver holding the steering wheel. A relax task and a position task with two different grip conditions, loose and tight, are performed. The results of the experiment show that there should be an inverse relationship between grip strength and admittance. Whether it is possible to differentiate admittance between tasks through grip strength alone remains to be seen as we could not measure grip strength directly. Future experiments with grip pressure sensors will be carried out to establish a quantitative correlation between grip strength and neuromuscular admittance.

Vehicle accelerations may feed through the human body, causing involuntary limb motions which may lead to involuntary control inputs. This phenomenon is called biodynamic feedthrough (BDFT). Signal cancellation is a possible way of mitigating biodynamic feedthrough. It makes use of a BDFT model to estimate the involuntary control inputs. The BDFT effects are removed by subtracting the modeled estimate of the involuntary control input from the total control signal, containing both voluntary and involuntary components. The success of signal cancellation hinges on the accuracy of the BDFT model used. In this study the potential of signal cancellation is studied by making use of a method called optimal signal cancellation. Here, an identified BDFT model is used off-line to generate an estimate of the involuntary control inputs based on the accelerations present. Results show that reliable signal cancellation requires BDFT models that are both subject and task dependent. The task dependency is of particular importance: failing to adapt the model to changes in the operator's neuromuscular dynamics dramatically decreases the quality of cancellation and can even lead to an increase in unwanted effects. As a reliable and fast on-line identification method of the neuromuscular dynamics of the human operator currently does not exist, real-time signal cancellation is currently not feasible.

The research presented in this paper focuses on the design of a driver support system for the manual longitudinal control of a car during car-following. The aim of the design was to develop a system that would cooperate with the driver in comfortably maintaining (safe) separation with a lead vehicle. Three important design issues for a haptic gas pedal feedback system can be distinguished: 1) quantification of intervehicle separation parameters; 2) the type of haptic feedback; and 3) the relation between haptic feedback and intervehicle separation. Because of the inverse relationship between time-to-contact (TTC) and time-headway (THW)-the smaller the THW, the more important the avoidance of high TTC-THW should act as an amplifier for the haptic gas pedal feedback based on TTC. Using gas pedal stiffness feedback is expected to better facilitate the manual control of intervehicle separation changes, quantified by THW and TTC, because stiffness feedback allows perception of force and force-slope changes. The force changes inform drivers of instantaneous changes in the environment. Force-slope changes prevent drivers from input to the car that would continue to reduce the following gap in situations where this would be undesirable. A review of fixed-base simulator and field tests confirms that haptic gas pedal feedback improves driver vigilance during car-following without increasing the workload.

In previous research, a driver support system that uses continuous haptic feedback on the gas pedal to inform drivers of the separation to the lead vehicle was developed. Although haptic feedback has been previously shown to be beneficial, the influence of the underlying biomechanical properties of the driver on the effectiveness of haptic feedback is largely unknown. The goal of this paper is to experimentally determine the biomechanical properties of the ankle-foot complex (i.e., the admittance) while performing a car-following task, thereby separating driver responses to visual feedback from those to designed haptic feedback. An experiment was conducted in a simplified fixed-base driving simulator, where ten participants were instructed to follow a lead vehicle, with and without the support of haptic feedback. During the experiment, the lead vehicle velocity was perturbed, and small stochastic torque perturbations were applied to the pedal. Both perturbations were separated in the frequency domain to allow the simultaneous estimation of frequency response functions of both the car-following control behavior and the biomechanical admittance. For comparison to previous experiments, the admittance was also estimated during three classical motion control tasks (resist forces, relax, and give way to forces). The main experimental hypotheses were that, first, the haptic feedback would encourage drivers to adopt a "give way to force task," resulting in larger admittance compared with other tasks and, second, drivers needed less control effort to realize the same car-following performance. Time- and frequency-domain analyses provided evidence for both hypotheses. The developed methodology allows quantification of the range of admittances that a limb can adopt during vehicle control or while performing a variety of motion control tasks. It thereby allows detailed computational driver modeling and provides valuable information on how to design and evaluate continuous haptic feedback systems.

Biodynamic feedthrough (BDFT) refers to a phenomenon where accelerations cause involuntary limb motions, which can result in unintentional control inputs that can substantially degrade manual control. It is known that humans can adapt the dynamics of their limbs by adjusting their neuromuscular settings, and it is likely that these adaptations have a large influence on BDFT. The goal of this paper is to present a method that can provide evidence for this hypothesis. Limb dynamics can be described by admittance, which is the causal dynamic relation between a force input and a position output. This paper presents a method to simultaneously measure BDFT and admittance in a motion-based simulator. The method was validated in an experiment. Admittance was measured by applying a force disturbance signal to the control device; BDFT was measured by applying a motion disturbance signal to the motion simulator. To allow distinguishing between the operator's responses to each disturbance signal, the perturbation signals were separated in the frequency domain. To show the impact of neuromuscular adaptation, subjects were asked to perform three different control tasks, each requiring a different setting of the neuromuscular system (NMS). Results show a dependence of BDFT on neuromuscular admittance: A change in neuromuscular admittance results in a change in BDFT dynamics. This dependence is highly relevant when studying BDFT. The data obtained with the proposed measuring method provide insight in how exactly the settings of the NMS influence the level of BDFT. This information can be used to gain fundamental knowledge on BDFT and also, for example, in the development of a canceling controller.

Most collision avoidance systems for highway scenarios are shifting the role of the driver from manual execution to a supervisory position. In the interface design of the proposed collision avoidance system proposed in this article, the human-machine issues complicating task automation were avoided by adopting a human-centered approach. In this paper a method to preserve driver's choice and maneuver initiation through the use of a temporarily unstable steering wheel is presented. In the particular driving condition investigated in a fixed base driving simulator, the driver has to avoid an obstacle following one of two equally safe escape paths (left or right evasive maneuver). The collision avoidance system aims at supporting both available solutions by means of a haptic steering wheel interface. Two feedback modalities are used, namely torque feedback and stiffness feedback. The results of the experiment show that the haptic interface effectively reduced the number of crashes, decreased response time with at least 100 ms while reducing the control effort and activity in the most critical situations.

Haptic guidance on the steering wheel has been shown to be useful in supporting drivers during lane keeping and curve negotiation. One successful realization is haptic guidance that continuously produces torques on the steering wheel depending on predicted lateral lane deviations. Thus, both driver and support system contribute to the steering wheel torque, sharing lateral control of the vehicle. However, current shared control systems do not support drivers during a lane change, a driving task diametrically opposed to lane keeping. This study describes the design and evaluation of an extension of lane-keeping haptic guidance, which also continuously and smoothly supports lane changes. An experiment in a fixed-base driving simulator was conducted to assess the effect of the support system on lane change behavior when haptic guidance was provided during both lane-keeping and lane-changing situations. In agreement with previous findings, objective measures and subjective responses showed that the presented haptic guidance was beneficial during lane-keeping tasks, resulting in small but significantly increased performance with a smoother and reduced steering activity. Although the developed system increased the measured steering wheel peak torque during the start of lane changes, subjective responses showed that drivers could comfortably make lane changes and felt in control during the maneuver. Concluding, the designed system provides drivers with the benefits of (previously designed) haptic guidance for lane-keeping, while allowing them to smoothly change lanes at any time.

Vehicle accelerations may lead to involuntary limb motions. These motions can result into involuntary control inputs when performing a manual control task. This phenomenon is called biodynamic feedthrough (BDFT). This paper aims to show that task interpretation plays an important role in the occurrence of BDFT. Results of an experiment are presented, in which biodynamic feedthrough was measured during three different control tasks. Each control task required the human operator to adapt his/her neuromuscular settings. The results show that the level of biodynamic feedthrough depends on the task the human operator is performing. From further analysis, it can be observed that the experiment results are in good agreement with BDFT measurements found in literature. The comparison confirms that the task interpretation plays an important role in BDFT which cannot be ignored when attempting to understand or mitigate BDFT in practical situations.

A haptic gas pedal feedback system is developed that provides car-following information via haptic cues from the gas pedal. During normal car-following situations, the haptic feedback (HF) cues were sufficient to reduce control activity and improve car-following performance. However, in more critical following situations, drivers use the brake pedal to maintain separation with the lead vehicle. A deceleration control (DC) algorithm is designed that, in addition to the HF, provided increased deceleration upon release of the gas pedal during car-following situations that required faster deceleration than releasing the gas pedal alone would do. For the design, a driver model for car following in different situations was estimated from driving simulator data. A Monte Carlo analysis with the driver model yielded subjective decision points, where drivers released the gas pedal to start pressing the brakes. This enabled the definition of a reaction field, which determined the needed deceleration input for the DC algorithm. The tuned DC algorithm was tested in a fixed-base driving simulator experiment. It was shown that the active deceleration support improved the car-following performance while reducing the driver brake pedal input magnitude in the conditions tested.

A haptic gas pedal feedback system was developed at Delft University of Technology that translated the separation to a lead vehicle into continuous haptic cues (force, stiffness) on the gas pedal. During normal car-following situations, the haptic feedback cues were sufficient to reduce control activity and improve car-following performance. However, in more critical following situations drivers use the brake pedal to maintain separation with the lead vehicle. A deceleration control algorithm was designed that, in addition to the haptic feedback, provided increased deceleration upon release of the gas pedal during critical car-following situations. The deceleration control algorithm was tested in a fixed-base driving simulator experiment. Deceleration control improved car-following performance while reducing driver brake pedal control activity in the conditions tested.

When an operator in a moving vehicle is performing a manual control task, the accelerations to which the operator is subjected can result in unintentional control inputs. This biodynamic feedthrough (BDFT) depends on the properties of the control device and of the control limb. Humans can adjust the dynamics properties of their limbs, effectively changing the limb admittance. Previous studies of BDFT did not consider the effect of this adjustment. This paper describes a model for BDFT and an experiment in a moving base simulator with subjects performing a control task with a side stick. During the experiment the neuromuscular admittance was varied by using different control tasks, each requiring a different neuromuscular setting. The non-parametric results of this experiment show that the level of feedthrough is strongly dependent on both the frequency of the disturbance and the neuromuscular admittance. The results furthermore suggest that a relationship can be established between admittance and biodynamic feedthrough.

Haptic feedback on the steering wheel is reported in literature as a promising way to support drivers during steering tasks. Haptic support allows drivers to remain in the direct manual control loop, avoiding known human factors issues with automation. This paper proposes haptic guidance based on the concept of shared control, where both the driver and the support system influence the steering wheel torque. The haptic guidance is developed to continuously generate relatively low forces on the steering wheel, requiring the driver's active steering input to safely negotiate curves. An experiment in a fixed-base driving simulator was conducted, in which 12 young, experienced drivers steered a vehicle - with and without haptic guidance - at a fixed speed along a road with varying curvature. The haptic guidance allowed drivers to slightly but significantly improve safety boundaries in their curve negotiation behavior. Their steering activity was reduced and smoother. The results indicated that continuous haptic guidance is a promising way to support drivers in actively producing (more) optimal steering actions during curve negotiation.

Driver assistance systems that supply force feedback (FF) on the accelerator commonly use relative distance and velocity with respect to the closest lead vehicle in front of the own vehicle. This 1-D feedback might not accurately represent the situation and can cause unwanted step-shaped changes in the FFs during lateral maneuvers. To address these shortcomings, a 2-D system is proposed that calculates FF using a weighted average of the influences of lead vehicles. Offline simulations and an experiment in a driving simulator were performed to compare no feedback, 1-D systems, and the novel 2-D system during a car-following task with cut-in maneuvers. Results show that the 2-D feedback resulted in lower mean forces, lower response times to cut-in vehicles, and favorable subjective experiences as compared to the 1-D systems.

A car-following experiment was conducted to investigate the effects of lead vehicle speed and separation distance on driver car-following behavior. Furthermore, the influence of a driver support system with haptic gas pedal feedback was investigated as well. The experiment was set up such that system identification techniques could be applied using a quasi-linear multi-loop car-following model. It was assumed that car-following is a compensatory task in which errors in relative velocity and relative distance are minimized. Results of the experiment show that drivers mainly use relative velocity for their control task. Low crossover frequencies and high phase margins indicate that car-following is a relatively simple and robust task, which is difficult to improve on when drivers can make full use of visual feedback.

A procedure is developed to identify the driver frequency response functions (FRF) of a serial multi-loop car-following model. It is shown that with a single forcing function both the inner-loop and outer-loop FRF's can be identified, although indirectly. Parameterisation of the driver FRF's is done using the concepts of the simplified precision model developed by McRuer and Krendel in the 1970's. The forcing function characteristics, data processing procedures, non-parametric and parametric identification are described. Experimental data are used to show the results of the identification procedure. In non-critical car-following situations (not requiring braking) the simplified precision model can be used as a linear description of driver car-following behaviours.