Sharing control between humans and automation using haptic interface: primary and secondary task performance benefits

Modeling Human Steering Behavior in Haptic Shared Control of Autonomy-Enabled Unmanned Ground Vehicles

OBJECTIVE

A human steering model for teleoperated driving is extended to capture the human steering behavior in haptic shared control of autonomy-enabled Unmanned Ground Vehicles (UGVs).

BACKGROUND

Prior studies presented human steering models for teleoperation of a passenger-sized Unmanned Ground Vehicle, where a human is fully in charge of driving. However, these models are not applicable when a human needs to interact with autonomy in haptic shared control of autonomy-enabled UGVs. How a human operator reacts to the presence of autonomy needs to be studied and mathematically encapsulated in a module to capture the collaboration between human and autonomy.

METHOD

Human subject tests are conducted to collect data in haptic shared control for model development and validation. The ACT-R architecture and two-point steering model used in the previous literature are adopted to predict the operator's desired steering angle. A torque conversion module is developed to convert the steering command from the ACT-R model to human torque input, thus enabling haptic shared control with autonomy. A parameterization strategy is described to find the set of model parameters that optimize the haptic shared control performance in terms of minimum average lane keeping error (ALKE).

RESULTS

The model predicts the minimum ALKE human subjects achieve in shared control.

CONCLUSIONS

The extended model can successfully predict the best haptic shared control performance as measured by ALKE.

APPLICATION

This model can be used in place of human operators, enabling fully simulation-based engineering, in the development and evaluation of haptic shared control technologies for autonomy-enabled UGVs, including control negotiation strategies and autonomy capabilities.

Anthropomorphism-based causal and responsibility attributions to robots

People tend to expect mental capabilities in a robot based on anthropomorphism and often attribute the cause and responsibility for a failure in human-robot interactions to the robot. This study investigated the relationship between mind perception, a psychological scale of anthropomorphism, and attribution of the cause and responsibility in human-robot interactions. Participants played a repeated noncooperative game with a human, robot, or computer agent, where their monetary rewards depended on the outcome. They completed questionnaires on mind perception regarding the agent and whether the participant's own or the agent's decisions resulted in the unexpectedly small reward. We extracted two factors of Experience (capacity to sense and feel) and Agency (capacity to plan and act) from the mind perception scores. Then, correlation and structural equation modeling (SEM) approaches were used to analyze the data. The findings showed that mind perception influenced attribution processes differently for each agent type. In the human condition, decreased Agency score during the game led to greater causal attribution to the human agent, consequently also increasing the degree of responsibility attribution to the human agent. In the robot condition, the post-game Agency score decreased the degree of causal attribution to the robot, and the post-game Experience score increased the degree of responsibility to the robot. These relationships were not observed in the computer condition. The study highlights the importance of considering mind perception in designing appropriate causal and responsibility attribution in human-robot interactions and developing socially acceptable robots.

Review on Haptic Assistive Driving Systems Based on Drivers’ Steering-Wheel Operating Behaviour

With the availability of modern assistive techniques, ambient intelligence, and the Internet of Things (IoT), various innovative assistive environments have been developed, such as driving assistance systems (DAS), where the human driver can be provided with physical and emotional assistance. In this human–machine collaboration system, haptic interaction interfaces are commonly employed because they provide drivers with a more manageable way to interact with other components. From the view of control system theory, this is a typical closed-loop feedback control system with a human in the loop. To make such a system work effectively, both the driving behaviour factors, and the electrical–mechanical components should be considered. However, the main challenge is how to deal with the high degree of uncertainties in human behaviour. This paper aims to provide an insightful overview of the relevant work. The impact of various types of haptic assistive driving systems (haptic guidance and warning systems) on driving behaviour performance is discussed and evaluated. In addition, various driving behaviour modelling systems are extensively investigated. Furthermore, the state-of-the-art driving behaviour controllers are analysed and discussed in driver–vehicle–road systems, with potential improvements and drawbacks addressed. Finally, a prospective approach is recommended to design a robust model-free controller that accounts for uncertainties and individual differences in driving styles in a haptic assistive driving system. The outcome indicated that the haptic feedback system applied to the drivers enhanced their driving performance, lowered their response time, and reduced their mental workload compared to a system with auditory or visual signals or without any haptic system, despite some annoyances and system conflicts. The driving behaviour modelling techniques and the driving behaviour control with a haptic feedback system have shown good matching and improved the steering wheel’s base operation performance. However, mathematical principles, a statistical approach, and the lack of consideration of the individual differences in the driver–vehicle–road system make the modelling and the controller less robust and inefficient for different driving styles.

Transitions Between Highly Automated and Longitudinally Assisted Driving: The Role of the Initiator in the Fight for Authority

OBJECTIVE

A driving simulator study explored how drivers behaved depending on their initial role during transitions between highly automated driving (HAD) and longitudinally assisted driving (via adaptive cruise control).

BACKGROUND

During HAD, drivers might issue a take-over request (TOR), initiating a transition of control that was not planned. Understanding how drivers behave in this situation and, ultimately, the implications on road safety is of paramount importance.

METHOD

Sixteen participants were recruited for this study and performed transitions of control between HAD and longitudinally assisted driving in a driving simulator. While comparing how drivers behaved depending on whether or not they were the initiators, different handover strategies were presented to analyze how drivers adapted to variations in the authority level they were granted at various stages of the transitions.

RESULTS

Whenever they initiated the transition, drivers were more engaged with the driving task and less prone to follow the guidance of the proposed strategies. Moreover, initiating a transition and having the highest authority share during the handover made the drivers more engaged with the driving task and attentive toward the road.

CONCLUSION

Handover strategies that retained a larger authority share were more effective whenever the automation initiated the transition. Under driver-initiated transitions, reducing drivers' authority was detrimental for both performance and comfort.

APPLICATION

As the operational design domain of automated vehicles (Society of Automotive Engineers [SAE] Level 3/4) expands, the drivers might very well fight boredom by taking over spontaneously, introducing safety issues so far not considered but nevertheless very important.

HapWheel: In-Car Infotainment System Feedback Using Haptic and Hovering Techniques

In-car devices are growing both in complexity and capacity, integrating functionalities that used to be divided among other controls in the vehicles. These systems appear increasingly in the form of touchscreens as a cost-saving measure. Screens lack the physicality of traditional buttons or switches, requiring drivers to look away from the road to operate them. This paper presents the design, implementation, and two studies that evaluated HapWheel, a system that provides the driver with haptic feedback in the steering wheel while interacting with an Infotainment System. Results show that the proposed system reduced both the duration of and the number of times a driver looked away from the road. HapWheel was also successful at reducing the number of mistakes during the interaction.

Haptic Lane-Keeping Assistance for Truck Driving: A Test Track Study

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.

Effect of Fixed and sEMG-Based Adaptive Shared Steering Control on Distracted Driver Behavior

Driver distraction is a well-known cause for traffic collisions worldwide. Studies have indicated that shared steering control, which actively provides haptic guidance torque on the steering wheel, effectively improves the performance of distracted drivers. Recently, adaptive shared steering control based on the forearm muscle activity of the driver has been developed, although its effect on distracted driver behavior remains unclear. To this end, a high-fidelity driving simulator experiment was conducted involving 18 participants performing double lane change tasks. The experimental conditions comprised two driver states: attentive and distracted. Under each condition, evaluations were performed on three types of haptic guidance: none (manual), fixed authority, and adaptive authority based on feedback from the forearm surface electromyography of the driver. Evaluation results indicated that, for both attentive and distracted drivers, haptic guidance with adaptive authority yielded lower driver workload and reduced lane departure risk than manual driving and fixed authority. Moreover, there was a tendency for distracted drivers to reduce grip strength on the steering wheel to follow the haptic guidance with fixed authority, resulting in a relatively shorter double lane change duration.

Long-Term Evaluation of Drivers' Behavioral Adaptation to an Adaptive Collision Avoidance System

OBJECTIVE

Taking human factors approach in which the human is involved as a part of the system design and evaluation process, this paper aims to improve driving performance and safety impact of driver support systems in the long view of human-automation interaction.

BACKGROUND

Adaptive automation in which the system implements the level of automation based on the situation, user capacity, and risk has proven effective in dynamic environments with wide variations of human workload over time. However, research has indicated that drivers may not efficiently deal with dynamically changing system configurations. Little effort has been made to support drivers' understanding of and behavioral adaptation to adaptive automation.

METHOD

Using a within-subjects design, 42 participants completed a four-stage driving simulation experiment during which they had to gradually interact with an adaptive collision avoidance system while exposed to hazardous lane-change scenarios over 1 month.

RESULTS

Compared to unsupported driving (stage i), although collisions have been significantly reduced when first experienced driving with the system (stage ii), improvements in drivers' trust in and understanding of the system and driving behavior have been achieved with more driver-system interaction and driver training during stages iii and iv.

CONCLUSION

While designing systems that take into account human skills and abilities can go some way to improving their effectiveness, this alone is not sufficient. To maximize safety and system usability, it is also essential to ensure appropriate users' understanding and acceptance of the system.

APPLICATION

These findings have important implications for the development of active safety systems and automated driving.

A Review of Occlusion as a Tool to Assess Attentional Demand in Driving

OBJECTIVE

The aim of this review is to identify how visual occlusion contributes to our understanding of attentional demand and spare visual capacity in driving and the strengths and limitations of the method.

BACKGROUND

The occlusion technique was developed by John W. Senders to evaluate the attentional demand of driving. Despite its utility, it has been used infrequently in driver attention/inattention research.

METHOD

Visual occlusion studies in driving published between 1967 and 2020 were reviewed. The focus was on original studies in which the forward visual field was intermittently occluded while the participant was driving.

RESULTS

Occlusion studies have shown that attentional demand varies across situations and drivers and have indicated environmental, situational, and inter-individual factors behind the variability. The occlusion technique complements eye tracking in being able to indicate the temporal requirements for and redundancy in visual information sampling. The proper selection of occlusion settings depends on the target of the research.

CONCLUSION

Although there are a number of occlusion studies looking at various aspects of attentional demand, we are still only beginning to understand how these demands vary, interact, and covary in naturalistic driving.

APPLICATION

The findings of this review have methodological and theoretical implications for human factors research and for the development of distraction monitoring and in-vehicle system testing. Distraction detection algorithms and testing guidelines should consider the variability in drivers' situational and individual spare visual capacity.

A workload adaptive haptic shared control scheme for semi-autonomous driving

Haptic shared control is used to manage the control authority allocation between a human and an autonomous agent in semi-autonomous driving. Existing haptic shared control schemes, however, do not take full consideration of the human agent. To fill this research gap, this study presents a haptic shared control scheme that adapts to a human operator's workload, eyes on road and input torque in real time. We conducted human-in-the-loop experiments with 24 participants. In the experiment, a human operator and an autonomy module for navigation shared the control of a simulated notional High Mobility Multipurpose Wheeled Vehicle (HMMWV) at a fixed speed. At the same time, the human operator performed a target detection task. The autonomy could be either adaptive or non-adaptive to the above-mentioned human factors. Results indicate that the adaptive haptic control scheme resulted in significantly lower workload, higher trust in autonomy, better driving task performance and smaller control effort.

Memory Pattern Identification for Feedback Tracking Control in Human-Machine Systems

OBJECTIVE

The aim of this paper was to identify the characteristics of memory patterns with respect to a visual input, perceived by the human operator during a manual control task, which consisted in following a moving target on a display with a cursor.

BACKGROUND

Manual control tasks involve nondeclarative memory. The memory encodings of different motor skills have been referred to as procedural memories. The procedural memories have a pattern, which this paper sought to identify for the particular case of a one-dimensional tracking task. Specifically, data recorded from human subjects controlling dynamic systems with different fractional order were investigated.

METHOD

A finite impulse response (FIR) controller was fitted to the data, and pattern analysis of the fitted parameters was performed. Then, the FIR model was further reduced to a lower order controller; from the simplified model, the stability analysis of the human-machine system in closed-loop was conducted.

RESULTS

It is shown that the FIR model can be used to identify and represent patterns in human procedural memories during manual control tasks. The obtained procedural memory pattern presents a time scale of about 650 ms before decay. Furthermore, the fitted controller is stable for systems with fractional order less than or equal to 1.

CONCLUSION

For systems of different fractional order, the proposed control scheme-based on an FIR model-can effectively characterize the linear properties of manual control in humans.

APPLICATION

This research supports a biofidelic approach to human manual control modeling over feedback visual perceptions. Relevant applications of this research are the following: the development of shared-control systems, where a virtual human model assists the human during a control task, and human operator state monitoring.

Data-driven Koopman operators for model-based shared control of human–machine systems

We present a data-driven shared control algorithm that can be used to improve a human operator’s control of complex dynamic machines and achieve tasks that would otherwise be challenging, or impossible, for the user on their own. Our method assumes no a priori knowledge of the system dynamics. Instead, both the dynamics and information about the user’s interaction are learned from observation through the use of a Koopman operator. Using the learned model, we define an optimization problem to compute the autonomous partner’s control policy. Finally, we dynamically allocate control authority to each partner based on a comparison of the user input and the autonomously generated control. We refer to this idea as model-based shared control (MbSC). We evaluate the efficacy of our approach with two human subjects studies consisting of 32 total participants (16 subjects in each study). The first study imposes a linear constraint on the modeling and autonomous policy generation algorithms. The second study explores the more general, nonlinear variant. Overall, we find that MbSC significantly improves task and control metrics when compared with a natural learning, or user only, control paradigm. Our experiments suggest that models learned via the Koopman operator generalize across users, indicating that it is not necessary to collect data from each individual user before providing assistance with MbSC. We also demonstrate the data efficiency of MbSC and, consequently, its usefulness in online learning paradigms. Finally, we find that the nonlinear variant has a greater impact on a user’s ability to successfully achieve a defined task than the linear variant.

Using Gaussian Mixture Models for Gesture Recognition During Haptically Guided Telemanipulation

Haptic guidance is a promising method for assisting an operator in solving robotic remote operation tasks. It can be implemented through different methods, such as virtual fixtures, where a predefined trajectory is used to generate guidance forces, or interactive guidance, where sensor measurements are used to assist the operator in real-time. During the last years, the use of learning from demonstration (LfD) has been proposed to perform interactive guidance based on simple tasks that are usually composed of a single stage. However, it would be desirable to improve this approach to solve complex tasks composed of several stages or gestures. This paper extends the LfD approach for object telemanipulation where the task to be solved is divided into a set of gestures that need to be detected. Thus, each gesture is previously trained and encoded within a Gaussian mixture model using LfD, and stored in a gesture library. During telemanipulation, depending on the sensory information, the gesture that is being carried out is recognized using the same LfD trained model for haptic guidance. The method was experimentally verified in a teleoperated peg-in-hole insertion task. A KUKA LWR4+ lightweight robot was remotely controlled with a Sigma.7 haptic device with LfD-based shared control. Finally, a comparison was carried out to evaluate the performance of Gaussian mixture models with a well-established gesture recognition method, continuous hidden Markov models, for the same task. Results show that the Gaussian mixture models (GMM)-based method slightly improves the success rate, with lower training and recognition processing times.

Does haptic steering guidance instigate speeding? A driving simulator study into causes and remedies

An important issue in road traffic safety is that drivers show adverse behavioral adaptation (BA) to driver assistance systems. Haptic steering guidance is an upcoming assistance system which facilitates lane-keeping performance while keeping drivers in the loop, and which may be particularly prone to BA. Thus far, experiments on haptic steering guidance have measured driver performance while the vehicle speed was kept constant. The aim of the present driving simulator study was to examine whether haptic steering guidance causes BA in the form of speeding, and to evaluate two types of haptic steering guidance designed not to suffer from BA. Twenty-four participants drove a 1.8m wide car for 13.9km on a curved road, with cones demarcating a single 2.2m narrow lane. Participants completed four conditions in a counterbalanced design: no guidance (Manual), continuous haptic guidance (Cont), continuous guidance that linearly reduced feedback gains from full guidance at 125km/h towards manual control at 130km/h and above (ContRF), and haptic guidance provided only when the predicted lateral position was outside a lateral bandwidth (Band). Participants were familiarized with each condition prior to the experimental runs and were instructed to drive as they normally would while minimizing the number of cone hits. Compared to Manual, the Cont condition yielded a significantly higher driving speed (on average by 7km/h), whereas ContRF and Band did not. All three guidance conditions yielded better lane-keeping performance than Manual, whereas Cont and ContRF yielded lower self-reported workload than Manual. In conclusion, continuous steering guidance entices drivers to increase their speed, thereby diminishing its potential safety benefits. It is possible to prevent BA while retaining safety benefits by making a design adjustment either in lateral (Band) or in longitudinal (ContRF) direction.

Haptic Guidance Needs to Be Intuitive Not Just Informative to Improve Human Motor Accuracy

Humans make both random and systematic errors when reproducing learned movements. Intuitive haptic guidance that assists one to make the movements reduces such errors. Our study examined whether any additional haptic information about the location of the target reduces errors in a position reproduction task, or whether the haptic guidance needs to be assistive to do so. Holding a haptic device, subjects made reaches to visible targets without time constraints. They did so in a no-guidance condition, and in guidance conditions in which the direction of the force with respect to the target differed, but the force scaled with the distance to the target in the same way. We examined whether guidance forces directed towards the target would reduce subjects’ errors in reproducing a prior position to the same extent as do forces rotated by 90 degrees or 180 degrees, as it might because the forces provide the same information in all three cases. Without vision of the arm, both the accuracy and precision were significantly better with guidance directed towards the target than in all other conditions. The errors with rotated guidance did not differ from those without guidance. Not surprisingly, the movements tended to be faster when guidance forces directed the reaches to the target. This study shows that haptic guidance significantly improved motor performance when using it was intuitive, while non-intuitively presented information did not lead to any improvements and seemed to be ignored even in our simple paradigm with static targets and no time constraints.

The Effect of Trial-by-Trial Adaptation on Conflicts in Haptic Shared Control for Free-Air Teleoperation Tasks

Haptic shared control can improve execution of teleoperation and driving tasks. However, shared control designs may suffer from conflicts between individual human operators and constant haptic assistance when their desired trajectories differ, leading to momentarily increased forces, discomfort, or even deteriorated performance. This study investigates ways to reduce conflicts between individual human operators and a haptic shared controller by modifying supported trajectories. Subjects (n=12) performed a repetitive movement task in an abstract environment with varying spatio-temporal constraints, both during manual control and while supported by haptic shared control. Four types of haptic shared control were compared, combining two design properties: the initial supported trajectory (either the centerline of the environment or an individualized trajectory based on manual control trials), and trial-by-trial adaptation of guidance towards previously performed trajectories (either present or absent). Trial-by-trial adaptation of guidance reduced conflicts compared to non-adaptive guidance, whether the initial trajectory was individualized or not. Without trial-by-trial adaptation, individualized trajectories also reduced conflicts, but not completely: when guided, operators adapt their preferred trajectories. In conclusion, trial-by-trial adaptation is the most promising approach to mitigate conflicts during repetitive motion tasks.

The Effect of Haptic Support Systems on Driver Performance: A Literature Survey

A large number of haptic driver support systems have been described in the scientific literature. However, there is little consensus regarding the design, evaluation methods, and effectiveness of these systems. This literature survey aimed to investigate: (1) what haptic systems (in terms of function, haptic signal, channel, and supported task) have been experimentally tested, (2) how these haptic systems have been evaluated, and (3) their reported effects on driver performance and behaviour. We reviewed empirical research in which participants had to drive a vehicle in a real or simulated environment, were able to control the heading and/or speed of the vehicle, and a haptic signal was provided to them. The results indicated that a clear distinction can be made between warning systems (using vibrations) and guidance systems (using continuous forces). Studies typically used reaction time measures for evaluating warning systems and vehicle-centred performance measures for evaluating guidance systems. In general, haptic warning systems reduced the reaction time of a driver compared to no warnings, although these systems may cause annoyance. Guidance systems generally improved the performance of drivers compared to non-aided driving, but these systems may suffer from after-effects. Longitudinal research is needed to investigate the transfer and retention of effects caused by haptic support systems.

Should drivers be operating within an automation-free bandwidth? Evaluating haptic steering support systems with different levels of authority

OBJECTIVE

The aim of this study was to compare continuous versus bandwidth haptic steering guidance in terms of lane-keeping behavior, aftereffects, and satisfaction.

BACKGROUND

An important human factors question is whether operators should be supported continuously or only when tolerance limits are exceeded. We aimed to clarify this issue for haptic steering guidance by investigating costs and benefits of both approaches in a driving simulator.

METHODS

Thirty-two participants drove five trials, each with a different level of haptic support: no guidance (Manual); guidance outside a 0.5-m bandwidth (Band1); a hysteresis version of Band1, which guided back to the lane center once triggered (Band2); continuous guidance (Cont); and Cont with double feedback gain (ContS). Participants performed a reaction time task while driving. Toward the end of each trial, the guidance was unexpectedly disabled to investigate aftereffects.

RESULTS

All four guidance systems prevented large lateral errors (>0.7 m). Cont and especially ContS yielded smaller lateral errors and higher time to line crossing than Manual, Band1, and Band2. Cont and ContS yielded short-lasting aftereffects, whereas Band1 and Band2 did not. Cont yielded higher self-reported satisfaction and faster reaction times than Band1.

CONCLUSIONS

Continuous and bandwidth guidance both prevent large driver errors. Continuous guidance yields improved performance and satisfaction over bandwidth guidance at the cost of aftereffects and variability in driver torque (indicating human-automation conflicts).

APPLICATION

The presented results are useful for designers of haptic guidance systems and support critical thinking about the costs and benefits of automation support systems.

Analysis of human-machine cooperation when driving with different degrees of haptic shared control

This study investigated human-machine cooperation when driving with different degrees of a shared control system. By means of a direct intervention on the steering wheel, shared control systems partially correct the vehicle's trajectory and, at the same time, provide continuous haptic guidance to the driver. A crucial point is to determine the optimal level of steering assistance for effective cooperation between the two agents. Five system settings were compared with a condition in which no assistance was present. In addition, road visibility was manipulated by means of additional fog or self-controlled visual occlusions. Several performance indicators and subjective assessments were analyzed. The results show that the best repartition of control in terms of cooperation between human and machine can be identified through an analysis of the steering wheel reversal rate, the steering effort and the mean lateral position of the vehicle. The best cooperation was achieved with systems of relatively low-level haptic authority, although more intervention may be preferable in poor visibility conditions. Increasing haptic authority did not yield higher benefits in terms of steering behavior, visual demand or subjective feeling.

Effect of haptic assistance on learning vehicle reverse parking skills

Compared to conventional visual- and auditory-based assisted driving technologies, haptic modality promises to be more effective and less disturbing assistance to the driver. However, in most previous studies, haptic assistance systems were evaluated from safety and stability viewpoints. Moreover, the effect of haptic assistance on human driving behavior has not been sufficiently discussed. In this paper, we introduce an assisted driving method based on haptic assistance for driver training in reverse parking, which is considered as an uncertain factor in conventional assisted driving systems. The proposed system assists the driver by applying a torque on the steering wheel to guide proper and well-timed steering. To design the appropriate assistance method, we conducted a measurement experiment to determine the qualitative reverse parking driver characteristics. Based on the determined characteristics, we propose a haptic assistance calculation method that utilizes the receding horizon control algorithm. For a simulation environment to assess the proposed assistance method, we also developed a scaled car simulator comprising a 1/10 scaled robot car and an omnidirectional camera. We used the scaled car simulator to conduct comparative experiments on subjects, and observed that the driving skills of the assisted subjects were significantly better than those of the control subjects.

Towards cooperative guidance and control of highly automated vehicles: H-Mode and Conduct-by-Wire

This article provides a general ergonomic framework of cooperative guidance and control for vehicles with an emphasis on the cooperation between a human and a highly automated vehicle. In the twenty-first century, mobility and automation technologies are increasingly fused. In the sky, highly automated aircraft are flying with a high safety record. On the ground, a variety of driver assistance systems are being developed, and highly automated vehicles with increasingly autonomous capabilities are becoming possible. Human-centred automation has paved the way for a better cooperation between automation and humans. How can these highly automated systems be structured so that they can be easily understood, how will they cooperate with the human? The presented research was conducted using the methods of iterative build-up and refinement of framework by triangulation, i.e. by instantiating and testing the framework with at least two derived concepts and prototypes. This article sketches a general, conceptual ergonomic framework of cooperative guidance and control of highly automated vehicles, two concepts derived from the framework, prototypes and pilot data. Cooperation is exemplified in a list of aspects and related to levels of the driving task. With the concept 'Conduct-by-Wire', cooperation happens mainly on the guidance level, where the driver can delegate manoeuvres to the automation with a specialised manoeuvre interface. With H-Mode, a haptic-multimodal interaction with highly automated vehicles based on the H(orse)-Metaphor, cooperation is mainly done on guidance and control with a haptically active interface. Cooperativeness should be a key aspect for future human-automation systems. Especially for highly automated vehicles, cooperative guidance and control is a research direction with already promising concepts and prototypes that should be further explored. The application of the presented approach is every human-machine system that moves and includes high levels of assistance/automation.

Associations between driving performance and engaging in secondary tasks: a systematic review

We conducted a systematic review and meta-analysis of the literature examining the relationship between driving performance and engaging in secondary tasks. We extracted data from abstracts of 206 empirical articles published between 1968 and 2012 and developed a logistic regression model to identify correlates of a detrimental relationship between secondary tasks and driving performance. Of 350 analyses, 80% reported finding a detrimental relationship. Studies using experimental designs were 37% less likely to report a detrimental relationship (P = .014). Studies examining mobile phone use while driving were 16% more likely to find such a relationship (P = .009). Quasi-experiments can better determine the effects of secondary tasks on driving performance and consequently serve to inform policymakers interested in reducing distracted driving and increasing roadway safety.

Exploring the design space of haptic assistants: the assistance policy module

Haptic assistants augment user commands to facilitate manipulation and to increase task performance. The strength of assistance, also referred to as assistance level, is one of the main design factors. While existing implementations mainly realize fixed assistance levels that are selected with respect to one design objective, we introduce an assistance policy module that dynamically changes assistance levels and can incorporate multiple performance measures. The design space of this assistance policy module is systematically analyzed and three design factors, 1) performance criteria, 2) performance reference, and 3) assistance policy, are identified. Different implementations of the assistance policy module are compared for a scenario involving guiding virtual fixtures. A single-user evaluation is used to illustrate the effect of the different implementations on the determined assistance levels, and a multi-user study allows for a statistical comparison of them. Results show that adaptive assistance policies can outperform constant assistance policies, switching assistance policies have advantages over continuously adapting policies, a multi-criteria performance measure should be favored if there is no single criterion that has an outstanding priority, and the selection of the performance reference is highly application dependent.

A task-specific analysis of the benefit of haptic shared control during telemanipulation

Telemanipulation allows human to perform operations in a remote environment, but performance and required time of tasks is negatively influenced when (haptic) feedback is limited. Improvement of transparency (reflected forces) is an important focus in literature, but despite significant progress, it is still imperfect, with many unresolved issues. An alternative approach to improve teleoperated tasks is presented in this study: Offering haptic shared control in which the operator is assisted by guiding forces applied at the master device. It is hypothesized that continuous intuitive interaction between operator and support system will improve required time and accuracy with less control effort, even for imperfect transparency. An experimental study was performed in a hard-contact task environment. The subjects were aided by the designed shared control to perform a simple bolt-spanner task using a planar three degree of freedom (DOF) teleoperator. Haptic shared control was compared to normal operation for three levels of transparency. The experimental results showed that haptic shared control improves task performance, control effort and operator cognitive workload for the overall bolt-spanner task, for all three transparency levels. Analyses per subtask showed that free air movement (FAM) benefits most from shared control in terms of time performance, and also shows improved accuracy.

Sharing control with haptics: seamless driver support from manual to automatic control

OBJECTIVE

Haptic shared control was investigated as a human-machine interface that can intuitively share control between drivers and an automatic controller for curve negotiation.

BACKGROUND

As long as automation systems are not fully reliable, a role remains for the driver to be vigilant to the system and the environment to catch any automation errors. The conventional binary switches between supervisory and manual control has many known issues, and haptic shared control is a promising alternative.

METHOD

A total of 42 respondents of varying age and driving experience participated in a driving experiment in a fixed-base simulator, in which curve negotiation behavior during shared control was compared to during manual control, as well as to three haptic tunings of an automatic controller without driver intervention.

RESULTS

Under the experimental conditions studied, the main beneficial effect of haptic shared control compared to manual control was that less control activity (16% in steering wheel reversal rate, 15% in standard deviation of steering wheel angle) was needed for realizing an improved safety performance (e.g., 11% in peak lateral error). Full automation removed the need for any human control activity and improved safety performance (e.g., 35% in peak lateral error) but put the human in a supervisory position.

CONCLUSION

Haptic shared control kept the driver in the loop, with enhanced performance at reduced control activity, mitigating the known issues that plague full automation.

APPLICATION

Haptic support for vehicular control ultimately seeks to intuitively combine human intelligence and creativity with the benefits of automation systems.

Influence of risk expectation on haptically cued corrective manoeuvres during near lane departure

Some driving devices are designed to prevent road departures. One such device, motor priming (MP), provides small pulses to the steering wheel towards the lane centre, without correcting the trajectory itself. Compared with the other lane departure warning systems, its higher efficacy has been demonstrated; it is hypothesised that this relies on the action of haptic cues at the sensorimotor level (Navarro, J., Mars, F., and Hoc, J.M., 2007. Lateral control assistance for car drivers: a comparison of motor priming and warning systems. Human Factors, 49 (5), 950-960). The way in which corrective manoeuvres, primed by MP, can be influenced by processes that operate at higher levels of cognitive control, such as risk evaluation, is an issue. Results showed that MP improved all indicators of steering efficiency, starting with reaction times. Risk expectation and situation analysis did not influence reaction times but came into play soon after the corrective manoeuvre was initiated. Thus, although MP triggered the response at the sensorimotor level, higher levels of cognition (symbolic control) quickly modulated the execution of the corrective manoeuvre.

PRACTITIONER SUMMARY

This paper showed that corrective manoeuvres following directional pulses on the steering wheel (motor priming) are modulated by risk expectation. The conclusion may be of interest for designers of haptics-based automation such as lane departure warning and lane keeping assistance systems.

Objective and subjective evaluation of motor priming and warning systems applied to lateral control assistance

Previous research has shown that a device called "motor priming" (MP) was more effective than other lane departure warning systems. MP prompts drivers to take action by means of small asymmetric oscillations of the steering wheel. The first objective of this experiment was to provide a deeper understanding of MP mechanisms through a series of comparisons with other haptic and auditory systems. The results suggest that much of the improvement in recovery manoeuvres observed with MP is due to the motor cue (proprioceptive pre-activation of the gesture). Other factors, such as delivering the signal directly to the hands (stimulation of response effectors) or using the tactile modality rather than auditory warning, play a lesser role. This supports the hypothesis that MP devices directly intervene at the motor level, in contrast to more traditional warning systems, which only improve situation diagnosis. The second objective was to assess drivers' acceptance of the assistance devices. A dissociation between efficiency and acceptance of the devices was observed: drivers globally preferred auditory warning to MP. The combination of auditory warning and motor priming appeared to be a good compromise to achieve both effectiveness and acceptance. This experiment illustrates the relevance of simulator studies when dangerous situations are the main targets of the investigation.

The effect of haptic guidance, aging, and initial skill level on motor learning of a steering task

In a previous study, we found that haptic guidance from a robotic steering wheel can improve short-term learning of steering of a simulated vehicle, in contrast to several studies of other tasks that had found that the guidance either impairs or does not aid motor learning. In this study, we examined whether haptic guidance-as-needed can improve long-term retention (across 1 week) of the steering task, with age and initial skill level as independent variables. Training with guidance-as-needed allowed all participants to learn to steer without experiencing large errors. For young participants (age 18–30), training with guidance-as-needed produced better long-term retention of driving skill than did training without guidance. For older participants (age 65–92), training with guidance-as-needed improved long-term retention in tracking error, but not significantly. However, for a subset of less skilled, older subjects, training with guidance-as-needed significantly improved long-term retention. The benefits of guidance-based training were most evident as an improved ability to straighten the vehicle direction when coming out of turns. In general, older participants not only systematically performed worse at the task than younger subjects (errors ∼3 times greater), but also apparently learned more slowly, forgetting a greater percentage of the learned task during the 1 week layoffs between the experimental sessions. This study demonstrates that training with haptic guidance can benefit long-term retention of a driving skill for young and for some old drivers. Training with haptic guidance is more useful for people with less initial skill.

Can robots help the learning of skilled actions?

Learning to move skillfully requires that the motor system adjusts muscle commands based on ongoing performance errors, a process influenced by the dynamics of the task being practiced. Recent experiments from our laboratories show how robotic devices can temporarily alter task dynamics in ways that contribute to the motor learning experience, suggesting possible applications in rehabilitation and sports training.

Passive and Active Discrimination of Natural Frequency of Virtual Dynamic Systems

It has been shown that humans use combined feedforward and feedback control strategies when manipulating external dynamic systems, and when exciting virtual dynamic systems at resonance, that they can tune their control parameters in response to changing natural frequencies. We present a study to determine the discrimination thresholds for the natural frequency of such resonant dynamic systems. Weber fractions (WFs in %) are reported for the discrimination of 1, 2, 4, and 8 Hz natural frequencies. Participants were instructed either to passively perceive or actively excite the virtual system via a one degree-of-freedom haptic interface with visual and/or haptic feedback. The average WF for natural frequency ranged from 4% to 8.5% for 1,2, and 4 Hz reference natural frequencies, while the WF was approximately 20% for systems with a reference natural frequency of 8 Hz. Results indicate that sensory feedback modality has a significant effect on WF during passive perception, but no significant effect in the active perception case. The data also suggest that discrimination sensitivity is not significantly affected by excitation mode. Finally, results for systems with equivalent natural frequencies but different spring stiffness indicate that participants do not discriminate natural frequency based on the maximum force magnitude perceived.

Lateral control assistance for car drivers: a comparison of motor priming and warning systems

OBJECTIVE

This paper's first objective is to determine whether motor priming assistance (consisting of directional steering wheel vibrations) can be of some benefit compared with more traditional auditory (lateralized sound) or vibratory (symmetric steering wheel oscillation) warning devices. We hypothesize that warning devices favor driving situation diagnosis, whereas motor priming can improve the initiation of action even further. Another objective is to assess the possible benefits of using multimodal information by combining auditory warning with simple steering wheel vibration or motor priming.

BACKGROUND

Within the context of active safety devices, the experiment dealt with moderately intrusive driving assistance devices that intervene when a certain level of risk in terms of lane departure is reached.

METHOD

An analysis of the steering behavior of 20 participants following episodes of visual occlusion was carried out. Five warning and motor priming devices were compared.

RESULTS

All tested devices improved the drivers' steering performance, although their effects were modulated by the drivers' risk assessment. However, performance improvements were found to be greater with a motor priming device. No additional performance enhancement was observed when auditory warning was added to steering wheel vibration or motor priming devices.

CONCLUSION

This study confirms the hypothesis that the direct intervention of motor priming at the action level is more effective than a simple warning, which intervenes upstream in situation diagnosis. Multimodal information did not seem to improve driver performance.

APPLICATION

This study proposes a new kind of lateral control assistance, which acts at a sensorimotor level, in contrast with traditional warning devices.