Measuring Neuromuscular Control Dynamics During Car Following ...

IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART B: CYBERNETICS, VOL. 41, NO. 5, OCTOBER 2011

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Measuring Neuromuscular Control Dynamics During Car Following With Continuous Haptic Feedback David A. Abbink, Mark Mulder, Member, IEEE, Frans C. T. van der Helm, Max Mulder, and Erwin R. Boer

Abstract—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. Index Terms—Car following, cybernetics, driver support systems, haptic feedback, human–machine systems, neuromuscular control, shared control.

Manuscript received May 17, 2010; revised December 16, 2010; accepted January 2, 2011. Date of publication April 29, 2011; date of current version September 16, 2011. This work was supported by Nissan Motor Company Ltd., Yokohama, Japan. The work of D. Abbink was supported by VENI Grant 10650 from the Netherlands Organization for Scientific Research (NWO). This paper was recommended by Associate Editor A. Tayebi. D. A. Abbink and F. C. T. van der Helm are with the Department of BioMechanical Engineering, Faculty of Mechanical, Maritime and Materials Engineering (3mE), Delft University of Technology, 2628 CD Delft, The Netherlands (e-mail: [email protected]). M. Mulder and M. Mulder are with the Aerospace Engineering Faculty, Delft University of Technology, 2629 HS Delft, The Netherlands. E. R. Boer is with Entropy Control, San Diego, CA 92122 USA. Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TSMCB.2011.2120606

N OMENCLATURE Abbreviations DSS EMG FRF FT GL GM H MVC PT RT STD SO TA V VH

Driver support system. Electromyography. Frequency response function. Force Maintenance Task (“give way to forces”). Gastrocnemius lateralis. Gastrocnemius medialis. Car following with only haptic feedback. Maximum voluntary contraction. Position Maintenance Task (“resist forces”). Relax Task (“ignore forces”). Standard deviation. Soleus. Tibialis anterior. Car following with only visual feedback. Car following with visual and haptic feedback.

Symbols B EMGrel EMGmax f fv ft Fsample Γ Hadm Hcar Hcontrol Hpedal iT T C Ic K R s t tanl Tc Tdist Tdss T HW TTC θc θref vcar vlead

1083-4419/$26.00 © 2011 IEEE

Damping. Relative EMG. Maximum EMG. Frequency. Velocity disturbance frequencies. Force disturbance frequencies. Sample frequency. Coherence. Admittance dynamics. Car dynamics. Driver dynamics. Pedal dynamics. Inverse time-to-contact. Inertia. Stiffness. Remnant. Laplace variable. Time. Analyzed duration of the signals. Driver torque. Torque disturbance. DSS torque. Time headway. Time-to-contact. Pedal displacement. Reference pedal displacement. Own car velocity. Lead car velocity.

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vrel xrel


Relative velocity. Relative distance. I. I NTRODUCTION

T

HE BENEFITS of using active gas pedals to assist drivers with speed adaptation has been widely reported (e.g., [1] and [2]). More recently, it has been proposed to use an active gas pedal to assist drivers with keeping a safe distance to a lead vehicle, in highway car-following situations. One of the possible approaches is to trigger gas pedal counterforces after a binary threshold is exceeded [3], [4]. Although, in general, relatively beneficial subjective results were reported, the authors caution for possible issues with nuisance and false alarms, which is inherently associated with binary warnings in continuously changing control tasks [5]. Another promising approach to improve driving comfort and safety during car following on highways is to support drivers with continuous haptic feedback [5]–[7]. This approach is an alternative to automating car following by means of an adaptive cruise control system, which is a system that has been extensively investigated and marketed by several automotive companies (e.g., [8] and [9]). Continuous haptic feedback is a special case of shared autonomy solutions [10], [11] that propose a cooperation somewhere between manual control and full automation, but, in this case, the human and machine cooperate on a physical level. Related work in this field includes cobots [12], solutions for vehicle steering (e.g., [13]–[16]), robotic surgery [17], [18], and others (for an overview, see [19]). Continuous haptic feedback during car following supplements the driver’s visual feedback with an additional haptic feedback channel by continuously transforming the dynamic separation to the lead vehicle to easily interpretable haptic information (e.g., pedal forces or pedal stiffness). In other words, if the lead vehicle slows down, the separation decreases, which the haptic support system continuously translates to an increasing force and stiffness on the gas pedal. The additional pedal force smoothly changes and directly suggests the proper control action: to release the gas pedal. Note that, essentially, the driver is always in control and can, at any time, choose either to overrule the haptic information and keep the pedal position constant (by pushing harder to resist the feedback forces) or to follow the haptic information and keep the pedal force constant (by giving way to the gas pedal). When designed well, the haptic DSS allows the driver to remain in the loop, is comfortable, and results in drivers being able to reach similar or slightly improved car-following performance at significantly reduced control effort [20], [21]. Although previous literature has realized the importance of the neuromuscular contributions to unsupported vehicular control (e.g., [22]–[24]), such insight has not been used in the design of haptic shared control systems. These systems are usually tuned, instead of designed based on quantitative driver models that include both visual and haptic feedback. This paper provides a control-theoretic approach to measure and separate the driver’s responses to visual feedback and the biomechanical responses to the additional feedback forces. The approach is illustrated in Fig. 1, which shows a simplified control-

Fig. 1. Simplified control-theoretical model of a closed-loop car-following situation with both visual and haptic feedback. The visual feedback path is shown by a dashed line to illustrate that it can momentarily be absent.

theoretical model of a closed-loop car-following situation where visual feedback is supplemented by continuous haptic feedback. Note that the haptic feedback is always available, even when the visual feedback is momentarily unavailable when the driver is looking elsewhere. In the modeled situation, the driver aims to reduce the impact of lead vehicle speed perturbations vlead , in order to minimize the changes in the separation (e.g., relative velocity vrel ). The driver is modeled as a cortical part that sends neural commands to a neuromuscular part that interacts with the gas pedal. The feedback torques from the driver support system Tdss , together with the contact torques Tc from the driver, determine the pedal displacements θc . That is, as illustrated in Fig. 1, the forces of the haptic feedback may cause changes in pedal position, depending on the combined impedances of the gas pedal and the driver’s ankle-foot complex in connection with the pedal. In previous research, several DSS prototypes were developed and tested in a fixed-base driving simulator. A number of experimental studies [7], [25]–[28] showed beneficial results while driving with a haptic DSS, compared with unassisted car following, with the general conclusion being that drivers need less effort to achieve the same level of performance. Although these results are promising, it is unclear how drivers exactly realize the benefits. Part of the response to the feedback forces does not arise from cognitive processes but from passive and reflexive responses. This neuromuscular response to the feedback forces has not been investigated yet but is crucial when attempting to understand the impact of the forces from a designed haptic support system or when attempting to improve future designs. It has been hypothesized that good haptic feedback induces drivers to change the task of their foot on the gas pedal from a “resist forces” or “relax” task to a “give way to forces” task [7], [29]. That is, by keeping the force constant, the driver will keep the separation constant. When the driver resists the forces, to maintain the gas pedal position, this is then a sign that the haptic feedback is not having the desired effect, and the driver does not agree with the feedback forces. A second hypothesis was that spinal reflexes are used to accomplish the force task, thereby considerably reducing response latency times [21] and the visual load. This latter hypothesis was indeed confirmed, as reported by [30]. Most of the hypotheses regarding the underlying mechanisms causing the performance benefit of haptic feedback cannot be tested, however, without a detailed biomechanical analysis of the human motion control of the lower limb. There is a

ABBINK et al.: MEASURING NEUROMUSCULAR CONTROL DYNAMICS DURING CAR FOLLOWING

substantial amount of literature available on limb dynamics. The human motion control dynamics are here captured by the admittance, which is defined as the causal dynamic relationship between the force acting on the limb (input) and the position of the limb (output). Techniques to estimate the admittance FRF have been successfully applied to upper extremity movements [31], as well as the ankle joint [32]–[36]. These investigations indicate that limbs behave approximately like mass-spring-damper systems and further show the human capability to change the visco-elastic properties of a limb by muscle (co-)contraction and by reflexive feedback. These techniques have been applied to the ankle-foot complex in a driving posture [29], where subjects showed a substantial increase in admittance during a “relax” task (RT ), compared with a “maintain position” task (P T or “resist forces”). Muscle activity was measured using EMG techniques, was very high during P T (due to maximal co-contraction), and negligible for a “relax task” (due to the muscle relaxation). It was also shown [37] that the “maintain force” task (F T or “give way to forces”) increases the admittance even further by actively giving way at low levels of muscle activity. The question remains however if human motion control behavior as measured during these classical tasks is, by any means, comparable to that measured while conducting a driving task (with or without a haptic driver support system). Unfortunately, at present, the literature offers no techniques to measure the human admittance while being engaged in another (continuous) control task. The goal of this paper is to develop such a technique and obtain a quantitative insight into how a haptic driver support system affects human neuromuscular control behavior and car following in general. The novel experimental technique is used to estimate the admittance of the ankle–foot complex during car following, both with and without the haptic DSS. These admittances will then be compared with the admittances that are estimated while performing the three classical tasks (RT, P T, F T ). In the next section, the experimental measurement technique will be described in detail. Section III will present the results of an experiment conducted to validate the technique, followed by a discussion in Section IV. This paper ends with conclusions and recommendations for further research. II. M ETHOD A. Subjects Ten subjects, i.e., five females and five males, between the age of 20 and 23 participated in the experiment. All subjects had their driver’s license for several years and had no medical record of neurological disorders or injuries to the lower extremities. The participants were not familiar with the purpose of the study, were paid for their efforts, and gave informed consent to the experimental procedure. B. Apparatus The experimental setup consisted of a driver seat, a computer screen for visualization, and a gas pedal. A high-fidelity

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Fig. 2. (Left) Participant driving in the simulator. (Right) Participant’s lower leg and foot pushing the gas pedal at the operating point of 25% pedal depression (= 5◦ ). TABLE I S TRUCTURE OF THE M AIN E XPERIMENT C ONSISTING OF C LASSICAL TASKS (PT, RT, FT) AND C AR -F OLLOWING TASKS (V, V H, H). Fsample I S THE S AMPLE F REQUENCY, AND tanl THE T IME D URATION OF A LL A NALYZED S IGNALS

force-controlled actuator was capable of imposing forces on the gas pedal, as well as simulating a range of stiffness and dampings. The conventional pedal characteristics were set to resemble those of a common gas pedal: the pedal force linearly progressed from 20 N at 0% pedal depression to 36 N at maximum pedal depression (with 0%–100% pedal depression being 0◦ –20◦ pedal rotation). This is how the pedal behaved without a vehicle in front. Additional forces and stiffness from the haptic gas pedal add to these conventional pedal characteristics when the separation to a lead vehicle decreased. The haptic feedback design is described in detail in literature [38]. Note that the pedal will still move back to 0% pedal depression if released, just as a conventional pedal. Subjects were asked to remove their footwear, place their right foot on the gas pedal, and sit in a way that felt comfortable for them as if preparing for a long drive (see Fig. 2). A 17-in screen (1024 × 768 pixels) approximately 1.5 m in front of the subject showed task-related information: force during a force task, position during a position task, and a road with a lead vehicle during the driving tasks. C. Experiment Protocol The experiment consisted of two parts: 1) admittance measurements during the three classical tasks and 2) car following during three conditions, with and without torque perturbation admittance measurements. Additional EMG measurements were performed at the start and end of the experiment in order to calibrate the EMG measurements during the main experiment. Table I summarizes the experimental conditions. 1) Electromyography: EMG activity was used to measure muscle activity for the relevant lower leg muscles. Discshaped (30 mm) differential (34-mm interspacing) electrodes (Ag/AgCl) were placed and oriented (standardized according

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to [39]) over the three plantar flexors, i.e., SO, GL, and GM , and over the dorsiflexing T A. Skin conduction was improved by using hydrogel, local shaving of the skin, abrasion with sandpaper, and cleaning with alcohol. The EMG signals were preamplified, high-pass filtered (analog third-order Butterworth with cutoff frequency at 20 Hz and 18 dB/oct) to prevent any motion artifacts, rectified, and low-pass filtered (analog third-order Butterworth with cutoff frequency at 100 Hz and 18 dB/oct) to prevent aliasing. The signals were measured at 250 Hz (DSpace AD converter with 16-bit resolution) and digitally stored for offline analysis. An isometric calibration experiment was done to calculate the MVCs and to relate EMG activity to a selection of forces that could reasonably be expected to be encountered while driving. The gas pedal position was fixed (θc = 5◦ or 25% pedal depression), and subjects were asked to alternately push maximally (plantar flexion) for approximately 3 s and then relax for a similar time, during an interval of 20 s. The same was done for a maximal pulling task (dorsal flexion), where the foot was strapped to the pedal to allow pulling. The entire sequence was repeated at the end of the main experiment to check for fatigue, which was negligible. The calibrations before and after the main experiment were averaged. 2) Classical Tasks: a) Task instruction: To quantify the range over which subjects could adapt their admittance, they were asked to perform three randomized classical tasks: minimize pedal deviations by resisting the pedal forces (position task, P T ), stay totally relaxed (relax task, RT ), and minimize force deviations by giving way to the pedal forces (force task, F T ). The subjects could see their performance (reference position or force against the actual value) on the screen in front of them, but during the relax task, the screen was turned off to prevent any distraction. Each task was repeated twice for averaging purposes and was preceded by training. b) Torque perturbation: While performing the task, a continuous stochastic torque perturbation Tdist was applied to the pedal. The perturbation was a multisine and generated offline in the frequency domain. The phase was randomized to yield an unpredictable signal, and the cresting technique ([40], [41]) was used to prevent large peaks in the time domain. Tdist was designed using the “Reduced Power Technique” ([42]), resulting in a signal that contained full power from 0.02 up to 0.5 Hz, and beyond that, a 5% fraction of that power at several logarithmically spaced frequency points up to 25 Hz (see Fig. 3). The technique allows estimation over a full frequency range while evoking only low-frequent behavior. Within the full-power section, only every third band of two frequency points contained power, in order to improve the signal-to-noise ratio and also to allow some “free” frequencies in between, where the visual perturbation signal could have power, as will be discussed in Section II-C3. An inverse fast Fourier transformation yielded a repeatable time-domain signal, which was cut to last 70 s. During the experiment, Tdist was scaled, so that the standard deviation of the resulting pedal displacements was approximately 0.5◦ (to ensure linearity).

Fig. 3. Generated perturbation signals Tdist and vlead in (left) frequency domain and (right) time domain.

c) Measured signals during classical tasks: The following signals were measured at 250 Hz: the contact torque Tc (t) (in Newton-meter), the torque perturbation Tdist (t) (in Newtonmeter), the pedal depression θc (t) (in radians), and the four EMG signals EMG(t) (in volts). 3) Driving Tasks: The main part of the experiment consisted of a simplified driving simulation. The computer screen showed a realistic representation of a straight road and a lead vehicle. Note that the driving simulator did not have a brake pedal or a steering wheel. The gas pedal depression provided the input to a nonlinear vehicle model, which included an automatic gear transmission. Linearization around a vehicle speed of 100 km/h, e.g., yields the following frequency response function between input gas pedal depression θc and output vehicle speed Vcar : Hcar =

0.03197 . s + 0.0443

(1)

a) Task instruction: Subjects were instructed to “maintain a constant T HW of 1 s to the lead vehicle, as good as possible.” Time headway is defined as T HW =

xrel vcar

(2)

with xrel being the relative position and vcar being the own vehicle velocity. The exact separation (T HW = 1 s) was shown at the start of each trial as a red square. When subjects had reached a constant operating point (vcar ≈ 28 m/s; θc ≈ 25% pedal depression), the red calibration square disappeared. Then, after a random amount of seconds, the lead vehicle speed was perturbed, and small torque perturbations were added to the gas pedal. After 94 s, the perturbations stopped, and the calibration square was shown again for 10 s. This indicated the end of the repetition and allowed subjects to correct for possible drift in T HW . A full trial consisted of four such repetitions, lasting about 8 min in total. Trials were done for three driving conditions: 1) normal driving with visual feedback (V ); 2) driving with the haptic feedback system and visual feedback (V H); and


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3) driving with only haptic feedback (H), during which the screen was turned off. The haptic feedback system was designed during previous research ([21], [38]) and caused changes in gas pedal stiffness as a result of changes in T T C (scaled by T HW in order to yield stronger feedback at closer separation). Here, time to contact T T C is defined as TTC =

xrel vrel

(3)

with vrel being the relative velocity between the lead vehicle and own vehicle. Each driving condition was trained for some time and pseudorandomized: the H condition was always preceded by the V H condition to make sure that drivers had a good feeling of how the haptic feedback related to visual information. The torque perturbations could be felt but were hypothesized to be small enough to not interfere with the car-following task. To check for this assumption, each driving condition was also repeated without the torque perturbations. These conditions will be referred to as V ∗ , V H ∗ , and H ∗ . b) Lead vehicle velocity perturbation: The lead vehicle velocity perturbation vlead (t) was designed in the same way as Tdist (t): a phase-randomized crested multisine containing power between 0.02 and 0.5 Hz. A different time realization of vlead was made for each driving condition (V, V H, H) in order to prevent driver anticipation after several repetitions. c) Frequency separation of the perturbations: A frequency separation method was employed in order to be able to separate the driver’s responses to both Tdist (t) and vlead (t). The two perturbations were designed in the frequency domain to contain power at different frequency points in the area of full power (between 0.02 and 0.5 Hz). The set of frequency points were divided into repetitive segments of three bands, each containing two frequency points. The first band of two frequency points fv only contained power for the visual perturbation, the following band ft only contained power for the torque perturbations, and the third band fr did not contain any power (see Fig. 3), after which the following contained power for fv , and so on. Beyond 0.5 Hz, the visual perturbation did not contain any power, but the torque perturbations were similar to those used for the classical tasks: designed according to the reduced power method ([42]) to contain full power up to 0.5 Hz and reduced power up to 25 Hz. d) Measured signals during car following: During the car-following experiment, all signals were measured at 200 Hz. In addition to the measured signals during the classical tasks, car-following data were measured as well: the lead vehicle perturbation vlead (t) (in meters per second), the own vehicle speed vcar (t) (in meters per second), and the relative separation xrel (t) (in meters). D. Data Analysis 1) Signals: The first seconds of each measured signal were discarded to reduce onset effects, leaving exactly 16 384 samples (213 , convenient for fast Fourier transforms) for identification. For all trials, the EMG(t) measured for each muscle was

Fig. 4. Measurement scheme during a classical position task. The pedal dy−1 form a closed-loop system. namics and the human ankle–foot dynamics Hadm −1 Hadm is modeled as a quasi-linear system, meaning that all nonlinearities are captured in the remnant R.

scaled by its respective EMGmax (the EMG measured during MVC) EMGrel (t) =

EMG(t) . EMGmax

(4)

The signals measured during car following were used to calculate two important metrics that are often used in literature as driving metrics, i.e., the time headway T HW and inverse time-to-contact iT T C. 2) Frequency-Domain Analysis: Frequency-domain analysis was conducted for each condition on the time average of that condition over all repetitions. a) Classical tasks: Fig. 4 shows a frequency-domain measurement scheme for a classical position task. The relationship between the torque Tc (input) and the pedal rotations θc (output), i.e., the admittance, is for each task estimated at the frequencies ft , according to ˆ ˆ adm (ft ) = H ˆ T θ (ft ) = STdist θc (ft ) . H c c ˆ STdist Tc (ft )

(5)

The term SˆTdist θc is the estimate for the cross-spectral density of disturbance Tdist (t) and θc (t), whereas SˆTdist Tc is the crossspectral density of disturbance Tdist (t) and Tc (t). All spectral densities were averaged over two adjacent frequencies to reduce the variance. The coherence function is used to determine the approximation involved by using linear models and is estimated according to 2 ˆ STdist θc (ft ) 2 ˆ Γ . (6) Tdist θc (ft ) = ˆ STdist Tdist (ft )Sˆθc θc (ft ) The coherence is an indication of the amount of linearity of the system in response to the external perturbation. For a linear system, the coherence function is equal to one when there is no noise (linearization or measurement noise) and zero in the worst case. b) Car-following tasks: The measurement scheme is slightly more complicated while car following (see Fig. 5). Here, the pedal deviations are the input of the longitudinal car dynamics Hcar . The relative velocity is perturbed by vlead (fv ), and the resulting separation is fed back to the driver either through visual (V ) or haptic feedback (H), or a combination of both (V H). At the center of the scheme, the admittance measurement scheme from Fig. 4 can still be recognized.

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TABLE II E XPERIMENTAL H YPOTHESES

Fig. 5. Measurement scheme of a driver in a car-following task. Hcontrol is the total driver FRF and consists of a visual part Hvis , a spinal control part −1 , and the pedal with a certain inertia Ic , represented by the admittance Hadm damping B, and stiffness K. The output of Hcontrol is a pedal position θc which is the input for the car kinematics Hcar . The velocity of the car is perturbed by vlead (fv ), which results in changes in vrel that the driver tries to control back to zero. When the DSS system is switched on, it will provide informational torques Tdss to the driver, as well as changes in the pedal stiffness K. Tdist (ft ) is applied to the pedal in order to estimate Hadm .

The admittance can be estimated at the frequencies ft , using the same procedures used for the classical tasks [see (5)]. Several other frequency response functions can now also be estimated: Hcontrol (the total driver dynamic response to vrel ) and the longitudinal car kinematics Hcar (with input θc and output vcar ). Hcontrol was estimated at the frequencies fv , where vlead had power, according to ˆ ˆ control (fv ) = Svlead θc (fv ) H Sˆvlead vrel (fv )

(7)

with the coherence ˆ2 Γ vlead vrel (fv ) =

2 ˆ Svlead vrel (fv ) Sˆvlead vlead (fv )Sˆvrel vrel (fv )

.

(8)

3) Time-Domain Analysis: For ease of interpretation, the torques were converted to forces at the contact point of the foot with the pedal (assuming a moment arm of 0.188 m), and the pedal rotations will be shown in degrees. The following car-following performance metrics were used (see [30]): the standard deviation of T HW (t) and the standard deviation of iT T C(t). The standard deviation of θc (t) is used as an objective metric of control effort. The means of the time-averaged EMGrel for each muscle are used as a measure of (co-)contraction and can be considered as an objective control effort metric during both the car-following and classical tasks. E. Hypotheses Since the prototype was designed to allow drivers to maintain a constant separation by maintaining a constant force, it was hypothesized that haptic feedback will enlarge the driver’s admittance (resembling a force task). The admittance during a force task is expected to be larger than that during a relax task, whereas a position task will decrease the admittance with respect to a relax task. Overall, the impact of haptic feedback on performance and control effort is expected to be beneficial.

Table II summarizes the hypotheses regarding the comparison of car-following performance (CFP ) and control effort (CFCE ), the frequency response function Hcontrol , the admittance, and the EMG for each different task. Recall that the three car-following tasks are driving with visual feedback only (V ), driving with visual and haptic feedback (V H), and driving with haptic feedback only (H). The three classical tasks are maintain position (PT), maintain force (FT), and relax (FT). III. R ESULTS A. FRFs of the Admittance All participants showed the ability to adapt their admittance, except for two subjects. These showed an increase in admittance neither during the classical FT (compared with the RT) nor during V H (compared with V ). Most likely, these subjects required more training than the rest, but they were removed from further analysis. For all remaining subjects (n = 8), the admittance could ˆ2 be estimated with high coherences (Γ Tdist θc ≥ 0.8), in both the classical tasks and the car-followings tasks. The coherence deteriorated for some subjects at the lowest frequencies, in particular during condition V . Overall, the high coherences indicate that linear techniques were indeed applicable. 1) Car Following: The admittance of a typical subject is shown in Fig. 6 for two of the driving conditions V and V H. Some intrasubject variability in the admittance is present, yet it can be clearly seen that, below 1 to 2 Hz, the admittance is larger during V H, compared with V . In general, the admittances estimated during driving were more noisy (i.e., lower coherences and more variability) than during the classical tasks, which was to be expected. ˆ adm 2) Classical Tasks: Fig. 7 shows the admittance H during classical tasks for another typical subject. The human operator ability to adapt the admittance to maximize task performance is clearly shown: the largest admittance is found during FT, the smallest admittance during PT and the admittance during RT lies in between. The intrasubject variability was substantially smaller for these classical tasks, compared with the car-following tasks. The intersubject variability during classical tasks is shown in Fig. 8, where the averaged admittances (over all eight subjects) are shown, together with the standard deviation, as thin dotted lines. 3) Comparison: In Fig. 9, the admittances as measured during the three car-following tasks are shown together with those measured during the classical tasks. The admittances were averaged over all eight subjects. It can be seen that the admittance during normal driving (V ) lies between RT and


ˆ adm of a typical subject estimated during two driving Fig. 6. Admittance H tasks. (Solid line) Driving with only visual feedback (V ). (Dashed line) Driving with visual and haptic feedback (V H). The top plot shows the magnitude of the admittance, the middle plot shows the phase, and the bottom plot shows the coherence squared. The thin lines show each of the four repetitions, and the thick line is the average.

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ˆ adm averaged over all subjects, which Fig. 8. Magnitude of the admittance H were estimated during each of the three classical tasks: maintain force (FT), relax (RT), and maintain position (PT). The thick line is the average, and the thin lines show the standard deviation over all subjects.

ˆ adm averaged over all subjects, which Fig. 9. Magnitude of the admittance H were estimated (dash-dotted lines) during every classical task (FT, RT, and PT) and (solid lines) during all driving tasks (V , V H, and H). ˆ adm of a typical subject estimated during each of the Fig. 7. Admittance H three classical tasks: maintain force (FT), relax (RT), and maintain position (PT). The top graph shows the magnitude of the admittance, the middle graph shows the phase, and the bottom graph shows the squared coherence. The thin dashed lines show each of the two repetitions, and the thick line shows the results for the time average over the two repetitions.

FT, whereas, with haptic feedback (both V H and H), the admittance increases even beyond FT. B. FRFs of Hcontrol Hcontrol (the driver’s total response to vrel ) could be estiˆ 2v v ≥ 0.8) during mated with relatively high coherences (Γ lead rel V and V H, although the coherences decreased somewhat at the higher frequencies (above 0.2 Hz), particularly during V . ˆ 2v v ≥ During H, the coherences were generally lower (Γ lead rel 0.6). Fig. 10 shows Hcontrol for a typical subject. When comparing Hcontrol during V H to that during V , a shift is noticeable: a smaller amplitude at lower frequencies and a larger amplitude at higher frequencies. This means that, at lower frequencies,

Fig. 10. Gain, phase, and coherence squared of the estimated Hcontrol (one typical subject). (Left to right) The solid lines depict conditions V , V H, and H. The striped lines show the same DSS conditions but then without the torque perturbations Tdist .

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Fig. 11. EMGrel of each muscle during [(top left) SO, (top right) GM , (bottom left) GL, (bottom right) T A] for all the classical tasks (P T, RT, F T ) averaged over all subjects. Small transparent bars show individual results.

drivers reacted with less pedal displacements θc to the same changes in vrel (t). The figure further shows that the driving trials with Tdist (V and V H: the solid lines) are very similar to the driving trials without the torque perturbations (V ∗ and V H ∗ : striped lines), except for driving with haptic feedback only. In that case, the coherence and Hcontrol were smaller when Tdist was present (H), compared with the situation where it was absent (H ∗ ).

not substantially influence the overall car-following control behavior. Apparently, the developed technique is indeed successful in quantifying the neuromuscular behavior of participants who were engaged with a different task, which, in this case, is car following, rather than primarily dealing with the torque perturbations, as in the classical setup to determine the human admittance.

A. Effect of DSS on Admittance C. Time-Domain Analysis The effect of the DSS on car-following performance and workload metrics in this experiment were the same as previously reported ([7], [30]). In short, a decreased physical workload (lower standard deviation of pedal depression θc ) was sufficient to realize the same performance (similar standard deviations of T HW and iT T C). Additionally, EMGrel was smaller during haptic feedback for every subject (not shown), independent of the torque perturbations. Fig. 11 shows the EMGrel results for the classical tasks: during the PT, there is considerable co-contraction; during the RT, there is very little muscle activation; and during the FT, there is some muscle activation, indicating that subjects actively used their muscles to give way to the forces. IV. D ISCUSSION The time- and frequency-domain results indicate that the control strategies and neuromuscular response of the subjects considerably changed when they received the additional haptic feedback from the driver support system. The objectives of the study, i.e., to simultaneously estimate FRFs of the driver’s response to lead vehicle speed perturbations and to gas pedal torque perturbations, were realized by separating the perturbations in the frequency domain and then applying them at the same time. Coherences were generally high, justifying the linear identification tools under these conditions. Additionally, the torque perturbations that were needed to estimate the motion control behavior of the ankle–foot complex (i.e., the admittance) did

The admittance was used to measure and describe the spinal contribution to the overall driver response Hcontrol . At frequencies above 10 Hz, the admittance of all tasks (car following and classical) converged. Here, inertial properties of the neuromuscular system dominate the behavior. At lower frequencies, subjects displayed a substantial adaptation range of the admittance: the smallest during the maximal PT and the largest during driving with haptic feedback. As hypothesized, the DSS leads drivers to adopt a force task strategy and to increase their admittance. Above 1 Hz, the admittances measured during RT and FT are roughly comparable in magnitude to studies that estimate ankle joint dynamics during similar tasks with higher bandwidth perturbations (e.g., see [32] and [35]). It must be noted that, in this paper, the measured dynamics do not describe those of the ankle joint but rather those of the ankle–foot complex at the contact point of the foot with gas pedal. The admittances estimated during PT and RT closely resemble those obtained in a previous study, which was performed on the ankle–foot complex manipulating a gas pedal [29], [37], [42]. The adaptiveness of the admittance is the result of—among others—muscle co-contraction and reflexive activity (by muscle spindles and Golgi tendon organs [20], [43]. At low frequencies, contributions of slower feedback systems (like visual feedback and tactile feedback) cannot immediately be excluded. The extent to which these mechanisms contribute to the admittance is an interesting question that has implications for the design. Driving with the DSS increased the admittance, even beyond the force task. However, the observed differences between


classical tasks and car-following tasks could partly be the effect of amplitude nonlinearity, which is a well-known biomechanical property ([44], [45]) that states that increased amplitudes of deviation result in increased admittance. The amplitudes during the three classical tasks were comparable (STD θc ≈ 0.7◦ ), but as a group also substantially smaller than those during the three car-following tasks (STD θc ≈ 3◦ ). This difference makes that direct comparisons must be taken with caution until it has been investigated how large the amplitude effect is for these experimental conditions. Results for admittance at frequencies well below 1 Hz—such as estimated in the current experiment—are usually not investigated. Instead, the admittance is usually estimated only above 1 Hz, which may be well suited to determine the mass-springdamper characteristics of a limb, but cannot be extended to fully explain human limb motion control during low-frequency control tasks such as, in this case, car following. The current method, however, is an important step toward such understanding and can be expected to also apply to other continuous control tasks. B. Effect of DSS on Car Following The general effect of the DSS on car following relates well to other studies with a haptic DSS [7], [25], [28]. Significantly reduced control effort was found when the system was active (standard deviation of pedal depressions went from 14% to 12%, and mean EMG levels for all four muscles decreased between 2% and 10% of M V C), which was still sufficient to realize similar car-following performance (a standard deviation of T HW of 0.2 s or standard deviations in iT T C of 0.07/s). In particular relevance is the similarity to an experiment done in the same period, using the same subjects and the same experimental conditions, but in a more realistic fixed-base driving simulator [7]. Both our time-domain metrics and the Hcontrol frequency response match the results of that experiment very well, indicating that the simplified driving simulator used in the current study was good enough to capture relevant carfollowing behavior for the given experimental conditions. As hypothesized—although still a remarkable result in itself—the haptic DSS allowed successful car following without any visual feedback (condition H). The system could keep the driver in the loop and assist in maintaining adequate performance in a very dynamic car-following task, without any visual sampling for over 90 s. In other words, the haptic feedback loop was informative enough that it could temporarily replace the visual feedback. C. Future Research Naturally, it would not be a desirable side effect of the DSS to induce drivers to refrain from visually sampling the driving scene for long periods of time, even though the current simulator experiment showed this could be done without compromising safety. Subjects reported that driving with only haptic feedback was uncomfortable: it required concentration, and they preferred to also have visual feedback available. However, the effect of the designed system on visual sampling, partic-

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ularly in long-term behavior, needs to be studied in simulator environments and real-world experiments. The long-term use of the system needs to be investigated as well to study possible changes in trust [46] or other adaptations that might degrade the short-term benefits found in this experiment. To some extent, the experimental results already show risk homeosthasis: the system is mainly used to reduce control effort while maintaining similar performance levels and not to increase performance while maintaining similar control effort levels. Two other important research questions remain. First, it was hypothesized that driving with the DSS would result in increased reflexive activity. Although, to fully answer this question, more research is necessary, two supportive results were found. First, the difference in admittance between V and V H is substantial and cannot be explained by the slight decrease in muscle co-contraction alone. Second, in a crossover model parameterization study [26], [30] performed on the same subjects, results showed a decrease in the total time delay. A faster response time could indeed be gained through an increased use of faster reflexive control responses. The second research question entailed the relative contributions of driver’s visual control actions (i.e., to what a driver sees) and spinal control actions (i.e., to what a driver feels) to car-following control behavior. It is hypothesized that the visual gains contained in Hvis will be smaller while using the haptic driver support system. Recall that Hcontrol is constituted by both Hvis and the admittance. Since the DSS yields an increased admittance while the total Hcontrol is similar or even smaller, it would seem that Hvis should be smaller. To better address these research questions and hypotheses, a more detailed driver model is required. Such a model needs to be detailed enough to describe changes in neuromuscular control at the level of reflexive response and muscle contractions [43]. It should also be able to describe the interaction between spinal and visual contributions to car-following control behavior. In an upcoming paper, such a model is proposed and parameterized on the data gathered in the present experiment. V. C ONCLUSION Neuromuscular properties strongly influence how humans will respond to force feedback systems. In order to estimate the neuromuscular properties during interaction with a haptic driver support system for car following, a novel technique has been developed. The technique allows simultaneous estimation of the frequency response functions in response to torque perturbations (the admittance) and total response to lead vehicle perturbations (Hcontrol ). Both FRFs could be estimated with high coherences while following a lead vehicle with and without the aid of a haptic driver support system. The torque perturbations that were required to estimate the admittance did not substantially influence the car-following behavior. It is further shown that the designed haptic DSS causes drivers to adopt a force task strategy. The admittance with the DSS has been substantially larger, compared to unassisted driving. The driver’s ability to adapt the admittance has also been shown for the so-called “classical tasks”: the largest

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admittance is measured during a force task, followed by a relax task and then the position task. Furthermore, when visual feedback is supplemented with haptic feedback, beneficial changes in car-following behavior were found: less control effort is needed to realize the same car-following performance. This was shown by a decrease in the standard deviation of gas pedal deflections, which was accompanied by a decrease in all lower leg muscle activity (measured through EMG), for the same performance metrics such as the standard deviation of time headway and time to contact. Finally, it is shown that the driver support system provides drivers with an additional haptic feedback loop for car following, which can even temporarily replace the visual feedback loop. R EFERENCES [1] H. Godthelp and J. Schumann, “The use of an intelligent accelerator as an element of a driver support system,” in Proc. 24th ISATA, Automot. Autom. Limited, Croydon, U.K., 1991, pp. 615–622. [2] A. Várhelyi, M. Hjälmdahl, C. Hydén, and M. Draskóczy, “Effects of an active accelerator pedal on driver behaviour and traffic safety after longterm use in urban areas,” Accid. Anal. Prev., vol. 36, no. 5, pp. 729–737, Sep. 2004. [3] E. Adell, A. Várhelyi, M. Alonso, and J. Plaza, “Developing HMI components for a driver assistance system for safe speed and safe distance,” IET Intell. Transp. Syst., vol. 2, no. 1, pp. 1–14, Mar. 2008. [4] W. B. Verwey, H. Alm, J. A. Groeger, W. H. Janssen, M. J. Kuiken, J. M. Schraagen, J. Schumann, W. van Winsum, and H. Wontorra, “GIDS functions,” in Generic Intelligent Driver Support, J. A. Michon, Ed. London, U.K.: Taylor & Francis, 1993, pp. 113–146. [5] D. A. Abbink, M. Mulder, and E. R. Boer, “Motivation for continuous haptic feedback,” in Proc. IEEE Intell. Vehicles Symp., Jun. 4–6, 2008, pp. 283–290. [6] N. Kuge, T. Yamamura, E. R. Boer, N. J. Ward, and M. P. Manser, “Study on driver’s car following abilities based on an active haptic support function,” presented at the Society Automotive Engineers Int., SAE Tech. Paper, Detroit, MI, 2006, Paper 2006–01–0344. [7] M. Mulder, D. A. Abbink, M. M. Van Paassen, and M. Mulder, “Haptic gas pedal feedback,” Ergonomics, vol. 51, no. 11, pp. 1710–1720, Nov. 2008. [8] M. A. Goodrich and E. R. Boer, “Model-based human-centered task automation: A case study in ACC design,” IEEE Trans. Syst., Man, Cybern. A, Syst., Humans, vol. 33, no. 3, pp. 325–336, May 2003. [9] H. H. Chiang, S. J. Wu, J. W. Perng, B. F. Wu, and T. T. Lee, “The humanin-the-loop design approach to the longitudinal automation system for an intelligent vehicle,” IEEE Trans. Syst., Man, Cybern. A, Syst., Humans, vol. 40, no. 4, pp. 708–720, Jul. 2010. [10] R. Parasuraman, T. B. Sheridan, and C. D. Wickens, “A model for types and levels of human interaction with automation,” IEEE Trans. Syst., Man, Cybern. A, Syst., Humans, vol. 30, no. 3, pp. 286–297, May 2000. [11] A. J. Sengstacken, D. A. DeLaurentis, and M. R. Akbarzadeh-T, “Optimization of shared autonomy vehicle control architectures for swarm operations,” IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 40, no. 4, pp. 1145–1157, Aug. 2010. [12] M. A. Peshkin, J. E. Colgate, W. Wannasuphoprasit, C. A. Moore, R. B. Gillespie, and P. Akella, “Cobot architecture,” IEEE Trans. Robot. Autom., vol. 17, no. 4, pp. 377–390, Aug. 2001. [13] P. Griffiths and R. B. Gillespie, “Sharing control between humans and automation using haptic interface: Primary and secondary task performance benefits,” Hum. Factors, vol. 47, no. 3, pp. 574–590, 2005. [14] K. H. Goodrich, P. C. Schutte, F. O. Flemisch, and R. A. Williams, “Application of the H-mode, a design and interaction concept for highly automated vehicles, to aircraft,” in Proc. 25th IEEE/AIAA Digital Avionics Syst. Conf., Portland, OR, 2006, pp. 1–13. [15] D. A. Abbink and M. Mulder, “Exploring the dimensionalities of haptic feedback support in manual control,” ASME Special Haptics Issue JCSIE, vol. 9, no. 1, pp. 011006.1–011006.9, 2009. [16] T. M. Lam, H. W. Boschloo, M. Mulder, and M. M. Van Paassen, “Artificial force field for haptic feedback in UAV teleoperation,”

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David A. Abbink was born in 1977. He received the M.Sc. degree in mechanical engineering from Delft University of Technology, Delft, The Netherlands, in 2002, and the Ph.D. degree in 2006. His Ph.D. dissertation was entitled “Neuromuscular Analysis of Haptic Feedback during Car Following.” Subsequently, he worked for three years on a research project funded by Nissan, during which he helped develop and evaluate a force feedback gas pedal to support driving with car following, which was released on American and Japanese markets in 2008. He continued his research on haptics and neuromuscular analysis with several projects for Nissan and Boeing and became an Assistant Professor in the Delft Haptics Laboratory, Department of BioMechanical Engineering, Faculty of Mechanical, Maritime and Materials Engineering, Delft University of Technology, in 2009. Prof. Abbink was the recipient of the best Ph.D. dissertation in the area of movement sciences in the Netherlands in 2006 and the Dutch “VENI grant” to further stimulate his work on the design of human-centered haptic guidance.

Mark Mulder (M’09) received the M.Sc. degree in aerospace engineering and the Ph.D. degree on haptic driver support systems from Delft University of Technology, Delft, The Netherlands, in 2000 and 2007, respectively. He is currently a Postdoctoral Research Fellow with the Aerospace Engineering Faculty, Aerospace Engineering Department, Delft University of Technology. His research interests include driver support systems, haptic guidance systems, neuromuscular control and modeling, and flight and driving simulation.

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Frans C. T. van der Helm received the M.Sc. degree in human movement science from the Vrije Universiteit Amsterdam, Amsterdam, The Netherlands, in 1985, and the Ph.D. degree in mechanical engineering from Delft University of Technology, Delft, The Netherlands, in 1991. He is currently a Professor of biomechatronics and biorobotics in the Department of BioMechanical Engineering, Faculty of Mechanical, Maritime and Materials Engineering (3mE), Delft University of Technology (TU Delft), Delft, The Netherlands, and an Adjunct Professor at the University of Twente, Enschede, The Netherlands, and Northwestern University, Chicago, IL. From 2005 to 2010, he was the Chair of the Department of Biomechanical Engineering. He was a member of the board of the International Society of Biomechanics from 2005 to 2009, and participates in the board of the Technical Group of Computer Simulation and the International Shoulder Group. He is one of the program leaders of the Medical Delta, which is the collaboration between Leiden University Medical Center (LUMC), Leiden, The Netherlands; Erasmus Medical Center Rotterdam, Rotterdam, The Netherlands; and TU Delft. He is the Principal Investigator in the TREND research consortium, investigating the complex regional pain syndrome as a neurological disorder. He has published more than 100 papers in international journals on topics such as biomechanics of the upper and lower extremities, neuromuscular control, eye biomechanics, pelvic floor biomechanics, human motion control, and posture stability.

Max Mulder received the M.Sc. and Ph.D. degrees (cum laude) in aerospace engineering from Delft University of Technology, Delft, The Netherlands, in 1992 and 1999, respectively, for his work on the cybernetics of tunnel-in-the-sky displays. He is currently a Full Professor and Head of the Control and Simulation Division, Aerospace Engineering Faculty, Delft University of Technology. His research interests include cybernetics and its use in modeling human perception and performance, and cognitive systems engineering and its application in the design of “ecological” human–machine interfaces.

Erwin R. Boer received the M.Sc. degree in electrical engineering from Twente University of Technology, Enschede, The Netherlands, in 1990, and the Ph.D. degree in electrical engineering from the University of Illinois, Chicago, in 1995. From 1995 to 2001, he was a Research Scientist with Nissan Cambridge Basic Research, Cambridge, MA, where he conducted basic research on driver behavior. In 2001, he founded his own research consulting company currently incorporated under the name Entropy Control, Inc., in San Diego, CA. He is currently active in the development of haptic driver support systems. His research interests include manual control, driver modeling, human operator performance assessment, driver support system design, and shared haptic control.