GB2504584A - A vehicle having a system and method of scheduling driver interface tasks - Google Patents

Abstract

A vehicle's dynamic handling state, driver inputs to the vehicle, etc. may be examined to determine one or more measures of driver workload. Driver interface tasks may then be delayed and/or prevented from executing based on the driver workload so as to not increase the driver workload. Alternatively, driver interface tasks may be schedule for execution based on the driver workload and caused to execute according to the schedule, for example, to minimize the impact the executing driver interface tasks have on driver workload. For example, driver interface tasks may include forwarding in-coming calls to voice mail and/or generating an audio output and/or generating a visual output and/or generating a tactile output. Some of the interface tasks are delayed or prevented from being executed based on a maximum value of generated parameters and/or if the driver workload fall within a predetermined range of values. In another embodiment some of the driver interface tasks are scheduled to be executed based on a maximum or aggregation of the generated parameters.

Description

SYSTEMS AND METHODS FOR SCHEDULING DRIVER

INTERFACE TASKS BASED ON DR1VER WORKLOAD

BACKGROUND

Certain vehicles may provide infotainment information, navigation information, etc. to enhance the driving experience. As the interaction between drivers and these vehicles increases, it maybe beneficial to facilitate such interaction without increasing 1 0 driver workload.

SUMMARY

Measures of driver workload may be determined from vehicle, driver and/or environmental information. Certain driver interface tasks may be selectively delayed or prevented from executing based on the determined driver workload. Alternatively, driver interface tasks may be scheduled for execution based on the determined driver workload and then caused to execute according to the schedule.

BRIEF DESCRIPTION OF THE DRAWINGS

Figure 1 is a block diagram of an example hybrid workload estimation system.

Figure 2 is a plot of example vehicle speed, traction and braking profiles.

Figures 3A through 3C are plots of example vehicle motion states of yaw rate and sideslip angle.

Figures 4A through 4C are plots of example yawing, longitudinal, and side slipping handling limit margins.

Figure 5 is a plot of example vehicle speed, traction arid braking profiles.

Figures 6A through 6C are plots of example vehicle motion states of yaw rate and sideslip angle.

Figures 7A through 7C are plots of example yawing. longitudinal, and side slipping handling limit margins.

Figures 8 and 9 are plots of examp'e final handling Umit margins and risk.

Figures 10 and 11 are plots of example accelerator pedal position for high demand circumstances and low demand circumstances respectively.

Figures 12 and 13 are histograms of the standard deviation of the accelerator pedal position of Figures 10 and ii respectively.

Figure 14 is a plot of curves fit to the histograms of Figures 12 and 13.

Figures iSA through 1SD are example plots of accelerator pedal position, steering wheel angle, Driver Control Action (DCA) Index, and vehicle speed respectively.

Figures 1 6A through I 6C are example plots of turn indicator activity, air conditioning control activity, and Instrument Panel (IP) Index respectively.

1 0 Figure 17 is a schematic diagram of a vehicle following another.

Figures 18, 19 and 20 are example plots of vehicle speed, closing velocity and range, respectively.

Figures 21 and 22 are example plots of headway and Headway(HW) Index respectively.

Figures 23A through 23E are example plots of a Rule-Based Index, IP Index, DCA Index, composite Workload Estimation (WLE) Index, and vehicle speed respectively.

Figure 24 is an examp'e plot of membership functions for characterizing driver demand based on the WLE Index.

DETAILED DESCRIPTION

I. Introduction

Driver workload/demand may refer to the visual, physical and cognitive demand that secondary activities such as infotainment, phone, proactive recommendations, etc. place on the driver above and beyond the primary activity of driving.

Drivers may sometimes incorrectly assume that they are able to divide their attention between the primary activity of driving and the secondary activities discussed above. Estimating the driving demand, therefore. may be of particular value if used to modulate communication and vehicle system interactions with the driver. Complex driving 3 0 contexts, however, may require innovative prognostic approaches to driver workload estimation. Development of intelligent systems that enable the identification of driver workload may be useful in tailoring human machine interface (I-lMl) outputs to the driver.

For continuous workload estimation, it may be useful to design an estimator that predicts the workload under different driving contexts and/or drivers. Adaptation of vehicle cabin communication services may be based on the context within which the driving demand is predicted and the value of the services to the driver. In addition, characterizing the driver workload over periods of time (e.g., long term characterization) may be beneficial.

Such assessment of the driver workload may allow vehicle cabin communication technologies to not only be suppressed or delayed during high workload periods, but in addition tailored to the long dr ving demand.

Certain embodiments described herein may provide methods and systems for Workload Estimation (WLE). The WLE may perform state estimation/classification of the driver workload from observable vehicle, dr ver and environment data for adaptive real-time 1 0 HMI task management. The WLE, in certain situations, may use separate real-time techniques and/or employ a real-time hybrid approach to workload estimation. A rule-based algorithm, for example, may be supplemented with additional continuous prediction of driver workload based on the driver, vehicle and environment interactions. The WLE algorithms may incorporate specialized learning and computational intelligence techniques to compute and predict an aggregated WLE Index (e.g., a continuous signal representing a workload load estimate for the driver). The driver's driving demand may be infelTed, in certain instances, from observable vehicle information including variations in speed, acceleration, braking, steering, headway, instrument panel and/or center stack interaction, etc. The WLE Index may be used, for example, to set/avoid/limit/tailor voice commands and/or other tasks/information presented to the driver to improve functionality.

Certain information for the driver may be limited/tailored/blocked during demanding vehicle handling manoeuvres, in hazardous driving environments, during periods of high activity with the instrument panel, etc. Intelligent hybrid algorithmic approaches may account for long-term as weB as short-term driver actions. WLE hybrid methods may capture the driver events, situations and behaviour for coordinating vehicle to driver communications. These and other techniques described herein may assist in predicting driver increasing/decreasing cognitive conditional states and use existing vehicle sensors.

The WLE Index may also allow a hierarchy of communication to be presented to the driver based on the driving demand workload. Message priority (e.g., low, high, etc.) may determine whether the message is delivered to the driver during a particular instant based on the predicted load. Specific HMl information may also be presented to the driver based on the driver's long-term driving demand. Alternatively, a Hybrid WLE framework may incorporate GPS and digital map databases to account for road scenario situations and conditions. Information about the driver's physiological state including heart-rate, eye-gaze, and respiration may, in addition, be incorporated as inputs to the WLE framework for anomaly detection. In other examples, the predicted WLE index may be communicated to the driver to remind them to avoid secondary tasks under high workload conditions. Other scenarios are also possihle.

Figure 1 is a block diagram of an embodiment of a WLE system 10 for a vehicle 11. The system 10 may include a rule-based workload index sub-system 12, a vehicle, driver and environment tracking and computation workload index sub-system 13, a context dependent workload index aggregation sub-system 14, and an overall aggregation/WLE long term characterization sub-system 16. The sub-systems 12, 13, 14, 16 (individually or in combination) may be implemented as one or more controllers/processing devices/etc.

The sub-system 12 (as explained in section VU below) may take as input vehicle information, driver information and/or environmental information (available, for example, from the vehicle's controller area network (CAN)), and output a rule-based index representing driver workload. The sub-system 13 (as explained in sections iU through VI below) may take as input vehicle information, driver information, and/or environmental information (available, for example, from the vehicle's CAN), and output one or more continuous indices (e.g., Handling Limit (HL) Index. Dnver Control Action (DCA) Index, Instrument Panel (IP) Index. Headway (HW) Index) representing driver workload. The sub-system 14 (as explained in section VIH below) may take as input the index/indices generated by the sub-system 13, and output a Tracking (T) Index. The sub-system 16 (as explained in section VIII below) may take as input the rule-based index and T index, and output a WLE Index and/or (as explained in section IX below) a long term characterization of the WLE Index.

The system 10, in other embodiments, may lack the sub-systems 12, 14, 16.

That is, certain embodiments may be configured to only generate one or more workload indices. As an example, the system 10 may be configured to only generate the IF Tndex based on certain vehicle information (discussed below). No aggregation is necessary in these 3 0 circumstances as there is only a single measure of driver workload. Hence the WLE Index, in this example, is the IF index. The dispatcher 18, in these and other embodiments, may be configured to generate the long term characterization of the WLE Index. Other arrangements are also possible.

The WLE Index may be sent to a dispatcher 18, which may be implemented as one or more controllers/processing devices/etc. The dispatcher 18 (as explained in section X below) may act as a filter-preventing/delaying information to be communicated to the driver from reaching the driver based on the WLE index. For example. if the WLE index is greater than 0.8, all information intended for the driver maybe blocked, If the WLE Index is approximately 0.5, only entertainment-type information may be blocked, etc. The dispatcher 18 may also schedule delivery of information to be communicated to the driver based on the WLE Index. For example, vehide maintenance information, text-to-speech readouts, incoming calls, etc. may be delayed during periods of high workload. In addition, the dispatcher i8 may enable vehicle outputs to be tailored to the driver based on a long term WLE hdex characterization as discussed in more detail below. For example, the output of certain vehicle systems induding cruise control, adaptive cruise controL music suggestion, configurable HMI, etc. may be based on the long term driving demand.

The driver's workload state may be inferred from observable vehicle information including variations in speed, acceleration, braking, steering, headway, instrument panel interaction, etc. Table 1 lists example features/metrics related to driver

Table 1

Example Features/Metrics Related to Driver Workload

________________________________ _______________________________________

Metric Behavioral Effect Intended to Quantify Mean Speed Large speed increase/reduction Maximum Speed Large speed increase Mean Time Headway (Gap Time) Reduced headway Minimum Time Headway Reduced minimum headway Brake Reaction Time (BRT) Reduced BRT Brake Jerks Increased frequency Steering Wheel Reversal Rate Increased frequency of small reversals Interaction with IP (e.g., pressing Increased frequency of IP buttons) Traffic Density Increased density Driving Location New driving environment Mean Speed Large speed increase/reduction Maximum Speed Large speed increase Tables 2a and 2b list example information which may be available/accessible via CAN as known in the art. The following information may be used as inputs to any of the algorithms described herein.

Table 2a

Example Information Available via CAN Accelerator pedal position Microphone inputs Steering wheel angle Cup holder sensor Seat sensor Vehicle speed TLLm signal Yaw rate Defrost signal Lateral acceleration Temperature control Longitudinal acceleration Headlamp status Wheel speeds High beam status Throttle position Fog lamp status Master cylinder pressure Radio tuner command Driver request torqlLe Wiper status Bus axle torque Gear position Bus torque distribution Rain sensor Roll rate Configurable FIMI Sideslip angie Touch HMI Relative roll angle

Table 2b

Example System Information Accessible via CAN Traction Control System Anti-Lock Braking System Electronic Stability Control Adaptive Cruise Control Collision Mitigation by Braking Blind Spot Monitoring Automatic Parking Aid II. A Brief Discussion Of Vehicle Stability Controls A vehicle's handling determines the vehicle's ability to corner and maneuver.

The vehicle needs to stick to the road with its four tire contact patches in order to maximize its handling capability. A tire which exceeds its limit of adhesion is either spinning, skidding or sliding. A condition where one or more tires exceed their limits of adhesion may be called a limit handling condition and the adhesion limit may be called a handling limit. Once a tire reaches its handling limit, the average driver is usually no longer in control. In the so-called understeer case, the car under performs a driver's steering input, its front tires pass their handling limit, and the vehicle continues going straight regardless of the driver's steering 1 0 request. In the so-called oversteer case, the car over performs the driver's steering inputs, its rear tires pass their handling limit, and the vehicle continues spinning. For safety purposes, most vehicles are built to understeer at their handling limits.

In order to compensate vehicle control in case a driver is unable to control the vehicle at or beyond the handling limit, electronic stability control (ESC) systems are designed to redistribute tire forces to generate a moment that can effectively turn the vehicle consistent with the driver's steering request. Namely, to control the vehicle to avoid understeer and oversteer conditions.

Since its debut in i995, ESC systems have been implemented in various platforms. Phasing in during model year 2010 and achieving full installation by model year 2012, Federal Motor Vehicle Safety Standard 126 requires ESC systems on any vehicle with a gross weight rating below 10,000 lb. ESC systems may be implemented as an extension of anti-lock braking systems (ABS) arid all-speed traction control systems (TCS). They may provide the yaw and lateral stability assist to the vehicle dynamics centered around the driver's intent. It may also proportion brake pressure (above or below the driver applied pressure) to individual wheel(s) so as to generate an active moment to counteract the unexpected yaw and lateral sliding motions of the vehicle. This leads to enhanced steering control at the handling limits for any traction surface during braking, accelerating or coasting.

More specifically, current ESC systems compare the driver's intended path to the actual vehicle response which is infelTed from onboard sensors. If the vehicle's response is different 3 0 from the intended path (either understeering or oversteering). the ESC controller applies braking at selected wheel(s) and reduces engine torque if required to keep the vehicle on the intended path and to minimize loss of control of the vehicle.

A limit handling condition can be detected using data already existing in ESC systems, so new sensors may not be required. Consider, for example, a vehicle equipped with an ESC system using a yaw rate sensor, a steering wheel sensor, a lateral accelerometer, a wheel speed sensors, a master cylinder brake pressure sensor, a longitudinal accelerometer, etc. The vehicle motion variables are defined in the coordinate systems as defined in ISO- 8855, where a frame fixed on the vehicle body has its vertical axis up, its axis along the longitudinal direction of the vehicle body, and its lateral axis pointed from the passenger side to the driver side.

Generally speaking, vehicle level feedback controls can be computed from individual motion variables such as yaw rate, sideslip angie, or their combination together with arbitrations among other control commands such as dr ver braking, engine torque request, ABS and TCS. Vehicle level control commands are discussed in the following.

The well-known bicycle model captures the vehicle dynamics, its yaw rate w along the vertical axis of the vehicle body and its sideslip angle fi, defined at its rear axle, and obeys the following equations: 1zz = -bfCf (T +b&,v' -8)+bcfl + M 15. (I) M(fi+Vfl+bth+WV)=-Cf(fi+hO)V-s)-Cfl where v, is the vehicle's travel speed, M and I are the total mass and the yaw moment of inertia of the vehicle, cf and C, are the cornering stiffness of the front and rear tires, bf and /,, are the distances from the center of gravity of the vehicle to the front and rear axles, 2 0 b = bf+ br, lvi. is the active moment applied to the vehicle, and 6 is the front wheel steeling angle.

A target yaw rate w, and a target sideslip angle fi used to reflect the driver's steering intent can be calculated from (1) using the measured steering wheel angle 6 and the estimated travel velocity v* as the inputs. hi such a computation, we assume that the vehicle is driven on a road of normal surface condition (e.g., high friction level with nominal cornering stiffness (7 and c,). Signal conditioning, filtering and nonlinear corrections for steady state limit cornering may also be performed in order to fine tune the target yaw rate and the target sideslip angle. These calculated target values characterize the driver's intended path on a normal road surface.

The yaw i'ate feedback controller is essentially a feedback controller computed from the yaw en-or (the difference between the measured yaw rate and the target yaw rate). If the vehicle is turning left and w? w+ WzJbo. (where w7ab0, is a time varying deadband), or the vehicle is turning right and to (iJzt 0zdhoc, the vehicle is oversteering and activating the oversteer control function in ESC. For instance, the active torque request (applied to the vehicle for reducing the oversteer tendency) might be computed as in the following during a left turn: M, = min(O,-k05 (w --(llzdbOs)) (2) during a right turn: M = max(O,-k0 (w -+ QdhOj) 1 0 where k,,. is a speed dependent gain which might be defined as in the following k =k --(v -v " (3 as 0 x xdb!) -1'sdhl with parameters Ico, kdbj, kdb1,, vdb,, 1vdbu being tunable.

If w -üzah,,s (where W5m115 is a time varying deadband) when the vehicle is turning eft or to.? cv. + cvdb,,. when the vehicle is turning right, the understeer control function in ESC is activated. The active torque request can be computed as in the following during a left turn: M, = max(O,-kwc (w. -+ 4) during a right turn: M = min(O,-k05 (w, --where k0, is a tunable parameter.

The sideslip angle controller is a supplementary feedback controller to the aforementioned oversteer yaw feedback controller. It compares the sideslip angie estimation fir to the target sideslip angle fiT. If the difference exceeds a threshold /J,*dh, the sideslip angle feedback control is activated. For instance, the active torque request is calculated as in the following 3 0 during a left turn, fir »= 0: = nnn(o,k55 (fir -B,5 - )-k,5cmpflrrnp) (5) during a right turn, fir <0: = rnax0, (ft -+ J3)-where k, and 1cscmp are tunable parameters and is a compensated time derivative of the sideslip angle.

Other feedback control terms based on variables such as yaw acceleration and sideslip gradient can be similarly generated. When the dominant vehicle motion variable is either the yaw rate or the sideslip angle, the aforementioned active torque can be directly used to determine the necessary control wheel(s) and the amount of brake pressures to be sent to corresponding control wheel(s). If the vehicle dynamics are dominated by multiple motion 1 0 variables, control arbitration and prioritization will be conducted. The final arbitrated active torque is then used to determine the final control wheel(s) and the corresponding brake pressure(s). For example. during an oversteer event, the front outside wheel is selected as the control wheel, while during an understeer event, the rear inside wheel is selected as the control wheel. During a large side-slipping case, the front outside wheel is always selected as the control wheel. When both the side slipping and oversteer yawing happen simultaneously.

the amoLLnt of brake pressure may be computed by integrating both yaw error and the sideslip angle control commands.

Besides the above cases where the handling limit is exceeded due to the driver's steeling manoeuvres, a vehicle can reach its limit handling condition in its longitudinal motion direction. For example. braking on a snowy and icy road can lead to locked wheels, which increases the stopping distance of the vehicle. Open throttling on a similar road can cause the drive wheels to spin without moving the vehicle forward. For this reason, the handling limit may also be used for these non-steering driving conditions. That is, the conditions where the tire longitudinal braking or driving forces reach their peak values may also be included in a definition of the handling limit.

The ABS function monitors the rotational motion of the individual wheels in relation to the vehicle's travel velocity, which can be characterized by the longitudinal slip ratios Lj, with i = 1. 2.3, 4 for the front-left, front-right, rear-left and rear-right wheels, computed as in the following 2= -l max((v1 + Wztf)cos(5)+ (v,, + oLb1)sin(8),v 2, = K,W. -l (6) -max((v + wt1)cos(ö) + (v + wb1)sin(ö), v11, ) ___________ K4W4 -1 max(v -Wi1, v) max(v + wç, v) where if and rare the half tracks for the front and rear axles, w1 is the th wheel speed sensor output, iq is the 1th wheel speed scaling factor, v is the lateral velocity of the vehicle at its e.g. location, and vmj11 is a preset parameter reflecting the allowable minimum longitudinal speed.

Notice that (6) is only valid when the vehicle is not in the reverse driving mode. When the driver initiated braking generates too much slip (e.g., -2? 2bp = 20%) at a wheel, the ABS module will rdease the hrake pressure at that wheel. Similarly, during a large throttle application causing a large slip on the t" driven wheel, the TCS module will request engine 1 0 torque reduction andJor brake pressure applied to the opposite wheel at the same axle.

Consequently, ABS or ItS activations can he predicted hy monitoring how close 21s are from 2hp and),.

111. Handling Limit Index While the aforementioned LSC (including ABS and ItS) is effective in achieving its safety goal, further enhancement is still possible. For example, augmentation of ESC systems may be desirable for roll stability control. The appropriate correction which ESC tries to make, however, may he counteracted by the driver or ambient conditions. A speeding vehicle whose tire forces go far beyond the traction capability of the roadJtires might not he able to avoid an understeer accident even with I SC intervention.

Generally speaking, accurate determination of the handling limit conditions would typically involve direct measurement of road and tire characteristics or intensive information from many related variables if direct measurements are not available. Cunently.

both of these methods are not mature enough for real-time implementation.

Due to their feedback feature, ESC systems may he configured to determine the potential limit handling conditions through monitoring the motion variables (vehicle handling parameters) of a vehicle such as those described in the last section. When the motion variables deviate from their reference values by a certain amount (e.g., beyond certain deadbands), the ESC systems may start to compute differential braking control command(s) and determine control wheel(s). The corresponding brake pressure(s) is then sent to the control wheel(s) to stabilize the vehicle. The starting point of the ESC activation can be thought of as the beginning of the handling limit.

More specifically, we may define a relative handling limit margin h as in the following h1 = (7) 0 otherwise where xis the deviation of a motion variable from its reference value and [, defines the deadhand interval within which x lalls without initiating the ESC, ABS or I'CS. x can he any of the control variables defined in the last section (or any other suitable control variable).

The benefit of h defined in (7) is that the driving condition can he quantitatively characterized into different categories. For instance, when h 10%, the driving condition maybe categorized as a red zone condition where the driver needs to have special attention or take some special actions (e.g., slowing down the vehicle); when 10% < h, c 40%, the driving condition may be categorized as a yellow zone condition which needs some level of special attention from the driver; when 40% <h. 100%, the driving condition maybe characterized as a normal condition. In the normal condition, the driver needs only to maintain his normal driving attention. Of course, other ranges may also be used.

2 0 More specifically, let us use the control variables computed in the last section to discuss the computation of hs. The vehicle's yaw handling limit margin during oversteer situations h05 (where w> W when the vehicle is turning to the left and w> w when the vehicle is turning to the right) can be computed from (7) by setting x = co. -co and I = = -x, where is the oversteer yaw rate deadband as defined in (2).

Similarly, the vehicle's yaw handling limit hus for understeer situations can be computed from 7) by setting x = co -co and I = = -x, where is the understeer yaw rate deadhand as defined in (4). Notice that the aforementioned deadhands might he functions of the vehicle speed. the magnitude of the target yaw rate, the magnitude of the measured yaw rate, etc. The deadbands for the understeer situation (x c 0) and the oversteer situation (x> 0) are different and they are tunable parameters.

The vehicle's sideslip handling limit margin hs5. can be computed from (7) by setting x = fir -fi1 and i = firim = -The longitudinal handing limits ol the vehide inv&ve the conditions when either the dnving or braking force of the tires approaches the handling limit. The traction control handling limit margin br the th driven wheel can he compiLted from (7) by setting x = x = 0, and = 2th The ABS handling limit margin for the th wheel h can also be computed from (7) by setting x = 2, x = 2bp' and = 0. The final traction and braking handling limit margins maybe defined as = mm h.%Bs,h5. = mm hjr (8) e{1,2.34} ,e{12.i4} Notice that further screening conditions may be used in computing the aforementioned handling limit margins. For instance, one of the following or the combination of some of the following conditions might be used to set the handling limit margin as 0: a magnitude of the target yaw rate is beyond a certain threshold; a magnitude of the measured yaw rate is greater than a certain threshoki; a driver's steering input exceeds a certain threshold; or. extreme conditions such as the vehicle's cornering acceleration is greater than 0.Sg, the vehicle's deceleration is greater than 0.7g, the vehicle is driven at a speed beyond a threshold (e.g.. 100 mph), etc. In order to test the aforementioned handling limit margin computations and verify their effectiveness with respect to known driving conditions, a vehicle equipped with a research FSC system developed at Ford Motor Company was used to conduct vehicle testing.

For the driving condition profiled by the vehicle speed, throttling, and braking depicted in Figure 2. the measured and computed vehicle motion variables are shown in Figures 3A through 3C. The colTesponding individual handling limit margins h. h05. hTCS, bAns, and hssj are shown in Figures 4A through 4c. This test was conducted as a free form slalom on a snow pad with all ESC computations running. The brake pressure apply was turned off in order for the vehicle to approach the true limit handling condition.

For another test, the vehicle was driven on a road surface with high friction level. The vehicle speed, traction and braking profiles for this test are depicted in Figure 5.

The vchicle motion states are shown in Figurcs 6A through MT. Thc corresponding individual handling limit margins h1, has, h4115, and are shown in Figures 7A and 7B.

An envelope variable of all the individual handling limit margins is defined as hem, = minh0, , . } (9) Considering that sudden changes in thc envelope handling limit margin might he due to signal noises, a low-pass filter E(z) is used to smooth he,,v so as to obtain the final Handling 1 0 Limit (IlL) Index or margin h = F(Z)hem (10) For the vehicle test data shown in Figure 2 and Figures 3A through 3G. the final handling limit margin is depicted in Figure 8. while for the vehicle test data shown in Figure 5 and Figures 6A through 6C, the final handling limiL margin is depicted in Figure 9.

the HL Index may provide a continuous variable between 0 and 1 and indicate how close the driver is to the vehicle's handling limit (where a value of 1 indicates that the driver is at the vehicle's handling limit). This model-based IlL Index may provide particularly important driving demand information during, for example, iow-road driving conditions.

Assuming that more visual, physica' and cognitive attention is required to maintain vehicle control as the vehicle approaches its handling limit, driver workload in[brmation maybe inFerred From the H1 Index. As the workload on (he driver increases, the 1-11. Index increases. As thc workload on the driver decreases, the HI. Index decreases.

IV. Dryer Control Action Index The Driver Control Action (DCA) Index may provide a continuous variahk between 0 and 1 that indicates the total variability of the driver's control action with respect to. for example, acceleration, braking and steering. Increased vanability from the driver's operational norm may reflect increased dnving demand and vise-versa. The DCA bdex may thus provide a measure of the variability (driving demand) associated with different drivers having different norms of vehicle control action.

Consider, for example. the impact of the accelerator pedal variability on driving demand. Referring to Figures 10 and 11, real-time accelerator pedal positions are plotted versus time for example high demand and low demand circumstances, respectively.

Considerably more variability of the accelerator pedal is evident in (he high demand case relative to the low demand case.

The standard deviation of the accelerator pedal positions of Figures 10 and 11 are plotted respectively in Figures 12 and 13.

Referring to Figure 14, probabilistic fits to the distributions of Figures 12 and 13 are generated using a gamma function of the standard form y = f(xI a,h)= bT(a) (11) where a is the scale factor and b is the shape factor. The dashed line represents the low driving demand standard deviation distribution and the solid Unc represents the high driving demand standard deviation distribution. These probabilistic distributions of the accelerator pedal variability show levels of distinction between the driving demand categor es and present opportunities for classification. For example, a standard deviation of 2% wouM have a higher probability of being characteristic of low drying demand, whereas a standard deviation of iO% would have a higher probability of being characteristic of high driving 2 0 demand, etc. This technique may similarly he applied to brake pedal position. steering wheel angie, andlor other driver control action parameters. Hence, the DCA Index may estimate the driving demand based on the variability of the driver action on the accelerator pedal. hrake pedal, steering wheel, etc. The averages of the standard deviation var ability shown in Figure 14 can change with different dnvers. The DCA Index computation may account for these changing averages and compute the relative variability. l'he derivative of the driver inputs may also he incorporated to capture anticipatory action. This variance computation may be obtained from analysis of the determinant of the covariance for each of the factors (e.g., accelerator pedal position/rate. brake pedal position/rate. steering wheel an&e position/rate. etc.) 3 0 The DCA Index, in certain embodiments, is computed by recursively calculating the determinant of the covariance affecting driving demand for each of the factors based on the lollowing equations: AXk-XkAk (12) k+1 =U-aEk -I-axk (13) (14) L () J = G, -a4 a (15) (1-a)+ a -AXk fA4 1 0 where Xk is a 2-dimensional vector for each of the driver control actions and its derivative (at time instant k), k is the mean (which may be continuously updated during each drive cycle, and reset after each drive cycle), is a calibrated forgetting factor, Gk is the estimated inverse covariance matrix, I is the identity matrix, I is the estimated covariance matrix, and Ag is the transpose of &k from (12).

The recursively computed determinant of the covariance matrix, det, is given by dec1 = (I-a)" dctk-(1+ a-Ac -Gk -At) (16) 2 0 where /1 is the size of the vector Xk. It provides a measure of the estimated variability of the driver acceleration, braking andlor steering performance relative to a particular driver's mean for these parameters. It also provides a single dimensional measure of total variance which may he tracked in capture significant changes in aggregated variability of driver control actions.

The final DCA Index may be scaled to a continuous signal between 0 and 1 and be given by DCA Index = max(Accel Fed Variance, Brake Fed Variance, Steering Variance) (17) Accderator pedal position as plotted in Figure ISA and steering wheel angle as plotted in Figure 1SB have been analyzed using the techniques above. Figure 15C shows example output for the DCA Index based on the input of Figures iSA and 15B. The determinant of the covariance matrix (16) provides a measure, in this example, of the estimated variability of the driver acceleration and steering performance. The respective variabilities have been normalized and aggregated by taking their maximum values to yield (he DCA Index as plotted in Figure 15C. Vehicle speed is plotted in Figure 1SD for reference. Increased variability is captured on the I)CA Index scale as values closer to I (indicative of higher driving demand), while decreased variability is captured on the scale as values between, for example, 0 and 0.2 (indicative of low drying demand).

V. Instrument Panel Index 1 0 Driver interaction with the instrument panel andlor other touch/voice related interfaces may provide an indication of driver activity. An increase in such driver activity level may increase the cognitive demands on the driver. As indicated in laNe 1. an increase in driver button pressing activity may increase the driver workload. The frequency of interaction with cabin controls including the wiper control, climate control, volume control, turn indicator, center stack console, window control, power seat control, voice command interface, etc. may be aggregated into a composite index. The Instrument Panel (IF) index thus provides a continuous output (between 0 and 1) representing the interaction of the driver with the instrument panel, electronics. andlor any other HMI.

When a button/interface device is pressediengaged at any time instant k, for example, the output is given by. (18)

When a button/interface device is not pressed/engaged, the output is given by BI(k)=a*BIk-l)+(I-a).0 (19) where HP1 is the button/interface pressed/engaged tracking value for each hutton/interface being tracked, and a is a calibratable forgetting factor.

l'he IF Index output may then he given by IP Index = max(Bi,HP,, ,HP4 HP,) (20) where n is the number of buttons/interfaces being tracked. The IP index may also be determined using any of the aggregation techniques described herein. As an example, techniques similar to those described with reference to (28) and (29) below may be used, etc. Example turn indicator and air conditioning activity inputs are plotted respectively in Figures 16A and IÔH. !he resulting IP Index is determined according to (18).

(19) and (20) and plotted in Figure 16C. The rise time and steady state value, in this example, are based on the duration of the activity.

VI. Ileadway Index 1 0 The Headway Index provides a continuous variable between 0 and 1 and indicates how close the vehicle being driven is to the vehicle (or other object) in front of (or beside) it. As indicated in fable 1, increased worldoad load maybe inferred from reduced mean time headway and/or reduced minimum headway.

Current velocity dependent headway may he obtained from H (ç(k-iyk)) ( Vf(k) where,(k) is the position of the preceding vehicle at any time instant k, i/k) is the position of the following vehicle and v/k) is the velocity of the following vehicle. The mean 2 0 headway, HW(k), may be obtained from HW (k) = HW (k-i) + a(H W -IIWM (k-i)) (22) where a is a time constant for exponential filtering, which may be selected as desired. The 11W Index may then obtained from HWInxlex= ;-HWu (23) HW) where y is the HW Index sensitivity gain and HWM is a calibrated value. The gain may be chosenladapted depending on the headway time required to meet a maximum index of 1.

The sensitivity gain, in other embodiments, may he chosen/adapted based on, for example, driver type. If a driver type such as "young," "old," "teen," "novice." exper," etc. is known, the sensitivity gain maybe adjusted accordingly. A driver maybe identified as "young," "old," "teen," etc. based on a token carried by them as known in the art. The token may be detected by the vehicle and used to identify the type of driver. Altematively, the vehicle may provide a sdect button that lets the driver identify themselves by type. Any suitable/known technique for classifying a dr ver by type, however, may be used. The 1 0 sensitivity gain may be increased for "teen" and "novice" drivers, while the sensitivity gain may be decreased for "expert" drivers. etc. The sensitivity gain, in other embodiments, may be selected to be higher for "teen" and "novice" drivers and selected to be lower for "expert" drivers, etc. Hence the HW Index, given the same headway, may be higher for a "teen" driver and lower for an "expert" driver, etc. Alternatively (or in addition to), the sensitivity gain may be chosen/adapted based on environmental conditions. Wet or icy road conditions, determined by any suitable/known technique such as through the detection of wheel slip, may result in the sensitivity gain being increased. Dry road conditions may result in the sensitivity gain being decreased. Any suitable environmental conditions including traffic density, geographic location, etc. maybe used to select/alter the sensitivity gain.

Ileadway proximity to infrastructure including intersections, roadwork, high-demand roadway geometry, etc. may also he similarly computed as in (2i). (22) and (23). In such cases, the 11W Index output may be given by HW Index = max(HW, HW, HW) (24) where n is the number of headway proximity items of high-driving demand being tracked. A weighted function for equation (24) may also be used.

Increased headway returns from increased traffic in adjacent lanes may be used as a bias input to the 11W Index in other embodiments. (Increased traffic density may increase driving demand as indicated in Ethic I.) The time-to-collision, in still other embodiments, may be tracked in the regime of less than 1000 ms. lii potential imminent crash conditions, the 11W Index output may default to the max value of I. Referring to Figure 17, the time to collision. t, may be computed as follows --vç ± (vc)2 + 2(A)(x) -(4) (25) x or /.=-v,, where V is the closing velocity, A. is the relative acceleration, and Xis the distance between the vehides. !he distance and closing velocity information maybe obtained from any 1 0 suitable/known radar system, vision system, lidar system, vehicle-to-vehicle communication system, etc. Considering the computation of the 11W Index in an example vehicle following scenario, Figures 18 through 20 show the host vehicle speed, inter-vehicle closing velocity and range during the scenario. Figures 21 and 22 show the mean headway (as computed via (22)) and the HW Index (as compLLted via (23)), respectively.

VU. Rule-Based Sub-System Referring again to Figure 1, the rule-based sub-system 12 may include a knowledge base and facts for determining an event binary output flag. I'he sub-system 12 2 0 may provide specilic expert engineering and vehicle-driver-environment interaction rules to supplement the other components of the system 10. lhe knowledge maybe represented as a set of rules. Specific activation of vehicle systems may be incorporated.

Each rule specifies a recommendation of the output workload, and has the IF (condition), TIIEN (action) structure. When the condition part of a rule is satisfied, the action part is executed. Each rule may specify a recommendation of the output workload (0 or 1). A number of vehicle parameters including longitudinal acceleration, lateral acceleration, deceleration, steering wheel angle, button usage, etc. (see, e.g.. l'ables 2a and 2h) may he monitoredJohtained in any suitable/known lashion by the sub-system 12 from, Rr example, the vehicle's CAN bus. Facts associated with these parameters and their 3 0 combination may be used to set the conditional rules.

A general rule implemented by the sub-system 12 may be of the form, If VehicIe_paraineierl)x and Vehicle_parameter 2)y Then (26) [1 if condition is satisfied Event -Flag = L° otherwise Specific delays or restriction of infotainment or vehicle cabin systems during events are enabled from the expert rules. I'he rule-based output maybe further processed to provide a relative output aggregation based on the usage of a specific feature and the expert notion of 1 0 the driving demand for the condition.

Rules may be based on the information, for example, listed in Tables 2a and 2h above. For example. if steering wheel angle> 105 degrees, then Even/_Flag = 1. Other rules may also, of course, be constructed.

VHI. Aggregation One or more of the HW index, DCA Index, B? Index, and HL index may he aggregated by the sub-system 14 to form a Tracking (I) Index using techniques described below, in embodiments where only one index is usedlcomputedJdetermined however, no aggregation may be necessary.

In certain embodiments, short-term aggregation maybe used to schedule/delay/suppress information/tasks to be communicated to the dryer. For conditions where the highest driving demand assessed is required. the T Index maybe given by T Index = max (DGA Index. IP Index, HL Index. HW Index) (27) In other embodiments, a context dependent aggregation is employed for mean/max output combinations of the index values as described below. With reference to Figure 1 for example, the DCA Index, B? Index. HL Index, and HW Index may he combined by the sub-system 14 to form a I Index given by T Index = (28) where w are context dependent weights depending on the driving demand value placed on the input. Expansion of (2) yields 1 Index = WLEDCA WDCA + + WLEHLWHL + WLEHW + ,js (29) + + + -w.Uw Max(Tracking -Index) = i.0 where WLEDC4, WLE1, WILE,11 and WLE, are the DCA Index, IP Index, IlL Index, and 11W Index outputs respectively. The corresponding weights are given by WDcA, wjp, WHL and WJJW 1 0 Tables 3 and 4 list example rules for aggregation.

Table 3

Example Rules for Context Based Aggregation

_______ _________ _________ _________ _________ ___________________________

Rule If DCA If IP If IlL If 11W Then Calculate WLE Index; Index is Index is Index is Index is Max=1.0 Min=0.0; _______ _________ _________ __________ _________ If night, then bias = + 0.2 1 Hi Hi Hi Hi Mean of 4 Indices: w(vector) = [11111 2 I-li Hi Hi Low Meanol3ollndices: w(veetor) = II I I UI 3 Iii IL Low Low Mean of 2 Indices: w(veetor)=[i 1001 4 Ui Low Low Low Max: w(veetor) =[0 1 001 Low Hi Low Low Max: w(veetor) =10 1 0 01 6 Low Low Hi Low Max: w(vector) =[0 0 1 0] 7 Low Low Low Ili Max: w(vector) =[0 00 1]

Table 4

More Example Rules for Context Based Aggregation Rule If DCA If IP If HLM If FIWM Then Calculate WLE Index; Index is Index is Index is Index is Max=1.0 Min=0.0; If night, then bias = + 0.2; If high traffic condition, then _____ _______ _______ _______ _______ bias = + 0.2 8 Low Low Low Low Mean of 4 Indices: w(vcctor)=[1 I I I] 9 Low Hi I-li Hi Meanof3oflndices: w(vector) = [01 1 1] Low Low IL IL Mean of 2 Indices: w(vector) = [00 1 1] 11 Hi Low Hi Low Mean of 2 Indices: w(vector) = [1 0 1 0] 12 Low Hi Low Hi Mean of 2 Indices: w(vcctor) = [0 1 0 1] 13 Low Hi Hi Low Mean of 2 Indices: w(vector) = [0 1 1 01 14 Hi Low Hi Hi Mean of 3 of Indices: w(vector) = [1 0 11] Hi Hi Low Hi Mean of 3 of Indices: w(vector) =[1 1 0 1] 16 Hi Low Low Hi Mean of 2 Indices: w(vector) 41 00 1] The sub-system 16 may use the techniques described above with reference to the sub-system 14 to aggregate the RuIn-Based Index and 1' Index into the WIA Index. As an example, the WLE Index may be give by: WLE Index = max(T Index, Rule -Based Index) (30) An example Rule-Based Index, IP Index, and DCA Index are plotted respectively in Figures 23A through 23C. I'hese indices have been aggregated using the techniques described herein and plotted in Figure 23D for conditions where the highest driving demand situations assessed are considered. Vehicle speed is plotted in Figure 23E for reference.

IX. Long Term Characterization The WLE Index, in other embodiments, may be characterized over time to provide for IIMI recommendations by the sub-system 16 and/or dispatcher 18 (depending on (he configuration). Long-term WLIIL characterization may enable HMI to be tailored to the driver based on the driving demand over time. Consider, for example, that rk is a variable reflecting the WLE Index value for the driver (at any time instant k). Assume the driving demand is categorized into 3 dasses as in fti,b,c} with luzzy membership functions as defined in Figure 24. lhen. the driving behavior. dk. can he inferred from the following example computation dk =LuCk),pb(rk),PC(rk)I (31) If for example i has a value of 0.4. then dk may be represented as 10.18, 0.62. 0] (according to Figure 24). The filtered (long term) version of the driving hehavior, df. can he expressed as in the following df =U-a)df 1k (32) where a is a calibratable forgetting factor (which thus specifies/determines the time period during which the long term version of the driving behavior, d1. is evaluated). l'he long-term probability for each of the classes, (Pk), may be obtained from (Pk)i = (1f (i (33) ic (ub According to (33), the filtered version of the driving behavior for each of the classes, df)., is divided by the sum of the flltered version of the driving behavior for all of the classes.

iffor example d. is represented as 10, 0.16, 0.381, then (Pja wouldbe je{üb.c} equal to 0 divided by 0+0.16+0.38 ((pt) would he equal to 0). (Pk)h would be equal to 0.16 divided by 0÷0.16+0.38 (&k)b would be equal to 0.29), and (Pk) would be equal to.38 divided by 0+0.16+0.38 ((pk) would be equal to 0.71).

l'he final long-term W1A Index characterization of driving demand, i,may then he inferred from (he following = arg max(pk), (34) Ia label Using the example above, the maximum of the (Pk) values is 0.71 ((Pk)) Hence, it may be infelTed from (34) that the driving behaviour is currently in the "high demand" class.

X. Dispatcher The dispatcher 18 may apply the computed WLE Index, the long term characterization of the WLE Index, or any one of the DCA Index, IP Index, IlL Index, and HW Index (in embodiments where only a single index is used/computed/determined) to modulate the interaction between the infotainment and/or other dialog systems and the driver.

The WLE Index provides the estimated workload load used to set/avoid/tailor/limit/schedule voice commands and other tasks to be presented to the driver to improve functionality and safety.

Example interaction with the driver may include generating text-to-speech 2 0 tell-tales, gcncrating avatar communications, generating notifications regarding in-coming phone calls, generating proactive powertrain commands, generating proactive voice recommendations, generating a tactile response via, icr example, a tactile steering wheeL or generating other audio, visual and/or tactile outputs, etc. Each of these example driver interface tasks may have a priority associated with it. For example. generating a notification regarding an in-coming phone call may have a high priority whereas generating a proactive voice recommendation may have a low priority.

Any suitable/known technique may be used to assign a priority type to a given driver interface task. As an example, the dispatcher 18 may implement a high/low priority convention wherein all notifications to he generated regarding in-coming phone calls are assigned a high priority and all vehicle initiated recommendations to be communicated to the driver arc assigned a low priorty. Other priority schemes, however, may be used. As an example, numbers between 0 and 1.0 may represent the priority of a task: certain tasks may be assigned a prority of 0.3 while other tasks may be assigned a priority of 0.8, etc. In other embodiments, the priority type associated with a driver interface task may he assigned by the controller/processor/subsystem (not shown) that generated the task as known in the art.

Certain embodiments may thus permit a modulated presentation of driver interface tasks based on workload and priority. If for example the WI] Index (or any one of the indices as the case may be) has a value between 0.4 and 0.6, the dispatcher 18 may only allow high priority driver interface tasks to be executed. The dispatcher 18 may schedule lower priority tasks for ater execution conditioned upon the WI] Index attaining a value thss than 0.4. If for example the WLE hidex has a value between 0.7 and 1.0, the dispatcher 18 1 0 may prevent all driver interface tasks from being executed. During these periods of high workload, the dispatcher 18 may schedule high priority tasks for later execution conditioned upon the WIE Index attaining a value less than 0.7 and schedifie lower priority tasks for later execution conditioned upon the WLE Index attaining a value less than 0.4.

Similarly, if the long term driving behavior is characterized as "high demand," certain/all tasks regardless of their priority may be suspendedldelayedlscheduled until the long term driving behavior is characterized as "medium demand" or "low demand." Alternatively, if the tong term driving behavior has any probability of being in, for example, the "high demand" class, certain/all tasks may he suspended/delayed/scheduled until the probability of being in "high demand" is zero. Other scenarios are, of course, also possible.

In embodiments where a priority type is not used to categorize tasks for example. all tasks may be suspended/delayed/scheduled depending on the infelTed workload.

In the case of an in-coming phone call received during penods of high workload, the dispatcher 18 may put the call through to a voice mail system. Once the WLE Index has attained an appropriate value, the dispatcher 1 8 may generate a notification indicating that a call was received.

The algorithms disclosed herein may be deliverable to a processing device, such as any/all of the systems 12, 13, 14, 16, 18, which may include any existing electronic control unit or dedicated electronic control unit, in many forms including, hut not limited to, information permanently stored on non-writable storage media such as ROM devices and 3 0 information alterably stored on wnteable storage media such as floppy disks, magnetic tapes, CDs, RAM devices, and other magnetic and optical media. The algorithms may also be implemented in a software executable object. Alternatively, the algorithms maybe embodied in whole or in part using suitable hardware components, such as Application Specific Integrated Circuits (ASICs). Field-Programmable Gate Arrays (FPGAs), state machines, controllers or other hardware components or devices, or a combination of hardware, software and fiimware components.

Whilst specific embodiments of the present invention have been described above, it will be appreciated that departures from the described embodiments may still fall within the scope of the present invention.

Claims (14)

1. CLAIMS1. A vehicle comprising: at least one processor configured to (i) monitor an activation state of each of a plurality of drivcr-vehicle interfaces, (ii) for each of the plurality of driver-vehicle interfaces.generate a parameter representing a level of interaction between a driver and the driver-vehicle interface based on the activation state and a previous value of the parameter, (iii) receive a plurality of driver interface tasks to he executed, and (iv) selectively delay or prevent at least some of the plurality of driver interface tasks from being executed based on a 1 0 maximum vake of the generated parameters.
2. 2. l'he vehicle of claim I wherein the plurality of driver interface tasks includes at least one of generating an audio output, generating a visual output, and generating a tactile output.
3. 3. The vehicle of claim i or 2 wherein each of the plurality of driver interface tasks includes a priority type and wherein the at least one processor is configured to selectively delay or prevent at least some of the plurality of dnver interface tasks from being executed further based on the priority type.
4. 4. The vehicle of claim 1, 2 or 3 wherein the plurality of driver interface tasks includes generating a notification regarding an in-coming call and wherein selectively delaying or preventing at least some of the plurality of driver interface tasks from being executed based on a maximlLm value ol the generated parameters includes Rrwarding the in-coming call to a voice mail system.
5. 5. The vehicle of claim i, 2, 3 or 4 wherein the plurality of driver-vehicle interfaces includes a wiper control, a climate control, a radio volume control, a turn indicator control, a center stack console, a door lock, a power seat control, or a voice command 3 0 interface.
6. 6. A vehide comprising: at least one processor configured to (i) determine a level of interaction between a driver and each of a plurality of driver-vehicle interfaces, (ii) determine a driver workload based on a maximum of the determined levels of interaction, (iii) receive a plurality of driver interface tasks to be executed, and (iv) selectively delay or prevent at least some of the plurality of driver interface tasks from being executed if the driver workload falls within a predetermined range of values.
7. 7. The vehicle of claim 6 wherein the at least one processor is configured to determine the level of interaction between the driver and each of the plurality of driver-vehicle interfaces based on a frequency of activation of the driver-vehicle interface by the driver.
8. 8. The vehicle of claim 6 or 7 wherein each of the plurality of driver interface tasks includes a priority type and wherein the predetermined range of values depends on the priority type.
9. 9. The vehicle of claim 6, 7 or 8 wherein the plurality of driver interface (asks includes generating a notification regarding an in-coming call and wherein selectively delaying or preventing at least some of the plurality of driver interface tasks from being executed if the driver workload falls within a predetermined range of values includes forwarding the in-coming call to a voice mail system.
10. 10. The vehicle of claim 6, 7, 8 or 9 wherein the plurality of driver-vehicle interfaces includes a wiper control, a climate control, a radio volume control, a turn indicator control, a center stack console, a door lock, a power seat control, or a voice command interface.
11. ii. The vehicle of any of claims, 6 to 10 wherein the plurality of driver interface tasks includes at least one of generating an audio output, generating a visual output, and generating a tactile output.3 0
12. 12. A method for managing driver interface tasks comprising: monitoring an activation state of each of a plurality of driver-vehicle interfaces; for each of the plurality of driver-vehicle interfaces, generating a parameter representing a level of interaction between a driver and the driver-vehicle inteitace based on the activation state and a previous value of the parameter; receiving a plurality of driver interface tasks to he executed; scheduling at thast some of the plurality of driver interface tasks for execution based on a maximum or aggregation of the generated parameters; and causing the plurality of dryer interface tasks to be executed.
13. 13. The method of claim 12 wherein each of the plurality of dryer 1 0 interface tasks includes a prionty type and wherein at least some of the plurality of driver interface tasks are scheduled for execution further based on the priority type.
14. 14. The method of claim 12 or 13 wherein the plurality of driver interface tasks includes at least one of generating an audio output, generating a visual output, and generating a tactile output.15. l'he method of claim 12, 13 or 14 wherein the pluralityof driver-vehicle interfaces includes a wiper control, a climate control, a radio volume control, a turn indicator control, a center stack console, a door lock, a power seat control, or a voice command interface.16. The method of any of claims i2. i3, 14 or 15 wherein the plurality of driver interface tasks includes generating a notification regarding an in-coming call and wherein scheduling at least some of the plurality oldriver interface tasks for execution based on a maximum or aggregation of the generated parameters includes forwarding the in-coming call to a voice mail system.