Patent Application
20240198941
In a passenger compartment or interior of a motor vehicle, vehicle seats are surrounded by or attached to one or more passenger restraint systems. For example, a modern vehicle interior is typically equipped with passenger restraint systems in the form of, e.g., lap-and-shoulder seatbelts, inflatable airbags, seatbelt pretensioners, adjustable head restraints, knee bolsters, energy-absorbing devices, etc.
With respect to airbags in particular, inflation of one or more airbags in the vehicle interior noted above is automatically triggered when onboard sensors detect a sudden threshold vehicle deceleration requiring airbag deployment. In response to measured forces or other characteristic values requiring such airbag deployment, the sensors transmit electronic signals to an airbag deployment control circuit. A typical airbag deployment circuit responds to the electronic signals by initiating a pyrotechnic process to generate and possibly release an inert inflation gas. The inflation gas is quickly released into an airbag cushion of the airbag, resulting in rapid inflation of the airbag cushion. The inflated airbag cushion then self-deflates to complete the airbag deployment process.
The control solutions described in detail below are collectively operable for regulating activation or deployment of an inflatable airbag aboard a motor vehicle based at least in part on an occupant's motion trajectory. As used herein for textual simplicity, “an airbag” means “at least one airbag” or “one or more airbags” unless otherwise specified. Those skilled in the art will appreciate that a given vehicle occupant may be restrained by multiple airbags in a modern vehicle interior, with the particular airbag(s) being deployed in response to a given airbag triggering event that itself may vary with the particular location of the occupant within the vehicle interior. Thus, the methodology described herein may be applied separately or collectively to each of the airbags used in the vehicle interior.
To help control airbag deployment within the scope of the present disclosure, interior sensors are outfitted to a given vehicle and configured to measure one or more parameters, e.g., occupant size, mass, position, etc. Airbag deployment decisions are made by an onboard controller based on these parameters, and in particular based on the changing position and resulting occupant trajectory or trajectories as set forth in detail below. One or more sensors may be used to detect and monitor changes in the occupant's location within the vehicle interior as part of the present solutions.
In particular, a method for controlling an airbag aboard a motor vehicle includes, in an exemplary embodiment, measuring, using a sensor suite of the motor vehicle, a respective position of one or more landmark points located on a body region of interest of an occupant of the motor vehicle, e.g., a point or points on an external surface of the occupant. The method according to this embodiment also includes receiving, via a controller, a set of occupant landmark signals from the sensor suite, as data points. The occupant landmark signals are indicative of, e.g., have a voltage or current level corresponding to, the respective position of the one or more landmark points. Additionally, the method includes determining, via the controller, a predicted trajectory of the occupant (“occupant trajectory”) using the occupant landmark signals, as well as automatically adjusting a restraint capacity setting of the airbag in response to the predicted occupant trajectory.
The method in one or more embodiments may include detecting at least one of a dynamic vehicle event, an oncoming object size, an oncoming object closing velocity, or an occupant movement event via the sensor suite. In response thereto, the method may include selectively adjusting a number of the landmark points used as data points during a sampling timeframe of interest, e.g., selectively repeating at least some of the data points during the sampling timeframe of interest to thereby increase a corresponding weight of the data points. The dynamic vehicle event may include a braking event of the motor vehicle, an avoidance maneuver event of the motor vehicle, an anticipated or imminent impact event of the motor vehicle, and/or an actual impact event of the motor vehicle in a possible implementation.
Selectively adjusting the number of the landmark points used as the sampled data points during the sampling timeframe of interest in response to the occupant movement event may occur in response to the occupant movement event, which in turn may include movement of a portion of one or more of a head, a neck, a torso, or a foot of the occupant within a predetermined distance of the airbag, e.g., within or predicted to be within an ASZ of the airbag.
Determining the predicted trajectory of the occupant may include extrapolating the predicted trajectory of the occupant from a time of a last sampling to at least one of a time of a potential contact by the occupant with the airbag, a time of a next sampling, or a point in time between the time of the potential occupant contact with the airbag and the time of the next sampling. Alternatively, determining the predicted trajectory could include using one or more mathematical curve fitting methods for the position of the occupant using a fixed window or a moving window, one or more predictive filtering techniques for signal processing, or artificial intelligence or machine learning methods.
The motor vehicle may include a seatbelt, in which case the method may include using a seatbelt usage status signal indicative of a usage of the seatbelt by the occupant to determine the location of an airbag suppression zone (ASZ) or adjust the restraint capacity setting of the airbag.
Receiving the occupant landmark signals, determining the predicted trajectory of the occupant, and automatically adjusting the restraint capacity setting of the airbag may be performed by the controller prior to and subsequent to an airbag deployment command decision of the controller. Additionally, determining the predicted trajectory of the occupant in one or more embodiments may include, subsequent to a deployment of the airbag in response to the deployment command decision, adjusting the restraint capacity setting when the predicted trajectory of the occupant will be within an ASZ when the occupant contacts the airbag.
At least one sensor of the sensor suite may have a field-of-view focus, in which case the method in some embodiments may include adjusting the field-of-view focus, via the controller, in response to at least one of the predicted trajectory, a dynamic vehicle event, an oncoming object, or an occupant movement event.
Automatically adjusting the restraint capacity setting may optionally include adjusting one or more of a deployment command decision of the controller, an inflator output, timing of an inflator output, a tether length of the airbag, an inflated cushion depth of the airbag, and a vent size of the airbag. The method may also include tracking a trajectory of a most forward point on the body region of interest of the occupant, with such a point predicted to be forward of an edge of an ASZ. Adjusting the restraint capacity setting in such an embodiment may include suppressing the airbag in response to the trajectory of the most forward point when the most forward point is a point on a head, a neck, a torso, or a leg of the occupant.
The motor vehicle may also include an instrument panel, in which case automatically adjusting the restraint capacity setting of the airbag may include suppressing the airbag when a leg of the occupant is placed on the instrument panel.
In one or more embodiments, the method may include determining a seated height of the occupant relative to the vehicle interior, automatically selecting at least one of the one or more landmark points, via the controller, based on the height of the occupant, as selected landmark points, and determining the predicted trajectory of the occupant using the selected landmark points.
Another aspect of the disclosure includes a motor vehicle having a vehicle body defining a vehicle interior, also referred to in the art as a passenger compartment. The motor vehicle includes an airbag positioned in the vehicle interior and having an ASZ, a sensor suite likewise positioned in the vehicle interior, and a controller. The sensor suite is configured to measure respective positions of one or more landmark points on a body region of interest of an occupant of the vehicle interior relative a position of the airbag. The controller in this representative implementation is operable for controlling a restraint capacity setting of the airbag, and for receiving occupant landmark signals from a sensor suite according to a sampling rate. Data points of the occupant landmark signals are indicative of respective locations of the one or more landmark points.
The controller is also programmed to determine a predicted occupant trajectory using the occupant landmark signals, and to automatically adjust a restraint capacity setting of the airbag in response to the predicted occupant trajectory. The landmark point(s) may include a landmark point located closest to the airbag relative to each of the one or more landmark points.
Also disclosed herein is a non-transitory, computer-readable storage medium on which is recorded an instruction set. Execution of the instruction set by a processor of a controller of a motor vehicle having an airbag causes the controller to receive occupant landmark signals from a sensor suite, with the occupant landmark signals being indicative of at least one landmark point on a body region of interest of the occupant, which in turn may be on an exterior surface of an occupant of the motor vehicle. Execution of the instruction set also causes the controller to determine a predicted occupant trajectory using the occupant landmark signals, and to automatically adjust a restraint capacity setting of the airbag in response to the predicted occupant trajectory.
The above features and advantages, and other features and advantages, of the present teachings are readily apparent from the following detailed description of some of the best modes and other embodiments for carrying out the present teachings, as defined in the appended claims, when taken in connection with the accompanying drawings.
The accompanying drawings, which are incorporated into and constitute a part of this specification, illustrate implementations of the disclosure and together with the description, serve to explain the principles of the disclosure.
The appended drawings are not necessarily to scale, and may present a simplified representation of various preferred features of the present disclosure as disclosed herein, including, for example, specific dimensions, orientations, locations, and shapes. Details associated with such features will be determined in part by the particular intended application and use environment.
The components of the disclosed embodiments may be arranged in a variety of configurations. Thus, the following detailed description is not intended to limit the scope of the disclosure as claimed, but is merely representative of possible embodiments thereof. In addition, while numerous specific details are set forth in the following description to provide a thorough understanding of various representative embodiments, some embodiments may be capable of being practiced without some of the disclosed details. Moreover, in order to improve clarity, certain technical material understood in the related art has not been described in detail. Furthermore, the disclosure as illustrated and described herein may be practiced in the absence of an element that is not specifically disclosed herein.
Associated restraint activation logic of an electronic control unit or “controller” as contemplated herein is determined and adjusted in real-time based on one or more predicted trajectories of different body regions of a passenger or occupant of a motor vehicle. Occupant trajectory predictions as described in detail below may include monitoring and measuring designated occupant landmark locations or points (“landmarks”) on a body region of interest of the occupant's body, and estimating where such landmarks will be in the immediate future. The predicted occupant trajectories are used as input conditions for restraint activation logic of the aforementioned controller, with the controller ultimately determining an actual restraint capacity setting of the airbag and possibly other passenger restraints. With respect to control decisions for inflation of the airbags, the inflation of associated airbag cushions is selectively enabled, suppressed, or disabled as a result of executing the disclosed restraint activation logic. In addition, airbag cushion parameters such as the quantity of inflator provided gas, cushion shape, and cushion venting can also be tailored. Other non-airbag restraints may also have their respective restraint capacities modified based on the predicted occupant trajectories within the scope of the disclosure.
Referring now to the drawings, wherein like reference numbers refer to like features throughout the several views,
The passenger restraint systems 18 may include one or more inflatable airbags 18A, which are referred to hereinafter in the singular (“an airbag 18A”, “the airbag 18A”, etc.) solely for illustrative and descriptive simplicity, and without limiting the actual number of airbags 18A used in a given construction of the vehicle interior 14, or the location of these airbags 18A within the vehicle interior 14. The passenger restraint systems 18 may also include additional restraint devices, including but not necessarily limited to seatbelts 18B, vehicle seats 20, head restraints 22, and one or more of the various devices described below with reference to
Within the scope of the present disclosure, the controller 50 shown schematically in
In particular, the controller 50 as contemplated herein is programmed or otherwise configured to predict individual trajectories of one or more different body regions of interest of the occupant 11. This occurs using measured occupant landmark locations or points, e.g., on external surfaces of the occupant 11, with such points referred to herein as “landmarks” for simplicity. In doing so, the controller 50 may employ mathematical methods and/or application-suitable data filtering techniques to predict a next point of each landmark on a straight or curved trajectory line. The predicted occupant trajectories are used by the controller 50 to selectively suppress or enable the airbag 18A as needed based on the present and impending proximity of the occupant 11 to the airbag 18A. The occupant trajectories may also be used to modify airbag restraint capacity/capacities by controlling the quantity of inflator gas, the airbag cushion shape, and/or airbag cushion venting. In addition, the present monitoring and control strategy may include modifying the restraint capacity of the seatbelts 18B and the vehicle seats 20 in one or more implementations. In performing its programmed functions, the controller 50 may continue to monitor actual or predicted movements of the occupant 11 after sampling, prior to, and possibly subsequent to deployment of the airbag 18A.
The representative vehicle interior 14 depicted in
Referring briefly to
Referring once again to
Referring to
As illustrated in
In contrast to the representative position x0, the occupant 11 when unbelted could possibly reach a forward position that is closer to the instrument panel 21 and similar to the position x1 during the dynamic vehicle event or the in-vehicle/occupant movement event, e.g., when a portion of one or more of a head, neck, torso, or foot of the occupant 11 is within a predetermined distance of the airbag 18A. Relative to the depicted position x0, the representative position x1 is closer to the airbag 18A in this exemplary deployment scenario.
Also shown in
A representative second airbag suppression zone (ASZ) is also shown in
In an optional approach, the controller 50 may monitor the position of the occupant 11, likely when the occupant 11 is wearing the seatbelt 18B, prior to a dynamic vehicle event. Exemplary dynamic vehicle events include, by way of example and not of limitation, a braking event of the motor vehicle 10, an avoidance maneuver event of the motor vehicle 10 such as aggressive steering action to avoid an obstacle in the path of the motor vehicle 10, an anticipated or imminent impact event of the motor vehicle 10, or an actual impact event of the motor vehicle 10, and/or in-vehicle/occupant movement event. The controller 50 may then determine if the airbag 18A is to be suppressed based on a position of the occupant 11 prior to the event, as at such a time, the occupant 11 could be in many possible locations. This action would eliminate the need to perform a dynamic occupant position assessment during the event. The second ASZ in this particular case would be located farther from the airbag 18A than is the first ASZ, so that the amount of motion from the belted occupant 11 during the event would not cause the occupant 11 to intrude into the first ASZ as demarcated by the edge 25. In an alternative approach, perhaps less likely to implement but nevertheless viable in certain constructions of the motor vehicle 10, the edge 26 of the second ASZ may be located closer to the airbag 18A than is the edge 25, i.e., the distance between the edge 26 and the airbag 18A could be smaller than the distance between the edge 25 and the airbag 18A.
As depicted in
For higher impact severity events, the present approach in one or more optional embodiments may include detecting an impact severity and/or an oncoming object closing velocity or speed relative to the motor vehicle 10 of
Occupant trajectory prediction and the consideration of such predicted occupant trajectories in subsequent airbag restraint deployment decisions of the controller 50 are explained for simplicity and clarity with respect to a non-limiting example of
In the simplified representative case of a head 110 of the occupant 11 as depicted in
In the non-limiting exemplary embodiment of
The landmarks 32 located on other body regions of interest of or on the occupant 11, such as shoulders, arms, legs, feet, etc., may be similarly designated and tracked in real time within the scope of the present disclosure, as noted above and as appreciated in the art. Likewise, additional landmarks 32 may be used on the head 110 or other body regions of interest. In addition, the most forward point of the external surface of the occupant 11 could be used for each body region of interest, regardless of the specific location on that specific region. The occupant trajectory may be calculated in some embodiments using a particular landmark point 32 having, relative to each of the one or more landmark points 32, a shortest distance to the airbag 18A, i.e., closest to the airbag 18A relative to the remaining landmark points 32.
Referring again to
The airbag vents 480 of
In a possible approach, the controller 50 of
In this manner, the controller 50 could measure a range of occupant classes using the sensor suite 30 and automatically adjust restraint capacity settings of the airbag 18A based on predicted occupant trajectories and this additional criteria. Such an action may entail establishing an unsuppressed state or a higher restraint capacity condition when the occupant class of the measured range is above a predetermined threshold or when the occupant 11 is verified as being within or matching a predetermined occupant class. Likewise, this action may entail establishing a suppressed state or a reduced restraint capacity state when the occupant class is below a predetermined threshold or when the occupant 11 is verified by the controller 50 as being within or matching a predetermined occupant class.
As appreciated by those skilled in the art, one or more sensors 40 of a typical sensing and airbag deployment system are configured to monitor or measure a host of variables, including but not necessarily limited to external impacts, wheel speeds, lateral and longitudinal accelerations, occupant presence status in a seating region, occupant position, object/animal presence in a seating region, object/animal position, seatbelt usage, brake pressure, steering angle, pitch, yaw, roll, etc., some of which may also be detected or may be separately detected by one or more sensors of the sensor suite 30 shown in
With respect to the sensor suite 30 shown schematically in
The onboard sensor suite 30 may also include one or more remote sensors 30B, i.e., “remote” with respect to the occupants(s) 11 and the vehicle seats 20. Such remote sensors 30B may be situated within the vehicle interior 14 of
Other possible sensors of the representative sensor suite 30 of
The collective set of information provide to the controller 50 by the onboard sensor suite 30 of
As noted below, collected data could be combined with data from the various other sensors 30A, 30B, 30C, 30D, and possibly one or more other sensors as represented by 30N, to “fine tune” the predictive accuracy of the controller 50 when predicting trajectories of the landmarks 32 and thereafter controlling the occupant restraint system(s) 18. The other sensors 30N could include a buckle switch 30E and possibly a seatbelt routing sensor 30F, the latter of which detects the presence of the identifiable characteristic 118 of the seatbelt 18A or the seatbelt webbing 27 in front of the occupant's torso and associates the identifiable characteristic 118 or absence thereof with a belted or unbelted occupant 11, respectively. The presence and use of other sensors not specifically mentioned herein is possible in other configurations. Note that some of the sensors 30A, . . . , 30N may detect more than one of the desired detection functions described herein.
With respect to the identifiable characteristics 118, in one or more embodiments the identifiable characteristics 118 may include a color and/or sheen of the seatbelt webbing 27 of the seatbelts 18B, specific identifiable patterns on the seatbelt webbing 27 such as stripes, checks, and/or other discrete markings. The identifiable characteristics 118 may include an embedded detectable element such as a magnet, a piece of metal, or another item that is detectable by the remote sensor 30B. Materials that reflect or block certain light wavelengths may be added as a coating on the webbing or on/within the threads within the seatbelt webbing 27, for instance infrared (IR)-absorbing and reflecting materials.
When an identifiable characteristic 118 is used to detect a presence of the seatbelt 18B, the seatbelt 18B may be deemed to be present/used by the occupant 11 when the shoulder belt present on the occupant's torso is detected, and not present/not used when the shoulder belt is not detected on the occupant's torso. Detection of the lap belt may not be part of the seatbelt usage determination logic in some embodiments, because the lap belt could be out of the field-of-view of relevant sensors of the sensor suite 30 or obscured by the occupant's body or objects on the occupant's lap. However, the present method in other implementations may include lap belt detection.
A seatbelt presence detection signal may also be generated by the remote sensor 30B indicative of a seatbelt usage status. Thus, occupant trajectory prediction and tracking may be evaluated by the controller 50 in conjunction with seatbelt usage and the presence and position of the occupant 11 relative to a defined limit or edge of the ASZ, possibly in conjunction with one or more other conditions as described below. The controller 50 may thereafter selectively adjust a location of one or more ASZs as a control action in response to the various data.
In accordance with the present disclosure, the controller 50 executes computer-readable instructions embodying the method 100 in response to electronic input signals (arrow CCI) to perform the various functions described herein, with the electronic input signals (arrow CCI) including the above described occupant landmark signals (arrow CCLM) of
The controller 50 of
Input/output circuit(s) and devices include analog/digital converters and related devices that monitor inputs from sensors, with such inputs monitored at a preset sampling frequency or in response to a triggering event. Software, firmware, programs, instructions, control routines, code, algorithms, and similar terms mean controller-executable instruction sets including calibrations and look-up tables. Each controller executes control routine(s) to provide desired functions. Ultimately, the controller 50 outputs control signals (arrow CCO) to one or more of the passenger restraint systems 18 described herein to regulate an actual restraint capacity setting thereof.
Referring now to
Within the scope of the disclosure, the controller 50 of
As shown in
Other approaches may be used to increase the data point weight or sampling rate, such as the detection of an oncoming object that is likely to contact the motor vehicle 10, either based on a detected object size, closing velocity or speed, or a combination of such factors. In addition, the amount of lateral movement or angular movement of the motor vehicle 10 could be used in one or more embodiments as a trigger for increasing the data point weight and/or sampling rate, as higher levels of these measurements may occur in an avoidance maneuver event.
During a vehicle event that triggers deployment of the airbag 18A, vehicle deceleration increases above a calibratable “panic braking” magnitude. This increased deceleration can cause the occupant 11 to move forward relative to the vehicle 10 in a quicker manner relative to normal braking. The present strategy may be modified to increase the corresponding weights of the data points occurring during events that result in increased vehicle deceleration. Since existing data points alone can be used, the data points collected during periods of higher deceleration may be repeated to provide such data points with additional weight, e.g., by using double or triple data points for a given data sample.
Mathematical curve fitting techniques using the data points from a moving window of time could have extra data points during the timeframe corresponding to the higher deceleration event. The higher deceleration rate may be identified based on the vehicle acceleration level. The number of extra data points during a higher deceleration event may be associated with the vehicle deceleration level to add additional data points when deceleration levels are higher. A lookup table could be used for this purpose in one or more embodiments, e.g., a lookup table indexed by deceleration level and the number of data points. For instance, a deceleration level (D) of less than or equal to about 1.5 g could be treated with one data point, while a deceleration level of between about 1.5 g and about 3.0 g could be treated with two data points, and a deceleration level of about 3.0 g or more could be treated with three data points. Acceleration level boundaries may be varied as a calibratable input or hard-coded into the particular algorithm(s) embodying the present method 100.
Referring briefly to table 80 of
Referring now to
The method 100 in this exemplary embodiment commences with terminal or logic block B102 (“REC CCI”) after initialization of the controller 50, e.g., a key-on event of the motor vehicle 10 shown in
At block B102, the controller 50 of
As also described above, the method 100 may include refining trajectory-based deployment decisions of the controller 50 using other data or performance factors, possibly including a belted/unbelted status of the occupant 11. This may include using the ASZ edge 25 for unbelted occupants 11 and the ASZ edge 26 for belted occupants 11 in some implementations. The determination of which ASZ edge 25 or 26 to use for a given occupant 11 is determined at block B102. Likewise, other adjustments may occur in block 102, including adjusting a field-of-view focus of one or more sensors of the sensor suite 30 and/or the sensor(s) 40 in response to the predicted occupant trajectories or the anticipation of an airbag deployment-triggering event based on vehicle deceleration, etc. Adjusting the field-of-view focus enables the controller 50 to focus on the body regions of interest, such as where the occupant 11 is presently located and predicted to be at one or more future points in time. The adjustment may also focus on predetermined “critical” body regions of interest, such as the occupant's head, neck, and chest/torso. The method 100 proceeds to block B104 once the controller 50 has received the electronic input signals (arrow CCI) inclusive of the occupant landmark signals (arrow CCLM).
Block B104 (“T11”) includes predicting a trajectory/multiple occupant trajectories using the occupant landmark signals (arrow CCLM of
As will be appreciated by those skilled in the art, such methods may include but are not limited to: (1) curve fitting methods, when there is no direct assumption about the noise and motion process, such as linear, polynomial, conic, geometric, etc., using various methods e.g., least square error, absolute error, etc.; (2) adaptive filtering methods or other predictive techniques for signal processing, such as but not limited to adaptive Wiener filtering, Kalman filtering, etc., which may include information or assumption about the noise distribution and/or process or motion equations; (3) artificial intelligence/machine learning methods in which a model/neural network is trained using a set of inputs and outputs, where the inputs consist of previous values of the occupant/landmark positions, and may also include environment values (vehicle kinematics, etc.) and the output is the current position(s) of the occupant/occupant landmarks. The controller 50 could determine the predicted trajectories of the occupant 11 at future time steps.
The method 100 may include extrapolating the occupant trajectories from a time of a last sampling to a time of a next sampling, i.e., t−1 and t+1, respectively, a time of a potential contact of the occupant 11 with the airbag 18A, e.g., an inflated cushion thereof if the airbag 18A was deployed before the next data sample, or a point in time between the time of the potential occupant contact with the airbag 18A and the time of the next sampling. Trajectories may also be extrapolated to predict occupant locations for future sample timeframes in a defined continuum of time. Trajectory predictions may continue to occur, and restraint capacity settings may continue to be adjusted, if the predicted occupant trajectory will be within the limits of the ASZ(s) currently, prior to contact with the airbag 18A, and at contact with the airbag 18A if the airbag 18A were to be deployed. The controller 50 could employ a moving window throughout time to determine a suitable curve fit equation for the occupant landmarks 32. The method 100 then proceeds to block B106.
The above-noted exemplary statistical techniques are appreciated and understood by those skilled in the art, and therefore may be extended to the present task of predicting occupant trajectories within the vehicle interior 14. As an illustrative working example, one may apply (linear) adaptive filtering techniques to assess curve fit to past trajectory locations, performed for each time step:
where Xk is the measured position of the occupant 11 and contains noise, Wk are the filter coefficients or weights, and yk is the estimated occupant location at time T, with yk=Σixikwik.
With dk being the actual occupant location, the error (ek) may be defined as:
With R being the mean of the error squared, if R=∈[ek2] and P=∈[dkXk] where ∈ is the mean operator, the mean square error (MSE) may be calculated as follows:
Differentiating with respect to W:
The minimum is found for D=0:
Approximation methods could also be used, e.g., gradient descent and least mean square, recursive mean square, etc. The particular approach selected and implemented by the controller 50 may vary with the intended application and accuracy requirements. For instance, the controller 50 could use a higher-order polynomial to accurately curve fit the trajectories of occupant landmarks for an occupant 11 moving forward or rearward within a moving trajectory evaluation window duration. Or, the controller 50 could perform a least squares fit assessment between the difference of each actual trajectory data point and each curve fit data point for the utilized curve fit equation approaches for the collective set of measured data points within a moving window and select the curve fit approach that offers the lowest least squares value. The lowest least squares value in turn would indicate the most accurate curve fit equation. The equation approach could vary for each occupant landmark being tracked. These or other approaches may be used within the intended scope of the present disclosure.
At block B106 (“T11>CAL?”), the controller 50 determines if the predicted occupant trajectories from block B104 are within a predetermined or calibrated limit. That is, the controller 50 may compare the predicted occupant trajectories, i.e., the trajectories of the various landmarks 32 to the first ASZ edge 25, and possibly the second ASZ edge 26 in embodiments utilizing the second ASZ, and then determine whether the one or more of the predicted occupant/landmark trajectories will result in the landmark(s) 32 moving forward of the ASZ edge 25, currently or within a forward-looking window of time, such as the time necessary to contact the airbag 18A in the event airbag 18A were to be deployed before the next sample.
As an example, the controller 50 could determine that a landmark 32 corresponding to exemplary point P1 of
Block B108 (“CA #1”) of the method 100 includes executing a first control action via the controller 50 in response to the suppressing one or more of the airbags 18A in associated restraint activation logic of the controller 50. For example, the controller 50 may temporarily suppress the airbag 18A located in the predicted path of the occupant 11 in restraint activation logic of the controller 50 before and possibly during the course of a triggering event. Block B108 in one or more embodiments may include processing additional evaluation criteria to further refine the restraint capacity setting adjustments and ASZ locations.
In block B108, the controller 50 may also look at the impact severity, object closing speed relative to the motor vehicle 10 of
Regarding the occupant movement event, such an event may result in the suppression of the airbag 18A when a portion of a head, neck, torso, and possibly feet of the occupant 11 are predicted to be within a predetermined distance of the airbag 18A, such as within the first or second ASZ edge 25 or 26, as the occupant 11 moves within the vehicle interior 14. Deployment of the airbag 18A may be enabled when the occupant 11 braces against the instrument panel 21 with one or more arms while the head, neck, and torso remain rearward of the ASZ edge 25 or 26. As another option, the airbag 18A may be suppressed if one or both arms of the occupant 11 are forward of the relevant first or second ASZ edge 25 or 26 depending on the implementation.
B110 (“CA #2”) may include controlling the corresponding restraint capacity settings of the various passenger restraint systems 18 in the typical manner. That is, execution of block B110 by the controller 50 occurs when the various landmarks 32 on various regions of interest of the occupant's body do not have corresponding predicted trajectories that would result in movement of the occupant's body forward of the ASZ edge 25, 26 or forward of other defined ASZs. Exemplary control actions performed in block B110 could include adjustments to restraint capacity settings based on the other criteria described above, including but not limited to modifying restraint capacity settings for one or more of the restraint systems 18 of
Block B110 in one or more embodiments may include processing additional evaluation criteria to further refine the restraint capacity setting adjustments and ASZ locations. For example, the controller 50, in addition to position, trajectories, and possible seatbelt usage status of the occupant 11, could look to the above-described optional occupant classes when determining how or when to adjust the restraint capacity settings, of one or more of the airbags 18A. To that end, an unsuppressed restraint state of each controlled airbag 18A or a higher restraint state of the airbag(s) 18A may be established in the logic of the controller 50 when the occupant class of the measured range is above a predetermined threshold or when the occupant 11 is verified as being within or matching a predetermined occupant class.
Likewise, a reduced restraint state of the airbag 18A or a suppressed state of the airbag 18A may be established in the restraint activation logic of the controller 50 when the occupant class of a measured range is below a predetermined threshold or when occupant 11 is verified as being within or matching a predetermined occupant class even if the occupant 11 has a certain body region outside the ASZ edge 25, 26. Automatically adjusting the restraint capacity settings may include adjusting, e.g., a restraint system deployment command decision of the controller 50, an inflator output of the inflator 380 of
When restraint capacity modification below a full inflation force of at least one of the airbags 18A is called for in an optional two-stage embodiment of the airbag 18A, the controller 50 of
For block B108 or B110, other control responses could be enacted for different restraint systems 18, including possibly adjusting a setting of the pretensioner 180 and/or energy-absorbing device 280 shown in
As will be appreciated by those skilled in the art, the above teachings enable occupant-specific levels of control to be applied to inflation of one or more airbags 18A shown in
Other possible refinements may be included within the scope of the disclosure, including but not limited to consideration of “occupant type”, i.e., whether the occupant 11 is non-human. Animals and objects, e.g., cargo, packages, luggage, etc., are examples of non-human occupants 11 within the scope of the disclosure. The controller 50 may be calibrated in one or more configurations to assess occupant type and respond accordingly when adjusting restraint capacities. In one or more embodiments, the controller 50 of
Additionally, the above-described strategy may be expanded to include transmitting, broadcasting, or otherwise providing an “ASZ intrusion” alert message or a series of alert messages to one or more predetermined message recipients, e.g., the occupant(s) 11, a ride share home office, a software application (“app”), etc. Such alert messages could be used to alert recipients that the occupant 11 was detected within the ASZ(s). For the occupant 11 or message recipients located inside of the vehicle interior 14, the alert message may be audible, visual, and/or haptic, and may be initiated by the controller 50 in quasi-static conditions, such as when the occupant 11 leans forward to access something or places their feet or leg(s) on the instrument panel 21. In one or more embodiments, the position of the occupant 11 could be recorded in memory of the controller 50, or in an “outside-the-vehicle” app so that the position/ASZ intrusion data is retrievable after a vehicle event resulting in deployment of the airbag(s) 18A, e.g., by emergency personnel, first responders, investigators, or other interested parties. These and other attendant benefits will be readily appreciated by those skilled in the art in view of the foregoing disclosure. These and other attendant benefits will be readily appreciated by those skilled in the art in view of the foregoing disclosure.
The detailed description and the drawings or figures are supportive and descriptive of the present teachings, but the scope of the present teachings is defined solely by the claims. While some of the best modes and other embodiments for carrying out the present teachings have been described in detail, various alternative designs and embodiments exist for practicing the present teachings defined in the appended claims.
