Restraint deployment control with central and frontal crash sensing

An improved restraint deployment control with central and frontal acceleration sensing, where the deployment is initiated when a filtered version of the central acceleration signal exceeds a deployment threshold that is periodically adjusted based on secondary measures of crash severity, the secondary measures being determined at least from the frontal acceleration signal. The threshold adjustment also takes into account the progression level of the crash event, as judged by the filtered version of the central acceleration signal. In a preferred implementation, the deployment threshold is set to a relatively high default level during periods of inactivity to provide good immunity to rough road impacts, while providing timely deployment for high speed crash events, and is periodically adjusted from the default level in the course of a sensed event. Preferably, the level of event progression is determined by deriving a .DELTA.V signal from the central acceleration sensor, and comparing such signal to a set of predefined event progression thresholds. At each level or stage of the event progression, the deployment threshold is adjusted within predefined boundaries based on central and frontal crash severity indications. Threshold adjustments based on the central and frontal severity indications are individually limited and then accumulated to determine the net threshold adjustment. In a particularly advantageous embodiment, the secondary measurements include an offset measure based on the difference between two frontal .DELTA.V signals, and corner crush measures based on differences between the frontal and central .DELTA.V signals.

TECHNICAL FIELD
 This invention relates to automotive passenger restraint systems, and more
 particularly to a control method that differentiates deployment events
 from non-deployment events in a restraint system having both central and
 frontal crash sensors.
 BACKGROUND OF THE INVENTION
 In general, automotive passenger restraint systems perform a number of
 functions including acceleration sensing, signal processing and analysis,
 and deployment of one or more restraint devices such as frontal or side
 air bags and seat belt pretensioners in response to a sensed crash event.
 Typically, an acceleration signal is monitored to detect a potential crash
 event, and then filtered or integrated over the course of the crash event
 to produce a velocity change or .DELTA.V signal. If the .DELTA.V signal
 exceeds a threshold, the crash event is determined to be sufficiently
 severe to warrant deployment of restraints. The threshold is typically
 time-dependent, and is calibrated based on data logged for different types
 of crash events, as well as data logged during rough road driving.
 Multiple distributed crash sensors are sometimes used in order to obtain
 faster deployment decisions and to distinguish a localized crash event
 from a full frontal crash event. For example, the system may include a
 central crash sensor located in or near the passenger compartment and one
 or more remote sensors located near the front corners of the vehicle.
 A problem with the above-described approach, with single or multiple
 sensors, is that it is often difficult to synchronize the time progression
 of the crash (that is, the event clock or timer) with the actual crash
 event. Various algorithms have been developed for determining if and when
 the event clock should be reset to improve synchronization. As a result,
 it can be difficult to distinguish between deployment events and
 non-deployment events, particularly in the first portion of the sensed
 event.
 A related problem in systems with multiple crash sensors is that it is
 difficult to quickly and reliably correlate the information from the
 various sensors. In particular, it is difficult to reliably distinguish
 between a localized crash event for which deployment is desired and a
 localized impact (such as a deer impact or an abuse event) for which
 deployment is not desired.
 SUMMARY OF THE INVENTION
 The present invention is directed to an improved restraint deployment
 control with central and frontal acceleration sensing, where the
 deployment is initiated when a filtered version of the central
 acceleration signal exceeds a deployment threshold that is periodically
 adjusted based on secondary measures of crash severity, the secondary
 measures being determined at least from the frontal acceleration signal.
 The threshold adjustment also takes into account the progression level of
 the crash event, as judged by the filtered version of the central
 acceleration signal.
 In a preferred embodiment, the deployment threshold is set to a relatively
 high default level during periods of inactivity to provide good immunity
 to rough road impacts, while providing timely deployment for high speed
 crash events, and is periodically adjusted from the default level in the
 course of a sensed event. Preferably, the level of event progression is
 determined by deriving a .DELTA.V signal from the central acceleration
 sensor, and comparing such signal to a set of predefined event progression
 thresholds. At each level or stage of the event progression, the
 deployment threshold is adjusted within predefined boundaries based on
 central and frontal crash severity indications. Threshold adjustments
 based on the central and frontal severity indications are individually
 limited and then accumulated to determine the net threshold adjustment. In
 a particularly advantageous embodiment, the secondary measurements include
 an offset measure based on the difference between two frontal .DELTA.V
 signals, and corner crush measures based on differences between the
 frontal and central .DELTA.V signals.

DESCRIPTION OF THE PREFERRED EMBODIMENT
 FIG. 1 generally depicts a vehicle 10 equipped with a supplemental
 restraint system 12 in which frontal air bags 14, 16, are deployed in a
 severe crash event to protect the vehicle occupants. The system 12 may
 include other restraints such as side air bags, belt pretensioners,
 inflatable tubular structures, side curtains, anti-whiplash devices, and
 so on, and it will be understood that the term "air bag" as used herein
 does not refer exclusively to a particular type of restraint. Restraint
 system 12 includes a central control module 18, which may be packaged as a
 single electronic module and mounted on a frame element in a central
 portion of the vehicle 10. Fundamentally, the central control module 18
 includes a longitudinal acceleration sensor 20 (which may comprise a
 single sensor or a pair of sensors mounted at an offset angle) for sensing
 longitudinal acceleration of the vehicle 10, a microprocessor (.mu.P) 22
 for receiving a central acceleration signal developed by the sensor 20,
 and a firing circuit (FC) 24 which is triggered by microprocessor 22 to
 deploy the air bags 14, 16 in the event of a severe crash. The
 microprocessor 22 also receives left and right frontal acceleration
 signals developed by the acceleration sensors 26 and 28, respectively,
 which are located in a crush zone near the frontal periphery of the
 vehicle 12.
 In general, the microprocessor 22 of the central control module 18 filters
 the central acceleration signal over a predefined interval, or window, to
 form a windowed velocity signal, referred to herein as .DELTA.V.sub.WIN,
 and adjusts a deployment threshold, referred to herein as the .DELTA.V
 Threshold, based on crash event progression, and various secondary crash
 severity indications obtained from the central and frontal acceleration
 signals. The windowed velocity signal .DELTA.V.sub.WIN is compared to the
 .DELTA.V Threshold, and the microprocessor 22 signals the firing circuit
 24 to deploy the air bags 14, 16 if and when .DELTA.V.sub.WIN crosses the
 .DELTA.V Threshold. Preferably, the .DELTA.V Threshold is set at a default
 level prior to initiation of a crash event and is periodically adjusted
 during the crash event. The progression of the crash event is determined
 by a .DELTA.V signal derived from the central acceleration signal, and the
 secondary measures are designed to identify the characteristics of various
 types of crash events so that the .DELTA.V Threshold can be adjusted
 accordingly.
 The windowed velocity signal .DELTA.V.sub.WIN may be calculated according
 to the expression:
EQU .DELTA.V.sub.WIN (n)=.SIGMA.[ACCEL(n-i)], for i=0 to (w-1) (1)
 where ACCEL is a filtered version of the central acceleration signal and w
 is the window size. In a digital implementation, the window w actually
 refers to a specified number of successive samples of the central
 acceleration signal. Since the samples are read at a predefined rate,
 however, the window w also may be viewed as a corresponding time interval.
 In the preferred embodiment, the level of event progression is determined
 by computing a velocity signal .DELTA.V.sub.bias that is biased toward
 zero. The biased velocity signal .DELTA.V.sub.bias may be determined by
 computing a filtered central acceleration signal .DELTA.V.sub.c, and then
 applying a bias "B". For example, .DELTA.V.sub.c may be calculated
 according to the expression:
EQU .DELTA.V.sub.c (n)=.DELTA.V.sub.c (n-1)+ACCEL(n)-.DELTA.V.sub.c (n-1)/C
 (2)
 where C is a constant, with .DELTA.V.sub.bias being defined as:
EQU .DELTA.V.sub.bias =.DELTA.V.sub.c -B (3)
 and the bias B being defined as:
 d if .DELTA.V.sub.c &gt;d, with d being a positive integer
EQU B=.DELTA.V.sub.c if.vertline..DELTA.V.sub.c.vertline..ltoreq.d (4)
 -d if .DELTA.V.sub.c &lt;-d
 Alternatively, the level of event progression can be determined by using
 windowed velocity .DELTA.V.sub.WIN within a limited acceleration range, or
 a filtered version of .DELTA.V.sub.WIN or .DELTA.V.sub.c. For purposes of
 this description, however, it will be assumed that .DELTA.V.sub.bias is
 used to determine the level of event progression. To this end,
 .DELTA.V.sub.bias is compared to a series of predefined velocity values,
 referred to herein as progression level thresholds a-d, thereby defining
 four corresponding stages or levels of event progression; obviously, the
 number of thresholds, and hence progression levels, may vary from one
 implementation to another. The approach is graphically depicted in FIG. 2,
 where Graphs A and B show exemplary values of .DELTA.V.sub.bias and a
 secondary measurement term .DELTA.V.sub.sec respectively, on a common time
 scale. The time designations t.sub.0 -t.sub.6 signify times that coincide
 with .DELTA.V.sub.bias crossing one of the thresholds a-d, and the event
 progression level at any given time is indicated at the top of Graph A,
 and below the time axis of Graph B. For example, progression level of the
 sensed event is "a" in the time interval t.sub.0 -t.sub.1, "b" in the time
 interval t.sub.1 -t.sub.2, "c" in the time interval t.sub.2 -t.sub.3, "b"
 in the time interval t.sub.3 -t.sub.4, and so on. The progression level
 "a" is indicative of no or very low activity. For each secondary measure
 .DELTA.V.sub.sec, each of the progression levels a-d have predefined
 regions corresponding to different levels of the secondary measure, as
 shown by the vertical columns in Graph B, and the threshold adjustment
 amount is determined based on which region the secondary signal is in. For
 example, if the sensed event in is progression level "b", a set of
 threshold adjustment rules might be: (1) increase the .DELTA.V Threshold
 by 5 counts if .DELTA.V.sub.sec is in region 1, (2) increase the .DELTA.V
 Threshold by 1 count if .DELTA.V.sub.sec is in region 2, and (3) decrease
 the .DELTA.V Threshold by 2 counts if .DELTA.V.sub.sec is in region 3.
 The usefulness of the above-described threshold adjustment technique can be
 illustrated by considering an example. Suppose it is determined through
 review of crash data that a certain type of non-deployment event such as a
 localized frontal impact (with a deer, for example) is characterized by
 high gradient of .DELTA.V.sub.bias at early and middle levels of the event
 progression. In such case, the progression levels a-d and the associated
 regions of secondary measurement representing high gradient of
 .DELTA.V.sub.bias can be determined by statistical analysis, and used to
 formulate rules such as described above to raise the .DELTA.V Threshold
 when the event progression level and secondary measurement characteristics
 are recognized in the course of a crash event so as to reduce the
 likelihood of an unwanted deployment. As a practical matter, there may be
 several secondary measurements, based on the longitudinal acceleration
 signal ACCEL, or on signals from other sensors such as a lateral
 acceleration sensor, a remote longitudinal acceleration sensor, or an
 intrusion sensor. In any event, the threshold adjustments associated with
 each such secondary measurement are summed to form a net adjustment value.
 If desired, weighting may be used to give more effect to adjustments
 associated with secondary measurement characteristics that are easily
 recognized, as compared to characteristics that tend to be variable and
 are less easily recognized.
 In a particularly advantageous embodiment, the secondary measurements
 include differential measures that combine the .DELTA.V information
 obtained from different acceleration sensors. These include an offset
 measure OM and left and right crush measures LC and RC. The offset measure
 OM is based on the difference in .DELTA.V signals obtained from the left
 and right frontal acceleration signals. That is:
EQU OM=.vertline..DELTA.V.sub.L -.DELTA.V.sub.R.vertline. (5)
 where .DELTA.V.sub.L is a filtered version of the left frontal acceleration
 signal, and .DELTA.V.sub.R is a filtered version of the right frontal
 acceleration signal. The left and right crush measures LC, RC are based on
 the differences between the individual frontal .DELTA.V signals and
 .DELTA.V.sub.c. Thus, the measures LC and RC are given by the expressions:
EQU LC=.vertline..DELTA.V.sub.L -.DELTA.V.sub.C.vertline., (6)
 and
EQU RC=.vertline..DELTA.V.sub.R -.DELTA.V.sub.C.vertline.. (7)
 FIG. 3 is a diagram illustrating an exemplary adjustment of the .DELTA.V
 Threshold. In the diagram, individual secondary measures of severity
 developed from the various acceleration sensors 20, 26, 28 are applied
 along with an event progression (EP) signal to Adaptation Logic blocks
 30-48. For each secondary measure, the respective Adaptation Logic block
 30-48 implements adaptation rules similar to those described above in
 reference to FIG. 2. The secondary measures applied to Adaptation Logic
 blocks 30, 32, 34, 36 are developed based on the frontal sensors 26, 28
 and the adaptation amounts developed by such blocks are summed by the
 summer 50 to form a frontal adaptation amount T.sub.A (f), and limited to
 a frontal adaptation limit amount by the limit block 52. Similarly, the
 secondary measures applied to Adaptation Logic blocks 38, 40, 42 are
 developed based on the central sensor 20 and the adaptation amounts
 developed by such blocks are summed by the summer 54 to form a central
 adaptation amount T.sub.A (c), and limited to a central adaptation limit
 amount by the limit block 56. And finally, the secondary measures applied
 to Adaptation Logic blocks 44, 46, 48 are developed based on the various
 combinations of the central and frontal sensors 20, 26, 28, and the
 adaptation amounts developed by such blocks are summed by the summer 58 to
 form a frontal/central adaptation amount T.sub.A (f/c), and limited to a
 central/frontal adaptation limit amount by the limit block 60. The summed
 and limited adaptation amounts from summers 52, 56, 60 are then summed in
 summer 62, along with the old .DELTA.V Threshold, forming the new .DELTA.V
 Threshold.
 The secondary measures indicated in the diagram of FIG. 3 are exemplary and
 non-exhaustive, as indicated by the dot trails between blocks 32, 34 and
 40, 42. The illustrated frontal secondary measures include the slope (SL)
 of signals developed from the frontal sensors 26, 28, and .DELTA.V signals
 developed from the frontal sensors 26, 28. The illustrated central
 secondary measures include the slope (SL) of .DELTA.V.sub.c, the
 oscillation (OSC) of ACCEL, and a band-pass (BP) filtered version of
 .DELTA.V.sub.c. The illustrated combined secondary measures include the
 offset measure OM described above in reference to equation 5, and the left
 and right crush measures LC, RC described above in reference to equations
 6, 7.
 A flow diagram representative of computer program instructions executed by
 the microprocessor .mu.P of FIG. 1 in carrying out the above-described
 deployment method is set forth in FIGS. 4-5. FIG. 4 depicts a main loop
 flow diagram, where the block 100 designates a series of instructions
 executed at the initiation of vehicle operation for initializing various
 registers, counters, flags and variables to a pre-defined state. For
 example, the .DELTA.V Threshold is initialized to a default value at this
 point. Thereafter, the block 102 is executed to read the output signal of
 the longitudinal acceleration sensor LAS, and to filter it to form a
 filtered acceleration signal ACCEL. The various severity measurements are
 then computed at block 104; these include, for example, .DELTA.V,
 .DELTA.V.sub.bias, .DELTA.V.sub.win, V.sub.sec1, V.sub.sec2 and so on,
 where V.sub.sec1 and V.sub.sec2 are secondary measurements such as OM, LC,
 RC, and so on. Block 106 is then executed to calculate frontal, central
 and frontal/cental threshold adaptation amounts T.sub.A (f), T.sub.A (C),
 T.sub.A (f/c) as described above in reference to FIG. 3. Block 108 then
 limits the adaptation amounts T.sub.A (f), T.sub.A (c), T.sub.A (f/c) to
 respective limit values LIMIT.sub.f, LIMIT.sub.c, LIMIT.sub.f/c, also as
 described above in reference to FIG. 3. The block 110 then sets the total
 adaptation amount T.sub.A to the sum of the limited frontal, central and
 frontal/central adaptation amounts, and block 112 sums T.sub.A with the
 old .DELTA.V Threshold to form the new .DELTA.V Threshold. If the windowed
 velocity .DELTA.V.sub.win exceeds the newly adjusted threshold, as
 determined at block 114, the block 116 is executed to deploy the
 restraints AB.
 The flow diagram of FIG. 5 sets forth the main flow diagram step of
 determining the net threshold adaptation amounts T.sub.A (block 106) in
 further detail. In the illustrated embodiment, the various event
 progression thresholds a-d, the regions 1-4 for each secondary
 measurement, and the associated threshold adjustment amounts are stored in
 an adaptation matrix within microprocessor .mu.P, and a series of
 progression level masks for each secondary measurement are used to
 identify corresponding regions and adjustment amounts. Blocks 120-132
 comprise a nested loop for determining the net frontal, central and
 frontal/central adaptation amounts T.sub.A (f), T.sub.A (c), T.sub.A
 (f/c), taking into account each of the secondary measurements. Thus, for
 each secondary measurement .DELTA.V.sub.sec, the microprocessor .mu.P
 executes the blocks 122-130 within the .DELTA.V.sub.sec loop boundary
 blocks 120 and 132, and for each progression level mask L, the
 microprocessor .mu.P executes the blocks 124-128 within the progression
 level loop boundary blocks 122 and 130. At block 124, the current mask L
 is applied to the matrix, and the microprocessor .mu.P determines if the
 biased velocity .DELTA.V.sub.bias is within the corresponding progression
 level thresholds. If not, the mask L for the next progression level is
 applied to the matrix, as indicated at block 130. If .DELTA.V.sub.bias is
 within the corresponding progression level thresholds, block 126
 determines if the respective secondary measurement .DELTA.V.sub.sec is
 within an adaptation region corresponding to the progression level of the
 mask L, and if so, block 128 sums the corresponding adaptation values to
 form the net threshold adaptation amounts T.sub.A (f), T.sub.A (c) and
 T.sub.A (f/c). In other words, the adaptation values derived from the
 frontal sensors 26, 28 are summed to form T.sub.A (f), the adaptation
 values derived from the central sensor 20 are summed to form T.sub.A (c),
 and the adaptation values derived from the combined outputs of the central
 and frontal sensors 20, 26, 28 are summed to form T.sub.A (f/c). As
 described above in reference to FIG. 2, the various adaptation values are
 stored in the matrix as a function of the secondary measurement
 .DELTA.V.sub.sec and the progression level mask L.
 After the net threshold adaptation amounts are determined for each
 progression level mask L of each secondary measurement .DELTA.V.sub.sec,
 the blocks 134-138 are executed to bias the .DELTA.V Threshold toward its
 default value (initialization threshold) if the event progression level is
 "a"--i.e., no activity. Block 134 determines if the event progression is
 at level "a". If so, block 136 compares the .DELTA.V Threshold to the
 Initialization Threshold. If the .DELTA.V Threshold has been adjusted to a
 value less than the Initialization Threshold, block 138 sets the net
 threshold adaptation amount T.sub.A to a positive incremental value,
 referred to in FIG. 5 as +Threshold Recover. Conversely, if the .DELTA.V
 Threshold has been adjusted to a value greater than the Initialization
 Threshold, block 138 sets the net threshold adaptation amount T.sub.A to a
 negative incremental value, referred to in FIG. 5 as -Threshold Recover.
 In summary, the deployment method of this invention provides a flexible
 framework for providing a high level of immunity to spurious acceleration
 signals and distinguishing between deployment events and non-deployment
 events based on the outputs of both central and remote sensors. The
 statistical frequency of various secondary measurements for different
 types of crash events (i.e., deployment, non-deployment, rough road, etc.)
 can be characterized as a function of .DELTA.V-based event progression
 level, and used to suitably adjust the .DELTA.V Threshold to increase or
 decrease the likelihood of deployment in the course of a crash event. When
 the crash event is over, the threshold is biased back to an initialization
 or default level providing the desired immunity to spurious events. While
 described in reference to the illustrated embodiment, it is expected that
 various modifications in addition to those suggested above will occur to
 those skilled in the art. For example, secondary measures based on other
 remote acceleration sensors, such as side or rear acceleration sensors,
 may be easily incorporated in a manner similar to that described with
 respect to the frontal sensors 26, 28. In this regard, it will be
 understood that this invention is not limited to the illustrated
 embodiment, and that deployment methods incorporating these and other
 modifications may fall within the scope of this invention, which is
 defined by the appended claims.