US5482314A
Expired / Lapsed
US
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Automotive occupant sensor system and method of operation by sensor fusion
Assignee: Robert Bosch LLC
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Filing: 1994-04-12
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Granted: 1996-01-09
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Expires: 2014-04-12
Abstract
Abstract A system for sensing the presence, position and type of an occupant in a passenger seat of a vehicle, as well as for sensing the presence of a rear-facing child seat therein, for use in enabling or disabling a related airbag activator. The sensor system employs sensor-fusion, a process of combining information provided by two or more sensors, each of which "sees" the world in a unique sense. In a preferred embodiment, occupancy sensor samples two detectable properties, a first being a thermal signature and associated motion, and a second is acoustically measured distance and the associated motion. Infrared sensor inputs and an ultrasonic sensor input are combined in a microprocessor circuit by means of a sensor fusion algorithm to produce an output signal to the air bag controller. The output signal results from preselected confidence weighing for feature parameters generated by the two sensors and upon a fusion process which ultimately makes a decision which is extremely reliable. The sensor fusion matrix processes the sensor outputs in a decision making operation which includes weighing inputs to guarantee reliability. All sensor outputs, along with calibration data, initial conditions and historical reference data are considered in the process of making a decision of whether or not to deploy the passenger-side air bag in a collision.
Inventors
- 👤 Anthony P. Corrado
- 👤 Stephen W. Decker
- 👤 Paul K. Benbow
Bibliographic Details
Application Number
—
Jurisdiction
US
Data Source
google_patents
Last Synchronized
2026-10-24 17:19
CPC Classifications
B; B60; B60R; B60R21/00; B60R21/01; B60R21/015; B60R21/01512; B60R21/0153; B60R21/01534; B60N; B60N2/00; B60N2/002; B60N2/0021; B60N2/0024; B60N2/0026; B60N2/0027; B60N2/003; B60N2/0035; B60N2/24; B60N2/26; B60N2/266; B60N2/267; B60R21/01532; B60R21/01536; B60R21/0154; B60R21/02; B60R21/16; B60R21/26; B60R21/261; G; G01; G01S; G01S15/00; G01S15/02; G01S15/04; G01S15/50; G01S15/52; G01S15/523; G01S15/86; G06; G06F; G06F18/00; G06F18/20; G06F18/25; G06F18/254; G06F18/256; G06V; G06V10/00; G06V10/10; G06V10/12; G06V10/14; G06V10/147; G06V10/70; G06V10/77; G06V10/80; G06V10/803; G06V20/00; G06V20/50; G06V20/59; G06V20/593; G06V40/00; G06V40/10; B60N2210/00; B60N2210/10; B60N2210/16; B60N2210/18; B60N2210/26; B60N2230/00; B60N2230/30; G01S15/88; G01S7/00; G01S7/52; G01S7/523; G01S7/526; G01S7/539
Claims Analysis
Total Claims: 68
Claim 1
Independent Claim
194 words
A method for determining whether or not to de-activate a vehicle's passenger passive restraint system as a function of a current state value determined by comparing measured signal features to a predetermined set of confidence values and empirical relationships obtained using various known occupancy scenarios and a set of state change criteria, comprising the steps of: (a) sensing the characteristics of occupancy of a particular passenger seat within the vehicle using a plurality of sensors functionally associated with said passenger seat and developing a set of corresponding electrical signals; (b) evaluating said electrical signals to determine a plurality of signal features included in each of said signals; (c) combining certain ones of said signal features to obtain a plurality of fused features; (d) associating said signal features and said fused features with the confidence values and empirical relationships to determine a feature state value; (e) identifying the feature state value as the current state value if the set of state change criteria is met; and (f) generating a de-activate signal if said current state value is one of a predetermined subset of state values for which said passive restraint system is to be de-activated.
Claim 2
Independent Claim
194 words
A method for determining whether or not to de-activate a vehicle's passenger passive restraint system as a function of a current state value determined by comparing measured signal features to a predetermined set of confidence values and empirical relationships obtained using various known occupancy scenarios and a set of state change criteria, comprising the steps of: (a) sensing the characteristics of occupancy of a particular passenger seat within the vehicle using a plurality of sensors functionally associated with said passenger seat and developing a set of corresponding electrical signals; (b) evaluating said electrical signals to determine a plurality of signal features included in each of said signals; (c) combining certain ones of said signal features to obtain a plurality of fused features; (d) associating said signal features and said fused features with the confidence values and empirical relationships to determine a feature state value; (e) identifying the feature state value as the current state value if the set of state change criteria is met; and (f) generating a de-activate signal if said current state value is one of a predetermined subset of state values for which said passive restraint system is to be de-activated.
Claim 3
Dependent Claim
42 words
A method according to claim 1, wherein said passenger passive restraint system includes an air bag deployment system having an air bag that is located for deployment proximate said passenger seat and that can be de-activated in response to said de-activate signal.
Claim 4
Dependent Claim
39 words
A method according to claim 2, wherein said predetermined set of state values includes values corresponding to an empty seat state, an occupied seat state, a rear-facing child seat state, an out-of-position passenger state, and an inanimate object state.
Claim 5
Dependent Claim
141 words
A method according to claim 3, wherein: (a) the occupied seat state corresponds to the scenarios of a person seated in said seat, a person seated in said seat holding a grocery bag, a child in a forward-facing child seat disposed in said seat, a child standing in said seat at a distance from the air bag deployment location, and a pet disposed in said seat; (b) the rear-facing child seat state corresponds to the scenario of a child in a rear-facing child seat disposed in said seat; (c) the out-of-position passenger state corresponds to the scenario of a person positioned in close proximity to the air bag deployment location; (d) the inanimate object state corresponds to the scenario of an inanimate object disposed in said seat; and (e) the empty seat state corresponds to the scenario of an empty seat.
Claim 6
Dependent Claim
120 words
A method according to claim 2, wherein said occupancy scenarios include a person seated in said passenger seat, a person seated in said seat holding a grocery bag, a child in a rear-facing child seat disposed in said seat, a child in a forward-facing child seat disposed in said seat, a child in a forward-facing child seat disposed in said seat, a person positioned in close proximity to the air bag deployment location, a child standing in said seat at a distance from the air bag deployment location, a standing child in close proximity to the air bag deployment location, an empty seat, an inanimate object disposed in said seat, a pet disposed in said seat, and an empty seat.
Claim 7
Dependent Claim
31 words
A method according to claim 1, wherein said plurality of sensors is selected from the group consisting of infrared sensors, ultrasound sensors, weight sensors, microwave sensors, light sensors, and laser sensors.
Claim 8
Dependent Claim
27 words
A method according to claim 6, wherein said sensing step includes the use of two infrared detectors placed close to each other and separated by a baffle.
Claim 9
Dependent Claim
43 words
A method according to claim 7, wherein said sensing step further includes using a multi-element Fresnel lens to focus one detector on a seat back of the passenger seat and to focus the other detector on a seat surface of the passenger seat.
Claim 10
Dependent Claim
42 words
A method according to claim 1, wherein said signal features include indicia of (a) motion, (b) frequency of motion, (c) levels of motion, (d) difference in motion levels, (e) distance, (f) relative distance, (g) thermal levels, and (h) difference in thermal levels.
Claim 11
Dependent Claim
19 words
A method according to claim 9, wherein said indicia of motion include indicia of lateral notion and longitudinal motion.
Claim 12
Dependent Claim
36 words
A method according to claim 1, wherein said fused features include indicia of (a) temperature, (b) temperature differences, (c) approximate size of objects, (d) distance, (e) motion, (f) frequency of motion, and (g) levels of motion.
Claim 13
Dependent Claim
115 words
A method according to claim 1, wherein said associating step (d) comprises the substeps of: i) using predetermined confidence values and said signal features and fused features to produce (1) a decision confidence matrix of confidence values for the signal features of the signals of each sensor, and (2) a decision confidence matrix of confidence values for said fused features; ii) using the empirical relationships to calculate a decision confidence vector corresponding to each of said decision confidence matrices; iii) weighing each decision confidence vector in a predetermined manner to produce weighted vectors; and iv) combining the weighted vectors to produce a resultant vector having state values from which the feature state value is selected.
Claim 14
Dependent Claim
25 words
A method according to claim 1, wherein said set of state change criteria includes consideration of previous feature state values and previous current state values.
Claim 15
Dependent Claim
48 words
A method according to claim 1, wherein the subset of said predetermined set of state values, for which said passive restraint system is to be de-activated, includes state values corresponding to a rear-facing child seat state, an empty seat state, an inanimate object state, and an out-of-position state.
Claim 16
Dependent Claim
19 words
A method according to claim 1, wherein said passive restraint system includes a single canister air bag deployment system.
Claim 17
Dependent Claim
30 words
A method according to claim 1, wherein said passive restraint system includes a multi-canister air bag deployment system capable of partially pressurizing an air bag to various degrees of pressure.
Claim 18
Dependent Claim
24 words
A method according to claim 12, further comprising modifying said confidence values over time to correspond to changes in environmental conditions of the vehicle.
Claim 19
Dependent Claim
25 words
A method according to claim 1, wherein said sensing step includes using an ultrasound sensor to transmit ultrasonic pulses and to receive ultrasonic return signals.
Claim 20
Dependent Claim
16 words
A method according to claim 18, further comprising varying the transmission times between said ultrasonic pulses.
Claim 21
Dependent Claim
167 words
A method as recited in claim 1, wherein said plurality of sensors include: (a) a first infrared detector for generating a first raw data signal; (b) a second infrared detector for generating a second raw data signal; and (c) an ultrasound detector for generating a third raw data signal; and wherein said evaluating step (b) includes: i) processing said first and second raw data signals to develop a first set of signals representing a first group of signal features and defining an infrared feature vector signal; ii) processing said third raw data signal to develop a second set of signals representing a second group of signal features and defining an ultrasound feature vector signal; iii) selecting a subset of said first group of signal features to develop a third group of signal features defining an infrared feature vector subset signal; and iv) selecting a subset of said second group of signal features to develop a fourth group of signal features defining an ultrasound feature vector subset signal.
Claim 22
Dependent Claim
33 words
A method as recited in claim 20 wherein said combining step includes processing said infrared feature vector subset signal and said ultrasound feature vector subset signal to develop a fused feature vector signal.
Claim 23
Dependent Claim
205 words
A method as recited in claim 21, wherein the processing of said infrared feature vector subset signal and said ultrasound feature vector subset signal includes: (a) correlating a first subset of said third group of signal features with a first subset of said fourth group of signal features to develop an infrared spatial frequency components signal; (b) processing a second subset of said third group of signal features with a second subset of said fourth group of signal features to develop an infrared first absolute surface temperature signal, an infrared second absolute surface temperature signal, and an infrared differential absolute surface temperature signal; (c) processing a third subset of said third group of signal features with a third subset of said fourth group of signal features to develop an infrared/ultrasound motion level correlation signal, an infrared/ultrasound motion level temporal correlation signal, and an infrared/ultrasound motion frequency correlation signal; and (d) the infrared spatial frequency components signal, the infrared first absolute surface temperature signal, the infrared second absolute surface temperature signal, the infrared differential absolute surface temperature signal, the infrared/ultrasound motion level correlation signal, the infrared/ultrasound motion level temporal correlation signal, and the infrared/ultrasound motion frequency correlation signal are to form said fused feature vector signal.
Claim 24
Dependent Claim
110 words
A method as recited in claim 22, wherein the signals representing the first group of said signal features include a first infrared lateral motion frequency signal, a first infrared thermal temporal signal, a first infrared thermal level signal, a first infrared lateral motion temporal signal, a first infrared lateral motion level signal, an infrared longitudinal motion level signal, an infrared differential motion level signal, an infrared differential motion temporal signal, an infrared differential motion frequency signal, a second infrared lateral motion frequency signal, a second infrared thermal temporal signal, a second infrared thermal level signal, a second infrared lateral motion temporal signal, and a second infrared lateral motion level signal.
Claim 25
Dependent Claim
89 words
A method as recited in claim 23, wherein the signals representing the second group of said signal features include an absolute range signal, a first return level rate of change signal, a first return level signal, an absolute range-1st return signal, a range motion signal, a range motion rate of change signal, a range motion temporal signal, a range motion frequency signal, a relative range level rate of change signal, a relative range level signal, a relative range value signal, a multipath triangulation signal, and an air temperature signal.
Claim 26
Dependent Claim
72 words
A method as recited in claim 24, wherein the signals representing the third group of said signal features include the first infrared lateral motion frequency signal, the first infrared thermal level signal, the first infrared lateral motion temporal signal, the infrared differential motion frequency signal, the second infrared lateral motion frequency signal, the second infrared thermal level signal, the second infrared lateral motion temporal signal, and the infrared differential motion frequency signal.
Claim 27
Dependent Claim
48 words
A method as recited in claim 25, wherein the signals representing the fourth group of said signal features include the absolute range signal, the absolute range-1st return signal, the range motion temporal signal, the range motion frequency signal, the relative range value signal, and the multipath triangulation signal.
Claim 28
Dependent Claim
40 words
A method as recited in claim 26, wherein the signals representing the first subset of said third group includes the first infrared lateral motion frequency signal, the second infrared lateral motion frequency signal, and the infrared differential motion frequency signal.
Claim 29
Dependent Claim
31 words
A method as recited in claim 27, wherein the signals representing second subset of said third group include the first infrared thermal level signal and the second infrared thermal level signal.
Claim 30
Dependent Claim
48 words
A method as recited in claim 28, wherein the signals representing the third subset of said third group include the first infrared lateral motion frequency signal, the second infrared lateral motion frequency signal, the first infrared lateral motion temporal signal, and the second infrared lateral motion temporal signal.
Claim 31
Dependent Claim
38 words
A method as recited in claim 29, wherein the signals representing the first subset of said fourth group include the absolute range signal, the absolute range-1st return signal, the relative range value signal, and the multipath triangulation signal.
Claim 32
Dependent Claim
29 words
A method as recited in claim 30, wherein the signals representing the second subset of said fourth group include the absolute range signal, and the absolute range-1st return signal.
Claim 33
Dependent Claim
29 words
A method as recited in claim 31, wherein the signals representing the third subset of said fourth group include the range motion temporal signal and range motion frequency signal.
Claim 34
Dependent Claim
209 words
Apparatus for determining whether or not to control a vehicle's passenger passive restraint system as a function of a current state value determined by comparing measured signal features to a predetermined set of confidence values and empirical relationships obtained using various known occupancy scenarios and a set of state change criteria, comprising: (a) means for sensing the characteristics of occupancy of a passenger seat within the vehicle using a plurality of sensors functionally associated with said passenger seat and a set of corresponding electrical signals; (b) means for evaluating said electrical signals to determine a plurality of signal features included in each of said signals; (c) means for combining certain ones of said signal features to obtain a plurality of fused features; (d) means for associating said signal features and said fused features with the confidence values and empirical relationships to determine a feature state value; (e) means for identifying the feature state value as the current state value if the set of state change criteria is met; and (f) means for generating a state of control signal if said current state value is one of a predetermined set of state values for which said passive restraint system is to be controlled, including possible deactivation of said restraint system.
Claim 35
Dependent Claim
209 words
Apparatus for determining whether or not to control a vehicle's passenger passive restraint system as a function of a current state value determined by comparing measured signal features to a predetermined set of confidence values and empirical relationships obtained using various known occupancy scenarios and a set of state change criteria, comprising: (a) means for sensing the characteristics of occupancy of a passenger seat within the vehicle using a plurality of sensors functionally associated with said passenger seat and a set of corresponding electrical signals; (b) means for evaluating said electrical signals to determine a plurality of signal features included in each of said signals; (c) means for combining certain ones of said signal features to obtain a plurality of fused features; (d) means for associating said signal features and said fused features with the confidence values and empirical relationships to determine a feature state value; (e) means for identifying the feature state value as the current state value if the set of state change criteria is met; and (f) means for generating a state of control signal if said current state value is one of a predetermined set of state values for which said passive restraint system is to be controlled, including possible deactivation of said restraint system.
Claim 36
Dependent Claim
46 words
Apparatus according to claim 33, wherein said passenger passive restraint system includes an air bag deployment system having an air bag that is poised for deployment proximate said passenger 'seat and that can be de-activated or inflation controlled in response to said state of control signal.
Claim 37
Dependent Claim
38 words
Apparatus according to claim 34, wherein said predetermined set of state values includes values corresponding to an empty seat state, an occupied seat state, a rear-facing child seat state, an out-of-position passenger state, and an inanimate object state.
Claim 38
Dependent Claim
140 words
Apparatus according to claim 35, wherein: (a) the occupied seat state corresponds to the scenarios of a person seated in said seat, a person seated in said seat holding a grocery bag, a child in a forward-facing child seat disposed in said seat, a child standing in said seat at a distance from the air bag deployment location, and a pet disposed in said seat; (b) the rear-facing child seat state corresponds to the scenario of a child in a rear-facing child seat disposed in said seat; (c) the out-of-position passenger state corresponds to the scenario of a person positioned in close proximity to the air bag deployment location; (d) the inanimate object state corresponds to the scenario of an inanimate object disposed in said seat; and (e) the empty seat state corresponds to the scenario of an empty seat.
Claim 39
Dependent Claim
108 words
Apparatus according to claim 34, wherein said occupancy scenarios include a person seated in said passenger seat, a person seated in said seat holding a grocery bag, a child in a rear-facing child seat disposed in said seat, a child in a forward-facing child seat disposed in said seat, a person positioned in close proximity to the air bag deployment location, a child standing in said seat at a distance from the air bag deployment location, a standing child in close proximity to the air bag deployment location, an empty seat, an inanimate object disposed in said seat, a pet disposed in said seat, and an empty seat.
Claim 40
Dependent Claim
30 words
Apparatus according to claim 33, wherein said plurality of sensors is selected from the group consisting of infrared sensors, ultrasound sensors, weight sensors, microwave sensors, light sensors, and laser sensors.
Claim 41
Dependent Claim
24 words
Apparatus according to claim 38, wherein said means for sensing includes two infrared detectors placed close to each other and separated by a baffle.
Claim 42
Dependent Claim
42 words
Apparatus according to claim 39, wherein said means for sensing further includes a multi-element Fresnel lens for focusing one detector on the seat back of the passenger seat and for focusing the other detector on the seat surface of the passenger seat.
Claim 43
Dependent Claim
41 words
Apparatus according to claim 33, wherein said signal features include indicia of (a) motion, (b) frequency of motion, (c) levels of motion, (d) difference in motion levels, (e) distance, (f) relative distance, (g) thermal levels, and (h) difference in thermal levels.
Claim 44
Dependent Claim
18 words
Apparatus according to claim 41, wherein said indicia of motion include indicia of lateral motion and longitudinal motion.
Claim 45
Dependent Claim
35 words
Apparatus according to claim 33, wherein said fused features include indicia of (a) temperature, (b) temperature differences, (c) approximate size of objects, (d) distance, (e) motion, (f) frequency of motion, and (g) levels of motion.
Claim 46
Dependent Claim
119 words
Apparatus according to claim 33, wherein said means for associating comprises: i) means for using predetermined confidence values and said signal features and fused features to produce (1) a decision confidence matrix of confidence values for the signal features of the signals of each sensor, and 2 a decision confidence matrix of confidence values for said fused features; ii) means for using the empirical relationships to calculate a decision confidence vector corresponding to each of said decision confidence matrices; iii) means for weighing each decision confidence vector in a predetermined manner to produce weighted vectors; and iv) means for combining the weighted vectors to produce a resultant vector having state values from which the feature state value is selected.
Claim 47
Dependent Claim
24 words
Apparatus according to claim 33, wherein said set of state change criteria includes consideration of previous feature state values and previous current state values.
Claim 48
Dependent Claim
47 words
Apparatus according to claim 33, wherein the subset of said predetermined set of state values, for which said passive restraint system is to be de-activated, includes state values corresponding to a rear-facing child seat state, an empty seat state, an inanimate object state, and an out-of-position state.
Claim 49
Dependent Claim
18 words
Apparatus according to claim 33, wherein said passive restraint system includes a single canister air bag deployment system.
Claim 50
Dependent Claim
29 words
Apparatus according to claim 33, wherein said passive restraint system includes a multi-canister air bag deployment system capable of partially pressurizing an air bag to various degrees of pressure.
Claim 51
Dependent Claim
25 words
Apparatus according to claim 33, further comprising means for modifying said confidence values over time to correspond to changes in environmental conditions of the vehicle.
Claim 52
Dependent Claim
24 words
Apparatus according to claim 33, wherein said means for sensing includes an ultrasound sensor for transmitting ultrasonic pulses and for receiving ultrasonic return signals.
Claim 53
Dependent Claim
17 words
Apparatus according to claim 50, further comprising means for varying the transmission times between said ultrasonic pulses.
Claim 54
Dependent Claim
188 words
Apparatus as recited in claim 33, wherein said plurality of sensors of element (a) include: i) a first infrared detector for generating a first raw data signal; ii) a second infrared detector for generating a second raw data signal; and iii) at least one ultrasound detector for generating at least one third raw data signal; and wherein said means for evaluating (b) includes: i) means for processing said first and second raw data signals and for developing a first set of signals representing a first group of signal features and defining an infrared feature vector signal; ii) means for processing said third raw data signal and for developing a second set of signals representing a second group of signal features and defining at least one ultrasound feature vector signal; iii) means for selecting a subset of said first group of signal features to develop a third group of signal features defining an infrared feature vector subset signal; and iv) means for selecting a subset of said second group of signal features to develop a fourth group of signal features defining at least one ultrasound feature vector subset signal.
Claim 55
Dependent Claim
35 words
Apparatus as recited in claim 52 wherein said means for combining includes means for processing said infrared feature vector subset signal and said ultrasound feature vector subset signal to develop a fused feature vector signal.
Claim 56
Dependent Claim
217 words
Apparatus as recited in claim 53, wherein said means for processing said infrared feature vector subset signal and said ultrasound feature vector subset signal includes (a) means for correlating a first subset of said third group of signal features with a first subset of said fourth group of signal features and for developing an infrared spatial frequency components signal; (b) means for processing a second subset of said third group of signal features with a second subset of said fourth group of signal features and for developing an infrared first absolute surface temperature signal, an infrared second absolute surface temperature signal, and an infrared differential absolute surface temperature signal; (c) means for processing a third subset of said third group of signal features with a third subset of said fourth group of signal features and for developing at least one infrared/ultrasound motion level correlation signal, an infrared/ultrasound motion level temporal correlation signal, and an infrared/ultrasound motion frequency correlation signal; and (d) the infrared spatial frequency components signal, the infrared first absolute surface temperature signal, the infrared second absolute surface temperature signal, the infrared differential absolute surface temperature signal, the infrared/ultrasound motion level correlation signal, the infrared/ultrasound motion level temporal correlation signal, and the infrared/ultrasound motion frequency correlation signal are combined to form said fused feature vector signal.
Claim 57
Dependent Claim
109 words
Apparatus as recited in claim 54, wherein the signals representing the first group of said signal features include a first infrared lateral motion frequency signal, a first infrared thermal temporal signal, a first infrared thermal level signal, a first infrared lateral motion temporal signal, a first infrared lateral motion level signal, an infrared longitudinal motion level signal, an infrared differential motion level signal, an infrared differential motion temporal signal, an infrared differential motion frequency signal, a second infrared lateral motion frequency signal, a second infrared thermal temporal signal, a second infrared thermal level signal, a second infrared lateral motion temporal signal, and a second infrared lateral motion level signal.
Claim 58
Dependent Claim
88 words
Apparatus as recited in claim 55, wherein the signals representing the second group of said signal features include an absolute range signal, a first return level rate of change signal, a first return level signal, an absolute range-1st return signal, a range motion signal, a range motion rate of change signal, a range motion temporal signal, a range motion frequency signal, a relative range level rate of change signal, a relative range level signal, a relative range value signal, a multipath triangulation signal, and an air temperature signal.
Claim 59
Dependent Claim
71 words
Apparatus as recited in claim 56, wherein the signals representing the third group of said signal features include the first infrared lateral motion frequency signal, the first infrared thermal level signal, the first infrared lateral motion temporal signal, the infrared differential motion frequency signal, the second infrared lateral motion frequency signal, the second infrared thermal level signal, the second infrared lateral motion temporal signal, and the infrared differential motion frequency signal.
Claim 60
Dependent Claim
51 words
Apparatus as recited in claim 57, wherein the signals representing the fourth group of said signal features include at least one of: the absolute range signal, the absolute range-1st return signal, the range motion temporal signal, the range motion frequency signal, the relative range value signal, and the multipath triangulation signal.
Claim 61
Dependent Claim
39 words
Apparatus as recited in claim 58, wherein the signals representing the first subset of said third group includes the first infrared lateral motion frequency signal, the second infrared lateral motion frequency signal, and the infrared differential motion frequency signal.
Claim 62
Dependent Claim
30 words
Apparatus as recited in claim 59, wherein the signals representing second subset of said third group include the first infrared thermal level signal and the second infrared thermal level signal.
Claim 63
Dependent Claim
47 words
Apparatus as recited in claim 60, wherein the signals representing the third subset of said third group include the first infrared lateral motion frequency signal, the second infrared lateral motion frequency signal, the first infrared lateral motion temporal signal, and the second infrared lateral motion temporal signal.
Claim 64
Dependent Claim
37 words
Apparatus as recited in claim 61, wherein the signals representing the first subset of said fourth group include the absolute range signal, the absolute range-1st return signal, the relative range value signal, and the multipath triangulation signal.
Claim 65
Dependent Claim
28 words
Apparatus as recited in claim 62, wherein the signals representing the second subset of said fourth group include the absolute range signal, and the absolute range-1st return signal.
Claim 66
Dependent Claim
28 words
Apparatus as recited in claim 63, wherein the signals representing the third subset of said fourth group include the range motion temporal signal and range motion frequency signal.
Claim 67
Dependent Claim
195 words
An application specific integrated circuit device for processing sensory input signals received from sensors adapted to sense the characteristics of occupancy of a particular passenger seat within a vehicle, and for determining whether or not to de-activate a vehicle's passenger passive restraint system as a function of a current state value determined by comparing measured signal features to a predetermined set of confidence values and empirical relationships obtained using various known occupancy scenarios and a set of state change criteria, comprising in one or more chips: (a) means for evaluating said input signals to determine a plurality of signal features; (b) means for combining certain ones of said signal features to obtain a plurality of fused features; (c) means for associating said signal features and said fused features with the confidence values and empirical relationships to determine a feature state value; (d) means for identifying the feature state value as the current state value if the set of state change criteria is met; and (e) means for generating a de-activate signal if said current state value is one of a predetermined set of state values for which said passive restraint system is to be de-activated.
Claim 68
Dependent Claim
195 words
An application specific integrated circuit device for processing sensory input signals received from sensors adapted to sense the characteristics of occupancy of a particular passenger seat within a vehicle, and for determining whether or not to de-activate a vehicle's passenger passive restraint system as a function of a current state value determined by comparing measured signal features to a predetermined set of confidence values and empirical relationships obtained using various known occupancy scenarios and a set of state change criteria, comprising in one or more chips: (a) means for evaluating said input signals to determine a plurality of signal features; (b) means for combining certain ones of said signal features to obtain a plurality of fused features; (c) means for associating said signal features and said fused features with the confidence values and empirical relationships to determine a feature state value; (d) means for identifying the feature state value as the current state value if the set of state change criteria is met; and (e) means for generating a de-activate signal if said current state value is one of a predetermined set of state values for which said passive restraint system is to be de-activated.
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USPTO Annuity & Maintenance Windows
| Annuity Stage | Window Opens | Payment Deadline | Surcharge Period Until | Estimated Fee | Status |
|---|---|---|---|---|---|
| 3.5 Years (Year 4) | 1999-01-08 | 1999-07-10 | 2000-01-09 | $2000.00 | Window Closed / Lapsed |
| 7.5 Years (Year 8) | 2003-01-08 | 2003-07-10 | 2004-01-09 | $3760.00 | Window Closed / Lapsed |
| 11.5 Years (Year 12) | 2007-01-08 | 2007-07-10 | 2008-01-09 | $7700.00 | Window Closed / Lapsed |