Occupant sensing system

• US 20030209893A1
• Filed: 04/14/2003
• Published: 11/13/2003
• Est. Priority Date: 05/05/1992
• Status: Active Grant

• Current Assignee: American Vehicular Sciences LLC (Acacia Research Corporation)
• Original Assignee: Automotive Technologies International Incorporated
• Inventors: Breed, David S., Johnson, Wendell C., DuVall, Wilbur E.
• Granted Patent: US 7,415,126 B2
• Time in Patent Office: Days
• US Class Current: 280/735
• CPC Class Codes: B60J 10/00 Sealing arrangements
• CPC Class Codes: B60N 2/002 Seats provided with an occupancy detection means mounted therein or thereon detection means responsive to presence or absence of children B60N2/266
• CPC Class Codes: B60N 2/015 Attaching seats directly to vehicle chassis
• CPC Class Codes: B60N 2/02246 Electric motors therefor
• CPC Class Codes: B60N 2/0244 with logic circuits
• CPC Class Codes: B60N 2/0248 with memory of positions
• CPC Class Codes: B60N 2/0252 with relations between different adjustments, e.g. height of headrest following longitudinal position of seat
• CPC Class Codes: B60N 2/0268 using sensors or detectors for adapting the seat or seat part, e.g. to the position of an occupant
• CPC Class Codes: B60N 2/0272 using sensors or detectors for detecting the position of seat parts
• CPC Class Codes: B60N 2/0276 reaction to emergency situations, e.g. crash
• CPC Class Codes: B60N 2/067 by linear actuators, e.g. linear screw mechanisms
• CPC Class Codes: B60N 2/28 Seats readily mountable on, and dismountable from, existing seats or other parts of the vehicle
• CPC Class Codes: B60N 2/2806 for securing the child seat to the vehicle
• CPC Class Codes: B60N 2/2863 backward facing
• CPC Class Codes: B60N 2/829 characterised by their adjusting mechanisms, e.g. electric motors
• CPC Class Codes: B60N 2/853 characterised by their adjusting mechanisms, e.g. electric motors
• CPC Class Codes: B60N 2/888 with arrangements for protecting against abnormal g-forces, e.g. by displacement of the head-rest
• CPC Class Codes: B60R 16/037 for occupant comfort , e.g. for automatic adjustment of appliances according to personal settings, e.g. seats, mirrors, steering wheel
• CPC Class Codes: B60R 2001/1223 with sensors or transducers
• CPC Class Codes: B60R 2001/1253 with cameras, video cameras or video screens
• CPC Class Codes: B60R 2021/0027 : Post collision measures, e.g. notifying emergency services
• CPC Class Codes: B60R 2021/01315 : monitoring occupant displacement
• CPC Class Codes: B60R 2021/23153 : specially adapted for rear seat passengers
• CPC Class Codes: B60R 2021/26094 : characterised by fluid flow controlling valves
• CPC Class Codes: B60R 2021/2765 : comprising means to control the venting
• CPC Class Codes: B60R 2022/208 : by automatic or remote control means
• CPC Class Codes: B60R 2022/288 : with means to adjust or regulate the amount of energy to be absorbed
• CPC Class Codes: B60R 2022/4685 : with means to adjust or regulate the tensioning force in relation to external parameters
• CPC Class Codes: B60R 2022/4825 : for sensing amount of belt winded on retractor
• CPC Class Codes: B60R 21/013 : including means for detecting collisions, impending collisions or roll-over
• CPC Class Codes: B60R 21/0132 : responsive to vehicle motion parameters , e.g. to vehicle longitudinal or transversal deceleration or speed value
• CPC Class Codes: B60R 21/0134 : responsive to imminent contact with an obstacle , e.g. using radar systems
• CPC Class Codes: B60R 21/0136 : responsive to actual contact with an obstacle , e.g. to vehicle deformation, bumper displacement or bumper velocity relative to the vehicle monitoring crash strength in combination with passenger seat detection B60R21/01558
• CPC Class Codes: B60R 21/015 : including means for detecting the presence or position of passengers, passenger seats or child seats , and the related safety parameters therefor, e.g. speed or timing of airbag inflation in relation to occupant position or seat belt use
• CPC Class Codes: B60R 21/01516 : using force or pressure sensing means
• CPC Class Codes: B60R 21/0152 : using strain gauges
• CPC Class Codes: B60R 21/0153 : using field detection presence sensors
• CPC Class Codes: B60R 21/01534 : using electromagneticwaves, e.g. infrared
• CPC Class Codes: B60R 21/01536 : using ultrasonic waves
• CPC Class Codes: B60R 21/01538 : for image processing, e.g. cameras or sensor arrays
• CPC Class Codes: B60R 21/01542 : detecting passenger motion
• CPC Class Codes: B60R 21/01544 : detecting seat belt parameters, e.g. length, tension or height-adjustment
• CPC Class Codes: B60R 21/01548 : sensing the amount of belt winded on retractor
• CPC Class Codes: B60R 21/01552 : detecting position of specific human body parts, e.g. face, eyes or hands
• CPC Class Codes: B60R 21/01554 : Seat position sensors
• CPC Class Codes: B60R 21/203 : in steering wheels or steering columns
• CPC Class Codes: B60R 21/21656 : Steering wheel covers or similar cup-shaped covers
• CPC Class Codes: B60R 21/276 : with means to vent the inflation fluid source, e.g. in case of overpressure B60R21/30 takes precedence; venting means on inflatable member walls B60R21/239
• CPC Class Codes: B60R 22/20 : adjustable in position, e.g. in height
• CPC Class Codes: B60R 22/201 : with the belt anchor connected to a slider movable in a vehicle-mounted track for passive restraint systems B60R22/06
• CPC Class Codes: B60R 25/25 : using biometry
• CPC Class Codes: B60R 25/252 : Fingerprint recognition
• CPC Class Codes: B60R 25/255 : Eye recognition
• CPC Class Codes: B60R 25/257 : Voice recognition
• CPC Class Codes: E05F 15/43 : responsive to disruption of energy beams, e.g. light or sound
• CPC Class Codes: E05F 15/431 : specially adapted for vehicle windows or roofs
• CPC Class Codes: E05F 2015/433 : using reflection from the obstruction
• CPC Class Codes: E05Y 2900/55 : Windows
• CPC Class Codes: G01S 13/04 : Systems determining presence of a target based on relative movement of target G01S13/56
• CPC Class Codes: G01S 15/04 : Systems determining presence of a target
• CPC Class Codes: G01S 15/06 : Systems determining the position data of a target
• CPC Class Codes: G01S 15/87 : Combinations of sonar systems
• CPC Class Codes: G01S 15/88 : Sonar systems specially adapted for specific applications seismic or acoustic prospecting or detecting G01V1/00
• CPC Class Codes: G01S 17/88 : Lidar systems specially adapted for specific applications
• CPC Class Codes: G01S 17/89 : for mapping or imaging
• CPC Class Codes: G01S 7/417 : involving the use of neural networks
• CPC Class Codes: G01S 7/4802 : using analysis of echo signal for target characterisation; Target signature; Target cross-section
• CPC Class Codes: G01S 7/539 : using analysis of echo signal for target characterisation; Target signature; Target cross-section
• CPC Class Codes: G06V 20/59 : inside of a vehicle, e.g. relating to seat occupancy, driver state or inner lighting conditions
• CPC Class Codes: G06V 40/10 : Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
• CPC Class Codes: G10K 2210/1282 : Automobiles
• CPC Class Codes: G10K 2210/3219 : Geometry of the configuration

Abstract

Optical classification method for classifying an occupant in a vehicle by acquiring images of the occupant from a single camera and analyzing the images acquired from the single camera to determine a classification of the occupant. The single camera may be a digital CMOS camera, a high-power near-infrared LED, and the LED control circuit. It is possible to detect brightness of the images and control illumination of an LED in conjunction with the acquisition of images by the single camera. The illumination of the LED may be periodic to enable a comparison of resulting images with the LED on and the LED off so as to determine whether a daytime condition or a nighttime condition is present. The position of the occupant can be monitored when the occupant is classified as a child, an adult or a forward-facing child restraint.

Claims

1. An optical classification method for classifying an occupant in a vehicle, comprising the steps of:

acquiring images of the occupant from a single camera; and

analyzing the images acquired from the single camera to determine a classification of the occupant.

2. The method of claim 1, wherein the single camera is selected from a group consisting of a digital CMOS camera, a high-power near-infrared LED, and the LED control circuit.

3. The method of claim 1, further comprising the steps of:

detecting brightness of the images and controlling illumination of an LED in conjunction with the acquisition of images by the single camera.

4. The method of claim 3, wherein the illumination of the LED is periodic to enable a comparison of resulting images with the LED on and the LED off so as to determine whether a daytime condition or a nighttime condition is present.

5. The method of claim 1, further comprising the step of monitoring the position of the occupant when the occupant is classified as a child, an adult or a forward-facing child restraint.

6. The method of claim 1, wherein the step of analyzing the images comprises the steps of:

pre-processing the images;

compressing the data from the pre-processed images;

determining from the compressed data or the acquired images a particular condition of the occupant or condition of the environment in which the images have been acquired;

providing a plurality of trained neural networks, each designed to determine the classification of the occupant for a respective one of the conditions;

inputting the compressed data into one of the neural networks designed to determine the classification of the occupant for the determined condition to thereby obtain a classification of the occupant; and

subjecting the obtained classification of the occupant to post-processing to improve the probability of the classification of the occupant corresponding to the actual occupant.

7. The method of claim 6, wherein the pre-processing step comprises the steps of removing random noise and enhancing contrast whereby the presence of unwanted objects other than the occupant are reduced.

8. The method of claim 6, further comprising detecting the presence of unwanted contents in the images other than the occupant and adjusting the camera to minimize the presence of the unwanted contents in the images.

9. The method of claim 6, wherein a particular condition of the occupant is determined from the compressed data or the acquired images.

10. The method of claim 6, wherein a particular condition of the environment in which the images have been acquired is determined from the compressed data or the acquired images.

11. The method of claim 6, wherein the post-processing comprises the step of filtering the classification of the occupant from the neural network to remove random noise.

12. The method of claim 6, wherein the post-processing comprises the steps of comparing the classification of the occupant from the neural network to a previously obtained classification of the occupant and determining whether any difference in the classification is possible.

13. The method of claim 6, further comprising the step of displaying the classification of the occupant from the neural network in a position visible to the occupant and enabling the occupant to change or confirm the classification.

14. The method of claim 6, further comprising the step of monitoring the position of the occupant when the occupant is classified as a child, an adult or a forward-facing child restraint.

15. The method of claim 14, wherein the step of monitoring the occupant comprises the step of inputting the compressed data or acquired images into an additional neural network designed to determine a recommendation for control of a system in the vehicle based on the monitoring of the position of the occupant.

16. The method of claim 14, wherein the step of monitoring the occupant comprises the steps of providing a plurality of additional neural networks, each of the additional neural networks being designed to determine a recommendation for control of a system in the vehicle for a particular classification of occupant;

and inputting the compressed data or acquired images into one of the neural networks designed to determine the recommendation for control of the system for the obtained classification of the occupant to thereby obtain a recommendation for the control of the system for the particular occupant.

17. The method of claim 16, wherein the system in the vehicle is an occupant restraint device, the additional neural networks being designed to determine a recommendation of a suppression of deployment of the occupant restraint device, a depowered deployment of the occupant restraint device or a full power deployment of the occupant restraint device.

18. The method of claim 6, further comprising the steps of

acquiring images of the occupant from an additional camera;

pre-processing the images acquired from the additional camera;

compressing the data from the pre-processed images acquired from the additional camera;

determining from the compressed data or the acquired images from the additional camera a particular condition of the occupant or condition of the environment in which the images have been acquired;

inputting the compressed data from the pre-processed images acquired by the additional camera into one of the neural networks designed to determine the classification of the occupant for the determined condition to thereby obtain a classification of the occupant;

subjecting the obtained classification of the occupant to post-processing to improve the probability of the classification of the occupant corresponding to the actual occupant; and

comparing the obtained classification using the images acquired form the additional camera to the images acquired from the initial camera to ascertain any variations in classification.