Vehicle occupant classification method and apparatus for use in a vision-based sensing system

• US 20060030988A1
• Filed: 06/20/2005
• Published: 02/09/2006
• Est. Priority Date: 06/18/2004
• Status: Abandoned Application

• Current Assignee: Eaton Corp. (Wisconsin) (Eaton Corporation PLC.)
• Original Assignee: Eaton Corp. (Wisconsin) (Eaton Corporation PLC.)
• Inventors: Farmer, Michael
• Application Number:
  US11/157,465
• Publication Number:
  US 20060030988A1
• Time in Patent Office: Days
• US Class Current: 701/45
• CPC Class Codes: G06V 40/10 Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands

Abstract

A method and apparatus for selectively deploying or suppressing automated safety equipment in a vehicle is disclosed. Employing methods obtained from the field of Evidential Reasoning, an occupant classification history process computes the most plausible occupant class, and then selects an appropriate piece of safety equipment to deploy or suppress, based at least in part upon the classification results.

Claims

1. A method of classifying objects in a historical classification system, wherein the objects are assigned one of a plurality of mutually exclusive classifications over a period of time, and wherein a history of previous classifications for each object is maintained as a set of historical classifications for each object, and wherein each historical classification in a set has a corresponding and associated belief and plausibility value, comprising:

(a) obtaining a current classification of a selected object;

(b) determining whether the current classification of the selected object is plausible based upon the set of historical classifications for the object;

(c) integrating the current classification with the set of historical classifications and thereby generating an updated set of historical classifications for the object if the current classification is determined to be plausible in step (b), else discarding the current classification and proceeding to step (d);

(d) updating the belief and plausibility values associated and corresponding to the historical classifications for the selected object;

(e) computing a complete power set vector containing all of the belief and plausibility values accumulated by the method;

(f) computing average belief and plausibility values based on the complete power set computed in step (e); and

(g) classifying the selected object as one of the mutually exclusive classifications, based upon the computed average belief and plausibility values computed in step (f).

2. The method of classifying objects as set forth in claim 1, wherein images of the selected objects are obtained by a vision-based vehicle safety system.

3. The method of classifying objects as set forth in claim 1, wherein the classification system further includes a reset trigger, further including, before the obtaining step (a), determining whether the reset trigger is activated, and if so activated, resetting all historical classifications, belief and plausibility values in the system to a complete ignorance condition.

4. The method of classifying objects as set forth in claim 3, wherein the classification system further includes a memory for storing all of the historical classifications, belief and plausibility values in the system, and wherein the memory is cleared of all data if the reset trigger is activated.

5. The method of classifying objects as set forth in claim 4, wherein the system also includes a history cache for storing the power set vector, and wherein the history cache is also cleared of all data if the reset trigger is activated.

6. The method of classifying objects as set forth in claim 1, wherein the step of determining whether the current classification is plausible comprises the following substeps:

(1) calculating a current system belief vector Bel_vector and a previous system belief vector Bel_last (Bel_last), wherein Bel_vector contains current belief values of all possible classifications, and assigning Bel_vector=Bel_last;

(2) obtaining a new set of masses for the selected object, and updating Bel_vector based on the new set of masses using Dempster-Shafer rules of combining;

(3) calculating a new belief vector Bel_vector_new (Bel_temp) based upon Bel_vector updated in step (2);

(4) calculating a delta_bel (Δ

Bel) value by summing absolute differences of the new belief vector Bel_vector_new and the previous belief vector Bel_last over all possible outcomes;

(5) comparing delta_bel to a threshold value; and

(6) determining that the current classification is not plausible if delta_bel exceeds the threshold value, else determining that the current classification is plausible.

7. The method of classifying objects as set forth in claim 6, wherein the step of calculating a delta_bel value is performed in accordance with the following mathematical expression:

$<br><br>\mathrm{\Delta <br><br>}<br><br>\text{\hspace{1em}<br><br>}<br><br>\mathrm{Bel}<br><br>=\underset{P<br><br>\left(\mathrm{\Theta <br><br>}\right)}{\sum <br><br>}<br><br>\text{\hspace{1em}<br><br>}<br><br><br><br>{\mathrm{Bel}}_{\mathrm{temp}}-{\mathrm{Bel}}_{\mathrm{last}}<br><br>.$

8. The method of classifying objects as set forth in claim 7, wherein the updating the belief and plausibility values step set forth in step (d) is performed in accordance with the following mathematical expression:

$m_1\oplus <br><br>m_2<br><br>\left(Z\right)=\frac{\underset{X\bigcap <br><br>Y=Z}{\sum <br><br>}<br><br>\text{\hspace{1em}<br><br>}<br><br>m_1<br><br>\left(X\right)·<br><br>m_2<br><br>\left(Y\right)}{1-\mathrm{\kappa <br><br>}},<br><br>\mathrm{where}<br><br>\text{\hspace{1em}<br><br>}<br><br>\mathrm{\kappa <br><br>}=\underset{X\bigcap <br><br>Y=\mathrm{\varphi <br><br>}}{\sum <br><br>}<br><br>\text{\hspace{1em}<br><br>}<br><br>m_1<br><br>\left(X\right)·<br><br>m_2<br><br>\left(Y\right).$

and wherein m₁ comprises an existing mass at a last iteration of the system, m₂ comprises a new mass, and a sum m₁ and m₂ is computed for each element in the complete power set.

9. The method of classifying objects as set forth in claim 5, wherein the complete power set vector is stored in the history cache.

10. The method of classifying objects as set forth in claim 9, wherein the computing average belief and plausibility values of step (f) further comprises averaging the values stored in the history cache, and wherein the history cache comprises a rolling buffer of previous N classifications.

11. The method of classifying objects as set forth in claim 10, wherein the step of averaging values stored in the history cache yields a first and a second set of belief and plausibility values for the complete power set, and wherein an average of the first and second sets is computed for each element in the power set, and wherein a final subset of the power set comprises a classification output by the historical classification system.

12. The method of classifying objects as set forth in claim 11, wherein a list of possible subsets output by the historical classification system comprises {RFIS}, {child}, {RFIS, child}, {adult}, {empty}.

13. The method of classifying objects as set forth in claim 1, wherein the method is used in a vision-based automated vehicle safety system, wherein the vehicle safety system includes a video camera mounted within the vehicle and wherein vehicle occupants are classified by the historical classification system into one of a plurality of mutually exclusive classifications over a period of time, and wherein the vehicle safety system selectively suppresses and deploys vehicle safety equipment responsive to the occupant classifications.

14. The method of claim 13, wherein the vehicle safety equipment comprises an airbag deployment system.

16. An automated vehicle safety system, comprising:

(a) an imaging device capable of obtaining images of a vehicle occupant;

(b) a computing device, operatively coupled to the imaging device, wherein the computing device is configured to classify objects in accordance with the method set forth in claim 1, and wherein the vehicle occupant is classified as one of a plurality of mutually exclusive classifications; and

(c) an automated safety device, responsive to the computing device, wherein the safety device is selectively deployed based on the vehicle occupant classification as determined by the computing device.

17. The automated vehicle safety system set forth in claim 16, wherein the imaging device comprises a camera.

18. The automated vehicle safety system set forth in claim 16, wherein the computing device comprises a DSP.

19. The automated vehicle safety system set forth in claim 16, wherein the automated safety device comprises an airbag deployment system.

15. An object classification system, wherein objects are assigned one of a plurality of mutually exclusive classifications over a period of time, and wherein a history of previous classifications for each object is maintained as a set of historical classifications for each object, and wherein each historical classification in a set has a corresponding and associated belief and plausibility value, comprising:

(a) means for determining a current classification of a selected object;

(b) means, coupled to the current classification determining means, for determining whether the current classification of the selected object is plausible based upon the set of historical classifications for the object;

(c) means, responsive to the means for determining the plausibility of the selected object, for integrating the current classification with the set of historical classifications and thereby generating an updated set of historical classifications for the object if the current classification is plausible and discarding the current classification if the current classification is not plausible;

(d) means, responsive to the integrating means, for updating the belief and plausibility values associated and corresponding to the historical classifications for the selected object;

(e) means for computing a complete power set vector containing all of the belief and plausibility values accumulated by the classification system;

(f) means, responsive to the complete power set computation means, for computing average belief and plausibility values based on the complete power; and

(g) means, responsive to the average belief and plausibility values computing means, for classifying the selected object as one of the mutually exclusive classifications, based upon the computed average belief and plausibility values.

20. A computer program executable on a general purpose computing device, wherein the program is executed to classify objects in a historical classification system, wherein the objects are assigned one of a plurality of mutually exclusive classifications over a period of time, and wherein a history of previous classifications for each object is maintained as a set of historical classifications for each object, and wherein each historical classification in a set has a corresponding and associated belief and plausibility value, comprising:

(a) a first set of instructions for obtaining a current classification of a selected object;

(b) a second set of instructions for determining whether the current classification of the selected object is plausible based upon the set of historical classifications for the object;

(c) a third set of instructions for integrating the current classification with the set of historical classifications and thereby generating an updated set of historical classifications for the object if the current classification is determined to be plausible, else discarding the current classification;

(d) a fourth set of instructions for updating the belief and plausibility values associated and corresponding to the historical classifications for the selected object;

(e) a fifth set of instructions for computing a complete power set vector containing all of the belief and plausibility values accumulated by the method;

(f) a sixth set of instructions for computing average belief and plausibility values based on the complete power set; and

(g) a seventh set of instructions for classifying the selected object as one of the mutually exclusive classifications, based upon the computed average belief and plausibility values.