Abstract:
A method for quantifying classification confidence of obstructions applied to a perception mergence system of a vehicular computer in a vehicle. The method includes steps of: the vehicular computer receiving obstruction information of at least obstruction, image information corresponding to the obstruction information and vehicle body signals, and using a classifier to classify them; calculating a detection result of each range sensor to calculate a existence confidence; using the existence confidences and precision of the classifier to calculate a classification belief assignment of each range sensor corresponding to each obstruction; performing mergence calculation on the classification belief assignments to respectively quantify an obstruction classification confidence of all the range sensor corresponding to each obstruction; and performing a classification ineffectiveness filtering mechanism to exclude the obstruction whose obstruction classification confidence less than a predetermined value. The present invention quantifies the obstruction classification confidence to improve the classification precision.

Description:
BACKGROUND OF THE INVENTION 
     Field of the Invention 
     The present invention relates to a method for quantifying classification confidence of objects, particularly to a method for quantifying classification confidence of obstructions applied to a perception mergence system. 
     Description of the Related Art 
     Presently, functions of vehicular computers become more and more perfect. In order to improve driving safety and consider the future of autonomous driving, the detection of obstructions in front of a vehicle and the confidence of classifying the obstructions are of importance. The obstructions are classified into different classifications, including vehicles, pedestrians, bicycles and utility poles. According to the system setting, classification items are decided. In this way, the system provides braking suggestion, automatically brakes quickly, or performs other activities according to the obstructions&#39; classification. 
       FIG. 1  is a block diagram showing detection for obstructions in front of a vehicle and perception mergence in the traditional technology. A camera  10  retrieves a front road image, and a plurality of sensors  11  and  12  detect distances between themselves and front obstructions, or retrieve a vehicle body signal of the vehicle. The heights and profiles of the obstructions are obtained from the distance detected by the sensors  11  and  12 . Then, the front road image, the obstruction information and the vehicle body signal are used to respectively analyzing the obstruction information  13  and to calculate mergence information for positions and classification  15  of the obstructions. Besides, the front road image, the obstruction information and the vehicle body signal are used to respectively estimating existence confidences  14 , namely precision that the front obstructions exist. Also, the existence confidences merged  16 . Finally, outputting information  17 , and the information includes the probabilities that the obstructions indeed exist, coordinates and possible classification of the obstructions. However, there is no mechanism to determine whether the existence confidences calculated by the system are correct again. Thus, the mergence results of the existence confidences are directly trusted. The existence confidences directly trusted will lead to serious results if misjudgment occurs, Take a real case for example. As shown in  FIG. 2 , a vehicle  18   a  is provided with a vehicular computer having a system for front obstruction detection and classification warning. The vehicle  18   a  is a safe distance from a front vehicle  18   b . A large tanker  18   c  drives on a right lane. When the tanker  18   c  passes by the vehicle  18   a , the microwaves reflected from the front vehicle  18   b  are diffused by the tanker  18   c . The system of the vehicle  18   a  receives the microwaves diffused to determine that the probability of hitting a front vehicle is very high, and then automatically brakes quickly. As a result, a rear vehicle  18   d  hits the vehicle  18   a  before reacting. In fact, there is no vehicle driving near the vehicle  18   a  and in front of the vehicle  18   a . Instead, the tanker  18   c  drives on the right lane neighboring the vehicle  18   a . The system misjudges that the noise represents a vehicle, which results in incorrect brake. 
     Accordingly, how to improve the precision of existence confidences and classification confidences and the reference for quantifying the mergence information to avoid misjudgment is an important problem. The present invention provides a method for quantifying classification confidence of obstructions and describes specific architectures and embodiments as followings: 
     SUMMARY OF THE INVENTION 
     A primary objective of the present invention is to provide a method for quantifying classification confidence of obstructions, which quantifies the confidence for classification information of obstructions to improve classification precision, lest an active safe system at a rear end do mistaken action to result in malfunction due to using incorrect information. 
     Another objective of the present invention is to provide a method for quantifying classification confidence of obstructions, which merges the information retrieved by range sensors, an image-retrieving unit and a vehicle body signal sensor to obtain information for position, classification and classification belief assignments of each obstruction corresponding to all the sensors. 
     Further objective of the present invention is to provide a method for quantifying classification confidence of obstructions, which further comprises a classification ineffectiveness filtering mechanism. When the quantified classification confidence of the obstruction is less than a predetermined value, which represents a classification error, the obstruction is ignored. When the classification confidence of the obstruction is larger than or equals to the predetermined value, the system informs a driver. In this case, the autonomous driving system can automatically brake. 
     To achieve the abovementioned objectives, the present invention provides a method for quantifying classification confidence of obstructions applied to a perception mergence system of a vehicular computer in a vehicle, and the vehicular computer is connected with an image-retrieving unit, a vehicle body signal sensor and a plurality of range sensors. The method comprises steps of: the vehicular computer receiving obstruction information of at least one obstruction, at least one piece of image information corresponding to the obstruction information and a plurality of vehicle body signals, and using a classifier to classify the obstruction information, the image information and the vehicle body signals; the perception mergence system calculating a detection result of each range sensor to calculate a existence confidence; using the existence confidences and precision of the classifier to calculate a classification belief assignment of each range sensor corresponding to each obstruction; performing mergence calculation on the classification belief assignments to respectively quantify an obstruction classification confidence of all the range sensors corresponding to each obstruction; and performing a classification ineffectiveness filtering mechanism according to the obstruction classification confidence to exclude the obstruction whose obstruction classification confidence less than a predetermined value. 
     Wherein, the existence confidence is a probability that the obstruction detected by the range sensor is an entity. Each range sensor tracks coordinates of the obstruction presently detected, compares with an actual value and a plurality of tracking values subsequently-received to determine a probability that the tracked obstruction presently exists whereby the probability is used as the existence confidence, wherein the tracking and comparison of the obstruction and determination of the existence confidence are calculated using joint integrated probabilistic association (JIPDA). 
     In the present invention, the mergence calculation uses the classification belief assignments, precision of the range sensors and at least one obstruction continuous detection probability to calculate the obstruction classification confidence, wherein the obstruction continuous detection probability is a probability that the range sensors continuously detect identical the obstruction. 
     Suppose that no image is used to determine classification of the obstructions. For example, only detection information for radars is used to determine classification of the obstructions. Thus, a determination way of the obstruction continuous detection probability comprises steps of: receiving the obstruction information detected by the range sensor to determine whether the obstruction is a vehicle; and comparing with the preceding obstruction information to determine whether the obstructions corresponding to the preceding obstruction information and the received obstruction information are identical: if no, determining that the obstruction is not a vehicle; and if yes, determining whether the identical obstruction is continuously detected more than predetermined times: if yes, determining the obstruction is a vehicle; and if no, determining the obstruction is not a vehicle. 
     The vehicular computer informs a driver of the vehicle of the obstruction information of the obstruction in front of the driver in hearing, touch or vision ways when the obstruction classification confidence is larger than or equals to the predetermined value. In this case, the vehicular computer informs the driver of a probability of the obstruction being a vehicle or a pedestrian. Meanwhile, the perception mergence system returns to the preceding step and retrieves another obstruction classification confidence to determine whether it less then the predetermined value. 
     The classification ineffectiveness filtering mechanism of the present invention comprises steps of: retrieving the obstruction classification confidence of one obstruction; and determining whether the retrieved obstruction classification confidence is less than the predetermined value: if yes, classifying the obstruction into misjudgment and filtering it out; and if no, retrieving the obstruction classification confidence of another obstruction for determining again. 
     The mergence calculation of the present invention comprises steps of: choosing a specific obstruction from the at least one obstruction and introducing the classification belief assignments of the range sensors detecting the specific obstruction, the precision of the range sensors and the obstruction continuous detection probability; calculating basic belief assignments of the range sensors according to existence or inexistence of the specific obstruction; the specific obstruction having four detection situations comprising null, existence, inexistence, possible existence, and possible inexistence and using the basic belief assignments of the range sensors to calculate a mergence belief assignment according to the four detection situations; and calculating an object existence probability of the specific obstruction according to the mergence belief assignment. 
     Below, the embodiments are described in detail in cooperation with the drawings to make easily understood the technical contents, characteristics and accomplishments of the present invention. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a block diagram showing detection for obstructions in front of a vehicle and perception mergence in the traditional technology; 
         FIG. 2  is a diagram schematically showing misjudgment in the traditional technology; 
         FIG. 3  is a diagram schematically showing a system of using a method for quantifying classification confidence of obstructions according to an embodiment of the present invention; 
         FIG. 4  is a flow chart showing a method for quantifying classification confidence of obstructions according to an embodiment of the present invention; 
         FIG. 5  is a flow chart determining an obstruction continuous detection probability according to an embodiment of the present invention; 
         FIG. 6  is a flow chart showing mergence calculation according to an embodiment of the present invention; and 
         FIG. 7  is a flow chart showing a classification ineffectiveness filtering mechanism according to an embodiment of the present invention. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     The present invention provides a method for quantifying classification confidence of obstructions. When each sensor detects obstructions in front of itself, the present invention tracks position information of the obstructions, quantifies a existence confidence of each sensor, and merges with a classification belief assignment of each sensor to quantify an obstruction classification confidence, which is provided to the system for classifying and filtering errors, so as to improve reliability and precision of overall classification. 
       FIG. 3  is a diagram schematically showing a system of using a method for quantifying classification confidence of obstructions according to an embodiment of the present invention. A vehicular computer  26  in a vehicle comprises a classifier  27  and a perception mergence system  28 , and the vehicular computer  26  is connected with an image-retrieving unit  20 , a vehicle body signal sensor  24  and a plurality of range sensors  22 . The range sensors  22  are radars or lidars (laser radars) and obtain obstruction information of at least one obstruction in front of the vehicle. The image-retrieving unit  20  retrieves at least one piece of image information corresponding to the obstruction information. The vehicle body signal sensor  24  obtains a plurality of vehicle body signals, including those of vehicle speed or a rotational angle of a steering wheel. 
       FIG. 4  is a flow chart showing a method for quantifying classification confidence of obstructions according to an embodiment of the present invention. In Step S 10 , the vehicular computer receives obstruction information of at least one obstruction, at least one piece of image information corresponding to the obstruction information and a plurality of vehicle body signals, and a classifier is used to classify the obstruction information, the image information and the vehicle body signals. The classifier is a function module in the vehicular computer. Then, in Step S 12 , the perception mergence system calculates a detection result of each range sensor to calculate a existence confidence, wherein the existence confidence is a probability that the obstruction detected by the range sensor is an entity. Each range sensor obtains the existence confidence corresponding to the obstruction. If a plurality of obstructions is detected, each range sensor obtains a plurality of existence confidences corresponding to the obstructions. In the present invention, each range sensor tracks coordinates of the obstruction presently detected, namely position information of the obstruction. Besides, each range sensor compares with an actual value and a plurality of tracking values subsequently-received to determine a probability that the tracked obstruction presently exists whereby the probability is viewed as the existence confidence. The tracking and comparison of the obstruction and determination of the existence confidence are calculated using joint integrated probabilistic association (JIPDA). 
     Then, in Step S 14 , precision of the classifier is read, wherein the precision is set by a developer of the classifier. 
     Then, in Step S 16 , the existence confidences and the precision of the classifier read in the preceding step are used to calculate a classification belief assignment of each range sensor corresponding to each obstruction. The classification belief assignment of each classifier equals to the existence confidence multiplied by the precision of the classifier. 
     Then, in Step S 18 , mergence calculation is performed on all the classification belief assignments to respectively quantify an obstruction classification confidence of all the range sensors corresponding to each obstruction. In the step, the perception mergence system firstly defines detection situations of each obstruction, including those of four situations {φ, {∃}, {           }, {∃,         }}. φ denotes null, and {∃} denotes existence of the obstruction, and {         } denotes inexistence of the obstruction, and {∃,         } denotes possible existence or possible inexistence of the obstruction. Meanwhile, three parameters are required to calculate an obstruction classification confidence of a specific obstruction. The three parameters include the classification belief assignment of each range sensor corresponding to the specific obstruction, precision of the range sensors and at least one obstruction continuous detection probability. The precision of the range sensors is provided by an industry in a factory. Not all precision of the range sensors are identical. In general, the precision of the range sensors is impossible to reach 100%. If the precision, for example, is lowered and different from an initial value thereof, the precision can be manually adjusted. As a result, the precision of each range sensor is a predetermined value in advance, and the obstruction continuous detection probability is a probability that the range sensors continuously detect the identical obstruction. If the vehicle is provided with the image-retrieving unit  20 , the images are used to directly determine whether the obstruction is a vehicle and the range sensors are responsible for auxiliary determination. If the vehicle does not be provided with the image-retrieving unit  20  to retrieve the images, only the range sensors are used to determine whether the obstruction is a vehicle. In such a case, a determination way of the obstruction continuous detection probability is shown in  FIG. 5 . Firstly, in Step S 30 , the obstruction information detected by the range sensor is received to determine whether the obstruction is a vehicle. Then, in Step S 32 , the preceding obstruction information is compared with the received obstruction information to determine whether the obstructions corresponding to the preceding obstruction information and the received obstruction information are identical. If the answer is no, the process determines that the obstruction is not a vehicle, as shown in Step S 34 . If the answer is yes, the process determines whether the identical obstruction is continuously detected more than predetermined times, as shown in Step S 36 . If the answer is yes, the process determines the obstruction is a vehicle, as shown in Step S 38 . If the answer is no, the process determines that the obstruction is not a vehicle, as shown in Step S 34 .
     In Step S 18 , the mergence calculation merges with all the information of the specific obstruction using Dempster-Shafer theory. Refer to  FIG. 6 , which is a flow chart showing the mergence calculation. Firstly, in Step S 182 , the classification belief assignments of all the range sensors detecting the specific obstruction, the precision of the range sensors and the obstruction continuous detection probability are introduced. Then, in Step S 184 , basic belief assignments of the range sensors according to existence or inexistence of the specific obstruction are calculated. Then, in Step S 186 , the specific obstruction has four detection situations comprising null, existence, inexistence, possible existence, and possible inexistence, and the basic belief assignments of Step S 184  is used to calculate a mergence confidence assignment according to the four detection situations. Finally, in Step S 188 , an object existence probability of the specific obstruction is calculated according to the mergence belief assignment, and the object existence probability is the obstruction classification confidence of the present invention. 
     The formulas of the mergence calculation are described as followings: 
     Firstly, in Step S 182 , the classification belief assignment p i  (∃x) of each range sensor corresponding to the obstruction, the precision p trust   i  of the range sensors and the obstruction continuous detection probability p p   i (x) are introduced. Then, in Step S 184 , the basic belief assignment of each range sensor is calculated, as shown by formulas (1) and (2).
 
 m   i ({∃})= p   p   i ( x )· p   trust   i   ·p   i (∃ x )  (1)
 
 m   i ({           })= p   p   i ( x )· p   trust   i ·[1− p   i (∃ x )]  (2)

     m i ({∃}) is the basic belief assignment during existence of the obstruction, and m i ({           }) is the basic belief assignment during inexistence of the obstruction. In the embodiment, existence {∃} and inexistence {         } of the obstruction are adopted without considering null φ, and possible existence and possible inexistence {∃,         } of the obstruction.
     Then, in Step S 186 , the mergence belief assignment is calculated only using {∃} and {           }, as shown by formula (3). Wherein, A={∃}, B={         }.
     
       
         
           
             
               
                 
                   
                     
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     Finally, in Step S 188 , an object existence probability of the specific obstruction is calculated according to the mergence belief assignment, as shown by formula (4). 
     
       
         
           
             
               
                 
                   
                     
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     Bel F ({∃}) is a situation without considering not determining existence or inexistence of the obstruction, and Pl F ({∃}) is a situation with considering not determining existence or inexistence of the obstruction. Formula (4) represents that the existence probabilities are weighted to obtain the average of various situations as the object existence probability of the obstruction, namely the obstruction classification confidence. 
     In  FIG. 4 , the final step is Step S 20 . In Step S 20 , a classification ineffectiveness filtering mechanism is performed according to the obstruction classification confidence. When the obstruction classification confidence is less than a predetermined value, the detection or classification of the obstruction is not reliable. In such a case, the perception mergence system excludes the obstruction, and the process is specifically shown in  FIG. 7 . Refer to  FIG. 7 . In Step S 202 , the obstruction classification confidence of one obstruction is retrieved, wherein the obstruction classification confidence is obtained from merging all the range sensors. Then, in Step S 204 , the process determines whether the retrieved obstruction classification confidence is less than the predetermined value. If the answer is yes, the process classifies the obstruction into misjudgment and filters it out, as shown in Step S 206 . If the answer is no, the process returns to Step S 202  and retrieves the obstruction classification confidence of another obstruction for determining again until the classification ineffectiveness filtering mechanism is performed on all the obstructions detected in front of the vehicle. In Step S 202 , the vehicular computer informs a driver of the vehicle of the obstruction information of the obstruction in front of the driver in hearing, touch or vision ways. Besides, the vehicular computer informs the driver of a probability of the obstruction being a vehicle or a pedestrian. In this case, the autonomous driving system can automatically brake. 
     In Step S 204 , the predetermined value of the obstruction classification confidence is adjusted by the driver. For example, if the driver starts a semi-autonomous driving system, the predetermined value should be increased to above 70% lest most obstructions be determined as vehicles so that the vehicular computer controls the semi-autonomous driving system to endlessly brake quickly when the predetermined value is decreased. When the driver drives the vehicle by himself and the obstruction classification confidence is used as auxiliary reference, the predetermined value, for example, is decreased to 30-50%. In this way, the driver himself can determine whether to slow down or brake although the perception mergence system usually sends notices such as the front obstructions being vehicles, slowing down or braking. 
     For example, suppose that the range sensor is a radar. The existence confidence of the range sensor is 0.9999, and the precision of the classifier is 0.87, and the classification belief assignment of the range sensor is 0.9999*0.87=0.8699. The existence confidence of the image-retrieving unit is 0.94, and the precision of the classifier is 0.95, and the classification belief assignment of the image-retrieving unit is 0.94*0.95=0.895. The obstruction classification confidence is 0.895 after the mergence calculation. The present invention retrieves an obstruction classification confidence to determine whether it is larger than a predetermined value such as 0.6. Due to 0.895 larger than 0.6, the present invention determines that the obstruction is a vehicle and informs the obstruction information of the driver. 
     The traditional technology obtains the classification and existence confidences of the obstructions, and then activates the system. However, the traditional technology does not check how high the classification confidence again. The classification is ineffective, which misjudges that the noise represents a vehicle and incorrectly activates a safe mechanism, and which results in quick braking whereby rear-end collisions of vehicles from the rear occur. On the contrary, the present invention quantifies the probabilities (the existence confidences) that the obstructions detected by the range sensors are entities, and then uses the existence confidences and the precision of the classifier to figure out the classification belief assignments, and finally adds a mechanism merging and quantifying the obstruction classification confidence to correct the ineffective classification and to improve the precision of classification lest the system at a rear end do mistaken action. The range sensors such as radars or lidars are used to classify the obstructions without images. The present invention never loses protection abilities because of no images to greatly improve classification confidence and driving safety. 
     The embodiments described above are only to exemplify the present invention but not to limit the scope of the present invention. Therefore, any equivalent modification or variation according to the shapes, structures, features, or spirit disclosed by the present invention is to be also included within the scope of the present invention.