WO2024009756A1

WO2024009756A1 - Object identification device and object identification method

Info

Publication number: WO2024009756A1
Application number: PCT/JP2023/022771
Authority: WO
Inventors: 都堀田; 英彰城戸; 郭介牛場
Original assignee: 日立Astemo株式会社
Priority date: 2022-07-08
Filing date: 2023-06-20
Publication date: 2024-01-11
Also published as: JP2024008516A

Abstract

The present invention comprises: a distance calculation unit which inputs external information acquired by an external information acquisition unit mounted to a vehicle and including an object outside the vehicle and which obtains the distance from the vehicle to the object; an identification score calculation unit that uses the external information to obtain an identification score indicating a degree of reliability that the object included in the external information is of a predetermined type; and an object identification unit that identifies that the object is of the type associated with the identification score when the identification score exceeds a threshold value defined in advance. The threshold value differs depending on the distance.

Description

Object identification device and object identification method

The present invention relates to an object identification device and an object identification method that identify the type of object on an image.

To support road traffic safety, technology that identifies objects in front of the vehicle and automatically applies the brakes is mandatory. As a method for identifying the type of a target object, a method using a classifier created by learning a large number of images of a specific object is generally used. The classifier learns the shape and texture features of objects obtained from the edges of the training image, and during inference, outputs a score for the input image that indicates the probability that it is the object to be identified. By setting a threshold in advance for the output score and determining it as "identified" when the score exceeds the threshold, it is possible to avoid misidentification and make more likely identification. This is commonly done.

For example, Patent Document 1 states, ``In areas that are frequently used by users (specific areas described later), such as home garages, by learning in advance the environment around the garage (scenery, installed objects, etc.), "Reducing the frequency of false positives without lowering the detection rate.", "The person detection unit may detect a person appearing in the surrounding image using a known image analysis method. In the first embodiment, The person detection unit detects the object as a person when the index value regarding the object appearing in the surrounding image is equal to or greater than the first threshold value.'' In other words, the pedestrian detection device disclosed in Patent Document 1 detects a target object in a limited area such as a home parking lot when an index value (score) related to the object exceeds a preset threshold. do.

JP 2021-144478 Publication

On the other hand, when performing identification assuming automatic braking while driving on a general road or expressway, the identification target is not limited to a limited area, but is identified from the nearest to far distance within the braking distance of the own vehicle. It is necessary. At this time, the farther the object is, the smaller the object becomes, and therefore the resolution of the object itself in the image becomes smaller. As the resolution decreases, the identification score value tends to decrease because the accuracy of extracting an object region from an image deteriorates, the outline of the object itself becomes unclear, and so on.

When setting thresholds for object identification, incorrect identification of objects near the vehicle can lead to incorrect braking. For this reason, it is desirable to set a strict threshold for object identification, but on the other hand, there is a problem in that the identification rate of distant identification targets decreases.

The present invention has been made in view of the above situation, and aims to suppress misidentification and improve object identification performance, regardless of whether the distance from the moving body to the object is near or far.

In order to solve the above problems, an object identification device according to one aspect of the present invention receives external world information including objects outside the vehicle acquired by an external world information acquisition unit mounted on the vehicle, and calculates the distance from the vehicle to the object. a distance calculation unit that calculates a distance, a discrimination score calculation unit that uses external world information to obtain a discrimination score that indicates confidence that the object included in the external world information is a predetermined type; and an object identification unit that identifies the object as being of a type associated with the identification score when the object is identified by the identification score. The threshold value differs depending on the distance.

According to at least one aspect of the present invention, by setting an appropriate object identification threshold that matches the actual situation by distance, it is possible to make errors regardless of whether the distance from a moving body such as a vehicle to the object is near or far. It is possible to suppress identification and improve object identification performance.
Problems, configurations, and effects other than those described above will be made clear by the following description of the embodiments.

FIG. 1 is a block diagram showing a configuration example of an object identification device according to a first embodiment of the present invention. FIG. 6 is a diagram illustrating an example of distance-based thresholds for object identification in the first embodiment of the present invention. It is a flowchart which shows the example of a procedure of object identification processing by an object identification device concerning a 1st embodiment of the present invention. FIG. 2 is a diagram showing an example of an image input to an object identification device. FIG. 3 is a diagram showing an example of detecting an object region from an image input to the object identification device according to the first embodiment of the present invention. It is a figure which shows the example of a distance, a score, a threshold value, and an identification result at the time of completion of the object identification process in the 1st Embodiment of this invention. FIG. 7 is a diagram showing an example of distance-based thresholds, scores, and identification results when the object identification process according to the first embodiment of the present invention is applied to a plurality of objects. It is a figure which shows the example of a score and an identification result when a fixed threshold value is used regardless of the conventional distance. It is a block diagram showing an example of composition of an object identification device concerning a 2nd embodiment of the present invention. FIG. 7 is a diagram illustrating an example of the configuration of a database of identification targets acquired in advance in the second embodiment of the present invention. FIG. 6 is a diagram showing an example of plotting the relationship between distance and score for each element of the identification target database in the object identification device according to the second embodiment of the present invention. FIG. 7 is a diagram showing a threshold value obtained from distance, a result of identification based on the threshold value, and a result of marking whether the identification result is correct or incorrect for each element of the identification target database in the second embodiment of the present invention. It is a block diagram showing an example of composition of an object identification device concerning a 3rd embodiment of the present invention. FIG. 7 is a diagram showing an example of distance-based thresholds, scores, and identification results for each braking distance when the object identification process according to the third embodiment of the present invention is applied to a plurality of objects. FIG. 7 is a diagram showing an example of the relationship between the distance to the object and the threshold value in a modification of the third embodiment of the present invention. It is a block diagram showing an example of composition of an object identification device concerning a 4th embodiment of the present invention. 1 is a block diagram showing an example of the hardware configuration of an object identification device according to first to fourth embodiments of the present invention. FIG.

Hereinafter, examples of modes for carrying out the present invention (hereinafter referred to as "embodiments") will be described with reference to the accompanying drawings. In this specification and the accompanying drawings, the same components or components having substantially the same functions are denoted by the same reference numerals, and redundant explanation will be omitted.

<First embodiment>
First, the configuration of an object identification device according to a first embodiment of the present invention will be described with reference to FIG.

[Configuration of object identification device]
FIG. 1 is a block diagram showing a configuration example of an object identification device according to a first embodiment. As shown in FIG. 1, the object identification device 2 includes an object area detection section 21, an object distance calculation section 22, an identification score calculation section 23, a distance-based identification method storage section 24, and a distance-based identification method selection section 25. and an object identification section 26.

The object area detection unit 21 detects an area in which an object appears ( object area). This is a process of detecting an object area made up of a group of pixels from an image, and various means can be used. For example, when a stereo camera is used as a camera for acquiring images, the distance between pixels with similar features on the left and right images can be determined based on parallax. An object area can be determined by grouping adjacent pixels (areas) that are close to each other on an image.

The object distance calculation unit 22 (an example of a distance calculation unit) calculates the distance from the stereo camera 1 to the object on the image detected by the object area detection unit 21 (hereinafter referred to as "object distance"). When a stereo camera is used, the object distance can be determined from the parallax value of the same object area in two images detected by the object area detection unit 21. Note that in this embodiment, the calculated value of the distance from the stereo camera 1 to the object is the distance from a moving object such as a vehicle to the object, but the mounting position of the stereo camera 1 on the moving object is reflected in the calculated value. The distance from the moving body to the object may also be determined by

Generally, based on stereo images taken by a stereo camera, the distance to an object within the camera's field of view is measured using the principle of triangulation. The principle of triangulation is to calculate the distance from the camera to the object using the positional shift (parallax) between the images of the same object photographed by the left and right cameras. The parallax is derived by specifying where the image of the object on one image exists on the other image.

Various methods have been proposed for deriving parallax. For example, as a classical method, block matching is known in which an image area consisting of a plurality of pixels in one image is searched for an image area having the lowest degree of dissimilarity in another image.

The identification score calculation unit 23 (an example of the identification score calculation unit) includes a classifier 231, and the classifier 231 performs a classification process on the object area detected by the object area detection unit 21, and identifies the object area. A score (sometimes referred to as a "discrimination score") that indicates object-likeness is calculated. This score can be said to be an index of the certainty that the object region is the identification target. For example, the classifier 231 is a machine learning model that has learned the shape and texture features of an object obtained from the edges of the training image, and during inference, it uses the object region of the input image to determine whether it is the object to be identified. Outputs a score that represents certainty.

The distance-based identification method storage unit 24 is a storage unit that stores score thresholds for determining "identification" for each distance to the object. The distance-specific thresholds stored in the distance-specific identification method storage unit 24 will be explained using FIG. 2.

[Threshold by distance]
FIG. 2 is a diagram illustrating an example of threshold values for each distance for object identification in this embodiment. In FIG. 2, the horizontal axis indicates the distance (m) to the object, and the vertical axis indicates the threshold value. As is clear from FIG. 2, the shorter the distance from the stereo camera 1 to the object, the higher the threshold value, and the farther the distance to the object, the lower the threshold value. By setting such a threshold for object identification, it is possible to suppress incorrect identification of objects other than pedestrians near the vehicle as pedestrians, and even if the object is far away and has a low score. This has the effect of identifying you as a pedestrian. The effect of adopting this threshold value for each distance will be explained in the following embodiments. Although FIG. 2 shows an example of distance-based thresholds for pedestrian identification, the identification target is not limited to this example. For example, it is possible to identify various objects such as obstacles such as structures and small animals, and it is sufficient to set a threshold value for each distance for each target to be identified.

The distance-based identification method selection unit 25 acquires the threshold value corresponding to the distance to the object calculated by the object distance calculation unit 22 from the distance-based identification method storage unit 24 .

The object identification unit 26 (an example of an object identification unit) compares the score calculated by the identification score calculation unit 23 with the distance-specific threshold value acquired by the distance-based identification method selection unit 25, and determines that the score is the threshold value. If it exceeds , the object of interest is identified as an identification target. The identification result Ob of the object identification unit 26 is output to the vehicle control device 3 that controls the operation of a moving object such as a vehicle on which the object identification device 2 is mounted. For example, the vehicle control device 3 is an electronic control unit (ECU) that controls automatic braking of a moving object, an electronic control unit (ECU) that controls steering of a moving object, or the like.

[Object identification processing procedure]
Next, the flow of object identification processing by the object identification device 2 will be explained using FIG. 3.
FIG. 3 is a flowchart showing an example of a procedure for object identification processing by the object identification device 2. As shown in FIG. In this embodiment, an example will be shown in which a pedestrian is identified as an identification target. Assume that a stereo camera is used as a means for acquiring images of the surroundings of the own vehicle, and a distance corresponding to the position of an object on the acquired stereo image Im can be acquired.

The object identification device 2 starts the object recognition process when the recognition process execution timing (current time) comes. First, in step S21, the object area detection section 21 inputs an image acquired by an image input section (not shown) of the object identification device 2.

FIG. 4 shows an example of the image 31 input to the object identification device 2. This image 31 can be considered to be one of the left and right images output by the stereo camera 1 and input to the object identification device 2. Image 31 shows pedestrians and trees on the right side of the road, a bus stop on the left side of the road, and a crosswalk and two pedestrians in front of the vehicle and in the distance of the road.

Next, in step S22, the object area detection unit 21 detects an object area from the image 31 acquired in step S21.

FIG. 5 is a diagram showing an example of detecting an object area from the image 31 input to the object identification device 2. Here, the object area detection unit 21 detects an object such as a pedestrian indicated by reference numeral 41 as object number. 0, an object such as a bus stop indicated by reference numeral 42 is designated as object number. 1. The tree-like object indicated by reference numeral 43 is designated as object number. 2. A pedestrian-like object indicated by reference numeral 44 is designated as object no. 3 and a pedestrian-like object indicated by the reference numeral 45 as object number. Detected as 4.

Next, in step S23, the object area detection unit 21 initializes a reference counter i to 0 (i=0) for sequentially identifying a plurality of detected objects (object areas). The object (object area) of reference counter i is expressed as "object i."

Next, in step S24, the object distance calculation unit 22 acquires distance information of the object i. The object distance calculation unit 22 obtains the distance of the object i to be processed from the parallax of the stereo image Im obtained by the stereo camera 1. Object No. 41 in FIG. It is assumed that object 0 is located at a distance of "30 m" from stereo camera 1 (own vehicle).

Next, in step S25, the identification score calculation unit 23 calculates the identification score of the object i to be processed. In this example, the degree of certainty of being a pedestrian is used as an index from 0 to 1, and the closer it is to 1, the more likely it is to be a pedestrian. Here, for example, object No. 41 is shown. It is assumed that the score of 0 is "0.98".

Next, in step S26, the distance-based identification method selection unit 25 selects the distance that corresponds to the distance of the object i acquired in step S24, from the distance-based thresholds (FIG. 2) stored in the distance-based identification method storage unit 24. Get the threshold. Here, object no. The threshold value corresponding to the distance “20 m” of 0 is “0.89”.

Next, in step S27, the object identification unit 26 determines whether the score of object i exceeds a threshold value. Object no. In the case of 0, the score calculated in step S25 is "0.98", which exceeds the threshold value "0.89". If the score of object i exceeds the threshold (YES in step S27), proceed to step S28; if the score of object i does not exceed the threshold (NO in step S27), proceed to step S29. .

In step S28, the object identification unit 26 determines the type of object i as a pedestrian.
On the other hand, in step S29, the object identification unit 26 determines the type of object i to be other than a pedestrian. For example, object No. 43. If the score of 2 is "0.80" and the distance is "40m", the threshold value corresponding to the distance "40m" is "0.83" (Figure 2), so the type of object i is other than a pedestrian. It is determined that

After the process in step S28 or S29, in step S30, the object area detection unit 21 increments the object reference counter i by 1 (i=i+1) and moves on to the identification process regarding the next object i.

Next, in step S31, the object area detection unit 21 determines whether the reference counter i has become equal to the number of object areas detected in step S22. If the reference counter i is not equal to the number of object regions detected in step S22 (NO in step S31), the object region detection unit 21 determines that the identification process has not been completed for all objects and returns to the process in step S24. .

On the other hand, when the reference counter i becomes equal to the number of object regions detected in step S22 (YES in step S31), the object region detection unit 21 completes the identification process for all objects at the current time. Then, the process moves on to the next time.

According to the flow described above, the score and distance of the object detected in the image acquired from the stereo camera 1 are calculated, and the object identification process is performed.

Here, an example of the distance, score, threshold value, and identification result at the time when the object identification process of FIG. Shown below.

FIG. 6 is a diagram showing an example of the distance, score, threshold value, and identification result at the time when the object identification process according to the present embodiment is completed. The table shown in FIG. 6 includes a field 51 for "object number", a field 52 for "object code", a field 53 for "object type", a field 54 for "distance", a field 55 for "score", and a field 55 for "threshold code". ” field 56, “identification result” field 57, and “identification result correct/incorrect” field 58.

“Object No.” is, for example, information that uniquely identifies a record in this table.
The "object code" is the object number. This is information such as a code attached to each object on the image in order to identify the object (object area) on the image. Although this object code is described to explain the object on the image of FIG. 5, it is not necessarily necessary.
“Object type” is information representing the actual type of the object on the image.
"Distance" is information representing the distance to the object acquired in step S24.
“Score” is information representing the score at the time of object identification calculated in step S25.
"Threshold value" is information representing a threshold value according to the distance to the object acquired in step S26.
"Identification result" is information representing the identification result determined in step S27.
“Correctness of identification result” is information indicating whether the identification result is correct or incorrect by comparing the actual “object type” in field 53 and “identification result” in field 57, for explanation.

Here, object No. shown in FIG. 0 (code 41) to object No. Each of the five objects No. 4 (reference numeral 45) will be explained.

Object No. shown in the first row. The actual object type of the object numbered 0 and 41 is "pedestrian", and since it is located at a distance of "20 m", the outline of the object area is clear, and the identification result score is as high as "0.98". can get. When the threshold value corresponding to “20 m” is obtained from the threshold values for each distance in FIG. ” becomes. Since the object for which the correct answer is a pedestrian is identified as a "pedestrian," the identification result is "correct" (○ in FIG. 6). This is an example in which the object is close, the object area can be accurately acquired, and the outline is clear, so a high score is obtained as a result of identification, and the identification judgment based on the threshold value is also made correctly.

Object No. shown on the second line. 1. The actual object type of the object numbered 42 is "other than pedestrian" (bus stop), and the outline of the object area is clear because it is at a distance of "30 m", but the shape of the bus stop is different from that of pedestrians. Since they are quite similar, the score is a high value of "0.88". When the threshold value for a distance of “30 m” is obtained from the threshold values for each distance in FIG. person”. Since an object whose correct answer is other than a pedestrian is identified as a "pedestrian," the identification result is "incorrect" (× in FIG. 6). This is because the object is close, the object area can be accurately acquired, and the outline is clear, but the shape is very similar to that of a pedestrian, so a high score is obtained as a result of identification, and the threshold is used. This is an example of a pedestrian being mistakenly identified as a result.

Object No. shown in the third row. 2. The actual object type of the object 43 is "non-pedestrian" (tree) and is located at a distance of "40 m", so the outline of the object area is somewhat clear. Since the shape is somewhat similar to that of a pedestrian, the score is "0.80", which is a rather high value. When the threshold value for a distance of "40 m" is obtained from the threshold values for each distance in FIG. 2, it is "0.83", and since the score does not exceed the threshold value in the process of step S27, the identification result is " ``Non-pedestrians''. Since objects for which the correct answer is other than pedestrians are identified as "other than pedestrians," the identification result is "correct." This is because the object is close, the object area can be accurately captured, and the outline is clear, so the shape of the object is similar to that of a pedestrian, but the short distance threshold is set high. As a result, this is an example in which erroneous identification as a pedestrian was avoided because the reliability as a pedestrian was less than the threshold.

Object No. shown on the 4th line. 3. The object 44 is an example in which the actual object type is a "pedestrian" and is located at a distance of "70 m", so the outline of the object area is less clear than in the case of a nearby object. Further, in this example, in the detected object region indicated by reference numeral 44 in FIG. 5, not all regions corresponding to the object have been extracted, and the head portion of the object is outside the object region. When detecting a distant object, it may be difficult to accurately detect the entire object area, as shown in this example, due to the low resolution of the object area. The discriminator 231 included in the discrimination score calculation unit 23 learns by considering the outline of the pedestrian's head as a feature, so if the object area does not include a part of the pedestrian's body, it is determined that the pedestrian is As a result, the identification score decreases. For these reasons, the score is a low value of "0.78". When the threshold value for a distance of “70 m” is obtained from the threshold values for each distance in FIG. person”. Since the object for which the correct answer is a pedestrian is identified as a "pedestrian," the identification result is "correct." This is an example where the object is far away and the object area cannot be accurately acquired, resulting in a lower score, but the threshold for each distance is set low for distant objects, so it can be identified by pedestrians. be.

Object No. shown in the 5th line. 4. The object 45 is an example in which the actual object type is a "pedestrian" and is located at a distance of "80 m", so the outline of the object area is unclear compared to the case in the vicinity. Further, in this example, since the resolution of the object region is low and the outline of the object region is unclear, the score is calculated as a low value of "0.75". When the threshold value for a distance of “80 m” is obtained from the threshold values for each distance in FIG. person”. Since the object for which the correct answer is a pedestrian is identified as a "pedestrian," the identification result is "correct." This is an example where the object is far away and the outline of the object area is unclear, resulting in a lower score, but because the distance-based threshold is set low for far away objects, it can be correctly identified as a pedestrian. It is.

[Example of scores and identification results according to this embodiment]
Examples of scores and identification results obtained by applying object recognition processing by the object identification device 2 according to the present embodiment to the five objects described above will be described using FIG. 7.

FIG. 7 is a diagram showing an example of distance-based thresholds, scores, and identification results when the object identification process by the object identification device 2 is applied to a plurality of objects. In FIG. 7, the horizontal axis indicates the distance (m) to the object, and the vertical axis indicates the threshold value. In the figure,

numerals

41, 42, 43, 44, and 45 indicate the distance and score from the object area indicated by the same numeral on the image 31 of FIG. 5. In FIG. 7, a distance-specific threshold 71 is a function (an example of a distance-specific threshold function) that indicates a threshold for the distance to an object, and is the same as the distance-specific threshold shown in FIG. It's the content. The marker indicated by “▲” indicates the score of the object region whose correct value is “pedestrian”. The marker indicated by "●" indicates the score of the object region whose correct value is "other than pedestrian."

For all

codes

41, 44, and 45 whose correct value is "pedestrian," the score exceeds the distance-specific threshold 71, and for

codes

42 and 43 whose correct value is "other than pedestrian," the score is A state in which the distance does not exceed the threshold value 71 is a 100% correct state. However, in the example of FIG. 7, one marker whose correct value is "other than pedestrian", indicated by reference numeral 42, is incorrectly identified as a pedestrian. However, by employing variable threshold values for each distance, such as increasing the threshold value for short distances and lowering the threshold value for long distances, it is possible to correctly identify 80% of object regions.

[Example of scores and identification results when applying conventional technology]
As a comparison with this embodiment, an example of scores and identification results when a constant threshold value is used regardless of distance will be described using FIG. 8.

FIG. 8 is a diagram showing an example of scores and identification results when a conventional constant (fixed) threshold value is used regardless of distance. In FIG. 8, the horizontal axis indicates the distance (m) to the object, and the vertical axis indicates the threshold value. In the figure,

numerals

41, 42, 43, 44, and 45 indicate the distance and score from the object area indicated by the same numeral on the image 31 of FIG. 5. In FIG. 8, similarly to FIG. 7, the marker indicated by "▲" indicates the score of the object region whose correct value is "pedestrian". The marker indicated by "●" indicates the score of the object region whose correct value is "other than pedestrian." Here, the threshold value 81 is set to a constant value of "0.9", the threshold value 82 is set to a constant value of "0.79", and the threshold value 83 is set to a constant value of "0.74". Three cases in which " is set to a constant value will be explained as examples.

If the threshold value is set to a high value of "0.9" such as threshold value 81, the score of code 41, where the correct answer is pedestrian, is correct because it exceeds the threshold value and is identified as "pedestrian". .

Codes

42 and 43, which are non-pedestrians, are identified as "non-pedestrians" because their scores do not exceed the threshold, and these two cases are also correct. On the other hand, the

numbers

44 and 45, which are pedestrians located far away, are identified as "other than pedestrians" because their scores do not exceed the threshold, and are therefore incorrect. In this way, setting a certain threshold value to a high value works well for distinguishing between "pedestrians" and "non-pedestrians" near the vehicle. On the other hand, for pedestrians who are far away and whose scores tend to be low even if they are pedestrians, if the threshold value is high, they will be determined to be other than pedestrians and cannot be identified, so it will not work well. As a result, in the conventional technology, the accuracy rate for the five objects is 60%, which is lower than in the present embodiment.

Furthermore, if the threshold value is set to a medium value of "0.78" like the threshold value 82, the score of code 41 whose correct answer is "pedestrian" will exceed the threshold value and be identified as "pedestrian". That's correct. The

scores

42 and 43, which are not pedestrians, are also determined to be "pedestrians" because their scores are equal to or higher than the threshold value, and these two cases are misidentified. Further, the

codes

44 and 45, which are long-distance pedestrians, are identified as "other than pedestrians" because their scores do not exceed the threshold value, resulting in erroneous identification. In this way, if the certain threshold value is set to a halfway value, the result will be that the identification results do not work well regardless of whether the object is located at a short distance or a long distance. As a result, the accuracy rate for the five objects is 20%.

Furthermore, if the threshold is set to a low value of "0.74" like threshold 83, the scores for all

codes

41, 44, and 45 for which the correct answer is "pedestrian" exceed the threshold and "pedestrian" ”. On the other hand,

codes

42 and 43 whose correct answer is other than pedestrian are also incorrectly identified as "pedestrian" because their scores exceed the threshold. In this way, setting a certain threshold to a low value has the effect of correctly identifying object regions that correspond to pedestrians with low scores in the distance, but objects other than pedestrians with high scores in the vicinity There is a problem in which areas are identified as pedestrians. As a result, the accuracy rate for the five objects is 60%.

In order to minimize misidentification and correctly identify pedestrian object regions with low scores at long distances, the values at short distances should be set high and the values at long distances should be set high, as shown in Figure 7. It is effective to make identification decisions using a threshold value set to a low value. The above is the content including the configuration and operation of the object identification device 2 according to the first embodiment of the present invention.

As described above, the object identification device (object identification device 2) according to the first embodiment is capable of identifying objects outside the vehicle (objects on images) acquired by the external world information acquisition unit (for example, the stereo camera 1) mounted on the vehicle. A distance calculation unit (object distance calculation unit 22) receives external world information (for example, a stereo image Im) including a region) and calculates the distance from the vehicle to the object, and uses the external world information to calculate the distance from the vehicle to the object. A discrimination score calculation unit (discrimination score calculation unit 23) that calculates a discrimination score indicating the degree of confidence that the object is of a predetermined type; and an object identification unit (object identification unit 26) that identifies the object to be of the attached type. Here, the threshold value differs depending on the distance from the vehicle to the object (FIG. 2).

According to this embodiment with the above configuration, an appropriate threshold value for object identification is set that matches the actual situation for each distance. Thereby, regardless of whether the distance from a moving body such as a vehicle to the object is near or far, misidentification can be suppressed and the performance (accuracy) of object identification can be improved.

<Second embodiment>
Next, as a second embodiment of the present invention, an example will be described in which the object identification device 2 (FIG. 1) according to the first embodiment is provided with a function of creating a threshold function for each distance. do.

[Configuration of object identification device]
FIG. 9 is a block diagram showing a configuration example of an object identification device according to the second embodiment. The object identification device 2A shown in FIG. 9 has a configuration in which a distance-based identification method creation unit 91 and an identification target database (DB) 92 are added to the object identification device 2 according to the first embodiment shown in FIG. be.

The distance-based identification method creation unit 91, which is an added component, will be further explained. The distance-based identification method creation unit 91 is a means for calculating a distance-based threshold function stored in the distance-based identification method storage unit 24 . The calculated distance-based threshold function is stored in the distance-based identification method storage unit 24 as a distance-based identification method.

The distance-based identification method selection unit 25 selects a threshold value obtained by inputting the distance to the object on the image calculated by the object distance calculation unit 22 into the distance-based threshold function of the distance-based identification method storage unit 24. get. Then, the object identification unit 26 compares the score calculated by the identification score calculation unit 23 with the threshold value obtained by the distance-based identification method selection unit 25 to identify the object on the image.

As described in the first embodiment, the distance-based threshold function is such that when the object is close to the vehicle, the outline of the object area on the input image is clearly detected, so the correct answer is that it is a pedestrian. The score for a certain object also becomes high, and objects other than pedestrians that are similar in shape to pedestrians also have high scores. Therefore, the threshold value is set high to suppress misidentification. On the other hand, at long distances, the score tends to be low due to reasons such as the outline of the object region becoming unclear or the detection region not properly extracting the object region. Therefore, the threshold value is set low.

The purpose of setting thresholds for each distance is to ensure a correct pedestrian identification rate for both short and long distances and to suppress false identification. Therefore, the distance-specific threshold function is a downward-sloping function in which the threshold value decreases as the distance increases, and any function may be used as long as it can appropriately achieve the purpose. Here, a description will be given assuming that the relationship between the distance and the threshold value is set using a linear function. An example will be explained in which pedestrians and others are identified as objects to be identified. In order to set appropriate threshold values for each distance, data in the identification target DB 92 acquired in advance is used.

FIG. 10 is a diagram showing a configuration example of the identification target DB 92 acquired in advance. FIG. 10 shows an example of fields (items) of the identification target database 92. The identification target DB 92 has an "ID" field 101, an "object type" field 102, a "distance" field 103, and a "score" field 104.

“ID” is information indicating an identifier for identifying an image (object) of an object region to be identified. If the correct answer is a pedestrian, it is a serial number with the symbol p added to the beginning of the identifier. If the correct answer is something other than a pedestrian, it is a serial number with a code n added to the beginning of the identifier. In total, it is assumed that there are Np identification objects whose correct answer is pedestrians and Nn identification objects other than pedestrians.
“Object type” is information representing the type of object to be identified.
“Distance” is information representing the distance to the object to be identified.
“Score” is information representing the score of the object to be identified.

These data are obtained by actually performing object identification processing according to the flowchart shown in Figure 3, and by identifying pedestrians and non-pedestrian objects at various distances, as shown in Figure 4 as an example. , is the obtained data. Note that these data may be fixed values, or may be values updated by performing object identification processing at any time. Further, the identification target DB 92 may be located outside the object identification device 2A and within the own vehicle, or the data corresponding to the identification target DB 92 may be acquired from outside the vehicle via a wide area network (not shown).

FIG. 11 is a diagram showing an example of plotting the relationship between distance and score for each element of the identification target DB 92 as shown in FIG. 10 in the object identification device 2A. For simplicity, the relationship between distance and score is shown as a score distribution every 10 m. In the figure, score

distributions

111, 113, 115, 117, 11a, 11c, 11e, and 11g shown by solid lines are score distributions of objects whose object type is "pedestrian."

Score distributions

112, 114, 116, 118, 11b, 11d, 11f, and 11h indicated by broken lines are score distributions of objects whose object type is "other than pedestrian."

When comparing the score distributions of two object types in the same distance range, it is expected that the score value will be higher if the object type is pedestrian, and the score value will be lower if the object type is non-pedestrian. In addition, as described in the first embodiment, as the object becomes far away, the outline of the object area on the image becomes unclear, the detection area does not extract the target area (object area) appropriately, etc. For this reason, scores tend to decrease relatively.

When a variable threshold value corresponding to the distance is set as the threshold value 110 for the distance and score identification target having the score distributions 111 to 11h, the field 103 is set for each element of the identification target DB 92 shown in FIG. The threshold value corresponding to the "distance" is determined. The variable threshold value assuming linearity can be defined by the following equation (1). Note that the coefficient a is a negative value in this embodiment.

Threshold = distance x a + b (1)

FIG. 12 is a diagram showing the threshold value obtained from the distance, the result of identification using the threshold value, and the correctness of the identification result for each element of the identification target DB 92 shown in FIG. 10. The table shown in FIG. 12 includes, in addition to fields 101 to 104, a field 121 for "threshold", a field 122 for "identification result", and a field 123 for "correctness of identification result".

“Threshold” is information representing a threshold corresponding to the distance to the object.
“Identification result” is information representing the result of identification from “score” and “threshold value”.
“Correctness of identification result” is information indicating whether the “identification result” is correct or incorrect by comparing the actual “object type” in field 102 and the “identification result” in field 122.

Once this has been determined, the correct classification rate and false classification rate when applying the threshold 110 shown in FIG. 11 to this data set can be determined using the following equations (2) and (3). However, the correct identification rate is the rate at which an object type whose correct answer is pedestrian is identified as a pedestrian. Further, the misidentification rate is the rate at which an object type other than a pedestrian is identified as a pedestrian.

Correct identification rate = (Number of objects with correct identification results among objects of pedestrian type) ÷ Np ... (2)
Misidentification rate = (Number of incorrect identification results among types other than pedestrians) ÷ Nn ... (3)

In formula (1), by determining the coefficient a and the coefficient b that minimize the false identification rate when the correct identification rate is a certain value or more, the identification target DB 92 of FIG. 10 can be used to ensure the correct identification rate. It is possible to determine a threshold value that minimizes false identification. By storing as much information about possible identification targets as possible in the identification target DB 92 shown in FIG. 10, it is possible to calculate distance-based thresholds that match the actual usage of the vehicle. The above is the content including the configuration and operation of the object identification device 2A according to the second embodiment of the present invention.

In this way, in the object identification device (object identification device 2A) according to the present embodiment, as a threshold value for each distance, information on objects for which correct values are known is stored in the image database (identification target DB 92) for each distance. , a threshold setting function (for example, threshold 110) with which the correct classification rate is a certain value or more and the misidentification is minimum is used, and the object identification unit (object identification unit 26) The above threshold value is calculated by inputting the distance to the object into the function.

In addition, in this embodiment, an example has been described in which the relationship between the distance to the target and the threshold value is expressed using a linear function, but the relationship is not necessarily limited to this. A relationship between distance and threshold can also be used. For example, instead of a linear function, the relationship between the distance to the object and the threshold may be expressed by a nonlinear function that satisfies the threshold conditions in the above embodiments. Further, the relationship between the distance to the target and the threshold value may be defined in the form of a table or map data.

<Third embodiment>
Next, as a third embodiment of the present invention, in addition to the distance from a moving object such as a vehicle to the object, a braking distance is added to the object identification device 2 (FIG. 1) according to the first embodiment. An example will be described in which a function is provided to determine a threshold value.

[Configuration of object identification device]
FIG. 13 is a block diagram showing a configuration example of an object identification device according to the third embodiment. The object identification device 2B shown in FIG. 13 adds a speed detection section 131 and a road surface condition detection section 132 to the object identification device 2 according to the first embodiment shown in FIG. 24 is replaced with a braking distance/distance-based identification method storage unit 133, and the distance-based identification method selection unit 25 is replaced with a braking distance/distance-based identification method selection unit 134. That is, the difference between this embodiment and the first embodiment is that each braking distance has a function of the distance to the object and the threshold value.

The main form of use of the present invention is to install it on a moving object such as a vehicle, identify an object in front that is at risk of collision, and automatically apply the brakes. That is, the present invention is suitable for application to vehicle control for advanced driver assistance systems (ADAS) and autonomous driving (AD). Braking distance varies depending on the speed of the vehicle and road surface conditions. What is important in operating automatic braking is to reliably identify objects within a braking distance range that can be calculated based on the vehicle's current speed and road surface conditions. Therefore, the braking distance is calculated based on the speed of the own vehicle and the road surface condition acquired by the speed detection section 131 and the road surface condition detection section 132. Then, a distance-specific threshold value that minimizes the false identification rate while ensuring a correct identification rate within the braking distance range is calculated and stored in the braking distance/distance-based identification method storage unit 133.

The speed detection unit 131 can be configured using, for example, a vehicle speed sensor mounted on the vehicle. The speed detection unit 131 outputs information on the detected speed of the own vehicle to the braking distance/distance-based identification method selection unit 134.

The road surface condition detection unit 132 determines the road surface condition from an image in front of the vehicle inputted from, for example, the stereo camera 1. For example, information on road surface conditions includes road surface irregularities, bumps, wet conditions, frozen conditions, and the like. The road surface condition detection section 132 outputs information on the detected road surface condition to the braking distance/distance-specific identification method selection section 134.

The braking distance/distance-based identification method storage unit 133 stores distance-based threshold values for each braking distance as shown in FIG. The contents of FIG. 14 will be described later.

The braking distance/distance-specific identification method selection unit 134 calculates the braking distance of the own vehicle based on the speed of the own vehicle detected by the speed detection unit 131 and the road surface condition detected by the road surface condition detection unit 132.
The braking distance is influenced by the weight of the own vehicle and the performance and specifications of the automatic brake, but such information can be prepared in advance and stored in the object identification device. For example, the braking distance can be calculated using a machine learning model (not shown) that is trained to output the braking distance by inputting the speed of the own vehicle and the road surface condition. Other information such as the weight of the vehicle and the performance and specifications of automatic braking are reflected in the parameters of the machine learning model in advance. The braking distance/distance-based identification method selection unit 134 uses this machine learning model to infer the braking distance from the speed of the own vehicle and the road surface condition. Then, the braking distance/distance-based identification method selection unit 134 acquires a threshold value corresponding to the inferred value of the braking distance from the braking distance/distance-based identification method storage unit 133 and outputs it to the object identification unit 26 .

Here, a method for identifying each braking distance and each distance to the object will be described with reference to FIG. 14.

FIG. 14 is a diagram showing an example of distance-based thresholds, scores, and identification results for each braking distance when the object identification process according to the present embodiment is applied to a plurality of objects. In FIG. 14, the horizontal axis indicates the distance (m) to the object, and the vertical axis indicates the threshold value. In the figure, a threshold value 141 indicated by a solid straight line (linear function) is a threshold value for each distance to the object when the braking distance is "50 m". Further, the threshold value 142 indicated by a broken straight line (linear function) is a threshold value for each distance to the object when the braking distance is "90 m".

Reference numerals

41, 42, 43, 44, and 45 indicate distances and scores from the object regions indicated by the same reference numerals on the image 31 in FIG.

If the vehicle's speed is faster than a certain speed and the braking distance is longer than the threshold 142 of "90 m", the long distance threshold is lower in order to identify pedestrians who are far away. Set a threshold with a large negative slope so that

On the other hand, when the speed of the own vehicle is low and the braking distance is "50 m" as the threshold value 141, it is easier to correctly identify a pedestrian closer than 50 m than to correctly identify a pedestrian further away than 50 m. Since priority can be given to suppressing erroneous identification of objects other than pedestrians, the negative slope of the threshold value can be reduced.

As a result, if the threshold value 141 for the case of braking distance "50 m" is adopted, the object that is a pedestrian indicated by the reference numeral 41 located in front of 50 m is correctly identified. In addition, since the score of the bus stop for non-pedestrians indicated by the code 42 located at a distance of 30 m is below the threshold, it can be correctly identified as non-pedestrians, and the bus stop indicated by the code 43 located at a distance of 40 m Trees other than pedestrians are also correctly identified as non-pedestrians because their scores do not exceed the threshold.

As described above, in this embodiment, the distance-based thresholds (

thresholds

141 and 142 in FIG. 14) are set according to the distance to the object and the braking distance. According to this embodiment, in addition to the effects similar to those of the first embodiment, the following effects can be obtained. That is, according to the present embodiment, by changing the distance-specific threshold value for each braking distance and narrowing down the application range of automatic braking, it is possible to perform object identification with higher accuracy within a necessary range.

Note that as data representing the relationship between the distance to the object and the threshold value for each braking distance, a table or map data may be prepared in which the relationship between the distance to the object and the threshold value is registered for each braking distance.

Further, in this embodiment, the threshold value for object identification is set based on the distance to the object and the braking distance, but the invention is not limited to this example. For example, since the braking distance is greatly affected by the speed of the moving object, the threshold value may be set depending on the distance and speed.

[Modification of third embodiment]
Further, in the above embodiment, the distance-based threshold function (threshold setting function) is a linear function, but the distance-based threshold function may be a fixed threshold value or a linear variable threshold value. It may also be defined by a combination. Hereinafter, an example in which a distance-specific threshold function is defined by a combination of a fixed threshold value and a linear variable threshold value will be described with reference to FIG. 15.

FIG. 15 is a diagram showing an example of the relationship between the distance to the object and the threshold value in a modification of the third embodiment. The threshold value 151 shown in the upper part of FIG. 15 is an example of a method in which a fixed threshold value is used when the distance to the object is short, and a linear variable threshold value is used when the distance to the object is long. This may be used when it is desired to strengthen the suppression of misidentification when the distance to the object is short. It is also effective for object identification when the braking distance is short.

The threshold value 152 shown in the middle part of FIG. 15 is an example of a method in which a linear variable threshold value is used when the distance to the object is short, and a fixed threshold value is used when the distance to the object is long. This may be used when it is desired to strengthen the suppression of misidentification even if the distance to the object is long. It is also effective for object identification when the braking distance is long. Note that the threshold functions for different distances may have different distances, for example, the fixed value of the threshold value 152 at a long distance in the middle row of FIG. 15 and the fixed value of the threshold value 153 at a long distance in the lower row of FIG. 15 are the same. You can also use it as

The threshold value 153 shown in the lower part of FIG. 15 is an example of a method using a high fixed threshold value for short distances, a linear variable threshold value for intermediate distances, and a low fixed threshold value for long distances. This is a combination of the threshold value 141 and the threshold value 142, and is effective when the braking distance is medium. Note that the fixed value of the threshold value 151 at a short distance in the upper row of FIG. 15 is the same as the fixed value of the threshold value 153 at a short distance in the lower row of FIG. You can also use it as

Note that when the distance to the object is shorter than the first distance, the distance is short distance, and when the distance to the object is farther than the second distance (first distance < second distance), the distance is long distance. It is assumed that when the distance to the object is between the first distance and the second distance, the distance is medium.

As described above, in the object identification devices (object identification devices 2 to 2B) according to the first to third embodiments described above, the distance-based threshold value is set when the distance to the object is longer than the second distance (long distance ) is set to be a larger value when the distance to the object is shorter than the first distance, which is shorter than the second distance (near distance). For example, the distance-based threshold used for object identification by the distance-based threshold function may be a linear variable value (in the first to third embodiments), or a fixed value and a linear variable value. (a modification of the third embodiment). Note that the distance-based thresholds may be set to values that vary stepwise from long distances to short distances. For example, if the distance from a moving object to an object is divided into three stages: long distance, medium distance, and short distance, the threshold value for each distance can be set to a small value for long distance, a medium value for medium distance, and a medium value for short distance. Set to a large value. The distance may be divided into four or more stages instead of three. In this case, compared to the above-described embodiment, it is possible to reduce the load required to determine the distance-based thresholds (stepwise different thresholds) to be stored in the distance-based identification method storage unit 24.

<Fourth embodiment>
Next, as a fourth embodiment of the present invention, the object identification device 2A (FIG. 9) according to the second embodiment is provided with a function of updating threshold values for each distance based on the object identification results. An example will be explained below.

FIG. 16 is a block diagram showing a configuration example of an object identification device according to the fourth embodiment. The object identification device 2C shown in FIG. 16 has a configuration in which an identification condition/result output unit 161 and a distance-based identification result storage unit 162 are added to the object identification device 2A according to the second embodiment shown in FIG. be. Note that in the object identification device 2C, the identification target DB 92 of the object identification device 2A in FIG. 9 has been deleted.

The identification condition/result output unit 161 acquires the identification results and identification conditions from the object identification unit 26 and outputs them to the distance-based identification result storage unit 162. Here, the identification condition/result output unit 161 outputs the image of the identification target (object region) identified by the object identification unit 26, the distance to the identification target, the score, and the threshold value used for identification. do.

The distance-based identification result storage unit 162 stores information such as the image of the identification target outputted by the identification condition/result output unit 161, the distance to the identification target, the score, and the threshold value used for identification.

The various information regarding the identification target stored in the distance-based identification result storage unit 162 can be used to check whether the object identification process is working well. Furthermore, by assigning an object type to each piece of information about the stored identification target, the distance-based identification method creating unit 91 can generate various types of information about the identification target stored in the distance-based identification result storage unit 162 (images of the identification target). , the distance to the identification target, and the score), an appropriate distance-specific threshold value can be calculated. The distance-based identification method creation unit 91 outputs the calculated distance-based threshold (for example, a distance-based threshold function) to the distance-based identification method storage unit 24 , and the distance-based identification method creation unit 91 outputs the calculated distance-based identification method storage unit 24 . Update another threshold.

Here, the purpose of assigning the object type is to assign the correct type to the identification target identified by the object identifying unit 26. Therefore, examples of methods for assigning object types include the following methods.

(1) A person visually observes the image output as an identification target (corresponding to an image of an object region), determines the type, and assigns the image.
(2) A classifier (object type assigning unit) identifies and assigns a type to an image output as a recognition target (corresponding to an image of an object region). At this time, it is desirable to use a discriminator whose discrimination performance is equal to or higher than that of humans. For example, ResNet described in Non-Patent Document 1 below can handle high-dimensional features by using a network as deep as 152 layers among convolutional neural networks.

(Non-patent document 1)
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. “Deep residual learning for image recognition” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.

The assignment of the object type can be carried out in an offline environment different from the vehicle by storing the information regarding the identification target stored in the distance-based identification result storage unit 162 in an external storage medium or the like. Furthermore, if a classifier configured with a high-performance network as described in Non-Patent Document 1 can be installed in a vehicle (for example, in an object recognition device or other electronic control device), the distance-based classification result storage unit 162 This can be achieved by reading the image of the object to be identified stored in and performing the identification.

As described above, in this embodiment, for each individual vehicle, the correct object type is determined based on various information regarding the identification target outputted by the identification condition/result output unit 161 and stored in the distance-based identification result storage unit 162. It is possible to create a data set to which a distance-based discrimination method creation unit 91 described in the second embodiment can set a distance-based threshold value for this data.

That is, the object identification device according to the present embodiment has an identification result storage that stores information such as an image of the object (object region) identified by the object identification unit (object identification unit 26), the distance to the object, and the identification score. (distance-based identification result storage unit 162) and a function creation unit (distance-based identification method creation unit) that creates a threshold setting function (distance-based threshold function) using each piece of information stored in the identification result storage unit 91).

For moving objects such as vehicles, the types of objects to be identified and objects other than pedestrians that are likely to be misidentified vary depending on the region in which they are used, environmental conditions, frequently used speed zones, etc. By using the object identification device according to this embodiment, it becomes possible to update the variable threshold value that is suitable for the driving environment of each vehicle at any time. For example, in the initial stage of operation of a system including the object identification device according to the present embodiment described above, a versatile distance-based threshold is created and used. On the other hand, the actual usage of mobile objects differs from user to user, that is, from vehicle to vehicle. According to the object identification device according to the present embodiment, the distance-based threshold is updated according to the actual usage situation. Note that the update frequency may be once, or may be updated multiple times periodically or by setting some conditions.

The above are examples of embodiments of the object identification device according to the present invention. In the embodiments described above, an example in which a pedestrian is identified as a non-pedestrian is used as an identification target, but the identification target may be a plurality of objects including a pedestrian.

Furthermore, although an example has been described in which outside world information obtained from a stereo camera is used as a means for calculating the distance to an object on an image, the outside world information acquisition unit is not limited to a stereo camera. For example, a monocular camera, a millimeter wave radar, or the like may be used as the external world information acquisition unit.

[Hardware configuration of object identification device]
Next, the configurations (hardware configurations) of the control systems of the object identification devices 2 to 2C according to the first to fourth embodiments described above will be described with reference to FIG. 17.

FIG. 17 is a block diagram showing an example of the hardware configuration of the object identification devices 2 to 2C according to the first to fourth embodiments. A computer 170 shown in FIG. 17 is hardware used as a so-called computer.

The computer 170 includes a CPU (Central Processing Unit) 171, a ROM (Read Only Memory) 172, a RAM (Random Access Memory) 173, a nonvolatile storage 176, and a network interface 177, each connected to a bus B.

The CPU 171 reads software program codes that implement each function of the passenger conveyor control system 100 according to the present embodiment from the ROM 172, expands them into the RAM 173, and executes them. Alternatively, the CPU 171 directly reads the program code from the ROM 172 and executes it as is. Note that the computer 170 may include a processing device such as an MPU (Micro-Processing Unit) instead of the CPU 171. Variables, parameters, etc. generated during arithmetic processing by the CPU 171 are temporarily written into the RAM 173. The functions of each block of the object identification devices 2 to 2C are realized by the CPU 171.

As the non-volatile storage 176, for example, an HDD (Hard Disk Drive), SSD (Solid State Drive), flexible disk, optical disk, magneto-optical disk, CD-ROM, CD-R, non-volatile memory card, etc. can be used. can. In this nonvolatile storage 176, in addition to the OS (Operating System) and various parameters, programs for operating the computer 170 and the like are recorded. The functions of the distance-based identification method storage unit 24, the identification target DB 92, the braking distance/distance-based identification method storage unit 133, and the braking distance/distance-based identification method selection unit 134 of the object identification devices 2 to 2C are performed by the non-volatile storage 176. Realized.

Note that the program may be stored in the ROM 172. The program is stored in the form of a computer-readable program code, and the CPU 171 sequentially executes operations according to the program code. That is, the ROM 172 or the nonvolatile storage 176 is used as an example of a computer-readable non-transitory recording medium that stores a program executed by a computer.

The network interface 177 is composed of a communication device or the like that controls communication with other devices. The communication function of the object identification devices 2 to 2C is realized by the network interface 177.

The embodiments and various modifications described above are merely examples, and the present invention is not limited to these embodiments and modifications as long as the characteristics of the invention are not impaired. In addition, the various embodiments and modifications described above are detailed and specific explanations of the configurations in order to explain the present invention in an easy-to-understand manner, and are not necessarily limited to those having all the described components. Other embodiments considered within the technical spirit of the present invention are also included within the scope of the present invention.

Further, each of the above-mentioned configurations, functions, processing units, etc. may be partially or entirely realized by hardware, for example, by designing an integrated circuit. As the hardware, a broadly defined processor device such as an FPGA (Field Programmable Gate Array) or an ASIC (Application Specific Integrated Circuit) may be used.

Further, each component of the object identification device according to the embodiment described above may be implemented in any hardware as long as the respective hardware can send and receive information to and from each other via a network. Moreover, the processing performed by a certain processing unit may be realized by one piece of hardware, or may be realized by distributed processing by a plurality of pieces of hardware.

In addition, in this specification, processing steps describing chronological processing are not only processes that are performed chronologically in the described order, but also processes that are not necessarily performed chronologically, but may be performed in parallel or It also includes processes that are executed individually (for example, processes by objects). Furthermore, the processing order of processing steps that describe time-series processing may be changed within a range that does not affect the processing results.

Furthermore, in the embodiments described above, the control lines and information lines indicated by arrows and solid lines indicate those considered necessary for explanation, and not all control lines and information lines are necessarily indicated in the product. . In reality, almost all the components may be considered to be interconnected.

1...Stereo camera, 2-2C...Object identification device, 22...Object distance calculation unit, 23...Identification score calculation unit, 26...Object identification unit

Claims

a distance calculation unit that receives external world information including an object outside the vehicle acquired by an external world information acquisition unit mounted on the vehicle, and calculates a distance from the vehicle to the object;
an identification score calculation unit that uses the external world information to calculate an identification score indicating a degree of confidence that the object included in the external world information is of a predetermined type;
an object identification unit that identifies the object as being of a type associated with the identification score when the identification score exceeds a predetermined threshold;
The threshold value varies depending on the distance. Object identification device.
The threshold value is set to be a larger value when the distance to the object is closer than a first distance shorter than the second distance than when the distance to the object is farther than a second distance. The object identification device according to claim 1, wherein the object identification device is set.
As the threshold value, a threshold setting function is used that provides a correct identification rate of a certain value or more and a minimum of misidentifications for an image database in which information about objects with known correct values is stored by distance. ,
The object identification device according to claim 2, wherein the object identification unit calculates the threshold by inputting a distance to the object into the threshold setting function.
The object identification device according to claim 1, wherein the threshold value is set according to a distance to the object and a braking distance.
The object identification device according to claim 3, wherein the threshold setting function is defined by a combination of a fixed threshold and a linear variable threshold.
an identification result storage unit that stores information such as an image of the object identified by the object identification unit, a distance to the object, and the identification score;
The object identification device according to claim 3, further comprising: a function creation unit that creates the threshold setting function based on the information stored in the identification result storage unit.
An object identification method using an object identification device in which external world information including objects outside the vehicle acquired by an external world information acquisition unit mounted on a vehicle is input,
a process of determining a distance from the vehicle to the object based on the external world information;
using the external world information to obtain an identification score indicating a degree of confidence that the object included in the external world information is of a predetermined type;
a process of identifying that the object is of a type associated with the identification score when the identification score exceeds a predetermined threshold;
The threshold value varies depending on the distance. Object identification method.