Patent Publication Number: US-10789489-B2

Title: Vehicle exterior environment recognition apparatus

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     The present application claims priority from Japanese Patent Application No. 2017-144321 filed on Jul. 26, 2017, the entire contents of which are hereby incorporated by reference. 
     BACKGROUND 
     The technology relates to a vehicle exterior environment recognition apparatus that identifies a specific object present in a traveling direction of an own vehicle. 
     A technique has been known that includes detecting a three-dimensional object, such as a vehicle located ahead of an own vehicle, and performing a control to avoid collision with a preceding vehicle (i.e., a collision avoidance control) or performing a control to keep a safe inter-vehicular distance from the preceding vehicle (i.e., a cruise control). For example, reference is made to Japanese Patent No. 3349060. 
     As a technique to detect the three-dimensional object, Japanese Unexamined Patent Application Publication (JP-A) No. 2008-134877 discloses a technique that includes detecting a parallel-traveling vehicle that travels parallel with the own vehicle, with reference to an image pattern photographed sideward of the own vehicle, on the basis of edge symmetry in a front-rear direction of the own vehicle. 
     SUMMARY 
     An aspect of the technology provides a vehicle exterior environment recognition apparatus that includes a three-dimensional object region identifier and a specific part identifier. The three-dimensional object region identifier is configured to identify a three-dimensional object region by monocular recognition based on a luminance image. The three-dimensional object region includes a three-dimensional object. The luminance image is generated by an image capturing unit configured to capture an image of vehicle exterior environment. The specific part identifier is configured to correlate the three-dimensional object region with a distance image, to identify a specific part of the three-dimensional object region on the basis of distance information. The distance image is generated from the luminance image. The distance information is calculated on the basis of the distance image. 
     An aspect of the technology provides a vehicle exterior environment recognition apparatus that includes circuitry. The circuitry is configured to identify a three-dimensional object region by monocular recognition based on a luminance image. The three-dimensional object region includes a three-dimensional object. The luminance image is generated by an image capturing unit configured to capture an image of vehicle exterior environment. The circuitry is configured to correlate the three-dimensional object region with a distance image, to identify a specific part of the three-dimensional object region on the basis of distance information. The distance image is generated from the luminance image. The distance information is calculated on the basis of the distance image. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a block diagram illustrating a relation of connection in a vehicle exterior environment recognition system. 
         FIGS. 2A and 2B  respectively describe a luminance image and a distance image. 
         FIG. 3  is a functional block diagram illustrating schematic functions of a vehicle exterior environment recognition apparatus. 
         FIG. 4  is a flowchart illustrating an example of a flow of a vehicle exterior environment recognition process. 
         FIG. 5A ,  FIG. 5B  and  FIG. 5C  describe examples of a three-dimensional object region identification process. 
         FIG. 6A ,  FIG. 6B  and  FIG. 6C  describe examples of a specific part identification process. 
     
    
    
     DETAILED DESCRIPTION 
     In the following, some preferred but non-limiting implementations of the technology are described in detail with reference to the accompanying drawings. Note that sizes, materials, specific values, and any other factors illustrated in respective implementations are illustrative for easier understanding of the technology, and are not intended to limit the scope of the technology unless otherwise specifically stated. Further, elements in the following example implementations which are not recited in a most-generic independent claim of the disclosure are optional and may be provided on an as-needed basis. The drawings are schematic and are not intended to be drawn to scale. Throughout the present specification and the drawings, elements having substantially the same function and configuration are denoted with the same reference numerals to avoid any redundant description. Further, elements that are not directly related to the technology are unillustrated in the drawings. 
     Non-limiting examples of a specific object present in a traveling direction of an own vehicle may include a preceding vehicle that travels in a same direction, and objects such as a pedestrian, i.e., a human, and a bicycle that cross a traveling path in a lateral direction of the own vehicle. Regarding the objects such as the pedestrian and the bicycle that cross the traveling path, it is desirable to determine their pedestrian-likeliness or bicycle-likeliness, on the basis of their outlines. In many cases, however, a pedestrian is smaller in absolute volume and more unstable in behavior, as compared to a vehicle or a bicycle. If a collision avoidance control is postponed until confirmation of presence of the pedestrian on the basis of, for example, their entire outline, a distance from the own vehicle to the pedestrian may become short during the postponement. This may necessitate an abrupt action as the collision avoidance control. 
     In particular, there are cases where a pedestrian jumps into the traveling path from behind a three-dimensional object such as a vehicle. In such cases, while a relative distance from the pedestrian to the own vehicle takes a continuous value with a relative distance from the three-dimensional object to the own vehicle, it is difficult to distinguish the pedestrian from the three-dimensional object solely on the basis of distance information. This may result in difficulty in early detection of the pedestrian. As used herein, the distance information refers to information regarding the relative distance as mentioned above. 
     It is desirable to provide a vehicle exterior environment recognition apparatus that makes it possible to detect a specific object such as a pedestrian early. 
     Vehicle Exterior Environment Recognition System  100   
       FIG. 1  is a block diagram illustrating a relation of connection in a vehicle exterior environment recognition system  100 . The vehicle exterior environment recognition system  100  may include image-capturing units  110 , a vehicle exterior environment recognition apparatus  120 , and a vehicle controller (e.g., an engine control unit (ECU))  130 . The implementation may include two image-capturing units  110  without limitation. 
     The two image-capturing units  110  may each include an imaging device such as, but not limited to, a charge-coupled device (CCD) and a complementary metal-oxide semiconductor (CMOS). The image-capturing units  110  may each be able to capture an image of vehicle exterior environment ahead of the own vehicle  1 , and to generate a luminance image that includes at least information on luminance. The luminance image may be a color image or a monochrome image. The two image-capturing units  110  may be so disposed that their respective optical axes become substantially parallel to each other along a traveling direction of the own vehicle  1 . The two image-capturing units  110  may be so disposed as to be separated away from each other in a substantially horizontal direction. The image-capturing units  110  may continuously generate the luminance image for each frame of, for example but not limited to, 1/60 second (at a frame rate of 60 fps). The luminance image may be an image that captures a three-dimensional object present in a detected region ahead of the own vehicle  1 . Non-limiting examples of the three-dimensional objects to be recognized by the image-capturing units  110  may include a three-dimensional object that is present independently, and an object as a part of the independently-present object. Non-limiting examples of the independently-present object may include a bicycle, a pedestrian (or a human), a vehicle, a traffic light, a road (or a traveling path), a road sign, a guardrail, and a building. Non-limiting examples of the object as a part of the independently-present object may include a part of a body of a pedestrian, e.g., a head or shoulders. 
     The vehicle exterior environment recognition apparatus  120  may obtain the luminance images from the respective image-capturing units  110 , and derive parallax information with use of so-called pattern matching. The pattern matching may involve extracting any block (e.g., an array of 4 pixels horizontally by 4 pixels vertically) from one of the luminance images, and searching for a corresponding block in another of the luminance images. The parallax information may include a parallax, and an on-screen position of any block. The on-screen position indicates a position of any block on a screen. In this implementation, the term “horizontally” refers to an on-screen lateral direction of the captured image, while the term “vertically” refers to an on-screen vertical direction of the captured image. A possible example of the pattern matching may be to compare a pair of images in terms of luminance (Y) block by block. Non-limiting examples may include techniques such as SAD (Sum of Absolute Difference), SSD (Sum of Squared intensity Difference), and ZNCC (Zero-mean Normalized Cross Correlation). The SAD includes obtaining differences in the luminance. The SSD includes using the differences squared. The ZNCC includes obtaining similarity of variance values obtained by subtracting an average value from luminance values of pixels. The vehicle exterior environment recognition apparatus  120  may perform such a block-by-block parallax derivation process, for all blocks displayed in the detected region of, for example, 600 pixels by 200 pixels. In this implementation, one block is assumed to be the array of 4 pixels by 4 pixels, but the number of the pixels inside one block may be set at any value. 
     It is to be noted that the vehicle exterior environment recognition apparatus  120  is able to derive the parallax for each of the blocks, but the vehicle exterior environment recognition apparatus  120  is not able to recognize what kind of object each of the blocks belongs to. The block serves as a unit of detection resolution. It follows, therefore, that the parallax information is derived not by the object but independently by the detection resolution in the detected region, e.g., by the block. In this implementation, an image with which the parallax information thus derived is correlated is referred to as a distance image, in distinction from the luminance image as mentioned above. 
       FIGS. 2A and 2B  respectively describe the luminance image  126  and the distance image  128 .  FIG. 2A  describes a non-limiting example in which the luminance image  126  as illustrated in  FIG. 2A  is generated for the detected region  124  by means of the two image-capturing units  110 . Note that  FIG. 2A  schematically illustrates only one of the two luminance images  126  generated by the respective image-capturing units  110  for easier understanding. The vehicle exterior environment recognition apparatus  120  may obtain the parallax for each of the blocks from the luminance images  126  to form the distance image  128  as illustrated in  FIG. 2B . Each of the blocks in the distance image  128  may be associated with the parallax of the relevant block. For description purpose, each of the blocks for which the parallax is derived is denoted by a black dot. 
     Moreover, the vehicle exterior environment recognition apparatus  120  may perform grouping of blocks, as an object. The grouping may be made with the use of luminance values, i.e., color values, based on the luminance image  126 , and with the use of three-dimensional positional information in real space. The three-dimensional positional information may be calculated on the basis of the distance image  128 , and include a relative distance to the own vehicle  1 . The blocks to be grouped may be of equal color values, and of close relative distances included in the three-dimensional positional information. The vehicle exterior environment recognition apparatus  120  may identify which specific object the object in the detected region ahead of the own vehicle  1  corresponds to. Non-limiting example of the specific object may include a preceding vehicle and a pedestrian. Moreover, upon identifying the three-dimensional object in this way, the vehicle exterior environment recognition apparatus  120  may further control the own vehicle  1 , to avoid collision with the three-dimensional object (i.e., the collision avoidance control) or to keep a safe inter-vehicular distance from the preceding vehicle (i.e., a cruise control). Note that the relative distance as mentioned above may be obtained by converting the parallax information for each of the blocks in the distance image  128  to the three-dimensional positional information with the use of a so-called stereo method. In this implementation, the stereo method refers to a method of deriving, from the parallax of the object, the relative distance of the relevant object with respect to the image-capturing units  110 , with the use of triangulation. 
     Returning to  FIG. 1 , the vehicle controller  130  may control the own vehicle  1  by accepting an operation input of the driver through a steering wheel  132 , an accelerator pedal  134 , and a brake pedal  136  and transmitting the operation input to a steering mechanism  142 , a drive mechanism  144 , and a brake mechanism  146 . The vehicle controller  130  may control the steering mechanism  142 , the drive mechanism  144 , and the brake mechanism  146 , in accordance with instructions from the vehicle exterior environment recognition apparatus  120 . 
     In the following, described in detail is a configuration of the vehicle exterior environment recognition apparatus  120 . A description is given here in detail of an identification process of the three-dimensional object (e.g., a pedestrian) in the detected region ahead of the own vehicle  1 . Note that a configuration less related to features of the implementation will not be described in detail. 
     Vehicle Exterior Environment Recognition Apparatus  120   
       FIG. 3  is a functional block diagram illustrating schematic functions of the vehicle exterior environment recognition apparatus  120 . Referring to  FIG. 3 , the vehicle exterior environment recognition apparatus  120  may include an interface (I/F)  150 , a data storage  152 , and a central controller  154 . 
     The interface  150  may be an interface that exchanges information bi-directionally between devices including, without limitation, the image-capturing units  110  and the vehicle controller  130 . The data storage  152  may include a random access memory (RAM), a flash memory, a hard disk drive (HDD), or any other suitable storage device. The data storage  152  may store various pieces of information necessary for processes to be carried out by the functional blocks to be described hereinafter. 
     The central controller  154  may include a semiconductor integrated circuit, and control devices including, without limitation, the interface  150  and the data storage  152  through a system bus  156 . The semiconductor integrated circuit may have devices such as, but not limited to, a central processing unit (CPU), a read only memory (ROM) in which programs, etc., are stored, and a random access memory (RAM) serving as a work area. In this implementation, the central controller  154  may function as a three-dimensional object region identifier  160 , a specific part identifier  162 , a speed-of-movement deriving unit  164 , and a collision avoidance control unit  166 . In the following, a detailed description is given, on the basis of operation of each functional block of the central controller  154  as well, of a vehicle exterior environment recognition process that involves, as a feature of the implementation, recognizing a pedestrian, i.e., a human. 
     Vehicle Exterior Environment Recognition Process 
       FIG. 4  is a flowchart illustrating an example of a flow of the vehicle exterior environment recognition process. The vehicle exterior environment recognition process may involve execution of the following processes: a three-dimensional object region identification process (S 200 ); a specific part identification process (S 202 ); a speed-of-movement derivation process (S 204 ); and a collision avoidance control process (S 206 ). In the three-dimensional object region identification process (S 200 ), the three-dimensional object region identifier  160  identifies a three-dimensional object region by monocular recognition based on the luminance image  126 . The three-dimensional object region includes the three-dimensional object, e.g., a pedestrian. In the specific part identification process (S 202 ), the specific part identifier  162  correlates the three-dimensional object region with the distance image  128 , to identify a specific part of the three-dimensional object region on the basis of the distance information. In the speed-of-movement derivation process (S 204 ), the speed-of-movement deriving unit  164  may derive a speed of movement of the specific part identified. Lastly, in the collision avoidance control process (S 206 ), the collision avoidance control unit  166  may execute the collision avoidance control. It is to be noted that the vehicle exterior environment recognition process may be repetitively executed for each frame of acquisition of the luminance image  126  and the distance image  128 . 
     Three-Dimensional Object Region Identification Process S 200   
       FIGS. 5A-5C  describe examples of the three-dimensional object region identification process S 200 . Described first is an attempt at identifying a pedestrian on the basis of the distance image  128  illustrated in  FIG. 5A . This attempt assumes a case where a three-dimensional object  212  jumps from behind a three-dimensional object  210  located in the distance image  128 . The three-dimensional object  212  corresponds to the pedestrian. The three-dimensional object  210  corresponds to an automobile. The automobile and the pedestrian are in separate and distinct relation from each other. However, while a distance from the automobile to the pedestrian is small, as illustrated in  FIG. 5B , the relative distance from the three-dimensional object  210  corresponding to the automobile with respect to the own vehicle  1  takes a continuous value with the relative distance from the three-dimensional object  212  corresponding to the pedestrian with respect to the own vehicle  1 . Accordingly, detecting a three-dimensional object on the basis of the distance image  128  causes a large three-dimensional object region  214  to be formed as illustrated in  FIG. 5A . The large three-dimensional object region  214  includes both the three-dimensional object  210  corresponding to the automobile and the three-dimensional object  212  corresponding to the pedestrian. This makes it difficult to distinguish the pedestrian from the automobile. 
     Described now is another attempt at identifying the pedestrian on the basis of the luminance image  126 , instead of the distance image  128 . In one specific but non-limiting example, as illustrated in  FIG. 5C , the pedestrian is identified, employing a recognition technique that includes recognizing a specific object with the use of machine learning on the basis of a shape or a pattern of any image in a monocular image, i.e., solely in one of the two luminance images  126 . This recognition technique is hereinafter simply referred to as the “monocular recognition”. In this case, as illustrated in  FIG. 5C , a three-dimensional object region  216  is formed that appropriately includes solely the pedestrian. 
     The monocular recognition as mentioned above identifies the three-dimensional object as the pedestrian with high probability, but precision of identification of a position of the three-dimensional object is not so high. Moreover, a shape of the three-dimensional object region  216  easily changes in accordance with behavior of the pedestrian. Therefore, the vehicle exterior environment recognition system  100  is able to grasp presence of the pedestrian on the traveling path, but may have difficulty in accurately identifying a speed of movement of the pedestrian. This may cause possibility of instability of the collision avoidance control with the pedestrian. 
     What is desired in this implementation is, therefore, to effectively unite identification of a three-dimensional object as a pedestrian by the monocular recognition, with identification of a position or a speed of movement of the pedestrian with the use of the distance image  128 , to detect a specific object such as a pedestrian early and stably. 
     Accordingly, as illustrated in  FIG. 5C , the three-dimensional object region identifier  160 , first, identifies the three-dimensional object region  216  by the monocular recognition based on the luminance image  126 . The three-dimensional object region  216  includes the pedestrian. However, the three-dimensional object region identifier  160  may refrain from deriving the speed of movement of the pedestrian from a result of the monocular recognition. 
     Specific Part Identification Process S 202   
       FIGS. 6A-6C  describe examples of the specific part identification process S 202 . The specific part identifier  162  correlates the three-dimensional object region  216  just as identified on the luminance image  126  by the monocular recognition, with the distance image  128 , to a corresponding position of the distance image  128 . Thus, as illustrated in  FIG. 6A , the three-dimensional object region  216  is formed on the distance image  128 . The three-dimensional object region  216  on the distance image  128  is identical to that on the luminance image  126 . In other words, a shape and area of the three-dimensional object region  216  on the distance image  128  are identical to those on the luminance image  126 . 
     Thereafter, as illustrated in  FIG. 6B , the specific part identifier  162  may equally divide the three-dimensional object region  216  on the distance image  128  into a predetermined number of divisions. In this example, the specific part identifier  162  may equally divide the three-dimensional object region  216  on the distance image  128  into, for example, eight vertically-arranged divisions each of which is shaped of a laterally-disposed strip. The specific part identifier  162  may extract the three-dimensional object  212  included in a division in a predetermined ordinal number from top of the screen. In this example, the specific part identifier  162  may extract the three-dimensional object  212  included in a division in the second place from the top, as illustrated in  FIG. 6C . In this implementation, the place of the division to be extracted is decided on an assumption that the shoulders of the pedestrian are located in the division in the second place from the top, among the eight vertically-arranged divisions of the pedestrian. 
     Thereafter, the specific part identifier  162  may identify a left end and a right end of a segment having the distance information, i.e., pixels or blocks having the distance information, out of the extracted division. In this implementation, the segment having the distance information refers to a segment the relative distance of which falls within a predetermined range with reference to an average relative distance of the three-dimensional object  212  with respect to the own vehicle  1 . The predetermined range may be, for example, ±1 meter. It is to be noted that in a case where the extracted division includes no segment having the distance information, a determination may be made that the relevant three-dimensional object  212  is not a pedestrian, and the vehicle exterior environment recognition process may be terminated. 
     Thereafter, as illustrated in  FIG. 6C , the specific part identifier  162  may identify, as a specific part  218 , a point that is positioned horizontally in the middle of the left end and the right end thus identified, and is positioned vertically in the middle of the extracted division. 
     Speed-of-Movement Derivation Process S 204   
     The speed-of-movement deriving unit  164  may derive a direction of movement and the speed of movement of the specific part  218 . The derivation may be made on the basis of a difference between a position of the lately-identified specific part  218  on the distance image  128  and a position of the preceding-identified specific part  218  on the distance image  128 , and on the basis of the relative distances thereof. The speed-of-movement deriving unit  164  may store the lately-identified specific part  218  to update a next-time preceding value. 
     Collision Avoidance Control Process S 206   
     On the ground that the three-dimensional object region  216  identified by the three-dimensional object region identifier  160  includes a pedestrian, and that the pedestrian is moving in the direction of movement and at the speed of movement derived by the speed-of-movement deriving unit  164 , the collision avoidance control unit  166  may execute the collision avoidance control, in order to avoid the collision with the pedestrian. 
     As described, in this implementation, first, the three-dimensional object is identified as a pedestrian by the monocular recognition. This makes it possible to detect the pedestrian earlier, as compared to a case solely with the use of the distance information. Hence, it is possible to detect a specific object such as a pedestrian early. 
     Moreover, the position and the speed of movement of the pedestrian may be derived with the use of the distance image  128 , without depending solely on the monocular recognition. This makes it possible to enhance precision of the identification of the position and the speed of movement. Hence, it is possible to detect the specific object such as a pedestrian early and stably. 
     Furthermore, in identifying the position of the pedestrian, the vertical position of the pedestrian may be set at a level corresponding to the shoulders of a human, i.e., a level between the neck and the chest. Because the shoulders are less likely to shift from a central axis of a human body, as compared to the head or the legs, it is possible to enhance the precision of the identification. It is to be noted that a lumbar part may serve as an alternative because the lumbar part is also unlikely to shift from the central axis of the human body. However, a locus of the lumbar part is sometimes unstable under an influence of arms and hands that move back and forth because of walking. Accordingly, it would be desirable to use the shoulders. 
     In addition, in identifying the position of the pedestrian, the horizontal position of the pedestrian may be set at a position corresponding to a midpoint of the shoulders of the human. Hence, it is possible to acquire a stable locus of movement, even in a case where the shoulders move back and forth because of walking. 
     The implementation also provides a program that causes a computer to function as the vehicle exterior environment recognition apparatus  120 , and a non-transitory recording medium that stores the program. The non-transitory recording medium is computer readable. Non-limiting examples of the non-transitory recording medium may include a flexible disk, a magneto-optical disk, ROM, CD, DVD (Registered Trademark), and BD (Registered Trademark). As used herein, the term “program” may refer to a data processor written in any language and any description method. 
     Although some preferred implementations of the technology have been described in the foregoing by way of example with reference to the accompanying drawings, the technology is by no means limited to the implementations described above. It should be appreciated that modifications and alterations may be made by persons skilled in the art without departing from the scope as defined by the appended claims. The technology is intended to include such modifications and alterations in so far as they fall within the scope of the appended claims or the equivalents thereof. 
     For instance, in one implementation described above, the description is made by giving an example where the three-dimensional object may be a pedestrian, or a human. The specific part of the three-dimensional object may be the horizontal midpoint between the vertical positions that correspond to the shoulders of the pedestrian. However, the technology is not limited to such an implementation. The technology may be targeted at various three-dimensional objects that are eligible to be targets of the monocular recognition, e.g., a bicycle, a motorcycle, and an automobile. 
     In one implementation described above, the description is made on an example where the specific part is vertically positioned at the level corresponding to the shoulders of the pedestrian. Specifically, the three-dimensional object region  216  is equally divided into the eight vertically-arranged divisions, and the division in the second place from the top is extracted. However, the number of the divisions and the place of the division to be extracted are not limited to as described above, and various values may be adopted. For example, the three-dimensional object region  216  may be equally divided into four divisions, and an uppermost division may be extracted. In another alternative, the three-dimensional object region  216  may be equally divided into five divisions, and a division in the second place from the top may be extracted. 
     A part or all of the processes in the vehicle exterior environment recognition process as disclosed herein does not necessarily have to be processed on a time-series basis in the order described in the example flowchart. A part or all of the processes in the vehicle exterior environment recognition process may involve parallel processing or processing based on subroutine. 
     The central controller  154  illustrated in  FIG. 3  is implementable by circuitry including at least one semiconductor integrated circuit such as at least one processor (e.g., a central processing unit (CPU)), at least one application specific integrated circuit (ASIC), and/or at least one field programmable gate array (FPGA). At least one processor is configurable, by reading instructions from at least one machine readable non-transitory tangible medium, to perform all or a part of functions of the central controller  154 . Such a medium may take many forms, including, but not limited to, any type of magnetic medium such as a hard disk, any type of optical medium such as a compact disc (CD) and a digital video disc (DVD), any type of semiconductor memory (i.e., semiconductor circuit) such as a volatile memory and a non-volatile memory. The volatile memory may include a dynamic random access memory (DRAM) and a static random access memory (SRAM), and the non-volatile memory may include a ROM and a non-volatile RAM (NVRAM). The ASIC is an integrated circuit (IC) customized to perform, and the FPGA is an integrated circuit designed to be configured after manufacturing in order to perform, all or a part of the functions of the central controller  154  illustrated in  FIG. 3 . 
     Although some implementations of the technology have been described in the foregoing by way of example with reference to the accompanying drawings, the technology is by no means limited to the implementations described above. The use of the terms first, second, etc. does not denote any order or importance, but rather the terms first, second, etc. are used to distinguish one element from another. It should be appreciated that modifications and alterations may be made by persons skilled in the art without departing from the scope as defined by the appended claims. The technology is intended to include such modifications and alterations in so far as they fall within the scope of the appended claims or the equivalents thereof.