Patent Publication Number: US-2022227396-A1

Title: Vehicle control system and vehicle control method

Description:
TECHNICAL FIELD 
     The present invention relates to a vehicle control system that estimates a state of an object by using information on the object detected by different types of sensors. 
     BACKGROUND ART 
     Background art of the present technical field includes the following prior art. In PTL (JP 2018-97765 A), when a radar target indicating an object detected by a radar and an image target indicating an object detected by an image pickup device are generated from the same object, a fusion target is generated by integrating the radar target and the image target. Then, calculation is performed by using the position of the image target used to generate the fusion target, in a width direction of the own vehicle, as a lateral position, and using a movement speed in the width direction of the own vehicle as a lateral speed. PTL 1 discloses an object detection device that, when a fusion target is not generated by acquiring a radar target and not acquiring an image target, generates a provisional fusion target by the lateral position and the lateral speed of the image target used to generate the fusion target and a radar target acquired by a radar target acquisition unit (see Abstract). 
     CITATION LIST 
     Patent Literature 
     PTL 1: JP 2018-97765 A 
     SUMMARY OF INVENTION 
     Technical Problem 
     In the technique disclosed in PTL 1, a constant value is used for an observation error of a sensor (radar and image pickup device). Thus, grouping of a target having a position estimated from a plurality of sensor values may be erroneously performed, and one object may be erroneously recognized as a plurality of objects. In addition, although the tendency of the error varies depending on the type of the sensor, the observation value of the sensor with high accuracy is not selected and the recognition result of the sensor is not integrated. Thus, the recognition accuracy may be lowered as a whole. Furthermore, the error of the sensor varies depending on the environment of the external field, and the influence of the external field is not taken into consideration. 
     Solution to Problem 
     A representative example of the invention disclosed in this application is as follows. That is, a vehicle control system includes an integration unit that estimates information on a position and a speed of a target existing in an external field, and errors of the position and the speed based on information from a sensor that acquires information on the external field of an own vehicle. The integration unit estimates an error of a detection result from the detection result of a sensor that detects an external field of a vehicle in accordance with a characteristic of the sensor, determines correlation between detection results of a plurality of the sensors, and integrates correlated detection results and calculates the errors of the position and the speed of the target. 
     Advantageous Effects of Invention 
     According to one aspect of the present invention, it is possible to accurately obtain an error of an observation value of a sensor and improve accuracy of a grouping process. Objects, configurations, and effects other than those described above will be clarified by the descriptions of the following embodiments. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG. 1  is a configuration diagram illustrating a vehicle control system according to an embodiment of the present invention. 
         FIG. 2  is a flowchart illustrating an entirety of integration processing in the present embodiment. 
         FIG. 3  is a flowchart of a prediction update process in Step S 2 . 
         FIG. 4  is a diagram illustrating a process in Step S 2 . 
         FIG. 5  is a diagram illustrating a grouping process (S 3 ) in the related art. 
         FIG. 6  is a diagram illustrating the grouping process (S 3 ). 
     
    
    
     DESCRIPTION OF EMBODIMENTS 
     An embodiment will be described below with reference to the drawings. 
       FIG. 1  is a configuration diagram illustrating a vehicle control system according to an embodiment of the present invention. 
     The vehicle control system in the present embodiment includes an own-vehicle movement recognition sensor D 001 , an external-field recognition sensor group D 002 , a positioning system D 003 , a map unit D 004 , an input communication network D 005 , a sensor recognition integration device D 006 , an autonomous-driving plan determination device D 007 , and an actuator group D 008 . The own-vehicle movement recognition sensor D 001  includes a gyro sensor, a wheel speed sensor, a steering angle sensor, an acceleration sensor, and the like mounted on the vehicle, and measures a yaw rate, a wheel speed, a steering angle, an acceleration, and the like representing the movement of the own vehicle. The external-field recognition sensor group D 002  detects a vehicle, a person, a white line of a road, a sign, and the like outside the own vehicle, and recognize information on the vehicle, the person, the white line, the sign, or the like. A position, a speed, and an object type of an object such as a vehicle or a person are recognized. The shape of the white line of the road including the position is recognized. For the expression, the position and the content of a sign are recognized. As the external-field recognition sensor group D 002 , sensors such as a radar, a camera, and a sonar are used. The configuration and number of sensors are not particularly limited. The positioning system D 003  measures the position of the own vehicle. As an example of the positioning system D 003 , there is a satellite positioning system. The map unit D 004  selects and outputs map information around the own vehicle. The input communication network D 005  acquires information from various information acquisition devices, and transmits the information to the sensor recognition integration device D 006 . As the input communication network D 005 , the controller area network (CAN), Ethernet, wireless communication, and the like are used. The CAN is a network generally used in an in-vehicle system. The sensor recognition integration device D 006  acquires own vehicle movement information, sensor object information, sensor road information, positioning information, and map information from the input communication network D 005 . Then, the sensor recognition integration device D 006  integrates the pieces of information as own vehicle surrounding information, and outputs the own vehicle surrounding information to the autonomous-driving plan determination device D 007 . The autonomous-driving plan determination device D 007  receives the information from the input communication network D 005  and the own-vehicle surrounding information from the sensor recognition integration device D 006 . The autonomous-driving plan determination device plans and determines how to move the own vehicle, and outputs command information to the actuator group D 008 . The actuator group D 008  operates the actuators in accordance with the command information. 
     The sensor recognition integration device D 006  in the present embodiment includes an information storage unit D 009 , a sensor object information integration unit D 010 , and an own-vehicle surrounding information integration unit D 011 . The information storage unit D 009  stores information (for example, sensor data measured by the external-field recognition sensor group D 002 ) from the input communication network D 005  and provides the information for the sensor object information integration unit D 010  and the own-vehicle surrounding information integration unit D 011 . The sensor object information integration unit D 010  acquires the sensor object information from the information storage unit D 009  and integrates the information of the same object, which is detected by a plurality of sensors, as the same information. Then, the sensor object information integration unit outputs the integration result to the own-vehicle surrounding information integration unit D 011 , as integration object information. The own-vehicle surrounding information integration unit D 011  acquires the integration object information, and the own vehicle movement information, the sensor road information, the positioning information, and the map information from the information storage unit D 009 . Then, the own-vehicle surrounding information integration unit D 011  integrates the acquired information as own-vehicle surrounding information, and outputs the own-vehicle surrounding information to the autonomous-driving plan determination device D 007 . 
     The sensor recognition integration device D 006  is configured by a computer (microcomputer) including an arithmetic operation device, a memory, and an input/output device. 
     The arithmetic operation device includes a processor and executes a program stored in the memory. A portion of the processing performed by the arithmetic operation device executing the program may be executed by another arithmetic operation device (for example, hardware such as a field programmable gate array (FPGA) and an application specific integrated circuit (ASIC)). 
     The memory includes a ROM and a RAM which are non-volatile storage elements. The ROM stores an invariable program (for example, BIOS) and the like. The RAM includes a high-speed and volatile storage element such as a dynamic random access memory (DRAM) and a non-volatile storage element such as a static random access memory (SRAM). The RAM stores a program executed by the arithmetic operation device and data used when the program is executed. The program executed by the arithmetic operation device is stored in a non-volatile storage element being a non-transitory storage medium of the sensor recognition integration device D 006 . 
     The input/output device is an interface that transmits processing contents by the sensor recognition integration device D 006  to the outside or receives data from the outside, in accordance with a predetermined protocol. 
       FIG. 2  is a flowchart illustrating an entirety of integration processing in the present embodiment. 
     The information storage unit D 009  stores sensor data. The sensor data is information of an object (target) recognized by various sensors (radar, camera, sonar, and the like) of the external-field recognition sensor group D 002 , and includes data of a relative position, a relative speed, and a relative position/speed of the recognized object in addition to data of a distance and a direction to the object. The relative position/speed can be represented by a range (for example, a Gaussian distribution type error ellipse) in which the object exists at a predetermined probability at a predetermined time. The Gaussian distribution type error ellipse can be represented by a covariance matrix shown in the following expression, and may be represented in another format. For example, as another form, the existence range of the object may be represented by general distribution other than the Gaussian distribution, which is estimated using the particle filter. 
     The covariance matrix shown in the following expression includes an element indicating a correlation between positions, an element indicating a correlation between speeds, and an element indicating a correlation between positions and speeds. 
     [Math. 1] 

 
     The memory of the sensor object information integration unit D 010  stores tracking data indicating a trajectory of an object recognized by the various sensors of the external-field recognition sensor group D 002 . 
     In the integration processing, first, the sensor object information integration unit D 010  estimates an error of sensor data (S 1 ). This error is determined by the type of sensor, the position of an object recognized within a recognition range (for example, if the distance to the object is long, the error is large, and the object recognized at the center of the recognition range has a small error), and an external environment (brightness of the external field, visibility, rainfall, snowfall, temperature, and the like). In addition, when coordinate systems of pieces of sensor data output from the various sensors of the external-field recognition sensor group D 002  are different from each other, a plurality of pieces of sensor data are converted into one common coordinate system, and then an error of the sensor data is estimated. Details of an error estimation process (S 1 ) will be described later. 
     The sensor object information integration unit D 010  updates prediction data of the tracking data (S 2 ). For example, assuming that the object represented by the tracking data performs a uniform linear motion from the previously recognized point without changing the moving direction and the speed, the position of the object at the next time is predicted, and the tracking data is updated. Details of a prediction data update process (S 1 ) will be described later. 
     Then, the sensor object information integration unit D 010  executes a grouping process of integrating data representing one object among the predicted position using the tracking data and the observed position using the sensor data (S 3 ). For example, an overlap between the error range of the predicted position using the tracking data and the error range of the observed position using the sensor data is determined, and the predicted position and the observed position where the error ranges overlap each other are grouped as data representing the same object. Details of a grouping process (S 3 ) will be described later. 
     Then, the sensor object information integration unit D 010  integrates the observation results by using the data determined as the group representing the same object (S 4 ). For example, a weighted average of the predicted positions and the observed positions grouped as the data representing the same object is calculated in consideration of errors of the predicted positions and the observed positions, and an integrated position of the object is calculated. 
     Then, the integrated position is output as a fusion result, and the tracking data is further updated (S 5 ). 
       FIG. 3  is a flowchart of the prediction update process in Step S 2  of  FIG. 2 .  FIG. 4  is a diagram illustrating a process in each step. In  FIG. 4 , the speed is represented by an arrow, the position is represented by a position on  FIG. 4 , and the position/relative speed is represented by an error ellipse. 
     First, the sensor object information integration unit D 010  acquires a first relative speed Vr_t 1 _t 1 , a first relative position X_t 1 _t 1 , and a first relative position/relative speed Pr_t 1 _t 1  of an object around a vehicle at a predetermined time t 1  (S 21 ). The relative speed, the relative position, and the relative position/relative speed are generally represented in a following coordinate system (also referred to as a relative coordinate system) based on a vehicle center position of the own vehicle, but may be represented in a coordinate system based on the position of the sensor that has measured the sensor data. 
     Then, the sensor object information integration unit D 010  converts the relative speed data in the following coordinate system into absolute speed data in a stationary coordinate system. For example, the sensor object information integration unit D 010  uses the first relative position X_t 1 _t 1  to convert the acquired first relative speed Vr_t 1 _t 1  and first relative position/relative speed Pr_t 1 _t 1  in the following coordinate system into a first absolute speed Va_t 1 _t 1  and a first relative position/absolute speed Pa_t 1 _t 1  in the stationary coordinate system (S 22 ). 
     Then, the sensor object information integration unit D 010  obtains the position at time t 2  from the position at time t 1 . For example, with the position O_t 1 _t 1  of the vehicle as the origin, the sensor object information integration unit D 010  converts the first absolute speed Va_t 1 _t 1 , the first relative position X_t 1 _t 1 , and the first relative position/absolute speed Pa_t 1 _t 1  at the time t 1  into the second absolute speed Va_t 2 _t 1 , the second relative position X_t 2 _t 1 , and the second relative position/absolute speed Pa_t 2 _t 1  at the time t 2  (S 23 ). 
     Then, the sensor object information integration unit D 010  updates the origin position of the coordinate system from the time t 1  to the time t 2 , that is, from the coordinate system at the time t 1  to the coordinate system at the time t 2 . For example, the sensor object information integration unit D 010  updates the second relative position X_t 2 _t 1 , the second absolute speed Va_t 2 _t 1 , and the second relative position/absolute speed Pa_t 2 _t 1  of the object with the position O_t 1 _t 1  of the vehicle at the time t 1  as the origin, to the second relative position X_t 2 _t 2 , the second absolute speed Va_t 2 _t 2 , and the second relative position/absolute speed Pa_t 2 _t 2  of the object with the position O_t 2 _t 1  of the vehicle at the time t 2  as the origin (S 24 ). 
     In the conversion from the origin position O_t 1 _t 1  at the time t 1  to the origin position O_t 2 _t 1  at the time t 2 , the measurement values (that is, the turning operation) of the vehicle speed and the yaw rate of the own vehicle are used. 
     Since the measured values of the vehicle speed and the yaw rate include errors, the error range indicated by the second relative position/absolute speed Pa_t 2 _t 2  may be increased in consideration of the error of the vehicle speed and the error of the yaw rate. 
     Then, the sensor object information integration unit D 010  converts the absolute speed data in the stationary coordinate system into relative speed data in the following coordinate system. For example, the sensor object information integration unit D 010  uses the second relative position X_t 2 _t 2  to convert the second absolute speed Va_t 2 _t 2  and the second relative position/absolute speed Pa_t 2 _t 2  in the stationary coordinate system into the second relative speed Vr_t 2 _t 2  and the second relative position/relative speed Pr_t 2 _t 2  in the following coordinate system in the updated coordinate system (S 25 ). 
     As described above, according to the prediction update process of the present embodiment, it is possible to more accurately calculate the relative position/relative speed (error range). 
     In addition, it is possible to improve grouping performance of the sensor data of the target, and improve determination performance of an operation plan. 
     Next, details of the grouping process (S 3 ) will be described. 
     For example, a case illustrated in  FIG. 5 , that is, a case where the observation values of a sensor A and a sensor B and the prediction update result are obtained, the error range of the observation value of the sensor is set to a constant value, and the error range after the prediction update is also set to a constant value is considered. At an observation point  1 , the error range of the observation value of the sensor A, the error range of the observation value of the sensor B, and the error range of the prediction update result overlap each other. Therefore, three targets observed at the observation point  1  are integrated into one and recognized as one object. At the observation point  1  illustrated in  FIG. 5 , the three error ranges overlap each other. Even in a case where the error range of the observation value of the sensor A overlaps the error range of the prediction update result, and the error range of the observation value of the sensor B overlaps the error range of the prediction update result, that is, a case where the error range of the observation value of the sensor A and the error range of the observation value of the sensor B overlap each other via the error range of the prediction update result, the three targets are integrated into one and recognized as one object. At an observation point  2 , there is no overlap between the error range of the observation value of the sensor A, the error range of the observation value of the sensor B, and the error range of the prediction update result. 
     Therefore, the three targets observed at the observation point  2  are not integrated into one and are recognized as three objects. 
       FIG. 6  is a diagram illustrating a grouping process (S 3 ) in the present embodiment. In the present embodiment, the grouping process is executed by using the error range calculated in accordance with the type of the sensor. The sensor A is, for example, a radar that measures a distance and a direction to a target. The sensor A has a small error in a distance direction (vertical direction) that is a direction from the sensor to the target, but has a large error in a rotation direction (lateral direction) perpendicular to the distance direction. The sensor B is, for example, a camera that picks up an image of the external field. The sensor B has a small error in the rotation direction (horizontal direction), but has a large error in the distance direction (vertical direction). Therefore, an error range is obtained in consideration of the error characteristic depending on the type of the sensor, as illustrated in  FIG. 6 . When the grouping process is executed by using the error range calculated in this manner, the error range of the observation value of the sensor A, the error range of the observation value of the sensor B, and the error range of the prediction update result overlap each other at the observation point  1 , similarly to the above description ( FIG. 5 ). Thus, the three targets observed at the observation point  1  are integrated into one and recognized as one object. In addition, at the observation point  2 , the error range of the observation value of the sensor A overlaps the error range of the prediction update result, and the error range of the observation value of the sensor B overlaps the error range of the prediction update result. Thus, the three targets observed at the observation point  2  are integrated into one and recognized as one object. 
     In the error estimation process (S 1 ) in the present embodiment, since the error is calculated in accordance with the type and characteristic of the sensor, and the error range is set, it is possible to accurately integrate targets observed by the plurality of sensors, and recognize the targets as one object. That is, since the accuracy of the grouping process is improved and the position of an object outside the vehicle can be accurately observed, it is possible to accurately control the vehicle. 
     In addition, in the present embodiment, the error may be calculated in accordance with the observation result of the sensor. Therefore, the sensor object information integration unit D 010  may determine the error range by using a function using the observation result (for example, the distance to the target) as a parameter. The sensor object information integration unit D 010  may determine the error range by using an error table set in advance instead of the function. 
     For example, the sensor generally has a larger error at a detection end than at the center of a detection range. Therefore, the error of the target detected at the center of the detection range may be set to be small, and the error of the target detected at a portion closer to the end of a detection displacement may be set to be larger. 
     In addition, the radar being a type of sensor has a small error in the distance direction (vertical direction) and a large error in the rotation direction (horizontal direction), but the error range varies depending on the distance to the target. That is, the error in the rotation direction (horizontal direction) increases in proportion to the distance, and the error in the distance direction (vertical direction) is substantially the same regardless of the distance. In addition, in the radar having a range switching function, the error in the rotation direction (horizontal direction) increases on the wide-angle side (short-distance side), and the error in the distance direction (vertical direction) is substantially the same regardless of the range. 
     The camera being a type of sensor has a small error in the distance direction (vertical direction) and a large error in the rotation direction (horizontal direction), but the error range varies depending on the distance to the target. That is, the error in the rotation direction (horizontal direction) increases in proportion to the distance, and the error in the distance direction (vertical direction) increases in proportion to the square of the distance. 
     As described above, in the present embodiment, since the error of the sensor is calculated in accordance with the position of the observed target and the error range is set, it is possible to accurately obtain the error of the observation value of the sensor. In particular, when the distance to the target is large, the error is increased, the error is changed in accordance with the detection direction of the target, the error of the target close to the end of the detection range is increased, and the error is increased on the wide-angle side. Therefore, it is possible to use an appropriate error range for the grouping process. Therefore, it is possible to accurately integrate targets observed by a plurality of sensors and recognize the targets as one object. That is, since the accuracy of the grouping process is improved and the position of an object outside the vehicle can be accurately observed, it is possible to accurately control the vehicle. 
     In the present embodiment, the error may be calculated in accordance with the environment of the external field. For example, the sensor error is small in good weather and large in rainy weather. In addition, the camera being a type of sensor has a small error during daytime when the illuminance of the external field is high, and has a large error during nighttime when the illuminance of the external field is low. 
     As described above, in the present embodiment, since the error is calculated in accordance with the environment outside the vehicle, it is possible to calculate a more accurate error, to improve the accuracy of the grouping process, and to accurately control the vehicle. 
     The present invention is not limited to the above-described embodiment, and includes various modifications and equivalent configurations within the spirit of the appended claims. For example, the above examples are described in detail in order to explain the present invention in an easy-to-understand manner, and the present invention is not necessarily limited to a case including all the described configurations. In addition, a portion of the configuration of one example may be replaced with the configuration of another example. Further, the configuration of one example may be added to the configuration of another example. 
     Regarding some components in the examples, other components may be added, deleted, and replaced. 
     In addition, some or all of the above-described configurations, functions, processing units, processing means, and the like may be realized by hardware by, for example, designing with an integrated circuit, or may be realized by software by a processor interpreting and executing a program for realizing each function. 
     Information such as a program, a table, and a file, that realizes each function can be stored in a memory, a storage device such as a hard disk and a solid state drive (SSD), or a recording medium such as an IC card, an SD card, and a DVD. 
     Control lines and information lines considered necessary for the descriptions are illustrated, and not all the control lines and the information lines in mounting are necessarily shown. In practice, it may be considered that almost all components are connected to each other. 
     REFERENCE SIGNS LIST 
     D 001  own-vehicle movement recognition sensor 
     D 002  external-field recognition sensor group 
     D 003  positioning system 
     D 004  map unit 
     D 005  input communication network 
     D 006  sensor recognition integration device 
     D 007  autonomous-driving plan determination device 
     D 008  actuator group 
     D 009  information storage unit 
     D 010  sensor object information integration unit 
     D 011  own-vehicle surrounding information integration unit