JP2020067402A - Sensor calibration method and sensor calibration apparatus - Google Patents

Abstract

To provide a sensor calibration method and a sensor calibration apparatus capable of improving a calibration accuracy of an onboard sensor.SOLUTION: An onboard ECU 100 is used in a vehicle A and functions as a sensor calibration apparatus which performs a calibration of multiple onboard sensors 30 mounted on the vehicle A. The onboard ECU 100 acquires measurement information on a ground-fixed object RO measured by the onboard sensors 30, and prepares infrastructural information on the ground-fixed object RO with reference to an information source 50 which is different from the onboard sensors 30. Furthermore, the onboard ECU 100 updates external parameters set between the multiple onboard sensors 30 by using the measurement information and the infrastructural information on the ground-fixed object.SELECTED DRAWING: Figure 1

Description

  The disclosure of this specification relates to a calibration technique for calibrating a plurality of in-vehicle sensors mounted on a vehicle.

  Conventionally, for example, Patent Document 1 discloses a calibration method for calibrating a rider or a camera mounted on a vehicle by using an object existing around the vehicle. Specifically, in the calibration method of Patent Document 1, GPS (Global Positioning System) information of another vehicle traveling in the vicinity of the own vehicle is acquired by inter-vehicle communication. Then, the process of comparing the information perceived by the rider or the camera with the information received from the other vehicle calibrates the rider or the camera of the own vehicle.

U.S. Patent Application Publication No. 2018/0196127

  A positioning error is likely to occur in the GPS information of another vehicle used in the calibration method disclosed in Patent Document 1, especially during traveling. Therefore, if the GPS information of another vehicle whose reliability is unknown is used for the calibration of the in-vehicle sensor such as the lidar or camera, it may be difficult to ensure the accuracy of the calibrated parameters.

  An object of the present disclosure is to provide a sensor calibration method and a sensor calibration device that can improve the calibration accuracy of an in-vehicle sensor.

  In order to achieve the above-mentioned object, one disclosed aspect is a sensor calibration method which is implemented by a computer (100) and calibrates a plurality of in-vehicle sensors (30) mounted on a vehicle (A), On at least one processor (61), the measurement information of the ground fixed object (RO) measured by the on-vehicle sensor is acquired (S102), and the known information of the ground fixed object by the information source (50) different from the on-vehicle sensor is acquired. A sensor calibration method including a step of preparing (S105) and updating external parameters set between a plurality of in-vehicle sensors using the measurement information and known information about the same ground fixed object (S107, S108). To be done.

  Further, one aspect disclosed is a sensor calibration device which is used in a vehicle (A) and calibrates a plurality of on-vehicle sensors (30) mounted on the vehicle, and the ground fixed object measured by the on-vehicle sensor. An information acquisition unit (71) that acquires measurement information of (RO), an information setting unit (84) that sets known information of a ground fixed object by an information source (50) different from the vehicle-mounted sensor, and the same ground fixed object An update execution unit (86) that updates external parameters set between a plurality of vehicle-mounted sensors by using the measurement information and known information about the sensor calibration device.

  In these aspects, the known information of the ground fixed object is used to update the external parameter. Therefore, it is possible to update the external parameter between the vehicle-mounted sensors without being substantially affected by the positioning error that occurs in another vehicle. According to the above, it is possible to improve the accuracy of calibration of the vehicle-mounted sensor.

  It should be noted that the reference numerals in the above parentheses only show an example of a correspondence relationship with a specific configuration in the embodiment described later, and do not limit the technical scope at all.

FIG. 3 is a diagram showing an overview of a configuration related to calibration in an embodiment of the present disclosure. It is a block diagram which shows the electric constitutions, such as vehicle-mounted ECU and an infrastructure module. It is a figure which shows an example of the reliability evaluation table used for digitizing the reliability of calibration. It is a flow chart which shows the details of sensor calibration processing carried out by in-vehicle ECU.

  In the embodiment of the present disclosure shown in FIGS. 1 and 2, the function of the sensor calibration device is implemented in an in-vehicle ECU (Electronic Control Unit) 100. The vehicle-mounted ECU 100 is one of a plurality of electronic control units mounted on the vehicle A, and is a vehicle-mounted computer that enables automatic driving or advanced driving assistance of the vehicle A. The vehicle-mounted ECU 100 is directly or indirectly electrically connected to the DCM 41, the V2I communication device 43, the bus of the vehicle-mounted network 45, the plurality of vehicle-mounted sensors 30, and the like.

  The DCM (Data Communication Module) 41 is a communication module mounted on the vehicle A. The DCM 41 transmits and receives radio waves to and from base stations around the vehicle A by wireless communication according to communication standards such as LTE (Long Term Evolution) and 5G. The DCM 41 enables cooperation (Cloud to Car) between the cloud CLD and the in-vehicle device. The DCM 41 shares various information such as map information and road information with the cloud database DBc installed on the cloud CLD. By mounting the DCM 41, the vehicle A becomes a connected car that can be connected to the Internet.

  The V2I (Vehicle to roadside Infrastructure) communication device 43 is a communication device for road-to-vehicle (V2I) communication. The V2I communication device 43 performs bidirectional communication with a roadside device installed on the road. For example, on the road sign 10 or the like, an infrastructure module 10a capable of wireless communication with the V2I communication device 43 is installed as a roadside device.

  The infrastructure module 10a includes an infrastructure communication device 11, an infrastructure database (hereinafter, “infrastructure DB”) 12, an infrastructure controller 13 that controls these, and the like. The infrastructure module 10a holds infrastructure information (details will be described later) related to the infrastructure such as the road sign 10 in the infrastructure DB 12. The infrastructure module 10a is connected to the Internet and can update the infrastructure information in the infrastructure DB 12 to the latest information at any time. The infrastructure module 10a transmits the latest infrastructure information stored in the infrastructure DB 12 to the V2I communication device 43 through the infrastructure communication device 11.

  A large number of in-vehicle devices are electrically connected to the communication bus of the in-vehicle network 45 directly or indirectly. The vehicle-mounted network 45 can provide various vehicle information output to the communication bus to the vehicle-mounted ECU 100. The vehicle-mounted network 45 provides the vehicle-mounted ECU 100 with, for example, information indicating the traveling speed of the vehicle A (hereinafter referred to as “vehicle speed information”) as information necessary for the calibration described below.

  The in-vehicle sensor 30 is mounted on the vehicle A and has a detection configuration that acquires various information necessary for automatic driving or advanced driving assistance. The vehicle-mounted sensor 30 includes a GNSS receiver 31, an IMU 32, a millimeter wave radar 33, a lidar 34, a camera 35, and a sonar 36. The on-vehicle sensors 30 are installed at different positions and in different postures.

  A GNSS (Global Navigation Satellite System) receiver 31 and an IMU (Inertial Measurement Unit) 32 are configured to identify the current position of the vehicle A. The GNSS receiver 31 receives positioning signals transmitted from a plurality of artificial satellites. The IMU 32 includes, for example, a triaxial gyro sensor, a triaxial acceleration sensor, and the like, and measures the inertial force acting on the vehicle A. The positioning signal and the measurement result of the IMU 32 received by the GNSS receiver are sequentially provided to the vehicle-mounted ECU 100 as the vehicle position information indicating the current position of the vehicle A.

  The millimeter wave radar 33, the rider 34, the camera 35, and the sonar 36 are autonomous sensors that recognize the surrounding environment of the vehicle A. These autonomous sensors detect moving objects such as pedestrians and other vehicles, and stationary objects such as traffic signals, road signs 10 and road markings 20 such as lane markings and pedestrian crossings. Each autonomous sensor sequentially outputs measurement information including detection results of a moving object and a stationary object to the vehicle-mounted ECU 100. Note that each autonomous sensor may be plural. Moreover, some autonomous sensors may be omitted.

  The millimeter wave radar 33 irradiates the millimeter wave toward the traveling direction of the vehicle A, and receives the millimeter wave reflected by a moving object, a stationary object, or the like existing in the traveling direction to obtain the detection result. The rider 34 irradiates the laser light toward the traveling direction of the vehicle A or to the front left and right, and acquires the detection result by a process of receiving the laser light reflected by a moving object, a stationary object, and the like existing in the irradiation direction. The lidar 34 may be a scanning type such as a rotating mirror type, a MEMS type, and a phased array type, or a non-scanning type such as a flash type. The camera 35 acquires the detection result by the process of analyzing the front image of the front area of the vehicle A. The sonar 36 irradiates ultrasonic waves to the surroundings of the vehicle A, and acquires detection information by a process of receiving ultrasonic waves reflected by a moving object, a stationary object, or the like existing in the irradiation direction.

  The vehicle-mounted ECU 100 is an arithmetic device that combines the vehicle position information and the measurement information acquired from the vehicle-mounted sensors 30 to recognize the traveling environment. The vehicle-mounted ECU 100 continuously repeats the process of specifying the position of the own vehicle, the process of calculating the relative distance to an object around the own vehicle, and the like. The vehicle-mounted ECU 100 mainly includes a control circuit including a processor 61, a RAM 62, a memory device 63, an input / output interface 64, and the like.

  The processor 61 is hardware for arithmetic processing combined with the RAM 62, and can execute various programs. The memory device 63 has a configuration including a non-volatile storage medium, and stores various programs executed by the processor 61. The memory device 63 stores at least a database update program, a sensor calibration program, and the like, in addition to the environment recognition program for recognizing the traveling environment. The database update program and the sensor calibration program are programs related to the calibration of each vehicle-mounted sensor 30.

  The vehicle-mounted ECU 100 includes functional units such as a sensor information acquisition unit 71, a parameter storage unit 72, and an object identification unit 73 by executing the environment recognition program. In addition, the vehicle-mounted ECU 100 includes the database updating unit 77 as a functional unit by executing the database updating program. Further, the vehicle-mounted ECU 100 includes functional units such as a stop determination unit 81, an environment estimation unit 82, a target selection unit 83, an infrastructure information setting unit 84, a reliability evaluation unit 85, and an update execution unit 86 by executing the sensor calibration program.

  The sensor information acquisition unit 71 acquires the vehicle position information provided by the GNSS receiver 31 and the IMU 32. In addition, the sensor information acquisition unit 71 acquires measurement information from each of the millimeter wave radar 33, the lidar 34, the camera 35, and the sonar 36.

  The parameter storage unit 72 stores external parameters for each on-vehicle sensor 30. The external parameter is a group of numerical values set between the two vehicle-mounted sensors 30, and is a group of numerical values that geometrically associates two pieces of measurement information acquired by the two vehicle-mounted sensors 30. Specifically, the external parameters are defined in a 6-axis (x, y, z, roll, pitch, yaw) format corresponding to the mounting position and mounting posture (orientation) of each vehicle-mounted sensor 30. In the present embodiment, one of the plurality of vehicle-mounted sensors 30 is used as a reference master sensor for setting external parameters. The parameter storage unit 72 stores the external parameters relative to the master sensor for each vehicle-mounted sensor 30 except the master sensor. According to the process of applying the external parameter to the measurement information of the vehicle-mounted sensor 30, the position coordinate of the detected object in the coordinate system of the vehicle-mounted sensor 30 can be converted into the position coordinate in the coordinate system of the master sensor.

  The object specifying unit 73, based on the measurement result acquired by the sensor information acquiring unit 71, the relative distance and direction, the shape (size, height, area, etc.) of the detected object detected from the vehicle surroundings, and the type, etc. Specify. In such a process, the object identifying unit 73 implements a process of combining measurement information of a plurality of autonomous sensors, so-called sensor fusion. In the sensor fusion, the object identifying unit 73 uses the external parameter stored in the parameter storage unit 72, and in the measurement results of the two different autonomous sensors, the coordinates indicating the same point geometrically are associated with each other to detect the detection accuracy. Improve.

  The database updating unit 77 updates the infrastructure information registered in the vehicle-mounted database (hereinafter, “vehicle-mounted DB”) 76 to the latest information. The vehicle-mounted DB 76 is a storage area reserved in the memory device 63 or the like for storing infrastructure information, and functions as a local database. The database updating unit 77 repeats the infrastructure information updating process in a predetermined cycle to maintain the infrastructure information in the vehicle-mounted DB 76 in the latest state.

  The infrastructure information is information associated with a specific infrastructure used in the calibration described later, specifically, the ground fixed object RO. The ground fixed object RO is an object which is fixed to the ground and whose shape is defined in advance by law or the like. The ground fixed object RO includes, for example, a road sign 10 and a road marking object 20. The infrastructure information has contents including shape information such as height and area from the ground in addition to absolute position information (latitude, longitude, altitude) of the ground fixed object RO. The road marking 20 drawn on the road surface is associated with infrastructure information indicating that the height is “0”, for example.

  The stop determination unit 81 determines the traveling state of the vehicle A based on the vehicle speed information of the vehicle A provided from the in-vehicle network 45. Specifically, the stop determination unit 81 determines that the vehicle A is stopped when the traveling speed indicated by the vehicle speed information is zero. The stop determination unit 81 may perform the stop determination of the vehicle A based on the vehicle position information provided from the GNSS receiver 31.

  The environment estimation unit 82 estimates the current environment around the vehicle based on the front image captured by the camera 35, the communication information received by the DCM 41 or the V2I communication device 43, and the like. The environment estimation unit 82 performs time (day / night or light / dark) and weather determination, for example.

  The target selection unit 83 updates the external parameters from the plurality of ground fixed objects RO when the millimeter wave radar 33, the lidar 34, the camera 35, and the sonar 36 detect a plurality of ground fixed objects RO. The ground fixed object RO used for is selected. The target selecting unit 83 refers to the identification result of the object identifying unit 73 and grasps the type of the ground fixed object RO around the vehicle detected by the autonomous sensor. The target selection unit 83 sets a priority order used for calibration based on the type of each ground fixed object RO. The target selection unit 83 sets a higher priority for a fixed object RO on the ground that is more likely to secure the reliability of calibration, which will be described later. Specifically, the ground fixed object RO having a high feature reliability (close to 1) defined in a reliability evaluation table (see FIG. 3) described later has a higher priority.

  The target selection unit 83 sets the priority of each ground fixed object RO, and then determines the presence or absence of infrastructure information in order from the ground fixed object RO having a higher priority. The target selection unit 83 selects the ground fixed object RO having the highest priority among the ground fixed objects RO in which the infrastructure information exists as the target to be used for updating the external parameter. The above-mentioned feature reliability is greater for the road sign 10 erected on the road surface than for the road marking 20 drawn on the road surface. Therefore, the target selecting unit 83 preferentially selects the road sign 10 as the use target rather than the road marking object 20.

  The infrastructure information setting unit 84 acquires the infrastructure information of the ground fixed object RO selected as the usage target by the target selection unit 83, and sets it to a state in which it can be compared with the measurement information. The infrastructure information setting unit 84 can acquire infrastructure information from the three information sources 50. The first information source 50 is a cloud database DBc. The map information and road information accumulated in the cloud database DBc are constantly updated with the latest information. The infrastructure information setting unit 84 acquires the infrastructure information included in the map information or the road information from the cloud CLD via the DCM 41. The second information source 50 is the infrastructure module 10a. The infrastructure information setting unit 84 acquires the infrastructure information stored in the infrastructure database 12 via the V2I communication device 43. The third information source 50 is an in-vehicle DB 76. The infrastructure information setting unit 84 acquires the infrastructure information stored in the vehicle-mounted DB 76 by the process of referring to the memory device 63.

  The reliability evaluation unit 85 evaluates the reliability of the measurement information by the in-vehicle sensor 30. Specifically, the reliability evaluation unit 85 digitizes the reliability of the measurement information and enables calibration according to the reliability. The higher the reliability, the larger the adjustment amount of the external parameter in updating. It is assumed that such reliability is influenced by the measurement environment such as time and weather, the type and size of the ground fixed object RO to be measured, and the type of the vehicle-mounted sensor 30 that outputs the measurement information. Therefore, a reliability evaluation table (see FIG. 3) that quantifies the influence of each factor on the reliability is defined in advance. The reliability evaluation unit 85 uses the reliability evaluation table to adjust the reliability value according to the measurement environment, the type of the fixed object RO on the ground, the type of the vehicle-mounted sensor 30, and the like.

  As shown in FIGS. 2 and 3, in the reliability evaluation table, evaluation criteria such as time reliability, weather reliability, feature reliability, and sensor reliability are set in advance. The time reliability is set to "1" in the daytime and "0.5" in the nighttime. The weather reliability is "1" in fine weather, "0.9" in cloudy weather, "0.8" in rainy weather, and "0.5" in snow and fog. Furthermore, if the wind speed is lower than a specific wind speed (for example, 5 m / s), the weather reliability is set to "1", and if there is a wind that exceeds the specific wind speed, the weather reliability is set to "1". 5 / wind speed (unit is m / s) ”.

  The feature reliability is set for each type of ground fixed object RO. As an example, in the reliability evaluation table, types such as road signs 10, traffic lights, street lights, buildings, road markings 20, road edges and roadside plants are set as the ground fixed objects RO. The reliability corresponding to the time or the weather is assigned to each type of the fixed object RO.

  The sensor reliability is set for each autonomous sensor that outputs the measurement result. In the present embodiment, the millimeter wave radar 33, the lidar 34, the camera 35, and the sonar 36 are set as the types of autonomous sensors. Then, for each type of autonomous sensor, the reliability corresponding to the time or the weather is given as in the case of the feature reliability. In an environment where it is difficult to recognize an object by an autonomous sensor, such as heavy rain, heavy snow, and heavy fog, the weather reliability and the sensor reliability may be set to lower values.

  Further, the reliability evaluation unit 85 uses the size information of the ground fixed object RO, specifically, the area information of the ground fixed object RO for the reliability evaluation. The area information of the ground fixed object RO may be a value based on the measurement result specified by the object specifying unit 73 or a value included in the infrastructure information. The reliability evaluation unit 85 sets the size reliability by a process of dividing the specific coefficient (for example, 0.04) by the area (unit: m ^ 2) of the ground fixed object RO.

  The maximum value of the size reliability is "1". Further, the area of the ground fixed object RO may be the projected area of the ground fixed object RO when viewed from the vehicle-mounted sensor 30, or may be the projected area when the ground fixed object RO is viewed from the front.

The reliability evaluation unit 85 uses the above-described time reliability, weather reliability, size reliability, minimum feature reliability, minimum first sensor reliability, and The reliability of the total measurement information is set by multiplying the minimum value of the second sensor reliability.
(Equation 1) Reliability = Time reliability × Weather reliability × Size reliability × Minimum value of feature reliability
× Minimum reliability of first sensor × Minimum reliability of second sensor

As an example, when the time is “night” and the weather is “rainy”, the reliability when the road sign 10 having the area “0.05 m ^ 2” is recognized by the rider 34 and the camera 35 is as follows (Formula 2) )become that way.
(Equation 2) Reliability = 0.5 × 0.8 × (0.04 / 0.05) × 0.8
× 0.7 × 0.7 = 0.125
Here, the minimum value of the feature reliability is the smaller one of “0.8” at night and “1.0” at rain. Similarly, the minimum sensor reliability of the lidar 34 is the smaller of "1.0" at night and "0.7" at rain, and the minimum sensor reliability of the camera 35 is "at night". It is the smaller of 0.7 and 0.7 of rain.

  The update execution unit 86 continuously performs the iterative calculation for updating the external parameter by using the measurement information and the infrastructure information about the same ground fixed object RO, specifically, by comparing the measurement information and the infrastructure information. The update execution unit 86 carries out the external parameter update processing while taking into account the reliability set by the reliability evaluation unit 85. The update process for optimizing such external parameters corresponds to the calibration of each in-vehicle sensor 30. This calibration is different from the calibration performed at the factory, dealer, etc., and is the on-road calibration performed while the user is using the calibration. In the calibration, the update execution unit 86 substantially treats the absolute position (and height information and the like) of the ground fixed object RO indicated by the infrastructure information as a true value. The update execution unit 86 sets the calibrated external parameter so that the error between the value obtained by applying the external parameter to the measurement result and the infrastructure information decreases.

  The details of the process of the sensor calibration method performed by the vehicle-mounted ECU 100 in cooperation with the above functional units will be described based on FIG. 4 and with reference to FIG. 2. The sensor calibration process shown in FIG. 4 is started by, for example, activation of the vehicle-mounted ECU 100 based on switching of the vehicle power supply to the ON state, and is continuously started until the vehicle power supply is switched to the OFF state.

  In S101, it is determined whether the vehicle A has stopped. When it is determined in S101 that the vehicle A is traveling, the determination in S101 is repeated. When it is determined in S101 that the vehicle A is stopped, the process proceeds to S102.

  In S102, the measurement information of each autonomous sensor is referred to, the ground fixed object RO existing around the vehicle A is grasped, and the process proceeds to S103. In S103, with respect to the ground fixed object RO grasped in S102, the priority order of selection is set in the order of increasing reliability in calibration, and the process proceeds to S104.

  In S104, it is determined whether or not there is infrastructure information in order from the ground fixed object RO having the highest priority set in S103. When it is determined in S104 that there is no infrastructure information, the presence or absence of infrastructure information is determined for the ground fixed object RO having the next highest priority. Then, if it is determined in S104 that there is infrastructure information, the ground fixed object RO is selected as a calibration target, and the process proceeds to S105.

  In S105, the infrastructure information of the ground fixed object RO selected in S104 is set. Specifically, in S105, the absolute position of the ground fixed object RO, the height, the area, and the like are acquired from any of the three information sources 50, and the process proceeds to S106. In S106, the reliability of this calibration is calculated using the reliability evaluation table (see FIG. 3) based on the information acquired in S102 and S105 and the like, and the process proceeds to S107.

  In S107, the absolute position of the ground fixed object RO in the infrastructure information set in S105 and the absolute position of the vehicle A based on the own vehicle position information are prepared, and based on the difference between these absolute positions, the ground fixed object RO is changed to the vehicle. Calculate the relative distance to A. Then, the calculated relative distance is compared with the measurement information, and the adjustment value EPt for updating the current external parameter EP1 is set.

  Specifically, when applied to the measurement results of the two vehicle-mounted sensors 30 (autonomous sensors), the value of the external parameter that minimizes the error with respect to the relative distance based on the difference between the absolute positions is searched for as the adjustment value EPt. It Then, the difference between the searched adjustment value EPt and the current external parameter EP1 is calculated as the offset OST, and the process proceeds to S108. Note that the absolute position of the vehicle A is corrected using the external parameter associated with the GNSS receiver 31 to the content based on the mounting position of the master sensor.

  In S108, the external parameter EP1 is adjusted in consideration of the reliability calculated in S106. In S108, adjustment is performed so that the current value of the external parameter EP1 approaches the adjustment value EPt. For example, a value obtained by integrating the reliability with the offset OST is set as the adjustment amount of the external parameter EP1. As a result, when the reliability is low, the difference between the external parameter EP1 before adjustment and the external parameter EP2 after adjustment becomes small (see the white arrow). On the other hand, when the reliability is high, the difference between the external parameter EP1 before adjustment and the external parameter EP2 after adjustment becomes large (see the dot arrow).

  In the present embodiment described up to this point, the infrastructure information, which is the known information about the ground fixed object RO, is used to update the external parameters. Therefore, the external parameters between the vehicle-mounted sensors 30 can be updated without being substantially affected by the positioning error that occurs in another vehicle. According to the above, the accuracy of the calibration of the vehicle-mounted sensor 30 can be improved.

  More specifically, since the mounting position of the GNSS receiver in another vehicle is unknown, the other vehicle position information received from the other vehicle causes an error due to the size of the other vehicle in the calibration. On the other hand, the ground fixed object RO used for calibration in this embodiment is smaller than other vehicles. Therefore, the position accuracy error due to the object size can be reduced.

  Further, if the existing ground fixed object RO is used for calibration as in the present embodiment, it is not necessary to install a special on-road object for calibration. As a result, it becomes possible to continuously update the external parameter in parallel with the normal use by the user.

  In addition, communication delays inevitably occur in vehicle-to-vehicle communication used when acquiring other vehicle position information. Therefore, an error occurs between the time when the relative position of the other vehicle is measured and the time when the other vehicle position information is acquired. As a result, the accuracy of calibration is also likely to decrease. On the other hand, in the present embodiment, the measurement result in the state where the vehicle A is stopped and the relative distance between the vehicle A and the ground fixed object is not substantially changed is used for updating the external parameter. The measurement result under such a state where the relative distance is constant is likely to be more accurate than the measurement result under the state where the relative distance is changing. Therefore, by using the measurement result acquired when the stop determination is established, it is possible to further improve the accuracy even in the case of on-road calibration.

  Further, in the present embodiment, in updating the external parameters, the object position information of the ground fixed object RO and the own vehicle position information of the vehicle A are used. In this way, if the information used as the true value can be set based on the two pieces of position information, the arithmetic processing for updating the external parameter can be simplified as compared with the case where the position information is not used.

  Further, in the present embodiment, the processing of updating the infrastructure information is repeated in each information source 50 that is the provider of the infrastructure information. Therefore, the infrastructure information used in the calibration is real-time information that reflects the latest state. According to the above, highly reliable calibration of external parameters becomes possible.

  In addition, the vehicle-mounted ECU 100 of the present embodiment can acquire infrastructure information from the cloud database DBc or the infrastructure DB 12, which is the information source 50 outside the vehicle. The information outside the vehicle A is regularly updated with high reliability. Therefore, it is possible to avoid adverse effects due to the replacement of the road sign 10 and erroneous recognition.

  Further, in the present embodiment, a process of selecting the ground fixed object RO used for calibration is performed. According to the above, in the situation where a plurality of ground fixed objects RO exist around the vehicle, the infrastructure information of the ground fixed objects RO suitable for ensuring the accuracy of calibration can be preferentially used. As a result, the accuracy of calibration is more easily ensured.

  Further, in the present embodiment, the road sign 10 standing on the road surface is preferentially selected as the use target of the calibration, rather than the road marking object 20 drawn on the road surface. Thus, the higher the fixed object RO on the ground, the easier it is to ensure the accuracy of the relative distance indicated by the measurement result. Therefore, if the road sign 10 is preferentially selected, the certainty of ensuring the accuracy of calibration can be further improved.

  In addition, in the mode in which the other vehicle position information is received from the other vehicle, it is difficult to distinguish the reliability of the other vehicle position information because the environment in which the other vehicle position information is measured by the other vehicle is unknown. . As a result, the calibration using the other vehicle position information may reflect low reliability information in the external parameter. On the other hand, in this embodiment, the reliability of the measurement result is possible, and after the reliability is evaluated, the external parameter is updated according to the reliability. Therefore, it is possible to avoid the situation where the external parameter is calibrated to an inappropriate value based on the measurement result measured under the bad condition. Further, when the measurement result can be obtained under favorable conditions, it becomes possible to effectively calibrate the external parameters.

  Further, the reliability of the measurement result in the present embodiment is adjusted according to time and weather, the type of the ground fixed object RO, the type of the vehicle-mounted sensor 30, and the like. As described above, by comprehensively incorporating the factors that affect the reliability in the evaluation of the reliability, the calibration of the external parameter can be performed more appropriately.

  In the above embodiment, the infrastructure information corresponds to “known information”, and the absolute position information of the fixed object RO on the ground corresponds to “object position information”. Further, the road sign 10 corresponds to a “road standing object”, the sensor information acquisition unit 71 corresponds to an “information acquisition unit”, and the infrastructure information setting unit 84 corresponds to an “information setting unit”. Then, the vehicle-mounted ECU 100 corresponds to a “computer” and a “sensor calibration device”.

(Other embodiments)
Although one embodiment of the present disclosure has been described above, the present disclosure is not construed as being limited to the above embodiment, and is applied to various embodiments and combinations without departing from the scope of the present disclosure. be able to.

  In the above embodiment, the own vehicle position is specified using the GNSS receiver and the IMU, but the method of acquiring the own vehicle position information may be changed as appropriate. In the first modification of the above-described embodiment, the vehicle position is specified by combining the measurement result of any one of the millimeter wave radar, the lidar, the camera, and the sonar with the high-precision map acquired from the cloud. Further, the vehicle position may be specified by sensor fusion combining a plurality of measurement results.

  In the second modification of the above-described embodiment, the external parameter related to the GNSS receiver is the calibration target. Furthermore, in the third modified example of the above-described embodiment, the external parameter associated with the IMU is the calibration target. As in Modifications 2 and 3 described above, calibration of external parameters can be applied to in-vehicle sensors other than autonomous sensors.

  In Modification 4 of the above-described embodiment, the external parameter is calibrated using the measurement result measured during traveling. In Modification Example 4, it is possible to promptly converge the external parameters by performing the calibration during traveling immediately after the replacement of the vehicle-mounted sensor.

  In the modified example 5 of the above embodiment, the DCM and the V2I communication device are omitted. The infrastructure information is acquired by referring to the vehicle-mounted DB. In the modified example 6 of the above-described embodiment, the process of selecting the fixed object on the ground is omitted, and, for example, the calibration using the fixed object on the ground closest to the own vehicle is sequentially performed.

  As in the above embodiment, the calculation method that adds the reliability to the calibration may be changed as appropriate. Further, the concrete parameter value of the reliability may be changed as appropriate. Further, in the seventh modification of the above embodiment, the evaluation of the reliability of the measurement information is omitted. Then, in the calibration, the adjustment value based on the measurement information is reflected in the current external parameter at a constant rate.

  In the modified example 8 of the above embodiment, the height reliability is used instead of or together with the size reliability. The height reliability is set based on the height of the ground fixture, and a higher value is given to the ground fixture having a height suitable for detection by the autonomous sensor. For example, if the height of the ground fixed object is in the range of 1 to 10 m, the height reliability is set to "1". For ground fixed objects having a height of less than 1 m or more than 10 m, the height reliability is set to "0.5".

  The processor of the above embodiment is a processing unit including one or more CPUs (Central Processing Units). Such a processor may be a processing unit including a GPU (Graphics Processing Unit) and a DFP (Data Flow Processor) in addition to the CPU. Further, the processor may be a processing unit including an FPGA (Field-Programmable Gate Array) and an IP core specialized for specific processing such as AI learning and inference. Each arithmetic circuit unit of such a processor may be mounted individually on a printed circuit board, or may be mounted on an ASIC (Application Specific Integrated Circuit), an FPGA, or the like.

  Various non-transitory tangible storage media such as a flash memory and a hard disk can be adopted as a memory device for storing the sensor calibration program and the like. The form of such a storage medium may be appropriately changed. For example, the storage medium may be in the form of a memory card or the like, and may be configured to be inserted into a slot portion provided in the vehicle-mounted ECU and electrically connected to the control circuit.

  The control unit and the method thereof described in the present disclosure may be implemented by a dedicated computer that configures a processor programmed to execute one or more functions embodied by a computer program. Alternatively, the apparatus and method described in the present disclosure may be realized by a dedicated hardware logic circuit. Alternatively, the device and method described in the present disclosure may be implemented by one or more dedicated computers configured by a combination of a processor that executes a computer program and one or more hardware logic circuits. Further, the computer program may be stored in a computer-readable non-transition tangible recording medium as an instruction executed by a computer.

A vehicle, RO ground fixed object, 10 road sign (road standing object), 20 road marking object, 30 vehicle sensor, 50 information source, 61 processor, 71 sensor information acquisition unit (information acquisition unit), 84 infrastructure information setting unit (Information setting unit), 86 Update execution unit, 100 In-vehicle ECU (computer, sensor calibration device)

Claims (12)

A sensor calibration method performed by a computer (100) for calibrating a plurality of on-vehicle sensors (30) mounted on a vehicle (A), comprising:
On at least one processor (61),
The measurement information of the fixed object on the ground (RO) measured by the vehicle-mounted sensor is acquired (S102),
Prepare known information of the ground fixed object by an information source (50) different from the in-vehicle sensor (S105),
Using the measurement information and the known information about the same fixed object on the ground, update the external parameters set between the plurality of vehicle-mounted sensors (S107, S108),
A method for calibrating a sensor including the step of. Further comprising the step of determining whether the vehicle is stopped (S101),
The sensor calibration method according to claim 1, wherein in the step of updating the external parameter, the measurement information measured while the vehicle is stopped is used. The known information includes object position information indicating the position of the ground fixed object,
From the vehicle-mounted sensor, the vehicle position information indicating the position of the vehicle is provided,
The sensor calibration method according to claim 1, wherein the object position information and the vehicle position information are used to update the external parameter.   The sensor calibration method according to claim 1, wherein in the step of preparing the known information, the known information is provided from the information source that repeatedly updates the latest information.   The method of calibrating a sensor according to claim 1, wherein in the step of preparing the known information, the known information is provided from the information source outside the vehicle through wireless communication.   6. The method according to claim 1, further comprising a step of selecting the ground fixed object used for updating the external parameter from a plurality of the ground fixed objects located around the vehicle (S103). The method for calibrating the sensor according to item.   In the step of selecting the ground fixed object, the road fixed object (10) erected on the road surface is used for updating the external parameters rather than the road marking object (20) drawn on the road surface. The sensor calibration method according to claim 6, wherein the sensor calibration method is selected with priority. The method further includes the step of evaluating the reliability of the measurement information (S106),
The sensor calibration method according to claim 1, wherein in the step of updating the external parameter, the external parameter is updated according to the reliability.   The sensor calibration method according to claim 8, wherein the reliability is adjusted according to at least one of time and weather when the measurement information is measured.   The sensor calibration method according to claim 8 or 9, wherein the reliability is adjusted according to a type of the ground fixed object that is a measurement target of the measurement information.   The sensor calibration method according to claim 8, wherein the reliability is adjusted according to the type of the vehicle-mounted sensor that outputs the measurement information. A sensor calibration device which is used in a vehicle (A) and calibrates a plurality of on-vehicle sensors (30) mounted on the vehicle,
An information acquisition unit (71) for acquiring measurement information of a fixed object on the ground (RO) measured by the in-vehicle sensor;
An information setting unit (84) for setting known information of the ground fixed object by an information source (50) different from the in-vehicle sensor;
An update execution unit (86) that updates external parameters set between the plurality of vehicle-mounted sensors using the measurement information and the known information about the same ground fixed object;
A sensor calibration device including the.

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