JP2019196941A - Own vehicle position estimating device - Google Patents

Abstract

To provide an own vehicle position estimating device which can appropriately determine the level of confidence of estimating the position of an own vehicle, using a target storing plural pieces of positional information.SOLUTION: The own vehicle position estimating device estimates the position of an own vehicle on a map on the basis of the positional information on the position on a map of a target used for estimating the position of the own vehicle and the position of the target recognized from the result of detection by the on-vehicle sensor of the own vehicle. The own vehicle position estimating device includes: a target database storing the positional information of a target on a map, the database storing more than one piece of positional information of at least one target; and a confidence level operation unit for operating the level of confidence of estimating the position of the own vehicle, using the target storing plural pieces of positional information. The confidence level operation unit operates the level of confidence of estimating the position of the own vehicle on the basis of the distribution of plural pieces of positional information.SELECTED DRAWING: Figure 2

Description

  The present invention relates to a host vehicle position estimation device.

  Conventionally, Japanese Unexamined Patent Application Publication No. 2009-205226 is known as a technical document related to the vehicle position estimation apparatus. In this publication, the distance between the target object (target object) and the mobile robot as a reference in the mobile environment of the mobile robot is measured by the distance sensor, and the map data corresponding to the measurement direction at the time of measurement and the measurement of the distance sensor are measured. It is described that the self-position of a mobile robot is estimated based on the distance information.

JP 2009-205226 A

  In estimating the position of the vehicle, which is the position on the map of the vehicle, position information on a map of a target such as a pole on the road is essential. As an example, the position of such a target on the map is measured by running a plurality of vehicles equipped with sensors for detecting the target. However, in measuring the position of the target on the map, not only the measurement direction but also various error factors such as variations in detection accuracy of the sensor influence. As a result, a plurality of different positions are measured for the same target in a plurality of measurements, and a plurality of position information for one target may be stored in the database.

  On the other hand, it is difficult to always ensure a certain accuracy for the vehicle position estimation, and the degree of confidence (probability) of the vehicle position estimation varies depending on various factors. The degree of confidence in the position estimation of the host vehicle is required to be appropriately calculated because it affects various vehicle controls and driver route guidance. For this reason, it is desirable to obtain the degree of confidence of the vehicle position estimation by appropriately considering the case where a plurality of pieces of position information are stored for one target.

  Therefore, in the present technical field, it is desired to provide a vehicle position estimation device that can appropriately determine the degree of confidence in vehicle position estimation using a target in which a plurality of pieces of position information are stored.

  In order to solve the above-described problem, one aspect of the present invention is based on position information on a map of a target used for vehicle position estimation and a position of the target recognized from a detection result of an in-vehicle sensor of the vehicle. A host vehicle position estimation device for estimating a host vehicle position that is a position on the map of the host vehicle based on a database for storing position information on the map of the target, the position information on at least one target And a confidence level calculation unit that calculates the confidence level of the vehicle position estimation using a target in which multiple pieces of position information are stored. Based on the distribution of the position information of the vehicle, the degree of confidence of the vehicle position estimation is calculated.

  According to the host vehicle position estimation device according to one aspect of the present invention, in the calculation of the degree of confidence of the host vehicle position estimation using the target in which the plurality of position information is stored, the confidence based on the distribution of the plurality of position information. Compared to the case where a plurality of position information is stored for a target, the vehicle position estimation using the target stored with a plurality of position information is performed. The degree of confidence can be calculated appropriately.

The host vehicle position estimation apparatus according to one aspect of the present invention further includes a host vehicle direction estimation unit that estimates a direction of the host vehicle on a map, and the confidence degree calculation unit includes the direction of the host vehicle and distribution of a plurality of position information. Based on the above, by calculating a front-rear direction distribution, which is a distribution of a plurality of position information in the front-rear direction of the host vehicle, the front-rear direction confidence level, which is the confidence level of the host vehicle position estimation in the front-rear direction, is calculated In addition, the vehicle width direction distribution is calculated from the vehicle width direction distribution by calculating the vehicle width direction distribution that is the distribution of the plurality of position information in the vehicle width direction of the own vehicle based on the direction of the own vehicle and the distribution of the plurality of position information. The vehicle width direction confidence level, which is the confidence level of the vehicle position estimation at, may be calculated.
According to this vehicle position estimation device, the vehicle in the front-rear direction of the host vehicle is calculated by calculating the front-rear direction distribution and the vehicle width direction distribution on the basis of the direction of the host vehicle from the distribution of the plurality of position information on the target. It is possible to appropriately obtain the front-rear direction confidence level that is the confidence level of the position estimation and the vehicle width direction confidence level that is the confidence level of the vehicle position estimation in the vehicle width direction.

The host vehicle position estimation apparatus according to one aspect of the present invention further includes a target recognition unit that recognizes a target based on a detection result of the in-vehicle sensor and recognizes a distance between the host vehicle and the target, and has confidence level. The calculation unit may calculate the degree of confidence of the host vehicle position estimation based on the distance between the target and the host vehicle in which the plurality of position information is stored and the distribution of the plurality of position information.
According to the own vehicle position estimation device, since the detection accuracy (the probability of detection) of the target changes depending on the distance between the target and the own vehicle, the distance between the own vehicle and the target is considered. The degree of confidence of the vehicle position estimation can be obtained appropriately.

  As described above, according to the own vehicle position estimation device according to one aspect of the present invention, the degree of confidence of the own vehicle position estimation using a target in which a plurality of position information is stored can be appropriately obtained. .

It is a block diagram which shows the own vehicle position estimation apparatus which concerns on 1st Embodiment. (A) It is a figure which shows the example of distribution of several positional information in case several positional information is memorize | stored in the target. (B) It is a figure which shows the case where the dispersion | variation in the distribution of several positional information is large. It is a figure which shows the case where two positional information is memorize | stored with respect to one target. It is a figure for demonstrating the calculation of the deterioration degree of the own vehicle position estimation on the basis of a vehicle. (A) It is a flowchart which shows the own vehicle position estimation process. (B) It is a flowchart which shows the confidence level calculation process of the own vehicle position estimation. It is a block diagram which shows the own vehicle position estimation apparatus which concerns on 2nd Embodiment. (A) It is a figure which shows the case where the dispersion | variation in the distribution of several positional information is large in the front-back direction. (B) It is a figure which shows the case where the dispersion | variation in the distribution of several positional information is large in the vehicle width direction. It is a flowchart which shows the confidence level calculation process of the own vehicle position estimation which concerns on 2nd Embodiment.

  Embodiments of the present invention will be described below with reference to the drawings.

[First Embodiment]
FIG. 1 is a block diagram illustrating a host vehicle position estimation apparatus according to the first embodiment. As shown in FIG. 1, a host vehicle position estimation apparatus 100 according to the first embodiment is an apparatus that estimates a host vehicle position that is a position on a map of the host vehicle in a host vehicle such as a passenger car. The own vehicle position estimating apparatus 100 estimates the own vehicle position using position information on the map of the target, and calculates the degree of confidence (probability) of the own vehicle position estimation using the target.

<Configuration of the vehicle position estimation device in the first embodiment>
As shown in FIG. 1, the vehicle position estimation device 100 includes a vehicle position estimation ECU [Electronic Control Unit] 10 that comprehensively manages the system. The own vehicle position estimation ECU 10 is an electronic control unit having a CPU [Central Processing Unit], a ROM [Read Only Memory], a RAM [Random Access Memory], a CAN [Controller Area Network] communication circuit, and the like. In the vehicle position estimation ECU 10, for example, various functions are realized by loading a program stored in the ROM into the RAM and executing the program loaded in the RAM by the CPU. The host vehicle position estimation ECU 10 may be composed of a plurality of electronic units.

  The vehicle position estimation ECU 10 is connected to the GPS receiver 1, an external sensor (vehicle sensor) 2, an internal sensor 3, a map database 4, a target database 5, an HMI [Human Machine Interface] 6, and an automatic operation ECU 50. .

  The GPS receiving unit 1 is a measuring unit that measures a position (for example, the latitude and longitude of a vehicle) of the host vehicle by receiving signals from three or more GPS satellites. The GPS receiver 1 transmits the measured position information of the host vehicle to the host vehicle position estimation ECU 10.

  The external sensor 2 is a detection device (on-vehicle sensor) that is mounted on the host vehicle and detects a situation around the host vehicle. The external sensor 2 includes at least one of a camera and a radar sensor.

  The camera is an imaging device that captures an external situation of the host vehicle. The camera is provided on the back side of the windshield of the host vehicle. The camera transmits imaging information related to the external situation of the host vehicle to the host vehicle position estimation ECU 10. The camera may be a monocular camera or a stereo camera. The stereo camera has two imaging units arranged so as to reproduce binocular parallax. The imaging information of the stereo camera includes information in the depth direction.

  The radar sensor is a detection device that detects an obstacle around the host vehicle using radio waves (for example, millimeter waves) or light. The radar sensor includes, for example, a millimeter wave radar or a lidar [LIDAR: Light Detection And Ranging]. The radar sensor detects an obstacle by transmitting radio waves or light to the vicinity of the host vehicle and receiving radio waves or light reflected by the obstacle. The radar sensor transmits the detected obstacle information to the vehicle position estimation ECU 10. Obstacles include fixed obstacles such as guardrails and buildings, as well as moving obstacles such as pedestrians, bicycles, and other vehicles.

  The internal sensor 3 is a detection device that detects the traveling state of the host vehicle. The internal sensor 3 includes a vehicle speed sensor, an acceleration sensor, and a yaw rate sensor. The vehicle speed sensor is a detector that detects the speed of the host vehicle. As the vehicle speed sensor, for example, a wheel speed sensor that is provided for a wheel of the host vehicle or a drive shaft that rotates integrally with the wheel and detects the rotational speed of the wheel is used. The vehicle speed sensor transmits the detected vehicle speed information (wheel speed information) to the host vehicle position estimation ECU 10.

  The acceleration sensor is a detector that detects the acceleration of the host vehicle. The acceleration sensor includes, for example, a longitudinal acceleration sensor that detects acceleration in the longitudinal direction of the host vehicle and a lateral acceleration sensor that detects lateral acceleration of the host vehicle. For example, the acceleration sensor transmits acceleration information of the host vehicle to the host vehicle position estimation ECU 10. The yaw rate sensor is a detector that detects the yaw rate (rotational angular velocity) around the vertical axis of the center of gravity of the host vehicle. As the yaw rate sensor, for example, a gyro sensor can be used. The yaw rate sensor transmits the detected yaw rate information of the host vehicle to the host vehicle position estimation ECU 10.

  The detection results (vehicle speed information, yaw rate information, etc.) of the internal sensor 3 may be used for measuring the position of the host vehicle on the map. In this case, the internal sensor 3 functions as a position measurement unit on the map of the host vehicle.

  The map database 4 is a database that stores map information. The map database 4 is formed, for example, in an HDD [Hard Disk Drive] mounted on the vehicle. The map information includes road position information, road shape information (for example, curves, straight line types, curve curvature, etc.), intersection and branch point position information, and structure position information. The map database 4 may be formed on a server that can communicate with the host vehicle.

  The target database 5 is a database that stores target information related to the target. The target has a known position on the map and is used as a reference for estimating the position of the vehicle. The target includes at least one of a structure provided on or around the road and a road marking displayed on the road surface. The target may include a part of the structure or a part of the road sign.

  The structures include at least one of curbs, poles, road signs, guardrails, denilators, walls, traffic lights, tunnel exits and entrances, ETC gates, and buildings. The road sign includes at least one of a regulation sign and an instruction sign. The regulatory marking includes a turn prohibition mark, a maximum speed mark, and the like. There are white lines (roadway center line, roadway outside line, lane boundary line, etc.), a rhombus mark indicating that there is a pedestrian crossing ahead, a triangle mark indicating that there is a priority road ahead, a traveling direction mark, a crossing Includes sidewalk markings, temporary stop lines, etc.

  When the white line is represented as a continuous dotted line, each dotted line may be treated as a target. Moreover, you may treat the edge part (front end, rear end) in the road extending direction of each dotted line as a target, respectively. In addition, as a target, a well-known thing can be employ | adopted in the technical field of the own vehicle position estimation.

  The target information stored in the target database 5 includes position information of the target on the map and target characteristic information. The target feature information is information for extracting the target from the detection result of the external sensor 2. Target feature information may include target type information (pole, white line, etc.), target shape information, and target size information. May be included.

  The target database 5 stores a plurality of pieces of position information for at least one target. Here, FIG. 2A is a diagram illustrating an example of distribution of a plurality of position information when a plurality of position information is stored in the target. FIG. 2A shows a plurality of position information distributions P1, P2, P3,... Constituting the own vehicle M, a plurality of position information distributions P for a certain target, and a plurality of position information distributions P. FIG. Further, an f-axis extending in the front-rear direction of the host vehicle M and a w-axis extending in the vehicle width direction of the host vehicle M are shown with the center of the distribution P of the plurality of position information as the origin.

  As an example, the position information of the target stored in the target database 5 is measured by running a vehicle equipped with a sensor for detecting the target. In this case, when the vehicle performs measurement for a single target multiple times, a plurality of different position information may be obtained for a single target due to various error factors such as the measurement direction and variation in detection accuracy of the sensor. It may be stored in the target database 5. For this reason, the target database 5 stores a plurality of pieces of position information for at least one target.

  Note that the measurement subject need not be a vehicle, and may be a drone (for example, a flying drone), a measurement device possessed by a person, or a satellite. In any measurement subject, when position measurement is performed a plurality of times, different positions may be measured in one target. In addition, the target database 5 may be formed in a server that can communicate with the host vehicle. The target database 5 may be configured as a database integrated with the map database 4.

  The HMI 6 is an interface for inputting and outputting information between the vehicle position estimation device 100 and an occupant (including a driver). The HMI 6 includes, for example, a display and a speaker. The HMI 6 performs display image output and sound output from the speaker in accordance with a control signal from the confidence calculation ECU 10. The display may be a head-up display. The HMI 6 includes, for example, input devices (buttons, touch panel, voice input device, etc.) for receiving input from the occupant. Note that the host vehicle position estimation ECU 10 is not necessarily connected to the HMI 6.

  The automatic driving ECU 50 is an electronic control unit that is mounted on the host vehicle and executes the driving of the host vehicle. Automatic driving is vehicle control in which the vehicle is automatically driven without driving operation by the driver. The automatic operation ECU 50 may be composed of a plurality of electronic units. Some of the functions of the automatic driving ECU 50 may be executed by a server that can communicate with the host vehicle.

  The automatic driving ECU 50 includes the vehicle position estimated by the vehicle position estimation device 100, the map information in the map database 4, the surrounding environment of the vehicle recognized from the detection result of the external sensor 2 (the position of another vehicle, etc.), and the internal Based on the vehicle state (vehicle speed, yaw rate, etc.) recognized from the detection result of the sensor 3, a travel plan along a preset target route is generated. The target route is set by a passenger of the own vehicle or a well-known navigation system. The navigation system may be integrated with the automatic driving ECU 50.

  The automatic operation ECU 50 performs automatic operation according to the travel plan. The automatic driving ECU 50 performs automatic driving by transmitting a control signal to an actuator (engine actuator, steering actuator, brake actuator, etc.) of the host vehicle. The automatic operation ECU 50 can generate a travel plan and execute an automatic operation by a known method. Note that the host vehicle position estimation ECU 10 is not necessarily connected to the automatic driving ECU 50.

  Next, the functional configuration of the vehicle position estimation ECU 10 will be described. The own vehicle position estimation ECU 10 includes a measurement position acquisition unit 11, a target recognition unit 12, an own vehicle position estimation unit 13, and a confidence level calculation unit 14.

  The measurement position acquisition unit 11 acquires a measurement position that is a position on the map of the host vehicle based on the position information of the host vehicle measured by the GPS reception unit 1. Based on the detection result of the internal sensor 3, the measurement position acquisition unit 11 acquires the measurement position of the host vehicle from the vehicle speed history (or the wheel rotation number history), the yaw rate history of the host vehicle, and the like. Also good. In other words, the measurement position acquisition unit 11 may acquire the measurement position of the host vehicle by so-called odometry using a known method.

  The target recognition unit 12 recognizes a target around the host vehicle based on the detection result of the external sensor 2, the measurement position of the host vehicle measured by the measurement position acquisition unit 11, and the target information in the target database 5. (To detect. For example, the target recognition unit 12 narrows down target candidates that can be detected by the external sensor 2 of the own vehicle from the measurement position of the own vehicle and the position information of the target, and the detection result of the external sensor 2 and the target By collating with the feature information, the target around the host vehicle is recognized (specified). The method for recognizing the target is not particularly limited, and a known method can be used.

  Further, the target recognition unit 12 recognizes the target around the host vehicle and the distance of the host vehicle based on the detection result of the external sensor 2. The distance between the target and the host vehicle means the detection distance detected by the external sensor 2 regardless of the position information in the target database 5.

  The host vehicle position estimation unit 13 estimates the host vehicle position, which is the position on the map of the host vehicle, using the target recognized by the target recognition unit 12. As for the own vehicle position, the use of the target is expected to improve the position accuracy as compared with the measurement position of the own vehicle acquired by the measurement position acquisition unit 11.

  Specifically, the host vehicle position estimation unit 13 obtains position information (one or a plurality of position information) on the map of the target recognized by the target recognition unit 12 based on the target information in the target database 5. recognize. The own vehicle position estimation unit 13 recognizes the position of the target recognized by the target recognition unit 12 (relative position with respect to the own vehicle) based on the detection result of the external sensor 2. The own vehicle position estimation unit 13 estimates the own vehicle position based on the position information on the map of the target stored in the target database 5 and the position of the target detected by the external sensor 2. In addition, the own vehicle position estimation unit 13 may estimate the own vehicle position using the target by a known method.

  Here, when a plurality of pieces of position information are stored for one target, the vehicle position estimation unit 13 uses the position information closest to the host vehicle among the plurality of pieces of position information as the position information of the target. Estimate the vehicle position.

  In addition, the own vehicle position estimation unit 13 may use the variation center of the plurality of pieces of position information as the position information of the target, and may use the position having the highest density of the plurality of pieces of position information as the position information of the target. The own vehicle position estimation unit 13 outputs the estimated own vehicle position to the automatic driving ECU 50.

  When the vehicle position estimation using the target is performed, the confidence level calculation unit 14 calculates the degree of confidence (probability) of the vehicle position estimation for the target. In the present embodiment, when the vehicle position estimation is performed using a target in which a plurality of pieces of position information are stored, the confidence level calculation unit 14 calculates the degree of confidence in the vehicle position estimation of the target.

  The degree-of-confidence calculating unit 14 calculates the degree of confidence of the vehicle position estimation using the target based on the distribution P of the plurality of position information in the target and the distance (detected distance) between the host vehicle and the target. To do. Specifically, the confidence level calculation unit 14 recognizes a distribution P of a plurality of position information in the target from the target information in the target database 5 for the target used for the vehicle position estimation. The degree-of-confidence calculation unit 14 calculates the degree of confidence of the vehicle position estimation as a large value when the variation in the distribution P of the plurality of position information is small compared to the case where the variation in the distribution P of the plurality of position information is large.

  Here, FIG. 2B is a diagram illustrating a case where the variation in the distribution P of the plurality of position information is large. The variation of the distribution P of the plurality of position information in FIG. 2B is larger than the variation of the distribution P of the plurality of position information in FIG.

  Specifically, when the plurality of position information distributions P falls within a preset setting range (when variation is small), the confidence level calculation unit 14 does not fit the plurality of position information distributions P within the setting range. Compared to the case (when the variation is large), the degree of confidence in the vehicle position estimation is calculated as a large value. The setting range can be, for example, a circular range.

  The confidence level calculation unit 14 performs the confidence level calculation when all the position information constituting the distribution P is within the set range when the set range is arranged at any position on the map, and the variation is small. Can do. Note that the setting range is not limited to a circular range, and may be a range of any appropriate shape. The set range may be a rectangular range, or a triangular or polygonal range.

  Alternatively, the degree-of-confidence calculating unit 14 may calculate the degree of confidence of the vehicle position estimation as a larger value as the variation in the distribution P of the plurality of position information is smaller. The degree-of-confidence calculating unit 14 may calculate the diameter of a circle range including all of the plurality of position information distributions P, and calculate the degree of confidence as a large value so as to be inversely proportional to the diameter.

  The confidence level calculation unit 14 may apply the concept of DOP [Dilution Of Precision] used in GPS position measurement to the calculation of confidence level. Here, FIG. 3 is a diagram illustrating a case where two pieces of position information P1 and P2 are stored for one target. In FIG. 3, the position Pm (x, y) of the host vehicle M, the first position information P1 (x1, y1) in the target, and the second position information P2 (x2, y2) are shown.

  In FIG. 3, x and y are position coordinates based on a map. For example, the position coordinates on the map can be shown with the width direction of the traveling road on which the host vehicle M is traveling in the x direction and the extending direction of the traveling road as the y direction. In this case, the distribution P of the plurality of position information in the target is P1 (x1, y1) and P2 (x2, y2). 3 shows a distance r1 between the position Pm of the host vehicle M and the position information P1, and a distance r2 between the position Pm of the host vehicle M and the position information P1. The position Pm of the host vehicle M can be set to an arbitrary position in the host vehicle M.

  In the situation shown in FIG. 3, the own vehicle position estimating apparatus 100 performs the own vehicle position estimation using only the target in which the position information P1 (x1, y1) and the position information P2 (x2, y2) are stored.

When the DOP concept is applied to the vehicle position estimation in this case, the following equation (1) can be obtained from the geometric positional relationship between the vehicle M and the target position information P1 and P2. The left side of Equation (1) is a covariance matrix of errors in the x direction. G on the right side of Equation (1) is an observation matrix. Superscript T indicates a transposed matrix.

Here, when a two-dimensional xy plane is considered without considering the height direction (z-axis direction), a covariance matrix of errors in the x direction can be expressed as the following equation (2). In Equation (2), σ ² xx is covariance in the x direction. σ ² yy is the covariance in the y direction. σ ² xy is a covariance between the x direction and the y direction.

On the other hand, the observation matrix G can be expressed by the following equation (3) in the situation shown in FIG. In Expression (3), r1 is the distance between the host vehicle M and the target position information P1. r2 is the distance between the host vehicle M and the target position information P2. From these formulas (1) to (3), the following formula (4) can be obtained.

The diagonal component in the above equation (4) corresponds to the degree of deterioration D of the vehicle position estimation caused by the distribution of the plurality of position information on the target. Deterioration degree D is shown as the following formula (5).

  In addition, although calculation of the degradation degree D of the own vehicle position estimation using the target which has the two position information P1 and P2 shown in FIG. 3 was demonstrated, naturally when the position information is three or more, it is applicable. . In this case, the number of rows of the matrix in the above formula increases according to the number of position information. Further, not only the x direction and the y direction but also the height direction (z direction) may be considered. In this case, the number of columns of the matrix in the above formula is three.

  The degree-of-confidence calculation unit 14 may calculate the degree of deterioration D of the vehicle position estimation based on the vehicle. FIG. 4 is a diagram for explaining the calculation of the deterioration degree of the vehicle position estimation based on the vehicle. A plurality of pieces of position information 131 to 133 on the map are stored in the target database 5 in the target 120 shown in FIG. In FIG. 4, the position of the target 120 indicates a relative position with respect to the host vehicle M recognized from the detection result of the external sensor 2. The plurality of pieces of position information (131 to 133) are shown based on the position of the host vehicle M on the map.

Here, the coordinates of the target 120 in the xy plane coordinate system of the position and the coordinate origin of the vehicle M (xs, ys), each coordinate of the location information _{131~133 (x 1, y 1)} , (x 2 , Y ₂ ), (x ₃ , y ₃ ). In this case, the degree of degradation D of the vehicle position estimation is the average distance between the coordinates of the target 120 obtained from the detection result of the external sensor 2 and the coordinates of the position information 131 to 133 stored in the target database 5. Can be obtained by taking That is, the deterioration degree D of the vehicle position estimation may be calculated by the following equation (6). In addition, the upper limit value 3 of the number of terms in Σ in Expression (6) is changed according to the number of pieces of position information on the map associated with the target object.

  In addition, when a plurality of targets are recognized from the detection result of the external sensor 2, the confidence level calculation unit 14 calculates the degree of deterioration D of the vehicle position estimation for each target by the above method, The average value can be used as the final degradation degree D of the vehicle position estimation.

  The degree-of-confidence calculating unit 14 calculates the degree of confidence of the vehicle position estimation in the target based on the degree of deterioration D of the vehicle position estimation. As an example, when the degree of deterioration D is less than a preset deterioration degree threshold, the degree-of-confidence calculating unit 14 increases the degree of confidence in the vehicle position estimation as compared with the case where the degree of deterioration D is greater than or equal to the deterioration degree threshold. Calculate as a value. A plurality of deterioration degree thresholds may be provided. In this case, the confidence level calculation unit 14 can change the confidence level in stages using a plurality of deterioration level thresholds. In addition, the confidence level calculation unit 14 may calculate the confidence level of the vehicle position estimation to be a larger value as the deterioration level D is smaller without using the threshold value.

  Further, the degree-of-confidence calculating unit 14 calculates the degree of confidence of the vehicle position estimation using the target in consideration of the distance (detected distance) between the target vehicle M recognized by the target recognition unit 12 and the target. To do. The degree-of-confidence calculating unit 14 uses the target when the distance between the host vehicle M and the target is less than the distance threshold as compared to when the distance between the host vehicle M and the target is equal to or greater than the distance threshold. The confidence level of the vehicle position estimation is calculated as a large value. A plurality of distance thresholds may be provided. In this case, the confidence level calculation unit 14 can change the confidence level in stages using a plurality of distance thresholds.

  Note that the confidence level calculation unit 14 may perform calculation so that the confidence level of the vehicle position estimation becomes larger as the distance between the host vehicle M and the target is smaller without using a threshold value. The degree-of-confidence calculating unit 14 may calculate the degree of confidence of the vehicle position estimation from the degree of deterioration D, the distance of the vehicle M, and the target using a preset equation.

  When the self-vehicle position estimation is performed using only one target in which a plurality of pieces of position information are stored, the self-confidence calculating unit 14 outputs the self-vehicle position estimation self-confidence degree of the target vehicle for output. Output as confidence of position estimation. The degree-of-confidence calculating unit 14 outputs the degree of confidence of the vehicle position estimation for output to the automatic driving ECU 50.

  On the other hand, when the vehicle position estimation is performed using a plurality of targets in which a plurality of pieces of position information are stored, the confidence level calculation unit 14 calculates the confidence level of the vehicle position estimation for each target. In this case, the confidence level calculation unit 14 calculates the confidence level of the vehicle position estimation for output from the confidence level of the vehicle position estimation for each target using a preset calculation formula. The confidence level of the vehicle position estimation for output may be an average value or a median value of the confidence level of the vehicle position estimation for each target.

  When calculating the confidence level of the vehicle position estimation for output, the confidence level calculation unit 14 may notify the occupant of the vehicle position estimation via the HMI 6 of the confidence level of the vehicle position estimation. The confidence level calculation unit 14 notifies the occupant by at least one of image display on the display and audio output from the speaker. The degree-of-confidence calculating unit 14 may notify the occupant of a decrease in the degree of confidence in the vehicle position estimation when the degree of confidence in the output vehicle position estimation is less than a preset notification threshold. Thereby, the confidence level calculation unit 14 can present appropriate information regarding the confidence level of the vehicle position estimation to the occupant.

  The automatic driving ECU 50 performs automatic driving of the host vehicle M using the input confidence level of the host vehicle position estimation. The automatic driving ECU 50 may change the control content of the automatic driving according to the degree of confidence of the vehicle position estimation. The automatic driving ECU 50 may end the automatic driving when the degree of confidence in the vehicle position estimation is less than a preset confidence level threshold. In this case, the automatic driving ECU 50 may ask the driver to shift to manual driving, or may cause the host vehicle M to evacuate to the side of the road.

<Processing of the vehicle position estimation apparatus according to the first embodiment>
Hereinafter, the process of the vehicle position estimation apparatus 100 according to the first embodiment will be described.

<Vehicle position estimation processing>
FIG. 5A is a flowchart showing the vehicle position estimation process. The process of the flowchart in FIG. 5A is executed, for example, while the vehicle is traveling.

  As shown in FIG. 5A, the host vehicle position estimation ECU 10 of the host vehicle position estimation apparatus 100 acquires the measurement position of the host vehicle M by the measurement position acquisition unit 11 as S10. The measurement position acquisition unit 11 acquires the measurement position of the vehicle based on the position information of the host vehicle M measured by the GPS reception unit 1. The measurement position of the host vehicle is obtained by predicting the position of the host vehicle M from the vehicle speed history and the yaw rate history of the host vehicle based on the host vehicle position estimated by the host vehicle position estimation unit 13. The position is also included.

  In S <b> 12, the host vehicle position estimation ECU 10 recognizes the target around the host vehicle M by the target recognition unit 12. The target recognizing unit 12 uses the detection result of the external sensor 2, the measurement position of the own vehicle M measured by the measurement position acquisition unit 11, and the target information in the target database 5, and targets around the own vehicle M. Recognize

  As S <b> 13, the vehicle position estimation ECU 10 estimates the vehicle position by the vehicle position estimation unit 13. The host vehicle position estimation unit 13 estimates the host vehicle position, which is the position of the host vehicle M on the map, using the target recognized by the target recognition unit 12. The own vehicle position estimation ECU 10 outputs the estimated own vehicle position to the automatic operation ECU 50. Thereafter, the host vehicle position estimation ECU 10 repeats the process from S10 again after a predetermined time has elapsed.

《Self-confidence calculation processing》
FIG. 5B is a flowchart showing the confidence level calculation process of the vehicle position estimation. The flowchart shown in FIG. 5B is executed when the vehicle position estimation process is performed.

  As shown in FIG. 5B, the host vehicle position estimation ECU 10 recognizes the distribution P of the plurality of position information on the target and the distance between the target and the host vehicle M by the confidence calculating unit 14 in S20. . The confidence level calculation unit 14 recognizes a distribution P of a plurality of pieces of position information in the target from the target information in the target database 5 for the target used for estimating the position of the host vehicle. Further, the confidence level calculation unit 14 recognizes the target and the distance of the host vehicle M from the recognition result of the target recognition unit 12.

  In S <b> 22, the vehicle position estimation ECU 10 calculates the confidence level of the vehicle position estimation by the confidence level calculation unit 14. The degree-of-confidence calculating unit 14 calculates the degree of confidence of the vehicle position estimation using the target based on the distribution P of the plurality of position information on the target and the distance between the target and the host vehicle M.

  As an example, the degree-of-confidence calculating unit 14 determines the degree of degradation D of the vehicle position estimation using the above-described equations (1) to (5) based on the distribution P of the plurality of position information in the target. The confidence level calculation unit 14 calculates the confidence level of the vehicle position estimation from a preset calculation formula or the like based on the deterioration level D and the distance between the vehicle and the target. The own vehicle position estimation ECU 10 outputs the calculated degree of confidence of the own vehicle position estimation to the automatic operation ECU 50. Thereafter, the host vehicle position estimation ECU 10 repeats the process from S20 again after a predetermined time has elapsed.

<Effects of the vehicle position estimation apparatus according to the first embodiment>
According to the vehicle position estimation device 100 according to the first embodiment described above, in the calculation of the degree of confidence of the vehicle position estimation using the target in which the plurality of position information is stored, the distribution of the plurality of position information Since the degree of confidence is calculated in consideration of P, a target in which a plurality of position information is stored is used as compared with a case where a plurality of position information is stored for the target. The self-confidence level of the estimated vehicle position can be obtained appropriately.

  Furthermore, according to the own vehicle position estimation device 100, the target detection accuracy (probability of detection) and the like vary depending on the distance between the target and the own vehicle. In addition, by taking into account the distance of the target, the degree of confidence in the vehicle position estimation can be obtained more appropriately.

[Second Embodiment]
Next, the own vehicle position estimation apparatus according to the second embodiment will be described with reference to the drawings. FIG. 6 is a block diagram illustrating a host vehicle position estimation apparatus according to the second embodiment. The own vehicle position estimation device 200 shown in FIG. 6 differs from the first embodiment in that the degree of confidence in the own vehicle position estimation is calculated in the front-rear direction and the vehicle width direction of the own vehicle, respectively. Constituent elements equivalent to the constituent elements of the first embodiment are denoted by the same reference numerals, and redundant description is omitted.

<Configuration of the vehicle position estimation device according to the second embodiment>
As shown in FIG. 6, the own vehicle position estimation device 200 according to the second embodiment is different from the first embodiment in that it has an own vehicle direction estimation unit 21 and the function of the confidence level calculation unit 22. Yes. The own vehicle direction estimation unit 21 estimates the direction of the own vehicle on the map. The method for estimating the direction of the host vehicle on the map is not particularly limited. For example, the host vehicle direction estimation unit 21 estimates the direction of the host vehicle on the map from the time change of the measurement position of the host vehicle (or the host vehicle position of the host vehicle). The host vehicle direction estimation unit 21 may estimate the direction of the host vehicle on the map based on the past map map of the host vehicle and the yaw rate of the host vehicle.

  The degree-of-confidence calculation unit 22 uses the direction on the map of the host vehicle estimated by the host vehicle direction estimation unit 21 and the degree of confidence in the front-rear direction, which is the degree of confidence in the vehicle position estimation in the front-rear direction of the host vehicle M The vehicle width direction confidence level, which is the confidence level of the vehicle position estimation in the vehicle width direction of the host vehicle M, is estimated.

  Specifically, the degree-of-confidence calculating unit 22 distributes a plurality of pieces of position information in the front-rear direction of the host vehicle M based on the direction of the host vehicle estimated by the host vehicle direction estimating unit 21 and the distribution P of the plurality of position information. The front-rear direction distribution is calculated. In addition, the self-confidence degree calculation unit 22 is based on the distribution of the plurality of position information in the vehicle width direction of the host vehicle M based on the direction of the host vehicle estimated by the host vehicle direction estimation unit 21 and the distribution P of the plurality of position information. A certain vehicle width direction distribution is calculated.

  Here, Fig.7 (a) is a figure which shows the case where the some positional information in a target has a big dispersion | variation in the front-back direction. The f-axis shown in FIG. 7A is an axis extending in the front-rear direction of the host vehicle M, and the w-axis is an axis extending in the vehicle width direction of the host vehicle M (same as FIGS. 2A and 2B). . The f axis and the w axis can be obtained from the direction of the host vehicle M on the map.

  In FIG. 7A, the front-rear direction distribution corresponds to a distribution when a plurality of position information distributions P are projected on the f-axis. The vehicle width direction distribution corresponds to a distribution when a plurality of position information distributions P are projected on the w-axis. Note that the f-axis and the w-axis are not necessarily used. The degree-of-confidence calculating unit 22 may calculate the front-rear direction distribution and the vehicle width direction distribution based on the orientation of the host vehicle M on the map from a plurality of position information distributions P in the target stored in the target database 5. .

  The degree-of-confidence calculating unit 22 calculates the degree of front-rear confidence, which is the degree of confidence of vehicle position estimation in the front-rear direction, based on the front-rear direction distribution. Specifically, the degree-of-confidence calculating unit 22 determines that the plurality of position information distributions P are within the preset front-rear direction range as compared to the case where the plurality of position information distributions P are not within the front-rear direction range. The front-rear direction confidence is calculated as a large value. The front-rear direction range is a range preset on the f-axis.

  Alternatively, the degree-of-confidence calculating unit 22 may calculate the degree of confidence in the front-rear direction as the value increases as the variation in the front-rear direction distribution decreases. The degree-of-confidence calculating unit 22 may calculate the degree of confidence in the front-rear direction as the distance between the position information located at the front end and the position information located at the rear end in the front-rear direction distribution is smaller.

  Since the degree of confidence in the front-rear direction distribution in FIG. 7 (b) is smaller than that in FIG. 7 (a), the confidence level calculation unit 22 converts the front-rear direction confidence level in FIG. 7 (b) to the front-rear direction confidence level in FIG. It is calculated as a larger value than

  Similarly, the confidence level calculation unit 22 calculates the vehicle width direction confidence level, which is the confidence level of the vehicle position estimation in the vehicle width direction, based on the vehicle width direction distribution. Specifically, the degree-of-confidence calculating unit 22 includes a case where the plurality of position information distributions P are within the preset vehicle width direction range, and a case where the plurality of position information distributions P are not within the vehicle width direction range. In comparison, the vehicle width direction confidence is calculated as a large value. The vehicle width direction range is a range set in advance on the w-axis.

  Alternatively, the degree-of-confidence calculation unit 22 may calculate the degree of confidence in the vehicle width direction as the value increases as the variation in the distribution in the vehicle width direction decreases. The degree-of-confidence calculation unit 22 may calculate the degree of confidence in the vehicle width direction as a larger value as the distance between the position information located at the rightmost position and the position information located at the leftmost position in the vehicle width direction distribution is smaller.

  Since the degree of confidence in the vehicle width direction distribution in FIG. 7 (a) is smaller than that in FIG. 7 (b), the confidence level calculation unit 22 determines the vehicle width direction confidence level in FIG. 7 (a) as the vehicle width in FIG. 7 (b). It is calculated as a larger value than the direction confidence.

  Similar to the first embodiment, the confidence level calculation unit 22 may calculate the front-rear direction confidence level and the vehicle width direction confidence level in consideration of the distance between the host vehicle M and the target. For example, when the distance between the host vehicle M and the target is less than the distance threshold, the confidence level calculation unit 22 displays the target compared to the case where the distance between the host vehicle M and the target is greater than or equal to the distance threshold. The degree of confidence in the front and rear direction and the degree of confidence in the vehicle width direction of the vehicle position estimation used are calculated as large values.

  When the self-vehicle position estimation is performed using a plurality of targets in which a plurality of pieces of position information are stored, the confidence level calculation unit 22 calculates the front-rear direction confidence level and the vehicle width direction confidence level for each target. In this case, the confidence level calculation unit 22 calculates a front-rear direction confidence level for output and a confidence level in the vehicle width direction for output by using a preset formula. The front-rear confidence level for output may be an average value or a median value of front-rear confidence levels for each target. The same applies to the degree of confidence in the vehicle width direction. The degree-of-confidence calculating unit 22 outputs the output front-rear direction confidence level and the output vehicle width direction confidence level to the automatic driving ECU 50.

<Confidentiality Calculation Processing of Own Vehicle Position Estimation Device According to Second Embodiment>
Next, the confidence level calculation process of the vehicle position estimation apparatus 200 according to the second embodiment will be described with reference to FIG. FIG. 8 is a flowchart showing the degree-of-confidence calculation processing of the vehicle position estimation according to the second embodiment. The flowchart shown in FIG. 8 is executed when the vehicle position is estimated.

  As shown in FIG. 8, the host vehicle position estimation ECU 20 of the host vehicle position estimation apparatus 200 uses a confidence degree calculation unit 22 to calculate a plurality of position information distributions P in the target and the distance between the target and the host vehicle M in S30. Recognize

  In S <b> 32, the host vehicle position estimation ECU 20 estimates the direction of the host vehicle on the map by the host vehicle direction estimation unit 21. For example, the host vehicle direction estimation unit 21 estimates the direction of the host vehicle on the map from the time change of the measurement position of the host vehicle (or the host vehicle position of the host vehicle). The order of S30 and S32 may be reversed and may be executed simultaneously.

  In S <b> 34, the vehicle position estimation ECU 20 calculates the front-rear direction confidence level and the vehicle width direction confidence level by the confidence level calculation unit 22. The confidence level calculation unit 22 calculates the front-rear direction distribution and the vehicle width direction distribution based on the direction of the host vehicle estimated by the host vehicle direction estimation unit 21 and the distribution P of the plurality of position information. The confidence level calculation unit 22 calculates the front-rear direction confidence level based on the front-rear direction distribution, and calculates the vehicle width direction confidence level based on the vehicle width direction distribution. The confidence level calculator 22 outputs the front-rear direction confidence level and the vehicle width direction confidence level to the automatic driving ECU 50. Thereafter, the host vehicle position estimation ECU 20 repeats the process from S30 again after a predetermined time has elapsed.

<Effects of the vehicle position estimation device according to the second embodiment>
According to the vehicle position estimation apparatus 200 according to the second embodiment described above, the front-rear direction distribution and the vehicle width direction distribution are calculated from the distribution P of the plurality of position information on the target with reference to the direction of the vehicle M. Thus, it is possible to appropriately obtain the front-rear direction confidence level that is the confidence level of the vehicle position estimation in the front-rear direction of the host vehicle M and the vehicle width direction confidence level that is the confidence level of the vehicle position estimation in the vehicle width direction. it can.

  As mentioned above, although preferred embodiment of this invention was described, this invention is not limited to embodiment mentioned above. The present invention can be implemented in various forms including various modifications and improvements based on the knowledge of those skilled in the art including the above-described embodiments.

  For example, when the vehicle position estimation using a plurality of targets is performed, the confidence level calculation units 14 and 22 do not necessarily have to calculate the confidence level of the output vehicle position estimation. The confidence level calculation units 14 and 22 may calculate only the confidence level of the vehicle position estimation for each target.

  The degree-of-confidence calculating units 14 and 22 do not necessarily have to estimate the degree of confidence (hereinafter including the degree of confidence in the front-rear direction and the degree of confidence in the vehicle width direction) using the distance (detected distance) between the host vehicle M and the target. . The confidence level calculation units 14 and 22 may estimate the confidence level based on the distribution P of a plurality of pieces of position information on the target.

  In this case, the vehicle position estimation devices 100 and 200 may predict the degree of confidence in the vehicle position estimation before performing the vehicle position estimation. The confidence level calculation units 14 and 22 use the target from the plurality of position information distributions P in the target before the host vehicle M reaches the distance at which the target can be detected by the external sensor 2. It is possible to predict the confidence level of the vehicle position estimation.

  The confidence level calculation units 14 and 22 predict the confidence level of the vehicle position estimation for each target detected by the external sensor 2 on the target route of the host vehicle M, and actually the external sensor 2 of the host vehicle M. However, after the target is detected and the vehicle position is estimated, the degree of confidence in the vehicle position estimation may be calculated using the prediction result.

  The confidence level calculation units 14 and 22 may output the prediction result of the confidence level of the vehicle position estimation for each target to the automatic driving ECU 50. The automatic driving ECU 50 changes the control content of the automatic driving based on the prediction result of the degree of confidence in estimating the vehicle position for each target. In addition, the automatic driving ECU 50 causes the own vehicle position estimation devices 100 and 200 to predict the degree of confidence in estimating the own vehicle position for each target for a plurality of route candidates, and the own vehicle M travels by automatic driving based on the prediction result. A target route may be set. For example, the automatic driving ECU 50 selects a route having the highest average value of the degree of confidence of the vehicle position estimation for each target as the target route.

  The host vehicle M does not necessarily include the automatic driving ECU 50. The own vehicle position estimated by the own vehicle position estimation device 100 and the degree of confidence of the own vehicle position estimation can be used in addition to automatic driving. The own vehicle position estimation devices 100 and 200 may transmit the own vehicle position and the degree of confidence of the own vehicle position estimation to the driving assistance ECU. The driving assistance ECU executes driving assistance control such as ACC [Adaptive Cruise Control] or LKA [Lane KeepAssist]. The own vehicle position estimation devices 100 and 200 may transmit the own vehicle position and the degree of confidence of the own vehicle position estimation to the navigation system.

  The vehicle position estimation device 200 according to the second embodiment calculates the target position from the front-rear direction distribution and the vehicle width direction distribution of the target without calculating the front-rear direction confidence level and the vehicle width direction confidence level. The degree of confidence of the vehicle position estimation may be calculated. In this case, the confidence level calculation unit 22 can weight the front-rear direction distribution and the vehicle width direction distribution, and can calculate the confidence level of the subject vehicle position estimation of the target in consideration of the weighted result. The confidence degree calculation unit 22 sets the weight of the front-rear direction distribution to a value greater than the weight of the vehicle width direction distribution because the larger the variation in the front-rear direction distribution has a greater influence on the own vehicle position estimation. The weighting can be a preset value. The degree-of-confidence calculating unit 22 is more confident in estimating the vehicle position when the variation in the front-rear direction distribution is small and the variation in the vehicle width direction distribution is larger than when the variation in the front-rear direction distribution is large and the variation in the vehicle width direction distribution is small. The degree is calculated as a large value.

  In addition, the case where the concept of DOP is applied to the calculation of the degree of confidence in the vehicle position estimation using one target in which a plurality of pieces of position information are stored has been described, but the concept of DOP is a case where a plurality of targets are used. It can also be applied to. That is, the confidence level calculation unit 14 can also use the above-described formulas (1) to (5) in the calculation of the confidence level of the vehicle position estimation using a plurality of targets each storing one piece of position information. . In this case, for example, the position information P1 (x1, y1) and the position information P2 (x2, y2) in FIG. 3 may be considered as position information of different targets.

  In this way, the confidence level calculation units 14 and 22 can calculate the degree of deterioration of the vehicle position estimation using a plurality of targets around the host vehicle M. Thereby, in the confidence calculation units 14 and 22, for example, when 10 targets exist around the host vehicle M, the ten targets are located in the same direction as viewed from the host vehicle M. Compared to the time, it can be appropriately calculated that the confidence level of the own vehicle position estimation becomes larger when the ten targets are located approximately 360 degrees around the own vehicle M. .

  DESCRIPTION OF SYMBOLS 1 ... GPS receiving part, 2 ... External sensor, 3 ... Internal sensor, 4 ... Map database, 5 ... Target database, 50 ... Automatic operation ECU, 6 ... HMI, 10, 20 ... Own vehicle position estimation ECU, 11 ... Measurement Position acquisition unit, 12 ... target recognition unit, 13 ... own vehicle position estimation unit, 14, 22 ... confidence level calculation unit, 21 ... own vehicle direction estimation unit, 100, 200 ... own vehicle position estimation device.

Claims (3)

Based on the position information on the map of the target used for estimating the position of the own vehicle and the position of the target recognized from the detection result of the vehicle-mounted sensor of the own vehicle, the position on the map of the own vehicle A vehicle position estimation device for estimating a vehicle position,
A database for storing position information on a map of the target, a target database storing a plurality of the position information in at least one target; and
A confidence level calculation unit that calculates the confidence level of the vehicle position estimation using the target in which a plurality of the position information is stored;
With
The said self-confidence degree calculating part is a self-vehicle position estimation apparatus which calculates the self-confidence degree of the said vehicle position estimation based on distribution of these several positional information. A host vehicle direction estimation unit that estimates a direction of the host vehicle on a map;
The degree-of-confidence calculating unit calculates a front-rear direction distribution that is a distribution of the plurality of position information in the front-rear direction of the host vehicle based on the orientation of the host vehicle and the distribution of the plurality of position information. While calculating the degree of confidence in the front-rear direction, which is the degree of confidence in the vehicle position estimation in the front-rear direction from the front-rear direction distribution,
Based on the direction of the host vehicle and the distribution of the plurality of position information, the vehicle width direction distribution, which is the distribution of the plurality of position information in the vehicle width direction of the host vehicle, is calculated. The own vehicle position estimation apparatus according to claim 1, wherein a degree of confidence in the vehicle width direction, which is a degree of confidence in the vehicle position estimation in the vehicle width direction, is calculated. While recognizing the target based on the detection result of the in-vehicle sensor, further comprising a target recognition unit for recognizing the distance between the target and the host vehicle,
The degree-of-confidence calculating unit calculates the degree of confidence of the vehicle position estimation based on the distance between the target in which the plurality of pieces of position information are stored and the own vehicle, and the distribution of the plurality of pieces of position information. The host vehicle position estimation apparatus according to claim 1.

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