JP2018021777A - Own vehicle position estimation device - Google Patents

Abstract

PROBLEM TO BE SOLVED: To improve the estimation accuracy of a position of an own vehicle in a map.SOLUTION: An own vehicle position estimation device includes: a vertical position recognition part for recognizing a vertical position which is a position of an own vehicle in an extention direction of a travel road on the basis of position information of a land mark in a map based on a first own vehicle position which is a position of the own vehicle in the map acquired by an on-vehicle positioning part and image position coordinates of the land mark included in a picked-up image; a vertical position error recognition part for recognizing a vertical position error which is a difference between the vertical position recognized by the vertical position recognition part and a vertical position of the own vehicle acquired from a second own vehicle position; and a third own vehicle position estimation part for estimating a third own vehicle position which is a position of the own vehicle in the map by preset Kalman filter processing on the basis of the second own vehicle position, the degree of collation between a road surface image and a pseudo road surface image, the curvature of the travel road, the vertical position error, the yaw-angle and the vehicle speed.SELECTED DRAWING: Figure 3

Description

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

  Conventionally, JP-A-2001-331787 is known as a technical document related to the vehicle position estimation apparatus. In this publication, a first shape candidate of a road on which the vehicle travels is extracted from image data captured by an in-vehicle camera, a second shape candidate of the road is extracted from map data, and a first shape candidate and a second shape candidate are extracted. An apparatus for estimating the position of the host vehicle on the road based on the overlapping state with the shape candidate is described. This apparatus uses a Kalman filter for collation of overlapping states.

JP 2001-331787 A

  By the way, in order to estimate the position of the own vehicle with high accuracy, a method for further considering the posture (yaw angle or the like) of the in-vehicle camera is known. However, there is a problem that the yaw angle error accumulates while the vehicle is traveling on a curve and the like, leading to a decrease in the estimation accuracy of the position of the host vehicle.

  Therefore, in the present technical field, it is desired to provide an own vehicle position estimation device that can improve the estimation accuracy of the position of the own vehicle on the map.

  In order to solve the above-described problem, an own vehicle position estimation device according to the present invention is based on map information and is based on a first own vehicle position, which is a position on a map of the own vehicle obtained from an in-vehicle positioning unit. A host vehicle position estimation device that generates a surrounding pseudo road surface image and estimates a second host vehicle position by comparing a road surface image obtained from a captured image of a camera of the host vehicle and a pseudo road surface image, A matching degree calculation unit that calculates a matching degree with the pseudo road surface image, and a curvature acquisition unit that acquires the curvature of the traveling road on which the host vehicle travels based on the first host vehicle position or the second host vehicle position and the map information. A landmark storage unit that stores position information of landmarks on the map, and a landmark recognition unit that recognizes image position coordinates of the landmarks included in the captured image based on the captured image captured by the camera of the host vehicle And the lander on the map Based on the position information and the image position coordinates of the landmark included in the captured image, the vertical position recognition unit that recognizes the vertical position that is the position of the vehicle in the extending direction of the traveling road, and the vertical position recognition unit A vertical position error recognition unit that recognizes a vertical position error that is a difference between the recognized vertical position and the vertical position of the host vehicle obtained from the second host vehicle position, and a yaw angle recognition unit that recognizes the yaw angle of the host vehicle; Based on the vehicle speed recognition unit that recognizes the vehicle speed of the host vehicle, the second host vehicle position, the matching degree, the curvature of the traveling road, the vertical position error, the yaw angle, and the vehicle speed, a map of the host vehicle is set by a preset Kalman filter process. A third host vehicle position estimating unit that estimates a third host vehicle position that is an upper position.

  As described above, according to the own vehicle position estimation device according to the present invention, it is possible to improve the estimation accuracy of the position of the own vehicle on the map.

It is a block diagram which shows the own vehicle position estimation apparatus which concerns on this embodiment. It is an example of the data table for calculating | requiring the value of GE (cc, err_lon) in Formula (5). It is a flowchart which shows the first own vehicle position estimation process. It is a flowchart which shows the own vehicle position estimation process after the 2nd time.

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

  A host vehicle position estimation apparatus 100 according to this embodiment shown in FIG. 1 is an apparatus that is mounted on a vehicle (host vehicle) and estimates the position of the host vehicle on a map. The own vehicle position estimation device 100 estimates the position of the own vehicle with high accuracy by using a collation result between a pseudo road surface image generated from map information and a road surface image captured by an in-vehicle camera and a Kalman filter.

[Configuration of the vehicle position estimation device]
As shown in FIG. 1, a host vehicle position estimation apparatus 100 according to the present embodiment includes an [Electronic Control Unit] 10 that comprehensively controls the apparatus. The 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 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 ECU 10 may be composed of a plurality of electronic control units.

  The ECU 10 is connected to a GPS [Global Positioning System] receiving unit 1, a camera 2, a vehicle speed sensor 3, an IMU [Inertial Measurement Unit] 4, a map database 5, and a landmark database 6.

  The GPS receiving unit 1 functions as an in-vehicle positioning unit that measures the position of the host vehicle (for example, the latitude and longitude of the 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 ECU 10. The on-vehicle positioning unit is not limited to the GPS receiving unit 1.

  The camera 2 is an imaging device that images the surroundings of the host vehicle. For example, the camera 2 is provided on the back side of the windshield of the vehicle and images the road surface ahead of the host vehicle. The camera 2 may be a monocular camera or a stereo camera. The camera 2 transmits captured image information to the ECU 10. The camera 2 may be composed of a plurality of cameras.

  The vehicle speed sensor 3 is a detector that detects the speed of the host vehicle. As the vehicle speed sensor 3, 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 rotation speed of the wheel is used. The vehicle speed sensor 3 transmits the detected vehicle speed information to the ECU 10.

  The IMU 4 is an inertial measurement unit that measures the roll angle, pitch angle, and yaw angle of the host vehicle. For example, the IMU 4 includes a gyro sensor and an acceleration sensor. The IMU 4 transmits the detected angle of the own vehicle to the ECU 10. Note that a yaw angle sensor may be employed instead of the IMU 4. Further, instead of the IMU 4, the yaw angle may be obtained from a collation result between a pseudo road surface image generated from map information and a road surface image captured by an in-vehicle camera.

  The map database 5 is a database that stores map information. The map database 5 is formed in, for example, an HDD [Hard Disk Drive] mounted on the host vehicle. Map information includes road position information, road shape information (eg, curves, straight line types, curve curvature, etc.), white line position information, intersection and branch point position information, and building position information. It is. The map database 5 may be stored in a computer of a facility such as a management center that can communicate with the host vehicle.

  The landmark database 6 is a landmark storage unit having landmark information. The landmark database 6 is formed in, for example, an HDD mounted on the host vehicle. A landmark has a fixed position on a road surface (including a road surface other than a vehicle lane) and serves as a reference for calculating an object position. The landmark includes a road sign and a road sign. Road signs include guide signs, warning signs, regulatory signs, instruction signs, and the like. The road marking includes a regulation sign and an instruction sign. The regulation 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 traffic light , Denilators, tunnel entrances, ETC gate entrances, etc.

  The landmark database 6 stores location information of landmarks on the map. That is, the landmark database 6 stores landmark position information associated with the map information stored in the map database 5. The landmark database 6 stores landmark image information for recognizing landmarks from images captured by the camera 2. Note that the map database 5 may also function as the landmark database 6.

  Next, a functional configuration of the ECU 10 will be described. The ECU 10 includes a first vehicle position estimation unit 11, a pseudo road surface image generation unit 12, a road surface image conversion unit 13, a matching degree calculation unit 14, a second vehicle position estimation unit 15, and a curvature acquisition unit 16. The ECU 10 also includes a landmark recognition unit 17, a vertical position recognition unit 18, a vertical position error recognition unit 19, a yaw angle recognition unit 20, a vehicle speed recognition unit 21, and a third own vehicle position estimation unit 22.

  The first vehicle position estimation unit 11 estimates the first vehicle position, which is the position of the vehicle on the map, based on the positioning result of the on-vehicle positioning unit such as the GPS reception unit 1 and the map information of the map database 5. To do. The first host vehicle position estimation unit 11 estimates the first host vehicle position (latitude and longitude) from the positioning result of the in-vehicle positioning unit by a known method.

  The simulated road surface image generation unit 12 generates a simulated road surface image around the first host vehicle position based on the first host vehicle position and map information (position information on the white line). A pseudo road surface image is an image of a road surface of a road that is generated in a pseudo manner from position information of a white line included in map information. The pseudo road surface image is generated, for example, so as to correspond to a road surface image in plan view. The pseudo road surface image generation unit 12 generates a pseudo road surface image from the first vehicle position and map information by a known method.

  The road surface image conversion unit 13 converts a captured image (an image including a road surface) of the camera 2 into a road surface image in plan view. The road surface image conversion unit 13 converts the captured image of the camera 2 into a road surface image by a known method (for example, viewpoint conversion processing) in order to be able to collate with the pseudo road surface image. Note that it is not necessary to convert the road image into a planar view as long as it can be collated with the pseudo road image.

  The matching degree calculation unit 14 calculates the matching degree between the pseudo road surface image and the road surface image. The matching degree corresponds to the degree of coincidence between the pseudo road surface image generated from the map information and the road surface image obtained from the captured image of the camera 2. The higher the matching degree is, the more the pseudo road image matches the road image. The matching degree calculation unit 14 calculates, for example, the matching degree of the road white line as the matching degree between the pseudo road surface image and the road surface image. The matching degree calculation unit 14 calculates the matching degree between the pseudo road surface image and the road surface image by a known method.

  The second host vehicle position estimation unit 15 estimates the second host vehicle position, which is the position on the map of the host vehicle, based on the pseudo road surface image and the road surface image. The second own vehicle position estimating unit 15 estimates the second own vehicle position (latitude and longitude) from the pseudo road surface image and the road surface image by a known method.

  The second vehicle position estimation unit 15 may use the matching degree calculated by the matching degree calculation unit 14. For example, the second vehicle position estimation unit 15 selects a pseudo road surface image having the highest matching degree among the pseudo road surface images whose matching degree with the road surface image obtained from the captured image of the in-vehicle camera 2 is a predetermined threshold value or more. Identify. The second host vehicle position estimation unit 15 estimates the second host vehicle position using position information on the map on which the identified pseudo road surface image is generated. The second host vehicle position estimation unit 15 estimates the second host vehicle position using the matching between the pseudo road surface image and the road surface image by a known method.

  The curvature acquisition unit 16 acquires the curvature of the traveling road on which the host vehicle travels based on the second host vehicle position and the map information. The curvature acquisition unit 16 specifies the traveling road on which the host vehicle travels from the second host vehicle position, and acquires the curvature of the traveling road based on the road curvature information included in the map information. The curvature acquisition unit 16 may acquire the curvature of the traveling road of the host vehicle using the first host vehicle position instead of the second host vehicle position.

  The landmark recognition unit 17 recognizes the landmark included in the captured image based on the captured image of the camera 2 and the landmark information stored in the landmark database 6. The landmark recognition unit 17 recognizes a landmark in the image by pattern recognition or edge extraction, for example.

  When the landmark recognition unit 17 recognizes a landmark included in the captured image, the landmark recognition unit 17 recognizes the image position coordinates of the landmark in the captured image by known image processing. The landmark image position coordinates are the coordinates of the position of the landmark image in the captured image. For example, the image position coordinates of a rhombus mark that is a road marking on the road surface can be the coordinates of the center of the rhombus mark in the captured image. The landmark recognition unit 17 recognizes the image position coordinates of the landmark by a known method.

  When the landmark recognizing unit 17 recognizes the image position coordinates of the landmark, the vertical position recognizing unit 18 recognizes the vehicle based on the image position coordinates of the landmark in the captured image and the position information of the landmark on the map. Recognize the vertical position. The vertical position of the host vehicle is the position of the host vehicle in the extending direction of the traveling road on which the host vehicle travels. The vertical position recognition unit 18 recognizes the vertical position of the host vehicle using landmarks by a known method.

  The vertical position error recognition unit 19 calculates a vertical position error when the vertical position recognition unit 18 recognizes the vertical position of the host vehicle. The vertical position error is a difference between the vertical position of the host vehicle recognized by the vertical position recognition unit 18 and the vertical position of the host vehicle obtained from the second host vehicle position. For example, the vertical position error recognizing unit 19 calculates the vertical position error by taking the difference between the second vehicle position and the vertical position of the own vehicle recognized by the vertical position recognizing unit 18 based on the extending direction of the traveling road. calculate. The vertical position error recognition unit 19 calculates the vertical position error by a known method.

  The yaw angle recognizing unit 20 recognizes the yaw angle of the host vehicle based on the yaw angle measured by the IMU 4. The vehicle speed recognition unit 21 recognizes the vehicle speed of the host vehicle based on the vehicle speed detected by the vehicle speed sensor 3.

  When the vertical position error recognition unit 19 recognizes the vertical position error of the host vehicle, the third host vehicle position estimation unit 22 estimates the third host vehicle position that is the position on the map of the host vehicle. The third host vehicle position estimation unit 22 estimates the third host vehicle position by a preset Kalman filter process based on the second host vehicle position, the matching degree, the curvature of the traveling road, the vertical position error, the yaw angle, and the vehicle speed. To do. Further, the third host vehicle position estimation unit 22 outputs the yaw angle of the host vehicle in addition to the third host vehicle position as the output result of the Kalman filter process. Hereinafter, the yaw angle estimated by the Kalman filter process is referred to as a second yaw angle.

Hereinafter, the Kalman filter processing according to the present embodiment will be described. The following expression (1) is an expression indicating the state quantity u in the Kalman filter processing. In Equation (1), E is the latitude of the host vehicle, N is the longitude of the host vehicle, φ is the yaw angle of the host vehicle, V is the vehicle speed of the host vehicle, and φ ^′ (dot in φ) is the yaw rate of the host vehicle. T represents the transpose of the matrix. Expression (2) is an expression indicating the observation amount v in the Kalman filter processing. In Equation (2), Ec is the latitude at the second vehicle position, Nc is the longitude at the second vehicle position, φc is the yaw angle recognized by the yaw angle recognition unit 20, Vc is the vehicle speed recognized by the vehicle speed recognition unit 21, and φ ^′ c is a yaw rate obtained by differentiating φc.

Next, the observation equation will be described. The observation equation for Kalman filter processing is shown as the following equation (3). In Expression (3), H is a unit matrix. Rn is observation noise. k represents a time series. In Expression (4), Expression (3) is represented by a determinant.

The following equation (5) is a determinant representing the observation noise Rn. In equation (5), cc is the matching degree calculated by the matching degree calculation unit 14. err_lon is a vertical position error recognized by the vertical position error recognition unit 19. A ₁ , A ₂ , and A ₃ are constants. G _E (cc, err_lon), G _R (cc, err_lon), and G _YR (R) are functions. Expression (6) is an expression indicating the function G _YR . In Expression (6), R is the curvature of the traveling road of the host vehicle. A ₄ , A ₅ , and A ₆ are constants. Equation (6) reflects the characteristic that the observation noise of the yaw rate tends to increase when the curvature R is small.

Here, FIG. 2 is an example of a data table for obtaining GE (cc, err_lon) in Expression (5). “A” shown in FIG. 2 is a constant. The data table in FIG. 2 is set so that the observation noise is small when the matching degree cc is large and when the vertical position error err_lon is small. As shown in FIG. 2, if the matching degree cc and the vertical position error err_lon are determined, the value of GE (cc, err_lon) can be obtained using a preset data table or the like. G _R (cc, err_lon) for also, it is possible to obtain the value by using the same data table. For example, by replacing the constant a in FIG. 2 to the appropriate constant _b, it can be a data table G R (cc, err_lon).

Next, the state equation will be described. A state equation of Kalman filter processing is shown as the following equation (7). In Expression (7), F is a matrix that can be partially differentiated. Qn is state noise (system noise). k + 1 means that it is a discrete time immediately after the discrete time k. Expressions (8) to (12) are shown below as expressions developed by omitting the state noise Qn from Expression (7). In addition, dt shown in the equations (8) to (10) means a transition time from the discrete time k to the discrete time k + 1.

The following equation (13) is a determinant representing the state noise Qn. In Expression (13), B1, B2, B3, and B4 are constants. Fpos (R) is a function of curvature R. Fyaw (φ) is a function of the yaw angle φ. Expression (14) is an expression indicating the function Fpos (R). In Formula (14), B ₅ , B ₆ , and B ₇ are constants. It is a formula which shows a function Fyaw (R). In the formula (15), B ₈ , B ₉ and B ₁₀ are constants.

  The third host vehicle position estimation unit 22 can estimate the third host vehicle position and the yaw angle by the Kalman filter process using the equations (1) to (15) described above. According to this Kalman filter process, the accuracy of estimation of the third vehicle position can be improved by considering the matching degree cc, the vertical position error err_lon, and the curvature R in the observation noise Rn as compared with the case where these are not taken into consideration. Can do. In addition, in the state noise Qn, by considering the vertical position error err_lon and the curvature R, it is possible to improve the estimation accuracy of the third host vehicle position compared to the case where these are not considered.

[Vehicle position estimation processing in the vehicle position estimation apparatus]
Hereinafter, the vehicle position estimation process in the vehicle position estimation apparatus 100 according to the present embodiment will be described with reference to the drawings. FIG. 3 is a flowchart showing a first vehicle position estimation process. The flowchart of FIG. 3 is implemented, for example, when the host vehicle starts traveling.

  As shown in FIG. 3, the ECU 10 of the host vehicle position estimation apparatus 100 estimates the first host vehicle position by the first host vehicle position estimation unit 11 as S10. The first own vehicle position estimating unit 11 estimates the first own vehicle position based on the positioning result of the on-vehicle positioning unit (for example, the GPS receiving unit 1).

  In S12, the ECU 10 causes the pseudo road surface image generation unit 12 to generate a pseudo road surface image. The simulated road surface image generation unit 12 generates a simulated road surface image around the first host vehicle position based on the first host vehicle position and map information (position information on the white line). Further, the ECU 10 converts the captured image of the camera 2 into a planar road surface image (a road surface image that can be compared with the pseudo road surface image) by the road surface image conversion unit 13.

  In S14, the ECU 10 calculates the matching degree between the pseudo road surface image and the road surface image by the matching degree calculation unit 14. The higher the matching degree, the higher the coincidence degree between the pseudo road surface image and the road surface image.

  In S <b> 16, the ECU 10 estimates the second vehicle position by the second vehicle position estimation unit 15. The second host vehicle position estimation unit 15 estimates the second host vehicle position based on the pseudo road surface image and the road surface image. For example, the second vehicle position estimation unit 15 selects a pseudo road surface image having the highest matching degree among the pseudo road surface images whose matching degree with the road surface image obtained from the captured image of the in-vehicle camera 2 is a predetermined threshold value or more. The second vehicle position is estimated using the position information of the map on which the identified pseudo road surface image is identified.

  In S <b> 18, the ECU 10 acquires the curvature of the traveling road on which the host vehicle travels by the curvature acquisition unit 16. The curvature acquisition unit 16 specifies a traveling road on which the host vehicle travels from the second host vehicle position based on the second host vehicle position and map information, and acquires the curvature of the traveling road from the map information.

  In S <b> 20, the ECU 10 determines whether or not the landmark recognition unit 17 has recognized a landmark included in the captured image of the camera 2. The landmark recognition unit 17 recognizes the landmark included in the captured image based on the captured image of the camera 2 and the landmark information stored in the landmark database 6. When it is determined that the landmark included in the captured image is recognized (S20: YES), the ECU 10 proceeds to S22.

  On the other hand, if the ECU 10 determines that the landmark included in the captured image is not recognized (the landmark is not present in the captured image) (S20: NO), the ECU 10 proceeds to S28.

  In S <b> 22, the ECU 10 recognizes the image position coordinates of the landmark in the captured image by the landmark recognition unit 17. For example, the landmark recognition unit 17 can recognize the coordinates of the center of the rhombus mark in the captured image as the image position coordinates of the rhombus mark that is a road marking.

  In S <b> 24, the ECU 10 recognizes the vertical position of the host vehicle by the vertical position recognition unit 18. The vertical position recognition unit 18 recognizes the vertical position of the host vehicle based on the image position coordinates of the landmark in the captured image and the position information of the landmark on the map.

  In S <b> 26, the ECU 10 calculates the vertical position error by the vertical position error recognition unit 19. The vertical position error recognition unit 19 calculates a vertical position error as a difference between the vertical position of the host vehicle recognized by the vertical position recognition unit 18 and the vertical position of the host vehicle obtained from the second host vehicle position.

  In S28, the ECU 10 estimates the third vehicle position and the second yaw angle by the third vehicle position estimation unit 22. The third vehicle position estimation unit 22 performs the third vehicle position and the second vehicle position by a preset Kalman filter process based on the second vehicle position, the matching degree, the curvature of the traveling road, the vertical position error, the yaw angle, and the vehicle speed. Estimate the 2 yaw angle. The third host vehicle position estimation unit 22 can estimate the third host vehicle position and the second yaw angle by, for example, Kalman filter processing using the above-described formulas (1) to (15). The ECU 10 outputs the estimated third host vehicle position to the outside (for example, a navigation system, a driving support device, an automatic driving device) as a position on the map of the host vehicle. Here, if NO in S20 (when the landmark is not recognized), the Kalman filter process is executed assuming that the vertical position error is a preset maximum value. In this case, in the table of FIG. 2, the value in the column “2 ≦ err_lon” that is the maximum value is used in relation to the matching degree cc.

  In addition to the flowchart of FIG. 3, the yaw angle recognition unit 20 recognizes the yaw angle of the host vehicle and the vehicle speed recognition unit 21 recognizes the host vehicle speed at predetermined intervals according to the performance on the sensor side. Yes.

  FIG. 4 is a flowchart showing the vehicle position estimation process for the second and subsequent times. The flowchart shown in FIG. 4 is executed, for example, when the third host vehicle position and the yaw angle are obtained by the first Kalman filter process after the host vehicle starts running.

  As shown in FIG. 4, ECU10 produces | generates a pseudo road surface image by the pseudo road surface image generation part 12 as S30. When the third vehicle position is estimated once, the pseudo road surface image generation unit 12 is based on the third vehicle position and map information (position information on the white line) with high estimation accuracy. A pseudo road surface image is generated. Further, the ECU 10 converts the captured image of the camera 2 into a planar road surface image (a road surface image that can be compared with the pseudo road surface image) by the road surface image conversion unit 13.

  In S32, the ECU 10 calculates the matching degree between the pseudo road surface image and the road surface image by the matching degree calculation unit 14. This pseudo road surface image is a pseudo road surface image around the third vehicle position.

  In S <b> 34, the ECU 10 estimates the second vehicle position by the second vehicle position estimation unit 15. The second vehicle position estimation unit 15 estimates the current second vehicle position based on the pseudo road surface image and the road surface image around the third vehicle position.

  In S <b> 36, the ECU 10 acquires the curvature of the traveling road on which the host vehicle travels by the curvature acquisition unit 16. The curvature acquisition unit 16 acquires the curvature of the traveling road based on the second host vehicle position and the map information.

  In S <b> 38, the ECU 10 determines whether or not the landmark recognition unit 17 has recognized a landmark included in the captured image of the camera 2. When it is determined that the landmark included in the captured image is recognized (S38: YES), the ECU 10 proceeds to S40.

  On the other hand, when the ECU 10 determines not to recognize the landmark included in the captured image (S38: NO), the ECU 10 proceeds to S46.

  Since S40 to S44 are the same processes as S22 to S26 shown in FIG. In S <b> 40, the ECU 10 recognizes the image position coordinates of the landmark in the captured image by the landmark recognition unit 17. In S <b> 42, the ECU 10 recognizes the vertical position of the host vehicle by the vertical position recognition unit 18. In S <b> 44, the ECU 10 calculates the vertical position error by the vertical position error recognition unit 19.

  In S <b> 46, the ECU 10 estimates the current third vehicle position and the current second yaw angle by the third vehicle position estimation unit 22. The third host vehicle position estimation unit 22 performs the current first vehicle position by a preset Kalman filter process based on the second host vehicle position, the matching degree, the curvature of the traveling road, the vertical position error, the previous second yaw angle, and the vehicle speed. 3 Estimate the vehicle position and the current second yaw angle.

  The third vehicle position estimation unit 22 can estimate the current third vehicle position and the current second yaw angle by, for example, the Kalman filter process using the above-described equations (1) to (15). . In this case, the previous second yaw angle (the yaw angle estimated by the previous Kalman filter process) is used as the yaw angle φ in equation (1).

  The previous second yaw angle is not necessarily used, and the Kalman filter process may be executed using the yaw angle of the host vehicle recognized by the yaw angle recognition unit 20. If NO in S38 (when no landmark is recognized), Kalman filter processing is performed using the vertical position error used in the previous processing. The ECU 10 outputs the estimated current third vehicle position to the outside as a position on the map of the own vehicle. Thereafter, the ECU 10 repeats the process from S30 again using the current third host vehicle position after the elapse of a predetermined time.

[Operational effect of own vehicle position estimation device]
According to the vehicle position estimation device 100 according to the present embodiment described above, the second vehicle position, yaw angle, and vehicle speed estimated by collating the pseudo road surface image generated from the map information with the road surface image captured by the in-vehicle camera are calculated. By performing Kalman filter processing considering the curvature and vertical position error of the driving road as input, it becomes possible to correct the yaw angle based on the road curvature compared to the case where the road curvature and vertical position error are not considered. The estimation accuracy of the position on the map can be improved.

  Moreover, according to the own vehicle position estimation apparatus 100, as shown in FIG. 4, after estimating the third own vehicle position once, the next own vehicle position estimation process is performed using the third own vehicle position. Therefore, it is not necessary to use an in-vehicle positioning unit such as the GPS receiving unit 1 every time, and high estimation accuracy can be maintained for the position of the host vehicle on the map.

  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, the second vehicle position estimation unit 15 does not need to use the matching degree in the estimation of the second vehicle position. In this case, the matching degree calculation unit 14 may calculate the matching degree between the pseudo road surface image and the road surface image after the estimation of the second vehicle position. That is, S14 in FIG. 3 may be performed after S16. Further, the order of acquisition of the curvature of the traveling road in S10 to S16 and S18 is not limited as long as it is completed before the Kalman filter processing in S28. Similarly, S32 of FIG. 4 may be performed after S34. The order of acquisition of the curvature of the traveling road of S30 to S34 and S36 is not limited as long as it is completed before the Kalman filter processing of S46. In the flowchart of the first vehicle position estimation process shown in FIG. 3, when NO in S20 (when no landmark is recognized), the Kalman filter process of S28 is not performed, and only when S20 is YES, S28. It may be an aspect in which the Kalman filter processing is executed.

  DESCRIPTION OF SYMBOLS 100 ... Own vehicle position estimation apparatus, 10 ... ECU, 1 ... GPS receiving part (positioning part), 2 ... Camera, 3 ... Vehicle speed sensor, 4 ... IMU, 5 ... Map database, 6 ... Landmark database (landmark memory | storage part) ), 11... First vehicle position estimation unit, 12... Pseudo road surface image generation unit, 13... Road surface image conversion unit, 14 .. matching degree calculation unit, 15 ... second vehicle position estimation unit, 16. DESCRIPTION OF SYMBOLS 17 ... Landmark recognition part, 18 ... Vertical position recognition part, 19 ... Vertical position error recognition part, 20 ... Yaw angle recognition part, 21 ... Vehicle speed recognition part, 22 ... 3rd own vehicle position estimation part

Claims (1)

Based on the first vehicle position, which is the position of the host vehicle on the map obtained from the on-vehicle positioning unit, a pseudo road surface image around the host vehicle is generated from the map information, and the captured image of the camera of the host vehicle A vehicle position estimation device that estimates a second vehicle position by comparing the road surface image obtained from the pseudo road surface image,
A matching degree calculation unit for calculating a matching degree between the road surface image and the pseudo road surface image;
A curvature acquisition unit that acquires a curvature of a traveling road on which the vehicle travels based on the first vehicle position or the second vehicle position and the map information;
A landmark storage unit for storing location information of landmarks on the map;
A landmark recognition unit that recognizes image position coordinates of the landmark included in the captured image based on a captured image captured by the camera of the host vehicle;
Based on position information of the landmark on the map and image position coordinates of the landmark included in the captured image, a vertical position that is the position of the host vehicle in the extending direction of the traveling road is recognized. A vertical position recognition unit;
A vertical position error recognizing unit for recognizing a vertical position error which is a difference between the vertical position recognized by the vertical position recognizing unit and the vertical position of the own vehicle obtained from the second own vehicle position;
A yaw angle recognition unit for recognizing the yaw angle of the host vehicle;
A vehicle speed recognition unit for recognizing the vehicle speed of the host vehicle;
Based on the second own vehicle position, the collation degree, the curvature of the traveling road, the vertical position error, the yaw angle, and the vehicle speed, a position on the map of the own vehicle by a preset Kalman filter process. A third vehicle position estimation unit for estimating a third vehicle position;
A vehicle position estimation device comprising:

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