JP2014034251A - Vehicle traveling control device and method thereof - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a vehicle traveling control device capable of obtaining stable estimation accuracy during traveling in a highly difficult section.SOLUTION: A ground own vehicle position estimation unit 3 estimates estimated values of own vehicle position and a posture and estimation accuracy on a ground fixed coordinate system. A relative own vehicle position estimation unit 4 integrates the changing amounts of the own vehicle position to estimate a relative positional relationship between a reference point, an azimuth, and the own vehicle position. An own vehicle position difficult section estimation unit 8 estimates a section where the difficulty of position estimation on a target traveling route by the ground own vehicle position estimation unit is high. A landmark retrieval unit 9 retrieves a landmark highly likely to be within the photographing range of an ambient environment photographing unit 1 in a region before the section estimated to be high in difficulty from map information. An own vehicle position estimation switching unit 11 switches from the ground own vehicle position estimation unit to the relative own vehicle position estimation unit during traveling in the section where the position estimation difficulty is high on the basis of the landmark. An own vehicle traveling control unit 7 subjects the own vehicle to traveling control on the basis of the output of the relative own vehicle position estimation unit during traveling in the section where the position estimation difficulty is high, and subjects the own vehicle to traveling control on the basis of the output of the ground own vehicle position estimation unit in sections other than the section where the position estimation difficulty is high.

Description

  The present invention relates to a vehicle travel control apparatus and method for causing a vehicle to follow a target travel route, and in particular, a vehicle travel control apparatus that obtains stable estimation accuracy even when traveling in a highly difficult section such as parking and passing through a narrow road. And its method.

  Conventionally, as this type of technology, for example, a parking assistance device described in Patent Document 1 is known. The parking assist device includes a camera that captures a mark placed at a parking position, and performs parking support by recognizing a relative positional relationship between the detected mark and the current position of the vehicle by image processing.

  The parking assist device records the ideal travel route from the parking start position to the parking completion position by causing the vehicle to travel in advance. When parking at the set parking position after recording the ideal travel route, the vehicle position is calculated by detecting the mark installed at the start of parking with a camera. An error between the calculated own vehicle position and the parking start position is estimated, an ideal travel route is corrected based on the estimated error, a parking guidance route is calculated, and automatic parking is executed.

JP 2008-174000

  However, in the parking assistance device described in Patent Document 1, it is necessary to previously install a mark at the parking position. In addition, automatic parking was not possible unless the vehicle was parked once in the past. Also, until the mark enters the camera field of view, the driver has to drive or otherwise guide the vehicle.

  That is, when traveling in a highly difficult section such as parking and passing through a narrow road, stable estimation accuracy cannot be obtained, and automatic parking cannot be performed with high accuracy.

  Therefore, the present invention has been proposed in view of the above situation, and a vehicle travel control device capable of obtaining stable estimation accuracy even when traveling in a highly difficult section such as parking and passing through a narrow road, and the like An object is to provide such a method.

  In the present invention, the estimated value and estimation accuracy of the position and orientation of the vehicle on the ground fixed coordinate system are estimated by the first vehicle position estimation unit based on the surrounding environment image of the vehicle. The relative position relationship between the reference point and direction and the vehicle position is estimated by integrating the amount of change in the vehicle position estimated based on the operation amount and the movement state of the vehicle by the second vehicle position estimation unit. Then, a section having a high degree of difficulty in position estimation by the first vehicle position estimation unit is estimated on the target travel route. A landmark having a high possibility of entering the shooting range of the surrounding environment shooting unit in the near side area of the section estimated to have a high degree of difficulty is detected from the map information. Based on the detected landmark, the vehicle position estimation switching unit switches from the first vehicle position estimation unit to the second vehicle position estimation unit while traveling in a section where the position estimation difficulty is high. While traveling in a section with a high degree of position estimation difficulty, a section other than the section with a high position estimation difficulty is executed by executing travel control of the own vehicle based on the output of the second vehicle position estimation section based on the result of the own vehicle position estimation switching section. Then, the traveling control of the host vehicle is executed based on the output of the first host vehicle position estimation unit based on the result of the host vehicle position estimation switching unit.

  According to the present invention, the vehicle position estimation is switched from the first vehicle position estimation unit to the second vehicle position estimation unit while traveling in a section with high position estimation difficulty, and the vehicle position estimation is performed while traveling in a section with high position estimation difficulty. Based on the output of the second host vehicle position estimation unit based on the result of the switching unit, the travel control of the host vehicle is executed. In a section other than the section where the position estimation difficulty is high, travel control of the host vehicle is executed based on the output of the first host vehicle position estimation unit based on the result of the host vehicle position estimation switching unit. Therefore, stable estimation accuracy can be obtained even when traveling in highly difficult sections such as parking and passing through narrow roads.

It is a block diagram which shows the structure of the vehicle travel control apparatus of the 1st Embodiment of this invention. It is a figure which shows the example to which the vehicle travel control apparatus of the 1st Embodiment of this invention is applied. It is a figure which shows the example which uses properly two self-position estimation methods from which a property differs in the example to which the vehicle travel control apparatus of 1st Embodiment shown in FIG. 2 is applied. It is a flowchart which shows the operation | movement procedure of the vehicle travel control apparatus of the 1st Embodiment of this invention. It is a block diagram which shows the structure of the own vehicle position and orientation estimation apparatus of the 2nd Embodiment of this invention. (A) is a photographed image, (b) is an edge image of (a), (c) is a virtual image, and (d) is a virtual image when the virtual position of (c) is shifted to the right. It is a flowchart which shows the operation | movement procedure of the own vehicle position and orientation estimation apparatus of the 2nd Embodiment of this invention. It is explanatory drawing explaining the operation | movement which moves a particle in the own vehicle position and orientation estimation apparatus of the 2nd Embodiment of this invention. It is a figure which shows the relationship between the distance from a vehicle, and likelihood like_p in the own vehicle position and orientation estimation apparatus of the 2nd Embodiment of this invention. It is a figure which shows the relationship between the distance from a vehicle, and likelihood like_a in the own vehicle position and orientation estimation apparatus of the 2nd Embodiment of this invention.

  Hereinafter, embodiments of the present invention will be described with reference to the drawings.

[First Embodiment]
FIG. 1 is a block diagram showing the configuration of the vehicle travel control apparatus according to the first embodiment of the present invention. The vehicle travel control device includes an ambient environment photographing unit 1, a vehicle state detection unit 2, a ground vehicle position estimation unit 3, a relative vehicle position estimation unit 4, a map database (map DB) 5, and a travel route calculation unit 6. . In addition, the vehicle travel control device includes an automatic travel control unit 7, a host vehicle position difficulty interval estimation unit 8, a landmark search unit 9, a landmark detection travel control unit 10, and a host vehicle position estimation switching unit 11.

  The ambient environment photographing unit 1 captures an image of the surrounding environment of the host vehicle and outputs the captured image to the ground host vehicle position estimating unit 3 and is a camera or the like. The own vehicle state detection unit 2 detects an operation amount and a movement state of the own vehicle, and outputs a detected value to the relative own vehicle position estimation unit 4, and is an encoder, a speed sensor, or the like.

  The ground vehicle position estimation unit 3 corresponds to the first vehicle position estimation means, and is realized by a VMM (Visual Map Matching) using a camera. Suppose. The ground vehicle position estimation unit 3 calculates the estimated value and estimation accuracy of the position and posture of the vehicle on the ground fixed coordinate system based on the image of the surrounding environment of the vehicle detected by the ambient environment photographing unit 1. . The ground vehicle position estimation unit 3 can continuously estimate the vehicle position in a travel section where map data is available, but the position estimation accuracy decreases in a high difficulty section.

  The relative vehicle position estimation unit 4 corresponds to the second vehicle position estimation unit, and integrates the amount of change in the vehicle position estimated based on the detection value of the vehicle state detection unit 2, thereby The relative positional relationship between the bearing and the vehicle position is estimated. Although the relative vehicle position estimation unit 4 can be expected to have high position estimation accuracy regardless of the location, since the error is integrated with the travel distance, continuous use is difficult.

  The map DB 5 acquires and stores linear information of a road on which the vehicle can travel and position information of structures around the road as map information, and includes a storage medium such as a hard disk or a memory. The travel route calculation unit 6 calculates a target travel route from the departure point of the own vehicle to the movement target point based on the map information stored in the map DB 5, and is a navigation or the like.

  The automatic travel control unit 7 automatically controls the steering system and the braking / driving system so that the host vehicle follows the target travel route calculated by the travel route calculation unit 6. The own vehicle position difficulty level section estimation unit 8 estimates a section on the target travel route calculated by the travel route calculation unit 6 where the position estimation difficulty by the ground vehicle position estimation unit 3 is high.

  The landmark search unit 9 corresponds to the landmark detection means, and can enter the shooting range of the surrounding environment shooting unit 1 within the area on the near side of the section estimated to be high by the own vehicle position difficulty section estimation unit 8. A highly characteristic landmark is detected from the map information in the map DB 5.

  The landmark detection travel control unit 10 corresponds to the landmark detection means, and the detected landmarks of the surrounding environment photographing unit 1 are detected until the vehicle position estimation accuracy by the ground vehicle position estimation unit 3 reaches a predetermined level. Control is performed so as to correct the vehicle position so as to be within the shooting range.

  The own vehicle position estimation switching unit 11 performs landmark detection travel control when the position estimation accuracy of the ground vehicle position estimation unit 3 exceeds a predetermined level with the landmark detection travel control unit 10 activated. The activation of the unit 10 is canceled. The own vehicle position estimation switching unit 11 switches from the ground own vehicle position estimation unit 3 to the relative own vehicle position estimation unit 4 while traveling in a section where the position estimation difficulty is high.

  The automatic travel control unit 7 travels the vehicle based on the output of the relative vehicle position estimation unit 4 based on the result of the vehicle position estimation switching unit 11 while traveling in a section where the position estimation difficulty is high. In a section other than the high section, the host vehicle is travel-controlled based on the output of the ground host vehicle position estimation unit 3 based on the result of the host vehicle position estimation switching unit 11.

  Ground vehicle position estimation unit 3, relative vehicle position estimation unit 4, travel route calculation unit 6, automatic travel control unit 7, own vehicle position difficulty section estimation unit 8, landmark search unit 9, landmark detection travel control unit 10 The own vehicle position estimation switching unit 11 is realized by the ECU executing an automatic traveling control program.

  Next, the operation of the vehicle travel control apparatus of the first embodiment configured as described above will be described in detail with reference to FIGS. FIG. 2 is a diagram illustrating an example to which the vehicle travel control device of the first embodiment of the present invention is applied. FIG. 3 is a diagram illustrating an example in which two self-position estimation methods having different properties are selectively used by detecting a landmark in the example to which the vehicle travel control device of the first embodiment illustrated in FIG. 2 is applied. FIG. 4 is a flowchart showing an operation procedure of the vehicle travel control apparatus according to the first embodiment of the present invention.

  First, in step S1, a destination is input by the operation unit. In step S2, the travel route calculation unit 6 calculates a target travel route from the departure point of the host vehicle to the destination based on the input destination and the map information from the map DB 5.

  Next, in step S <b> 3, the own vehicle position difficulty section estimation unit 8 estimates a section on which the position estimation difficulty by the ground host vehicle position estimation unit 3 is high on the target travel route calculated by the travel route calculation unit 6. Extract. In step S4, the landmark search unit 9 has a high possibility of entering the shooting range of the surrounding environment shooting unit 1 in the near side area of the section estimated as having a high difficulty level by the own vehicle position difficulty section estimation unit 8. The mark is detected from the map information from the map DB 5. In step S5, the vehicle starts to travel.

  Next, the ground vehicle position estimation unit 3 estimates the position and orientation of the vehicle on the ground fixed coordinate system and the estimation accuracy based on the surrounding environment image detected by the ambient environment photographing unit 1. Is calculated. The relative vehicle position estimation unit 4 accumulates the amount of change in the vehicle position estimated based on the detection value of the vehicle state detection unit 2, so that the relative positional relationship between the reference point and direction and the vehicle position is calculated. Is estimated. In step S <b> 6, the ECU acquires the vehicle position estimation results of the ground vehicle position estimation unit 3 and the relative vehicle position estimation unit 4.

  Next, in step S7, the ECU determines whether or not the vehicle has arrived at the destination. In step S8, if the vehicle has not arrived at the destination, the ECU Determine if you are driving.

  In step S <b> 9, when the host vehicle is not traveling in the high difficulty section, the host vehicle position estimation switching unit 11 switches from the relative host vehicle position estimation unit 4 to the ground host vehicle position estimation unit 3. In step S10, the automatic travel control unit 7 controls the automatic travel of the vehicle based on the vehicle position from the ground vehicle position estimation unit 3 switched by the vehicle position estimation switching unit 11. Calculate the value.

  In the application example shown in FIG. 2, when the host vehicle 13 is traveling on the road 12a (before time t1 in FIG. 3), the host vehicle position estimation unit 3 estimates the host vehicle position with reasonable estimated position accuracy. Is done.

  On the other hand, in step S8, when the vehicle is traveling in the high difficulty section, in step S11, the ECU determines whether or not the high difficulty section has approached forward. In step S12, when the high difficulty section approaches, it is switched to the landmark detection travel control unit 10, and the landmark detection travel control unit 10 is activated.

  In step S <b> 13, the landmark detection travel control unit 10 determines whether the landmark searched by the landmark search unit 9 has been detected. In step S14, when a landmark is detected, the landmark detection travel control unit 10 determines whether the vehicle position estimation accuracy by the ground vehicle position estimation unit 3 is equal to or higher than a predetermined level. That is, it is determined whether the vehicle position estimation accuracy is good.

  At this time, the landmark detection travel control unit 10 makes the own vehicle so that the landmark falls within the photographing range of the surrounding environment photographing unit 1 until the own vehicle position estimation accuracy by the ground own vehicle position estimating unit 3 reaches a predetermined level. Control to correct the position.

  In this case, at time t1 in FIG. 3, as shown in FIG. 2, when deceleration control is performed in front of the landmark 14 and the vehicle 13 detects the landmark 14, the landmark detection travel control unit 10 The vehicle 13 is temporarily stopped. Then, at time t1 to t2, the vehicle position is corrected based on the detected landmark 14. Then, the host vehicle 13 is restarted at time t2 when the host vehicle position estimation accuracy is equal to or higher than a predetermined level.

  In step S15, when the vehicle position estimation accuracy becomes a predetermined level or higher, the vehicle position estimation switching unit 11 switches from the ground vehicle position estimation unit 3 to the relative vehicle position estimation unit 4.

  In this case, after the time t2, the vehicle position estimation switching unit 11 is traveling to the ground vehicle position estimation unit while traveling in a highly difficult section, that is, the vehicle 13 is traveling around the parking lot 15 of the road 12b. 3 to switch to the relative vehicle position estimation unit 4, and the relative vehicle position estimation unit 4 controls the vehicle 13. The relative host vehicle position estimation unit 4 can control the traveling of the host vehicle stably at a short distance with high accuracy.

  As described above, according to the vehicle travel control apparatus of the first embodiment, in the travel section in which the map information of the map DB 5 can be used, the host vehicle position estimation unit 3 continuously estimates the host vehicle position, and the degree of difficulty is high. Correct the position of the vehicle by the landmark before the section. Since the vehicle position estimation unit 3 switches to the relative vehicle position estimation unit 4 while traveling in a section with high position estimation accuracy, the vehicle position estimation accuracy is stable even in highly difficult sections such as parking and passing through narrow roads. Travel control can be continued.

  In addition, the vehicle position is corrected so that the detected landmark falls within the camera viewing angle until the vehicle position estimation accuracy by the ground vehicle position estimation unit 3 reaches a predetermined level. Can be improved.

  The ground vehicle position estimation unit 3 compares a captured image of the surrounding environment of the host vehicle captured by the ambient environment capturing unit 1 with a virtual image obtained by capturing the map information of the map DB 5 from the virtual position and the virtual attitude angle. To do. Thereby, the attitude angle and position of the actual vehicle can also be estimated, and the position estimation result can be obtained continuously using only the captured image.

  Further, when the landmark detection travel control unit 10 detects the landmark searched by the landmark search unit 9, the landmark detection travel control unit 10 temporarily stops the vehicle, so that the correction by the landmark can be performed and the vehicle position estimation accuracy is increased. be able to.

  The landmark search unit 9 searches the map DB 5 for a plurality of landmarks that satisfy a predetermined condition, and selects a landmark that is highly likely to be detected from traffic conditions from the searched plurality of landmarks. The selected landmark is used as the target landmark of the landmark detection travel control unit 10. Thereby, even when the landmark is shielded from the field of view of the own vehicle by another vehicle or the like, the robustness to the environment can be improved by performing the correction using the substitute.

  The present invention may also include an obstacle detection unit that detects the position and size of an obstacle present in front of the host vehicle. In this case, the landmark detection is performed by selecting an appropriate landmark after excluding those landmarks that are likely to be shielded by the obstacle detected by the obstacle detection unit. The process of the traveling control unit 10 can be executed.

  Further, when it is determined that sufficient position estimation accuracy is achieved in the vicinity of the landmark, the process of the landmark detection travel control unit 10 is skipped and the switching to the relative vehicle position estimation unit 4 is immediately performed. May be executed.

  In addition, when the set destination is a parking lot composed of a plurality of parking frames, the own vehicle position difficulty level section estimation unit 8 may set the parking lot as a section with a high own vehicle position estimation difficulty level. good. Since the same parking frame is continuous, the position estimation accuracy in the parking area where the position estimation accuracy by the camera is difficult to increase can be stably improved.

  In addition, the vehicle position difficulty level section estimation unit 8 is a section where the road width is smaller than a predetermined value, and a landmark that can be detected from the road is not recorded in the map information. You may set as a section with a high vehicle position estimation difficulty.

  Further, the landmark detection travel control unit 10 may change the target travel speed of the vehicle based on the current level of the vehicle position estimation accuracy. Also, among the extracted landmarks, a landmark first detected by the ambient environment photographing unit 1 may be selected.

  The ground vehicle position estimation unit 3 may be realized by a high-precision GPS (Global Positioning System) instead of the VMM using the camera, and as a high-difficulty section, the tunnel, the valley of the building, the indoor parking lot Etc. may be assumed.

[Second Embodiment]
The own vehicle position / posture estimation apparatus according to the second embodiment of the present invention shows a specific configuration of the ground own vehicle position estimation unit 3 described in the vehicle travel control apparatus according to the first embodiment. As shown in FIG.

  This own vehicle position / posture estimation apparatus includes an ECU 21, a camera (imaging means) 22, a three-dimensional map database 23, and a vehicle sensor group 40. The vehicle sensor group 40 includes a GPS receiver 41, an accelerator sensor 42, a steering sensor 43, a brake sensor 44, a vehicle speed sensor 45, an acceleration sensor 46, a wheel speed sensor 47, and other sensors 48 such as a yaw rate sensor. The ECU 21 is actually composed of a ROM, a RAM, an arithmetic circuit, and the like. The ECU 21 performs processing according to the vehicle position / posture estimation program stored in the ROM, thereby realizing functions (virtual image acquisition means, likelihood setting means, own vehicle position / posture estimation means) described later.

  The camera 22 uses a solid imaging element such as a CCD. The camera 22 is installed, for example, in the front portion of the vehicle, and is installed in a direction in which the front of the vehicle can be photographed. The camera 22 captures the periphery at predetermined time intervals to acquire a captured image and supplies it to the ECU 21.

  The three-dimensional map database 23 stores, for example, three-dimensional position information such as edges of the surrounding environment including road surface display. In the present embodiment, the 3D map database 23 includes edge information of structures such as curbs and buildings in addition to road surface displays such as white lines, stop lines, pedestrian crossings, and road surface marks. Each map information such as a white line is defined by an aggregate of edges. In the case where the edge is a long straight line, for example, since it is divided every 1 m, there is no extremely long edge. In the case of a straight line, each edge has three-dimensional position information indicating both end points of the straight line. In the case of a curve, each edge has three-dimensional position information indicating both end points and a center point of the curve.

  The vehicle sensor group 40 is connected to the ECU 21. The vehicle sensor group 40 supplies various sensor values detected by the sensors 41 to 48 to the ECU 21. The ECU 21 uses the output value of the vehicle sensor group 40 to calculate the approximate position of the vehicle and the odometry that indicates the amount of movement of the vehicle per unit time.

The ECU 21 is an electronic control unit that estimates the position and posture angle of the host vehicle using the captured image captured by the camera 22 and the three-dimensional position information stored in the three-dimensional map database 23. Note that the ECU 21 may also be used as an ECU used for other controls.
In particular, the vehicle position / orientation estimation apparatus compares a captured image captured by the camera 22 with a virtual image obtained by converting 3D map data into an image captured from a virtual position and a virtual attitude angle. And the posture angle is estimated. Here, it is assumed that a captured image as shown in FIG. 6A is obtained and an edge image as shown in FIG. 6B is obtained. On the other hand, assume that a virtual image obtained by projecting the three-dimensional position information onto the position and posture angle of the camera 22 is as shown in FIG. When the captured image of FIG. 6B and the virtual image of FIG. 6C are compared, both the far position (A) and the near position (B) match, so the virtual position where the virtual image is generated and It can be estimated that the virtual posture angle corresponds to the position and posture angle of the host vehicle. However, when the virtual position is displaced in the right direction, the virtual image is as shown in FIG. In this case, comparing the captured image of FIG. 6A and the captured image of FIG. 6D, the distant position (A) is coincident, but the nearby position (B) is greatly shifted. Conversely, when the virtual posture angle of the virtual image in FIG. 6C is shifted (not shown) and compared with the captured image in FIG. 6A, the near position (B) matches, but the far position (A) Deviates greatly.

  Paying attention to such a phenomenon, the vehicle position / orientation estimation apparatus determines that the virtual position of the virtual image is likely when the neighboring position pixel in the captured image matches the neighboring position pixel in the virtual image. . Conversely, the host vehicle position / posture estimation apparatus determines that the virtual posture angle of the virtual image is likely when the far position pixel in the captured image matches the far position pixel in the virtual image.

  Hereinafter, the operation of the vehicle position / orientation estimation apparatus will be described with reference to the position / orientation estimation algorithm of FIG. In this embodiment, it is assumed that the position of six degrees of freedom (front-rear direction, lateral direction, vertical direction) and posture angle (roll, pitch, yaw) of the vehicle are estimated. Further, the position / orientation estimation algorithm of FIG. 7 is continuously performed by the ECU 21 at intervals of, for example, about 100 msec.

  First, in step S51, the ECU 21 acquires a video image captured by the camera 22, and calculates an edge image from the captured image included in the video image. An edge in the present embodiment refers to a location where the luminance of a pixel changes sharply. For example, the Canny method can be used as the edge detection method. The edge detection method is not limited to this, and various other methods such as differential edge detection may be used.

  Further, it is desirable for the ECU 21 to extract from the captured image of the camera 22 the direction of edge brightness change, the color near the edge, and the like. Thereby, in step S55 and step S56 described later, the position estimation likelihood and the posture angle estimation likelihood are set using information other than the edges recorded in the three-dimensional map database 23, The position and posture angle of the host vehicle may be estimated.

  In the next step S52, the ECU 21 calculates an odometry that is the amount of movement of the vehicle from the sensor value obtained from the vehicle sensor group 40 after the calculation in step S52 of the position and orientation estimation algorithm before one loop. In the case of the first loop after starting the position / orientation estimation algorithm, the odometry is calculated as zero.

  The ECU 21 uses various sensor values obtained from the vehicle sensor group 40 to calculate odometry that is the amount of movement of the vehicle per unit time. As the odometry calculation method, for example, the movement amount and the rotation amount per unit time may be calculated from the wheel speed of each wheel and the yaw rate sensor after limiting the vehicle motion to a plane. Further, the ECU 21 may substitute the wheel speed by the difference between the vehicle speed and the positioning value of the GPS receiver 41, or may substitute the yaw rate sensor by the steering angle. Various calculation methods can be considered as a method for calculating odometry, but any method may be used as long as odometry can be calculated.

  In the next step S53, the ECU 21 calculates a plurality of virtual (predicted) position and virtual (predicted) posture angle candidates from the odometry calculated in step S52. The plurality of virtual position and virtual posture angle candidates are candidates for the vehicle position and the posture angle. At this time, the ECU 21 moves from the vehicle position estimated in the previous step S56 by the odometry calculated in the current step S52. The ECU 21 calculates a plurality of virtual position and virtual attitude angle candidates in the vicinity of the moved vehicle position. However, in the case of the first loop after starting the position / orientation estimation algorithm, the ECU 21 does not have the previous vehicle position information. Therefore, the ECU 21 uses the data from the GPS receiver 41 included in the vehicle sensor group 40 as initial position information. Or ECU21 memorize | stores the vehicle position and attitude | position angle which were calculated at the last time at the time of a stop last time, and may make it into initial position and attitude | position angle information.

  At this time, the ECU 21 takes into account measurement errors of the vehicle sensor group 40, odometry errors caused by communication delays, and vehicle dynamics that cannot be taken into account by the odometry, so that the true value of the vehicle position and attitude angle can be taken. A plurality of candidates for a plurality of virtual positions and virtual posture angles are generated. The candidates for the virtual position and the virtual attitude angle set the upper and lower limits of the error with respect to the 6-degree-of-freedom parameters of the position and attitude angle, and randomly use a random number table or the like within the upper and lower limits of the error. Set.

  In the present embodiment, 500 candidates for the virtual position and the virtual posture angle are created. The upper and lower limits of the error for the 6-degree-of-freedom parameters of position and posture angle are ± 0.05 [m], ± 0.05 [m], ± 0.05 [ m], ± 0.5 [deg], ± 0.5 [deg], ± 0.5 [deg]. The number of virtual position and virtual attitude angle candidates to be created and the upper and lower limits of the error with respect to the 6-degree-of-freedom parameters of position and attitude angle are changed as appropriate by detecting or estimating the driving state of the vehicle and the road surface condition. It is desirable. For example, when a sudden turn or slip occurs, the error in the plane direction (front / rear direction, lateral direction, yaw) is likely to increase, so the upper and lower limits of the error of these three parameters are increased. In addition, it is desirable to increase the number of virtual position and virtual attitude angle candidates created.

  In step S53, the ECU 21 may set a plurality of virtual position and virtual attitude angle candidates using a so-called particle filter. At this time, the ECU 21 moves the position and posture angle of each of the particles (candidate points) that are candidates for the plurality of virtual positions and virtual posture angles generated in step S56 one loop before by odometry. Specifically, as shown in FIG. 8, the particle P and the surrounding particles P <b> 1 to P <b> 5 of the position and posture angle of the vehicle V (t <b> 1) estimated one loop before are moved by the odometry. As a result, the ECU 21 sets the particles P10 to 15 for estimating the position and posture angle of the new vehicle V (t2). Thereby, the ECU 21 calculates a plurality of virtual position and virtual attitude angle candidates at this time. That is, the position and posture angle of each particle are set as candidates for a plurality of virtual positions and virtual posture angles. The position information and attitude angle information of each particle are moved by the odometry, taking into account measurement errors of the vehicle sensor group 40, odometry errors caused by communication delays, and vehicle dynamics that cannot be taken into account by odometry. Thereafter, as described above, it is more preferable to change the position and orientation angle randomly using a random number table or the like within the range of the upper and lower limits of the error with respect to the 6-degree-of-freedom parameter.

  However, in the case of the first loop after starting the position / orientation estimation algorithm, each particle does not have position information and attitude angle information. For this reason, it is good also considering the detection data of the GPS receiver 41 contained in the vehicle sensor group 40 as initial position information. Alternatively, the position information and attitude angle information of each particle may be set from the vehicle position estimated last when the vehicle stopped last time. In the present embodiment, in the case of the first loop, the upper and lower limits of the error with respect to the 6-degree-of-freedom parameter of position and posture angle from the vehicle position estimated last when the vehicle stopped last time, with respect to the 6-degree-of-freedom parameter of position and posture angle Set. Within the upper and lower limits of this error, the position information and posture angle of each particle are set at random using a random number table or the like. In the present embodiment, 500 particles are created in the case of the first loop. In addition, the upper and lower limits of the error with respect to the 6-degree-of-freedom parameter of each particle are ± 0.05 [m], ± 0.05 [m], ± 0.05 [m] in the order of the front and rear direction, the horizontal direction, the vertical direction, the roll, the pitch, and the yaw. , ± 0.5 [deg], ± 0.5 [deg], ± 0.5 [deg].

  In the next step S54, the ECU 21 creates a virtual (projection) image for each of the plurality of virtual positions and virtual attitude angle candidates created in step S53. At this time, the ECU 21 creates a virtual image by projecting and converting, for example, three-dimensional position information such as an edge stored in the three-dimensional map database 23 into a camera image taken from the virtual position and the virtual attitude angle. This virtual image is an image for evaluation that evaluates whether each virtual position and virtual posture angle candidate matches the actual position and posture angle of the host vehicle. In projection conversion, an external parameter indicating the position of the camera 22 and an internal parameter of the camera 22 are required. The external parameter may be calculated from the virtual position and the virtual attitude angle candidate by measuring in advance the relative position from the vehicle position (for example, the center position) to the camera 22. The internal parameters may be calibrated in advance.

  In addition, when it can extract from a camera image also about the direction of the brightness | luminance change of an edge, the color of the edge vicinity, etc. from the picked-up image image | photographed with the camera 22 in step S51, three-dimensional position information is also recorded on the 3D map database 23 about them. A virtual image may be created.

  In step S55, the ECU 21 compares the edge image created in step S51 with the virtual image created in step S54 for each of the plurality of virtual position and virtual attitude angle candidates set in step S53. The ECU 21 sets the position estimation likelihood and the posture angle estimation likelihood for each virtual position and virtual posture angle candidate based on the comparison result. The likelihood is an index indicating how likely the virtual position and virtual attitude angle candidates are to the actual vehicle position and attitude angle. The ECU 21 sets the likelihood to be higher as the matching degree between the virtual image and the edge image is higher. The method for obtaining the likelihood will be described later.

  In particular, the ECU 21 compares the captured image with the virtual image, and increases the attitude angle likelihood of the virtual image when the far position pixel in the captured image matches the far position pixel in the virtual image (likelihood. Setting means). The ECU 21 compares the captured image with the virtual image, and increases the position likelihood of the virtual image when the neighboring position pixel in the photographed image matches the neighboring position pixel in the virtual image (likelihood setting means). . The far position pixel and the near position pixel may be set according to the positions of the captured image and the virtual image, for example. For example, a range that is lower than the intermediate position in the vertical direction in the captured image and the virtual image by a predetermined width may be set as the image area in which the far position pixel exists. In addition, a range upward by a predetermined width from the bottom surface position in the vertical direction in the captured image and the virtual image may be set as an image region in which the neighboring position pixel exists. This distance can be obtained because the position of the camera 22 is assumed when the virtual image when the three-dimensional map is projected onto the camera 22 is obtained.

  Specifically, the ECU 21 determines whether or not the edge portion on the virtual image matches the edge portion on the edge image, that is, whether the edge on the edge image exists at the pixel coordinate position where the edge on the virtual image exists. Judging. When the edge portions on the virtual image and the edge image match, the three-dimensional map database 23 is referred to for the matching edge portion, and the position of the matching edge portion in the three-dimensional space is obtained. Then, the distance L (unit: m) between the position information of the candidate of the virtual position and the virtual attitude angle for which the position estimation likelihood is obtained and the matching edge portion is obtained, and the reciprocal of the distance L is the likelihood like_p (Unit: None) When the edge portion on the virtual image and the edge image do not match, 0 is set in the likelihood like_p.

  The ECU 21 performs this process for all the pixels on the virtual image. ECU21 sets large likelihood like_p to the said pixel, when an edge image and a virtual image correspond in the part close | similar to a vehicle. Conversely, when the edge image and the virtual image coincide with each other at a portion far from the vehicle, the ECU 21 sets a small likelihood like_p for the pixel. The ECU 21 sets the sum of the likelihoods like_p of all the pixels as the position estimation likelihood LIKE_P (unit: none) for the virtual image.

  Moreover, when calculating | requiring likelihood like_p, ECU21 may provide an upper limit according to the distance from a vehicle, as shown in FIG. The ECU 21 sets the likelihood like_p so that the predetermined upper limit value lthp is obtained when the pixel of the edge image (captured image) matches the pixel of the virtual image in the vicinity of the predetermined distance Lthp or more. Or ECU21 may reduce the increase width of likelihood like_p, so that the distance from a vehicle becomes near. In this embodiment, the distance Lthp is set to 1.0 [m].

  Specifically, the ECU 21 prepares a position likelihood setting map that describes the relationship between the distance from the vehicle and the likelihood like_p in advance. The ECU 21 refers to the position likelihood setting map and sets the likelihood like_p according to the distance from the vehicle where the edge image and the virtual image match.

  As a result, even if there is noise or an error in the edge extracted for a portion that is extremely close to the vehicle (camera 22), the influence can be suppressed, and the position estimation error can be reduced.

  The ECU 21 also determines the likelihood for posture angle estimation. When the edge part on the virtual image and the edge image match, the ECU 21 calculates the distance L (unit: m) from the vehicle of the matched pixel as in the case of determining the position estimation likelihood LIKE_P. . The ECU 21 sets the value obtained by dividing the distance L from the vehicle by 10 as the likelihood like_a (unit: none). Note that if the edge portion on the virtual image and the edge portion on the edge image do not match, 0 is set in the likelihood like_a.

  The ECU 21 performs this process for all the pixels on the virtual image. ECU21 sets large likelihood like_a to the said pixel, when an edge image and a virtual image correspond in the part far from a vehicle. Conversely, when the edge image and the virtual image coincide with each other in a portion close to the vehicle, the ECU 21 sets a small likelihood like_a for the pixel. The ECU 21 sets the sum of the likelihoods like_a of all the pixels as the posture angle estimation likelihood LIKE_A (unit: none) for the virtual image.

  Moreover, when calculating | requiring likelihood like_a, ECU21 may provide an upper limit according to the distance from a vehicle, as shown in FIG. The ECU 21 sets the likelihood likelihood like_a so that the predetermined upper limit value ltha is obtained when the pixel of the edge image (captured image) and the pixel of the virtual image coincide with each other at a distance greater than or equal to the predetermined distance Ltha. Or ECU21 may reduce the increase width of likelihood like_a, so that the distance from a vehicle becomes far. In this embodiment, the distance Ltha is set to 30.0 [m].

  Specifically, the ECU 21 prepares a position likelihood setting map that describes the relationship between the distance from the vehicle and the likelihood like_a in advance. The ECU 21 refers to the position likelihood setting map and sets the likelihood like_a according to the distance from the vehicle where the edge image and the virtual image match.

  As a result, even if there is noise or an error in the edge extracted for a part that is extremely far from the vehicle (camera 22), the influence can be suppressed, and the attitude angle estimation error can be reduced. .

  The ECU 21 obtains the position estimation likelihood LIKE_P and the posture angle estimation likelihood LIKE_A for each virtual image, and further calculates the position estimation likelihood LIKE_P and the posture angle estimation likelihood LIKE_A for the virtual position and virtual posture angle candidates. calculate. The ECU 21 normalizes the total value of the position estimation likelihood LIKE_P and the attitude angle estimation likelihood LIKE_A to 1 using all the results for the plurality of virtual position and virtual attitude angle candidates.

  In the next step S56, the ECU 21 uses the plurality of virtual position and virtual attitude angle candidates for which the position estimation likelihood LIKE_P and the attitude angle estimation likelihood LIKE_A were obtained in step S55, to determine the final own vehicle. Is calculated. The ECU 21 estimates the actual posture angle of the vehicle with respect to the virtual posture angle of the virtual image based on the likelihood LIKE_A for posture angle estimation of the virtual image (own vehicle position and posture estimation means). The ECU 21 estimates the actual vehicle position relative to the virtual position of the virtual image based on the position estimation likelihood LIKE_P of the virtual image (own vehicle position / posture estimation means).

  At this time, for example, the ECU 21 calculates the virtual position and the virtual attitude angle as the actual position and attitude angle of the vehicle by using the virtual position and virtual attitude angle candidate having the highest position estimation likelihood LIKE_P. Also good. Alternatively, the ECU 21 may calculate the virtual position and the virtual attitude angle as the actual position and attitude angle of the vehicle using the virtual position and virtual attitude angle candidate having the highest attitude angle estimation likelihood LIKE_A. . Alternatively, the ECU 21 uses the virtual position and the virtual attitude angle candidate having the highest sum of the position estimation likelihood LIKE_P and the attitude angle estimation likelihood LIKE_A to obtain the virtual position and the virtual attitude angle from the actual vehicle position. You may calculate as a position and a posture angle. Alternatively, the ECU 21 calculates the actual position and posture angle of the vehicle by taking a weighted average corresponding to the position estimation likelihood LIKE_P of each virtual image for a plurality of virtual position and virtual posture angle candidates. Also good. Alternatively, the ECU 21 calculates the actual position and posture angle of the vehicle by taking a weighted average corresponding to the posture angle estimation likelihood LIKE_A of each virtual image for a plurality of virtual position and virtual posture angle candidates. May be. Alternatively, the ECU 21 may calculate the actual position and posture angle of the vehicle by taking a weighted average corresponding to the sum of the position estimation likelihood LIKE_P and the posture angle estimation likelihood LIKE_A of each virtual image.

  When a particle filter is used in step S53, first, in step S56, two weights for position estimation likelihood LIKE_P and posture angle estimation likelihood LIKE_A may be set as the weight of each particle. The ECU 21 sets the position information of the particles having the highest position estimation likelihood LIKE_P as the actual vehicle position. Alternatively, the ECU 21 may calculate the actual vehicle position by taking a weighted average corresponding to the position estimation likelihood LIKE_P for the position information of the plurality of particles. Alternatively, the ECU 21 may use the posture angle information of the particles having the highest posture angle estimation likelihood LIKE_A as the actual posture angle of the vehicle. Alternatively, the ECU 21 may take the weighted average corresponding to the attitude angle estimation likelihood LIKE_A with respect to the attitude angle information of a plurality of particles, and obtain the actual attitude angle of the vehicle.

  Furthermore, the ECU 21 performs resampling of each particle based on the position estimation likelihood LIKE_P and the posture angle estimation likelihood LIKE_A. That is, the plurality of virtual positions and the virtual posture angles are reset based on the posture angle likelihood of the plurality of virtual images and the position likelihood of the plurality of virtual images.

  Specifically, the ECU 21 resamples each particle around the particle having the highest sum of the position estimation likelihood LIKE_P and the posture angle estimation likelihood LIKE_A. Alternatively, the ECU 21 once separates the position information and the posture angle information of each particle, and performs resampling on the particles having only the position information based on the position estimation likelihood LIKE_P. For particles having only posture angle information, resampling is performed based on posture angle estimation likelihood LIKE_A. Thereafter, the ECU 21 may randomly reconstruct particles having position information and posture angle information by randomly combining the position information and posture angle information of particles having only position information and particles having only posture angle information. .

  The ECU 21 can sequentially calculate the position and posture angle of the vehicle by repeatedly performing steps S51 to S56 as described above.

  As described above in detail, according to the vehicle position / posture estimation apparatus shown as the second embodiment, the captured image and the virtual image are compared, and the far position pixel in the captured image and the far position in the virtual image are compared. When the pixel matches, the attitude angle likelihood of the virtual image is increased. On the other hand, if the neighboring position pixel in the captured image matches the neighboring position pixel in the virtual image, the position likelihood of the virtual image is increased. Then, the own vehicle position / posture estimation apparatus estimates the actual posture angle of the own vehicle with respect to the virtual posture angle of the virtual image based on the posture angle likelihood of the virtual image. On the other hand, the actual position of the host vehicle with respect to the virtual position of the virtual image is estimated based on the position likelihood of the virtual image.

  Therefore, according to this own vehicle position / posture estimation apparatus, different likelihoods are considered for the position and the posture angle, and the position and posture angle are set according to the distance to the place where the real image and the virtual image match. It can be adjusted separately and estimated. Specifically, if these two images match at a location far from the camera 22, the attitude angle of the virtual image is close to the true value, but there may be a large error in the position information of the virtual image. The likelihood of posture angle information can be set larger. Conversely, the likelihood of position information is not set too large. On the other hand, when the two images match at a location close to the camera 22, the likelihood of the position information is set larger, and conversely, the likelihood of the posture angle information is not set too large.

  When the place where the two images match is far from the camera 22, the position error may be large. Conversely, when the position where the two images match is close to the camera 22, there may be a large error in the posture angle. However, according to this vehicle position and posture estimation device, the position and posture angle of the vehicle can be accurately determined. Can be estimated.

  Further, according to this vehicle position / posture estimation apparatus, when the pixel of the captured image and the pixel of the virtual image coincide with each other at a distance of a predetermined distance or more, the likelihood increase is reduced or becomes a predetermined upper limit value. Is set to the attitude angle likelihood of the virtual image. Thus, according to the vehicle position / posture estimation apparatus, when a portion that is extremely far from the camera 22 is included in the video to be matched, the likelihood of the posture angle information depends on the degree of matching in this portion. It can be prevented from being determined. For this reason, even if there is noise or an error in the edge extracted for a part that is extremely far from the camera 22, the influence can be suppressed, and the estimation error of the posture angle can be reduced.

  Furthermore, according to this vehicle position / posture estimation apparatus, when the pixel of the captured image and the pixel of the virtual image coincide in the vicinity of a predetermined distance or more, the increase in likelihood is reduced or becomes a predetermined upper limit value. Is set to the position likelihood of the virtual image. This prevents the likelihood of the position information from becoming too large when the two videos match within a certain distance from the camera 22. Therefore, according to this own vehicle position / posture estimation apparatus, even if there is noise or an error in the extracted edge with respect to a portion where the distance from the camera 22 is extremely close, the influence can be suppressed and the position estimation can be performed. The error can be reduced.

  Furthermore, according to this vehicle position / posture estimation apparatus, a plurality of particles (candidate points) are set, and the position estimation likelihood LIKE_P and the posture angle estimation likelihood LIKE_A for each particle are set to determine the position of the vehicle. And determine the attitude angle. Particles can be resampled based on the position estimation likelihood LIKE_P and the posture angle estimation likelihood LIKE_A.

  Here, in the particle filter, when the estimated order is n, in order to increase the estimation accuracy by a times, it is necessary to increase the number of particles to be scattered to a power of n times in principle (probability robotics Chapter 4 Section 3 ((Author) Sebastian Thrun / Wolfram Burgard / Dieter Fox, (Translated) Ryuichi Ueda, (Published) Mainichi Communications Inc.). In the existing technology, for example, when estimating position information and posture angle information in three dimensions at the same time, the order becomes 6th order, and if the estimation accuracy is to be doubled, the calculation time is 64 times and the estimation accuracy is tripled. However, the computation time increases by a factor of 729.

  On the other hand, according to the own vehicle position / posture estimation apparatus, position information and posture angle information can be handled separately. Therefore, in principle, the calculation time is 23 × 2 = 16 times when the estimation accuracy is to be doubled, and the calculation time is 33 × 2 = 54 times when the estimation accuracy is to be tripled. Therefore, the calculation load can be greatly reduced.

  The above-described embodiment is an example of the present invention. For this reason, the present invention is not limited to the above-described embodiment, and various modifications can be made depending on the design and the like as long as the technical idea according to the present invention is not deviated from this embodiment. Of course, it is possible to change.

  In the above-described embodiment, a vehicle is taken as an example. However, any vehicle that has at least one camera mounted thereon can be applied to an aircraft, a ship, and the like.

  In the above-described embodiment, the position (front-rear direction, lateral direction, vertical direction) and posture angle (roll, pitch, yaw) of the six degrees of freedom of the vehicle are obtained, but the present invention is not limited to this. For example, this embodiment is also used when estimating a position (front-rear direction, lateral direction) and posture angle (yaw) with three degrees of freedom, such as an automatic guided vehicle used in a factory without a suspension or the like. Is applicable. Specifically, in such a vehicle, the vertical position and the posture angle such as roll and pitch are fixed, so these parameters are measured in advance or the 3D map database 23 is referred to. And ask for it.

DESCRIPTION OF SYMBOLS 1 Surrounding environment imaging | photography part 2 Own vehicle state detection part 3 Ground own vehicle position estimation part 4 Relative own vehicle position estimation part 5 Map DB (map database)
6 travel route calculation unit 7 automatic travel control unit 8 own vehicle position difficulty section estimation unit 9 landmark search unit 10 landmark detection travel control unit 11 own vehicle position estimation switching unit 12a, 12b road 13 own vehicle 14 landmark 15 parking lot 21 ECU
22 camera 23 dimensional map database 40 vehicle sensor group 41 GPS receiver 42 accelerator sensor 43 steering sensor 44 brake sensor 45 vehicle speed sensor 46 acceleration sensor 47 wheel speed sensor 48 other sensors

Claims (7)

Ambient environment photographing means for photographing an image of the surrounding environment of the own vehicle,
Own vehicle state detection means for detecting an operation amount and a movement state of the own vehicle;
A travel route calculating means for calculating a target travel route from the departure point of the own vehicle to the movement target point based on the linear information of the road on which the host vehicle can travel and the map information obtained from the position information of the structures around the road; ,
First vehicle position estimation means for calculating an estimated value and estimation accuracy of the position and posture of the vehicle on the ground fixed coordinate system based on the output of the ambient environment photographing means;
Second vehicle position estimation means for estimating a relative positional relationship between the reference point and direction and the vehicle position by integrating the amount of change in the vehicle position estimated based on the output of the vehicle state detection means When,
Own vehicle position difficulty section estimation means for estimating a section on which the position of the first vehicle position estimation means is high on the target travel route;
Landmark detection for detecting, from the map information, a landmark that is highly likely to be in the photographing range of the surrounding environment photographing means within the near side region of the section that is estimated to be difficult by the own vehicle position difficulty section estimating means. Means,
Vehicle position estimation switching means for switching from the first vehicle position estimation means to the second vehicle position estimation means while traveling in a section with a high position estimation difficulty based on the landmark detected by the landmark detection means When,
While traveling in a section with a high degree of position estimation difficulty, the travel control of the own vehicle is executed based on the output of the second vehicle position estimation means based on the result of the own vehicle position estimation switching means. Automatic travel control means for executing travel control of the vehicle based on the output of the first vehicle position estimation means based on the result of the vehicle position estimation switching means;
A vehicle travel control device comprising: The landmark detection means detects the vehicle position so that the detected landmark falls within the shooting range of the surrounding environment shooting means until the position estimation accuracy of the first vehicle position estimation means reaches a predetermined level. To correct
If the position estimation accuracy of the first vehicle position estimation means is equal to or higher than the predetermined level in a state where the landmark detection means is activated, the vehicle position estimation switching means 2. The vehicle travel control according to claim 1, wherein the vehicle travel control is switched from the first vehicle position estimation means to the second vehicle position estimation means while traveling in a section having a high position estimation difficulty. apparatus.   The first vehicle position estimation means compares a photographed image of the surrounding environment of the vehicle photographed by the surrounding environment photographing means with a virtual image obtained by photographing the map information from a virtual position and a virtual attitude angle. The vehicle travel control device according to claim 1, wherein an actual attitude angle and position of the vehicle are estimated by the following. The first vehicle position estimation means compares the captured image with the virtual image, and if the far position pixel in the captured image matches the far position pixel in the virtual image, the virtual image The likelihood angle setting means for increasing the position likelihood of the virtual image when the position angle likelihood of the virtual image coincides with the vicinity position pixel in the captured image and the vicinity position pixel in the virtual image,
Based on the attitude angle likelihood of the virtual image set by the likelihood setting means, an actual attitude angle of the own vehicle with respect to the virtual attitude angle of the virtual image is estimated, and the virtual image set by the likelihood setting means Vehicle position and orientation estimation means for estimating the actual position of the vehicle relative to the virtual position of the virtual image based on the position likelihood;
The vehicle travel control device according to claim 3, further comprising:   The vehicle travel control device according to any one of claims 1 to 4, wherein the landmark detection means temporarily stops the own vehicle when the landmark is detected.   The landmark search means searches a plurality of landmarks satisfying a predetermined condition from the map information, selects a landmark suitable for the situation from the plurality of searched landmarks, and selects the selected landmark as the landmark. The vehicle travel control device according to any one of claims 1 to 5, wherein the vehicle travel control device is used as a target landmark of a mark detection travel control means. Photographing an image of the surrounding environment of the vehicle by the surrounding environment photographing means;
Detecting the operation amount and the movement state of the own vehicle by the own vehicle state detecting means;
Calculating a target travel route from the departure point of the host vehicle to the moving target point based on the map information obtained by acquiring linear information of the road on which the host vehicle can travel and position information of structures around the road; and
Estimating an estimated value and estimation accuracy of the position and orientation of the vehicle on the ground fixed coordinate system based on the output of the surrounding environment photographing unit by the first vehicle position estimation unit;
A relative positional relationship between the reference point and direction and the vehicle position is estimated by integrating the amount of change in the vehicle position estimated based on the output of the vehicle state detection unit by the second vehicle position estimation unit. And steps to
Estimating a section having a high degree of difficulty in position estimation by the first vehicle position estimating means on the target travel route;
Detecting from the map information landmarks that are likely to be in the shooting range of the surrounding environment shooting means in the near side area of the section estimated to be high in difficulty;
A vehicle position estimation switching step for switching from the first vehicle position estimation means to the second vehicle position estimation means while traveling in a section having a high position estimation difficulty based on the detected landmark;
While traveling in a section where the position estimation difficulty is high, the vehicle is controlled based on the output of the second vehicle position estimation means based on the result of the vehicle position estimation switching step. In the section, executing the running control of the own vehicle based on the output of the first own vehicle position estimating means based on the result of the own vehicle position estimation switching step;
The vehicle travel control method characterized by including.

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