Location Estimation System

Abstract

To provide a position estimation system that is capable of estimating a position of an own vehicle more accurately according to a travel state and a travel environment of the own vehicle.SOLUTION: A position estimation system comprises: a front sensor for detecting an object in front of a vehicle; a peripheral sensor for detecting objects in a periphery of a vehicle; a position estimation device for detecting a plurality of feature points of an object from at least one detection result out of a detection result of the front sensor and a detection result of the peripheral sensor, and for estimating a position of the vehicle using the plurality of feature points detected; and a control device for controlling operation of the vehicle on the basis of an estimation result of the position estimation device. The position estimation device sets a front area corresponding to the front sensor and a peripheral area corresponding to the peripheral sensor, applies weights to a plurality of pieces of information respectively related to a state of the vehicle, applies weights to information related to a peripheral environment of the vehicle, and sets an area to be used in position estimation according to a result of the weighting, in order to conduct position estimation using a feature point group of the set area.SELECTED DRAWING: Figure 4

Description

[Technical field]

[0001] The present invention relates to a position estimation system. [Background technology]

[0002] As a technology for safe driving and automatic driving, there is known a technology for determining the position of a vehicle based on information obtained from sensors installed in the vehicle. For example, Patent Document 1 discloses a position determination device that stores a plurality of reference data obtained by extracting characteristic parts of a scenic image taken by an on-board camera at intervals in the driving direction on a road in association with corresponding positions on a map, performs a matching process to determine whether or not the scenic image taken by the on-board camera matches the plurality of reference data, and determines the position of the vehicle on the map based on the position on the map corresponding to the reference data determined to match by the matching process, in which the matching process is performed by determining that the determination data obtained by performing a predetermined image processing for extracting characteristic parts from the scenic image taken by the vehicle matches the reference data when the degree of match is equal to or greater than a determination threshold, and determining that the reference data does not match when the degree of match is less than the determination threshold, and the determination threshold is adjusted to tend to become smaller as the vehicle speed increases and to tend to become smaller as the steering amount increases. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2005-318568 A Summary of the Invention [Problem to be solved by the invention]

[0004] However, in the above-mentioned conventional technology, the conditions for determining that the landscape image captured by the vehicle-mounted camera matches the reference data when it becomes difficult for the two to match are relaxed, which unduly increases the opportunities for the landscape image to match the reference data, and there is a risk that the accuracy of determining the vehicle's position will decrease.

[0005] The present invention has been made in consideration of the above, and has an object to provide a position estimation system that can estimate the position of a vehicle with higher accuracy depending on the driving state and driving environment of the vehicle. [Means for solving the problem]

[0006] In order to achieve the above-mentioned object, the present invention provides a position estimation system provided in a vehicle, the system comprising: a forward sensor that detects objects in front of the vehicle; a peripheral sensor that has a detection range that is closer than the forward sensor and includes at least a range in the left and right directions of the vehicle and detects objects around the vehicle in the detection range; a position estimation device that detects a plurality of feature points of an object from at least one of the detection results of the forward sensor and the detection results of the peripheral sensor and estimates the position of the vehicle using the detected plurality of feature points; and a control device that controls the operation of the vehicle based on the estimation result of the position estimation device, the position estimation device sets a forward area corresponding to a feature point group consisting of feature points detected from the detection result of the forward sensor, and a peripheral area corresponding to a feature point group consisting of feature points detected from the detection result of the peripheral sensor, weights each of a plurality of pieces of information related to the state of the vehicle, and weights information related to the surrounding environment of the vehicle, and sets the forward area and the peripheral area to either an area to be used for the position estimation or an area not to be used for the position estimation depending on the weighting result, and performs the position estimation using the feature point group of the area set as the area to be used for the position estimation. Effect of the Invention

[0007] According to the present invention, the position of the host vehicle can be estimated with higher accuracy according to the traveling state and traveling environment of the host vehicle. [Brief description of the drawings]

[0008] [Figure 1] FIG. 1 is a diagram illustrating an example of position estimation by a position estimation system mounted on a vehicle. [Diagram 2] FIG. 1 is a diagram illustrating an example of position estimation by a position estimation system mounted on a vehicle. [Diagram 3] FIG. 1 is a diagram illustrating an example of position estimation by a position estimation system mounted on a vehicle. [Figure 4] 1 is a functional block diagram illustrating an overall configuration of a position estimation system. [Diagram 5] 4 is a functional block diagram illustrating an overall configuration of a used feature point area determination unit. FIG. [Figure 6] FIG. 11 is a diagram illustrating the process contents of a speed determination unit. [Figure 7] FIG. 11 is a diagram showing the processing contents of a turning determination unit. [Figure 8] FIG. 4 is a diagram showing the processing contents of a driving environment determination unit. [Figure 9] 13 is a diagram showing the processing contents of a predicted curvature determining unit; FIG. [Figure 10] 11 is a diagram showing the processing contents of a surrounding environment determination unit; FIG. [Figure 11] FIG. 13 is a diagram illustrating the processing contents of an area cost calculation unit. [Figure 12] FIG. 2 is a diagram showing an example of three-dimensional point cloud data indicating feature points. [Figure 13] FIG. 4 is a diagram illustrating an example of detection ranges of a front sensor and a surrounding sensor. [Figure 14] FIG. 2 is a diagram illustrating a detection region. [Figure 15] FIG. 2 is a diagram illustrating a detection region. [Figure 16] FIG. 1 is a diagram illustrating an IPC technique. [Figure 17] FIG. 1 is a diagram illustrating an IPC technique. [Figure 18]FIG. 1 is a diagram illustrating an IPC technique. [Figure 19] FIG. 2 is a diagram illustrating an example of a surrounding environment of a host vehicle. [Figure 20] FIG. 2 is a diagram illustrating an example of a surrounding environment of a host vehicle. [Figure 21] FIG. 2 is a diagram illustrating an example of a surrounding environment of a host vehicle. [Figure 22] 4 is a flowchart showing a process performed by the vehicle position estimation device. [Diagram 23] 13 is a flowchart showing the processing content of an area selection process in a used feature point area determination unit; [Figure 24] FIG. 2 is a diagram illustrating an example of a driving situation. [Diagram 25] FIG. 2 is a diagram illustrating an example of a driving situation. [Figure 26] FIG. 2 is a diagram illustrating an example of a driving situation. [Figure 27] FIG. 1 is a diagram showing an example of a fusion result in the prior art. [Figure 28] FIG. 13 is a diagram illustrating another example of a sensor configuration. [Figure 29] FIG. 13 is a diagram illustrating another example of a sensor configuration. [Diagram 30] FIG. 13 is a diagram illustrating another example of a sensor configuration. [Diagram 31] FIG. 13 is a diagram illustrating another example of a sensor configuration. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, an embodiment of the present invention will be described with reference to the drawings.

[0010] <First embodiment> A first embodiment of the present invention will be described with reference to FIGS.

[0011] 1 to 3 are diagrams showing an example of position estimation by a position estimation system mounted on a vehicle.

[0012] There are several methods for estimating the position of a moving body such as a vehicle (so-called vehicle position estimation), including estimating the latitude and longitude of the vehicle on Earth as an absolute position using the GNSS (Global Navigation Satellite System), as shown in Figure 1, estimating the vehicle's position as a relative position to surrounding structures (such as lines on the road or other objects) from information obtained from a camera, etc., as shown in Figure 2, and estimating the vehicle's position in an environment such as inside a tunnel by combining and processing information from various sensors such as a gyro sensor and an acceleration sensor with information from the GNSS, etc. (so-called dead reckoning), as shown in Figure 3.

[0013] The position estimation system according to the present embodiment estimates the position of the vehicle by comparing information obtained from detection results of various sensors mounted on the vehicle with information on a map prepared in advance. More specifically, the sensor mounted on the vehicle detects feature points of objects around the vehicle, and the feature points are associated with a map including information on the feature points that has been prepared in advance by prior measurement, thereby estimating the position of the vehicle on the map. Details of the position estimation system according to the present embodiment will be described below.

[0014] FIG. 4 is a functional block diagram showing an overall configuration of the position estimation system according to the present embodiment.

[0015] 4, the position estimation system is roughly composed of a forward sensor 201, a surrounding sensor 202, a vehicle internal sensor 203, a GNSS 204, a map storage unit 205, a target route setting unit 206, an external sensor 207, and a vehicle position estimation device 100.

[0016] The forward sensor 201 is a long-distance narrow-angle sensor that detects an object in front of the vehicle 1 (see FIG. 12 below, etc.), and for example, in this embodiment, a stereo camera is used to detect an object from a measurement result (image) obtained by the stereo camera, and output as point cloud data indicating the position of the surface (or interior) of the object. Note that the forward sensor 201 only needs to be able to detect an object in front of the vehicle 1, and may use, for example, a method of detecting an object from an image obtained by a camera, or a method of detecting an object using technology such as radar (Radio Detecting and Ranging), sonar (SONAR: Sound navigation and ranging), or lidar (Light Detection and Ranging).

[0017] The surrounding sensor 202 is a short-distance omnidirectional sensor that detects objects around the vehicle 1, and in this embodiment, for example, is composed of sensors 202a, 202b, 202c, and 202d using multiple LIDARs with detection ranges in the front, left side, rear, and right side, respectively, and detects objects from the obtained measurement results and outputs them as point cloud data indicating the position of the surface (or interior) of the object. Note that the surrounding sensor 202 only needs to be able to detect objects around the vehicle 1, and may use, for example, a method of detecting objects from an image obtained by a camera, or a method of detecting objects using technology such as radar (Radio Detecting and Ranging) or sonar (SONAR: Sound navigation and ranging).

[0018] The in-vehicle sensors 203 are various sensors that detect information about the host vehicle 1, such as a vehicle speed sensor that detects the traveling speed of the host vehicle, a steering angle sensor that detects the steering angle, and the like.

[0019] The GNSS 204 is known as the Global Navigation Satellite System, and measures the position (i.e., latitude and longitude) of the host vehicle 1 in the Earth coordinate system.

[0020] The map storage unit 205 stores map information including the driving environment of the vehicle 1. In the map information, position information of characteristic points of structures such as roads and buildings is measured in advance and stored as, for example, three-dimensional point cloud data indicating the characteristic points as shown in FIG.

[0021] The target route setting unit 206 sets the driving route and its curvature of the vehicle 1. For example, when the vehicle 1 is traveling automatically, the curvature is set from the driving route of the vehicle set for automatic traveling. When the vehicle 1 is traveling manually, the curvature of the driving route of the vehicle is set based on the detection results from sensors that acquire information about the surroundings of the vehicle, including the forward sensor 201 and the surrounding sensor 202.

[0022] The external sensor 207 detects information on the driving environment of the vehicle (for example, information on the presence or absence and position of other vehicles around the vehicle) as a recognition result, and may be shared by the forward sensor 201 and the peripheral sensor 202, that is, the detection results of the forward sensor 201 and the peripheral sensor 202 may be used as the detection results of the external sensor 207. Alternatively, a separate sensor may be provided as the external sensor 207. Note that, as a method for using the external sensor 207, for example, a method for detecting an object from a measurement result (image) obtained by a stereo camera, a method for detecting an object from an image obtained by a camera, or a method for detecting an object using a technology such as Radar (Radio Detecting and Ranging), Sonar (SONAR: Sound navigation and ranging), or LiDAR (Light Detection and Ranging) may be used.

[0023] The vehicle position estimation device 100 estimates the position of the vehicle based on information from a forward sensor 201, a peripheral sensor 202, an internal vehicle sensor 203, a GNSS 204, a map memory unit 205, a target route setting unit 206, and an external sensor 207, and outputs the estimation result to a control device 300 that controls operations such as automatic driving and manual driving of the vehicle 1, and is generally composed of a feature point detection unit 110, a feature point area generation unit 120, a forward matching unit 130, a peripheral matching unit 140, a used feature point area determination unit 150, a vehicle movement amount estimation unit 160, and a vehicle position fusion unit 170.

[0024] The feature point detection unit 110 detects a plurality of feature points of a detected object from point cloud data that is the measurement results of the forward sensor 201 and the peripheral sensor 202 .

[0025] The feature point area generating section 120 associates the multiple feature points detected by the feature point detecting section 110 with any of a plurality of predetermined detection areas, thereby dividing the area into multiple feature point groups, each of which is made up of multiple feature points.

[0026] FIG. 13 is a diagram showing an example of the detection ranges of the front sensor and the surrounding sensors, and FIGS. 14 and 15 are diagrams for explaining the detection areas defined in this embodiment.

[0027] 13, the front sensor 201 has a detection range of a long-distance narrow angle in front of the vehicle and detects objects within the detection range. The surrounding sensor 202 has a detection range of the entire short-distance range around the vehicle by multiple sensors 202a, 202b, 202c, and 202d installed around the vehicle and detects objects within the detection range.

[0028] 14 and 15, a plurality of detection areas are defined in advance in front of and around the vehicle 1. In this embodiment, for example, a forward area E is defined as a detection area including a certain range in front of the vehicle 1 at a relatively long distance. Similarly, for the periphery in the short distance of the vehicle 1, a peripheral area A is defined as a forward detection area, a peripheral area B is defined as a left side detection area, a peripheral area C is defined as a rear detection area, and a peripheral area D is defined as a right side detection area.

[0029] In this embodiment, a plurality of feature points (a plurality of feature point groups) detected from the measurement results obtained by the forward sensor 201 and the peripheral sensor 202 (sensors 202a, 202b, 202c, 202d) are associated with preset detection areas (peripheral areas A, B, C, D, forward area E), respectively, to divide the plurality of feature points into a plurality of feature point groups. That is, the plurality of feature points detected from the detection results of the forward sensor 201 are included in the feature point group corresponding to the forward area E. Similarly, the feature points detected from the detection results of the peripheral sensor 202 (sensors 202a, 202b, 202c, 202d) are included in the feature point groups corresponding to the peripheral area A, the peripheral area B, the peripheral area C, and the peripheral area D, respectively.

[0030] The use feature point area determination unit 150 determines whether or not to use each of the feature points belonging to each detection area (forward area E, surrounding areas A, B, C, D) in the estimation process of the position of the vehicle 1 based on information from the vehicle interior sensor 203, the GNSS 204, the map storage unit 205, the target route setting unit 206, and the external sensor 207. That is, for example, when it is determined that the feature points of the forward area E are not to be used in the estimation process of the vehicle 1, it outputs the determination result (selected area value) to the forward matching unit 130. Similarly, when it is determined that the feature points of the surrounding area A are not to be used in the estimation process of the vehicle 1, it outputs the determination result (selected area value) to the surrounding matching unit 140. The details of the determination process in the use feature point area determination unit 150 will be described later.

[0031] The forward matching unit 130 estimates and outputs the position of the vehicle 1 on the map by comparing and associating a plurality of feature points (here, feature points belonging to the forward area E) obtained from the detection result of the forward sensor 201 with feature points of the map stored in the map storage unit 205. At the same time, it also outputs information on whether the result of the position estimation has been updated since the previous association process.

[0032] There are various methods for estimating the position of the vehicle 1 by associating the feature points of the forward area E with the feature points of the map. For example, it is possible to use the IPC (Iterative Closest Point) method shown in Fig. 16 to Fig. 18. In this case, first, for the feature points of the feature points of the feature points of the forward area E shown in Fig. 16 and the feature points of the feature points of the map, the nearest feature points are searched for and associated with each other as shown in Fig. 17, and the positions of the feature points of the forward area E are adjusted so that the difference in the positions of the associated feature points is minimized as shown in Fig. 18. Then, when the process of Fig. 16 to Fig. 18 is repeated and the difference between the feature points satisfies a predetermined condition, it is determined that the feature points of the forward area E and the feature points on the map represent the same object, and the position of the vehicle 1 on the map is estimated.

[0033] In addition, if the used feature point area determination unit 150 described later determines that the feature points of the forward area E will not be used for position estimation (i.e., if the selected area value of the forward area E = 0), the forward matching unit 130 will not perform position estimation using the feature points of the forward area E or output the results.

[0034] Similarly, the surrounding matching unit 140 estimates and outputs the position of the vehicle 1 on the map by comparing and associating a plurality of feature points (here, feature points belonging to the surrounding areas A, B, C, and D, respectively) obtained from the detection results of the surrounding sensor 202 (sensors 202a, 202b, 202c, and 202d) with the feature points of the map stored in the map storage unit 205. In addition, information on whether the result of the position estimation has been updated since the previous association process is simultaneously output. Note that the method of estimating the position of the vehicle 1 by associating the feature points belonging to the surrounding areas A, B, C, and D, respectively, with the feature points of the map can be the same as that of the forward matching unit 130.

[0035] Furthermore, for surrounding areas where the feature points used in the feature point area determination unit 150 described below have determined that the feature points are not to be used for position estimation (i.e., surrounding areas with a selected area value = 0), the surrounding matching unit 140 does not perform position estimation using the feature points of the surrounding areas or output the results.

[0036] FIG. 5 is a functional block diagram showing an outline of the overall configuration of the used feature point area determination section according to the present embodiment.

[0037] 5, the use feature point area determination unit 150 is roughly composed of a speed determination unit 151, a turning determination unit 152, a driving environment determination unit 153, a predicted curvature determination unit 154, a surrounding environment determination unit 155, and an area cost calculation unit 156.

[0038] FIG. 6 is a diagram showing the process contents of the speed determination unit.

[0039] As shown in Fig. 6, the speed determination unit 151 calculates a weight based on the vehicle speed information acquired from the vehicle interior sensor 203 (i.e., weights the information related to the vehicle speed) and outputs the calculation result (weight A). For example, the speed determination unit 151 prepares a table in advance in which the weight A increases with an increase in the vehicle speed, and calculates the weight A according to the table, with weight A = 1.0 when the vehicle speed is 80 km / h or more and 0 (zero) km / h or more, and with weight A = 0.1 when the vehicle speed is 0 (zero) km / h. Note that the various numerical values ​​in the above processing are merely examples and can be changed as appropriate.

[0040] FIG. 7 is a diagram showing the processing contents of the turning determination unit.

[0041] As shown in Fig. 7, the turning determination unit 152 calculates a weight based on steering angle information acquired from the vehicle interior sensor 203 (i.e., weights information related to the steering angle) and outputs the calculation result (weight B). For example, the turning determination unit 152 prepares a table in advance in which the weight B decreases as the steering angle (absolute value) increases, and when the absolute value of the steering angle is 90°, the weight B is set to 0.1, when the absolute value of the steering angle is less than 90° and greater than 0.0°, the weight B is calculated according to the table, and when the steering angle is 0° (zero degree), the weight B is set to 0.9. Note that the various numerical values ​​in the above processing are merely examples and can be changed as appropriate.

[0042] FIG. 8 is a diagram showing the processing contents of the driving environment determination unit.

[0043] As shown in Fig. 8, the driving environment determination unit 153 determines the driving environment based on the position information from the GNSS 204 and the map information from the map storage unit 205, calculates a weight according to the driving environment (i.e., weights the information related to the driving environment), and outputs the calculation result (weight C). The driving environment determination unit 153 has weighting information according to the driving environment of the vehicle 1 in advance, and for example, if the driving environment of the vehicle 1 is an expressway, the weight C = 0.9, if it is a national highway or a ring road, the weight C = 0.7, if it is other roads, the weight C = 0.4, and if it is a parking lot of a supermarket, the weight C = 0.1. Note that the various numerical values ​​in the above processing are merely examples and can be changed as appropriate.

[0044] FIG. 9 is a diagram showing the process performed by the predicted curvature determining unit.

[0045] 9, the predicted curvature of the driving route of the vehicle 1 is determined based on the predicted curvature determination unit 154, map information from the map storage unit 205, route information and curvature information from the target route setting unit 206, and recognition result information from the external sensor 207, and a weight according to the predicted curvature is calculated (i.e., information related to the driving environment is weighted), and the calculation result (weight D) is output. The predicted curvature determination unit 154 obtains the weight D by averaging the weight obtained when the vehicle 1 is in autonomous driving and the weight obtained when the vehicle is in non-autonomous driving within a predetermined time range.

[0046] During autonomous driving, the predicted curvature determination unit 154 acquires the target route and curvature, and, for example, sets the weight to 0.5 when the curvature is small (i.e., in the case of a gentle curve), sets the weight to 0.1 when the curvature is large (i.e., in the case of a sharp curve), and sets the weight to 0.9 when the curvature is 0 (zero) (i.e., in the case of a straight line).

[0047] Similarly, during non-automated driving, the curvature obtained from the external sensor 207 is obtained, and, for example, when the curvature is small (i.e., in the case of a gentle curve), the weight is set to 0.5, when the curvature is large (i.e., in the case of a sharp curve), the weight is set to 0.1, and when the curvature is 0 (zero) (i.e., in the case of a straight line), the weight is set to 0.9.

[0048] Then, the weight D is obtained by calculating the time average of the weights during autonomous driving and non-autonomous driving.

[0049] In the above example, the degree of weighting for the curvature is set to be the same during autonomous driving and during non-autonomous driving, but it may be set to be different. Also, for example, a table may be prepared in advance in which the weight decreases as the predicted curvature increases, and the weight may be calculated using this table. Also, the various numerical values ​​in the above process are merely examples and may be changed as appropriate.

[0050] FIG. 10 is a diagram showing the processing contents of the surrounding environment determination unit.

[0051] 10, the surrounding environment determination unit 155 determines the presence or absence of other dynamic objects (e.g., other vehicles, motorcycles, bicycles, etc.) in the surrounding environment of the vehicle 1 based on the recognition result from the external sensor 207, calculates a weight according to the determination result for each detection area (forward area E, surrounding areas A, B, C, D) (i.e., weights information related to the surrounding environment), and outputs the calculation result (area weight). For example, the surrounding environment determination unit 155 sets the weight of a detection area where a dynamic object exists to 0.0 (zero), and the weight of a detection area where no dynamic object exists to 1.0.

[0052] 19 to 21 are diagrams showing an example of the surrounding environment of the host vehicle.

[0053] For example, when another vehicle 2 exists in front of the vehicle 1 as shown in FIG. 19, that is, when a dynamic object exists in the forward detection area (forward area E, peripheral area A), the surrounding environment determination unit 155 sets the weights of the forward area E and the peripheral area A to 0.0, and the weights of the peripheral areas B, C, and D to 1.0. Also, for example, when other vehicles 2, 3, 4, and 5 exist in front, behind, to the right, and diagonally forward right of the vehicle 1 as shown in FIG. 20, that is, when a dynamic object exists in the forward and peripheral detection areas (forward area E, peripheral areas A, C, and D), the surrounding environment determination unit 155 sets the weights of the forward area E and the peripheral areas A, C, and D to 0.0, and the weight of the peripheral area B to 1.0. Also, for example, when other vehicles 6 and 7 exist relatively far away in front and behind the vehicle 1 as shown in FIG. 21, that is, when a dynamic object exists in the forward detection area (forward area E), the weight of the forward area E is set to 0.0, and the weights of the peripheral areas A, B, C, and D are set to 1.0.

[0054] FIG. 11 is a diagram showing the processing contents of the area cost calculation unit.

[0055] 11, the area cost calculation unit 156 calculates and outputs a selection area value for each detection area (forward area E, surrounding areas A, B, C, D) according to the calculation results (weights A, B, C, D, area weights) from the speed judgment unit 151, the turning judgment unit 152, the driving environment judgment unit 153, the predicted curvature judgment unit 154, and the surrounding environment judgment unit 155. The area cost calculation unit 156 calculates a weight multiplication value for each detection area, and sets a selection area value for each detection area according to the weight multiplication value.

[0056] That is, the area cost calculation unit 156 first calculates the weight multiplication value = weight A (speed) x weight B (turning) x weight C (driving environment) x weight D (predicted curvature) x area weight (surrounding environment) for each detection area.

[0057] Next, for the surrounding areas A, B, C, and D, the selected area value of a detection area with a weighted multiplication value greater than 0.0 is set to 1, and the selected area value of a detection area with a weighted multiplication value of 0.0 is set to 0. Similarly, for the forward area E, if the weighted multiplication value is equal to or greater than a predetermined threshold, the selected area value is set to 1, and if the weighted multiplication value is less than the predetermined threshold, the selected area value is set to 0.

[0058] As a specific example of the processing contents in the area cost calculation unit 156, for example, when the vehicle 1 is traveling at 100 km / h on a straight highway and there are no other dynamic objects in the vicinity, the weights A to D and the area weights are as follows: weight A (speed) = 1.0, weight B (turning) = 0.9, weight C (traveling environment) = 0.9, weight D (predicted curvature) = 0.9, area weight (surrounding environment) = 1.0 (all detection areas). Here, the weight multiplication value of the front area E = 0.729. Therefore, when the threshold value is smaller than 0.729, the selected area value of the front area E = 1. Also, the weight multiplication values ​​of the surrounding areas A, B, C, and D are each 0.0, so the selected area values ​​of the surrounding areas A, B, C, and D = 0.

[0059] Also, for example, when the vehicle 1 is traveling at 50 km / h on a curve at a JCS (junction) on a highway and there are no other dynamic objects in the vicinity, the weights A to D and area weights are as follows: weight A (speed) = 0.5, weight B (turning) = 0.5, weight C (traveling environment) = 0.9, weight D (predicted curvature) = 0.5, area weight (surrounding environment) = 1.0 (all detection areas). Here, the weight multiplication value of the forward area E = 0.1125, so when the threshold is greater than 0.1125, the selected area value of the forward area E = 0. Also, the weight multiplication values ​​of the surrounding areas A, B, C, and D are each 0.0125, so the selected area value of the surrounding areas A, B, C, and D = 1.

[0060] Also, for example, when the vehicle 1 is caught in a traffic jam on a national highway and is repeatedly stopping and starting, and is traveling in the left lane and there are other dynamic objects (other vehicles) in front, behind, and to the right, the weights A to D are as follows: weight A (speed) = 1.0, weight B (turning) = 0.9, weight C (driving environment) = 0.7, and weight D (predicted curvature) = 0.9. Also, the area weight (surrounding environment) of the surrounding area B = 1.0, and the area weights (surrounding environment) of the front area E and the surrounding areas A, C, and D = 0.0. Here, the weight multiplication value of the front area E = 0, so when the threshold is greater than 0.0, the selected area value of the front area E = 0. Also, the weight multiplication values ​​of the surrounding areas A, C, and D are each 0.0, so the selected area value of the surrounding areas A, C, and D = 0. Also, the weight multiplication value of the surrounding area B is 0.0027, so the selected area value of the surrounding area B = 1.

[0061] Also, for example, when the vehicle 1 is waiting for a traffic light at the head position of the right lane at an intersection and there are other dynamic objects (other vehicles) behind and to the left, the weights A to D are as follows: weight A (speed) = 0.1, weight B (turning) = 0.9, weight C (driving environment) = 0.5, and weight D (predicted curvature) = 0.9. The area weights (surrounding environment) of the front area E and the surrounding areas A and D = 1.0, and the area weights (surrounding environment) of the surrounding areas B and C = 0.0. Here, the weight multiplication value of the front area E = 0.045, so when the threshold is smaller than 0.0045, the selected area value of the front area E = 1. The weight multiplication values ​​of the surrounding areas A and D are each 0.0045, so the selected area value of the surrounding areas A and D = 1. The weight multiplication values ​​of the surrounding areas B and C are 0.0, so the selected area value of the surrounding areas B and C = 0.

[0062] Return to Figure 4.

[0063] The vehicle movement amount estimation unit 160 estimates the amount of movement and the direction of movement of the vehicle using the vehicle speed information and steering angle information acquired from the vehicle interior sensor 203. There are various methods for estimating the amount of movement and the direction of movement of the vehicle, and for example, a dead reckoning technique that estimates the relative position from a certain reference point can be used.

[0064] The vehicle position fusion unit 170 calculates the position of the vehicle 1 by fusing the position information of the vehicle 1 estimated by the forward matching unit 130, the position information of the vehicle 1 estimated by the surrounding matching unit 140, and the movement amount of the vehicle 1 estimated by the vehicle movement amount estimation unit 160, and outputs the position of the vehicle 1 to the control device 300.

[0065] There are various possible methods for calculating the position of the host vehicle 1, and for example, an extended Kalman filter may be used. In this case, the vehicle position fusion unit 170 performs fusion using an extended Kalman filter using the speed, angular velocity, sideslip angle, and time of the host vehicle 1 estimated by the vehicle movement amount estimating unit 160 and the position, attitude, and time of the host vehicle estimated by the front matching unit 130 and the surrounding matching unit 140, to calculate the position of the host vehicle 1.

[0066] FIG. 22 is a flowchart showing the process performed by the vehicle position estimation device.

[0067] In FIG. 22, the feature point detection unit 110 of the vehicle position estimation device 100 first acquires point cloud data from the forward sensor 201 and the surrounding sensor 202 (step S100), and detects feature points of surrounding objects (step S110).

[0068] Next, the feature point area generation unit 120 divides the feature points detected by the feature point detection unit 110 into a plurality of feature point groups, each of which is made up of a plurality of feature points, by associating the feature points with any of a plurality of predetermined detection areas (step S120).

[0069] Next, in the area selection process, the use feature point area determination unit 150 determines whether or not to use each of the feature point groups belonging to each detection area (forward area E, surrounding areas A, B, C, D) in the estimation process of the position of the vehicle 1 (i.e., whether or not to set the selected area value = 1) based on information from the vehicle internal sensor 203, the GNSS 204, the map memory unit 205, the target route setting unit 206, and the external sensor 207 (step S200).

[0070] Next, the vehicle movement amount estimating unit 160 estimates the amount of movement of the host vehicle 1 (step S130).

[0071] Next, the forward matching unit 130 determines whether the forward area E has been selected in the area selection process (step S200), i.e., whether the selected area value of the forward area E is 1 or not, and if the determination result is YES, it estimates the position of the vehicle 1 using the feature point group of the forward area E (step S150).

[0072] When the processing in step S150 is completed, or when the judgment result in step S140 is NO, the surrounding matching unit 140 then judges whether or not the surrounding areas A, B, C, and D were each selected in the area selection processing (step S200), i.e., whether or not the selected area value of each of the surrounding areas A, B, C, and D is 1 (step S160).

[0073] If the determination result in step S160 is YES, the position of the vehicle 1 is estimated using the group of feature points in the surrounding area where the selected area value is 1 (step S170).

[0074] When the processing in step S170 is completed, or when the judgment result in step S160 is NO, the vehicle position fusion unit 170 then fuses the calculation results in the forward matching unit 130 and the surrounding matching unit 140 to calculate the position of the host vehicle 1 (step S180), and ends the processing.

[0075] FIG. 23 is a flowchart showing the details of the area selection process in the used feature point area determining section.

[0076] In FIG. 23, the speed determination unit 151 and the turning determination unit 152 of the use feature point area determination unit 150 acquire host vehicle information such as vehicle speed information and steering angle information from the vehicle interior sensor 203 (step S210).

[0077] Furthermore, the driving environment determining unit 153 acquires position information from the GNSS 204 (step S220), and the driving environment determining unit 153 and the predicted curvature determining unit 154 acquire map information from the map storage unit 205 (step S230).

[0078] In addition, the predicted curvature determination unit 154 acquires target information such as route information and curvature information from the target route setting unit 206 (step S240), and the surrounding environment determination unit 155 and the predicted curvature determination unit 154 acquire recognition result information from the external sensor 207 (step S250).

[0079] Next, the speed judgment unit 151, the turning judgment unit 152, the driving environment judgment unit 153, the predicted curvature judgment unit 154, and the surrounding environment judgment unit 155 perform weight calculation processing (see Figures 6 to 10) to calculate weights A, B, C, D and area weights (step S300), and the area cost calculation unit 156 performs cost calculation processing (see Figure 11) to calculate the selected area value (step S400), and the processing is terminated.

[0080] The effects of the present embodiment configured as above will be described.

[0081] As a technology for safe driving and autonomous driving, there is known a technology that determines the position of a vehicle based on information obtained from sensors installed in the vehicle. In such a technology, the detection accuracy of the sensors affects the accuracy of the vehicle's position estimation, but the detection accuracy of the sensors varies depending on the driving conditions of the vehicle.

[0082] For example, as shown in Fig. 24, in driving conditions where a large steering angle is required while driving at low speed, such as turning left (or right) at an intersection, the amount of change (amount of lateral movement) in the detection range of the long-distance, narrow-angle front sensor becomes large, so the detection accuracy tends to decrease, and the impact on the accuracy of position estimation is large. On the other hand, the amount of change (amount of lateral movement) in the detection range of the short-distance, omnidirectional peripheral sensor is relatively small, so the detection accuracy tends not to decrease easily, and the impact on the accuracy of position estimation is small.

[0083] Also, for example, as shown in Fig. 25, in driving conditions where the vehicle is traveling at high speeds such as on a highway, the change in the detection range of the long-distance narrow-angle front sensor is relatively small, so the detection accuracy is less likely to decrease and the impact on the accuracy of position estimation is small. On the other hand, the change in the detection range of the short-distance omnidirectional surrounding sensor is large, so the detection accuracy is likely to decrease and the impact on the accuracy of position estimation is large.

[0084] Also, for example, as shown in Fig. 26, in driving conditions where the vehicle is traveling at a low speed, such as in a traffic jam, the detection range of the long-distance, narrow-angle front sensor is blocked by other vehicles ahead, etc., so the detection accuracy is likely to decrease and the impact on the accuracy of position estimation is large. On the other hand, the detection range of the short-distance, omnidirectional surrounding sensor changes little, so the detection accuracy tends not to decrease even when the range from which information can be obtained is limited, and the impact on the accuracy of position estimation is small.

[0085] In such various driving conditions, when the vehicle position is estimated from feature points detected from the detection results of each sensor and feature points on a map, and the vehicle position is estimated by fusing the position estimation results, the fusion result is affected by the detection results with reduced detection accuracy, resulting in a reduced position estimation result, as shown in FIG. 27.

[0086] Therefore, in this embodiment, a position estimation system provided in a vehicle includes a forward sensor that detects objects in front of the vehicle, a peripheral sensor whose detection range is a range closer than the forward sensor and including at least the left and right directions of the vehicle and that detects objects around the vehicle in the detection range, a position estimation device that detects multiple feature points of the object from at least one of the detection results of the forward sensor and the peripheral sensor and estimates the position of the vehicle using the detected multiple feature points, and a control device that controls the operation of the vehicle based on the estimation results of the position estimation device, and the position estimation device is configured to weight each of the multiple pieces of information related to the state of the vehicle and to weight information related to the environment surrounding the vehicle, and to select for each feature point group whether or not to use a group of feature points consisting of some of the multiple feature points detected by the forward sensor and the peripheral sensor for position estimation based on the weighting results, so that the position estimation system can estimate the position of the vehicle with greater accuracy depending on the driving state and driving environment of the vehicle.

[0087] <First Modification> In the first embodiment, a case where a plurality of feature points (a plurality of feature point groups) detected from the measurement results obtained by the forward sensor 201 and the peripheral sensor 202 (sensors 202a, 202b, 202c, 202d) are associated with a preset detection area (forward area E, peripheral areas A, B, C, D) is illustrated as an example, but for example, as shown in FIG. 28, a single sensor that detects objects around the vehicle 1 at once may be used as the peripheral sensor, and the detection range of the peripheral sensor may be divided in advance according to the relative position with the vehicle 1, and the divided detection range may be associated in advance with the detection area to generate a feature point group. Also, in the case where a forward sensor and a rear sensor are used as shown in FIG. 29, a forward sensor and left and right side sensors are used as shown in FIG. 30, or a peripheral sensor having a long detection range in the front and rear directions as shown in FIG. 31, a detection area (here, a sensor) may be selected according to the driving situation.

[0088] <Second Modification> In addition to the first embodiment, the configuration may be such that the number of samples of the sensors related to the feature point group (i.e., the detection area with the selected area value = 1) selected for use in position estimation among the multiple sub-sensors (sensors 202a, 202b, 202c, 202d) of the forward sensor 201 and the peripheral sensor 202 is increased.

[0089] In position estimation, the accuracy of position estimation improves as the number of feature points used in the calculation (point density) increases, but the calculation time increases exponentially, so it is necessary to appropriately allocate the position estimation accuracy and the calculation time.

[0090] Therefore, in this modified example, in contrast to the first embodiment configured to perform position estimation using the detection results of sensors that have little effect on detection accuracy according to the driving conditions (i.e., the selected area value of the detection area related to the sensor is set to 1) and not to use the detection results of sensors that have a large effect on detection accuracy for position estimation (i.e., the area value of the detection area related to the sensor is set to 0), the number of samples of sensors related to detection areas with selected area value = 1 is increased according to the number of sensors related to detection areas with selected area value = 0. At this time, the calculation resources used for calculation of position estimation based on the detection results of sensors that were not selected are used for calculation related to the feature points that have increased due to the increase in the number of samples.

[0091] This makes it possible to estimate the vehicle position with greater accuracy while suppressing an increase in calculation time.

[0092] <Third modified example> In the first embodiment, the feature point area used determination unit 150 weights information, and the detection area of ​​the feature points used for estimating the position of the vehicle 1 is selected based on a selection area value that is set according to the weighting result. However, it may also be configured to select a detection area with a large number of feature points, and use those feature points for position estimation.

[0093] <Fourth modified example> In the first embodiment, the forward sensor 201 and the peripheral sensor 202 detect the surface position of an object as a point cloud, and then estimate the position using feature points detected from the point cloud (so-called point cloud matching). However, other methods may be used, and for example, the system may be configured to match road paint information such as white lines and stop lines on a high-precision map with road paint information on the map based on the results obtained from the sensors, or to match information based on landmark information such as signs, traffic lights, billboards, and telephone poles.

[0094] <Additional Notes> The present invention is not limited to the above-described embodiments, but includes various modifications and combinations. For example, the above-described embodiments have been described in detail to clearly explain the present invention, and are not necessarily limited to those having all of the described configurations. In addition, the above-described configurations, functions, etc. may be realized by designing a part or all of them, for example, in an integrated circuit. In addition, the above-described configurations, functions, etc. may be realized by software, in which a processor interprets and executes a program that realizes each function. [Explanation of symbols]

[0095] 1... host vehicle, 2 to 7... other vehicles, 100... host vehicle position estimation device, 110... feature point detection unit, 120... feature point area generation unit, 130... forward matching unit, 140... surrounding matching unit, 150... used feature point area determination unit, 151... speed determination unit, 152... turning determination unit, 153... driving environment determination unit, 154... predicted curvature determination unit, 155... surrounding environment determination unit, 156... area cost calculation unit, 160... vehicle movement amount estimation unit, 170... vehicle position fusion unit, 201... forward sensor, 202... surrounding sensor, 202a to 202d... sensor, 203... vehicle internal sensor, 204... GNSS, 205... map memory unit, 206... target route setting unit, 207... external sensor, 300... control device

Claims

1. A position estimation system provided in a vehicle, a forward sensor for detecting an object in front of the vehicle; a surrounding sensor that detects objects around the vehicle within a detection range that is closer than the front sensor and includes at least a left-right range of the vehicle; a position estimation device that detects a plurality of feature points of an object from at least one of a detection result of the forward sensor and a detection result of the peripheral sensor, and estimates a position of the vehicle using the detected plurality of feature points; a control device that controls an operation of the vehicle based on an estimation result of the position estimation device; Equipped with The position estimation device includes: a forward area corresponding to a group of feature points detected from the detection results of the forward sensor, and a peripheral area corresponding to a group of feature points detected from the detection results of the peripheral sensor; weighting each of a plurality of pieces of information relating to the state of the vehicle and weighting information relating to the surrounding environment of the vehicle; setting the forward area and the peripheral area to either one of an area to be used for the position estimation and an area not to be used for the position estimation according to a result of the weighting; A position estimation system, comprising: a position estimation unit that estimates the position by using a group of feature points of an area that is set as an area to be used for the position estimation.

2. A position estimation system provided in a vehicle, a forward sensor for detecting an object in front of the vehicle; a surrounding sensor that detects objects around the vehicle within a detection range that is closer than the front sensor and includes at least a left-right range of the vehicle; a position estimation device that detects a plurality of feature points of an object from at least one of a detection result of the forward sensor and a detection result of the peripheral sensor, and estimates a position of the vehicle using the detected plurality of feature points; a control device that controls an operation of the vehicle based on an estimation result of the position estimation device; Equipped with The surrounding sensor is composed of a plurality of sub-sensors each having a detection range in a different direction around the vehicle, The position estimation device includes: a forward area corresponding to a group of feature points detected from a detection result of the forward sensor, and a plurality of peripheral areas corresponding to a group of feature points detected from each detection result of a plurality of sub-sensors of the peripheral sensor; weighting each of a plurality of pieces of information relating to the state of the vehicle and weighting information relating to the surrounding environment of the vehicle; According to a result of the weighting, the forward area and the plurality of surrounding areas are set as either an area to be used for the position estimation or an area not to be used for the position estimation, A position estimation system, comprising: a position estimation unit that estimates the position by using a group of feature points of an area that is set as an area to be used for the position estimation.

3. 3. The position estimation system according to claim 1, The position estimation device includes: a forward position estimation unit that estimates a position of the vehicle on a map by comparing the plurality of characteristic points detected from a detection result of the forward sensor with a plurality of characteristic points on a predetermined map; a surrounding position estimation unit that compares the plurality of feature points detected from the detection result of the surrounding sensor with feature points of the map to estimate a position of the vehicle on the map; A position estimation system comprising:

4. 3. The position estimation system according to claim 1, The position estimation device includes: a speed determination unit that weights information related to the speed of the vehicle; a steering angle determination unit that weights information related to a steering angle of the vehicle; A driving environment determination unit that weights information related to the driving environment of the vehicle; a predicted curvature determination unit that weights information related to a curvature of a travel route of the vehicle; a surrounding environment determination unit that weights information related to the surrounding environment of the vehicle; an area cost calculation unit that selects whether or not to set the forward area and the peripheral area as an area to be used for the position estimation based on the results of weighting by the speed determination unit, the steering angle determination unit, the driving environment determination unit, the predicted curvature determination unit, and the peripheral environment determination unit; A position estimation system comprising:

5. 3. The position estimation system according to claim 1, The position estimation device includes: a surrounding environment determination unit that obtains, as information related to the surrounding environment of the vehicle, information on whether or not there is a vehicle different from the vehicle in the forward area that is the detection range of the forward sensor, and information on whether or not there is a vehicle different from the vehicle in the surrounding area that is the detection range of the surrounding sensor, and weights the information; a surrounding environment determining unit that determines whether or not the forward area and the surrounding area are to be used in the position estimation based on a result of weighting by the surrounding environment determining unit;

6. 2. The position estimation system according to claim 1, A position estimation system characterized in that the position estimation device increases the number of samplings of the forward sensor or the peripheral sensor for an area set as an area to be used for the position estimation, among the forward area and the peripheral area.

7. 3. The position estimation system according to claim 2, A position estimation system characterized in that the position estimation device increases the number of samplings of the forward sensor or the sub-sensor for an area set as an area to be used for the position estimation, among the forward area and multiple surrounding areas.

8. 3. The position estimation system according to claim 1, The position estimation system is characterized in that the position estimation device sets an area having a large number of feature points as an area to be used for the position estimation.

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