Lane boundary line detection method and lane boundary line detection device

Abstract

The present invention provides a lane boundary line detection method and a lane boundary line detection device for, when detecting lane boundary lines of a road using map information including a link line (Z) representing the road, detecting the lane boundary lines from positional information of feature points relative to the link line (Z).

Description

Lane boundary line detection method and lane boundary line detection device

[0001] The present invention relates to a lane boundary line detection method and a lane boundary line detection device.

[0002] A lane marking recognition device is known that extracts feature points that make up white line candidates from camera images, converts the coordinates of the feature points from the local coordinate system of each camera to a world coordinate system common to multiple cameras, rotates the feature points around the vehicle so that the row of converted feature points is parallel to the Y axis of the world coordinate system, extracts the X coordinate where the cumulative value of the edge strength of the feature points is equal to or greater than a predetermined threshold and is a maximum, and detects the white line from the extracted X coordinate (Patent Document 1).

[0003] Japanese Patent Application Laid-Open No. 2017-156795

[0004] In the above-described conventional technology, when a vehicle is traveling around a curve, the feature points are dispersed in the X-axis direction of the world coordinate system, and therefore it is not possible to extract an X-coordinate that is equal to or greater than a predetermined threshold and that is a maximum, and therefore it is not possible to accurately detect the white line.

[0005] SUMMARY OF THE INVENTION An object of the present invention is to provide a method and apparatus for detecting lane boundary lines that can accurately detect lane boundary lines.

[0006] The present invention solves the above problem by detecting lane boundary lines of a road from position information of feature points relative to the link lines when detecting lane boundary lines of a road using map information including link lines representing the road.

[0007] According to the present invention, lane boundary lines can be detected accurately.

[0008] Fig. 4 is a block diagram showing an example of an embodiment of a driving assistance device including a lane boundary line detection device according to the present invention. Fig. 5 is a plan view showing an example of a driving scene in which driving assistance is performed by the driving assistance device of Fig. 1. Fig. 6 is a plan view showing an example of feature points extracted in the driving scene of Fig. 2. Fig. 7 is a plan view showing an example of a link line in the driving scene of Fig. 2. Fig. 8 is a plan view showing the feature points of Fig. 3 in a local coordinate system along the link line of Fig. 4. Fig. 9 is a plan view showing another example of a link line of this embodiment. Fig. 10 is a plan view showing yet another example of a link line of this embodiment. Fig. 11 is a flowchart showing an example of a processing procedure in the driving assistance device of Fig. 1. Fig. 12 is a flowchart showing another example of a processing procedure in the driving assistance device of Fig. 1.

[0009] Hereinafter, an embodiment of the present invention will be described with reference to the drawings. The following description is based on the assumption that vehicles are driven on the left side of the road in countries where left-hand traffic regulations apply. In countries where right-hand traffic regulations apply, the left and right in the following description should be interpreted as symmetrical.

[0010] [Configuration of Driving Assistance Device] FIG. 1 is a block diagram showing a driving assistance device 10 according to the present invention. The driving assistance device 10 is a group of devices that perform driving assistance for a vehicle. Driving assistance includes driving a vehicle using autonomous driving control and providing information to the driver of the vehicle to assist the driver in driving operations. As an example, the driving assistance device 10 generates a driving route to a destination set by an occupant and controls the vehicle's actuators to drive the vehicle along the driving route. As another example, the driving assistance device 10 notifies the driver of the risk of lane departure when the vehicle deviates from its lane. The driving assistance device 10 may be an in-vehicle system, or some of its components may be provided outside the vehicle.

[0011] Autonomous driving control refers to autonomously controlling the driving behavior of a vehicle using a vehicle control device, and driving behavior includes all driving behaviors such as acceleration, deceleration, starting, stopping, and steering. Autonomously controlling driving behavior means that the control device controls driving behavior using the vehicle's devices. The control device controls these driving behaviors within a predetermined range, and driving behaviors that are not controlled by the control device are manually operated by the driver. When the vehicle is driven manually by the driver without autonomous driving control, the control device does not perform autonomous control of driving behavior, and the vehicle's driving behavior is controlled by the driver's operation.

[0012] As shown in Fig. 1, the driving assistance device 10 includes a map database 11, a navigation device 12, an imaging device 13, a distance measuring device 14, and a control device 15. The control device 15 in this embodiment also includes a lane boundary detection device 20 as part thereof. These devices are connected via a Controller Area Network (CAN) or other in-vehicle LAN, and can exchange information with each other. Information is exchanged with devices installed outside the vehicle via a network such as the Internet or a local area network (LAN).

[0013] The map database 11 is a storage medium storing map information and is provided inside or outside the vehicle. The control device 15 acquires map information from the map database 11 as needed. The map information is used for generating a driving route, etc., and includes information on nodes corresponding to specific points on roads where the vehicle's direction of travel changes (intersections, branching points, etc.) and information on links corresponding to road sections connecting the nodes. Node information includes location information (e.g., latitude and longitude) and information on entering and exiting intersections and branching points. Link information includes road width, road curvature radius, road shoulder structures, road traffic regulations (e.g., traffic direction), merging points, branching points, etc. The map information may be high-precision map information (HD map) that can grasp the movement trajectory for each lane.

[0014] The navigation device 12 is a device that references map information and generates a driving route from the current position of the vehicle detected by a positioning system (not shown) to a set destination. The navigation device 12 uses node and link information in the map information to search for a driving route for the vehicle to reach the destination. The driving route includes at least information on the roads the vehicle will travel on and the vehicle's direction of travel, and is displayed, for example, by nodes and link lines.

[0015] The imaging device 13 is a device that captures images of objects around the vehicle, and examples thereof include a camera equipped with an imaging element such as a CCD, an ultrasonic camera, an infrared camera, etc. In order to reduce blind spots when detecting objects, multiple imaging devices 13 are installed on the front grille of the vehicle, below the left and right door mirrors, near the rear bumper, etc.

[0016] The ranging device 14 is a device that acquires the relative distance and relative speed between the ranging device 14 and an object, and examples thereof include laser radar, millimeter-wave radar, and LiDAR (Light Detection and Ranging) units. In order to reduce blind spots when detecting an object, multiple ranging devices 14 are installed at the front, right side, left side, and rear of the vehicle.

[0017] The object refers to an object existing on or around the road, including lane boundaries, centerlines, road markings, medians, guardrails, curbs, road signs, traffic lights, and crosswalks. The object also includes obstacles that may affect the traveling of a vehicle, such as automobiles other than the host vehicle, motorcycles, bicycles, and pedestrians. In this embodiment, the object particularly refers to an object indicating a lane boundary on the road. The control device 15 acquires the detection results of the imaging device 13 and the ranging device 14 at predetermined time intervals (e.g., every 0.1 to 1 millisecond). The control device 15 may also integrate or synthesize (sensor fusion) the detection results of the imaging device 13 and the ranging device 14 to supplement missing information.

[0018] The ranging device 14 may generate point cloud data in which information about ranging points of surrounding objects is arranged two-dimensionally in the left-right and up-down directions of the vehicle. The ranging points of the object are points on the object whose distances to the ranging device 14 are measured. The ranging point information includes not only position information of the ranging points but also the reflectivity of electromagnetic waves at the ranging points. The position information of the ranging points includes information on the coordinates of the ranging points and information on the distance from the ranging device 14 to the ranging points.

[0019] The distance measuring device 14 generates point cloud data by scanning electromagnetic waves in the left-right direction of the vehicle. For example, the distance measuring device 14 emits electromagnetic waves along the vehicle width direction, detects the reflected electromagnetic waves (reflected waves), and calculates the distance to the distance measurement point and the direction of the distance measurement point. The irradiation range of the electromagnetic waves is not particularly limited. Examples of electromagnetic waves include millimeter waves, infrared rays, and lasers.

[0020] The control device 15 is a device that performs driving assistance by controlling and cooperating with the constituent devices of the driving assistance device 10. The control device 15 is, for example, a computer, and includes a CPU (Central Processing Unit) that is a processor, a ROM (Read Only Memory) that stores programs, and a RAM (Random Access Memory) that functions as an accessible storage device. The CPU is an operating circuit that executes the programs stored in the ROM and realizes the functions of the control device 15.

[0021] The control device 15 has a driving assistance function that performs driving assistance for the vehicle. The control device 15 also includes a lane boundary detection device 20 as part thereof, and the lane boundary detection device 20 has a lane boundary detection function that detects lane boundary lines as part of the driving assistance function. The ROM stores a program for realizing the driving assistance function, and the CPU executes the program stored in the ROM to realize the driving assistance function including the lane boundary detection function.

[0022] 1 illustrates, for convenience, the driving control unit 16, the feature acquisition unit 21, the position acquisition unit 22, and the boundary detection unit 23 as functional blocks that realize the driving assistance function. Of these functional blocks, the feature acquisition unit 21, the position acquisition unit 22, and the boundary detection unit 23 are functional blocks that realize the lane boundary line detection function, and are included in the lane boundary line detection device 20. The functions of each functional block will be described below with reference to FIG. 2.

[0023] [Function of Lane Boundary Detection Device] FIG. 2 is a plan view showing an example of a driving scene in which the control device 15 executes autonomous driving control using the driving assistance function. In the driving scene shown in FIG. 2, a lane W, demarcated (defined) by white lines L1 and L2, extends in the Y-axis direction (the up-down direction in the drawing) of a global coordinate system used in map information. The coordinate system of the map information is not limited to the global coordinate system, and various coordinate systems may be used. Furthermore, a vehicle V is traveling at position Y1 in the lane W. If the traffic direction of the lane W is the positive direction of the Y-axis (the direction from the bottom to the top in the drawing), the control device 15, using the function of the driving control unit 16, generates a driving route X for traveling along the lane W from position Y1 to position Y2. Then, the control device 15 autonomously controls the driving operation of the vehicle V so that the vehicle V travels along the driving route X.

[0024] If the map information stored in the map database 11 is high-precision map information, the lanes W can be recognized from lane information on the road, but if the map information does not include lane information, the lanes W are recognized by detecting white lines L1, L2 from images acquired by the imaging device 13, etc. In this lane recognition that does not rely on map information, the feature points of the white lines L1, L2 extracted from the image may overlap at curved portions of the lane W, making it impossible to accurately recognize the lane W. Therefore, the control device 15 of this embodiment detects the white lines L1, L2 from position information of the feature points relative to the link lines, using the processing described below.

[0025] 1 has a function of generating a driving route for the vehicle V and activating actuators of the vehicle V so that the vehicle V travels along the driving route. When performing driving assistance using the functions of the driving control unit 16, the control device 15 determines whether to use map information including link lines representing roads. If it is determined that driving assistance should be performed using map information including link lines representing roads, the control device 15 instructs the lane boundary detection device 20 to detect lane boundary lines using map information including link lines representing roads in order to generate a driving route X.

[0026] The feature acquisition unit 21 has a function of acquiring feature points of objects around the vehicle V. Feature points are characteristic parts of an object that are used to detect the object, and examples of such feature points include edges that capture the outline of the object, corners of the object, and parts of the object where the brightness changes. The lane boundary detection device 20 acquires images from the imaging device 13 using the function of the feature acquisition unit 21, and extracts feature points from the images using a known algorithm such as SIFT (Scale-Invariant Feature Transform).

[0027] Alternatively, the lane boundary detection device 20 may acquire point cloud data from the distance measurement device 14 and acquire feature points from the point cloud data. For example, the lane boundary detection device 20 generates an image on which distance measurement points are plotted from the point cloud data, and performs feature point extraction processing on the generated image. In this case, by incorporating information on the reflectance of electromagnetic waves into the generated image, road markings such as lane boundary lines can be detected from the difference in reflectance between the road surface and road markings.

[0028] The lane boundary detection device 20 of this embodiment particularly acquires information on feature points of objects that indicate lane boundary lines of the road on which the vehicle V is traveling from a sensor mounted on the vehicle V. The objects that indicate lane boundary lines include white solid lines, yellow solid lines, white dashed lines, etc., and the information on the feature points of the objects includes position information of the edges of the white solid lines, information on the width of the yellow solid lines, information on the length of the white dashed lines in the traveling direction, etc. Examples of sensors include an imaging device 13 and a distance measuring device 14.

[0029] FIG. 3 is a plan view showing an example of feature points extracted from an image acquired by the image capture device 13 in the driving scene shown in FIG. 2. The coordinate system shown in FIG. 3 is a global coordinate system. In the driving scene shown in FIG. 2, as shown in FIG. 3, feature points A1 to A15 are extracted for the white line L1 on the left side of the vehicle V in the direction of travel, and feature points B1 to B13 are extracted for the white line L2 on the right side of the vehicle V in the direction of travel. The extracted feature points are arranged on the XY plane in FIG. 3 based on the coordinate information of the feature points. Note that the number of feature points and the spacing between the feature points can be set to appropriate values ​​depending on the feature point extraction process.

[0030] The position acquisition unit 22 has a function of acquiring position information of feature points relative to link lines. A link line is, for example, a line along the road that indicates the shape of the road corresponding to the road section between nodes. As an example, the link line is located on the center line in the width direction of the road, and one link line is set for each road section between nodes. Information about the link lines is included in the information about the links.

[0031] Nodes are placed on the link lines at predetermined distances (for example, every 1 to 3 meters). The nodes on the link lines have position information for the nodes. Examples of position information include coordinate information in the global coordinate system used in map information, information on the angle between a predetermined direction and the direction of the link line (direction of travel), and information on the travel distance from one node to another node on the link line. For example, the node closest to the start point of travel route X is set as the one node serving as the starting point.

[0032] Examples of the predetermined direction include the north direction on the map and the direction along the axes of the global coordinate system. The direction of the link line is, for example, the tangent direction of the link line at a node on the link line. The travel distance between nodes is calculated as the travel distance when traveling from one node to another node along the link line. In addition, the nodes on the link line may have information on whether or not the node is located within an intersection.

[0033] Fig. 4 is a plan view showing an example of link lines set for the road shown in Fig. 2. Link line Z shown in Fig. 4 is a line that runs along the lane W and is located on the center line of the lane W in the width direction, and indicates the curvature of the lane W, etc. Nodes N1 to N14 are provided on link line Z, and each of nodes N1 to N14 has information on its coordinates in the global coordinate system, information on the angle formed between the Y-axis direction and the tangent direction of link line Z, information on the distance traveled from node N1, which is the starting point, and the like.

[0034] The lane boundary detection device 20 acquires position information in a coordinate system (hereinafter also referred to as the original coordinate system) of the information used to extract the feature points, using the function of the position acquisition unit 22. If the feature points are extracted from an image, the original coordinate system is the coordinate system used for the image, and if the feature points are extracted from point cloud data, the original coordinate system is the coordinate system used for the point cloud data. Next, the lane boundary detection device 20 converts the position information in the original coordinate system into position information in the global coordinate system. As an example, the lane boundary detection device 20 converts the original coordinate system into the global coordinate system using the relative position of the feature points with respect to the vehicle V in the original coordinate system.

[0035] The lane boundary detection device 20 then converts the position information in the global coordinate system into position information in the local coordinate system, and acquires position information of the feature point in the local coordinate system along the link line Z. The local coordinate system along the link line Z is a coordinate system in which the position information of the feature point is expressed by the distance from the link line Z and the traveling distance from a predetermined node. The lane boundary detection device 20 associates the feature point with the closest node on the link line Z, and rotates the feature point around the associated node by the angle formed by the predetermined direction and the direction of the link line. Furthermore, the lane boundary detection device 20 moves the rotated feature point along the link line Z by the traveling distance of the node at the center of rotation.

[0036] The position information of a feature point is information relating to the position of the feature point, and is not particularly limited. Examples include information on the coordinates of the feature point in an arbitrary coordinate system such as a local coordinate system, information on the distance and direction of one feature point relative to another feature point, and information on the distance between the feature point and the link line Z. Note that the position information of the feature point relative to the link line Z does not necessarily have to be position information of the feature point in a local coordinate system along the link line Z, and is not particularly limited as long as it is position information based on the link line Z.

[0037] In the example shown in Figure 4, the coordinates of each feature point in the global coordinate system have been acquired, so the lane boundary line detection device 20 associates each feature point with a node on the link line Z. Specifically, feature points A1 to A8 and feature points B1 to B8 are associated with nodes N1 to N8, respectively. Furthermore, feature points A9 and B9 are associated with node N9, feature points A10 and A11 are associated with node N10, and feature points A12 and B10 are associated with node N11. Furthermore, feature points A13 to A15 and feature points B11 to B13 are associated with nodes N12 to N14, respectively.

[0038] Next, the lane boundary line detection device 20 rotates each of the feature points A1 to A15 and B1 to B13 by the angle formed by a predetermined direction and the direction of the link line, using information on the angle formed by the associated nodes N1 to N14. The lane boundary line detection device 20 then moves each of the rotated feature points A1 to A15 and B1 to B13 along the link line Z, using information on the travel distance held by the associated nodes N1 to N14.

[0039] The process of converting to the local coordinate system will be specifically described using the example of feature point A9 associated with node N9. As shown in Figure 4, if the angle formed by the Y-axis direction C1 and the tangent direction C2 of the link line Z at node N9 is θ, the direction (travel direction) R of the link line Z at node N9 is expressed by the following equation (1): Here, x ａ and y ａ are the X and Y coordinates of the node N9 in the global coordinate system. In this case, the coordinates P of the feature point A9 in the global coordinate system ｒａｗis expressed by the following equation (2), the coordinates P of the feature point A9 in the global coordinate system are ｒａｗ is rotated counterclockwise by an angle θ ｎｏｄｅ is expressed by the following equation (3). Here, x ｂ and y ｂ are the X and Y coordinates of the feature point A9 in the global coordinate system. ｎｏｄｅ coordinate P ｗａｙ is expressed by the following equation (4). The travel distance s is the travel distance from node N1 to node N9.

[0040] The lane boundary line detection device 20 performs the above-described series of processes on each feature point shown in Fig. 4 to convert the coordinates of the feature point in the global coordinate system shown in Fig. 4 into coordinates in the local coordinate system shown in Fig. 5. The horizontal axis of the local coordinate system in Fig. 5 represents the distance d between the feature point and the link line Z, and the vertical axis represents the travel distance s from node N1.

[0041] The boundary detection unit 23 has a function of detecting lane boundary lines that separate lanes on a road from position information of the feature points relative to the link line Z. As an example, in a local coordinate system along the link line Z, feature points corresponding to lane boundary lines are aligned at approximately the same distance from the link line Z, and therefore the lane boundary line detection device 20, using the function of the boundary detection unit 23, searches for peak positions where a predetermined number or more of feature points exist from the position information of the feature points in the local coordinate system, and detects lane boundary lines from the searched peak positions.

[0042] The predetermined number of peak positions can be set to any value within a range in which lane boundary lines can be accurately detected. In addition, in searching for peak positions, the lane boundary line detection device 20 may generate a histogram indicating the distribution of feature points from the position information of the feature points in the local coordinate system.

[0043] 5, the lane boundary detection device 20 searches for the number of feature points in the s-axis direction from an arbitrary position (e.g., origin O) along the positive direction of the d-axis (i.e., the direction from left to right in the drawing). This search detects the presence of a peak consisting of feature points A1 to A15 at position d1 and a peak consisting of feature points B1 to B13 at position d2. Then, from the peak at position d1, the lane boundary detection device 20 detects the white line L1 on the left side of the vehicle V in the traveling direction, and from the peak at position d2, the lane boundary detection device 20 detects the white line L2 on the right side of the vehicle V in the traveling direction.

[0044] The lane boundary line detection device 20 outputs information about the detected lane boundary lines to the driving control unit 16. The control device 15 generates a driving route X shown in Fig. 2 using the information about the lane boundary lines obtained from the lane boundary line detection device 20, and causes the vehicle V to drive along the driving route X by autonomous driving control.

[0045] When performing driving assistance using lane boundary lines, the lane boundary line detection device 20 may determine whether or not a branch point at which a link line branches off exists on the driving route X to the destination of the vehicle V. For example, as shown in Fig. 6 , if it is determined that a branch point D at which the link line Z1 branches off into link lines Z2 and Z3 exists on the link line Z1, the lane boundary line detection device 20 acquires position information for the link line heading towards the destination, out of the link lines Z2 and Z3 that branch off at the branch point D.

[0046] 6, if link line Z2 is a link line leading to the destination, the lane boundary line detection device 20 acquires the coordinates of the feature point in a local coordinate system along link lines Z1 and Z2. On the other hand, if it is determined that no branch point D exists on link line Z1, the lane boundary line detection device 20 acquires the position information of the feature point in the local coordinate system along link line Z1.

[0047] The lane boundary line detection device 20 may acquire position information relative to link lines for feature points other than those within intersections. In other words, the lane boundary line detection device 20 may exclude position information for feature points within intersections from the position information for feature points. This is because feature points within intersections may not be accurately extracted. Specifically, since each node on a link line has information indicating whether the node is located within an intersection, the lane boundary line detection device 20 does not acquire position information for feature points associated with nodes located within an intersection when converting the coordinate system.

[0048] When the road is a one-way road, the lane boundary line detection device 20 may acquire position information relative to the link line for feature points on both sides of the link line Z. Alternatively or in addition, when the road is a two-way road, the lane boundary line detection device 20 may acquire position information relative to the link line for feature points on the side of the link line Z that is farther from the oncoming lane.

[0049] For example, in the driving scene shown in Fig. 2, there are no lanes other than lane W and the road is one-way, so the coordinates of feature points A1 to A15 and B1 to B13 on both sides of link line Z among the feature points shown in Fig. 5 are obtained, and lane boundary line detection processing is performed. In contrast, in the driving scene shown in Fig. 2, if an oncoming lane exists to the right of lane W, the road becomes a two-way road, so the coordinates of feature points A1 to A15 on the side farther from the oncoming lane than link line Z among the feature points shown in Fig. 5 are obtained, and lane boundary line detection processing is performed.

[0050] The lane boundary detection device 20 may associate a feature point with each detected lane boundary line, convert the position information of the feature point into map position information in the map information, and estimate the shape of the lane boundary line for each associated feature point from the map position information. In this case, the lane boundary detection device 20 may convert the position information of the feature point into a coordinate system with the position of the vehicle V as the origin. In the example shown in FIG. 5 , if the lane boundary detection device 20 detects a white line L1 on the left side of the vehicle V's traveling direction and a white line L2 on the right side of the vehicle V's traveling direction from the coordinates of the feature points shown in FIG. 5 , the lane boundary detection device 20 associates feature points A1 to A15 corresponding to the white line L1 with feature points B1 to B13 corresponding to the white line L2, respectively. The coordinate system is then converted from the local coordinate system shown in FIG. 5 to the global coordinate system shown in FIG. 4 , and the shapes of the white lines L1 and L2 are estimated for each associated feature point using a known fitting method. Associating feature points with each white line (clustering) facilitates fitting.

[0051] For example, before acquiring position information in the local coordinate system, the lane boundary detection device 20 may determine whether the angle formed between a link line ahead of a certain node on the link line in the traveling direction and a link line behind the node in the traveling direction is equal to or greater than a predetermined angle. If the lane boundary detection device 20 determines that the angle formed between a link line ahead of a certain node in the traveling direction and a link line behind the node in the traveling direction is equal to or greater than the predetermined angle, it corrects the shape of the link line at the node and before and after it. The predetermined angle can be set to any appropriate value within a range in which conversion to the local coordinate system can be appropriately performed.

[0052] FIG. 7 is a plan view showing another example of link lines set for the road shown in FIG. 2 . In the link line Zx shown in FIG. 7 , node N9x is shifted to the left of the other nodes in the direction of travel, resulting in a distorted shape of the link line Zx before and after node N9x. In this case, the lane boundary detection device 20 determines whether the angle θa formed by the direction C3 of the link line between nodes N8 and N9x and the direction of the link line between nodes N9x and N10 is equal to or greater than a predetermined angle. If it determines that the angle θa is equal to or greater than the predetermined angle, it corrects the shape of the link line Zx at node N9x and the nodes N8 and N10 before and after it. On the other hand, if it determines that the angle θa is less than the predetermined angle, it does not perform correction of the link line Zx.

[0053] As a method for correcting the shape of the link line Zx, for example, as shown in FIG. 7, an inscribed circle E tangent to nodes N8 and N10 is generated, and node N9x is moved to the position of node N9y on the inscribed circle E, or node N9x is replaced with node N9y. Node N9y is the closest point to node N9x on the inscribed circle E. Alternatively, instead of the inscribed circle E, a Bezier curve connecting nodes N8 and N10 may be generated, and node N9x may be moved to the closest point on the Bezier curve from node N9x. Furthermore, an approximation curve may be generated by polynomial approximation using the coordinates of nodes N8, N9x, and N10, and node N9x may be moved to the closest point on the approximation curve from node N9x.

[0054] 8 and 9, the procedure for processing information by the control device 15 will be described. The process described below is executed by a processor (CPU) included in the control device 15 at predetermined time intervals (for example, every 0.1 to 1 millisecond).

[0055] FIG. 8 is an example of a flowchart showing information processing executed by the driving assistance device 10.

[0056] First, in step S1, the feature acquisition unit 21 functions to acquire information on feature points of an object that indicates lane boundary lines from an image or the like, and then in step S2, the position acquisition unit 22 functions to acquire position information of the feature points relative to the link line Z. In step S3, the boundary detection unit 23 functions to detect lane boundary lines from the position information of the feature points relative to the link line, and then in step S4, the driving control unit 16 functions to perform driving assistance using the detected lane boundary lines.

[0057] Next, FIG. 9 is a flowchart showing another example of information processing executed by the driving assistance device 10. In FIG.

[0058] First, in step S11, feature points are extracted from the image and / or point cloud data by the function of the feature acquisition unit 21. In step S12, coordinates of the feature points in the original coordinate system of the image and / or point cloud data are acquired by the function of the position acquisition unit 22. In the subsequent step S13, the coordinate system of the acquired coordinates is converted from the original coordinate system to a global coordinate system. In the subsequent step S14, each feature point is associated with the nearest node on the link line Z.

[0059] In step S15, it is determined from the information held by the node whether the node exists within an intersection. If it is determined that the node exists within the intersection, the process proceeds to step S16, where the coordinates of feature points associated with nodes existing within the intersection are excluded, and the process proceeds to step S17. On the other hand, if it is determined that the node does not exist within the intersection, the process proceeds to step S17, where the coordinate system is converted from the global coordinate system to the local coordinate system.

[0060] In step S18, the coordinates of the feature points in the local coordinate system are acquired, and in the following step S19, it is determined whether the road to which the node belongs is a one-way street. If it is determined that the road to which the node belongs is not a one-way street, the process proceeds to step S20, where the coordinates of the feature points on the side of the link line Z closer to the oncoming lane are excluded, and the process proceeds to step S21. On the other hand, if it is determined that the road to which the node belongs is a one-way street, the process proceeds to step S21, where the boundary detection unit 23 detects lane boundary lines from the peak positions in the local coordinate system.

[0061] In step S22, feature points are associated with each boundary line, and in the subsequent step S23, the coordinate system is converted from the local coordinate system to the global coordinate system. Then, in step S24, fitting is performed for each associated feature point to estimate the shape of the lane boundary line.

[0062] Note that step S4 in Fig. 8 is not essential to the present invention and may be omitted as necessary. Also, steps S15, S16, S19, and S20 in Fig. 9 are not essential to the present invention and may be omitted as necessary.

[0063] [Embodiments of the Present Invention] According to this embodiment, there is provided a lane boundary line detection method executed by a lane boundary line detection device 20 that detects lane boundary lines of a road using map information including link lines Z that represent the road, in which the lane boundary line detection device 20 acquires information on feature points of an object that indicates the lane boundary lines of the road on which a vehicle V is traveling from a sensor mounted on the vehicle V, and detects the lane boundary lines from position information of the feature points relative to the link lines Z. This enables lane boundary lines to be detected accurately.

[0064] In the lane boundary line detection method of this embodiment, when performing driving assistance using the lane boundary lines, the lane boundary line detection device 20 may determine whether or not a branch point D at which the link line Z branches off is present on the driving route X to the destination of the vehicle, and if it determines that the branch point D is present, may acquire the position information for the link line Z heading towards the destination out of the link lines Z branching off at the branch point D. This makes it possible to eliminate unnecessary feature points that hinder the detection of lane boundary lines.

[0065] In the lane boundary line detection method of this embodiment, the lane boundary line detection device 20 may acquire the position information relative to the link line for the feature points other than intersections, thereby suppressing erroneous detection of lane boundary lines within intersections.

[0066] In the lane boundary line detection method of this embodiment, the lane boundary line detection device 20 may acquire the position information relative to the link line for the feature points on both sides of the link line Z if the road is a one-way road, and may acquire the position information relative to the link line for the feature points on the side of the link line Z farther from the oncoming lane if the road is a two-way road. This makes it possible to suppress erroneous detection of lane boundary lines for oncoming traffic.

[0067] In the lane boundary line detection method of this embodiment, the lane boundary line detection device 20 may associate the feature points with each of the detected lane boundary lines, convert the position information of the feature points into position information on the map of the map information, and estimate the shape of the lane boundary line for each of the associated feature points from the position information on the map. This allows for more accurate estimation of the shape of the lane boundary lines.

[0068] In the lane boundary line detection method of this embodiment, the lane boundary line detection device 20 determines whether the angle formed between the link line ahead of the node on the link line in the traveling direction and the link line behind the node in the traveling direction is equal to or greater than a predetermined angle, and if it determines that the angle formed between the link line ahead of the node in the traveling direction and the link line behind the node in the traveling direction is equal to or greater than the predetermined angle, may correct the shape of the link line at the node and before and after it, thereby suppressing erroneous detection of lane boundary lines.

[0069] Furthermore, according to this embodiment, there is provided a lane boundary line detection device 20 that detects lane boundary lines of a road using map information including link lines Z that represent the road, the lane boundary line detection device 20 including: a feature acquisition unit 21 that acquires information on feature points of an object that indicates the lane boundary lines of the road on which a vehicle V is traveling from a sensor mounted on the vehicle V; and a boundary detection unit 23 that detects the lane boundary lines from position information of the feature points relative to the link lines Z. This enables lane boundary lines to be detected accurately.

[0070] 10...driving assistance device, 11...map database, 12...navigation device, 13...imaging device, 14...distance measuring device, 15...control device, 16...driving control unit, 20...lane boundary detection device, 21...feature acquisition unit, 22...position acquisition unit, 23...boundary detection unit, A1 to A15, B1 to B13...feature points, C1...Y-axis direction, C2...tangential direction, C3...direction of link line, D...branch point, L1, L2...white line, N1 to N14...node, V...vehicle, W...lane, X...driving route, Y1, Y2...position, Z, Z1, Z2, Z3, Zx...link line

Claims

1. A lane boundary detection method performed by a lane boundary detection device that detects lane boundary lines of a road using map information including link lines representing the road, The lane boundary detection device is, Information on the characteristic points of an object indicating the lane boundary line of the road on which the vehicle is traveling is obtained from a sensor mounted on the vehicle. A method for detecting lane boundary lines, which involves detecting the lane boundary line from the positional information of the feature points in a local coordinate system along the link line.

2. The lane boundary detection device is, When performing driving assistance using the lane boundary line, it is determined whether or not there is a branching point on the vehicle's route to its destination where the link line branches off. The lane boundary detection method according to claim 1, wherein if it is determined that the aforementioned branching point exists, the method acquires the position information in the local coordinate system along the link line that branches off at the branching point and heads toward the destination.

3. The lane boundary detection method according to claim 1 or 2, wherein the lane boundary detection device acquires the position information in the local coordinate system for the feature points other than intersections.

4. The lane boundary detection device is, If the road is one-way, the positional information in the local coordinate system is obtained for the feature points on both sides of the link line. The lane boundary detection method according to any one of claims 1 to 3, wherein, if the road is a two-way road, the position information in the local coordinate system is obtained for the feature point on the side furthest from the opposing lane relative to the link line.

5. The lane boundary detection device is, The feature points are associated with each detected lane boundary line. The location information of the feature point is converted into location information on the map of the map information, A method for detecting lane boundaries according to any one of claims 1 to 4, wherein the shape of the lane boundary is estimated for each associated feature point from the location information on the map.

6. The lane boundary detection device is, It is determined whether the angle formed by the link line forward in the direction of travel from the node on the link line and the link line backward in the direction of travel from the node is greater than or equal to a predetermined angle. The lane boundary detection method according to any one of claims 1 to 5, wherein if it is determined that the angle formed by the link line forward of the node in the direction of travel and the link line backward of the node in the direction of travel is greater than or equal to the predetermined angle, the shape of the link line at the node and before and after it is corrected.

7. A lane boundary detection device that detects the lane boundary of a route using map information including link lines representing roads, A feature acquisition unit acquires information on the feature points of an object that indicates the lane boundary line of the road on which the vehicle is traveling, from a sensor mounted on the vehicle. A lane boundary detection device comprising a boundary detection unit that detects the lane boundary line from the positional information of the feature point in a local coordinate system along the link line.

8. The lane boundary detection method according to any one of claims 1 to 6, wherein the local coordinate system is a coordinate system that represents the position information of the feature point using the distance from the link line and the travel distance from a predetermined node.

9. The lane boundary detection device is, The positional information of the feature points represented in the global coordinate system used in the aforementioned map information is obtained, The positional information of the feature point in the global coordinate system is converted to the positional information in the local coordinate system. A method for detecting a lane boundary line according to any one of claims 1 to 6 and 8, wherein the lane boundary line is detected from the position information of the converted feature points.