Driving control method and driving control device

Abstract

To improve accuracy of estimating a shape of an own lane by using map data in which a road shape is represented for each road by the arrangement of a plurality of nodes.SOLUTION: A travel control method includes: extracting an own vehicle road shape of an own vehicle road on which an own vehicle travels, and an opposite road shape of an opposite road, from map data in which a road shape is represented for each road by the arrangement of a plurality of nodes (S3); translating the own vehicle road shape so that a point on the own vehicle road shape in the vicinity of an own vehicle position coincides with the own vehicle position, and translating the opposite road shape so that a point on the opposite road shape in the vicinity of the own vehicle position coincides with the own vehicle position (S5); calculating, in a section ahead in the route of the own vehicle, an amount of deviation between the own vehicle road shape after being translated and the opposite road shape after being translated (S6); and estimating the shape of a right-turn lane on the basis of a shape obtained by correcting the own vehicle road shape after being translated so that the amount of deviation increases to a value obtained by multiplication by a constant (S7).SELECTED DRAWING: Figure 10

Description

[Technical Field]

[0001] The present invention relates to a cruise control method and a cruise control device. [Background technology]

[0002] Patent document 1 describes a technology that reads position data of nodes, which are road markings located before and after the vehicle's current position, from a road map database, estimates the shape of the vehicle's road from multiple nodes, and controls the headlight illumination. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent No. 4363640 specification Summary of the Invention [Problem to be solved by the invention]

[0004] In map data in which road shapes are represented by an array of multiple nodes on a road-by-road basis, each node indicates a position near the center of the road. Therefore, in places where lanes increase, such as near the entrance to an intersection, the position of the node in the lane width direction also changes in the direction in which the lanes increase, so the shape represented by the array of nodes differs from the actual lane shape. Therefore, if the shape of the lane in which the vehicle is traveling near the entrance to an intersection is estimated based on the road shape in the map data, the estimation accuracy may be low. An object of the present invention is to improve the accuracy of estimating the shape of the current lane using map data in which road shapes are expressed on a road-by-road basis by an arrangement of multiple nodes. [Means for solving the problem]

[0005] In one aspect of the driving control method of the present invention, a host vehicle position, which is the current position of the host vehicle, is detected, and whether or not the lane on which the host vehicle is traveling will branch into a straight lane for traveling straight through an intersection and a right-turn lane for turning right at the intersection is estimated. If it is estimated that the lane on which the host vehicle is traveling will branch into a straight lane and a right-turn lane, a host road shape, which is the road shape of the host road on which the host vehicle is traveling, and an oncoming road shape, which is the road shape of an oncoming road on which an oncoming vehicle to the host vehicle is traveling, are extracted from map data in which road shapes are expressed on a road-by-road basis by an array of multiple nodes, and a vehicle position on the host road shape near the host vehicle position is calculated. The shape of the road on which the vehicle is traveling is translated so that a point on the shape of the oncoming road near the position of the vehicle becomes the vehicle position, and the shape of the oncoming road is translated so that a point on the shape of the road on which the vehicle is traveling becomes the vehicle position. An inter-shape deviation amount, which is the amount of deviation between the shape of the road on which the vehicle is traveling after the parallel translation and the shape of the oncoming road after the parallel translation, is calculated in the section ahead of the path of the vehicle. By correcting the shape of the road on which the vehicle is traveling after the parallel translation, the amount of deviation between the corrected shape of the road on which the vehicle is traveling after the parallel translation and the shape of the oncoming road after the parallel translation is increased to a value obtained by multiplying the inter-shape deviation amount by a constant greater than 1, and the shape of the right-turn lane is estimated based on the corrected shape of the road on which the vehicle is traveling after the parallel translation. [Effects of the Invention]

[0006] According to the present invention, it is possible to improve the accuracy of estimating the shape of the current lane by using map data in which the road shape is expressed on a road-by-road basis by an arrangement of a plurality of nodes. [Brief explanation of the drawings]

[0007] [Figure 1] 1 is a diagram showing an example of a schematic configuration of a vehicle equipped with a cruise control device according to an embodiment; [Figure 2] FIG. 2 is a schematic diagram illustrating an example of a road shape obtained from map data. [Figure 3] FIG. 10 is a schematic diagram illustrating an example of the shape of the road on which the vehicle is traveling and the shape of the oncoming road after translation. [Figure 4] FIG. 10 is a schematic diagram of an example of the shape of the road on which the vehicle is traveling after the deviation amount from the shape of the oncoming road has been corrected; [Figure 5] 2 is a block diagram illustrating an example of a functional configuration of a controller in FIG. 1. FIG. [Figure 6] FIG. 1 is a schematic diagram of an example of a road marking that warns of a branching lane into a straight lane and a right-turn lane. [Figure 7] 10(a) and 10(b) are diagrams illustrating a method for calculating the degree of difference between the shape of the road on which the vehicle is traveling and the shape of an oncoming road. [Figure 8] 10 is a schematic diagram showing an example of a similarity calculation section for calculating the similarity between the shape of a road on which one vehicle runs and the shape of an oncoming road; FIG. [Figure 9] 10(a) and 10(b) are schematic diagrams illustrating an example of setting a similarity calculation interval. [Figure 10] 3 is a flowchart illustrating an example of a driving control method according to an embodiment. [Figure 11] 10(a) and 10(b) are explanatory diagrams of an example of a driving control method according to a second embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0008] Hereinafter, embodiments of the present invention will be described with reference to the drawings. Note that the drawings are schematic and may differ from the actual product. Furthermore, the embodiments of the present invention shown below are examples of devices and methods for embodying the technical concept of the present invention, and the technical concept of the present invention does not limit the structure, arrangement, etc. of component parts to those described below. The technical concept of the present invention can be modified in various ways within the technical scope defined by the claims.

[0009] (First embodiment) (composition) The host vehicle 1 is equipped with a driving control device 10 that supports the driving of the host vehicle 1. The driving control device 10 detects the driving environment around the host vehicle 1 and automatically controls the driving of the host vehicle 1 based on the detected driving environment, thereby supporting the driving of the host vehicle 1. For example, the driving assistance of the host vehicle 1 by the driving control device 10 may include autonomous driving control in which the host vehicle 1 is automatically driven without the involvement of an occupant (e.g., a driver). Also, for example, the driving assistance of the host vehicle 1 by the driving control device 10 may include automatic control of at least one of the driving force, braking force, or steering angle of the host vehicle 1.

[0010] The cruise control device 10 includes a positioning device 11, a map database 12, an external sensor 14, a vehicle sensor 15, a controller 16, and an actuator 17. In the drawings, the map database is referred to as a "map DB." The positioning device 11 measures the current position of the vehicle 1. In the following description, the current position of the vehicle 1 is referred to as "vehicle position." The positioning device 11 may include, for example, a Global Positioning System (GNSS) receiver. The GNSS receiver is, for example, a Global Positioning System (GPS) receiver, and receives radio waves from multiple navigation satellites to measure the vehicle position.

[0011] Map information is stored in the map database 12. The map information stored in the map database 12 may be, for example, map data for navigation that includes information on a road-by-road basis. The map data for navigation may be data that expresses road shapes on a road-by-road basis using a node string. In this specification, a node string may be, for example, an array of multiple nodes, or data that includes an array of multiple nodes and line segments that connect these nodes with curves or straight lines. In other words, a node string represents an array of multiple nodes, i.e., the shape of a road on a map, and is not limited to an array of multiple nodes as long as it includes an array of multiple nodes.

[0012] The external sensor 14 detects various information (driving environment information) about the driving environment around the vehicle 1. For example, the external sensor 14 detects objects around the vehicle 1. The external sensor 14 detects the environment around the vehicle 1, such as objects present around the vehicle 1, the relative position between the vehicle 1 and the object, the distance between the vehicle 1 and the object, and the direction in which the object exists. The external sensor 14 outputs the detected information about the driving environment to the controller 16 as driving environment information. For example, the external sensor 14 detects the relative positions of other vehicles and targets around the vehicle 1 relative to the vehicle 1. Here, targets include, for example, traffic lights provided on the road on which the vehicle 1 is traveling, lines on the road surface (stop lines, lane boundaries, lane markings, etc.), curbs on the shoulders of the road, guardrails, etc.

[0013] The external sensor 14 may include a monocular camera such as a full HD color camera. The camera captures an image including a recognition target in the environment surrounding the vehicle 1, and outputs the captured image to the controller 16 as driving environment information. The external sensor 14 may also include a distance measuring device such as a laser range finder (LRF), radar, or a laser radar such as LiDAR (Light Detection and Ranging). The distance measuring device detects the relative position of the vehicle, which is determined by the relative distance and direction to an object present around the vehicle. The distance measuring device outputs the detected distance data to the controller 16 as driving environment information.

[0014] The vehicle sensor 15 detects various information (vehicle information) obtained from the host vehicle 1. The vehicle sensor 15 includes, for example, a vehicle speed sensor that detects the traveling speed (vehicle speed) of the host vehicle 1, a wheel speed sensor that detects the rotational speed of each tire equipped on the host vehicle 1, a three-axis acceleration sensor (G sensor) that detects the acceleration (including deceleration) in three axial directions of the host vehicle 1, a steering angle sensor that detects the steering angle of the steering wheel, a turning angle sensor that detects the turning angle of the steered wheels, a gyro sensor that detects the angular velocity generated in the host vehicle 1, a yaw rate sensor that detects the yaw rate, an accelerator sensor that detects the amount of operation of the accelerator pedal of the host vehicle 1, and a brake sensor that detects the amount of brake operation by the driver.

[0015] The controller 16 is an electronic control unit (ECU) that controls the driving of the host vehicle 1. When controlling the driving of the host vehicle 1, the controller 16 automatically controls the driving of the host vehicle 1 based on the surrounding driving environment. The controller 16 may be configured as a single electronic control unit or as a collection of multiple electronic control units. The controller 16 includes a processor 20 and peripheral components such as a storage device 21. The processor 20 may be, for example, a CPU (Central Processing Unit) or an MPU (Micro-Processing Unit). The storage device 21 may include a semiconductor storage device, a magnetic storage device, an optical storage device, etc. The storage device 21 may include memories such as a register, a cache memory, a ROM (Read Only Memory) used as a main memory device, and a RAM (Random Access Memory). The functions of the controller 16 described below are realized by, for example, the processor 20 executing a computer program stored in the storage device 21 .

[0016] The controller 16 may be formed of dedicated hardware for executing the information processing described below. For example, the controller 16 may include a functional logic circuit configured in a general-purpose semiconductor integrated circuit. For example, the controller 16 may include a programmable logic device (PLD) such as a field-programmable gate array (FPGA).

[0017] The actuator 17 operates the accelerator opening and braking device of the host vehicle 1 in response to a control signal from the controller 16 to generate a driving force for driving the host vehicle 1 or a braking force for braking the host vehicle 1. The actuator 17 includes an accelerator opening actuator and a brake control actuator. The accelerator opening actuator controls the accelerator opening of the host vehicle 1. The brake control actuator controls the braking operation of the braking device of the host vehicle 1. The actuator 17 may also include a steering actuator that controls the steering direction and steering amount of the steering mechanism of the host vehicle 1. The actuator 17 may operate the steering mechanism of the host vehicle 1 in response to a control signal from the controller 16.

[0018] Next, a description will be given of the driving control of the host vehicle 1 by the controller 16. The driving control by the controller 16 may include automatic control that estimates the lane shape of the host lane, which is the lane on which the host vehicle 1 is traveling, and controls at least the steering angle of the host vehicle 1 based on the estimated lane shape of the host lane. For example, the driving control by the controller 16 may include lane departure prevention control (lane keeping control) that controls the steering angle so that the host vehicle 1 travels along the estimated lane shape of the host lane. The controller 16 acquires the road shape of the road on which the vehicle is traveling (hereinafter, sometimes referred to as "vehicle road shape") from the map data stored in the map database 12, and estimates the lane shape of the vehicle's lane.

[0019] Here, the map data stored in the map database 12 is data that expresses road shapes on a road-by-road basis using node strings. 2 is a schematic diagram of an example of road shapes acquired from map data. For example, dashed line 30 indicates the road shape of the host road R1, and dashed line 31 indicates the road shape of the oncoming road R2 on which the host vehicle 1 is traveling (hereinafter, sometimes referred to as the "oncoming road shape"). 2 shows an example in which the current road shape 30 and the oncoming road shape 31 are each represented by separate node strings. For example, if the current road R1 and the oncoming road R2 are separated by a central reservation, the current road shape 30 and the oncoming road shape 31 are each represented by separate node strings.

[0020] Now, let us consider a case in which the number of lanes on the road R1 increases as a result of the lane on which the vehicle 1 is traveling branching into a straight-ahead lane Ls and a right-turn lane Lr adjacent to the straight-ahead lane Ls in a section Sc near the entrance to the intersection C0. The straight-ahead lane Ls is a lane exclusively for vehicles traveling straight through the intersection C0, and the right-turn lane Lr is a lane exclusively for vehicles turning right at the intersection C0. As described above, in the case of map data expressed by a node string on a road-by-road basis, each node indicates a position near the center of the road. Therefore, as the number of lanes on a road increases, the position of the node in the lane width direction changes in the direction in which the number of lanes increases (toward the right-turn lane Lr in the example of FIG. 2). Therefore, the current road shape 30 expressed by the node string may differ from the actual lane shape of the lanes on the current road R1. As a result, there is a risk that the estimation accuracy will be low if the shape of the current lane is estimated based on the road shape of the map data.

[0021] In this invention, we focus on changes in the road width direction position of the vehicle road shape 30 in the section Sc near the entrance to the intersection C0. As described above, the vehicle road shape 30 passes through approximately the center of the vehicle road R1. Therefore, when the right-turn lane Lr with lane width W increases, the vehicle road shape 30 after the right-turn lane Lr is increased passes through a position that is shifted (offset) toward the right-turn lane Lr by half the lane width W (W / 2) in the road width direction compared to before the right-turn lane Lr was increased. Therefore, the controller 16 translates the host road shape 30 so that a point on the host road shape that is close to the host vehicle position becomes the host vehicle position. The controller 16 also translates the oncoming road shape 31 so that a point on the oncoming road shape that is close to the host vehicle position becomes the host vehicle position. In other words, the controller 16 aligns the road width direction positions of the host road shape 30 and the oncoming road shape 31 with the host vehicle position as the reference.

[0022] 3, a solid line 32 indicates the shape of the oncoming road after the parallel movement, and a dashed line 33 indicates the shape of the road on which the vehicle will be traveling after the parallel movement. Section Sc, which is near the entrance to intersection C0 on the own road R1, is near the exit from intersection C0 on the oncoming road shape 31. Because the number of lanes on the oncoming road shape 31 does not increase at the exit from intersection C0, oncoming road shape 31 closely matches the shape of the actual lanes of oncoming road R2. Furthermore, it can be assumed that the lanes of oncoming road R2 at the exit from intersection C0 are approximately parallel to the lanes of own road R1 before the right-turn lane Lr is added and the straight-ahead lane Ls.

[0023] Therefore, when the road width direction positions of the vehicle road shape 33 and the oncoming road shape 32 are aligned based on the vehicle position, in the section where the right-turn lane Lr becomes parallel to the straight-ahead lane Ls, the deviation τ between the vehicle road shape 33 and the oncoming road shape 32 becomes approximately half (W / 2) of the increased lane width W of the right-turn lane Lr.

[0024] Furthermore, if the oncoming road shape 32 is translated in parallel so as to pass through the position of the vehicle 1 traveling in the lane of the vehicle road R1 before it branches into the straight lane Ls and the right-turn lane Lr, the oncoming road shape 32 will have a shape that enters the intersection C0 from the lane of the vehicle road R1 before it branches through the straight lane Ls. Therefore, in the section where the right-turn lane Lr becomes parallel to the straight-ahead lane Ls, the road shape 33 of the vehicle will pass through a position shifted toward the right-turn lane Lr by a deviation amount τ=W / 2 from the oncoming road shape 32 that passes through the straight-ahead lane Ls.

[0025] Therefore, the controller 16 corrects the host vehicle road shape 33 after the parallel shift, thereby increasing the deviation between the corrected host vehicle road shape and the oncoming road shape 32 after the parallel shift to a value (K × τ) obtained by multiplying the deviation τ by a constant K greater than 1. The dashed dotted line 34 in FIG. 4 shows an example of the road shape after correction so as to increase the amount of deviation from the oncoming road shape 32 after translation. By increasing the deviation amount from the oncoming road shape 32 after parallel movement by a constant multiple (K×τ) of the deviation amount τ, the modified host vehicle road shape 34 will have a shape that enters the intersection C0 along the right-turn lane Lr. For example, if the constant multiple is set to 2, the deviation amount (K×τ) will be approximately equal to the lane width W of the right-turn lane Lr, and the host vehicle road shape 34 will have a shape that passes through approximately the center of the right-turn lane Lr.

[0026] Therefore, the controller 16 can estimate the shape of the right-turn lane Lr with high accuracy by estimating the shape of the right-turn lane Lr based on the modified host road shape 34. The drawings in this specification illustrate a driving scene in which a one-lane road R1 branches into a straight lane Ls and a right-turn lane Lr near the entrance to an intersection, changing into two lanes, but the present invention is not limited to such a scene. The present invention can also be applied to a case in which the lane closest to the oncoming road, among the lanes of a road with n lanes (n is an integer greater than 1), branches into a straight lane and a right-turn lane near the entrance to an intersection, making the road have (n+1) lanes.

[0027] Next, the driving control device 10 in the embodiment will be described in more detail. Figure 5 is a block diagram showing an example of the functional configuration of the controller 16 in Figure 1. The controller 16 functions as a recognition unit 40, a navigation system 41, and a determination unit 42. The recognition unit 40 estimates whether the lane ahead of the vehicle 1 on which the vehicle 1 is traveling will branch into a straight lane Ls and a right-turn lane Lr. For example, the recognition unit 40 may include a road marking detection unit 40a. The road marking detection unit 40a analyzes an image captured by the camera of the external sensor 14 to detect road markings on the road surface of the vehicle's own lane ahead of the vehicle 1 that indicate a branch into a straight lane and a right-turn lane.

[0028] 6 is a schematic diagram of an example of a road marking Ts that warns of a branch into a straight-ahead lane Ls and a right-turn lane Lr. The road marking Ts is a road marking in which an arrow indicating straight-ahead and an arrow indicating a right-turn are arranged within a predetermined distance of each other in the road width direction, and indicates that the lane on which the road marking Ts is installed branches into the straight-ahead lane Ls and the right-turn lane Lr ahead. When the road marking Ts is detected, it is assumed that a right-turn lane will be added to the lane in which the host vehicle 1 is currently traveling. The recognition unit 40 may estimate whether the lane on which the host vehicle is traveling branches into a straight lane Ls and a right-turn lane Lr by means other than the road markings Ts. For example, the recognition unit 40 may estimate whether the lane on which the host vehicle is traveling branches into a straight lane Ls and a right-turn lane Lr based on map information.

[0029] Referring to Fig. 5, the positioning device 11 is connected to a GNSS antenna installed outside the vehicle cabin, and estimates the current position (vehicle position) and angle (attitude) of the vehicle 1 in a fixed coordinate system of map information stored in the map database 12. Based on the estimated vehicle position, the navigation system 41 identifies the road R1 on which the vehicle 1 is currently traveling from among the roads described in the map data. Furthermore, the navigation system 41 sets a destination through a user operation, searches for a route from the current position to the destination, and stores the route as a planned travel route for the vehicle 1 to travel.

[0030] The navigation system 41 includes an intersection detection unit 41a. Based on map data of the host road R1, the intersection detection unit 41a detects an intersection C0 where the host road R1 intersects with a cross road within a first predetermined distance ahead of the path of the host vehicle 1. When an intersection is detected, the intersection detection unit 41a extracts the host road shape 30 of the host road R1 and the oncoming road shape 31 of the oncoming road R2 from the map data.

[0031] The determination unit 42 includes a usability determination unit 42a, a road shape estimation unit 42b, and a vehicle control unit 42c. The usability determination unit 42a determines whether or not the oncoming road shape 31 can be used to estimate the vehicle lane shape of the right-turn lane Lr or the straight lane Ls. The usability determination unit 42a determines that the oncoming road shape 31 can be used to estimate the vehicle lane shape when the correlation between the host vehicle road shape 30 extracted from the map data and the oncoming road shape 31 is low in sections near intersections (i.e., when the degree of difference is high) or high in sections other than near intersections (i.e., when the degree of similarity is high).

[0032] First, we will explain how the usability determination unit 42a calculates the degree of difference between the host road shape 30 and the oncoming road shape 31. When calculating the degree of difference between the host road shape 30 and the oncoming road shape 31, the usability determination unit 42a sets a difference calculation section Sd for calculating the degree of difference. 7(a) is a schematic diagram of an example of the difference calculation section Sd. For example, the availability determination unit 42a may set as the difference calculation section Sd a range a predetermined distance r in front of the nearest intersection C0 ahead of the vehicle 1. In this case, the position of the intersection C0 may be information embedded in the map data, or a point Pc1 where the node string of the road on which the vehicle is traveling intersects with the intersecting road.

[0033] Fig. 7(b) is a schematic diagram of an example of a method for calculating the dissimilarity. In Fig. 7(b), triangular plots indicate host vehicle road points, which are points on the host vehicle road shape 30 included in the dissimilarity calculation section Sd, and circular plots indicate on-coming road points, which are points on the on-coming road shape 31. As the host vehicle road points and on-coming road points, nodes in a node string included in the map data may be used, or points on a curve or line that interpolates between nodes (connecting between nodes) may be used.

[0034] For example, the usability determination unit 42a moves one or both of the vehicle road shape 30 and the oncoming road shape 31 parallel in the road width direction, thereby aligning the road width direction positions of the vehicle road shape 30 and the oncoming road shape 31 at the end point p0 close to the vehicle position of the dissimilarity calculation section Sd. Thereafter, the usability determining unit 42a may calculate the degree of difference D using the following equation (1).

number

[0035] Next, we will explain how the usability determination unit 42a calculates the similarity between the host road shape 30 and the oncoming road shape 31. When calculating the similarity between the host road shape 30 and the oncoming road shape 31, the usability determination unit 42a sets a similarity calculation section Ss for calculating the similarity. FIG. 8 is a schematic diagram showing an example of the similarity calculation section Ss. Reference symbols C0, Cf1, Cb1, and Cb2 all indicate intersections where the host vehicle road R1 intersects with a cross road, with reference symbol C0 indicating the nearest intersection ahead of the host vehicle 1, and reference symbol Cf1 indicating the intersection next to reference symbol C0 (i.e., the second nearest intersection ahead of the host vehicle 1). Reference symbol Cb1 indicates the nearest intersection behind the host vehicle 1, and reference symbol Cb2 indicates the second nearest intersection behind the host vehicle 1.

[0036] The availability determination unit 42a detects, from the map data, an inter-intersection section where the current road R1 is sandwiched between adjacent intersections within a predetermined range from the current vehicle position. In the example of Fig. 8, the availability determination unit 42a detects an inter-intersection section R0 sandwiched between adjacent intersections C0 and Cb1. The availability determining unit 42a sets a local range including the middle part of the inter-intersection section R0 as the similarity calculation section Ss.

[0037] The usability determination unit 42a translates one or both of the host road shape 30 and the oncoming road shape 31 in the road width direction in the same way as in the calculation of the dissimilarity D described above, and calculates the positional deviation d in the road width direction between the host road point and the oncoming road point in the similarity calculation section Ss. i Based on this, the similarity S may be calculated using the following formula (2).

number

[0038] 9A is a schematic diagram of an example of setting the similarity calculation section Ss. The usability determination unit 42a sets the section from the adjacent intersection C0 to the intersection Cb1 as the maximum range Ss of the similarity calculation section Ss. max Set as. The usability determination unit 42a sets a candidate range for the similarity calculation section Ss (hereinafter referred to as a "candidate range") to a maximum range Ss max The degree of difference between the road shape 30 and the oncoming road shape 31 within the candidate range is calculated successively by gradually reducing the size of the candidate range from .

[0039] For example, if the direction in which the vehicle 1 is moving is defined as the forward direction and the opposite direction as the backward direction, the range may be narrowed by moving the end point on the backward side of the candidate range of the similarity calculation section Ss forward as shown by the arrow, or by moving the end point on the forward side backward, or both. The dissimilarity may be calculated in the same manner as the calculation of the dissimilarity D described above. The usability determining unit 42a narrows the candidate range until the amount of change in dissimilarity changes from a decrease to an increase, and sets the candidate range when the amount of change in dissimilarity changes to an increase as the similarity calculation section Ss.

[0040] This is the maximum range Ss max In the case of R0, the degree of difference between the host road shape 30 and the oncoming road shape 31 is high due to an increase in the number of lanes near the entrance to the intersection, and as the candidate range is gradually narrowed, the degree of difference within the candidate range decreases. However, once the host road shape 30 and the oncoming road shape 31 within the candidate range become similar, further narrowing of the candidate range may not only decrease but also increase the degree of difference. For this reason, by setting the candidate range when the amount of change in the degree of difference changes to an increase as the similarity calculation section Ss, it is possible to set the similarity calculation section Ss that includes the middle part of the inter-intersection section R0 so as not to include the part near the entrance to the intersection where there is a large difference between the host road shape 30 and the oncoming road shape 31.

[0041] 9(b) is a schematic diagram of another example of setting the similarity calculation section Ss. For example, the usability determination unit 42a may set, as the similarity calculation section Ss, a section of length L extending from a center Pc2 of the inter-intersection section R0 between the adjacent intersections C0 and Cb1 on both sides in the extension direction of the road. In the above explanation, an example was given of setting the similarity calculation section Ss in the intersection-to-intersection section R0 from intersection C0 to intersection Cb1, but using a similar method, the similarity calculation section Ss may also be set in the intersection-to-intersection section Rb1 from the adjacent intersection Cb1 to intersection Cb2, or in the intersection-to-intersection section Rf1 from the adjacent intersection C0 to intersection Cf1. In the above example, the dissimilarity D and similarity S are calculated based on the positional deviation between the own vehicle road point and the oncoming road point. Alternatively or in addition to this, the dissimilarity D and similarity S may be calculated based on the deviation in angle between the own vehicle road shape 30 and the oncoming road shape 31 at the own vehicle road point and the oncoming road point.

[0042] The usability determination unit 42a may determine that the oncoming road shape 31 can be used to estimate the traffic lane shape when the dissimilarity D calculated as described above is higher than a threshold value, may determine that the oncoming road shape 31 can be used to estimate the traffic lane shape when the similarity S is higher than a threshold value, or may determine that the oncoming road shape 31 can be used to estimate the traffic lane shape when the dissimilarity D and similarity S are both higher than a threshold value.

[0043] Referring to Fig. 5, the roadway shape estimation unit 42b determines whether the recognition unit 40 has estimated that the lane on which the vehicle 1 is traveling is branched into a straight lane Ls and a right-turn lane Lr, and whether the usability determination unit 42a has determined that the oncoming road shape 31 can be used to estimate the road lane shape. If it is not estimated that the lane on which the vehicle 1 is traveling will branch into a straight lane Ls and a right-turn lane Lr, or if it is not determined that the oncoming road shape 31 can be used, the road shape estimation unit 42b will not estimate the shape of the straight lane Ls or the right-turn lane Lr.

[0044] When it is estimated that the lane on which the vehicle 1 is traveling branches into a straight lane Ls and a right-turn lane Lr, and it is determined that the oncoming road shape 31 can be used, the road shape estimation unit 42b applies the vehicle road shape 30 and the oncoming road shape 31 to the vehicle position. Here, fitting the vehicle road shape 30 to the vehicle position means translating the vehicle road shape 30 so that a point on the vehicle road shape 30 near the vehicle position becomes the vehicle position. Similarly, fitting the oncoming road shape 31 to the vehicle position means translating the oncoming road shape 31 so that a point on the oncoming road shape 31 near the vehicle position becomes the vehicle position.

[0045] For example, "a point on the vehicle road shape 30 near the vehicle position" and "a point on the oncoming road shape 31 near the vehicle position" may be the nearest point on the vehicle road shape 30 and the nearest point on the oncoming road shape 31 that is located closest to the vehicle position. They may also be the feet of perpendicular lines dropped from the vehicle position to the vehicle road shape 30 and the oncoming road shape 31, respectively. 3, a solid line 32 indicates the shape of the oncoming road after the parallel movement, and a dashed line 33 indicates the shape of the road on which the vehicle will be traveling after the parallel movement. The lane shape estimation unit 42b calculates the deviation amount τ in the road width direction between the host road shape 33 after parallel movement and the oncoming road shape 32. In this specification, the deviation amount τ in the road width direction between the host road shape 33 after parallel movement and the oncoming road shape 32 is referred to as the "inter-shape deviation amount τ." The lane shape estimation unit 42b corrects the host road shape 33 after translation, thereby increasing the deviation between the corrected host road shape and the oncoming road shape 32 after translation to a value (K×τ) obtained by multiplying the inter-shape deviation amount τ by a constant K greater than 1. The constant K may be 2, for example.

[0046] The corrected own road shape 34 shown in Figure 4 has the deviation amount from the oncoming road shape 32 changed from the inter-shape deviation amount τ to 2 × τ, and it can be seen that it is closer to the shape of the right-turn lane Lr than the shape before correction. Note that it is sufficient to be able to estimate the right-turn lane Lr up to just before entering the intersection C0, so it is sufficient to correct the deviation amount only in the section up to approximately the center of the intersection C0 on the map. The road shape estimation unit 42b estimates the oncoming road shape 32 after the parallel movement as the shape of the straight lane Ls, and estimates the modified own road shape 34 as the shape of the right-turn lane Lr.

[0047] See Fig. 5. The vehicle control unit 42c drives the actuator 17 to control the accelerator and brake, as well as the steering direction and steering amount of the steering mechanism, so that the host vehicle 1 travels along the shape of the lane that matches the estimated shapes of the straight lane Ls and right-turn lane Lr and the planned travel route set by the navigation system 41.

[0048] (operation) FIG. 10 is a flowchart of an example of a driving control method according to an embodiment. In step S1, the positioning device 11 detects the current position of the vehicle 1 (the vehicle position). In step S2, the road shape estimation unit 42b determines whether the road marking detection unit 40a has detected a road marking Ts that indicates an upcoming branch between the straight lane Ls and the right-turn lane Lr. If the road marking detection unit 40a has detected the road marking Ts (step S2: Y), the process proceeds to step S3. If the road marking detection unit 40a has not detected the road marking Ts (step S2: N), the process ends.

[0049] In step S3, the intersection detection unit 41a extracts the current road shape 30 of the current road R1 and the opposite road shape 31 of the opposite road R2 from the map data. In step S4, the usability determination unit 42a determines whether or not the oncoming road shape 31 can be used to estimate the traffic lane shape. If the oncoming road shape 31 can be used to estimate the traffic lane shape (step S4: Y), the process proceeds to step S5. If the oncoming road shape 31 cannot be used to estimate the traffic lane shape (step S4: N), the process ends.

[0050] In step S5, the road shape estimation unit 42b translates the host road shape 30 so that a point on the host road shape 30 that is near the host vehicle position becomes the host vehicle position. Similarly, the oncoming road shape 31 is translated so that a point on the oncoming road shape 31 that is near the host vehicle position becomes the host vehicle position. In step S6, the road shape estimation unit 42b calculates the amount of deviation τ between the host road shape 33 and the oncoming road shape 32 in the road width direction after the parallel movement. In step S7, the lane shape estimation unit 42b corrects the host road shape 33 after translation, thereby increasing the deviation between the corrected host road shape 34 and the oncoming road shape 32 after translation to a value (K × τ) obtained by multiplying the inter-shape deviation amount τ by a constant K greater than 1. Then, the processing ends.

[0051] (Second embodiment) 11(a), the lane shape estimation unit 42b of the second embodiment applies the host vehicle road shape 30 and the oncoming road shape 31 to the host vehicle position, and then identifies parallel sections Sp1 and Sp2 in the area before the intersection C0, where the degree of parallelism between the oncoming road shape 32 after parallel movement and the host vehicle road shape 33 is equal to or greater than a threshold. The parallel section Sp1 is the section before the straight lane Ls and the right-turn lane Lr branch off (before the right-turn lane Lr is added), and in the parallel section Sp1, the oncoming road shape 32 and the host road shape 33 substantially overlap. The parallel section Sp2 is the section after the straight lane Ls and the right-turn lane Lr become parallel. The section Si is an intermediate section between the parallel sections Sp1 and Sp2.

[0052] The lane shape estimation unit 42b of the second embodiment corrects the host road shape 33 so that the deviation amount in the road width direction of the host road shape from the oncoming road shape 32 after parallel movement in these parallel sections Sp1 and Sp2 is a constant multiple (K × τ) of the inter-shape deviation amount τ, and so that the host road shapes of the parallel sections Sp1 and Sp2 are smoothly connected in the intermediate section Si. For example, the lane shape estimation unit 42b may connect the host road shapes of the parallel sections Sp1 and Sp2 with a clothoid curve in the intermediate section Si.

[0053] 11(b) shows an example of the host road shape after the deviation amount has been corrected. Note that in the parallel section Sp1, the oncoming road shape 32 and the host road shape 33 are substantially overlapping as described above, so the deviation amount τ between the shapes is almost 0, and the position of the host road shape in the road width direction remains almost unchanged even when multiplied by a constant. In this way, it is sufficient that the deviation between the vehicle road shape 34 and the oncoming road shape 32 at the point of entering the intersection C0 is a constant multiple (K×τ) of the inter-shape deviation amount τ, and the deviation may be a constant multiple (K×τ) of the inter-shape deviation amount τ for the entire sections Sp1, Si, and Sp2 as in the first embodiment, or the deviation may be a constant multiple (K×τ) of the inter-shape deviation amount τ for only the parallel sections Sp1 and Sp2 as in the second embodiment.

[0054] (Effects of the embodiment) (1) The positioning device 11 detects the host vehicle position, which is the current position of the host vehicle 1. The controller 16 estimates whether the lane on which the host vehicle 1 is traveling will branch into a straight lane for traveling straight through an intersection and a right-turn lane for turning right at the intersection ahead of the host vehicle 1, and if it estimates that the lane on which the host vehicle 1 is traveling will branch into a straight lane and a right-turn lane, it extracts the host road shape, which is the road shape of the host road on which the host vehicle 1 is traveling, and the oncoming road shape, which is the road shape of the oncoming road on which the oncoming vehicle of the host vehicle 1 is traveling, from map data in which the road shape is expressed on a road-by-road basis by an arrangement of multiple nodes, and calculates the host vehicle position so that a point on the host road shape that is close to the host vehicle position is the host vehicle position. The shape of the road to which the host vehicle is traveling is translated, the shape of the oncoming road is translated so that a point on the shape of the oncoming road near the host vehicle's position becomes the host vehicle's position, an inter-shape deviation amount, which is the amount of deviation between the shape of the host vehicle's road after the parallel translation and the shape of the oncoming road after the parallel translation, is calculated in the section ahead of the path of the host vehicle (1), and by correcting the shape of the host vehicle's road after the parallel translation, the amount of deviation between the corrected shape of the host vehicle's road and the shape of the oncoming road after the parallel translation is increased to a value obtained by multiplying the inter-shape deviation amount by a constant greater than 1, and the shape of the right-turn lane is estimated based on the corrected shape of the host vehicle's road. In this way, by estimating the shape of a right-turn lane based on a modified shape of the road on which the vehicle is traveling so as to change the amount of deviation of the road shape from the shape of the oncoming road, it is possible to estimate the shape of a right-turn lane that is not included in the map data.

[0055] (2) When the controller 16 detects road markings on the road ahead of the vehicle 1 that indicate a branch into a straight lane and a right-turn lane, the controller 16 may determine that the lane in which the vehicle 1 is traveling will branch into a straight lane and a right-turn lane. This makes it possible to determine whether the lane in which the vehicle 1 is traveling branches into a straight lane and a right-turn lane. (3) The controller 16 may identify a parallel section where the degree of parallelism between the vehicle road shape and the oncoming road shape is equal to or greater than a threshold value, and may modify the vehicle road shape after parallel movement so that the deviation amount between the modified vehicle road shape in the parallel section and the oncoming road shape after parallel movement is a constant multiple of the deviation amount between the shapes. In this way, by taking the amount of deviation between the parallel sections of the road shape of the vehicle and the oncoming road shape as the deviation in the road width direction, the amount of deviation can be calculated accurately.

[0056] (4) The constant multiple may be, for example, two times. In map data where road shapes are represented by an array of multiple nodes on a road-by-road basis, each node indicates a position near the center of the road. Therefore, when a right-turn lane is added near the intersection entrance, the deviation in the road width direction of the node position before and after the right-turn lane is added is half the lane width. Therefore, by correcting the deviation amount of the road shape of the vehicle so that it is doubled, the deviation amount becomes one lane and the vehicle will follow the shape of the right-turn lane.

[0057] (5) The controller 16 may detect from the map data an inter-intersection section, which is a section of the vehicle road sandwiched between adjacent intersections within a predetermined range from the vehicle position, calculate the similarity between the shape of the vehicle road in a range including the middle part of the inter-intersection section and the shape of the oncoming road in the part corresponding to the range, and if the similarity is equal to or greater than a threshold, estimate the shape of the straight lane based on the shape of the oncoming road after parallel movement. Only when the shape of the road on which the vehicle is traveling and the shape of the oncoming road are highly similar, the straight lane is estimated using the shape of the oncoming road, thereby preventing erroneous shape estimation. (6) The controller 16 may estimate the shape of a right-turn lane or a straight-through lane when an intersection exists a predetermined distance ahead from the vehicle position. In this way, the shapes of right-turn lanes and straight-through lanes are estimated only near intersections, so that it is possible to prevent erroneous estimation of right-turn lanes at locations other than intersections. [Explanation of symbols]

[0058] 1...Own vehicle, 10...cruising control device, 12...map database, 14...external sensor, 15...vehicle sensor, 16...controller, 17...actuator, 20...processor, 21...storage device, 40...recognition unit, 40a...road marking detection unit, 41...navigation system, 41a...intersection detection unit, 42...determination unit, 42a...usability determination unit, 42b...road shape estimation unit, 42c...vehicle control unit

Claims

1. Detecting the current location of the vehicle; estimating whether a lane in which the vehicle is traveling will branch into a straight lane for going straight through an intersection and a right-turn lane for turning right at the intersection ahead of the vehicle's path; When it is estimated that the lane on which the host vehicle is traveling will branch into a straight lane and a right-turn lane, a host road shape that is the road shape of the host road on which the host vehicle is traveling and an oncoming road shape that is the road shape of an oncoming road on which an oncoming vehicle to the host vehicle is traveling are extracted from map data in which road shapes are expressed on a road-by-road basis by an arrangement of a plurality of nodes; translating the vehicle road shape so that a point on the vehicle road shape that is in the vicinity of the vehicle position becomes the vehicle position; translating the shape of the oncoming road so that a point on the shape of the oncoming road that is in the vicinity of the vehicle position becomes the vehicle position; calculating an inter-shape deviation amount, which is an amount of deviation between the host vehicle road shape after translation and the oncoming road shape after translation, in a section ahead of the host vehicle's path; by correcting the host road shape after the parallel movement, increasing the amount of deviation between the corrected host road shape and the oncoming road shape after the parallel movement to a value obtained by multiplying the amount of deviation between the shapes by a constant greater than 1; estimating the shape of the right-turn lane based on the modified shape of the own road; A driving control method characterized by:

2. 2. The driving control method according to claim 1, wherein when a road marking indicating a branch into a straight lane and a right-turn lane is detected on the road surface ahead of the vehicle, it is determined that the lane in which the vehicle is traveling will branch into a straight lane and a right-turn lane.

3. identifying a parallel section where the parallelism between the host road shape and the oncoming road shape is equal to or greater than a threshold value; correcting the host road shape after the parallel movement so that the amount of deviation between the corrected host road shape and the oncoming road shape after the parallel movement in the parallel section is a constant multiple of the amount of deviation between the shapes; 3. The method for controlling travelling according to claim 1 or 2.

4. 4. The cruise control method according to claim 1, wherein the constant multiple is two.

5. 5. The cruise control method according to claim 1, wherein the shape of the right-turn lane is estimated when an intersection exists a predetermined distance ahead from the vehicle position.

6. detecting, from the map data, an inter-intersection section in which the road to be driven is sandwiched between adjacent intersections within a predetermined range from the vehicle position; calculating a similarity between the shape of the own road in a range including an intermediate portion of the section between the intersections and the shape of the oncoming road in a portion corresponding to the range; If the degree of similarity is equal to or greater than a threshold, the shape of the straight-through lane is estimated based on the shape of the oncoming road after the parallel movement.

6. The method for controlling driving according to claim 1.

7. 7. The cruise control method according to claim 6, wherein the shape of the straight-through lane is estimated when an intersection exists a predetermined distance ahead from the vehicle position.

8. The vehicle is equipped with a positioning device that detects a vehicle position, which is the current position of the vehicle, and a controller, The controller estimating whether a lane in which the host vehicle is traveling will branch into a straight lane and a right-turn lane ahead of the host vehicle's path; extracting a host road shape, which is the road shape of the host road on which the host vehicle is traveling, and an oncoming road shape, which is the road shape of an oncoming road on which an oncoming vehicle to the host vehicle is traveling, from map data in which road shapes are expressed on a road-by-road basis by an arrangement of a plurality of nodes, when it is estimated that the lane on which the host vehicle is traveling will branch into a straight lane for traveling straight through the intersection and a right-turn lane for turning right at the intersection; translating the vehicle road shape so that a point on the vehicle road shape that is in the vicinity of the vehicle position becomes the vehicle position; translating the shape of the oncoming road so that a point on the shape of the oncoming road that is in the vicinity of the vehicle position becomes the vehicle position; calculating an inter-shape deviation amount, which is an amount of deviation between the host vehicle road shape after translation and the oncoming road shape after translation, in a section ahead of the host vehicle's path; by correcting the host road shape after the parallel movement, increasing the amount of deviation between the corrected host road shape and the oncoming road shape after the parallel movement to a value obtained by multiplying the amount of deviation between the shapes by a constant greater than 1; estimating the shape of the right-turn lane based on the modified shape of the own road; A driving control device characterized by:

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