JP2023151393A - Travel control method and travel control apparatus - Google Patents

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

The present invention relates to a travel control method and a travel control device.

Patent Document 1 discloses a method that reads position data of nodes that are road marking points located before and after the current position of the vehicle from a road map database, estimates the shape of the vehicle's road from a plurality of nodes, and controls the illumination of headlights. The techniques used are described.

Patent No. 4363640 specification

In the case of map data in which the shape of a road is expressed on a road-by-road basis by an arrangement of a plurality of nodes, each node indicates a position near the center of the road. Therefore, in places where the number of lanes increases, such as near the entrance of an intersection, the position of the node in the lane width direction also changes in the direction of the increase in the number of lanes, so the shape represented by the arrangement of nodes differs from the actual lane shape. For this reason, if the shape of the own lane in which the own vehicle travels near the entrance of an intersection is estimated based on the road shape of the map data, there is a risk that the estimation accuracy will be lowered.
An object of the present invention is to improve the accuracy of estimating the shape of the own lane using map data in which the road shape is expressed for each road by an arrangement of a plurality of nodes.

In a travel control method according to one aspect of the present invention, the current position of the own vehicle is detected, and the lane in which the own vehicle is traveling is set to the straight lane that goes straight through the intersection and turns right at the intersection. A map in which the road shape is expressed for each road by an array of multiple nodes, when it is estimated whether the lane in which the vehicle is traveling will branch into a straight lane and a right turn lane. From the data, the own vehicle road shape, which is the road shape of the own vehicle road on which the own vehicle is traveling, and the oncoming road shape, which is the road shape of the oncoming road on which the own vehicle's oncoming vehicle travels, are extracted, and the own vehicle position is extracted. Move the own road shape in parallel so that the point on the own vehicle road shape that is nearby becomes the own vehicle position, and move the oncoming road shape so that the point on the oncoming road shape that is near the own vehicle position becomes the own vehicle position. The shape is moved in parallel, and in the section in front of the vehicle's path, the amount of deviation between the shapes, which is the amount of deviation between the shape of the own road after parallel movement and the shape of the oncoming road after parallel movement, is calculated, and the amount of deviation between shapes is calculated. By correcting the own road shape, the amount of deviation between the corrected own road shape and the oncoming road shape after parallel movement can be obtained by multiplying the amount of deviation between the shapes by a constant greater than 1. The shape of the right turn lane is estimated based on the modified shape of the vehicle's road after the modification.

According to the present invention, it is possible to improve the accuracy of estimating the shape of the own lane using map data in which the road shape is expressed for each road by an arrangement of a plurality of nodes.

1 is a diagram illustrating an example of a schematic configuration of a vehicle equipped with a travel control device according to an embodiment. It is a schematic diagram of an example of the road shape acquired from map data. FIG. 3 is a schematic diagram of an example of the shape of the own vehicle road and the shape of the oncoming road after parallel movement. FIG. 3 is a schematic diagram of an example of the vehicle road shape after the deviation amount has been corrected with respect to the oncoming road shape. FIG. 2 is a block diagram of an example of the functional configuration of the controller in FIG. 1. FIG. FIG. 3 is a schematic diagram of an example of a road marking that foretells a branch between a straight lane and a right turn lane. (a) and (b) are explanatory diagrams of a method of calculating the degree of difference between the vehicle road shape and the oncoming road shape. FIG. 3 is a schematic diagram showing an example of a similarity calculation section in which the similarity between the vehicle road shape and the oncoming road shape is calculated. (a) and (b) are schematic diagrams of examples of setting similarity calculation intervals. It is a flow chart of an example of a traveling control method of an embodiment. (a) and (b) are explanatory diagrams of an example of the travel control method of the second embodiment.

Embodiments of the present invention will be described below with reference to the drawings. Note that each drawing is schematic and may differ from the actual drawing. In addition, the embodiments of the present invention shown below illustrate devices and methods for embodying the technical idea of the present invention. is not limited to the following: The technical idea of the present invention can be modified in various ways within the technical scope defined by the claims.

(First embodiment)
(composition)
The host vehicle 1 includes a travel control device 10 that supports travel of the host vehicle 1. The driving control device 10 supports the driving of the own vehicle 1 by detecting the driving environment around the own vehicle 1 and automatically controlling the driving of the own vehicle 1 based on the detected driving environment.
For example, the driving support for the own vehicle 1 by the driving control device 10 may include autonomous driving control in which the own vehicle 1 is automatically driven without involvement of a passenger (for example, a driver). Further, for example, the travel support of the host vehicle 1 by the travel control device 10 may include automatically controlling at least one of the driving force, braking force, or steering angle of the host vehicle 1.

The travel 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 . Note that in the drawings, the map database is referred to as "map DB."
The positioning device 11 measures the current position of the own vehicle 1. In the following description, the current position of the own vehicle 1 will be referred to as "the own 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 or the like, and measures the position of the vehicle by receiving radio waves from a plurality of navigation satellites.

The map database 12 stores map information. The map information stored in the map database 12 may be, for example, navigation map data including information for each road. The map data for navigation may be data in which the shape of a road is expressed on a road-by-road basis by a string of nodes. In this specification, a node string may be, for example, an array of multiple nodes, or data including an array of multiple nodes and line segments connecting these nodes with curves or straight lines. good. That is, the node string represents the arrangement of a plurality of nodes, that is, the shape of a road on a map, and may include the arrangement of a plurality of nodes, and is not limited to the arrangement of a plurality of nodes.

The external sensor 14 detects various information about the driving environment around the own vehicle 1 (driving environment information). For example, the external sensor 14 detects objects around the host vehicle 1 . The external sensor 14 detects the surrounding environment of the own vehicle 1, such as objects existing around the own vehicle 1, the relative position between the own vehicle 1 and the object, the distance between the own vehicle 1 and the object, and the direction in which the object exists. . The external sensor 14 outputs the detected driving environment information 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 host vehicle 1 with respect to the host vehicle 1. Here, the target object is, for example, a traffic light provided on the road on which the host vehicle 1 is traveling, a line on the road surface (stop line, lane boundary line, lane marking line, etc.), a curb on the shoulder of the road, a guardrail, or the like.

The external sensor 14 may include a monocular camera, such as a full HD resolution color camera. The camera captures an image that includes a recognition target of the surrounding environment of the host vehicle 1, and outputs the captured image to the controller 16 as driving environment information.
Further, the external sensor 14 may include a distance measuring device such as a laser range finder (LRF), a radar, or a LiDAR (Light Detection and Ranging) laser radar. The distance measuring device detects, for example, a relative position determined by a relative distance and direction to objects existing around the host vehicle. The distance measuring device outputs the detected distance data to the controller 16 as driving environment information.

Vehicle sensor 15 detects various information (vehicle information) obtained from own vehicle 1 . The vehicle sensor 15 includes, for example, a vehicle speed sensor that detects the running speed (vehicle speed) of the own vehicle 1, a wheel speed sensor that detects the rotational speed of each tire included in the own vehicle 1, and an acceleration ( a 3-axis acceleration sensor (G sensor) that detects the steering angle (including deceleration), 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, and the steering angle sensor that detects the turning angle of the steering wheel. It includes a gyro sensor that detects angular velocity, a yaw rate sensor that detects 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.

The controller 16 is an electronic control unit (ECU) that controls the running of the host vehicle 1 . When controlling the running of the own vehicle 1, the controller 16 automatically controls the running of the own vehicle 1 based on the surrounding running environment. Note that the controller 16 may be configured as a single electronic control unit, or may be configured as a set 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, or the like. The storage device 21 may include memories such as a register, a cache memory, a ROM (Read Only Memory) used as a main storage device, and a RAM (Random Access Memory).
The functions of the controller 16 described below are realized, for example, by the processor 20 executing a computer program stored in the storage device 21.

Note that the controller 16 may be formed from dedicated hardware for executing each information process described below. For example, the controller 16 may include a functional logic circuit set 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).

The actuator 17 operates the accelerator opening and the brake device of the own vehicle 1 in response to a control signal from the controller 16 to generate a driving force for driving the own vehicle 1 or a braking force for braking the own 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 brake device of the own vehicle 1.
Furthermore, the actuator 17 may include a steering actuator that controls the steering direction and amount of steering of the steering mechanism of the own 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.

Next, driving control of the host vehicle 1 by the controller 16 will be explained. The driving control by the controller 16 includes automatic control that estimates the lane shape of the own lane in which the own vehicle 1 is traveling, and controls at least the steering angle of the own vehicle 1 based on the estimated lane shape of the own lane. That's fine. 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 vehicle 1 travels along the estimated lane shape of the own lane.
The controller 16 acquires the road shape of the vehicle's own road (hereinafter sometimes referred to as "the vehicle's road shape") from the map data stored in the map database 12, and estimates the lane shape of the vehicle's own lane.

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.
FIG. 2 is a schematic diagram of an example of a road shape obtained from map data. As an example, a broken line 30 indicates the shape of the own vehicle road R1, and a broken line 31 indicates the road shape of the oncoming road R2 on which the oncoming vehicle of the own vehicle 1 runs (hereinafter referred to as "oncoming road shape"). be).
The map data example in FIG. 2 shows an example in which the own vehicle road shape 30 and the oncoming road shape 31 are each expressed by separate node strings. For example, when the own vehicle road R1 and the oncoming road R2 are separated by a median strip, the own vehicle road shape 30 and the oncoming road shape 31 are each represented by separate node strings.

Now, the number of lanes on the own vehicle road R1 increases as the lane in which the own vehicle 1 is traveling branches into a straight lane Ls and a right turn lane Lr adjacent to the straight lane Ls in the section Sc near the entrance of the intersection C0. Assume that The straight lane Ls is a lane exclusively for vehicles going straight through the intersection C0, and the right-turn lane Lr is a lane exclusively for right-turn vehicles making a right turn at the intersection C0.
As described above, in the case of map data expressed in units of roads by node strings, each node indicates a position near the center of the road. Therefore, when 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 (in the example of FIG. 2, to the right-turn lane Lr side). Therefore, the vehicle road shape 30 represented by the node string may differ from the actual lane shape of the vehicle road R1. As a result, if the shape of the vehicle's own lane is estimated based on the road shape in the map data, the estimation accuracy may become low.

In the present invention, attention is paid to changes in the position of the vehicle road shape 30 in the road width direction in the section Sc near the entrance of the intersection C0. As described above, the vehicle road shape 30 passes approximately near the center of the vehicle road R1. Therefore, when the right turn lane Lr with the lane width W increases, the own vehicle road shape 30 after the right turn lane Lr increases is half the lane width W (W /2) to the right-turn lane Lr side (offset).
Therefore, the controller 16 moves the own vehicle road shape 30 in parallel so that a point on the own vehicle road shape that is near the own vehicle position becomes the own vehicle position. Further, the oncoming road shape 31 is translated in parallel so that a point on the oncoming road shape near the own vehicle position becomes the own vehicle position. That is, the road width direction positions of the own vehicle road shape 30 and the oncoming road shape 31 are aligned based on the own vehicle position.

A solid line 32 in FIG. 3 indicates the shape of the oncoming road after parallel movement. A dashed line 33 indicates the shape of the own vehicle's road after parallel movement.
The section Sc, which is near the entrance to the intersection C0 with respect to the vehicle road R1, is near the exit from the intersection C0 with respect to the oncoming road shape 31. Since the number of lanes of the oncoming road shape 31 does not increase at the exit from the intersection C0, the oncoming road shape 31 matches well with the actual lane shape of the oncoming road R2. Further, it can be assumed that the lane of the oncoming road R2 at the exit from the intersection C0 is substantially parallel to the lane of the own vehicle road R1 before the right turn lane Lr is increased and the straight lane Ls.

Therefore, when aligning the road width direction positions of the own vehicle road shape 33 and the oncoming road shape 32 based on the own vehicle position, in the section where the right turn lane Lr is parallel to the straight lane Ls, the own vehicle road shape 33 and the opposite road shape 32 are aligned. The amount of deviation τ from the road shape 32 becomes approximately half (W/2) of the increased lane width W of the right turn lane Lr.

Further, when the oncoming road shape 32 is moved in parallel so as to pass through the own vehicle position of the own vehicle 1 traveling in the lane of the own vehicle road R1 before branching into the straight lane Ls and the right turn lane Lr, the oncoming road shape 32 has a shape in which the vehicle enters the intersection C0 from the lane of the vehicle road R1 before the branch, passing through the straight lane Ls.
Therefore, in the section where the right turn lane Lr is parallel to the straight lane Ls, the own vehicle road shape 33 is at a position shifted toward the right turn lane Lr by the deviation amount τ=W/2 from the oncoming road shape 32 passing through the straight lane Ls. It will pass through.

Therefore, by correcting the own vehicle road shape 33 after parallel movement, the controller 16 adjusts the amount of deviation between the own vehicle road shape after correction and the oncoming road shape 32 after parallel movement using a constant K larger than 1. The deviation amount τ is increased to a value (K×τ) obtained by multiplying the deviation amount τ by a constant.
A dashed-dotted line 34 in FIG. 4 shows an example of the own vehicle's road shape after being corrected to increase the amount of deviation from the oncoming road shape 32 after parallel movement.
By increasing the amount of deviation from the oncoming road shape 32 after parallel movement to a constant multiple (K×τ) of the amount of deviation τ, the corrected own vehicle road shape 34 can be adjusted to the intersection along the right turn lane Lr. It has a shape that invades C0. For example, if the constant multiplier is set to 2, the amount of deviation (K×τ) becomes approximately equal to the lane width W of the right-turn lane Lr, so the host vehicle road shape 34 becomes a shape that passes approximately through the center of the right-turn lane Lr.

Therefore, the controller 16 can accurately estimate the shape of the right-turn lane Lr by estimating the shape of the right-turn lane Lr based on the corrected vehicle road shape 34.
Note that the drawings in this specification illustrate a driving scene in which a one-lane vehicle road R1 branches into a straight lane Ls and a right-turn lane Lr near the entrance of an intersection and changes to two lanes. It is not limited to certain scenes. In the present invention, the lane closest to the oncoming road among the n lanes (n is an integer greater than 1) of the own vehicle road branches into a straight lane and a right turn lane near the entrance of an intersection, so that the own vehicle road ( It can also be applied to cases where the lane is n+1).

Next, the travel control device 10 in the embodiment will be described in more detail. FIG. 5 is a block diagram of an example of the functional configuration of the controller 16 in FIG. 1. As shown in FIG. The controller 16 functions as a recognition section 40, a navigation system 41, and a determination section 42.
The recognition unit 40 estimates whether or not the lane in which the vehicle 1 travels ahead of the vehicle 1 branches 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 detects a road marking on the road surface of the own lane in front of the own vehicle 1, which foretells the branching of the straight lane and the right turn lane, by analyzing the image taken by the camera of the external sensor 14.

FIG. 6 is a schematic diagram of an example of a road surface marking Ts that foretells a branch between the straight lane Ls and the right turn lane Lr. The road marking Ts is a road marking in which an arrow indicating going straight and an arrow indicating a right turn are arranged within a predetermined distance from each other in the road width direction, and the lane where the road marking Ts is installed is the straight lane Ls and the right turn lane Lr in front. It shows that it is branched into. When the road marking Ts is detected, it is assumed that the number of right turn lanes will be increased compared to the own lane in which the own vehicle 1 is currently traveling.
Note that the recognition unit 40 may estimate whether the lane in which the host vehicle is traveling branches into a straight lane Ls and a right-turn lane Lr by means other than the road marking Ts. For example, the recognition unit 40 may estimate whether the lane in which the host vehicle is traveling branches into a straight lane Ls and a right-turn lane Lr based on map information.

See FIG. 5. The positioning device 11 is connected to a GNSS antenna installed outside the vehicle interior, and determines the current position (own vehicle position) and angle (attitude) of the own vehicle 1 in a fixed coordinate system of map information stored in the map database 12. presume.
Based on the estimated vehicle position, the navigation system 41 identifies the vehicle road R1 on which the vehicle 1 is currently traveling among the roads described in the map data.
Furthermore, the navigation system 41 sets a destination through a user's operation, searches for a route from the current position to the destination, and stores it as a planned travel route for the host vehicle 1 to travel.

The navigation system 41 includes an intersection detection section 41a. The intersection detection unit 41a detects an intersection C0 where the own vehicle road R1 intersects with a crossroad within a first predetermined distance ahead of the course of the own vehicle 1, based on the map data of the own vehicle road R1. When an intersection is detected, the intersection detection unit 41a extracts the own vehicle road shape 30 of the own vehicle road R1 and the oncoming road shape 31 of the oncoming road R2 from the map data.

The determination unit 42 includes an availability determination unit 42a, a road shape estimation unit 42b, and a vehicle control unit 42c.
The availability determining unit 42a determines whether the oncoming road shape 31 can be used to estimate the lane shape of the right-turn lane Lr and the straight lane Ls. The availability determining unit 42a determines whether the correlation between the own vehicle's road shape 30 and the oncoming road shape 31 extracted from the map data is low in a section near an intersection (that is, when the degree of dissimilarity is high), When the degree of similarity is high in a section other than the above (that is, when the degree of similarity is high), it is determined that the oncoming road shape 31 can be used for estimating the lane shape.

First, the calculation of the degree of difference between the vehicle's road shape 30 and the oncoming road shape 31 by the usability determination unit 42a will be described. When calculating the degree of difference between the own vehicle road shape 30 and the oncoming road shape 31, the availability determining unit 42a sets a degree of difference calculation section Sd for calculating the degree of difference.
FIG. 7A is a schematic diagram of an example of the difference calculation section Sd. For example, the availability determining unit 42a may set a range of a predetermined distance r from the nearest intersection C0 in front of the own vehicle 1 as the difference calculation section Sd. At this time, the position of the intersection C0 may be information embedded in the map data, or a point Pc1 where the node strings of the vehicle road and the intersecting road intersect.

FIG. 7(b) is a schematic diagram of an example of a method for calculating the degree of dissimilarity. In FIG. 7(b), the triangular plot indicates the own vehicle road point, which is a point on the own vehicle road shape 30 included in the difference degree calculation section Sd, and the circular plot indicates the oncoming road point, which is a point on the oncoming road shape 31. show. As the own vehicle road point and the oncoming road point, nodes in a node string included in the map data may be used, or points on a curve or straight line that interpolates between nodes (connecting between nodes) may be used.

上式（１）のｄ_ｉは、自車道路点と対向道路点との間の道路幅方向の位置偏差であり、Ｎは自車道路点又は対向道路点の数である（すなわち積算数に該当する）。
利用可否判定部４２ａは、相違度Ｄが閾値以上である場合に、自車道路形状３０と対向道路形状３１との間の相関が、交差点付近の区間で低くなると判定する。 For example, the availability determination unit 42a moves the own vehicle road shape 30 and the oncoming road shape 31 in parallel in the road width direction, so that the own vehicle reaches the end point p0 near the own vehicle position of the difference degree calculation section Sd. The positions of the road shape 30 and the oncoming road shape 31 in the road width direction are made to match.
Thereafter, the availability determining unit 42a may calculate the degree of difference D using the following equation (1).

In the above formula (1), d _i is the positional deviation in the road width direction between the own vehicle's road point and the oncoming road point, and N is the number of the own vehicle's road point or oncoming road point (i.e., the cumulative number (applicable).
The availability determining unit 42a determines that the correlation between the own vehicle road shape 30 and the oncoming road shape 31 is low in the section near the intersection when the degree of difference D is equal to or greater than the threshold value.

Next, calculation of the degree of similarity between the own vehicle's road shape 30 and the oncoming road shape 31 by the availability determining unit 42a will be explained. When calculating the degree of similarity between the vehicle road shape 30 and the oncoming road shape 31, the availability determining unit 42a sets a similarity calculation section Ss in which the degree of similarity is calculated.
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 own vehicle road R1 intersects with a cross road, reference symbol C0 indicates the nearest intersection in front of the own vehicle 1, and reference symbol Cf1 indicates the intersection with reference symbol C0. indicates the next intersection (that is, the second closest intersection in front of the host vehicle 1). Reference code Cb1 indicates the nearest intersection behind the host vehicle 1, and reference code Cb2 indicates the second closest intersection behind the host vehicle 1.

The availability determining unit 42a detects, from the map data, an inter-intersection section where the vehicle road R1 is sandwiched between intersections adjacent to each other within a predetermined range from the vehicle position. In the example of FIG. 8, an inter-intersection section R0 sandwiched between adjacent intersections C0 and Cb1 is detected.
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.

上式（２）の定数Ｂは補正値であり、想定されるずれ量として半車線幅の値としてもよい。 Similar to the calculation of the degree of difference D described above, the availability determination unit 42a moves one or both of the own vehicle road shape 30 and the oncoming road shape 31 in parallel in the road width direction to determine whether the own vehicle is within the similarity calculation section Ss. Based on the positional deviation d _i in the road width direction between the road point and the oncoming road point, the degree of similarity S may be calculated using the following equation (2).

The constant B in the above equation (2) is a correction value, and may be a value of half lane width as the expected deviation amount.

FIG. 9A is a schematic diagram of a setting example of the similarity calculation section Ss. The availability determination unit 42a sets the section from the adjacent intersection C0 to the intersection Cb1 as the maximum range Ss _max of the similarity calculation section Ss.
The availability determination unit 42a gradually reduces the range that is a candidate for the similarity calculation section Ss (hereinafter referred to as "candidate range") from the maximum range Ss _max , and in the candidate range, the own vehicle road shape 30 and the oncoming road The degree of difference between the shape 31 and the shape 31 is sequentially calculated.

For example, if the direction in which the host vehicle 1 is traveling is forward and the opposite direction is backward, the range may be reduced by moving the rear end point of the candidate range of the similarity calculation section Ss forward as shown by the arrow. The range may be reduced by moving the front end point backward, or both. Note that the degree of difference may be calculated in the same manner as the calculation of the degree of difference D described above.
The availability determining unit 42a reduces the candidate range until the amount of change in the degree of dissimilarity changes from decreasing to increasing, and sets the candidate range when the amount of change in the degree of dissimilarity changes to increase as the similarity calculation section Ss.

This is because in the maximum range Ss _max , the degree of difference between the own vehicle's road shape 30 and the oncoming road shape 31 is high due to the increase in lanes near the entrance of the intersection, and as the candidate range is gradually reduced, the difference is high. However, when the own vehicle road shape 30 and the oncoming road shape 31 in the candidate range reach a similar state, the degree of difference may not only decrease but also increase by further reducing the candidate range. It is from. Therefore, by setting the candidate range when the amount of change in the degree of difference changes to increase as the similarity calculation section Ss, the area where the difference between the own vehicle road shape 30 and the oncoming road shape 31 near the entrance of the intersection is large The similarity calculation section Ss can be set to include the middle part of the inter-intersection section R0 so as not to include.

FIG. 9(b) is a schematic diagram of another setting example of the similarity calculation section Ss. For example, the availability determining unit 42a sets a section having a length L extending from the center Pc2 of the inter-intersection section R0 from the adjacent intersection C0 to the intersection Cb1 to both sides in the road extension direction as the similarity calculation section Ss. It's okay.
In addition, in the above explanation, the case where the similarity calculation section Ss is set in the inter-intersection section R0 from the intersection C0 to the intersection Cb1 was explained as an example, but by the same method, the intersection between the adjacent intersection Cb1 and the intersection Cb2 is set. The similarity calculation section Ss may be set in the inter-intersection section Rb1, or the similarity calculation section Ss may be set in the inter-intersection section Rf1 from the adjacent intersection C0 to the intersection Cf1.
Furthermore, in the above example, the degree of difference D and the degree of similarity S are calculated based on the positional deviation between the own vehicle's road point and the oncoming road point. The degree of difference D and the degree of similarity S may be calculated based on the deviation of the angle between the own vehicle's road shape 30 and the oncoming road shape 31 at the road point.

The availability determining unit 42a may determine that the oncoming road shape 31 can be used for estimating the lane shape when the degree of difference D calculated as described above is higher than the threshold, and when the degree of similarity S is higher than the threshold. It may be determined that the oncoming road shape 31 can be used for estimating the lane shape when the difference degree D and the similarity degree S are higher than the respective thresholds. It may be determined that it is possible.

See FIG. 5. The road shape estimating unit 42b determines whether or not the recognition unit 40 estimates that the lane in which the vehicle 1 is traveling branches into a straight lane Ls and a right-turn lane Lr, and that the oncoming road shape 31 can be used for estimating the lane shape. It is determined whether the section 42a has made the determination.
If it is not estimated that the lane in which the host vehicle 1 is traveling is divided 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 determines whether the lane in which the host vehicle 1 is traveling is divided into the straight lane Ls or the right turn lane Lr. The shape of lane Lr is not estimated.

When it is estimated that the lane in which the host vehicle 1 is traveling is divided 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 estimating unit 42b determines whether the host vehicle road shape 30 and the oncoming road shape 31 can be used. The oncoming road shape 31 is applied to the own vehicle position.
Here, applying the own vehicle road shape 30 to the own vehicle position means moving the own vehicle road shape 30 in parallel so that a point on the own vehicle road shape 30 near the own vehicle position becomes the own vehicle position. means. Similarly, applying the oncoming road shape 31 to the own vehicle position means moving the oncoming road shape 31 in parallel so that a point on the oncoming road shape 31 near the own vehicle position becomes the own vehicle position. .

For example, "a point on the own vehicle road shape 30 near the own vehicle position" and "a point on the oncoming road shape 31 near the own vehicle position" are a point on the own vehicle road shape 30 and the oncoming road shape, respectively. It may be the nearest point located closest to the own vehicle position among the points on 31. It may be the foot of a perpendicular line drawn from the own vehicle position to the own vehicle road shape 30 and the oncoming road shape 31, respectively.
A solid line 32 in FIG. 3 indicates the shape of the oncoming road after parallel movement. A dashed line 33 indicates the shape of the own vehicle's road after parallel movement.
The road shape estimation unit 42b calculates the amount of deviation τ in the road width direction between the own vehicle road shape 33 and the oncoming road shape 32 after parallel movement. In this specification, the amount of deviation τ in the road width direction between the own vehicle road shape 33 and the oncoming road shape 32 after parallel movement is referred to as "the amount of deviation τ between shapes."
By correcting the own vehicle road shape 33 after the parallel movement, the driving road shape estimation unit 42b calculates the amount of deviation between the own vehicle road shape after the correction and the oncoming road shape 32 after the parallel movement by a constant larger than 1. K is increased to a value (K×τ) obtained by multiplying the amount of deviation τ between shapes by a constant. The constant K may be 2, for example.

The corrected vehicle road shape 34 shown in FIG. 4 is one in which the amount of deviation from the oncoming road shape 32 is changed from the amount of deviation between shapes τ to 2×τ, and the shape of the road for the right turn lane Lr is more narrow than the shape before correction. It can be seen that the shape is close. Note that it is sufficient to estimate the right turn lane Lr up to just before entering the intersection C0, so it is sufficient to correct the amount of deviation only in the section up to approximately the center of the intersection C0 on the map.
The driving road shape estimation unit 42b estimates the oncoming road shape 32 after parallel movement as the shape of the straight lane Ls, and estimates the own vehicle road shape 34 after the correction as the shape of the right turn lane Lr.

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

(motion)
FIG. 10 is a flowchart of an example of the travel control method according to the embodiment.
In step S1, the positioning device 11 detects the current position of the own vehicle 1 (own vehicle position).
In step S2, the road shape estimating unit 42b determines whether the road marking detecting unit 40a has detected a road marking Ts that foretells a divergence between the straight lane Ls and the right turn lane Lr. When the road marking detection unit 40a detects the road marking Ts (step S2: Y), the process proceeds to step S3. If the road marking detection unit 40a does not detect the road marking Ts (step S2: N), the process ends.

In step S3, the intersection detection unit 41a extracts the own vehicle road shape 30 of the own vehicle road R1 and the oncoming road shape 31 of the oncoming road R2 from the map data.
In step S4, the usability determining unit 42a determines whether the oncoming road shape 31 can be used to estimate the lane shape. If the oncoming road shape 31 can be used to estimate the lane shape (step S4: Y), the process proceeds to step S5. If the oncoming road shape 31 cannot be used to estimate the lane shape (step S4: N), the process ends.

In step S5, the driving road shape estimating unit 42b moves the own vehicle road shape 30 in parallel so that a point on the own vehicle road shape 30 near the own vehicle position becomes the own vehicle position. Similarly, the oncoming road shape 31 is translated in parallel so that a point on the oncoming road shape 31 near the own vehicle position becomes the own vehicle position.
In step S6, the road shape estimating unit 42b calculates the amount of deviation τ between the shapes of the own vehicle road shape 33 and the oncoming road shape 32 in the road width direction after parallel movement.
In step S7, the driving road shape estimating unit 42b corrects the own vehicle road shape 33 after the parallel movement, thereby calculating the amount of deviation between the modified own vehicle road shape 34 and the oncoming road shape 32 after the parallel movement. With a constant K greater than 1, the amount of deviation τ between shapes is increased to a value (K×τ) obtained by multiplying the amount τ by a constant. The process then ends.

(Second embodiment)
Refer to FIG. 11(a). After applying the own vehicle road shape 30 and the oncoming road shape 31 to the own vehicle position, the driving road shape estimating unit 42b of the second embodiment applies the oncoming road shape 32 after parallel movement and the oncoming road shape 32 to the own vehicle position in the area before the intersection C0. Parallel sections Sp1 and Sp2, which are sections whose degree of parallelism with the vehicle road shape 33 is equal to or greater than a threshold value, are specified.
The parallel section Sp1 is a section before the straight lane Ls and the right turn lane Lr diverge (before the right turn lane Lr increases), and in the parallel section Sp1, the oncoming road shape 32 and the own vehicle road shape 33 substantially overlap. . The parallel section Sp2 is a section after the straight lane Ls and the right turn lane Lr become parallel. Moreover, the section Si is an intermediate section between the parallel sections Sp1 and Sp2.

The road shape estimation unit 42b of the second embodiment calculates that in these parallel sections Sp1 and Sp2, the amount of deviation in the road width direction of the own vehicle's road shape with respect to the oncoming road shape 32 after parallel movement is a constant times the amount of deviation between shapes τ. (K×τ), and the vehicle road shape 33 is corrected so that the vehicle road shapes of the parallel sections Sp1 and Sp2 are smoothly connected in the intermediate section Si. For example, the road shape estimation unit 42b may connect the vehicle road shapes of the parallel sections Sp1 and Sp2 with a clothoid curve in the intermediate section Si.

A dashed-dotted line 34 in FIG. 11(b) shows an example of the vehicle road shape after the deviation amount has been corrected. In addition, in the parallel section Sp1, the oncoming road shape 32 and the own vehicle road shape 33 substantially overlapped as described above, so the amount of deviation between the shapes τ is almost 0, and even if multiplied by a constant, the road width direction position of the own vehicle road shape remains almost unchanged.
In this way, the amount of deviation between the own vehicle's road shape 34 and the oncoming road shape 32 at the portion entering the intersection C0 only needs to be a constant times (K×τ) the amount of deviation between shapes τ, and as in the first embodiment, The amount of deviation may be a constant multiple (K×τ) of the amount of deviation between shapes τ in the entire sections Sp1, Si, and Sp2, and the amount of deviation may be a constant multiple (K × τ) of the amount of deviation between shapes τ in the entire sections Sp1, Si, and Sp2. It may be a constant multiple (K×τ) of the deviation amount τ.

(Effects of embodiment)
(1) The positioning device 11 detects the own vehicle position, which is the current position of the own vehicle 1. The controller 16 estimates whether or not the lane in which the own vehicle 1 is traveling branches into a straight lane for going straight through an intersection and a right turn lane for turning right at the intersection in front of the route of the own vehicle 1, and the controller 16 estimates whether or not the lane in which the own vehicle 1 is traveling branches into a straight lane for going straight through an intersection and a right turn lane for turning right at the intersection. When it is estimated that the lane will diverge into a straight lane and a right-turn lane, the road shape that is the road shape of the own vehicle road on which the own vehicle 1 is traveling is calculated from map data in which the road shape is expressed for each road by an array of multiple nodes. The vehicle road shape and the oncoming road shape, which is the road shape of the oncoming road on which the oncoming vehicle of own vehicle 1 is traveling, are extracted, and the point on the own vehicle road shape that is near the own vehicle position is the own vehicle position. The shape of the oncoming road is moved in parallel so that the point on the shape of the oncoming road near the position of the own vehicle becomes the position of the own vehicle. By calculating the amount of discrepancy between the shape of the own vehicle's road after the movement and the shape of the oncoming road after the parallel movement, and correcting the shape of the own road after the parallel movement, the corrected own vehicle's road shape can be calculated. The amount of deviation between the road shape and the shape of the oncoming road after parallel movement was increased to a value obtained by multiplying the amount of deviation between the shapes by a constant larger than 1, and the corrected road shape of the own vehicle was corrected. Estimate the shape of the right turn lane based on its shape.
In this way, by estimating the shape of the right turn lane based on the shape of the own vehicle's road modified to change the amount of deviation of the own vehicle's road shape from the oncoming road shape, right turns that are not included in the map data can be estimated. The shape of the lane can be estimated.

(2) When the controller 16 detects a road marking on the road ahead of the host vehicle 1 that foretells a branch between the straight lane and the right turn lane, the controller 16 determines whether the lane in which the host vehicle 1 is traveling is divided into the straight lane and the right turn lane. It may be determined that there is a branch.
Thereby, it can be determined whether the lane in which the host vehicle 1 is traveling branches into a straight-on lane and a right-turn lane.
(3) The controller 16 identifies a parallel section, which is a section in which the degree of parallelism between the vehicle's road shape and the oncoming road shape is equal to or greater than a threshold, and determines the vehicle's road shape after correction in the parallel section and the oncoming road after parallel movement. The vehicle road shape after parallel movement may be corrected so that the amount of deviation between the shapes is a constant multiple of the amount of deviation between the shapes.
In this way, by using the deviation amount of the parallel section between the own vehicle's road shape and the oncoming road shape as the deviation in the road width direction, the deviation amount can be calculated accurately.

(4) The constant multiple may be, for example, twice.
In the case of map data in which the shape of a road is expressed on a road-by-road basis by an arrangement of a plurality of nodes, each node indicates a position near the center of the road. Therefore, when the number of right turn lanes is increased by one lane near the entrance of an intersection, the amount of deviation of the node position in the road width direction before and after the increase in the number of right turn lanes is half the lane width. Therefore, by correcting the deviation amount of the vehicle's road shape to double, the deviation amount becomes one lane and follows the shape of the right turn lane.

(5) The controller 16 detects from the map data an inter-intersection section, which is a section where the own vehicle road is sandwiched between adjacent intersections, within a predetermined range from the own vehicle position, and The degree of similarity between the road shape and the shape of the oncoming road corresponding to the range is calculated, and if the degree of similarity is greater than a threshold, the shape of the straight lane is estimated based on the shape of the oncoming road after parallel movement. It's fine.
Since the oncoming road shape is used to estimate the straight lane only when the similarity between the own vehicle's road shape and the oncoming road shape is high, it is possible to prevent incorrect shape estimation.
(6) The controller 16 may estimate the shape of a right turn lane or a straight lane when an intersection exists a predetermined distance ahead of the vehicle position.
In this way, since the shapes of the right turn lane and the straight lane are estimated only in the vicinity of the intersection, it is possible to prevent the right turn lane from being erroneously estimated at a location other than the intersection.

DESCRIPTION OF SYMBOLS 1... Own vehicle, 10... Driving control device, 12... Map database, 14... External sensor, 15... Vehicle sensor, 16... Controller, 17... Actuator, 20... Processor, 21... Storage device, 40... Recognition part, 40a... Road marking detection section, 41... Navigation system, 41a... Intersection detection section, 42... Determination section, 42a... Availability determination section, 42b... Road shape estimation section, 42c... Vehicle control section

Claims (8)

Detects the own vehicle position, which is the current position of the own vehicle,
In front of the course of the own vehicle, it is estimated whether the lane in which the own vehicle is traveling branches into a straight lane that goes straight through an intersection and a right turn lane that turns right at the intersection;
When it is estimated that the lane on which the host vehicle is traveling is divided into a straight lane and a right-turn lane, the vehicle road on which the host vehicle is traveling is determined from map data in which the road shape is expressed for each road by an array of a plurality of nodes. Extracting the own vehicle road shape, which is the road shape of the own vehicle, and the oncoming road shape, which is the road shape of the oncoming road on which the oncoming vehicle of the own vehicle runs,
Translating the own vehicle road shape so that a point on the own vehicle road shape near the own vehicle position becomes the own vehicle position,
moving the oncoming road shape in parallel so that a point on the oncoming road shape near the own vehicle position becomes the own vehicle position;
In a section in front of the course of the own vehicle, calculate a shape-to-shape deviation amount that is a deviation amount between the own vehicle road shape after parallel movement and the oncoming road shape after parallel movement;
By correcting the shape of the road for the own vehicle after the parallel movement, the amount of deviation between the shape of the road for the own vehicle after the correction and the shape of the oncoming road after the parallel movement is set by a constant larger than 1 to the amount of deviation between the shapes. Increase to the value obtained by multiplying by a constant,
estimating the shape of the right turn lane based on the modified shape of the vehicle road shape after the modification;
A travel control method characterized by: The present invention is characterized by determining that the lane in which the host vehicle is traveling is branching into a straight-forward lane and a right-turn lane when a road surface marking that foretells a branch between a straight-ahead lane and a right-turn lane is detected on the road surface in front of the host vehicle. The travel control method according to claim 1. identifying a parallel section where the degree of parallelism between the own vehicle road shape and the oncoming road shape is equal to or greater than a threshold;
The shape of the road for the own vehicle after the parallel movement is adjusted such that the amount of deviation between the corrected shape of the road for the own vehicle in the parallel section and the shape of the oncoming road after the parallel movement is a constant times the amount of deviation between the shapes. fix,
The travel control method according to claim 1 or 2, characterized in that: The traveling control method according to any one of claims 1 to 3, wherein the constant multiplication is twice. 5. The travel control method according to claim 1, wherein the shape of the right turn lane is estimated when an intersection exists a predetermined distance ahead of the vehicle position. detecting from the map data an inter-intersection section where the own vehicle road is sandwiched between adjacent intersections within a predetermined range from the own vehicle position;
Calculating the degree of similarity between the own vehicle road shape in a range including a middle part of the inter-intersection section and the oncoming road shape in a portion corresponding to the range;
If the degree of similarity is greater than or equal to a threshold, estimating the shape of the straight lane based on the shape of the oncoming road after the parallel movement;
The travel control method according to any one of claims 1 to 5, characterized in that: 7. The driving control method according to claim 6, wherein the shape of the straight lane is estimated when an intersection exists a predetermined distance ahead of the vehicle position. comprising a positioning device that detects the current position of the own vehicle, and a controller;
The controller includes:
estimating whether or not the lane in which the own vehicle is traveling branches into a straight lane and a right-turn lane ahead of the course of the own vehicle;
When it is estimated that the lane in which the host vehicle is traveling is divided into a straight lane for going straight through an intersection and a right-turn lane for turning right at the intersection, the road shape is determined based on the map data in which the road shape is expressed for each road by an array of a plurality of nodes. extracting the own vehicle road shape which is the road shape of the own vehicle road on which the own vehicle is traveling, and the oncoming road shape which is the road shape of the oncoming road on which the oncoming vehicle of the own vehicle is traveling;
Translating the own vehicle road shape so that a point on the own vehicle road shape near the own vehicle position becomes the own vehicle position,
moving the oncoming road shape in parallel so that a point on the oncoming road shape near the own vehicle position becomes the own vehicle position;
In a section in front of the course of the own vehicle, calculate a shape-to-shape deviation amount that is a deviation amount between the own vehicle road shape after parallel movement and the oncoming road shape after parallel movement;
By correcting the shape of the road for the own vehicle after the parallel movement, the amount of deviation between the shape of the road for the own vehicle after the correction and the shape of the oncoming road after the parallel movement is set by a constant larger than 1 to the amount of deviation between the shapes. Increase to the value obtained by multiplying by a constant,
estimating the shape of the right turn lane based on the modified shape of the vehicle road shape after the modification;
A travel control device characterized by:

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