JP2019043396A - Driving control method and driving control device for driving support vehicle - Google Patents

Abstract

PROBLEM TO BE SOLVED: To suppress a sense of discomfort given to an occupant when traveling on a road in which a plurality of inflection points exist in a short section. SOLUTION: In a driving control device for a driving support vehicle including an automatic driving recognition determination processor 3 that controls for turning the own vehicle based on a target driving route, a GPS / navigation system 2, map data 4, and the like. It has a recognition sensor 1 that detects an oncoming vehicle A2, a left / right turning point detection unit 32 that detects a turning point IP, and an obstacle determination unit 34 that determines the presence or absence of an oncoming vehicle on the opposite side in front of the own vehicle. When it is determined that there is an oncoming vehicle, the target travel route generation unit 35 generates the first target travel route R1 based on the route, and when it is determined that there are a plurality of inflection point IPs and there is no oncoming vehicle. The relative distances D1'and D2' of each inflection point IP1 and IP2 are longer than one inflection point IP1 and shorter than the other inflection point IP2 than when the first target travel path R1 is generated. The second target travel path R2 is generated. [Selection diagram] FIG. 10B

Description

  The present disclosure relates to a driving control method and a driving control device for a driving assistance vehicle.

  Conventionally, a vehicle that performs vehicle control to generate a target route along a road boundary and to follow a turning target value calculated based on a forward gazing point for the purpose of achieving both tracking and stability with respect to the target route. A driving assistance control device is known (see, for example, Patent Document 1). In such a conventional device, in a turning traveling scene on a curved road, the target route is generated along the road boundary, and the vehicle travels in the center of the road to increase the margin between the vehicle position and the left and right road boundaries. It is common to generate a route. Further, in such a conventional apparatus, when the travel lane is clearly defined by the landmarks on the road, a route with a reduced curvature may be generated within the travel lane.

Japanese Unexamined Patent Publication No. 2015-013545

  Here, among general public roads, there are roads having a plurality of inflection points in a short section. Typical examples include crank roads, S-curve roads, and irregular roads / intersections. When a route is generated based on a conventional device on a road having such a shape, the route generated along the road boundary naturally also has a shape along the road shape. Further, when trying to follow the route, the vehicle turns immediately in the opposite direction after the turn, and the vehicle behavior becomes larger than when traveling on a route with a reduced curvature within the travel lane. In addition, when the route is generated based on the conventional device, a route with a reduced curvature may be generated within the range of the traveling lane, but if there is a three-dimensional object that becomes an obstacle on the opposite side of the own vehicle, There is a risk of the three-dimensional object approaching. As described above, when the route is generated based on the conventional device, there is a problem that when the vehicle travels on a road having a plurality of inflection points in a short section, there is a possibility that the passenger may feel uncomfortable.

  The present disclosure has been made paying attention to the above problem, and provides a driving control method and a driving control device for a driving assistance vehicle that suppress a sense of incongruity given to an occupant when driving on a road having a plurality of inflection points in a short section. The purpose is to provide.

In order to achieve the above object, the present disclosure includes a controller that performs control for turning the host vehicle based on a target travel route.
In this driving support vehicle driving control method, a route to a destination is set.
Get road information around the route.
Detect obstacles around your vehicle.
From the road information, an inflection point of curvature existing within a predetermined distance is detected with respect to a road boundary that forms a road ahead of the host vehicle.
The presence or absence of an obstacle is determined on the opposite side in front of the host vehicle.
When there are no more than two inflection points, or when it is determined that there is an obstacle, the first target travel route is generated based on the route.
When it is determined that there are two or more inflection points and there is no obstacle, a second target travel route is generated. In the second target travel route, the relative distance of each inflection point is shorter than one inflection point and longer than the other inflection point than when the first target travel route is generated.

As described above, by generating the first target travel route and the second target travel route, it is possible to suppress a sense of discomfort given to the occupant when traveling on a road having a plurality of inflection points in a short section.

BRIEF DESCRIPTION OF THE DRAWINGS It is a whole block diagram which shows the automatic driving system structure of the automatic driving vehicle to which the driving control method and driving control apparatus of the automatic driving vehicle of Example 1 were applied. It is a block diagram which shows the traveling control process of the automatic driving vehicle performed by the recognition judgment processor for automatic driving of Example 1. FIG. 3 is a flowchart illustrating a flow of a traveling control process for an autonomous driving vehicle that is executed by a recognition determination processor for autonomous driving according to a first embodiment. It is a figure which shows an example of the road scene which does not have a center line in a crank road and is not one-way. It is a figure which shows an example of the road scene which is not a one-way street without the center line in a S-shaped curve road. It is a figure which shows an example of the road scene which does not have the center line in the intersection which crosses irregularly, and a stagnation road. It is a figure which shows an example of the road scene from which landmark information cuts in the middle of the intersection which crosses irregularly, and a tangential road. It is explanatory drawing which shows an example of the interpolation of the landmark information in FIG. 4A. It is explanatory drawing which shows an example of the interpolation of the landmark information in FIG. 4B. It is explanatory drawing which shows an example of the interpolation of the landmark information in FIG. 4C. It is explanatory drawing which shows an example of the interpolation of the landmark information in FIG. 4D. It is a figure which shows the detection and determination of the inflection point in a right curve road. It is a figure which shows the detection and determination of the inflection point in a crank path. It is a figure which shows the detection and determination of the inflection point in an S-shaped curve road. It is a figure which shows the detection and determination of the inflection point in the intersection which cross | intersects irregularly, and a choji. It is a figure which shows the target travel path | route produced | generated in Example 1, and the node information of each node. It is a figure which shows the 1st target travel path | route when it determines with there being no inflection point in Example 1. FIG. It is a figure which shows the 1st target travel path | route at the time of determining with the oncoming lane landmark in Example 1. It is a figure which shows the 1st target travel path | route at the time of determining with the obstruction in Example 1. FIG. It is a figure which shows the 2nd target driving | running route | path at the time of determining with multiple inflection points in Example 1, no oncoming lane landmark, and no obstacle. It is explanatory drawing explaining the relative distance of the inflection point in Example 1, and a 1st target travel path | route. It is explanatory drawing explaining the relative distance of the inflection point in Example 1, and a 2nd target travel path | route. It is a flowchart which shows the flow of the traveling control process of the automatic driving vehicle performed by the recognition judgment processor for automatic driving of Example 2. It is explanatory drawing explaining the future movement prediction of the own vehicle and an obstruction in Example 2. FIG. It is a figure which shows an example when the future movement prediction of the obstruction in Example 2 comes off. 12 is a flowchart illustrating a flow of a traveling control process for an autonomous driving vehicle that is executed by a recognition determination processor for autonomous driving according to a third embodiment. It is a figure which shows an example when the future movement prediction of the obstruction in Example 3 comes off, and is explanatory drawing explaining regeneration of the 3rd target travel route in Example 3. FIG. 12 is a flowchart illustrating a flow of a traveling control process for an autonomous driving vehicle that is executed by a recognition determination processor for autonomous driving according to a fourth embodiment. It is a figure which shows the target driving | running route produced | generated in Example 4, and the node information of each node. It is a figure which shows an example when the future movement prediction of the obstruction in Example 4 comes off, and is a figure which stops the own vehicle in Example 4.

  Hereinafter, a best mode for realizing a driving control method and a driving control device of a driving assistance vehicle (own vehicle / own vehicle) according to the present disclosure will be described based on Examples 1 to 4 shown in the drawings.

  First, the configuration will be described. The travel control method and the travel control apparatus according to the first embodiment are based on a hybrid vehicle (an example of an electric vehicle) that is driven by a motor, and an autonomous driving vehicle (an example of a driving support vehicle) that can externally control steering / driving / braking. ). Hereinafter, the configuration of the first embodiment will be described by dividing it into “automatic driving system configuration”, “detailed configuration of recognition determination processor for automatic driving”, and “travel control processing configuration of automatic driving vehicle”.

[Automatic driving system configuration]
FIG. 1 shows an automatic driving system configuration of an automatic driving vehicle to which a driving control method and a driving control device for an automatic driving vehicle according to a first embodiment are applied. Hereinafter, the overall configuration of the automatic driving system will be described with reference to FIG.

  The automatic driving system includes a recognition sensor 1 (obstacle detection unit), a GPS / navigation system 2 (route setting unit), an automatic driving recognition determination processor 3 (controller), and map data 4 (road shape acquisition unit). It is equipped with. The automatic driving system includes an automatic driving control controller 5 (vehicle control unit), an electric power steering 6, a drive / regenerative motor 7, and a hydraulic brake 8. That is, the automatic driving recognition determination processor 3 and the automatic driving control controller 5 that calculates and transmits each control command value to each actuator ECU are mounted on the vehicle as a processing system. Note that description of each actuator ECU is omitted.

  The recognition sensor 1 is a sensor provided to recognize an external environment (such as a road boundary) around the host vehicle such as the front of the host vehicle or the rear of the host vehicle. The recognition sensor 1 is also a sensor that detects an obstacle around the host vehicle. Typically, it refers to an in-vehicle camera, a laser radar, or the like mounted on the front part of the host vehicle and the rear part of the host vehicle. Here, the “runway boundary” is a boundary such as a road width, a road shape, and a lane.

  GPS of the GPS / navigation system 2 is a position detection means that is mounted on the own vehicle and detects the traveling position (latitude / longitude) of the traveling vehicle. Note that “GPS” is an abbreviation for Global Positioning System. The navigation system (route setting unit) is route setting means for automatically calculating a route from the initial location to the destination based on the destination input of the occupant or the outside operator. As a definition of this route, each branch point from the initial location to the destination is considered comprehensively considering traffic rules such as route length, road width, one-way traffic, and traffic changes during construction and traffic jams. It shall be created by joining together. In addition, the resolution is at a level where the branch points are connected, and it is assumed that the lanes and the line taking are not detailed. A detailed description of the route setting method is omitted.

  The automatic driving recognition determination processor 3 integrates the map data 4, the GPS / navigation system 2 and the information of the recognition sensor 1, and calculates each profile such as a target speed profile (= target vehicle speed profile). That is, the basic route to the destination designated by the occupant and the vehicle speed are calculated based on the map data 4 stored in the in-vehicle memory. Further, while following the basic route and the vehicle speed based on the position information by GPS, based on the sensing result around the vehicle by the on-board recognition sensor 1, a nearby target travel route (target route) and target speed (target vehicle speed) are used as a profile. Correct sequentially.

  The map data 4 is map data that is stored in an in-vehicle memory and road information such as a gradient and a speed limit is written. In the map data 4, when the traveling position of the host vehicle traveling by GPS is detected, map information centered on the traveling position of the host vehicle is read from the automatic driving recognition determination processor 3.

  The automatic driving controller 5 determines each command value of the steering amount, the driving amount, and the braking amount based on the profile information (target travel route, target vehicle speed, etc.) from the automatic driving recognition determination processor 3. Steering control is performed by the electric power steering 6 that is a steering actuator. Drive control is performed by the drive / regeneration motor 7 which is a drive source actuator. The braking control is performed by distributing the regeneration by the drive / regeneration motor 7 and the mechanical brake by the hydraulic brake 8. Steering control, drive control, and braking control are performed by each ECU provided for each actuator.

  The electric power steering 6 is a steering actuator that automatically steers in accordance with a control command value from the control controller 5 for automatic driving. When the turning target value is calculated, the electric power steering 6 realizes the turning target value based on at least one of the turning of the own vehicle (drive wheel turning) and the braking / driving force difference generated in each wheel, Turn control.

  The drive / regenerative motor 7 is a drive source actuator that performs constant speed traveling or acceleration traveling by driving or decelerating traveling by regeneration in accordance with a control command value from the controller 5 for automatic operation.

  The hydraulic brake 8 is a brake actuator that operates hydraulic braking in accordance with a control command value from the automatic operation controller 5.

[Detailed configuration of recognition judgment processor for automatic driving]
FIG. 2 is a block diagram of the traveling control process for the autonomous driving vehicle executed by the recognition determination processor for autonomous driving according to the first embodiment. The detailed configuration of the automatic driving recognition determination processor will be described below with reference to FIG.

  The automatic driving recognition determination processor 3 includes a road boundary interpolation unit 31, a left / right turning point detection unit 32, an oncoming lane landmark determination unit 33, an obstacle determination unit 34, a target travel route generation unit 35, and a target profile. And a generation unit 36. Information such as a route is input to the automatic driving recognition determination processor 3.

  The runway boundary interpolation unit 31 inputs landmark information from the recognition sensor 1, the map data 4, and the like. The runway boundary interpolation unit 31 interpolates a runway boundary from the acquired landmark information in an area without a landmark that defines the runway boundary (see FIGS. 4A to 4D) (see FIGS. 5A to 5D). The road boundary interpolation unit 31 outputs the result of interpolation of the road boundary to the left / right turning point detection unit 32.

  Here, “landmark information” is information that defines the road boundary. This information includes lane markings by road surface paint, curbstones and walls, solid objects such as poles and guardrails, side grooves, etc., and boundaries where vehicles cannot physically enter or traffic rules. It defines the boundaries that should not be used. In addition, as in the convection zone (zebra zone), entry is not prohibited as a traffic rule, but a boundary that psychologically prevents entry is also included.

  The left / right turning point detection unit 32 inputs the road information such as the recognition sensor 1 and the map data 4 and the interpolation result of the road boundary. The left / right turning point detection unit 32 detects inflection points and the number of curvatures existing within a predetermined distance with respect to road information, an interpolation result, and a road boundary constituting a road ahead of the host vehicle. The left / right turning point detection unit 32 determines whether the number of detected inflection points is two or more (plural). The left / right turning point detection unit 32 outputs the detection result of the inflection point to the oncoming lane landmark determination unit 33 and the target travel route generation unit 35.

  The oncoming lane landmark determination unit 33 determines whether there is an oncoming lane landmark that defines the oncoming lane from the landmark information. The oncoming lane landmark determination unit 33 outputs an oncoming lane landmark presence signal or no signal to the target travel route generation unit 35. The oncoming lane landmark determination unit 33 outputs an oncoming lane landmark presence signal to the obstacle determination unit 34.

  The obstacle determination unit 34 determines the presence or absence of an obstacle on the opposite side in front of the host vehicle from the obstacle information of the recognition sensor 1. The obstacle determination unit 34 outputs an obstacle presence signal or an absence signal to the target travel route generation unit 35.

  The target travel route generation unit 35 inputs the map data 4, the GPS / navigation system 2 and the information of the recognition sensor 1 (environmental information around the vehicle). In addition, the target travel route generation unit 35 inputs an inflection point detection result, an oncoming lane landmark presence signal or absence signal, and an obstacle presence signal or absence signal. The target travel route generation unit 35 generates a first target travel route or a second target travel route as a target travel route based on such information and the like. That is, the target travel route generation unit 35 sets the future position of the host vehicle through which the host vehicle will pass in the future and the future posture of the host vehicle when the host vehicle passes. Next, the target travel route generation unit 35 smoothly connects the current position and current posture of the host vehicle to the future position of the host vehicle and the future posture of the host vehicle, so that the first target travel route or the second target travel route. Is generated. The target travel route generation unit 35 outputs the first target travel route or the second target travel route to the target profile generation unit 36.

  The target profile generation unit 36 generates a target travel route profile, a target speed profile, a steering angle profile, and the like based on the first target travel route or the second target travel route. The target profile generation unit 36 outputs the generated target travel route profile, target speed profile, steering angle profile, and the like to the automatic operation control controller 5.

[Running control processing configuration of autonomous driving vehicle]
FIG. 3 shows a flow of the traveling control process for the autonomous driving vehicle executed by the recognition determination processor for autonomous driving of the first embodiment. That is, FIG. 3 shows the flow of the traveling control process of the autonomous driving vehicle in FIG. Hereinafter, each step of the flowchart shown in FIG. 3 showing the traveling control processing configuration of the autonomous driving vehicle will be described with reference to FIGS. Note that the traveling control process for the autonomous vehicle shown in FIG. 3 is repeatedly executed at a predetermined control cycle. Moreover, in FIGS. 4-7, the own vehicle is shown by "A1" and an oncoming vehicle (an oncoming vehicle, an obstruction) is shown by "A2".

  In the flowchart of FIG. 3, it is assumed that the route has been set, the conditions necessary for automatic driving are satisfied, and the vehicle is traveling in the automatic driving mode. In order to improve the performance of automatic driving, the GPS is a system that can acquire position information with high accuracy, and the map data 4 is digital data that accurately represents the real environment with high accuracy.

  In step S11, the road information is acquired, the road boundary is interpolated, and the process proceeds to step S12. That is, in step S <b> 11, landmark information that defines the road boundary existing on both sides of the route near the host vehicle is acquired from the recognition sensor 1 and the map data 4 according to the set route. In step S11, as shown in FIGS. 4A to 4D, in an area where there is no landmark that defines the road boundary, as shown in FIGS. 5A to 5D, the road boundary is interpolated from the acquired landmark information. In step S <b> 11, the road boundary interpolation corresponds to the road boundary interpolation unit 31. Hereinafter, interpolation of the road boundary will be described.

  First, the landmark information is identified from the information of the recognition sensor 1, the first landmark information that defines the road boundary as a real environment, the GPS data and the map data 4, and the road boundary on both sides of the route is defined. And second landmark information.

  The first landmark information and the second landmark information are selectively used in a portion where the recognition sensor 1 can detect the first landmark information in the vicinity of the host vehicle, using the first landmark information. On the other hand, the second landmark information is used for a portion in which the first landmark information cannot be detected by the recognition sensor 1 far away from the subject vehicle or by occlusion. “Occlusion” means that the first landmark cannot be recognized by the recognition sensor 1 because it is blocked by an obstacle or the like.

  Specifically, in a crank road as shown in FIG. 4A, where the center line is not present and the oncoming vehicle A2 can travel instead of one-way, if only the road width RW can be acquired as the first landmark information, As shown in FIG. 5A, a half width of the road width RW is set as the travel path C. 4B, a half width of the road width RW is set as the traveling road C as shown in FIG. 5B, as in FIG. 4A.

  In addition, in a scene where an oncoming vehicle A2 can travel on an irregularly shaped intersection or a straight road as shown in FIG. 4C, where there is no center line and one-way traffic, the road width RW1 before getting on and the road width RW2 after entering A case where landmark information on the opposite side to the route can be acquired will be described. In the case as shown in FIG. 4C, based on the acquired landmark information, as shown in FIG. 5C, smooth connection is performed by a curved line expression such as a spline curve or a polynomial function.

  Further, a description will be given of a road scene in which landmark information is cut off in the middle of an intersection or a tangential road at an irregularly shaped intersection or a tangential road as shown in FIG. 4D. In this scene, the case where the first landmark information M1 before and after cutting and the second landmark information M2 on the opposite side to the route can be acquired will be described. In the case as shown in FIG. 4D, using the acquired landmark information, as shown in FIG. 5D, the portions before and after being cut by the curve expression such as a spline curve or a polynomial function are smoothly connected.

  In step S12, it is determined whether two or more inflection points have been detected following the acquisition of the road information and the interpolation of the road boundary in step S11. If YES (there is a plurality of inflection points), the process proceeds to step S13, and if NO (there is no plurality of inflection points), the process proceeds to step S15. Step S <b> 12 corresponds to the left / right turning point detection unit 32.

  Here, the “inflection point” refers to a point at which the curvature change rate reaches a peak, paying attention to the curvature of the road boundary that forms the road ahead of the host vehicle acquired in step S11. Then, it is determined whether there are two or more inflection points between the inflection point closest to the vehicle and a predetermined distance. In addition, the section length of the “predetermined distance” is variable according to the host vehicle traveling speed, and the section length is set shorter as the host vehicle traveling speed decreases. In addition, the definition of the distance at this time is ideally the distance converted along the road boundary, but in order to reduce the calculation load, the distance spread in a circle around the host vehicle may be used instead. Is possible. In addition, which of the left and right sides of the road boundary is used for the determination is preferentially determined based on the actual number of landmarks. Specifically, the road length shown in FIGS. When the inflection point IP is detected and determined using the left-side lane boundary in FIGS. 6A to 6D, in the case of FIG. 6A, since one inflection point IP is detected, it is determined that there are not a plurality of inflection points. . 6B to 6D, in any case, since two inflection points IP are detected, it is determined that there are a plurality of inflection points.

  In step S13, following the determination that there are a plurality of inflection points in step S12, it is determined whether there is an oncoming lane landmark MM. If YES (with oncoming lane landmark), the process proceeds to step S15, and if NO (no oncoming lane landmark), the process proceeds to step S14. Step S13 corresponds to the oncoming lane landmark determination unit 33.

  Here, the “opposite lane landmark MM” is a road landmark that defines the host vehicle travel lane and the opposite lane, and is an actual landmark. For example, the opposite lane landmark MM in FIG. This information is the track information acquired in step S11. Further, as shown in FIGS. 5A to 5C, when the road boundary is interpolated, it is determined that there is no oncoming lane landmark.

  In step S14, following the determination that there is no oncoming lane landmark in step S13, it is determined whether or not there is an obstacle on the opposite side in front of the host vehicle. If YES (no obstacle), the process proceeds to step S16, and if NO (has an obstacle), the process proceeds to step S15. Step S14 corresponds to the obstacle determination unit 34.

  Here, the “opposite side in front of the host vehicle” is the opposite side in the case of left-hand traffic and the opposite side in the case of right-hand traffic. The “obstacle” is an obstacle that moves in a direction approaching the host vehicle. For example, the oncoming vehicle A2 traveling in the oncoming lane (see FIGS. 4 to 7).

  Both the next step S15 and step S16 are steps for generating a target travel route. For this reason, after explaining matters common to both steps, each step will be explained.

  “Target travel route” is a method for generating a target travel route by using a method based on optimization calculation based on the map data 4, GPS, or recognition sensor 1 or a method for selecting the best-rated route from a plurality of routes. To do. When the target travel route is generated, it is generated based on a route to a preset destination. As an index for generating the target travel route, the path curvature is assumed on the premise that it does not exceed the road boundary where the vehicle can travel and that it does not contact with three-dimensional obstacles around the vehicle such as other vehicles and pedestrians. Take into account that the is not excessive.

In addition, as shown in FIG. 7, the target travel route is set with coordinates where the vehicle position at the time of route generation is the origin, the direction of the vehicle is x, and the width direction of the vehicle is y. Thus, it is handled with discrete node information that is equally spaced or divided according to a certain rule. In the first embodiment, each node constituting the target travel route is recorded with two-dimensional coordinate information x _i and y _i . Each information is recorded in a recording unit (not shown) until the vehicle reaches the end of the route.

  In step S15, following the determination that there are no inflection points in step S12, the determination that there is an oncoming lane landmark in step S13, or the determination that there is an obstacle in step S14, along the road boundary A first target travel route is generated and the process proceeds to the end. Step S15 corresponds to the target travel route generation unit 35.

  When this first target travel route is generated, when it is determined that there are not a plurality of inflection points at step S12, when it is determined that there is an oncoming lane landmark at step S13, or at step S14 an obstacle This is the case when it is determined that there is none. Here, the “first target travel route” is an immediate route that the vehicle can follow based on the route, and that is free from deviation from the boundary of the travel route, contact with an obstacle, and contact. That is, the route is within a range that does not depart from the road boundary. That is, among the indices used when the first target travel route is generated, the index “does not exceed the road boundary on which the car can travel” is “does not exceed the travel path boundary on which the vehicle travels”.

  In step S16, following the determination that there is no obstacle in step S14, a second target travel route with a curvature lower than that of the first target travel route is generated, and the process proceeds to the end. Step S16 corresponds to the target travel route generation unit 35.

  When this second target travel route is generated, it is determined that there are a plurality of inflection points in step S12, it is determined that there is no oncoming lane landmark in step S13, and it is determined that there is no obstacle in step S14. It is. Therefore, there is no actual oncoming lane landmark MM, and there is no need to worry about the obstacle on the opposite side in front of the host vehicle. As a result, there is no problem with the part where the road boundary is interpolated in step S11, even in terms of traffic rules, and “the road does not exceed the road boundary where the car can physically travel” is an indicator for generating the second target travel route. It becomes.

Next, the operation will be described.
The operation of the first embodiment will be described by dividing it into “automatically driven vehicle travel control processing effect” and “automatically driven vehicle travel control characteristic effect”.

[Operation control processing operation of autonomous driving vehicle]
Hereinafter, the processing operation of the travel control of the autonomous driving vehicle will be described with reference to FIGS. 3, 6, and 8 to 10 in the flowchart of FIG. 3. 8 to 10, the current position of the vehicle is indicated by “A1”, the traveling direction of the vehicle is indicated by “DM” and the future position of the vehicle is indicated by “P11 to P13” in FIGS. 8 and FIG. 10, an oncoming vehicle (an oncoming vehicle or an obstacle) is indicated by “A2”.

  First, when there is one inflection point IP as shown in FIG. 6A, the process proceeds from step S11 → step S12 → step S15 → end. At this time, in step S15, a first target travel route R1 shown in FIG. 8A is generated.

  Next, there are two inflection points IP as shown in FIG. 6B, but when the oncoming lane landmark MM exists, the process proceeds from step S11 to step S12 to step S13 to step S15 to end. At this time, in step S15, a first target travel route R1 shown in FIG. 8B is generated.

  Next, as shown in FIG. 6B, there are two inflection points IP and no oncoming lane landmark MM, but when the oncoming vehicle A2 is on the opposite side in front of the host vehicle, step S11 → step S12 → step S13 → The process proceeds from step S14 to step S15 to the end. At this time, in step S15, as in FIG. 8B, the first target travel route R1 shown in FIG. 8C is generated.

  Next, as shown in FIG. 6B, there are two inflection points IP, no oncoming lane landmark MM, and no oncoming vehicle A2 on the opposite side in front of the host vehicle, step S11 → step S12 → step S13 → step. The process proceeds from S14 to step S16 to the end. At this time, in step S16, the second target travel route R2 shown in FIG. 9 is generated.

  Here, the first target travel route R1 and the second target travel route R2 are compared based on the inflection point IP. For example, as shown in FIG. 10A under the same conditions as FIG. 8C, in the first target travel route R1 (dashed line), the relative distances D1, D2 between the inflection points IP1, IP2 and the first target travel route R1 are: The relative distance D1 is longer than the relative distance D2 (D1> D2). On the other hand, relative distances D1 ′ and D2 ′ between the inflection points IP1 and IP2 and the second target travel route R2 (broken line) are as shown in FIG. 10B under the same conditions as in FIG. That is, in the second target travel route R2, compared to the first target travel route R1, the relative distances D1 ′ and D2 ′ between the inflection points IP1 and IP2 and the second target travel route R2 are one inflection point. IP1 is long (D1 <D1 ′) and the other inflection point IP2 is short (D2> D2 ′). That is, in the route along which the vehicle travels, the inflection point IP1 on the near side is long (D1 <D1 ′) and the inflection point IP2 on the back side is short (D2> D2 ′). For this reason, the second target travel route is a route in which the curvature is suppressed more than that of the first target travel route by changing the relative distance between the inflection points IP1 and IP2. In addition, the road shape etc. of FIG. 10A and FIG. 10B are the same.

  Thus, when traveling on a route having a plurality of inflection points in a short section, depending on the number of inflection points IP, the presence or absence of the oncoming lane landmark MM, and the presence or absence of the oncoming vehicle A2, the first target travel route R1 or A two target travel route R2 is generated. That is, when the first target travel route R1 is generated, a route along the travel route boundary is generated, so that the approach of the host vehicle A1 and the oncoming vehicle A2 is suppressed. Further, when the second target travel route R2 is generated, a route with a reduced curvature is generated, so that excessive vehicle behavior is suppressed. Therefore, when traveling on a road having a plurality of inflection points in a short section, excessive vehicle behavior and the approach of the host vehicle A1 and the oncoming vehicle A2 are suppressed. In addition, when there is an oncoming lane landmark MM that defines the oncoming lane, the first target travel route R1 along the road boundary is generated, so the traffic rules can be set without causing the vehicle to protrude from the oncoming lane landmark MM. Enables safe driving.

  Note that after the generation of the first target travel route R1 in step S15 or after the generation of the second target travel route R2 in step S16, the process proceeds to the end. Thereafter, the generated target travel route is stored in the memory of the automatic driving recognition determination processor 3. Next, a forward gazing point is set based on the generated target travel route and the current movement information of the host vehicle, and a turning target value is calculated. Thereafter, the steering angle target value (the command value of the steering amount) of the electric power steering 6 is calculated by the controller 5 for automatic driving, and the steering control of the electric power steering 6 is performed by the ECU using this as external command information.

[Characteristic effects of driving control for autonomous vehicles]
In the first embodiment, when there are not two or more inflection points IP or when it is determined that there is an obstacle, the first target travel route R1 is generated based on the route. When it is determined that there are two or more inflection points and there is no obstacle A2, the second target travel route R2 is generated. In the second target travel route R2, the relative distances D1 ′ and D2 ′ between the inflection points IP1 and IP2 and the second target travel route R2 are one of the inflections as compared with the case where the first target travel route R1 is generated. The point IP1 is long (D1 <D1 ′) and the other inflection point IP2 is short (D2> D2 ′). That is, when the first target travel route R1 is generated, a route along the travel route boundary is generated, so that the approach of the host vehicle A1 and the oncoming vehicle A2 is suppressed. Further, when the second target travel route R2 is generated, a route with a reduced curvature is generated, so that excessive vehicle behavior is suppressed. As a result, when driving on a road having a plurality of inflection points IP in a short section, an uncomfortable feeling given to the occupant is suppressed.

  In the first embodiment, the section length of the predetermined distance is variable according to the host vehicle traveling speed, and the section length is set shorter as the host vehicle traveling speed decreases. Accordingly, an appropriate route is generated according to the traveling speed of the vehicle.

  In the first embodiment, with respect to a place where there is no landmark, interpolation is performed with the landmark on the opposite side that defines the road boundary or landmarks before and after the landmark is interrupted. Therefore, a route can be appropriately generated even for a road that does not have a landmark that defines the road boundary.

Next, the effect will be described.
In the traveling control method and the traveling control device for the autonomous driving vehicle according to the first embodiment, the effects listed below can be obtained.

(1) A controller (automatic driving recognition determination processor 3) that performs control for turning the vehicle based on the target travel route is provided.
In the driving control method for this driving assistance vehicle (automatic driving vehicle), a route to the destination is set.
Get road information around the route.
An obstacle (oncoming vehicle A2) around the host vehicle A1 is detected.
From the road information, the inflection point IP of the curvature existing within a predetermined distance is detected with respect to the road boundary constituting the road ahead of the host vehicle A1.
The presence or absence of an obstacle (oncoming vehicle A2) is determined on the opposite side in front of the host vehicle A1.
When there are no more than two inflection points IP, or when it is determined that there is an obstacle (oncoming vehicle A2), the first target travel route R1 is generated based on the route.
When it is determined that there are two or more inflection points IP and there is no obstacle (oncoming vehicle A2), the second target travel route R2 is generated. In the second target travel route R2, the relative distances D1 ′ and D2 ′ of the inflection points IP1 and IP2 are longer than the one inflection point IP1 (D1 < D1 ′), the other inflection point IP2 becomes shorter (D2> D2 ′).
For this reason, when driving on a road having a plurality of inflection points IP in a short section, it is possible to provide a driving control method for a driving assistance vehicle (automatic driving vehicle) that suppresses discomfort given to the occupant.

(2) The section length of the predetermined distance is variable according to the host vehicle traveling speed, and the section length is set shorter as the host vehicle traveling speed is lower.
For this reason, in addition to the effect of (1), an appropriate route can be generated according to the traveling speed of the host vehicle.

(3) Refer to landmarks that define the driving lanes that exist in the left-right direction of the route as the road boundary in the road information around the route.
For the places where there are no landmarks, interpolation is performed with landmarks on the opposite side that define the road boundary or landmarks before and after the landmark is interrupted.
For this reason, in addition to the effects (1) and (2), a route can be appropriately generated even for a road that does not have a landmark that defines the road boundary.

(4) A controller (automatic driving recognition determination processor 3) that performs control for turning the vehicle based on the target travel route is provided.
In the driving control device of the driving assistance vehicle (automatic driving vehicle), the controller (automatic driving recognition determination processor 3) includes a route setting unit (GPS / navigation system 2), a road shape acquisition unit (map data 4), Have The controller (automatic driving recognition determination processor 3) includes an obstacle detection unit (recognition sensor 1), a left / right turning point detection unit 32, an obstacle determination unit 34, and a target travel route generation unit 35. .
The route setting unit (GPS / navigation system 2) sets a route to the destination.
The road shape acquisition unit (map data 4) acquires road information around the route.
The obstacle detection unit (recognition sensor 1) detects an obstacle (oncoming vehicle A2) around the own vehicle A1.
The left / right turning point detection unit 32 detects, from the road information, an inflection point IP having a curvature existing within a predetermined distance with respect to a road boundary constituting the road ahead of the host vehicle A1.
The obstacle determination unit 34 determines the presence or absence of an obstacle (oncoming vehicle A2) on the opposite side in front of the host vehicle A1.
The target travel route generation unit 35 generates the first target travel route R1 based on the route when there are not two or more inflection points IP or when it is determined that there is an obstacle (oncoming vehicle A2). The target travel route generation unit 35 generates the second target travel route R2 when it is determined that there are two or more inflection points IP and there is no obstacle (oncoming vehicle A2). In the second target travel route R2, the relative distances D1 ′ and D2 ′ of the inflection points IP1 and IP2 are longer than the one inflection point IP1 (D1 < D1 ′), the other inflection point IP2 becomes shorter (D2> D2 ′).
For this reason, when driving on a road having a plurality of inflection points IP in a short section, it is possible to provide a driving control device for a driving assistance vehicle (automatic driving vehicle) that suppresses the uncomfortable feeling given to the occupant.

  Example 2 is an example in which the future travel of an obstacle is predicted and a target travel route is generated. Further, in the case where the movement prediction is deviated, the target travel route is continuously switched to the route on the side away from the obstacle.

  First, the configuration will be described. The travel control method and the travel control apparatus according to the second embodiment are based on a hybrid vehicle (an example of an electric vehicle) that is driven by a motor, and an autonomous driving vehicle (an example of a driving support vehicle) that can externally control steering / driving / braking. ). Hereinafter, the configuration of the second embodiment will be described by dividing it into “a detailed configuration of the recognition determination processor for automatic driving” and “a travel control processing configuration of the automatic driving vehicle”. The “automatic operation system configuration” of the second embodiment is the same as that of the first embodiment, and thus the description thereof is omitted.

[Detailed configuration of recognition judgment processor for automatic driving]
The automatic driving recognition determination processor 3 includes a movement prediction unit in addition to the detailed configuration of the automatic driving recognition determination processor 3 of the first embodiment.

  The movement predicting unit inputs information of the recognition sensor 1 (environmental information around the vehicle) and an obstacle signal. The movement prediction unit performs future movement prediction of the obstacle based on the relative distance and relative speed between the host vehicle and the obstacle by inputting the obstacle presence signal. The movement prediction unit outputs the result of the movement prediction to the target travel route generation unit 35.

  The target travel route generation unit 35 inputs the result of the movement prediction in addition to the input of various information and signals as in the first embodiment. The target travel route generation unit 35 generates the first target travel route R1 or the second target travel route R2 as the target travel route based on various information and signals as in the first embodiment. The target travel route generation unit 35 outputs the generated target travel route to the target profile generation unit 36.

  Further, when various information and an obstacle presence signal are input, the target travel route generation unit 35 generates both the first target travel route R1 and the second target travel route R2. Subsequently, the target travel route generation unit 35 determines the presence or absence of an obstacle approaching the host vehicle by inputting the result of the movement prediction. Next, the target travel route generation unit 35 generates both the first target travel route R1 and the second target travel route R2 based on the presence or absence of an obstacle approaching the host vehicle, and the first target travel route. The switching timing between R1 and the second target travel route R2 and the setting of the weight of each route are adjusted. Also, this weight is changed to a new weight when the actual approach degree is close to the approach degree of the movement prediction. When the weight change occurs, the target travel route generation unit 35 generates a target travel route that is continuously switched to a route on the side away from the obstacle. The target travel route generation unit 35 outputs the generated target travel route to the target profile generation unit 36.

  Since other configurations are the same as those of the first embodiment, illustration and description thereof are omitted.

[Running control processing configuration of autonomous driving vehicle]
FIG. 11 shows the flow of a traveling control process for an autonomous driving vehicle executed by the recognition determination processor for autonomous driving according to the second embodiment. Hereafter, each step of the flowchart shown in FIG. 11 showing the traveling control processing configuration of the autonomous driving vehicle will be described with reference to FIGS. 11 and 12. In addition, the traveling control process of the automatic driving vehicle shown in FIG. 11 is repeatedly executed at a predetermined control cycle. In FIG. 12, the current position of the host vehicle is indicated by “P11”, the future position of the host vehicle is indicated by “P12 to P14”, and the current position of the obstacle (oncoming vehicle) is indicated by “P21”. Is indicated by “P22 to P24”.

  In the flowchart of FIG. 11, it is assumed that the route has been set, the conditions necessary for automatic driving are satisfied, and the vehicle is traveling in the automatic driving mode. In order to improve the performance of automatic driving, the GPS is a system that can acquire position information with high accuracy, and the map data 4 is digital data that accurately represents the real environment with high accuracy.

  Since each of step S21-step S26 respond | corresponds to each of step S11-step S16, description is abbreviate | omitted. However, when it is determined that there is no obstacle in step S24, the process proceeds to step S26, and when it is determined that there is an obstacle in step S24, the process proceeds to step S27.

  In step S27, following the determination that there is an obstacle in step S24, both the first target travel route R1 and the second target travel route R2 are generated, and the process proceeds to step S28 (see FIGS. 8C and 9). That is, in step S27, both steps S25 and S26 are performed. Step S27 corresponds to the target travel route generation unit 35. Further, both the first target travel route R1 and the second target travel route R2 generated in step S27 are stored in the memory of the automatic driving recognition determination processor 3.

  In step S28, following the generation of both the first target travel route R1 and the second target travel route R2 in step S27, the future movement of the obstacle is predicted. Step S28 corresponds to a movement prediction unit.

Here, “movement prediction” performs the movement prediction of the obstacle until a predetermined time in the future based on the relative distance and relative speed between the vehicle and the obstacle. As for the relative distance and the relative speed, two-dimensional information is acquired by the recognition sensor 1 as shown in FIG. That is, information on relative distance and relative speed is acquired as (D _x , D _y , V _x , V _y ). Further, the “predetermined time” is a time until at least the own vehicle passes an obstacle. For example, as shown in FIG. 12, when the obstacle is the current position P21, the future movement prediction of the obstacle is performed from positions P22 to P24. In step S28, the relative distance and relative speed between the host vehicle and the obstacle are recorded, and TTC (Time to Collision) and THW (Time HeadWay) are calculated from the relative distance and relative speed information. Relative information with the obstacle is acquired by digitizing the degree of approach of the object.

  In step S29, following the prediction of the future movement of the obstacle in step S28, weights are set for the first target travel route R1 and the second target travel route R2, a target travel route is generated, and the process proceeds to the end. Step S29 corresponds to the target travel route generation unit 35.

  In step S29, specifically, based on the movement prediction in step S28, as shown in FIG. 12, from the time-series movement transition between the own vehicle and the obstacle, Based on the approaching degree of the obstacle, the presence / absence of an obstacle approaching the host vehicle is determined. In FIG. 12, when the vehicle is at the position P12, the obstacle is predicted to move to the position P22. The “degree of approach” is TTC or THW calculated from the relative distance and relative speed between the vehicle and the obstacle in step S28. The presence / absence of an obstacle approaching the host vehicle is determined based on the approach degree and an approach degree threshold value set in advance by a test or the like. When the approach degree is less than or equal to the approach degree threshold value, it is determined that there is an obstacle approaching the host vehicle, and when the approach degree is greater than the approach degree threshold value, it is determined that there is no obstacle approaching the host vehicle. In addition, the determination result of the presence or absence of an obstacle approaching the own vehicle varies depending on changes in relative speed and relative distance.

  Next, a target travel route is generated from the two routes of the first target travel route R1 and the second target travel route R2 stored in the memory in step S27 depending on whether there is an obstacle approaching the host vehicle. Specifically, first, the second target travel route R2 is read out from the two routes of the first target travel route R1 and the second target travel route R2 stored in the memory in step S27. Next, based on the movement prediction in step S28, as shown in FIG. 12, the closest approach point is determined from the time-series movement transition of the vehicle and the obstacle. For example, when the vehicle travels on the second target travel route R2, in FIG. 12, when it is predicted that the vehicle moves to the vehicle position P13 and the obstacle moves to the obstacle position P23 at the same time, The point is the closest point. Then, each route of the first target travel route R1 and the second target travel route R2 is weighted from the degree of approach of this closest approach point.

  Here, the sum of the weights of the first target travel route R1 and the second target travel route R2 is “1”. A weighted average obtained from the weights corresponding to the two route information stored in the memory is used as a target travel route for finally calculating the turning target value. For example, when it is determined that there is an obstacle approaching the host vehicle, the weight of the first target travel route R1 or the second target travel route R2 on the side away from the obstacle is increased. On the other hand, if it is determined that there is no obstacle approaching the host vehicle, the weight of the second target travel route R2 increases. In this way, since the presence or absence of an obstacle approaching the host vehicle is determined based on the future movement prediction between the host vehicle and the obstacle, an appropriate target driving route (predicted target driving route) based on the future is determined. Settings can be made. That is, based on the movement prediction, an appropriate target travel route can be set using as an index how close the travel locus of the host vehicle and the obstacle are.

  Further, the movement prediction is performed sequentially, and the weighting based on the prediction of the degree of approach to each other is performed sequentially. For example, movement prediction is performed for each control period, and weighting is set. For this reason, when the approach degree of the movement prediction is compared with the approach degree (actual approach degree) between the actual vehicle and the obstacle, and the actual approach degree is close to the approach degree of the movement prediction, the weight change is performed. Occur. That is, if the prediction is not met, a weight change occurs. That is, this is the timing for switching the target travel route.

  As described above, when the movement prediction is deviated, the target travel route is continuously switched from the target travel route calculated with the current weight to the target travel route calculated with the new weight. That is, the target travel route is continuously switched from the target travel route calculated with the weight so far to the route away from the obstacle. This continuous switching is performed by continuously changing the weights of the first target travel route R1 and the second target travel route R2. A route that can be continuously switched to a route away from the obstacle is also referred to as a “switching target travel route”.

Next, the operation will be described.
The operation of the second embodiment is similar to that of the first embodiment, and shows “characteristic operation of travel control of an automatically driven vehicle”. Further, the operation of the second embodiment will be described by dividing it into “a travel control processing operation of an automatically driven vehicle” and “a characteristic operation of the second embodiment”.

[Operation control processing operation of autonomous driving vehicle]
Hereinafter, the processing operation of the travel control of the autonomous driving vehicle will be described with reference to FIGS. 11 and 13 in the flowchart of FIG. In FIG. 13, the position of the own vehicle is indicated by “P11, P12”, and the position of the obstacle (oncoming vehicle) is indicated by “P21, P22, P22 ′”.

  However, in FIG. 11, the flow from step S21 to step S22 is the same as the flow from step S11 to step S12 in the first embodiment, and thus the description thereof is omitted. Further, since the flow from step S22 to step S25 to the end is the same as the flow from step S12 to step S15 to the end of the first embodiment, description thereof is omitted. Further, the flow of going from step S22 → step S23 → step S25 → end is the same as the flow going from step S12 → step S13 → step S15 → end of the first embodiment, so the description is omitted. Furthermore, since the flow of going from step S22 → step S23 → step S24 → step S26 → end is the same as the flow going to step S12 → step S13 → step S14 → step S16 → end of the first embodiment, the explanation will be given. Omitted.

  Then, there are two inflection points IP and no oncoming lane landmark, but when there is an obstacle on the opposite side in front of the host vehicle, the process proceeds from step S22 to step S23 to step S24. This is the same as the flow from step S12 to step S13 to step S14 in the first embodiment, and a description thereof will be omitted.

  In step S24, it is determined that there is an obstacle, and the process proceeds from step S27 → step S28 → step S29 → end. At this time, in step S27, both the first target travel route R1 and the second target travel route R2 are generated. In step S28, the movement prediction of the obstacle up to a predetermined time in the future is performed based on the relative distance and relative speed between the vehicle and the obstacle. Furthermore, in step S29, the presence or absence of an obstacle approaching the host vehicle is determined based on the degree of approach between the host vehicle and the obstacle accompanying the movement prediction. Next, based on the presence or absence of an obstacle approaching the host vehicle, a predicted target travel route is generated by setting the weights of the first target travel route R1 and the second target travel route R2. For example, based on the movement prediction, the first target travel route R1 and the second target travel route R2 are weighted based on the presence or absence of an obstacle approaching the host vehicle and the approach degree of the closest approach point. Then, the target travel route on which the host vehicle travels is set as the predicted target travel route calculated by weight.

  On the other hand, as shown in FIG. 13, when the movement prediction is lost, a weight change occurs. For example, as shown in FIG. 13, when the vehicle is at the position P11, an obstacle is detected at the position P21. In the movement prediction, based on the relative distance and relative speed at that time, it is predicted that the obstacle will reach the positions P21 to P22 when the vehicle moves from the positions P11 to P12. However, in practice, when the own vehicle moves to the position P12, if the obstacle increases in speed and reaches the position P21 ′ from the position P21, a weight change occurs. In this case, the target travel route is continuously switched from the target travel route calculated with the weight so far to the route away from the obstacle. This “route on the side away from the obstacle” is, for example, the first target travel route R1 in FIG.

  As described above, when traveling on a route having a plurality of inflection points in a short section, excessive vehicle behavior and the approach of the host vehicle and the three-dimensional object are suppressed as in the first embodiment. In addition, when there is a landmark that defines the oncoming lane, the first target travel route R1 along the road boundary is generated, so that it is possible to travel according to traffic rules without the vehicle protruding from the landmark. To do. In addition, when it is determined that there is an obstacle, it is possible to predict the future movement of the obstacle and determine whether there is an obstacle approaching the host vehicle. A target travel route is generated. Furthermore, by performing sequential movement prediction and weighting based on this prediction, even if the movement prediction is deviated, the target travel route is continuously switched to a route on the side away from the obstacle. For this reason, even when the movement prediction is deviated, the vehicle is appropriately controlled according to the situation.

  After the generation of the first target travel route R1 in step S25, after the generation of the second target travel route R2 in step S26, or after the generation of the target travel route in step S29, the process proceeds to the end. . Since the subsequent steps are the same as those in the first embodiment, description thereof is omitted.

[Characteristic Action of Example 2]
In Example 2, when it is determined that there is an obstacle, future movement prediction of the obstacle is performed based on the relative distance and relative speed between the vehicle and the obstacle. And the presence or absence of the obstruction which approaches the own vehicle is determined based on the approach degree of the own vehicle and an obstruction accompanying movement prediction. That is, a target travel route is generated using as an index how close the travel locus of the host vehicle and the obstacle are based on the degree of approach between the host vehicle and the obstacle accompanying the movement prediction. Accordingly, an appropriate target travel route is generated in consideration of situations that may occur in the future.

  In the second embodiment, when the actual approach degree is close to the approach degree of the movement prediction, the target travel route is continuously switched to the first target travel route R1 on the side away from the obstacle. That is, when the movement prediction is deviated and the vehicle approaches the obstacle more than predicted, the target travel route is continuously switched to a route on the side away from the obstacle. Therefore, even when the movement prediction is lost, the vehicle is appropriately controlled according to the situation.

Next, the effect will be described.
In the traveling control method and the traveling control device for the autonomous driving vehicle according to the second embodiment, the effects described in the first embodiment (1) to (3) can be obtained. Moreover, in the traveling control method for an autonomous driving vehicle according to the second embodiment, the following effects (4) to (5) can be obtained.

(4) When it is determined that there is an obstacle (oncoming vehicle A2), future movement prediction of the obstacle (oncoming vehicle A2) based on the relative distance and relative speed between the own vehicle A1 and the obstacle (oncoming vehicle A2) I do.
The presence / absence of an obstacle (an oncoming vehicle A2) approaching the own vehicle A1 is determined based on the degree of approach between the own vehicle A1 and the obstacle (oncoming vehicle A2) associated with the movement prediction.
For this reason, it is possible to generate an appropriate target travel route in consideration of situations that may occur in the future.

(5) When the approach degree of the actual vehicle and the obstacle is close to the approach degree of the movement prediction, the target travel path is continuously connected to the first target travel path or the second target travel path on the side away from the obstacle. Switch.
For this reason, in addition to the effect of (4), the vehicle can be appropriately controlled according to the situation even when the movement prediction is lost.

  The third embodiment is an example in which a future travel prediction of an obstacle is performed and a target travel route is generated. Further, in the case where the movement prediction is deviated, the target travel route in which the setting of the future position of the own vehicle and the future posture of the own vehicle is corrected is regenerated.

  First, the configuration will be described. The travel control method and travel control apparatus according to the third embodiment is based on a hybrid vehicle (an example of an electric vehicle) that is driven by a motor, and is an automatic driving vehicle (an example of a driving support vehicle) that can externally control steering / driving / braking. ). Hereinafter, the configuration of the third embodiment will be described by dividing it into “a detailed configuration of a recognition determination processor for automatic driving” and “a travel control processing configuration of an automatic driving vehicle”. The “automatic operation system configuration” of the third embodiment is the same as that of the first embodiment, and thus the description thereof is omitted.

[Detailed configuration of recognition judgment processor for automatic driving]
The automatic driving recognition determination processor 3 includes a movement prediction unit and a path following determination unit in addition to the detailed configuration of the automatic driving recognition determination processor 3 of the first embodiment. Note that the movement prediction unit is the same as that of the second embodiment, and thus the description thereof is omitted.

  The route tracking determination unit inputs a predicted target travel route and a switching target travel route. The route follow-up determination unit determines whether or not it is difficult for the vehicle to follow the predicted target travel route and the switching target travel route. This route follow-up determination unit outputs a follow-up difficulty signal or a followable signal to the target travel route generation unit 35.

  The target travel route generation unit 35 generates a predicted target travel route and a switching target travel route. Next, when the followable signal is input, the target travel route generation unit 35 outputs the generated target travel route to the target profile generation unit 36. On the other hand, the target travel route generation unit 35 regenerates a target travel route that can be followed by the host vehicle when a follow-up difficulty signal is input. That is, the third target travel route R3 is generated (see FIG. 15). Then, the target travel route generation unit 35 outputs the generated third target travel route R3 to the target profile generation unit 36. Since the configuration of the other target travel route generation unit 35 is the same as that of the second embodiment, the description thereof is omitted.

  Since other configurations are the same as those of the first embodiment, illustration and description thereof are omitted.

[Running control processing configuration of autonomous driving vehicle]
FIG. 14 shows the flow of a traveling control process for an autonomous driving vehicle that is executed by the autonomous driving recognition determination processor of the third embodiment. Hereinafter, each step of the flowchart shown in FIG. 14 showing the traveling control processing configuration of the autonomous driving vehicle will be described with reference to FIGS. 14 and 15. Note that the traveling control process for the autonomous vehicle shown in FIG. 14 is repeatedly executed at a predetermined control cycle. In FIG. 15, the position of the vehicle is indicated by “P11, P12, P14, P14 ′”, and the position of the obstacle is indicated by “P21, P22, P22 ′”.

  Except for step S31 and step S32, each of step S21 to step S29 is the same as that of the second embodiment, and therefore, the corresponding step is denoted by the same reference numeral and description thereof is omitted. However, after step S29, the process proceeds to step S31.

  In step S31, following the generation of the target travel route in step S29, it is determined whether it is difficult for the vehicle to follow the target travel route. If YES (difficult to follow), the process proceeds to step S32. If NO (followable), the process proceeds to the end. Step S31 corresponds to a path follow-up determination unit.

  Here, first, the target route in step S31 is the target travel route generated when the prediction is out of the target travel routes generated in step S29. That is, it is a switching target travel route. For this reason, when a predicted target travel route is generated in step S29, it is determined in step S31 that follow-up is possible. Next, in the case of “it is difficult for the vehicle to follow the target travel route” in step S31, the switching target travel route generated in step S29 is a route that exceeds the hardware performance of the own vehicle. This is the case. This “exceeding the hardware performance of the own vehicle” may be a case where the target traveling route is less than the minimum turning radius of the own vehicle or a case where the limit of the steering speed that the electric power steering 6 can output is exceeded. .

  Hereinafter, the case where it is determined in step S31 that tracking is difficult will be described. For example, as described in the second embodiment, when the actual approach degree is close to the approach degree of the movement prediction due to the sudden acceleration of the obstacle, the weight change occurs (see FIGS. 13 and 15). Specifically, as shown in FIG. 15, when the host vehicle is at position P11, an obstacle is detected at position P21. In the movement prediction, based on the relative distance and relative speed at that time, it is predicted that the obstacle will reach the positions P21 to P22 when the vehicle moves from the positions P11 to P12. However, in actuality, when the vehicle moved to the position P12, the obstacle increased in speed and reached from the position P21 to P22 ′. This case is a case where the prediction is lost, and a weight change occurs. In this way, when the prediction is lost, it may be difficult to follow the route of the host vehicle by switching from the target travel route generated with the current weight to the target travel route generated with the new weight. In such a case, it is determined in step S31 that it is difficult to follow.

  In step S32, following the determination that tracking is difficult in step S31, the target travel route is regenerated starting from the current position P12 of the host vehicle, and the process proceeds to the end. That is, a third target travel route R3 that can be followed by the host vehicle is generated. Step S32 corresponds to the target travel route generation unit 35.

  At this time, the new host vehicle future position Pn and the new host vehicle future posture P14 ′, which are the end points of the regenerated third target travel route R3, are shown in FIG. The future position Po and the old vehicle future attitude P14 need not be the same. That is, the setting of the old vehicle future position Po and the old vehicle future posture P14 is corrected to the setting of the new vehicle future position Pn and the new vehicle future posture P14 ′. The new vehicle future position Pn and the new vehicle future posture P14 ′ are set within the range of the road boundary as shown in FIG. Then, the third target travel route R3 is regenerated by smoothly connecting the start point P12 (the current position of the host vehicle) and the end point (the new host vehicle future position Pn and the new host vehicle future posture P14 ').

Next, the operation will be described.
The operation of the third embodiment is similar to that of the first embodiment, and shows “characteristic operation of travel control of an autonomous driving vehicle”. Further, the action of the third embodiment shows the “characteristic action of the second embodiment” as in the second embodiment. Further, the operation of the third embodiment will be described by dividing it into “a travel control processing operation of an autonomous driving vehicle” and “a characteristic operation of the third embodiment”.

[Operation control processing operation of autonomous driving vehicle]
Hereinafter, based on the flowchart of FIG. 14, the processing operation of the traveling control of the autonomous driving vehicle will be described. However, the flow from Step S21 to Step S25 via one or more steps and the flow from Step S21 to Step S26 via three steps are the same as in Example 2. Description is omitted. Further, the flow from step S21 to step S29 via five steps is the same as that in the second embodiment, and thus the description thereof is omitted.

  In step S29, a switching target travel route is generated. Next, when it is difficult for the vehicle to follow the switching target travel route, the process proceeds from step S31 to step S32 to end. In step S32, as shown in FIG. 15, a third target travel route R3 that can be followed by the host vehicle is generated. That is, the target travel route is regenerated.

  Further, when the vehicle can follow the switching target travel route generated in step S29, the process proceeds from step S31 to end. At this time, it is not the third target travel route R3 in step S32 but the switching target travel route or the predicted target travel route generated in step S29.

  As described above, when traveling on a route having a plurality of inflection points in a short section, excessive vehicle behavior and the approach of the host vehicle and the three-dimensional object are suppressed as in the first embodiment. In addition, when there is a landmark that defines the oncoming lane, the first target travel route along the track boundary is generated, so that the vehicle can travel while following the traffic rules without protruding from the landmark. . Similarly to the second embodiment, when it is determined that there is an obstacle, an appropriate target travel route is generated in consideration of a situation that may occur in the future. Furthermore, by performing sequential movement prediction and weighting based on this prediction, even if the movement prediction is deviated, the target travel route is continuously switched to a route on the side away from the obstacle. In addition, when it is difficult for the vehicle to follow the switching target travel route in step S29, the third target travel route R3 is generated, so that trajectory tracking that is unreasonable for the vehicle is suppressed. Even when the movement prediction is deviated, the vehicle is appropriately controlled according to the situation.

  After the generation of the first target travel route R1 in step S25, after the generation of the second target travel route R2 in step S26, or after the generation of the target travel route in step S29, the process proceeds to the end. . Further, after the generation of the third target travel route R3 in step S32, the process proceeds to the end. Since the subsequent steps are the same as those in the first embodiment, description thereof is omitted.

[Characteristic Action of Example 3]
In the third embodiment, when the approach degree of the actual vehicle and the obstacle is close to the approach degree of the movement prediction, the target travel route in which the settings of the old car future position Po and the old car future posture P14 are corrected is set. Regenerated. That is, when the prediction is not true, the third target travel route R3 is generated. Therefore, even when the movement prediction is lost, the vehicle is appropriately controlled according to the situation.

  In addition, in the third embodiment, it is determined whether or not it is difficult for the vehicle to follow the switching target travel route. When it is determined that it is difficult to follow, the target travel route in which the settings of the old vehicle future position Po and the old vehicle future posture P14 are corrected is regenerated. Accordingly, when it is determined that it is difficult for the host vehicle to follow, trajectory tracking that is impossible for the host vehicle is suppressed.

Next, the effect will be described.
In the traveling control method and the traveling control device for the autonomous driving vehicle according to the third embodiment, the effects described in the first embodiment (1) to (3) and the second embodiment (4) to (5) can be obtained. . Further, in the traveling control method for an automatically driven vehicle according to the third embodiment, the following effect (6) can be obtained.

(6) Based on the route, set the future position of the vehicle that the vehicle A1 will pass in the future (old vehicle future position Po) and the future posture of the vehicle when the vehicle A1 passes (old vehicle future position P14). .
A target travel route that smoothly connects the current position and current posture of the host vehicle, the host vehicle future position (old host vehicle future position Po), and the host vehicle future posture (old host vehicle future posture P14) is generated.
When the actual vehicle A1 and the obstacle (the oncoming vehicle A2) are close to the degree of approach of the movement prediction, the vehicle future position (old vehicle future position Po) and the vehicle future attitude (old vehicle) The target travel route (third target travel route R3) in which the setting of the future posture P14) is corrected is regenerated.
For this reason, even when the movement prediction is deviated, the vehicle can be appropriately controlled according to the situation.

  The fourth embodiment is an example in which a future travel of an obstacle is predicted and a target travel route is generated. In addition, in the case where the movement prediction is lost, the own vehicle is temporarily stopped.

  First, the configuration will be described. The travel control method and travel control apparatus according to the fourth embodiment is based on a motor-driven hybrid vehicle (an example of an electric vehicle), and is an automatic driving vehicle (an example of a driving support vehicle) capable of externally controlling steering / driving / braking. ). Hereinafter, the configuration of the fourth embodiment will be described by dividing it into “a detailed configuration of the recognition determination processor for automatic driving” and “a travel control processing configuration of the automatic driving vehicle”. The “automatic driving system configuration” of the fourth embodiment is the same as that of the first embodiment, and thus the description thereof is omitted.

[Detailed configuration of recognition judgment processor for automatic driving]
In addition to the detailed configuration of the automatic driving recognition determination processor 3 of the first embodiment, the automatic driving recognition determination processor 3 includes a movement prediction unit, a path follow-up determination unit, and a target speed setting unit. Note that the movement prediction unit is the same as that of the second embodiment, and thus the description thereof is omitted.

  The route follow-up determination unit outputs a follow-up difficulty signal or a followable signal to the target speed setting unit and the target travel route generation unit 35. Since the configuration of the other path follow-up determination unit is the same as that of the third embodiment, description thereof is omitted.

  The target speed setting unit includes a target travel route, a preset lateral acceleration limit (horizontal G limit) and a yaw rate limit of the own vehicle, an obstacle signal or an obstacle signal, a tracking difficulty signal or a tracking enable signal, Enter. The target speed setting unit sets a target speed when traveling on the target travel route. The target speed is set to a speed that does not exceed the preset maximum lateral G and maximum yaw rate (the lateral acceleration limit and the yaw rate limit of the host vehicle). Further, the target speed setting unit corrects (adjusts) the target speed in order to temporarily stop the own vehicle when the follow-up difficulty signal is input. In addition, the target speed setting unit corrects the target speed for the restart after the temporary stop. The target speed setting unit outputs the set target speed to the target travel route generation unit 35 and the target profile generation unit 36.

  The target travel route generation unit 35 generates a predicted target travel route and a switching target travel route. Next, when the followable signal is input, the target travel route generation unit 35 outputs the generated target travel route to the target speed setting unit and the target profile generation unit 36. On the other hand, when the follow difficult signal is input, the target travel route generation unit 35 sets the target travel route as the predicted target travel route. The target travel route generation unit 35 outputs the predicted target travel route to the target speed setting unit and the target profile generation unit 36. Since the configuration of the other target travel route generation unit 35 is the same as that of the second embodiment, the description thereof is omitted.

  Since other configurations are the same as those of the first embodiment, illustration and description thereof are omitted.

[Running control processing configuration of autonomous driving vehicle]
FIG. 16 shows the flow of a traveling control process for an autonomous driving vehicle that is executed by the autonomous driving recognition determination processor of the fourth embodiment. Hereinafter, each step of the flowchart shown in FIG. 16 showing the traveling control processing configuration of the autonomous driving vehicle will be described with reference to FIGS. 16 and 17. In addition, the traveling control process of the autonomous driving vehicle shown in FIG. 16 is repeatedly executed at a predetermined control cycle. In FIG. 17, the own vehicle is indicated by “A1”, and the oncoming vehicle (oncoming vehicle, obstacle) is indicated by “A2”.

  Except for step S41 and step S42, each of step S21 to step S29 is the same as that of the second embodiment, and therefore, the corresponding step is denoted by the same reference numeral and description thereof is omitted. However, steps S25, S26, and S27 are partially different. Further, after step S29, the process proceeds to step S41.

First, step S25 to step S27 will be described. In step S27 of the second embodiment, it has been described that both the processes of step S25 and step S26 are performed. In the second embodiment, it has been described that step S25 and step S26 correspond to step S15 and step S16. Further, in the first embodiment, it has been described in steps S15 and S16 that each node constituting the target travel route is recorded with two-dimensional coordinate information x _i and y _i .

On the other hand, in step S25 and step S26 of the fourth embodiment, each node constituting the target travel route includes, as shown in FIG. 17, curvature information ρ in addition to x _i and y _i that are two-dimensional coordinate information. _i and velocity information ν _i are recorded together. The curvature information ρ _i is information calculated from the preceding and following node information, and the velocity information ν _i is information when passing through each node. Each information is recorded in a recording unit (not shown) until the vehicle reaches the end of the route.

Here, the speed information ν _i is determined based on the curvature information ρ _i so that the generated lateral G and yaw rate do not become excessive. For example, the maximum lateral G and the maximum yaw rate are set in advance as Gy _max and γ _max , respectively, and the following is calculated using Expression (1) for the node with the largest curvature among the nodes constituting the generation path.
... (1)

The target speed for each node is calculated in such a manner that it is stored in this speed information ν _i and is continuously connected within a range of Gx _max that is a preset maximum vertical G (maximum longitudinal G). Here, “the lateral acceleration limit and the yaw rate limit of the own vehicle” are set in advance by experiments or the like for each vehicle type. The same applies to the maximum vertical G. Since each of other step S25-step S27 is the same as that of step S25-step S27 of Example 2, description is abbreviate | omitted.

  Next, in step S41, following the generation of the target travel route in step S29, it is determined whether it is difficult for the vehicle to follow the target travel route. If YES (following difficulty), the process proceeds to step S42, and if NO (following is possible), the process proceeds to the end. Step S41 corresponds to a path follow-up determination unit. The rest is the same as step S31 of the third embodiment, and a description thereof will be omitted.

  In step S42, following the determination that it is difficult to follow in step S41, the host vehicle is temporarily stopped and the process proceeds to the end. That is, the target speed of the own vehicle is corrected. Step S42 corresponds to the target speed setting unit and the target travel route generation unit 35.

  Here, the “temporary stop” is performed by the drive / regenerative motor 7 and the hydraulic brake 8. That is, first, the target speed corrected by the target speed setting unit is input to the automatic operation controller 5 via the target profile generation unit 36. In other words, the target speed profile is corrected based on the corrected target speed. Next, a control command value is output from the controller 5 for automatic operation to the drive / regenerative motor 7 and the hydraulic brake 8. Next, according to the control command value, the drive / regenerative motor 7 performs decelerating traveling by regeneration, or the hydraulic brake 8 activates hydraulic braking to temporarily stop the vehicle. In addition, after the vehicle is temporarily stopped, the obstacle is allowed to pass. Next, the suspension of the host vehicle is released, and the host vehicle is restarted on the predicted target travel route instead of the switching target travel route.

Next, the operation will be described.
The operation of the fourth embodiment shows the “characteristic operation of the traveling control of the autonomous driving vehicle” as in the first embodiment. Further, the action of the fourth embodiment shows the “characteristic action of the second embodiment” as in the second embodiment. Further, the operation of the fourth embodiment will be described by dividing it into “a travel control processing operation of an autonomous driving vehicle” and “a characteristic operation of the fourth embodiment”.

[Operation control processing operation of autonomous driving vehicle]
Hereinafter, the processing operation of the traveling control of the autonomous driving vehicle will be described with reference to FIGS. In FIG. 18, the own vehicle is indicated by “P11 to P13”, and the obstacle is indicated by “P21, P22, P22 ′ to P24 ′”. However, the flow from Step S21 to Step S25 via one or more steps and the flow from Step S21 to Step S26 via three steps are the same as in Example 2. Description is omitted. Further, the flow from step S21 to step S29 via five steps is the same as that in the second embodiment, and thus the description thereof is omitted.

  In step S29, the target travel route is generated by setting the weights of the first target travel route and the second target travel route. Next, when it is difficult for the vehicle to follow the target travel route, the process proceeds from step S41 to step S42 to the end.

  For example, as shown in FIG. 18, in the movement prediction, it is predicted that the obstacle will reach the positions P21 to P22 when the host vehicle moves from the positions P11 to P12 based on the relative distance and the relative speed at that time. did. However, in practice, when the own vehicle moves to the position P12, if the obstacle increases in speed and reaches the position P21 ′ from the position P21, a weight change occurs. At this time, in step S29, the target travel route becomes a target travel route calculated with a new weight from the target travel route calculated with the weight up to now. However, when it is difficult for the host vehicle to follow the target travel route calculated with the new weight, the target travel route is set as the predicted target travel route calculated with the current weight. Then, the vehicle is temporarily stopped at position P13. Next, during the temporary stop of the own vehicle, the target obstacle is passed (position P23 ′). Next, after the obstacle reaches the position P24 ′, it is confirmed that there is no obstacle on the opposite side, and the temporary stop of the host vehicle is released. Subsequently, the vehicle is restarted by returning the speed again, and the vehicle follows the predicted target travel route.

  Further, when the vehicle can follow the switching target travel route generated in step S29, the process proceeds from step S41 to end. At this time, the vehicle is not temporarily stopped in step S42, but becomes the switching target travel route or the predicted target travel route generated in step S29.

  As described above, when traveling on a route having a plurality of inflection points in a short section, excessive vehicle behavior and the approach of the host vehicle and the three-dimensional object are suppressed as in the first embodiment. In addition, when there is a landmark that defines the oncoming lane, the first target travel route along the track boundary is generated, so that the vehicle can travel while following the traffic rules without protruding from the landmark. . Similarly to the second embodiment, when it is determined that there is an obstacle, an appropriate target travel route is generated in consideration of a situation that may occur in the future. Furthermore, by performing sequential movement prediction and weighting based on this prediction, even if the movement prediction is deviated, the target travel route is continuously switched to a route on the side away from the obstacle. In addition, when it is difficult for the host vehicle to follow the switching target travel route in step S29, the host vehicle is temporarily stopped. Even when the movement prediction is deviated, the vehicle is appropriately controlled according to the situation.

  Note that after the generation of the first target travel route in step S25, the generation of the second target travel route in step S26, the generation of the target travel route in step S29, or the predicted target in step S42. After making the travel route, proceed to the end. Since the subsequent steps are the same as those in the first embodiment, description thereof is omitted.

[Characteristic Action of Example 4]
In the fourth embodiment, the target speed is set to a speed that does not exceed the preset maximum lateral G and maximum yaw rate according to the curvature of the target travel route. That is, the target speed during the turn for following the target travel route is set to a speed at which the maximum lateral G and the maximum yaw rate do not exceed predetermined values. Accordingly, excessive vehicle behavior can be suppressed during traveling.

  In the fourth embodiment, when the actual vehicle and the obstacle are close to the movement predicted approach, the vehicle is temporarily stopped. In other words, if the prediction is wrong, the host vehicle is temporarily stopped. Therefore, even when the movement prediction is lost, the vehicle is appropriately controlled according to the situation.

  In addition, in Example 4, it is determined whether it is difficult for the vehicle to follow the switching target travel route. When it is determined that it is difficult to follow, the host vehicle is temporarily stopped. Accordingly, when it is determined that it is difficult for the host vehicle to follow, trajectory tracking that is impossible for the host vehicle is suppressed.

Next, the effect will be described.
In the traveling control method and the traveling control device for the autonomous driving vehicle according to the fourth embodiment, the effects described in the first embodiment (1) to (3) and the second embodiment (4) to (5) can be obtained. . Further, in the traveling control method for an autonomous driving vehicle according to the fourth embodiment, the following effects (7) to (8) can be obtained.

(7) Generate a target speed when traveling on the target travel route.
The target speed is set to a speed that does not exceed the preset lateral acceleration limit and yaw rate limit (maximum lateral G and maximum yaw rate) of the vehicle according to the curvature of the target travel route.
For this reason, excessive vehicle behavior can be suppressed during traveling.

(8) When the approach degree of the actual vehicle A1 and the obstacle (the oncoming vehicle A2) is close to the approach degree of the movement prediction, the host vehicle A1 is temporarily stopped.
For this reason, in addition to the effect of (7), the vehicle can be appropriately controlled according to the situation even when the movement prediction is lost.

  As described above, the traveling control method and the traveling control device for the autonomous driving vehicle according to the present disclosure have been described based on the first to fourth embodiments. However, the specific configuration is not limited to the first to fourth embodiments, and design changes and additions are allowed without departing from the gist of the invention according to each claim of the claims. Is done.

  In Example 1 to Example 4, the second target travel route R2 is longer than the first target travel route R1 in that the relative distances D1 ′ and D2 ′ are longer than one inflection point IP1 (D1 <D1 ′). ), An example in which the other inflection point IP2 is shorter (D2> D2 ′) is shown. However, it is not limited to this. For example, in the case of right-hand traffic, such as in the United States, the relative distance between each inflection point and the second target travel route is greater than that of the first target travel route R1. The inflection point is short, and the inflection point on the near side of Examples 1 to 4 is long. That is, the relative distance between each inflection point and the second target travel route is different in the case of the left-hand traffic as in Japan and the right-hand traffic as in the United States than in the case of generating the first target travel route R1. . Even if comprised in this way, the effect described in Example 1- Example 4 is acquired.

  In Examples 1 to 4, the relative distances D1 ′ and D2 ′ between the inflection points IP1 and IP2 and the second target travel route R2 in the case where it is determined that there is no obstacle and there are two inflection points are exemplified. . However, it is not limited to this. For example, when it is determined that there are no obstacles and there are three inflection points, the relative distance between each inflection point and the second target travel route is greater than that in the case where the first target travel route R1 is generated. In the path traveled by, the inflection point on the near side is long, the inflection point in the middle between the near side and the farthest side is short, and the path that is longer than the inflection point on the farthest side is generated . That is, a second target travel route that alternately changes the length of the inflection point is generated. Even if comprised in this way, the effect described in Example 1- Example 4 is acquired.

  In Examples 1 to 4, an example in which the “obstacle” is an obstacle that moves in a direction approaching the host vehicle is shown. However, it is not limited to this. For example, an obstacle that does not move may be used. In short, any obstacle on the opposite side in front of the host vehicle may be used. In the case of an obstacle that does not move, the vehicle is temporarily decelerated in the fourth embodiment. Even if comprised in this way, the effect described in Example 1- Example 4 is acquired.

  In the first to fourth embodiments, the electric power steering 6 is a steering actuator, the drive / regenerative motor 7 is a drive source actuator, and the hydraulic brake 8 is a brake actuator. However, it is not limited to this. That is, each control system can be replaced with other than the above means (each actuator) as long as the steering / driving / braking control can be performed on the driving wheel (tire) based on the external command. For example, in the case where an in-wheel motor or the like has a means capable of braking / driving each wheel independently, a method of assisting the turning operation with a braking / driving force difference in addition to the control of the electric power steering 6 is conceivable.

  In the second to fourth embodiments, the example in which the information on the relative distance and the relative speed is acquired as the two-dimensional information by the recognition sensor 1 has been described. However, it is not limited to this. For example, information on relative distance and relative speed may be acquired by the recognition sensor 1 as one-dimensional information. Even when acquiring with one-dimensional information, it is possible to predict the movement of the obstacle until a predetermined time in the future based on the relative distance and relative speed between the vehicle and the obstacle.

  In the second to fourth embodiments, the example in which the target travel route is continuously switched to the route on the side away from the obstacle is shown. However, it is not limited to this. For example, the target travel route may be switched stepwise to a route on the side away from the obstacle. Even if comprised in this way, the effect described in Example 2-Example 4 is acquired. Further, the route on the side away from the obstacle is not limited to the first target travel route, and becomes the first target travel route or the second target travel route depending on the position of the host vehicle.

  In the third embodiment, an example in which the third target travel route R3 is generated when the prediction is lost, a switching target travel route is generated, and the vehicle is determined to be difficult to follow the route is shown. However, it is not limited to this. For example, when the prediction is wrong, the third target travel route R3 may be generated without generating the switching target travel route. Even if comprised in this way, the effect described in Example 3 is acquired.

  In the fourth embodiment, an example is shown in which a prediction target is lost, a switching target travel route is generated, and the vehicle is temporarily stopped when it is determined that the vehicle is difficult to follow the route. However, it is not limited to this. For example, when the prediction is wrong, the host vehicle may be temporarily decelerated or temporarily stopped without generating the switching target travel route. Even if comprised in this way, the effect described in Example 4 is acquired.

  In Example 4, the example which pauses the own vehicle was shown. However, it is not limited to this. For example, the host vehicle may be temporarily decelerated. Further, the host vehicle may be temporarily decelerated or temporarily stopped according to the situation. Even if comprised in this way, the effect described in Example 4 is acquired.

  In the first to fourth embodiments, the traveling control method and the traveling control device of the present disclosure are based on a hybrid vehicle (an example of an electric vehicle) that is motor-driven, and can automatically control steering / driving / braking. The example applied to a driving vehicle was shown. However, the traveling control method and the traveling control device of the present disclosure may be a driving support vehicle that supports a part of driving among steering driving / driving driving / braking driving by a driver. In short, the present invention can be applied to any vehicle that supports driving by a driver by displaying a travel route. Moreover, the traveling control method and the traveling control device of the present disclosure are not limited to hybrid vehicles, and can be applied to electric vehicles and engine vehicles.

1 Recognition sensor (obstacle detection unit)
2 GPS / navigation system (route setting unit)
3 Recognition judgment processor (controller) for automatic driving
31 Road boundary interpolation unit 32 Left / right turning point detection unit 33 Oncoming lane landmark determination unit 34 Obstacle determination unit 35 Target travel route generation unit 36 Target profile generation unit 4 Map data 4 (road shape acquisition unit)
5. Controller 5 for automatic driving (vehicle control unit)
A1 Own vehicle (own vehicle)
A2 Obstacle (oncoming vehicle, oncoming vehicle)
D1, D2, D1 ′, D2 ′ Relative distances IP, IP1, IP2 Inflection point R1 First target travel route R2 Second target travel route

Claims (9)

In a driving control method for a driving assistance vehicle including a controller that performs control for turning the vehicle based on a target driving route,
Set the route to the destination,
Get road information around the route,
Detect obstacles around your vehicle,
From the road information, the inflection point of the curvature existing between a predetermined distance is detected with respect to the road boundary constituting the road ahead of the host vehicle,
Determine the presence or absence of the obstacle on the opposite side in front of the vehicle,
When there are no more than two inflection points, or when it is determined that there is an obstacle, a first target travel route is generated based on the route,
When it is determined that there are two or more inflection points and there is no obstacle, the relative distance of each inflection point is set as one inflection point, compared to the case of generating the first target travel route. And a second target travel route that is shorter than the other inflection point is generated. In the driving control method of the driving assistance vehicle according to claim 1,
The section length of the predetermined distance is variable in accordance with the host vehicle traveling speed, and the section length is set shorter as the host vehicle traveling speed is lower. In the driving control method of the driving assistance vehicle according to claim 1 or 2,
Refer to landmarks that define the driving lanes that exist in the left-right direction of the route as the road boundary in the road information around the route,
A driving control method for a driving assistance vehicle, characterized in that, with respect to a place without the landmark, interpolation is performed using a landmark on the opposite side that defines the road boundary or a landmark before and after the landmark is interrupted. In the driving control method of the driving assistance vehicle according to any one of claims 1 to 3,
When it is determined that there is an obstacle, a future movement of the obstacle is predicted based on a relative distance and a relative speed between the vehicle and the obstacle,
A driving control method for a driving assistance vehicle, wherein the presence or absence of the obstacle approaching the host vehicle is determined based on a degree of approach between the host vehicle and the obstacle accompanying the movement prediction. In the driving control method of the driving assistance vehicle according to claim 4,
When the approach degree of the actual vehicle and the obstacle is close to the approach degree of the movement prediction, the first target travel path or the second target travel on the side away from the obstacle on the target travel path A driving control method for a driving assistance vehicle, wherein the driving assistance vehicle is switched to a route continuously or stepwise. In the driving control method of the driving assistance vehicle according to claim 4 or claim 5,
Based on the route, set the future position of the vehicle where the vehicle will pass in the future and the future posture of the vehicle when the vehicle passes,
Generating a target travel route that smoothly connects the current position and current posture of the host vehicle to the future position of the host vehicle and the future posture of the host vehicle;
When the degree of approach between the actual vehicle and the obstacle is close to the degree of approach in the movement prediction, the target travel route in which the settings of the future position of the own vehicle and the future posture of the own vehicle are corrected is regenerated. A driving control method for a driving support vehicle. In the driving control method of the driving assistance vehicle according to any one of claims 4 to 6,
Generate a target speed when traveling on the target travel route,
The target speed is set to a speed that does not exceed at least one of a lateral acceleration limit and a yaw rate limit set in advance according to the curvature of the target travel route. Method. In the driving control method of the driving assistance vehicle according to claim 7,
A driving control method for a driving assistance vehicle, characterized in that the host vehicle is temporarily decelerated or temporarily stopped when the approaching degree of the actual vehicle and the obstacle is close to the approaching degree of the movement prediction. In a driving control device for a driving assistance vehicle including a controller that performs control for turning the vehicle based on a target driving route,
The controller is
A route setting section for setting a route to the destination;
A road shape acquisition unit for acquiring road information around the route;
An obstacle detector for detecting obstacles around the vehicle;
From the road information, a left and right turning point detection unit that detects an inflection point of curvature existing between a predetermined distance with respect to a road boundary constituting a road ahead of the host vehicle,
An obstacle determination unit for determining the presence or absence of the obstacle on the opposite side in front of the vehicle;
When there are no more than two inflection points, or when it is determined that there is an obstacle, a first target travel route is generated based on the route,
When it is determined that there are two or more inflection points and there is no obstacle, the relative distance of each inflection point is set as one inflection point, compared to the case of generating the first target travel route. A target travel route generating unit that generates a second target travel route that is short and is long with the other inflection point;
A driving control device for a driving assistance vehicle, comprising: