Driving control method and driving control device

Abstract

To prevent automatic travel of an own vehicle along an inappropriate target travel trajectory from giving an occupant a feeling of insecurity when generating the target travel trajectory on the basis of a travel trajectory learned or stored during manual operation.SOLUTION: In a travel control method, a first target travel trajectory is generated on the basis of a travel trajectory when an own vehicle 1 is operated through manual operation by a driver of the own vehicle 1 (S5); positions of traffic lane boundary lines of a traffic lane on which the own vehicle 1 travels are acquired (S1); a second target travel trajectory which passes a predetermined position in a traffic lane width direction in the traffic lane is generated on the basis of the acquired positions of the traffic lane boundary lines (S5); a third target travel trajectory is set at a position between the first target travel trajectory and the second target travel trajectory in the traffic lane width direction (S9); and the own vehicle 1 is controlled to travel along the third target travel trajectory (S10).SELECTED DRAWING: Figure 6

Description

Technical Field

[0005] , ,

[0001] The present invention relates to a driving control method and a driving control device.

Background Art

[0002] Patent Document 1 proposes a vehicle driving control device that stores the driving trajectory of a host vehicle when the host vehicle is manually driven and uses it as the target driving trajectory of the host vehicle when performing automatic driving.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] When generating a target driving trajectory based on a driving trajectory learned or memorized during manual driving, an appropriate target driving trajectory may not be generated in a situation that is significantly different from the situation during learning or memorization. As a result, there is a risk of causing anxiety to the passengers by automatically driving the host vehicle along an inappropriate target driving trajectory. An object of the present invention is to suppress the sense of anxiety given to passengers by automatically driving the host vehicle along an inappropriate target driving trajectory when generating a target driving trajectory based on a driving trajectory learned or memorized during manual driving.

Means for Solving the Problems

[0005] <00000In a driving control method according to one aspect of the present invention, a first target driving trajectory is generated based on the driving trajectory when the vehicle is driven by the driver of the vehicle, the position of the lane boundary line of the lane in which the vehicle is traveling is obtained, a second target driving trajectory is generated that passes through a predetermined position in the lane width direction within the lane based on the obtained position of the lane boundary line, a third target driving trajectory is set at a position between the first target driving trajectory and the second target driving trajectory in the lane width direction, and the vehicle is driven along the third target driving trajectory. [Effects of the Invention]

[0006] According to the present invention, when generating a target driving trajectory based on a driving trajectory learned or memorized during manual driving, it is possible to suppress the feeling of anxiety caused to the occupants by having the vehicle automatically drive along an inappropriate target driving trajectory. [Brief explanation of the drawing]

[0007] [Figure 1] This is a schematic diagram of an example of a driving control device according to an embodiment. [Figure 2] (a) is a schematic diagram illustrating an example of a driving control method of the embodiment, and (b) is a schematic diagram of the first and second directions of travel, and an explanatory diagram of an example of a method for setting the third direction of travel. [Figure 3] This is a block diagram showing an example of the controller's functional configuration. [Figure 4] (a) and (b) are explanatory diagrams illustrating the first and second examples of methods for calculating the risk of deviating from the first lane. [Figure 5] This is an explanatory diagram illustrating an example of the process when the first evaluation position is set outside the driving lane. [Figure 6] This is a flowchart of an example of a driving control method according to the embodiment. [Modes for carrying out the invention]

[0008] Embodiments of the present invention will be described below with reference to the drawings. Note that the drawings are schematic and may differ from actual ones. Furthermore, the embodiments of the present invention described below are illustrative examples of devices and methods for realizing the technical concept of the present invention, and the technical concept of the present invention is not limited to the structure, arrangement, etc., of the components described below. The technical concept of the present invention can be modified in various ways within the technical scope defined by the claims described in the patent claims.

[0009] (composition) Figure 1 is a schematic diagram of an example of a driving control device according to an embodiment. The vehicle 1 is equipped with a driving control device 10. The driving control device 10 sets a target driving trajectory for the vehicle 1 based on the driving environment around the vehicle 1 and controls at least the steering device of the vehicle 1 so that the vehicle 1 automatically drives along the target driving trajectory. For example, the driving control by the driving control device 10 may be autonomous driving control that causes the vehicle 1 to autonomously drive to a destination set based on the driving environment around the vehicle 1. Alternatively, the driving control by the driving control device 10 may be lane keeping control that controls the steering of the vehicle 1 to prevent it from deviating from the driving lane, or obstacle avoidance control that controls the steering of the vehicle 1 to avoid obstacles in the path ahead of the vehicle 1. The following explanation describes an example where the driving control by the driving control device 10 is autonomous driving control that causes the vehicle 1 to drive autonomously to a set destination.

[0010] The driving control device 10 includes an object sensor 11, a vehicle sensor 12, a positioning device 13, a map database (map DB) 14, a communication device 15, a navigation device 16, an actuator 17, and a controller 18. The object sensor 11 comprises multiple different types of object detection sensors that detect objects around the vehicle 1, such as laser radar, millimeter-wave radar, cameras, and LIDAR (Light Detection and Ranging, Laser Imaging Detection and Ranging) mounted on the vehicle 1.

[0011] The vehicle sensor 12 is mounted on the vehicle 1 and detects various information (vehicle signals) obtained from the vehicle 1. The vehicle sensor 12 includes, for example, a vehicle speed sensor that detects the vehicle speed of the vehicle 1, a wheel speed sensor that detects the rotational speed of the tires of the vehicle 1, a three-axis acceleration sensor that detects the acceleration and deceleration of the vehicle 1 in three axes, a steering angle sensor that detects the steering angle of the steering wheel, a steering angle sensor that detects the turning angle of the steering wheels, a gyro sensor that detects the angular velocity of the vehicle 1, a yaw rate sensor that detects the yaw rate, an accelerator sensor that detects the accelerator opening of the vehicle, and a brake sensor that detects the amount of brake operation by the driver.

[0012] The positioning device 13 is equipped with a Global Navigation Satellite System (GNSS) receiver and measures the current position of the vehicle 1 by receiving radio waves from multiple navigation satellites. The GNSS receiver may be, for example, a GPS receiver. The positioning device 13 may also be, for example, an inertial navigation device. Map database 14 stores road map data. Map database 14 may also store navigation map data (hereinafter sometimes referred to as "navigation map data") as road map data. Navigation map data includes information on individual roads. This road-level information includes, for example, information on road nodes indicating reference points on road baselines, and information on road links indicating the road section configurations between road nodes. The road node information includes location coordinates, the number of connected road links, and identification information for the connected road links.

[0013] The map database 14 may store high-precision map data as road map data. High-precision map data is suitable as map information for autonomous driving and includes lane-level information that is more detailed than road-level information. As information on a lane-by-lane basis, for example, high-precision map data includes information on lane nodes that indicate reference points on lane baselines (e.g., the center line within a lane) and information on lane links that indicate the lane section configuration between lane nodes. The information of the lane node includes the position coordinates of the lane node, the number of connected lane links, and the identification information of the connected lane links. The information of the lane link includes the position coordinates of the lane link, the type of the lane, the width of the lane, the type of the lane boundary line, the shape of the lane, the shape of the lane dividing line, and the shape of the lane reference line.

[0014] The communication device 15 performs wireless communication with a communication device outside the host vehicle 1. The communication method by the communication device 15 may be, for example, wireless communication via a public mobile phone network, vehicle-to-vehicle communication, road-to-vehicle communication, or satellite communication. The navigation device 16 recognizes the current position of the host vehicle by the positioning device 13 and acquires the navigation map data at the current position from the map database 14. The navigation device 16 sets a target driving route to the destination input by the occupant and guides the occupant along the target driving route. In addition, the navigation device 16 outputs the information of the set target driving route to the controller 18. During autonomous driving control, the controller 18 automatically drives the host vehicle 1 to travel along the target driving route set by the navigation device 16.

[0015] The actuator 17 operates the steering device, the accelerator opening, and the brake device of the host vehicle 1 according to a control signal from the controller 18 to generate the vehicle behavior of the host vehicle 1. The actuator 17 includes a steering actuator, an accelerator opening actuator, and a brake control actuator. The steering actuator controls the steering direction and the steering amount of the steering device of the host vehicle 1. The accelerator opening actuator controls the accelerator opening of the host vehicle 1. The brake control actuator controls the braking operation of the brake device of the host vehicle.

[0016] The controller 18 is an electronic control unit that performs the driving control of the host vehicle 1. The controller 18 includes a processor 18a and peripheral components such as a storage device 18b. The processor 18a may be, for example, a CPU or an MPU. The memory device 18b may include a semiconductor memory device, a magnetic memory device, an optical memory device, etc. The memory device 18b may include memories such as registers, cache memories, ROM and RAM used as main memory devices. The functions of the controller 18 described below are realized, for example, when the processor 18a executes a computer program stored in the memory device 18b. Note that the controller 18 may be formed of dedicated hardware for executing each information processing described below. For example, the controller 18 may include a functional logic circuit set in a general-purpose semiconductor integrated circuit. For example, the controller 18 may have a PLD such as an FPGA.

[0017] Next, an example of the travel control by the controller 18 will be described. The controller 18 calculates a target travel trajectory for the host vehicle 1 to travel based on the current position and traveling direction of the host vehicle 1, the target travel route by the navigation device 16, the map information, the surrounding environment of the host vehicle 1 detected by the object sensor 11, and the vehicle information detected by the vehicle sensor 12. The controller 18 drives the actuator 17 so that the host vehicle 1 travels along the generated target travel trajectory.

[0018] The controller 18 has a function of learning or storing the travel trajectory of the host vehicle when the host vehicle 1 travels in manual driving. For example, the controller 18 acquires data on the relative position of the lane boundary line with respect to the host vehicle 1 in the vehicle width direction when the host vehicle 1 travels in manual driving. In the following description, the relative position of the lane boundary line with respect to the host vehicle 1 in the vehicle width direction may be simply referred to as the "relative position of the lane boundary line".

[0019] The relative position of the lane boundary line may be recognized, for example, based on an image of the area in front of the vehicle 1 taken by the camera of the object sensor 11, or it may be detected based on the distance measurement results of a laser radar, millimeter-wave radar, LIDAR, etc. The position and orientation of the vehicle 1 on the map coordinate system may be obtained based on the positioning device 13 or map matching, and the relative position of the lane boundary line may be obtained from high-precision map data.

[0020] The controller 18 creates the driving trajectory of its own vehicle 1 from the history of acquired data on the relative position of lane boundary lines. The controller 18 learns or stores the driving trajectory of the vehicle 1 by associating the relative position of the lane boundary line with the vehicle speed of the vehicle 1 detected by the vehicle sensor 12. For example, the controller 18 may store the driving trajectory of the vehicle 1 in the memory device 18b by associating the relative position of the lane boundary line with the vehicle speed of the vehicle 1 detected by the vehicle sensor 12. Alternatively, the controller 18 may use the relative position of the lane boundary line, the vehicle speed, and the generated driving trajectory when the vehicle 1 is driven manually as training data (supervised data) and learn using a convolutional neural network (CNN) or other machine learning algorithms.

[0021] When generating a target driving trajectory in autonomous driving control, the controller 18 obtains the relative position of the current lane boundary line from the object sensor 11 or high-precision map data, and also obtains the current vehicle speed of the vehicle 1 from the vehicle sensor 12. The controller 18 predicts the vehicle's trajectory by obtaining the driving trajectory learned or memorized during past manual driving from the storage device 18b or a CNN, etc., based on the current relative position of the lane boundary and the current vehicle speed. In the following description, the driving trajectory predicted based on the driving trajectory learned or memorized during past manual driving may be referred to as the AI ​​(Artificial Intelligence) trajectory.

[0022] Figure 2(a) is a schematic diagram illustrating an example of a driving control method according to an embodiment. The area LT between solid lines Lb1 and Lb2 represents the driving lane in which the vehicle 1 is traveling, and solid lines Lb1 and Lb2 represent the left and right lane boundary lines of the driving lane LT, respectively. The dashed line Lc represents the lane center of the driving lane LT in the lane width direction. In the following description, lane boundary lines Lb1 and Lb2 may be collectively referred to as "lane boundary line Lb".

[0023] The controller 18 predicts the AI ​​trajectory Tai by obtaining the driving trajectory learned or stored during past manual driving from the storage device 18b or a CNN, etc., based on the current relative position of the lane boundary line Lb and the vehicle speed of its own vehicle 1. When predicting an AI trajectory (Tai) based on previously learned or memorized driving trajectories, if the current driving conditions of vehicle 1 differ significantly from past conditions for which the driving trajectory was learned or memorized, the AI ​​trajectory (Tai) may not be an appropriate target driving trajectory. As a result, when vehicle 1 is driven along the AI ​​trajectory Tai, the risk of vehicle 1 deviating from the driving lane LT (hereinafter sometimes referred to as "lane departure risk") increases, which may cause anxiety among the occupants.

[0024] The risk of lane departure may be, for example, the distance in the lane width direction from vehicle 1 to the lane boundary line Lb. The shorter the distance in the lane width direction from vehicle 1 to the lane boundary line Lb, the higher the risk of lane departure. The lane departure risk may be, for example, the margin of error for lane departure relative to the lane boundary line Lb. The margin of error for lane departure is a value obtained, for example, by dividing the distance between vehicle 1 and the lane boundary line Lb in the direction of travel of vehicle 1 by the speed of vehicle 1. The shorter the margin of error for lane departure, the higher the risk of lane departure.

[0025] On the other hand, even if the system generates a target driving trajectory so that vehicle 1 travels along the lane center Lc of the driving lane LT, it may still cause discomfort to the occupants of vehicle 1. This is because when a person is manually driving a vehicle, they do not always drive on the lane center. For example, in driving scenes such as curves, the driver may drive outside the lane center Lc at the corner entrance and exit, and inside the lane center Lc while passing through the corner, following what is known as an "out-in-out" driving line.

[0026] The controller 18 then generates a first target driving trajectory T1 based on the AI ​​trajectory Tai, and a second target driving trajectory T2 that passes through a predetermined position in the lane width direction (e.g., the lane center Lc) within the driving lane LT. The steering system of the vehicle 1 is then controlled to drive along a third target trajectory T3, which is set to be located between the first target trajectory T1 and the second target trajectory T2 in the lane width direction.

[0027] This makes it possible to lower the lane departure risk when driving vehicle 1 along the third target driving trajectory T3 compared to the lane departure risk when driving vehicle 1 along the first target driving trajectory T1 based on the AI ​​trajectory Tai. Furthermore, by driving the vehicle 1 along the third target trajectory T3, the discomfort experienced by the occupants can be reduced compared to when the vehicle 1 is driven along the second target trajectory T2, which passes through a predetermined position in the lane width direction (for example, the lane center Lc).

[0028] Next, the functions of the controller 18 will be explained in detail. Figure 3 is a block diagram of an example of the controller's functional configuration. The controller 18 includes a lateral position acquisition unit 30, an AI trajectory generation unit 31, an evaluation section length setting unit 32, a first target trajectory generation unit 33, a second target trajectory generation unit 34, a risk calculation unit 35, a third target trajectory generation unit 36, and a vehicle control unit 37.

[0029] The lateral position acquisition unit 30 acquires information on the relative position of the current lane boundary line Lb from the object sensor 11 or high-precision map data. The AI ​​trajectory generation unit 31 acquires information on the current vehicle speed of the vehicle 1 from the vehicle sensor 12. It also acquires information on the relative position of the current lane boundary line Lb from the object sensor 11 or high-precision map data. Based on the current vehicle speed of the vehicle 1 and the relative position of the current lane boundary line Lb, the AI ​​trajectory generation unit 31 generates an AI trajectory Tai.

[0030] The lateral position acquisition unit 30 and the AI ​​trajectory generation unit 31 may select whether to acquire information on the relative position of the lane boundary line Lb from the object sensor 11 or high-precision map data, based on the detectable distance Ld at which the vehicle sensor 12 (e.g., camera) can detect the lane boundary line Lb. For example, if the detectable distance Ld is shorter than the forward gaze distance Lg = V × Tg determined by the vehicle speed V of the vehicle 1, information on the relative position of the lane boundary line Lb may be acquired from high-precision map data, and if the detectable distance Ld is greater than or equal to the forward gaze distance Lg, information on the relative position of the lane boundary line Lb may be acquired from the object sensor 11. Time Tg is a predetermined constant and may be set to, for example, 2 to 3 seconds.

[0031] The evaluation section length setting unit 32 sets the length of the evaluation section (hereinafter referred to as "evaluation section length Le") in front of the vehicle 1 for evaluating the target driving trajectory of the vehicle 1. For example, when acquiring information on the relative position of the lane boundary line Lb from the object sensor 11, the shortest distance among the forward gaze distance Lg, the detectable distance Ld, or the distance to the position Pm of maximum curvature of the driving lane LT within a predetermined distance in front of the vehicle 1 may be set as the evaluation section length Le. For example, when obtaining information on the relative position of lane boundary lines Lb from high-precision map data, the longer of the distance to the forward gaze point Lg or the distance to the maximum curvature point Pm may be set as the evaluation section length Le.

[0032] The first target trajectory generation unit 33 and the second target trajectory generation unit 34 each generate a first target travel trajectory T1 based on the AI ​​trajectory Tai and a second target travel trajectory T2 that passes through a predetermined position in the lane width direction within the travel lane LT. Refer to Figure 2(A). The first target trajectory generation unit 33 sets a position Pe1 as the first evaluation position, which is ahead of the vehicle 1 by an evaluation section length Le and on the AI ​​trajectory Tai. The first target trajectory generation unit 33 generates a first target travel trajectory T1 that passes through the first evaluation position Pe1 from the current position of the vehicle 1. For example, the first target trajectory generation unit 33 may generate the first target travel trajectory T1 using known methods such as forward gaze point control or model prediction control.

[0033] On the other hand, the second target trajectory generation unit 34 sets the second evaluation position Pe2 as a predetermined position in the lane width direction within the driving lane LT, which is forward of the evaluation section length Le from the vehicle 1. In the example in Figure 2(A), the second evaluation position Pe2 is set at the lane center Lc as the predetermined position in the lane width direction. However, the position in which the second evaluation position Pe2 is set is not limited to the lane center Lc, but may be set at any position in the lane width direction within the driving lane LT.

[0034] For example, in a driving scene such as a curve, if the second target driving trajectory T2 is generated so that the vehicle 1 follows the aforementioned "out-in-out" driving line, the second evaluation position Pe2 may be set outside the course relative to the lane center Lc at the corner entrance and exit, and inside the course relative to the lane center Lc while passing through the corner. The second target trajectory generation unit 34 generates a second target travel trajectory T2 that passes through the second evaluation position Pe2 from the current position of the vehicle 1. For example, the second target trajectory generation unit 34 may generate the second target travel trajectory T2 using known methods such as forward gaze point control or model prediction control.

[0035] Refer to Figure 3. The risk calculation unit 35 calculates the first lane departure risk R1, which is the lane departure risk when the vehicle 1 travels along the first target travel trajectory T1, and the second lane departure risk R2, which is the lane departure risk when the vehicle 1 travels along the second target travel trajectory T2. As described above, the lane departure risk may be the distance in the lane width direction from the vehicle 1 to the lane boundary line Lb, or it may be the margin of error for departure relative to the lane boundary line Lb.

[0036] When calculating the lane departure margin relative to the lane boundary line Lb as a lane departure risk, the risk calculation unit 35 predicts, for example, the vehicle speed V1 of the vehicle 1 and the direction of travel dt1 of the vehicle 1 (hereinafter referred to as "first direction of travel dt1") at the time when the vehicle 1 travels along the first target travel trajectory T1 and reaches the first evaluation position Pe1. Furthermore, the system predicts the vehicle speed V2 and the direction of travel dt2 (hereinafter referred to as "second direction of travel dt2") of vehicle 1 at the point when vehicle 1 travels along the second target trajectory T2 and reaches the second evaluation position Pe2.

[0037] Figure 2(b) is a schematic diagram of the first direction of travel dt1 and the second direction of travel dt2. The risk calculation unit 35 may, for example, predict the tangential direction of the first target travel trajectory T1 at the first evaluation position Pe1 as the first direction of travel dt1, and predict the tangential direction of the second target travel trajectory T2 at the second evaluation position Pe2 as the second direction of travel dt2. The risk calculation unit 35 may also obtain the current speed of the vehicle 1 from the vehicle sensor 12 and calculate the current acceleration / deceleration of the vehicle 1 by differentiating the speed with respect to time. Based on the current speed and acceleration / deceleration of the vehicle 1, the risk calculation unit 35 may predict the speed V1 of the vehicle 1 when the vehicle 1 travels along the first target travel trajectory T1 and reaches the first evaluation position Pe1, and the speed V2 of the vehicle 1 when the vehicle 1 travels along the second target travel trajectory T2 and reaches the second evaluation position Pe2.

[0038] Figure 4(a) is an explanatory diagram of a first example of a method for calculating the first lane departure risk R1. The risk calculation unit 35 may calculate the first lane departure risk R1 as the departure margin time (L1 / V1) obtained by dividing the distance L1 between the first evaluation position Pe1 along the first direction of travel dt1 and the lane boundary line Lb by the vehicle speed V1 of the vehicle 1. Similarly, the risk calculation unit 35 may calculate the second lane departure risk R2 as the deviation margin time (L2 / V2) obtained by dividing the distance L2 between the second evaluation position Pe2 and the lane boundary line Lb along the second direction of travel dt2 by the vehicle speed V2 of the vehicle 1.

[0039] Figure 4(b) is an explanatory diagram of a second example of a method for calculating the first lane departure risk R1. The risk calculation unit 35 may set a plurality of evaluation positions P1 to P4 on the first target driving trajectory T1 and predict the vehicle speed V11 to V14 of the vehicle 1 at the time the vehicle 1 travels along the first target driving trajectory T1 and reaches each of the evaluation positions P1 to P4, and the distance L11 to L14 from the evaluation positions P1 to P4 to the lane boundary line Lb in the direction of travel of the vehicle 1. For example, the risk calculation unit 35 may set evaluation positions P1 to P4 at equal intervals on the first target travel trajectory T1. The number of evaluation positions is not limited to four; it may be two, three, or five or more.

[0040] The risk calculation unit 35 may calculate the sum of the deviation margin times at each of the evaluation positions P1 to P4 (L11 / V11 + L12 / V12 + L13 / V13 + L14 / V14) as the first lane departure risk R1. Similarly, the risk calculation unit 35 may set multiple evaluation positions on the second target driving trajectory T2 and calculate the sum of the deviation margin times at these evaluation positions as the second lane departure risk R2.

[0041] Refer to Figure 3. The third target trajectory generation unit 36 ​​sets the third target trajectory T3 at a position between the first target trajectory T1 and the second target trajectory T2 in the lane width direction of the travel lane LT. Refer to Figure 2(a). For example, the third target trajectory generation unit 36 ​​may set the target position Pt on a straight line connecting the first evaluation position Pe1 and the second evaluation position Pe. The third target trajectory generation unit 36 ​​may set the travel trajectory from the current position of the vehicle 1 to the target position Pt as the third target travel trajectory T3. For example, the third target trajectory generation unit 36 ​​may generate the third target travel trajectory T3 using known methods such as forward gaze point control or model prediction control.

[0042] For example, the third target trajectory generation unit 36 ​​may set the target position Pt such that the higher the first lane departure risk R1 is compared to the second lane departure risk R2, the closer it gets to the second evaluation position Pe2. For example, the third target trajectory generation unit 36 ​​may set the target position Pt such that the ratio (d1 / d2) between the distance d1 from the first evaluation position Pe1 to the target position Pt and the distance d2 from the second evaluation position Pe2 to the target position Pt matches the ratio (R2 / R1) between the first lane departure risk R1 and the second lane departure risk R2.

[0043] Refer to Figures 2(b) and 2(c). For example, the third target trajectory generation unit 36 ​​may calculate the direction between the first direction of travel dt1 and the second direction of travel dt2 as the target direction of travel dt3. The third target trajectory generation unit 36 ​​may set the third target travel trajectory T3 such that the direction of travel of the vehicle 1 at the target position Pt is the target direction of travel dt3. For example, the third target trajectory generation unit 36 ​​may set the target direction of travel dt3 such that the higher the first lane departure risk R1 is compared to the second lane departure risk R2, the closer it gets to the second direction of travel dt2. For example, the third target trajectory generation unit 36 ​​may set the target direction of travel dt3 such that the ratio (dif1 / dif2) between the difference (dif1) between the first direction of travel dt1 and the target direction of travel dt3 and the difference (dif2) between the second direction of travel dt2 and the target direction of travel dt3 matches the ratio (R2 / R1) between the first lane departure risk R1 and the second lane departure risk R2.

[0044] Furthermore, the third target trajectory generation unit 36 ​​may determine whether the first lane departure risk R1 and the second lane departure risk R2 are each less than a predetermined value. For example, when calculating the deviation margin time relative to the lane boundary line Lb as the first lane departure risk R1 and the second lane departure risk R2, it may be determined that the first lane departure risk R1 and the second lane departure risk R2 are less than a predetermined value if the deviation margin time is greater than or equal to a predetermined threshold. For example, it may be determined that the first lane departure risk R1 and the second lane departure risk R2 are less than a predetermined value if the deviation margin time is infinite.

[0045] The third target trajectory generation unit 36 ​​may set the first target driving trajectory T1 as the third target driving trajectory T3 regardless of the second target driving trajectory R2 if the first lane departure risk R1 is less than a predetermined value. If the risk of deviating from the first lane R1 is not below a predetermined value, and the risk of deviating from the second lane R2 is also below a predetermined value, the second target driving trajectory T2 may be set as the third target driving trajectory T3.

[0046] Furthermore, for example, if the first evaluation position Pe1 is set outside the driving lane LT, even if the third target driving trajectory T3 is set to pass through the target position Pt set between the first evaluation position Pe1 and the second evaluation position Pe2, there is a risk that the lane departure risk of the vehicle 1 traveling along the third target driving trajectory T3 will increase. Therefore, the third target trajectory generation unit 36 ​​may modify the target position Pt so that it gradually moves towards the second evaluation position Pe2 over time along a straight line connecting the initially calculated target position Pt and the second evaluation position Pe2, and set the modified target position Pt to the second evaluation position Pe2 or to a position closer to the second evaluation position Pe2 than to the first evaluation position Pe1 on the straight line connecting the first evaluation position Pe1 and the second evaluation position Pe2. The third target trajectory generation unit 36 ​​may also set the third target trajectory T3 so that it passes through the modified target position Pt.

[0047] Figure 5 is an explanatory diagram illustrating an example of the process when the first evaluation position Pe1 is set outside the driving lane LT. The triangular plots Pe1(t1), Pe2(t1), and Pt(t1) in Figure 5 show the first evaluation position, second evaluation position, and target position set at the first time t1. Now, let's assume that at the current time t2, which is in a control cycle later than the first time t1, the first evaluation position Pe1(t2) is set outside the driving lane LT.

[0048] In this case, the third target trajectory generation unit 36 ​​modifies the target position Pt so that it gradually moves towards the second evaluation position Pe2(t1) as time progresses, along the straight line connecting the target position Pt(t1) at the first time t1 and the second evaluation position Pe2(t1), and sets the modified target position Pt to the second evaluation position Pe2 or to a position closer to the second evaluation position Pe2 than to the first evaluation position Pe1 on the straight line connecting the first evaluation position Pe1 and the second evaluation position Pe2. The third target trajectory generation unit 36 ​​sets the third target trajectory T3 so that it passes through the modified target position Pt. For example, the third target trajectory generation unit 36 ​​may modify the target position Pt to move from the target position Pt(t1) to an intermediate point Pi between the target position Pt(t1) and the second evaluation position Pe2(t1) within a predetermined time (for example, 2 seconds).

[0049] Refer to Figure 3. The vehicle control unit 37 controls the vehicle 1 to travel along the third target travel trajectory T3 set by the third target trajectory generation unit 36. For example, by driving the actuator 17, at least the steering device of the vehicle 1 is controlled to travel along the third target travel trajectory T3.

[0050] (operation) Figure 6 is a flowchart of an example of a driving control method according to the embodiment. In step S1, the lateral position acquisition unit 30 and the AI ​​trajectory generation unit 31 acquire information on the relative position of the current lane boundary line Lb. The AI ​​trajectory generation unit 31 acquires information on the current vehicle speed of the vehicle 1. In step S2, the AI ​​trajectory generation unit 31 generates an AI trajectory Tai based on the current vehicle speed of the vehicle 1 and the relative position of the current lane boundary line Lb.

[0051] In step S3, the evaluation interval length setting unit 32 sets the evaluation interval length Le. In step S4, the first target trajectory generation unit 33 sets a position on the AI ​​trajectory Tai that is forward of the vehicle 1 by an evaluation section length Le as the first evaluation position Pe1. The second target trajectory generation unit 34 sets a position forward of the vehicle 1 by an evaluation section length Le as the second evaluation position Pe2, which is a predetermined position in the lane width direction within the driving lane LT. In step S5, the first target trajectory generation unit 33 and the second target trajectory generation unit 34 generate the first target travel trajectory T1 and the second target travel trajectory T2, respectively.

[0052] In step S6, the risk calculation unit 35 predicts the first direction of travel dt1 and the second direction of travel dt2. In step S7, the risk calculation unit 35 calculates the first lane departure risk R1 and the second lane departure risk R2. In step S8, the third target trajectory generation unit 36 ​​sets the target position Pt and the target direction of travel dt3.

[0053] In step S9, the third target trajectory generation unit 36 ​​sets the third target travel trajectory T3. In step S10, the vehicle control unit 37 controls the vehicle 1 to travel along the third target travel trajectory T3 set by the third target trajectory generation unit 36. The process will then be completed.

[0054] (Effects of the embodiment) (1) The controller 18 generates a first target driving trajectory based on the driving path when the vehicle 1 is driven by the driver of the vehicle 1, obtains the position of the lane boundary line of the lane in which the vehicle 1 is traveling, generates a second target driving trajectory that passes through a predetermined position in the lane width direction within the lane based on the obtained position of the lane boundary line, sets a third target driving trajectory at a position between the first target driving trajectory and the second target driving trajectory in the lane width direction, and drives the vehicle 1 along the third target driving trajectory.

[0055] This makes it possible to reduce the risk of lane departure when driving vehicle 1 along the third target driving trajectory compared to the risk of lane departure when driving vehicle 1 along the first target driving trajectory. As a result, the feeling of anxiety experienced by the occupants can be reduced. Furthermore, it reduces the discomfort experienced by the occupants compared to when driving vehicle 1 along the second target driving trajectory that passes through a predetermined position in the lane width direction (e.g., the lane center Lc).

[0056] (2) The controller 18 may set a target position in front of its own vehicle 1 and on a straight line connecting the first evaluation position and the second evaluation position, which are points on the first target travel trajectory and the second target travel trajectory, and set the trajectory from the current position of the own vehicle 1 to the target position as the third target travel trajectory. This allows a third target driving trajectory to be set at a position between the first target driving trajectory and the second target driving trajectory in the lane width direction.

[0057] (3) The controller 18 calculates the risk of the vehicle 1 deviating from its lane when traveling along the first target travel trajectory as the first risk, and the risk of the vehicle 1 deviating from its lane when traveling along the second target travel trajectory as the second risk. The higher the first risk is compared to the second risk, the controller 18 may set the target position to be closer to the second evaluation position. For example, the controller 18 may calculate a first risk based on the predicted speed and direction of travel of its own vehicle 1 at one or more positions on the first target travel trajectory, and calculate a second risk based on the predicted speed and direction of travel of its own vehicle 1 at one or more positions on the second target travel trajectory. This allows for the setting of a third target driving trajectory with a lower lane departure risk, even if the risk of lane departure is high when the vehicle 1 is driven along the first target driving trajectory.

[0058] (4) The controller 18 may set the target position such that the ratio of the distance from the first evaluation position to the target position to the distance from the second evaluation position to the target position matches the ratio of the first risk to the second risk. This allows the target position to be set such that even if the first evaluation position is close to the lane boundary line and therefore the first risk is high, the lane departure risk of the third target driving trajectory is reduced.

[0059] (5) The controller 18 may predict the direction of travel of the vehicle 1 when it travels along the first target travel trajectory and reaches the first evaluation position as the first direction of travel, predict the direction of travel of the vehicle 1 when it travels along the second target travel trajectory and reaches the second evaluation position as the second direction of travel, calculate the direction between the first direction of travel and the second direction of travel as the target direction of travel, and set the third target travel trajectory so that the direction of travel of the vehicle 1 at the target position is the target direction of travel.

[0060] In this case, the controller 18 calculates the risk of the vehicle 1 deviating from its lane when traveling along the first target travel trajectory as the first risk, and the risk of the vehicle 1 deviating from its lane when traveling along the second target travel trajectory as the second risk. The higher the first risk is compared to the second risk, the controller 18 may set the target travel direction to be closer to the second travel direction. For example, the controller 18 may set the target direction of travel such that the ratio of the difference between the first direction of travel and the target direction of travel and the ratio of the difference between the second direction of travel and the target direction of travel match the ratio between the first risk and the second risk. This allows the third target driving trajectory to be set in such a way that the risk of lane departure is reduced, even if the first direction of travel of the vehicle 1, which is traveling along the first target driving trajectory, is directed outwards from the lane, thus increasing the first risk.

[0061] (6) When the first evaluation position is set outside the lane, the controller 18 may gradually change the target position in the direction of the second evaluation position along the straight line connecting the target position and the second evaluation position. This reduces the risk of lane departure when the vehicle 1 is driven along the third target driving trajectory, even if the first evaluation position is set outside the lane. [Explanation of symbols]

[0062] 1...Self-vehicle, 10...Driving control device, 11...Object sensor, 12...Vehicle sensor, 13...Positioning device, 14...Map database, 15...Communication device, 16...Navigation device, 17...Actuator, 18...Controller, 18a...Processor, 18b...Storage device, 30...Lateral position acquisition unit, 31...AI trajectory generation unit, 32...Evaluation section length setting unit, 33...First target trajectory generation unit, 34...Second target trajectory generation unit, 35...Risk calculation unit, 36...Third target trajectory generation unit, 37...Vehicle control unit

Claims

1. A first target driving trajectory is generated based on the driving trajectory when the vehicle is driven by the driver of the vehicle. The position of the lane boundary line of the lane in which the vehicle is traveling is obtained, Based on the acquired position of the lane boundary line, a second target driving trajectory is generated that passes through a predetermined position in the lane width direction within the lane. The target position is set in front of the vehicle and on a straight line connecting the first evaluation position and the second evaluation position, which are points on the first target travel trajectory and the second target travel trajectory, respectively. A third target driving trajectory is set as the driving trajectory from the current position of the vehicle to the target position at a position between the first target driving trajectory and the second target driving trajectory in the lane width direction. The vehicle is driven along the third target trajectory. The risk of the vehicle deviating from the lane when traveling along the first target trajectory is calculated as the first risk. The risk of the vehicle deviating from the lane when traveling along the second target trajectory is calculated as the second risk. The higher the first risk is compared to the second risk, the closer the target position is to the second evaluation position. A driving control method characterized by the following:

2. Based on the predicted results of the vehicle speed and direction of travel of the vehicle at one or more positions on the first target travel trajectory, the first risk is calculated. The second risk is calculated based on the predicted results of the vehicle speed and direction of travel of the vehicle at one or more positions on the second target travel trajectory. The driving control method according to feature 1.

3. The driving control method according to claim 1, characterized in that the target position is set such that the ratio of the distance from the first evaluation position to the target position and the distance from the second evaluation position to the target position matches the ratio of the first risk and the second risk.

4. A first target driving trajectory is generated based on the driving trajectory when the vehicle is driven by the driver of the vehicle, The position of the lane boundary line of the lane in which the vehicle is traveling is obtained, Based on the acquired position of the lane boundary line, a second target driving trajectory is generated that passes through a predetermined position in the lane width direction within the lane. The target position is set in front of the vehicle and on a straight line connecting the first evaluation position and the second evaluation position, which are points on the first target travel trajectory and the second target travel trajectory, respectively. A third target driving trajectory is set as the driving trajectory from the current position of the vehicle to the target position at a position between the first target driving trajectory and the second target driving trajectory in the lane width direction. The vehicle is driven along the third target trajectory. The direction of travel of the vehicle when it travels along the first target travel trajectory and reaches the first evaluation position is predicted as the first direction of travel. The direction of travel of the vehicle when it travels along the second target travel trajectory and reaches the second evaluation position is predicted as the second direction of travel. The direction between the first direction of travel and the second direction of travel is calculated as the target direction of travel. The third target travel trajectory is set such that the direction of travel of the vehicle at the target position is the target direction of travel. The risk of the vehicle deviating from the lane when traveling along the first target trajectory is calculated as the first risk. The risk of the vehicle deviating from the lane when traveling along the second target trajectory is calculated as the second risk. The higher the first risk is compared to the second risk, the closer the direction is to the second direction of travel. Set the target direction of travel as described above. A driving control method characterized by the following:

5. The driving control method according to claim 4, characterized in that the target direction of travel is set such that the ratio of the difference between the first direction of travel and the target direction of travel and the ratio of the difference between the second direction of travel and the target direction of travel are equal to the ratio between the first risk and the second risk.

6. The driving control method according to any one of claims 1 to 3, characterized in that when the first evaluation position is set outside the lane, the target position is set to the second evaluation position or to a position closer to the second evaluation position than to the first evaluation position on the straight line connecting the first evaluation position and the second evaluation position.

7. The system includes a controller that performs the following processes: generating a first target driving trajectory based on the driving trajectory when the vehicle is driven by the driver of the vehicle; acquiring the position of the lane boundary line of the lane in which the vehicle is traveling; generating a second target driving trajectory that passes through a predetermined position in the lane width direction within the lane based on the acquired position of the lane boundary line; setting a target position in front of the vehicle and on a straight line connecting a first evaluation position and a second evaluation position, which are points on the first target driving trajectory and the second target driving trajectory; setting a third target driving trajectory as a driving trajectory from the current position of the vehicle to the target position at a position between the first target driving trajectory and the second target driving trajectory in the lane width direction; and driving the vehicle along the third target driving trajectory. The aforementioned controller, The risk of the vehicle deviating from the lane when traveling along the first target trajectory is calculated as the first risk. The risk of the vehicle deviating from the lane when traveling along the second target trajectory is calculated as the second risk. The higher the first risk is compared to the second risk, the closer the target position is to the second evaluation position. A driving control device characterized by the following:

8. A controller that performs the following processes: generating a first target driving trajectory based on the driving trajectory when the vehicle is driven by the driver of the vehicle; acquiring the position of the lane boundary line of the lane in which the vehicle is traveling; generating a second target driving trajectory that passes through a predetermined position in the lane width direction within the lane based on the acquired position of the lane boundary line; setting a target position in front of the vehicle and on a straight line connecting a first evaluation position and a second evaluation position which are points on the first target driving trajectory and the second target driving trajectory; setting a third target driving trajectory as a driving trajectory from the current position of the vehicle to the target position at a position between the first target driving trajectory and the second target driving trajectory in the lane width direction; and driving the vehicle along the third target driving trajectory. The aforementioned controller, The direction of travel of the vehicle when it travels along the first target travel trajectory and reaches the first evaluation position is predicted as the first direction of travel. The direction of travel of the vehicle when it travels along the second target travel trajectory and reaches the second evaluation position is predicted as the second direction of travel. The direction between the first direction of travel and the second direction of travel is calculated as the target direction of travel. The third target travel trajectory is set such that the direction of travel of the vehicle at the target position is the target direction of travel. The risk of the vehicle deviating from the lane when traveling along the first target trajectory is calculated as the first risk. The risk of the vehicle deviating from the lane when traveling along the second target trajectory is calculated as the second risk. The higher the first risk is compared to the second risk, the more the target direction of travel is set to be closer to the second direction of travel. A driving control device characterized by the following:

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