Mobile control device, mobile control method, and program

Abstract

To suitably utilize, for travel of a mobile object, information obtained by converting an image captured by a camera into a bird's-eye view coordinate system.SOLUTION: A mobile object control device comprises: a target trajectory generation unit which generates a target trajectory showing a path on which a mobile object travels in future on the basis of division line information expressed on a space where an image showing the peripheral situation of the mobile object imaged by a camera mounted on the mobile object is converted into a bird's-eye view coordinate system; a curvature estimation unit which calculates the curvature of the target trajectory; a curvature correction unit which corrects the curvature by applying a correction filter for correcting the curvature to the estimated curvature; a speed plan generation unit which generates a speed plan indicating a future target speed of the mobile object on the basis of the corrected curvature; and a travel control unit which causes the mobile object to travel according to the speed plan.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to a movement control device, a movement control method, and a program.

Background Art

[0002] Conventionally, a technique is known in which an image representing the surrounding situation of a vehicle captured by an in-vehicle camera is converted into a bird's-eye view coordinate system, and the information in the bird's-eye view coordinate system is utilized to assist the driving of the vehicle. For example, Patent Document 1 discloses a technique of displaying a bird's-eye view image of a road created from an image of an in-vehicle camera on a display.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] The technique described in Patent Document 1 generates a bird's-eye view image of the vicinity of the vehicle and displays it on a display. However, when an image captured by a camera is converted into a bird's-eye view coordinate system, the accuracy of the information in the bird's-eye view coordinate system may decrease as the distance from the camera increases. As a result, the information in the bird's-eye view coordinate system may not be suitably utilized for the running of the vehicle.

[0005] The present invention has been made in consideration of such circumstances, and one of its objects is to provide a movement control device, a movement control method, and a program that can suitably utilize the information obtained by converting an image captured by a camera into a bird's-eye view coordinate system for the running of a moving body.

Means for Solving the Problems

[0006] The mobile body control device, mobile body control method, and program according to the present invention adopt the following configurations. (1): The mobile body control device according to one aspect of the present invention includes a target trajectory generation unit that generates a target trajectory indicating a route along which the mobile body will travel in the future based on section line information represented in a space obtained by converting an image representing the surrounding situation of the mobile body captured by a camera mounted on the mobile body into a bird's-eye view coordinate system, a curvature estimation unit that calculates the curvature of the target trajectory, a curvature correction unit that corrects the curvature by applying a correction filter for correcting the curvature to the estimated curvature, a speed plan generation unit that generates a speed plan indicating a future target speed of the mobile body based on the corrected curvature, and a travel control unit that causes the mobile body to travel according to the speed plan.

[0007] (2): In the aspect of (1) above, the correction filter sets an upper limit value of the curvature.

[0008] (3): In the aspect of (2) above, the correction filter sets the upper limit value of the curvature such that the upper limit value of the curvature at a point on the target trajectory tends to decrease as the distance between the mobile body and the point on the target trajectory increases.

[0009] (4): In any of the aspects of (1) to (3) above, the generation unit generates the speed plan such that the lateral acceleration of the mobile body becomes a predetermined value on the condition of the corrected curvature.

[0010] (5): In any of the aspects of (1) to (4) above, when the speed of the mobile body is greater than the target speed, the control unit calculates a distance required for deceleration from the speed to the target speed, and decelerates the mobile body so that the mobile body reaches the target speed within the calculated distance range.

[0011] (6) The method for controlling the movement according to another aspect of the present invention is such that a computer generates a target trajectory indicating a route along which the moving body will travel in the future based on the section line information obtained by converting an image representing the surrounding situation of the moving body captured by a camera mounted on the moving body into a bird's-eye view coordinate system, calculates the curvature of the target trajectory, corrects the curvature by applying a correction filter for correcting the curvature to the estimated curvature, generates a speed plan indicating the future target speed of the moving body based on the corrected curvature, and causes the moving body to travel according to the speed plan.

[0012] (7) The program according to another aspect of the present invention causes a computer to generate a target trajectory indicating a route along which the moving body will travel in the future based on the section line information obtained by converting an image representing the surrounding situation of the moving body captured by a camera mounted on the moving body into a bird's-eye view coordinate system, calculate the curvature of the target trajectory, correct the curvature by applying a correction filter for correcting the curvature to the estimated curvature, generate a speed plan indicating the future target speed of the moving body based on the corrected curvature, and cause the moving body to travel according to the speed plan.

Advantages of the Invention

[0013] (1) According to the aspects (1) to (7), the information obtained by converting the image captured by the camera into the bird's-eye view coordinate system can be suitably utilized for the traveling of the moving body.

[0014] (2) According to the aspect (2) or (3), abnormal values of the curvature generated when the image captured by the camera is converted into the bird's-eye view coordinate system can be excluded.

[0015] (4) According to the aspect (4) or (5), the moving body can be caused to travel without giving discomfort to the passengers of the moving body.

Brief Description of the Drawings

[0016]

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Figure 7

Figure 8

Figure 9

Figure 10

Mode for Carrying Out the Invention

[0017] Hereinafter, with reference to the drawings, a movement control device, a movement control method, and a program according to an embodiment of the present invention will be described. The movement control device is a device that controls the movement of a moving body. The moving body includes vehicles such as three-wheeled or four-wheeled vehicles, two-wheeled vehicles, micromobility, etc., and may include, for example, any moving body on which a person (occupant) can ride and that can move on a road surface where lanes exist. In the following description, the moving body is assumed to be a four-wheeled vehicle, and a vehicle equipped with a driving support device is referred to as the host vehicle M.

[0018] [Overall Configuration] FIG. 1 is a configuration diagram of a vehicle system 1 using a movement control device according to an embodiment. The drive source of the vehicle on which the vehicle system 1 is mounted is an internal combustion engine such as a diesel engine or a gasoline engine, an electric motor, or a combination thereof. The electric motor operates using electric power generated by a generator connected to the internal combustion engine, or discharge power of a secondary battery or a fuel cell.

[0019] The vehicle system 1 includes, for example, a camera 10, a radar device 12, an object recognition device 16, a communication device 20, an HMI (Human Machine Interface) 30, a vehicle sensor 40, a navigation device 50, an MPU (Map Positioning Unit) 60, a driving operator 80, an automatic driving control device 100, a traveling driving force output device 200, a brake device 210, and a steering device 220. These devices and apparatuses are connected to each other by a multiplex communication line such as a CAN (Controller Area Network) communication line, a serial communication line, a wireless communication network, or the like. Note that the configuration shown in FIG. 1 is merely an example, and a part of the configuration may be omitted, or another configuration may be added.

[0020] The camera 10 is, for example, a digital camera using a solid-state imaging device such as a CCD (Charge Coupled Device) or a CMOS (Complementary Metal Oxide Semiconductor). The camera 10 is attached to an arbitrary position of a vehicle (hereinafter, the host vehicle M) on which the vehicle system 1 is mounted. When imaging the front, the camera 10 is attached to the upper part of the front windshield, the back surface of the rearview mirror, or the like. The camera 10, for example, periodically and repeatedly images the periphery of the host vehicle M. The camera 10 may be a stereo camera.

[0021] The radar device 12 radiates radio waves such as millimeter waves around the host vehicle M, and detects the radio waves (reflected waves) reflected by an object to detect at least the position (distance and azimuth) of the object. The radar device 12 is attached to an arbitrary position of the host vehicle M. The radar device 12 may detect the position and speed of an object by an FM-CW (Frequency Modulated Continuous Wave) method.

[0022] The object recognition device 16 analyzes an image representing the front situation of the host vehicle M captured by the camera 10 and extracts necessary information. Then, the object recognition device 16 performs sensor fusion processing on the detection results of the camera 10 and the radar device 12 to recognize the position, type, speed, etc. of the object, and outputs the recognition result to the automatic driving control device 100. In the present invention, the radar device 12 may be omitted. In that case, the object recognition device 16 may have only the function of analyzing an image. Further, without performing sensor fusion processing, the detection result of the radar device 12 may be directly output to the automatic driving control device 100.

[0023] Based on the image captured by the camera 10, the object recognition device 16 extracts information on the lane lines (hereinafter referred to as "lane line information") related to the lane in which the host vehicle M travels. FIG. 2 is a diagram showing an example of a method for the object recognition device 16 to extract lane line information. First, the object recognition device 16 inputs an image representing the front situation of the host vehicle M captured by the camera 10, and inputs it to a learned model such as a DNN (deep neural network) that has been trained to output the lane lines included in the image as a point cloud, thereby extracting the point cloud representing the lane lines. The point cloud extracted at this time is a point cloud defined in the camera coordinate system.

[0024] Next, the object recognition device 16 converts the point cloud in the extracted camera coordinate system into a point cloud in the bird's-eye view coordinate system by any method. The point cloud obtained by this conversion is arranged irregularly. Therefore, the object recognition device 16 converts the point cloud arranged irregularly in the bird's-eye view coordinate system into a point cloud P at equal intervals (for example, 1 m intervals) by function fitting using, for example, a quadratic curve or the like. Thereby, the object recognition device 16 acquires the point cloud P at equal intervals representing the partition line in the bird's-eye view coordinate system as partition line information.

[0025] FIG. 3 is a graph for explaining the characteristics of the point cloud P as the partition line information acquired by the object recognition device 16. In the graph of FIG. 3, the horizontal axis indicates the distance from the host vehicle M to each point constituting the point cloud P, and the vertical axis indicates the curvature of the point cloud P. The dotted line in FIG. 3 indicates the curvature of the point cloud P in the bird's-eye view coordinate system, and the solid line in FIG. 3 indicates the curvature of the actual road. As can be seen from FIG. 3, the curvature of the point cloud P in the bird's-eye view coordinate system has a small deviation from the curvature of the actual road when the distance from the host vehicle M is short, while the deviation from the curvature of the actual road tends to increase when the distance from the host vehicle M is long.

[0026] Therefore, generally in automatic driving or assist driving for acceleration and deceleration, when generating a speed plan of the host vehicle M based on the point cloud P converted from the camera coordinate system to the bird's-eye view coordinate system, it is assumed that the host vehicle M is excessively decelerated due to overestimating the curvature of the road more than the actual situation. As a result, the passengers of the host vehicle M may feel discomfort or uneasiness about the excessive deceleration of the host vehicle M. As will be described later, the automatic driving control device 100 according to the present embodiment solves such problems. Note that the image recognition function of the object recognition device 16 may be mounted on the automatic driving control device 100.

[0027] The communication device 20 communicates with other vehicles existing around the host vehicle M using, for example, a cellular network, a Wi-Fi network, Bluetooth (registered trademark), DSRC (Dedicated Short Range Communication), or the like, or communicates with various server devices via a wireless base station.

[0028] The vehicle sensor 40 includes a vehicle speed sensor that detects the speed of the host vehicle M, an acceleration sensor that detects acceleration, a yaw rate sensor that detects the angular velocity around the vertical axis, a direction sensor that detects the direction of the host vehicle M, and the like.

[0029] The navigation device 50 includes, for example, a GNSS (Global Navigation Satellite System) receiver 51, a navigation HMI 52, and a route determination unit 53. The navigation device 50 holds first map information 54 in a storage device such as an HDD (Hard Disk Drive) or a flash memory. The GNSS receiver 51 identifies the position of the host vehicle M based on the signals received from the GNSS satellites. The position of the host vehicle M may be identified or supplemented by an INS (Inertial Navigation System) using the output of the vehicle sensor 40. The navigation HMI 52 includes a display device, a speaker, a touch panel, keys, and the like. The navigation HMI 52 may share part or all of the functions with the aforementioned HMI 30. The route determination unit 53 determines, for example, a route (hereinafter, a map route) from the position of the host vehicle M identified by the GNSS receiver 51 (or an arbitrary input position) to the destination input by the occupant using the navigation HMI 52 with reference to the first map information 54. The first map information 54 is information in which the road shape is represented by, for example, links indicating roads and nodes connected by the links. The first map information 54 may include the curvature of the road, POI (Point Of Interest) information, and the like. The map route is output to the MPU 60. The navigation device 50 may perform route guidance using the navigation HMI 52 based on the map route. The navigation device 50 may be realized, for example, by the functions of a terminal device such as a smartphone or a tablet terminal held by the occupant. The navigation device 50 may transmit the current position and the destination to the navigation server via the communication device 20 and acquire a route equivalent to the map route from the navigation server.

[0030] The MPU 60 includes, for example, a recommended lane determination unit 61, and holds second map information 62 in a storage device such as an HDD or a flash memory. The recommended lane determination unit 61 divides the on-map route provided from the navigation device 50 into a plurality of blocks (for example, divides every 100 [m] in the vehicle traveling direction), and determines a recommended lane for each block with reference to the second map information 62. The recommended lane determination unit 61 makes a determination as to which lane from the left the vehicle should travel in. When there is a branch point on the on-map route, the recommended lane determination unit 61 determines the recommended lane so that the host vehicle M can travel on a reasonable route for proceeding to the branch destination.

[0031] The second map information 62 is map information with higher accuracy than the first map information 54. The second map information 62 includes, for example, information on the center of a lane or information on the boundary of a lane. Further, the second map information 62 may include road information, traffic regulation information, address information (address and postal code), facility information, telephone number information, and the like. The second map information 62 may be updated at any time when the communication device 20 communicates with other devices.

[0032] The driving operator 80 includes, for example, an accelerator pedal, a brake pedal, a shift lever, a steering wheel, a non-standard steering device, a joystick, and other operators. A sensor for detecting an operation amount or the presence or absence of an operation is attached to the driving operator 80, and the detection result is output to some or all of the automatic driving control device 100, the traveling driving force output device 200, the brake device 210, and the steering device 220.

[0033] Prior to the description of the automatic driving control device 100, the traveling driving force output device 200, the braking device 210, and the steering device 220 will be described. The traveling driving force output device 200 outputs the traveling driving force (torque) for the vehicle M to travel to the drive wheels. The traveling driving force output device 200 includes, for example, a combination of an internal combustion engine, an electric motor, and a transmission, etc., and a power ECU (Electronic Control Unit) that controls these. The power ECU controls the above configuration according to the information input from the automatic driving control device 100 or the information input from the operation operator 80.

[0034] The braking device 210 includes, for example, a brake caliper, a cylinder that transmits hydraulic pressure to the brake caliper, an electric motor that generates hydraulic pressure in the cylinder, and a brake ECU. The brake ECU controls the electric motor according to the information input from the automatic driving control device 100 or the information input from the operation operator 80, so that the braking torque corresponding to the braking operation is output to each wheel.

[0035] The steering device 220 includes, for example, a steering ECU and an electric motor. The electric motor acts on, for example, a rack and pinion mechanism to change the direction of the steered wheels. The steering ECU drives the electric motor according to the information input from the automatic driving control device 100 or the information input from the operation operator 80 to change the direction of the steered wheels.

[0036] The automatic driving control device 100 includes, for example, a first control unit 120 and a second control unit 160. The first control unit 120 and the second control unit 160 are each realized, for example, by a hardware processor such as a CPU (Central Processing Unit) executing a program (software). Also, some or all of these components may be realized by hardware (including a circuitry such as LSI (Large Scale Integration), ASIC (Application Specific Integrated Circuit), FPGA (Field-Programmable Gate Array), GPU (Graphics Processing Unit)), or may be realized by the cooperation of software and hardware. The program may be stored in advance in a storage device (a storage device having a non-transitory storage medium) such as an HDD or a flash memory of the automatic driving control device 100, or may be stored in a removable storage medium such as a DVD or a CD-ROM, and may be installed in the HDD or the flash memory of the automatic driving control device 100 when the storage medium (non-transitory storage medium) is mounted on a drive device. The automatic driving control device 100 is an example of a "travel control device".

[0037] [Configuration of Automatic Driving Control Device 100] FIG. 4 is a functional configuration diagram of a first control unit 120 and a second control unit 160 included in the automatic driving control device 100. The first control unit 120 includes, for example, a recognition unit 130 and a behavior plan generation unit 140. The behavior plan generation unit 140 includes, for example, a target trajectory generation unit 142, a curvature estimation unit 144, a curvature correction unit 146, and a speed plan generation unit 148.

[0038] The recognition unit 130 recognizes the position of an object around the host vehicle M and the state such as the speed and acceleration based on the information input from the camera 10 and the radar device 12 via the object recognition device 16. In particular, the recognition unit 130 acquires a point group P representing a road lane line from the object recognition device 16, and recognizes the region surrounded by the acquired point group P as the lane (travel lane) in which the host vehicle M is traveling.

[0039] The target trajectory generation unit 142 generates a target trajectory TT indicating a route along which the host vehicle M will travel in the future based on the point cloud P acquired from the object recognition device 16. FIG. 5 is a diagram for explaining an example of a method by which the target trajectory generation unit 142 generates the target trajectory TT. First, the target trajectory generation unit 142 uses, as a starting point, the point P1 closest to the host vehicle M among the point cloud P representing the road lane line on one side, and as an end point, the point P2 closest to the host vehicle M among the point cloud P representing the road lane line on the other side to obtain a vector V1. In the example of FIG. 5, the target trajectory generation unit 142 uses, as a starting point, the point P1 closest to the host vehicle M among the point cloud P representing the left road lane line, and as an end point, the point P2 closest to the host vehicle M among the point cloud P representing the road lane line on the other side to obtain a vector V1, but this may be a reverse vector.

[0040] Next, the target trajectory generation unit 142 acquires the point P3 that is the second closest to the host vehicle M among the point cloud P representing the left road lane line or the point cloud P representing the right road lane line. In FIG. 5, the target trajectory generation unit 142 acquires the point P3 that is the second closest to the host vehicle M among the point cloud P representing the left road lane line. Next, the target trajectory generation unit 142 calculates a unit vector V2' of a vector V2 having the point P1 as a starting point and the point P3 as an end point, and obtains a unit vector V2'' by rotating the calculated unit vector V2' by -90 degrees. The target trajectory generation unit 142 obtains an orthogonal projection vector V3 of the vector V1 with respect to the unit vector V2'', and estimates the magnitude of the orthogonal projection vector V3 as the width w of the road along which the host vehicle M travels. In other words, it can be said that the target trajectory generation unit 142 calculates an orthogonal projection vector of the vector V1 with respect to the unit vector in the normal direction of the road lane line passing through the point P1.

[0041] Next, the target trajectory generation unit 142 determines which of the point group P representing the left road lane line and the point group P representing the right road lane line has a higher recognition accuracy among the point group P representing the road lane lines. More specifically, the target trajectory generation unit 142 determines which of the point group P representing the left road lane line and the point group P representing the right road lane line has a higher recognition accuracy based on, for example, whether the tracking of the point group P is successful, the number of times the point group P is recognized, the length of the line connecting the point group P, and the like. In the situation shown in the left part of FIG. 5, since it is detected that the line connecting the point group P representing the right road lane line is longer than the line connecting the point group P representing the left road lane line, the target trajectory generation unit 142 determines that the recognition accuracy of the point group P representing the right road lane line is higher.

[0042] Next, as shown in the right part of FIG. 5, the target trajectory generation unit 142 generates the target trajectory TT by offsetting half of the estimated road width w, i.e., w / 2, in the normal direction of the road lane line based on the road lane line on the side determined to have a higher recognition accuracy. More specifically, the target trajectory generation unit 142 obtains a point TP obtained by offsetting half of the estimated road width w, i.e., w / 2, in the normal direction of the road lane line from each point of the point group P on the side determined to have a higher recognition accuracy, and generates the target trajectory TT by connecting the points TP to each other.

[0043] In the example of FIG. 5, the case where both the point group P representing the left road lane line and the point group P representing the right road lane line are recognized has been described. However, if the recognition of either the point group P representing the left road lane line or the point group P representing the right road lane line fails, the target trajectory TT is generated by the above-described method based on the point group P on the side where the recognition has not failed, using the point group P acquired immediately before the recognition failure and the road width w estimated at that time. Thereby, even when the recognition of either the point group P representing the left road lane line or the point group P representing the right road lane line fails, the target trajectory TT can be generated.

[0044] The curvature estimation unit 144 estimates the curvature of the target trajectory TT generated by the target trajectory generation unit 142. More specifically, when the longitudinal direction of the host vehicle M is defined as the x-axis, the lateral direction as the y-axis, and the distance from the host vehicle M as s, the curvature estimation unit 144 estimates by calculating the curvature of each point TP of the target trajectory TT according to the following formula (1). The curvature estimation unit 144 specifically calculates formula (1) using a numerical analysis method such as the central difference method. In formula (1), κ(s) represents the curvature of the point TP at the position of distance s from the host vehicle M, and R(s) represents the radius of curvature of the point TP at the position of distance s from the host vehicle M. As described above, since the point group P in the bird's-eye view coordinate system representing the road lane markings is set at equal intervals, each point TP of the target trajectory TT obtained by offsetting the point group P is also set at equal intervals, and the application of a numerical analysis method such as the central difference method becomes effective.

[0045]

Number

[0046] Figure 6 is a graph showing an example of the estimated curvature EC of the target trajectory TT calculated by the curvature estimation unit 144. In Figure 6, the solid line indicated by EC represents the estimated curvature of the target trajectory TT estimated by the curvature estimation unit 144, and the dotted line indicated by TC represents the true curvature of the road on which the host vehicle M travels. Thus, the estimated curvature EC of the target trajectory TT set based on the point group P converted from the camera coordinate system to the bird's-eye view coordinate system tends to have a larger error as the distance from the host vehicle M increases. As a result, when the host vehicle M is decelerated according to the speed plan based on the estimated curvature EC, the passengers of the host vehicle M may feel a sense of discomfort or unease about the excessive deceleration of the host vehicle M.

[0047] Against the backdrop of the above circumstances, the curvature correction unit 146 corrects the estimated curvature EC estimated by the curvature estimation unit 144 by applying a correction filter for correcting the curvature to the estimated curvature EC. More specifically, the curvature correction unit 146 applies a correction filter that sets the upper limit value of the estimated curvature EC such that the upper limit value of the curvature at the point TP decreases as the distance between the host vehicle M and the point TP of the target trajectory TT increases.

[0048] FIG. 7 is a graph showing an example of the corrected curvature CC of the target trajectory TT corrected by the curvature correction unit 146. In FIG. 7, the two-dot chain line indicated by CF represents the correction filter, and the solid line indicated by CC represents the corrected curvature CC of the target trajectory TT corrected by the curvature correction unit 146. As shown in FIG. 7, when the distance s from the host vehicle M is within a predetermined value d1, the value of the estimated curvature EC is below the set value of the correction filter CF, and the value of the corrected curvature CC coincides with the value of the estimated curvature EC.

[0049] On the other hand, when the distance s from the host vehicle M is greater than the predetermined value d1, since the value of the estimated curvature EC exceeds the set value of the correction filter CF, the curvature correction unit 146 applies the correction filter CF and limits the value of the estimated curvature EC to the upper limit value C1 to obtain the corrected curvature CC. In this way, by setting the upper limit value of the estimated curvature EC to be smaller as the distance between the host vehicle M and the point TP of the target trajectory TT increases, only the estimated curvature EC that is assumed to have a larger error can be corrected.

[0050] The speed plan generation unit 148 generates a speed plan indicating the future target speed of the host vehicle M based on the corrected curvature CC corrected by the curvature correction unit 146. More specifically, the speed plan generation unit 148 generates a speed plan such that the lateral acceleration of the host vehicle M becomes a predetermined value G that does not give discomfort to the occupant, with the corrected curvature CC as a condition. The speed plan generation unit 148 has a lateral acceleration v 2 / R = v 2The relational expression of κ = G is transformed, and a speed plan is generated so that the host vehicle M travels at the speed shown by the following formula (2). In this embodiment, for the sake of convenience, a relational expression is established to calculate the lateral acceleration so that the lateral acceleration of the host vehicle M becomes a predetermined value G. However, the predetermined value G in this case is not limited to a specific value and may be set to any value within a range that does not give discomfort or a sense of strangeness to the occupants.

[0051]

Number

[0052] FIG. 8 is a diagram showing an example of the speed plan generated by the speed plan generation unit 148. The speed plan shown in FIG. 8 is generated by the speed plan generation unit 148 corresponding to the corrected curvature CC shown in FIG. 7. As shown in FIG. 8, when the distance s from the host vehicle M is within a range of a predetermined value d1 or less, the target speed of the host vehicle M decreases as the distance s from the host vehicle M increases. This is because as the distance s from the host vehicle M increases, the corrected curvature CC shown in FIG. 7 also increases (that is, it is assumed that there is a curve in front of the host vehicle M).

[0053] However, when the distance s from the host vehicle M is greater than the predetermined value d1, since the corrected curvature CC shown in FIG. 7 takes a constant value, the target speed calculated based on the corrected curvature CC also takes a constant value. By such processing, it is possible to suppress the host vehicle M from being excessively decelerated due to the curvature of the point group P converted into the bird's-eye view coordinate system being overly evaluated as the distance from the host vehicle M increases.

[0054] FIG. 9 is a graph showing an example of the corrected curvature CC of the target trajectory TT corrected by the curvature correction unit 146 when the host vehicle M travels along a curve. As shown in FIG. 9, the estimated curvature EC of the target trajectory TT exceeds the above-described upper limit value C1 in the range where the distance s from the host vehicle M is equal to or less than a predetermined value d1. However, since it is assumed that the estimation accuracy of the curvature at a position close to the host vehicle M is high, the curvature correction unit 146 applies a higher upper limit value C2 to the estimated curvature EC. That is, when the distance s from the host vehicle M is large, a smaller upper limit value is applied as a correction filter, and when the distance s from the host vehicle M is small, a larger upper limit value is applied as a correction filter, thereby correcting the estimated curvature EC that is assumed to have a large error while the estimated curvature EC that is assumed to have a small error can be utilized for generating a speed plan.

[0055] Returning to FIG. 4, the second control unit 160 controls the travel driving force output device 200, the brake device 210, and the steering device 220 so that the host vehicle M passes along the target trajectory TT generated by the action plan generation unit 140 in accordance with the speed plan generated by the speed plan generation unit 148.

[0056] The second control unit 160 includes, for example, an acquisition unit 162, a speed control unit 164, and a steering control unit 166. The acquisition unit 162 acquires information on the target trajectory (trajectory points) generated by the action plan generation unit 140 and stores it in a memory (not shown). When the current speed of the host vehicle M is higher than the target speed generated by the speed plan generation unit 148, the speed control unit 164 calculates the distance required to decelerate from the current speed to the target speed and decelerates the host vehicle M so that the host vehicle M reaches the target speed within the calculated distance range. The steering control unit 166 controls the steering device 220 according to the degree of curvature of the target trajectory stored in the memory.

[0057] [Flow of processing] Next, with reference to FIG. 10, the flow of processing executed by the automatic driving control device 100 will be described. FIG. 10 is a diagram showing an example of the flow of processing executed by the automatic driving control device 100. First, the recognition unit 130 acquires a point group P representing a lane line in the bird's-eye view coordinate system from the object recognition device 16 (step S100). Next, the target trajectory generation unit 142 generates a target trajectory TT of the host vehicle M based on the acquired point group P (step S101).

[0058] Next, the curvature estimation unit 144 estimates the curvature of the generated target trajectory TT (step S102). Next, the curvature correction unit 146 corrects the curvature by applying a correction filter to the estimated curvature (step S103). Next, the speed plan generation unit 148 generates a speed plan indicating the future target speed of the host vehicle M based on the corrected curvature (step S104). Next, the second control unit 160 drives the host vehicle M according to the target trajectory TT generated by the target trajectory generation unit 142 and the speed plan generated by the speed plan generation unit 148 (step S105). Thereby, the processing of this flowchart ends.

[0059] In the above embodiment, an example in which the automatic driving control device 100 automatically drives the host vehicle M according to the speed plan generated by the speed plan generation unit 148 has been described. However, the present invention is not limited to such a configuration and can also be applied to assist driving for manual driving by a driver. For example, when the host vehicle M driven manually by the driver is traveling on a curve and the driver applies the brake device 210, the brake device 210 may be operated to assist deceleration so that the current speed of the host vehicle M reaches the target speed indicated by the speed plan generated by the speed plan generation unit 148. Further, for example, when the host vehicle M driven manually by the driver is traveling on a curve and the deviation between the current speed and the target speed indicated by the speed plan is equal to or greater than the threshold value, the brake device 210 may be operated so that the current speed approaches the target speed even if the driver does not apply the brake device 210.

[0060] According to the embodiment described above, based on the lane line information represented in the space obtained by converting the image captured by the camera into the bird's-eye view coordinate system, a target trajectory of the host vehicle M is generated, the curvature of the generated target trajectory is estimated and corrected, a speed plan for the host vehicle M is generated based on the corrected curvature, and the host vehicle M is caused to travel according to the generated speed plan. Thereby, the information obtained by converting the image captured by the camera into the bird's-eye view coordinate system can be suitably utilized for the travel of the moving body.

[0061] The embodiment described above can be expressed as follows. A storage device storing a program, A hardware processor, and configured to: By the hardware processor executing the program stored in the storage device, Based on the lane line information represented in the space obtained by converting the image representing the surrounding situation of the moving body captured by the camera mounted on the moving body into the bird's-eye view coordinate system, a target trajectory indicating the route that the moving body will travel in the future is generated, Calculate the curvature of the target trajectory, The curvature is corrected by applying a correction filter for correcting the curvature to the estimated curvature, Based on the corrected curvature, a speed plan indicating the future target speed of the moving body is generated, The moving body is caused to travel according to the speed plan, A moving body control device configured as such.

[0062] As described above, the embodiments for carrying out the present invention have been described using the embodiments. However, the present invention is not limited to such embodiments, and various modifications and substitutions can be made without departing from the gist of the present invention.

Description of Reference Numerals

[0063] 10 Camera 16 Object recognition device 100 Automatic driving control device 120 First control unit 130 Recognition Unit 140 Action Plan Generation Unit 142 Target Trajectory Generation Unit 144 Curvature Estimation Unit 146 Curvature Correction Unit 148 Speed Plan Generation Unit 160 Second Control Unit 162 Acquisition Unit 164 Speed Control Unit 166 Steering Control Unit

Claims

1. A target trajectory generation unit that generates a target trajectory indicating a path along which the moving body will travel in the future, based on section line information represented in a space obtained by converting an image representing the surrounding situation of the moving body captured by a camera mounted on the moving body into a bird's-eye view coordinate system; A curvature estimation unit that calculates the curvature of the target trajectory; A curvature correction unit that corrects the curvature by applying a correction filter for correcting the curvature to the estimated curvature; A speed plan generation unit that generates a speed plan indicating a future target speed of the moving body based on the corrected curvature; A travel control unit that causes the moving body to travel according to the speed plan; Comprising: The correction filter increases the degree of correction of the curvature at a point on the target trajectory as the distance between the moving body and the point on the target trajectory increases. A moving body control device.

2. The correction filter sets an upper limit value of the curvature. The moving body control device according to claim 1.

3. The correction filter sets the upper limit value of the curvature such that the upper limit value of the curvature at a point on the target trajectory tends to decrease as the distance between the moving body and the point on the target trajectory increases. The moving body control device according to claim 2.

4. The speed plan generation unit generates the speed plan such that the lateral acceleration of the moving body becomes a predetermined value on the condition of the corrected curvature. The moving body control device according to any one of claims 1 to 3.

5. When the speed of the moving body is greater than the target speed, the travel control unit calculates a distance required to decelerate from the speed to the target speed, and decelerates the moving body so that the moving body reaches the target speed within the calculated distance range. The moving body control device according to any one of claims 1 to 4.

6. A computer generates a target trajectory indicating a path along which the moving body will travel in the future, based on section line information obtained by converting an image representing the surrounding situation of the moving body captured by a camera mounted on the moving body into a bird's-eye view coordinate system, calculates the curvature of the target trajectory, corrects the curvature by applying a correction filter for correcting the curvature to the estimated curvature, generates a speed plan indicating a future target speed of the moving body based on the corrected curvature, causes the moving body to travel according to the speed plan, The correction filter increases the degree of correction of the curvature at a point on the target trajectory as the distance between the moving body and the point on the target trajectory increases. Moving body control method.

7. Cause a computer to Generate a target trajectory indicating a route that the moving body will travel in the future based on the section line information obtained by converting an image representing the surrounding situation of the moving body captured by a camera mounted on the moving body into a bird's-eye view coordinate system. Cause the curvature of the target trajectory to be calculated. Apply a correction filter for correcting the curvature to the estimated curvature to correct the curvature. Generate a speed plan indicating the future target speed of the moving body based on the corrected curvature. Cause the moving body to travel according to the speed plan. The correction filter increases the degree of correction of the curvature at a point on the target trajectory as the distance between the moving body and the point on the target trajectory increases. Program.

8. A target trajectory generation unit that generates a target trajectory indicating a route that the moving body will travel in the future based on the section line information represented in the space obtained by converting an image representing the surrounding situation of the moving body captured by a camera mounted on the moving body into a bird's-eye view coordinate system. A curvature estimation unit that calculates the curvature of the target trajectory. A curvature correction unit that corrects the curvature by applying a correction filter with a preset upper limit value of the curvature to correct the error of the curvature generated due to the conversion to the estimated curvature. A speed plan generation unit that generates a speed plan indicating the future target speed of the moving body based on the corrected curvature. A travel control unit that causes the moving body to travel according to the speed plan. A moving body control device comprising:

9. A computer Generates a target trajectory indicating a route that the moving body will travel in the future based on the section line information obtained by converting an image representing the surrounding situation of the moving body captured by a camera mounted on the moving body into a bird's-eye view coordinate system. Calculates the curvature of the target trajectory. Applies a correction filter with a preset upper limit value of the curvature to correct the error of the curvature generated due to the conversion to the estimated curvature to correct the curvature. Generates a speed plan indicating the future target speed of the moving body based on the corrected curvature. Causes the moving body to travel according to the speed plan. Moving body control method.

10. Cause a computer to generate a target trajectory indicating a route along which the moving body will travel in the future, based on section line information obtained by converting an image representing the surrounding situation of the moving body captured by a camera mounted on the moving body into a bird's-eye coordinate system; calculate the curvature of the target trajectory; correct the curvature by applying a correction filter having a preset upper limit value of the curvature for correcting the curvature error generated due to the conversion to the estimated curvature; generate a speed plan indicating a future target speed of the moving body based on the corrected curvature; cause the moving body to travel according to the speed plan; program.

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