Travel control device, travel control method, and program

Abstract

To sufficiently enhance the travel safety of an own vehicle.SOLUTION: A travel control device 13 comprises: an image recognition unit 3B that executes image recognition of a pattern PT drawn on a road surface RS by light emitted from a surrounding vehicle SV; a prediction unit 3C that predicts the behavior of the surrounding vehicle SV on the basis of the result of the image recognition of the pattern PT executed by the image recognition unit 3B; and a travel control unit 3D that controls the travel of an own vehicle 1 on the basis of the behavior of the surrounding vehicle SV predicted by the prediction unit 3C.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present disclosure relates to a driving control device, a driving control method, and a program.

Background Art

[0002] Patent Document 1 describes that a sensing system including a camera for photographing a road surface and an arithmetic processing unit is mounted on a vehicle (own vehicle), that a pattern drawn on the road surface by another traffic participant (for example, a surrounding vehicle or the like) is captured by the camera, that the arithmetic processing unit detects the states of the own vehicle and other traffic participants based on the pattern captured in the image of the camera, and that the vehicle (own vehicle) equipped with the sensing system controls at least one of steering, accelerator, and brake according to the output of the sensing system.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] By the way, in the technique described in Patent Document 1, the pattern drawn on the road surface by another traffic participant does not predict the future behavior of another traffic participant (for example, the surrounding vehicle as another traffic participant reverses, the door of the surrounding vehicle as another traffic participant (specifically, the surrounding vehicle with the door closed) opens, etc.). Therefore, with the technique described in Patent Document 1, the own vehicle cannot predict the future behavior of another traffic participant based on the pattern drawn on the road surface by another traffic participant. As a result, with the technique described in Patent Document 1, the driving of the own vehicle cannot be appropriately controlled based on the pattern drawn on the road surface by another traffic participant, and the driving safety of the own vehicle cannot be sufficiently improved.

[0005] In view of the above points, an object of the present disclosure is to provide a driving control device, a driving control method, and a program that can sufficiently improve the driving safety of a host vehicle.

Means for Solving the Problems

[0006] (1) One aspect of the present disclosure is a driving control device including an image recognition unit that executes image recognition of a pattern drawn on a road surface by light irradiated from a surrounding vehicle, a prediction unit that predicts the behavior of the surrounding vehicle based on the result of the image recognition of the pattern executed by the image recognition unit, and a driving control unit that controls the driving of the host vehicle based on the behavior of the surrounding vehicle predicted by the prediction unit.

[0007] (2) In the driving control device of (1), the driving control unit may drive the host vehicle while avoiding the pattern drawn on the road surface by the light irradiated from the surrounding vehicle.

[0008] (3) The driving control device of (1) may include a determination unit that determines whether or not the pattern that is the object of the image recognition executed by the image recognition unit is a road marking.

[0009] (4) One aspect of the present disclosure is a driving control method including an image recognition step in which a driving control device executes image recognition of a pattern drawn on a road surface by light irradiated from a surrounding vehicle, a prediction step in which the driving control device predicts the behavior of the surrounding vehicle based on the result of the image recognition of the pattern executed in the image recognition step, and a driving control step in which the driving control device controls the driving of the host vehicle based on the behavior of the surrounding vehicle predicted in the prediction step.

[0010] (5) One aspect of the present disclosure is a program for causing a processor to execute an image recognition step of recognizing an image of a pattern drawn on a road surface by light irradiated from a surrounding vehicle, a prediction step of predicting the behavior of the surrounding vehicle based on the result of the image recognition of the pattern executed in the image recognition step, and a travel control step of controlling the travel of the host vehicle based on the behavior of the surrounding vehicle predicted in the prediction step.

Advantages of the Invention

[0011] According to the present disclosure, the driving safety of the host vehicle can be sufficiently improved.

Brief Description of the Drawings

[0012]

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Modes for Carrying Out the Invention

[0013] Hereinafter, embodiments of the travel control device, travel control method, and program of the present disclosure will be described with reference to the drawings.

[0014] <First Embodiment> FIG. 1 is a diagram showing a first example of a host vehicle 1 to which a traveling control device 13 according to the first embodiment is applied. In the example shown in FIG. 1, the host vehicle 1 includes a camera 11, an HMI (Human Machine Interface) 12, a traveling control device 13, a steering actuator 14, a braking actuator 15, and a driving actuator 16. The camera 11 captures, for example, the surroundings (e.g., the front, side, rear, etc.) of the host vehicle 1 and transmits a camera image to the traveling control device 13. The HMI 12 has a function of receiving various operations of the driver of the host vehicle 1 and transmits a signal indicating the operation of the driver of the host vehicle 1 to the traveling control device 13.

[0015] In the example shown in FIG. 1, the traveling control device 13 is configured by, for example, a driving support ECU (Electronic Control Unit) (i.e., by one ECU). In other examples, the traveling control device 13 may be configured by, for example, a driving support ECU and an image processing ECU (i.e., by a plurality of ECUs).

[0016] In the example shown in FIG. 1, the traveling control device 13 controls the steering actuator 14, the braking actuator 15, and the driving actuator 16 based on, for example, the camera image transmitted from the camera 11, the signal indicating the operation of the driver of the host vehicle 1 transmitted from the HMI 12, etc. The steering actuator 14 has a function of steering the host vehicle 1. The steering actuator 14 includes, for example, a power steering system, a steer-by-wire steering system, a rear-wheel steering system, etc. The braking actuator 15 has a function of decelerating the host vehicle 1. The braking actuator 15 includes, for example, a hydraulic brake, an electric regenerative brake, etc. The driving actuator 16 has a function of accelerating the host vehicle 1. The driving actuator 16 includes, for example, an engine, an EV (electric vehicle) system, a hybrid system, a fuel cell system, etc.

[0017] The travel control device 13 is composed of a microcomputer including a communication interface (I / F) 131, a memory 132, and a processor 133. The communication interface 131 has an interface circuit for connecting the travel control device 13 to the camera 11, the HMI 12, the steering actuator 14, the braking actuator 15, the driving actuator 16, and the like. The memory 132 stores programs and various data (such as camera images transmitted from the camera 11) used in the processes executed by the processor 133. The processor 133 has functions as an acquisition unit 3A, an image recognition unit 3B, a prediction unit 3C, and a travel control unit 3D. The acquisition unit 3A acquires camera images. In addition, the acquisition unit 3A acquires signals indicating operations of the driver of the host vehicle 1 received by the HMI 12 (for example, operations for turning ON / OFF driving support functions (such as ACC (Adaptive Cruise Control), FCW (Forward Collision Warning), AEBS (Autonomous Emergency Braking), emergency steering assist (with active steering function), etc.), steering operations, brake pedal operations, accelerator pedal operations, etc.).

[0018] Based on the camera image acquired by the acquisition unit 3A, the image recognition unit 3B recognizes a pattern PT (see FIGS. 2 and 3) drawn on the road surface RS (see FIGS. 2 and 3) by the light irradiated from the surrounding vehicle SV (see FIGS. 2 and 3).

[0019] FIG. 2 is a diagram showing a first example of the pattern PT drawn on the road surface RS by the light irradiated from the surrounding vehicle SV. In the example shown in FIG. 2, when the surrounding vehicle SV is reversing, the surrounding vehicle SV irradiates light to draw a pattern PT indicating that the surrounding vehicle SV is reversing on the road surface RS, to alert pedestrians (not shown), vehicles (not shown), etc. around the surrounding vehicle SV and prevent accidents.

[0020] In the example shown in FIG. 1 (the first example of the host vehicle 1 to which the traveling control device 13 of the first embodiment is applied), the image recognition unit 3B uses teacher data, which is a dataset of a learning camera image and a label indicating whether or not the pattern included in the learning camera image is a pattern PT indicating that the surrounding vehicle SV for learning is reversing, drawn on the road surface RS by the light irradiated from the surrounding vehicle SV for learning when the surrounding vehicle SV for learning is reversing, as shown in FIG. 2 for example. By using the model obtained by performing learning, the image recognition unit 3B executes image recognition of the pattern PT (the pattern PT indicating that the surrounding vehicle SV is reversing) drawn on the road surface RS by the light irradiated from the surrounding vehicle SV included in the camera image acquired by the acquisition unit 3A.

[0021] In the example shown in FIG. 1, the prediction unit 3C predicts the behavior of the surrounding vehicle SV based on the result of the image recognition of the pattern PT executed by the image recognition unit 3B. Specifically, in the example shown in FIG. 1, when the image recognition of the pattern PT indicating that the surrounding vehicle SV is reversing is executed by the image recognition unit 3B, the prediction unit 3C predicts that the surrounding vehicle SV will reverse as the behavior of the surrounding vehicle SV.

[0022] In the example shown in FIG. 1, the traveling control unit 3D controls the traveling of the host vehicle 1 based on the behavior of the surrounding vehicle SV predicted by the prediction unit 3C. Specifically, in the example shown in FIG. 1, when the prediction unit 3C predicts that the surrounding vehicle SV will reverse as the behavior of the surrounding vehicle SV, the traveling control unit 3D causes the host vehicle 1 to travel while avoiding the pattern PT (the pattern PT indicating that the surrounding vehicle SV is reversing) drawn on the road surface RS by the light irradiated from the surrounding vehicle SV. That is, the traveling control unit 3D causes the host vehicle 1 to travel so that the host vehicle 1 and the surrounding vehicle SV do not come into contact even if the surrounding vehicle SV reverses. Specifically, for example, when ACC and emergency steering assist are in the ON state, the traveling control unit 3D controls the steering actuator 14 and the braking actuator 15 so that the host vehicle 1 travels while avoiding the pattern PT indicating that the surrounding vehicle SV is reversing, without the driver of the host vehicle 1 having to perform a steering operation and a brake pedal operation. In another example, when the driving support function is in the ON state, the travel control unit 3D may cause the HMI 12 to output an alert that prompts the driver of the host vehicle 1 to perform a steering operation and a brake pedal operation to avoid a pattern PT indicating that the surrounding vehicle SV is reversing, so that the host vehicle 1 travels.

[0023] FIG. 3 is a diagram showing a second example of the pattern PT drawn on the road surface RS by the light irradiated from the surrounding vehicle SV. In the example shown in FIG. 3, when there is a possibility that the occupant of the surrounding vehicle SV may open the door SVD of the surrounding vehicle SV, the surrounding vehicle SV irradiates light to draw a pattern PT indicating that the door SVD of the surrounding vehicle SV is opened on the road surface RS, and pedestrians (not shown), vehicles (not shown), etc. around the surrounding vehicle SV are alerted to prevent an accident.

[0024] In a second example of the host vehicle 1 to which the travel control device 13 of the first embodiment is applied, the image recognition unit 3B uses teacher data, which is a dataset of a learning camera image and a label indicating whether the pattern included in the learning camera image is a pattern PT indicating that the door SVD of the learning surrounding vehicle SV may be opened by the light irradiated from the learning surrounding vehicle SV on the road surface RS, for example, as shown in FIG. 3, when the occupant of the learning surrounding vehicle SV may open the door SVD of the learning surrounding vehicle SV. By performing learning using the obtained model, image recognition of the pattern PT (pattern PT indicating that the door SVD of the surrounding vehicle SV is opened) drawn on the road surface RS by the light irradiated from the surrounding vehicle SV included in the camera image acquired by the acquisition unit 3A is executed.

[0025] In a second example of the host vehicle 1 to which the travel control device 13 of the first embodiment is applied, when the image recognition unit 3B executes image recognition of the pattern PT indicating that the door SVD of the surrounding vehicle SV is opened, the prediction unit 3C predicts that the door SVD of the surrounding vehicle SV will be opened as the behavior of the surrounding vehicle SV.

[0026] In the second example of the host vehicle 1 to which the travel control device 13 of the first embodiment is applied, when the prediction unit 3C predicts that the door SVD of the surrounding vehicle SV will open as the behavior of the surrounding vehicle SV, the travel control unit 3D avoids the pattern PT (pattern PT indicating that the door SVD of the surrounding vehicle SV will open) drawn on the road surface RS by the light irradiated from the surrounding vehicle SV and makes the host vehicle 1 travel. That is, the travel control unit 3D makes the host vehicle 1 travel so that even if the door SVD of the surrounding vehicle SV opens, the door SVD of the surrounding vehicle SV and the host vehicle 1 do not come into contact with each other. Specifically, for example, when ACC and emergency steering assistance are in the ON state, the driver of the host vehicle 1 does not need to perform a steering operation and a brake pedal operation, and the travel control unit 3D controls the steering actuator 14 and the braking actuator 15 so that the host vehicle 1 travels while avoiding the pattern PT indicating that the door SVD of the surrounding vehicle SV will open. In another example, when the driving support function is in the ON state, the travel control unit 3D may cause the HMI 12 to output an alert that prompts the driver of the host vehicle 1 to perform a steering operation and a brake pedal operation for the host vehicle 1 to travel while avoiding the pattern PT indicating that the door SVD of the surrounding vehicle SV will open.

[0027] FIG. 4 is a flowchart for explaining an example of the processing executed by the processor 133 of the travel control device 13 according to the first embodiment. In the example shown in FIG. 4, in step S10, the image recognition unit 3B performs image recognition of the pattern PT (see FIGS. 2 and 3) drawn on the road surface RS (see FIGS. 2 and 3) by the light irradiated from the surrounding vehicle SV (see FIGS. 2 and 3) based on the camera image acquired in a step not shown. In step S11, the image recognition unit 3B determines whether or not the pattern PT drawn on the road surface RS by the light irradiated from the surrounding vehicle SV is included in the result of the image recognition executed in step S10. If YES, the process proceeds to step S12, and if NO, the process shown in FIG. 4 ends. In step S12, the prediction unit 3C predicts the behavior of the surrounding vehicle SV based on the result of the image recognition of the pattern PT executed in step S10. In step S13, the travel control unit 3D controls the travel of the host vehicle 1 based on the behavior of the surrounding vehicle SV predicted in step S12.

[0028] In the host vehicle 1 to which the travel control device 13 of the first embodiment is applied, the behavior of the surrounding vehicle SV is predicted, and the travel of the host vehicle 1 is controlled based on the predicted behavior of the surrounding vehicle SV. Therefore, the travel safety of the host vehicle 1 can be sufficiently improved as compared with the case where the behavior of the surrounding vehicle SV is not predicted.

[0029] <Second Embodiment> The host vehicle 1 to which the travel control device 13 of the second embodiment is applied is configured in the same manner as the host vehicle 1 to which the travel control device 13 of the first embodiment described above is applied, except for the points described below.

[0030] FIG. 5 is a diagram showing an example of the host vehicle 1 to which the travel control device 13 of the second embodiment is applied. In the example shown in FIG. 1, the host vehicle 1 does not include a LiDAR (Laser Imaging Detection and Ranging) 17 (see FIG. 5), but in the example shown in FIG. 5, the host vehicle 1 includes a LiDAR 17. The LiDAR 17 detects the surrounding situation of the host vehicle 1 and transmits the detection result to the travel control device 13. The acquisition unit 3A acquires the detection result of the LiDAR 17.

[0031] In the example shown in FIG. 1, the processor 133 does not have the function as the determination unit 3E (see FIG. 5), but in the example shown in FIG. 5, the processor 133 has the function as the determination unit 3E. The determination unit 3E determines whether the pattern PT (see FIGS. 2 and 3) image-recognized by the image recognition unit 3B is a road marking (for example, a lane line, a road surface marking, etc.). Specifically, when the intensity of the reflected light from the pattern PT received by the light receiving unit (not shown) of the LiDAR 17 is the same as the intensity of the reflected light from the portion of the road surface RS other than the pattern PT (see FIGS. 2 and 3), the determination unit 3E determines that the pattern PT image-recognized by the image recognition unit 3B is not a road surface paint (road marking), but a pattern PT drawn on the road surface RS by the light irradiated from the surrounding vehicle SV. When the intensity of the reflected light from the pattern PT received by the light receiving unit of the LiDAR 17 is significantly different from the intensity of the reflected light from the portion of the road surface RS other than the pattern PT, the determination unit 3E determines that the pattern PT image-recognized by the image recognition unit 3B is a road surface paint (road marking).

[0032] FIG. 6 is a flowchart for explaining an example of the process executed by the processor 133 of the travel control device 13 according to the second embodiment. In the example shown in FIG. 6, in step S20, the image recognition unit 3B performs image recognition of the pattern PT drawn on the road surface RS by the light irradiated from the surrounding vehicle SV based on the camera image acquired in a step not shown. In step S21, the image recognition unit 3B determines whether the result of the image recognition executed in step S20 includes the pattern PT drawn on the road surface RS by the light irradiated from the surrounding vehicle SV. If YES, the process proceeds to step S22; if NO, the process shown in FIG. 6 ends. In step S22, the determination unit 3E determines whether the pattern PT image-recognized in step S20 is a road marking (e.g., lane line, road surface marking, etc.). If NO, the process proceeds to step S23; if YES, the process shown in FIG. 6 ends. In step S23, the prediction unit 3C predicts the behavior of the surrounding vehicle SV based on the result of the image recognition of the pattern PT executed in step S20. In step S24, the travel control unit 3D controls the travel of the host vehicle 1 based on the behavior of the surrounding vehicle SV predicted in step S23.

[0033] As described above, in the host vehicle 1 to which the traveling control device 13 of the second embodiment is applied, when the pattern PT recognized by the image recognition unit 3B is determined by the determination unit 3E not to be a road marking (for example, a lane line, a road surface marking, etc.), the traveling control unit 3D executes traveling control of the host vehicle 1 based on the behavior of the surrounding vehicle SV. Therefore, it is possible to suppress the possibility that a road marking (for example, a lane line, a road surface marking, etc.) is misrecognized as the pattern PT drawn on the road surface RS by the light irradiated from the surrounding vehicle SV, and the traveling control of the host vehicle 1 is inappropriately executed based on the misrecognition result.

[0034] <Third Embodiment> The host vehicle 1 to which the traveling control device 13 of the third embodiment is applied is configured in the same manner as the host vehicle 1 to which the traveling control device 13 of the first embodiment described above is applied, except for the points described later.

[0035] In the example shown in FIG. 1 (the first example of the host vehicle 1 to which the traveling control device 13 of the first embodiment is applied), the processor 133 does not have the function as the determination unit 3E (see FIG. 5). However, in an example of the host vehicle 1 to which the traveling control device 13 of the third embodiment is applied, the processor 133 has the function as the determination unit 3E. The determination unit 3E determines whether or not the pattern PT (see FIGS. 2 and 3) recognized by the image recognition unit 3B is a road marking (for example, a lane line, a road surface marking, etc.) in the same manner as the example shown in FIG. 5.

[0036] In an example of the host vehicle 1 to which the travel control device 13 according to the third embodiment is applied, unlike the example shown in FIG. 5, the determination unit 3E determines whether the pattern PT (see FIGS. 2 and 3) recognized by the image recognition unit 3B is a road marking (for example, a lane line, a road surface marking, etc.) by using a machine learning model. Specifically, the determination unit 3E uses teacher data, which is a dataset of a learning camera image and a label indicating whether the pattern included in the learning camera image is the pattern PT drawn on the road surface RS by the light emitted from the surrounding vehicle SV for learning or a road surface paint (road marking), to perform learning. By using the obtained machine learning model, the determination unit 3E determines whether the pattern PT recognized by the image recognition unit 3B is a road marking. In an example of the host vehicle 1 to which the travel control device 13 according to the third embodiment is applied, when the determination unit 3E determines that the pattern PT recognized by the image recognition unit 3B is not a road marking, the prediction unit 3C predicts the behavior of the surrounding vehicle SV, and the travel control unit 3D controls the travel of the host vehicle 1.

[0037] <Fourth Embodiment> The host vehicle 1 to which the travel control device 13 according to the fourth embodiment is applied is configured in the same manner as the host vehicle 1 to which the travel control device 13 according to the third embodiment described above is applied, except for the points described later.

[0038] In an example of the host vehicle 1 to which the travel control device 13 according to the third embodiment is applied, as described above, the determination unit 3E determines whether the pattern PT (see FIGS. 2 and 3) recognized by the image recognition unit 3B is a road marking (for example, a lane line, a road surface marking, etc.) by using a machine learning model. On the other hand, in an example of the host vehicle 1 to which the travel control device 13 of the fourth embodiment is applied, the determination unit 3E determines whether the pattern PT image-recognized by the image recognition unit 3B is a road marking based on the presence or absence of a temporal change in the relative positional relationship between the pattern PT image-recognized by the image recognition unit 3B and the surrounding vehicle SV. When there is a temporal change in the relative positional relationship between the pattern PT image-recognized by the image recognition unit 3B and the surrounding vehicle SV (specifically, when the position of the pattern PT has not changed and the position of the surrounding vehicle SV has changed), the determination unit 3E determines that the pattern PT image-recognized by the image recognition unit 3B is a road marking.

[0039] <Fifth Embodiment> The host vehicle 1 to which the travel control device 13 of the fifth embodiment is applied is configured in the same manner as the host vehicle 1 to which any one of the travel control devices 13 of the first to fourth embodiments described above is applied, except for the points described later.

[0040] As described above, in the host vehicle 1 to which any one of the travel control devices 13 of the first to fourth embodiments is applied, the travel control device 13 is constituted by, for example, a driving support ECU. On the other hand, in the host vehicle 1 to which the travel control device 13 of the fifth embodiment is applied, the travel control device 13 is constituted by, for example, an automatic driving ECU. The travel control device 13 controls the steering actuator 14, the braking actuator 15, and the driving actuator 16 based on the camera image transmitted from the camera 11, the signal indicating the operation of the driver of the host vehicle 1 to put the host vehicle 1 in the automatic driving mode transmitted from the HMI 12, and the like.

[0041] In the host vehicle 1 to which the travel control device 13 of the fifth embodiment is applied, the acquisition unit 3A acquires a signal indicating the operation of the driver of the host vehicle 1 received by the HMI 12 (for example, an operation to put the host vehicle 1 in the automatic driving mode, an operation to cancel the automatic driving mode of the host vehicle 1, etc.).

[0042] In an example of the host vehicle 1 to which the travel control device 13 according to the fifth embodiment is applied, when the prediction unit 3C predicts that the surrounding vehicle SV will reverse as the behavior of the surrounding vehicle SV, the travel control unit 3D avoids the pattern PT (the pattern PT indicating that the surrounding vehicle SV will reverse) drawn on the road surface RS by the light emitted from the surrounding vehicle SV, generates a travel plan for traveling the host vehicle 1, and controls the steering actuator 14 and the braking actuator 15 based on the travel plan. In another example of the host vehicle 1 to which the travel control device 13 according to the fifth embodiment is applied, when the prediction unit 3C predicts that the door SVD of the surrounding vehicle SV will open as the behavior of the surrounding vehicle SV, the travel control unit 3D avoids the pattern PT (the pattern PT indicating that the door SVD of the surrounding vehicle SV will open) drawn on the road surface RS by the light emitted from the surrounding vehicle SV, generates a travel plan for traveling the host vehicle 1, and controls the steering actuator 14 and the braking actuator 15 based on the travel plan.

[0043] As described above, the embodiments of the travel control device, the travel control method, and the program of the present disclosure have been described with reference to the drawings. However, the travel control device, the travel control method, and the program of the present disclosure are not limited to the above-described embodiments, and can be appropriately modified without departing from the spirit of the present disclosure. The configurations of the respective examples of the above-described embodiments may be appropriately combined. In each example of the above-described embodiments, the processing performed in the travel control device 13 (for example, the driving support ECU, the automatic driving ECU, etc.) has been described as software processing performed by executing a program. However, the processing performed in the travel control device 13 may be processing performed by hardware. Alternatively, the processing performed in the travel control device 13 may be processing that combines both software and hardware. Further, the program (the program that realizes the functions of the processor 133 of the travel control device 13) stored in the memory 132 of the travel control device 13 may be recorded, provided, distributed, etc. on a computer-readable storage medium such as a semiconductor memory, a magnetic recording medium, an optical recording medium, etc.

Description of Reference Numerals

[0044] 1... own vehicle, 11... camera, 12... HMI, 13... driving control device, 131... communication interface, 132... memory, 133... processor, 3A... acquisition unit, 3B... image recognition unit, 3C... prediction unit, 3D... driving control unit, 3E... determination unit, 14... steering actuator, 15... braking actuator, 16... driving actuator, 17... LiDAR, SV... surrounding vehicle, SVD... door, RS... road surface, PT... pattern

Claims

1. An image recognition unit that performs image recognition of a pattern drawn on a road surface by light irradiated from surrounding vehicles; A prediction unit that predicts the behavior of the surrounding vehicles based on the result of the pattern image recognition executed by the image recognition unit; A driving control device comprising a driving control unit that controls the driving of the host vehicle based on the behavior of the surrounding vehicles predicted by the prediction unit.

2. The driving control device according to claim 1, wherein the driving control unit drives the host vehicle while avoiding the pattern drawn on the road surface by light irradiated from the surrounding vehicles.

3. The driving control device according to claim 1, further comprising a determination unit that determines whether or not the pattern that is the object of the image recognition executed by the image recognition unit is a road sign.

4. A driving control method, comprising: an image recognition step in which a driving control device performs image recognition of a pattern drawn on a road surface by light irradiated from surrounding vehicles; A prediction step in which the driving control device predicts the behavior of the surrounding vehicles based on the result of the pattern image recognition executed in the image recognition step; A driving control step in which the driving control device controls the driving of the host vehicle based on the behavior of the surrounding vehicles predicted in the prediction step.

5. A program for causing a processor to execute: An image recognition step of performing image recognition of a pattern drawn on a road surface by light irradiated from surrounding vehicles; A prediction step of predicting the behavior of the surrounding vehicles based on the result of the pattern image recognition executed in the image recognition step; A driving control step of controlling the driving of the host vehicle based on the behavior of the surrounding vehicles predicted in the prediction step.

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