Vehicle-mounted device, image quality adjustment method, and program

Abstract

To provide a vehicle-mounted device, an image quality adjustment method, and a program capable of suppressing a risk of delay in adjustment of image quality.SOLUTION: A vehicle-mounted device 10 includes: an imaging section 1 for imaging a periphery of a vehicle and generating image data; an information acquisition section 2 for acquiring information around a vehicle from a navigation device 20 for navigating the vehicle; a prediction section 3 for predicting brightness in a scheduled point in which a vehicle travels on the basis of information around the vehicle; and an image quality adjustment section 4 for adjusting image quality of an image shown by image data on the basis of predicted brightness.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present disclosure relates to an in-vehicle device, an image quality adjustment method, and a program. [Background technology]

[0002] The surrounding environment recognition device disclosed in Patent Document 1 uses brightness information from the image of a camera installed in front of the vehicle to predict how the brightness of the field of view of the camera behind the vehicle will change, and adjusts the gain and exposure time of the camera behind the vehicle. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2008-021196 A Summary of the Invention [Problem to be solved by the invention]

[0004] The present inventors have found the following technical problems. In such a surrounding environment recognition device, if the brightness around the vehicle changes suddenly, there is a risk that image quality adjustment will be delayed.

[0005] The present disclosure has been made in consideration of the above-mentioned problems, and provides an in-vehicle device, an image quality adjustment method, and a program that can reduce the risk of delays in image quality adjustment. [Means for solving the problem]

[0006] The in-vehicle device according to the present disclosure includes: an imaging unit that captures an image of the surroundings of the vehicle and generates image data; an information acquisition unit that acquires information about the surroundings of the vehicle from a navigation device that navigates the vehicle; a prediction unit that predicts brightness at a location where the vehicle is scheduled to travel based on information about the surroundings of the vehicle; and an image quality adjustment unit that adjusts the image quality of the image represented by the image data based on the predicted brightness.

[0007] The image quality adjustment method according to the present disclosure is an image quality adjustment method for adjusting image quality by an in-vehicle device. imaging an area around the vehicle to generate image data; acquiring information about the surroundings of the vehicle from a navigation device that navigates the vehicle; predicting brightness at a location where the vehicle is scheduled to travel based on information about the surroundings of the vehicle; and adjusting the image quality of the image represented by the image data based on the predicted brightness.

[0008] The program according to the present disclosure is provided for a computer operating as an in-vehicle device, imaging an area around the vehicle to generate image data; acquiring information about the surroundings of the vehicle from a navigation device that navigates the vehicle; predicting brightness at a location where the vehicle is scheduled to travel based on information about the surroundings of the vehicle; and adjusting the image quality of the image represented by the image data based on the predicted brightness. Effect of the Invention

[0009] According to the present disclosure, it is possible to provide an in-vehicle device, an image quality adjustment method, and a program that can reduce the risk of delays in image quality adjustment. [Brief description of the drawings]

[0010] [Figure 1] 1 is a block diagram showing a configuration example of an in-vehicle device according to a first embodiment; [Diagram 2] 4 is a flowchart showing an example of a process performed by the in-vehicle device according to the first embodiment. [Diagram 3] FIG. 11 is a schematic diagram showing an example of an image quality adjustment method. [Figure 4] 1 is a schematic diagram showing an example of an image to which an example of an image quality adjustment method is applied. [Diagram 5] 1 is a schematic diagram showing an example of an image to which an example of an image quality adjustment method is applied. [Figure 6] 1 is a schematic diagram showing an example of an image to which an example of an image quality adjustment method is applied. [Figure 7] 1 is a schematic diagram showing an example of an image to which an example of an image quality adjustment method is applied. [Figure 8] 1 is a schematic diagram showing an example of an image to which an example of an image quality adjustment method is applied. [Figure 9] 1 is a schematic diagram showing an example of an image to which an example of an image quality adjustment method is applied. [Figure 10] FIG. 2 is a diagram illustrating an example of a hardware configuration included in an in-vehicle device. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0011] (Embodiment 1) <Configuration> An example of a configuration of an in-vehicle device according to the first embodiment will be described with reference to FIG.

[0012] As shown in FIG. 1, the in-vehicle device 10 works in conjunction with the navigation device 20. The navigation device 20 navigates the vehicle 30. The in-vehicle device 10 and the navigation device 20 are mounted on the vehicle 30. The in-vehicle device 10 and the navigation device 20 are connected to and communicate with each other via a wired line or a wireless line. The in-vehicle device 10 and the navigation device 20 may exchange various data with each other. The navigation device 20 may acquire various data from a server (not shown) via a network. The navigation device 20 may be a car navigation device dedicated to being mounted on a vehicle, or may be a communication device such as a smartphone or tablet capable of executing a navigation application. When the navigation application is executed using a communication device, the communication device may not be mounted on the vehicle 30. The communication device may be used outside the vehicle 30.

[0013] The in-vehicle device 10 includes an imaging unit 1, an information acquisition unit 2, a prediction unit 3, and an image quality adjustment unit 4.

[0014] The imaging unit 1 captures an image of the periphery of the vehicle 30 to generate image data. The periphery of the vehicle 30 is, for example, a front area, a rear area, a side area, etc. of the vehicle 30. The subject captured by the imaging unit 1 is not particularly limited, but includes objects not described in the map information of the navigation device 20 and moving objects. Objects not described in the map information are, for example, structures, natural objects, moving objects, etc. Examples of structures are tunnels, buildings, etc. Examples of natural objects are mountains, rivers, seas, etc. The imaging unit 1 is, for example, an in-vehicle camera. The imaging unit 1 may be provided in a location where it can capture an image of the periphery of the vehicle 30, and may be attached, for example, to the rearview mirror of the front window of the vehicle 30 or near the rear window.

[0015] The information acquisition unit 2 acquires information about the periphery of the vehicle 30 from the navigation device 20. The information about the periphery of the vehicle 30 includes, for example, a planned point where the vehicle 30 is to travel, a travel direction of the vehicle 30, traffic congestion information about the periphery of the vehicle 30, the current time, weather, the vehicle status, the route of the vehicle 30, road information on the route, map information, and the like. The road information includes, for example, buildings arranged on the left and right of the route. The planned point where the vehicle 30 is to travel is a point ahead of the current position on the route of the vehicle 30, for example, a point about 5 to 50 m away from the vehicle 30, which is difficult to recognize in the image captured by the imaging unit 1.

[0016] The prediction unit 3 predicts the brightness at a location where the vehicle 30 is scheduled to travel, based on information about the periphery of the vehicle 30. The prediction unit 3 predicts the brightness at a location where the vehicle 30 is scheduled to travel, based on information about the location where the vehicle 30 is scheduled to travel, the traveling direction of the vehicle 30, and traffic congestion information about the periphery of the vehicle 30. The prediction unit 3 may predict the brightness of the entire image related to the image data generated by the imaging unit 1. The prediction unit 3 may also predict the brightness of each of a plurality of regions obtained by dividing the entire image.

[0017] The image quality adjustment unit 4 adjusts the image quality of the image represented by the image data based on the brightness predicted by the prediction unit 3. The image quality adjustment unit 4 may start adjusting the image quality of the image represented by the image data at a time before the vehicle 30 reaches the scheduled location. The time before the vehicle 30 reaches the scheduled location may be set arbitrarily based on the distance between the current position of the vehicle 30 and the scheduled location and the speed of the vehicle 30. When the image represented by the image data shows a moving object moving around the vehicle 30, the image quality adjustment unit 4 may adjust the image quality of the image captured by the imaging unit 1 based on the brightness predicted by the prediction unit 3 so that a predetermined part of the moving object moving around the vehicle 30 can be recognized. The moving object is, for example, a vehicle or a bicycle other than the vehicle 30. The predetermined part of the moving object is, for example, a license plate number. Specifically, the image quality adjustment unit 4 may adjust the image quality by performing white balance adjustment, exposure control, and more specifically, aperture control.

[0018] The in-vehicle device 10 may further include a recording unit. The recording unit records image data in association with the time point when the image data was captured. The recorded image data is at least one of image data generated by the imaging unit 1 and image data showing an image whose image quality has been adjusted by the image quality adjustment unit 4.

[0019] <Processing> Next, an example of processing by the in-vehicle device 10 will be described with reference to FIG.

[0020] The surroundings of the vehicle 30 are imaged to generate image data (step ST1).

[0021] Next, information about the surroundings of the vehicle 30 is acquired from the navigation device 20 (step ST2).

[0022] Next, the brightness at the location where the vehicle 30 is scheduled to travel is predicted based on information about the surroundings of the vehicle 30 (step ST3).

[0023] Finally, the image quality of the image represented by the image data is adjusted based on the brightness predicted in step ST3 (step ST4). Specifically, the image includes at least one of a bright area and a dark area. The bright area is brighter than the surroundings of the bright area. The dark area is darker than the surroundings of the dark area. The bright area is darkened so that whiteout does not occur in the bright area. The dark area is brightened so that the dark area is recognized.

[0024] For example, in the example of image IM1 shown in FIG. 3, the image quality is adjusted for the total area T1. Note that the example of image IM1 shown in FIG. 3 shows a road IM1a, a sky IM1b, a grassland IM1c, etc., but the subject shown in image IM1 is not limited to these. Each area in image IM1 shown in FIG. 3 is shown by hatching. The lower the luminance of each area, the higher the line density of each hatching corresponding to each area. The total area T1 in the example of image IM1 is divided into a plurality of divided areas M1. The total area T1 may be divided into a mesh-like shape into a plurality of divided areas M1. The number of divided areas M1 is not particularly limited and can be selected from a wide range. Weighting is performed for each of the plurality of divided areas M1 according to the brightness predicted in step ST3. This generates a weighted total area WT1. As shown in FIG. 3, the weighted total area WT1 indicates the magnitude of weighting of each divided area WM1. In an example of the weighted total area WT1 shown in FIG. 3, the weighting of the divided area WM1 near the center of the weighted total area WT1 is larger than the weighting of the divided area WM1 at the outer edge of the weighted total area WT1. In this example, the weighting of the divided area WM1 decreases stepwise from the center to the outer edge of the weighted total area WT1. Specifically, the divided area WM1a, the divided area WM1b, the divided area WM1c, the divided area WM1d, and the divided area WM1e are arranged in this order from the center to the outer edge of the weighted total area WT1. The weighting of the divided area WM1a, the divided area WM1b, the divided area WM1c, the divided area WM1d, and the divided area WM1e increases in this order. The luminance of the divided area M1 is changed according to the magnitude of the weighting of each divided area WM1. For example, when the divided area WM1 is heavily weighted, the luminance of the divided area M1 is reduced. In other words, when the divided area M1 is bright, the luminance of the divided area M1 is changed so that whiteout does not occur. Also, if the divided region WM1 is lightly weighted, the luminance of the divided region M1 is increased. In other words, if the divided region M1 is dark, the luminance of the divided region M1 is changed so that the divided region M1 is recognizable.

[0025] As described above, the brightness at the scheduled location is predicted, and the image quality of the image represented by the image data is adjusted based on the predicted brightness. Therefore, before the scheduled location is reached, the brightness at the scheduled location can be obtained, and image quality adjustment based on the obtained brightness at the scheduled location can be started. In other words, the brightness at the scheduled location can be predicted in advance, and the image quality can be adjusted. This reduces the risk of image quality adjustment being delayed.

[0026] (Specific examples of processing) Next, specific examples of steps ST2 to ST4 of the process by the in-vehicle device 10 will be described with reference to Figs. 4 to 9. In particular, specific examples according to the information on the periphery of the vehicle 30 acquired in step ST2 will be described. Although an example of images IM2 to IM6 shown in Figs. 4 to 9 shows a road or the like, the subject shown in images IM2 to IM6 is not limited to this. Each area in images IM2 to IM6 shown in Figs. 4 to 9 is shown using hatching. The lower the brightness of each area, the higher the line density of the hatching corresponding to each area. Moreover, the area with the highest brightness in each area is not hatched.

[0027] <Current time> The information about the surroundings of the vehicle 30 acquired in step ST2 may include the current time.

[0028] In this case, when the current time is daytime, it is predicted in step ST3 that the brightness is generally bright at the planned travel location of the vehicle 30. In step ST4, the image quality is adjusted so that the entire image becomes dark.

[0029] On the other hand, when the current time is night, in step ST3, it is predicted that the brightness at the planned travel location of the vehicle 30 will be dark overall. In step ST4, the image quality is adjusted so that the entire image becomes brighter.

[0030] <Current time and weather> The information about the surroundings of the vehicle 30 acquired in step ST2 includes the current time and weather, and the current time may be daytime.

[0031] In such a case, when the weather is fine, in step ST3, it is predicted that the brightness at the planned travel location of the vehicle 30 will be particularly bright in the upper half of the image, i.e., the area from the center to the top edge. In step ST4, the image quality is adjusted so that the upper half of the image is particularly dark.

[0032] On the other hand, when the weather is cloudy, in step ST3, it is predicted that the upper half of the image will be bright at the planned travel location of the vehicle 30. In step ST4, the image quality is adjusted so that the upper half of the image is darkened.

[0033] Also, when the weather is rainy, in step ST3, it is predicted that the brightness at the planned travel location of the vehicle 30 will be somewhat dark in the upper half of the image. In step ST4, the image quality is adjusted so that the upper half of the image becomes somewhat bright.

[0034] The information on the surroundings of the vehicle 30 acquired in step ST2 is the current time and weather, and the current time may be night. In such a case, when the weather is rainy, in step ST3, it is predicted that the brightness at the planned point where the vehicle 30 is to travel will be affected by the rain. The effect of the rain is, for example, the reflection of light from a road surface wet with rain. In step ST4, the image quality is adjusted taking into account the effect of the rain.

[0035] <Vehicle status> The information about the surroundings of the vehicle 30 acquired in step ST2 may include the vehicle status.

[0036] In such a case, when the vehicle status is that the vehicle is parked at home, in step ST3, it is predicted that the brightness at the planned travel location of the vehicle 30 will have little change other than the current time and weather. In step ST4, the image quality is adjusted taking into account the current time and weather.

[0037] On the other hand, when the host vehicle is traveling, in step ST3, it is predicted that the brightness at the planned location where the vehicle 30 is traveling will vary according to the vehicle speed. For example, when the vehicle speed is high, the headlights of the vehicle 30 are turned on, so it is predicted that the planned location where the vehicle 30 is traveling will be bright. When the vehicle speed is approximately 0 (zero), the headlights of the vehicle 30 are turned off, so it is predicted that the planned location where the vehicle 30 is traveling will be bright. In step ST4, the image quality is adjusted according to the vehicle speed.

[0038] <Travel position> The information about the surroundings of the vehicle 30 acquired in step ST2 may include the traveling position.

[0039] In such a case, the driving position may be a position without an intersection, for example, a single road. In such a case, in step ST3, it is predicted that the brightness at the planned driving point of the vehicle 30 will be affected by buildings on the left and right sides of the vehicle 30, and pedestrians and bicycles moving on the sidewalk near the vehicle 30. In step ST4, the image quality is adjusted taking into account the effects of such buildings, pedestrians, and bicycles.

[0040] In addition, in such a case, when the traveling position is an intersection, in step ST3, it is predicted that the brightness at the planned traveling point of the vehicle 30 will be affected by vehicles moving on the left and right of the vehicle 30. In step ST4, the image quality is adjusted taking into account the influence of such vehicles.

[0041] In such a case, when the driving location is an expressway, in step ST3, it is predicted that the brightness at the planned driving location of the vehicle 30 will be affected by the one-way street. In step ST4, the image quality is adjusted taking into account the effect of the one-way street.

[0042] Also, when the driving position is near the entrance of a tunnel, in step ST3, it is predicted that the brightness at the planned point where the vehicle 30 is to travel will be dark only in the center of the image. In step ST4, the image quality is adjusted taking into account the influence of one-way traffic. For example, in the image IM2 shown in FIG. 4, the center C2 of the general area T2 indicates the tunnel entrance, which is dark. The image quality of the image IM2 is adjusted so that the center C2 becomes bright. Note that an example of the image IM2 shown in FIG. 4 shows a road IM2a, sky IM2b, wall IM2c, etc.

[0043] Also, when the driving position is inside a tunnel, in step ST3, it is predicted that the brightness at the planned driving point of the vehicle 30 will be slightly dark in the entire image. In step ST4, the image quality is adjusted so that the entire image becomes slightly bright.

[0044] Furthermore, when the driving position is on the exit side of a tunnel, in step ST3, it is predicted that the brightness at the planned driving point of the vehicle 30 will be such that only the center of the image will be bright. As the driving position approaches the exit from inside the tunnel, the brighter the area becomes. In step ST4, the image quality is adjusted so that the center of the image becomes darker. For example, in the image IM3 shown in FIG. 5, the center C3 of the general area T3 indicates the tunnel exit, which is bright. The image quality of the image IM3 is adjusted so that the center C3 becomes darker. Note that the image IM3 shown in FIG. 5 indicates the road IM3a, the tunnel wall IM3c, etc.

[0045] <Travel location and current time> The information about the surroundings of the vehicle 30 acquired in step ST2 may include the traveling position and the current time.

[0046] In such a case, when the driving location is a downtown area and the current time is night, in step ST3, it is predicted that the brightness at the planned driving location of the vehicle 30 will be affected by the light of buildings and their signs on the left and right sides of the vehicle 30. In step ST4, the image quality is adjusted taking into account the influence of the light of such buildings and their signs.

[0047] In this case, when the driving location is a country road and the current time is night, in step ST3, it is predicted that the brightness at the planned driving point of the vehicle 30 will be affected only by the light from the vehicle 30, vehicles moving on the left and right of the vehicle 30, and bicycles. In step ST4, the image quality is adjusted taking into account the influence of such light.

[0048] <Travel direction> The information about the surroundings of the vehicle 30 acquired in step ST2 may include the traveling direction.

[0049] In such a case, when the driving direction indicates the direction in which the vehicle 30 turns at the intersection, in step ST3, it is predicted that the brightness at the planned point where the vehicle 30 will travel will be affected by road information at the point where the vehicle turns at the intersection and the direction of the turn. In step ST4, the image quality is adjusted taking into consideration the influence of such road information and the direction of the turn. Specifically, if the turn direction is to the right, the image quality is adjusted so that the left half of the image has priority. Similarly, if the turn direction is to the left, the image quality is adjusted so that the right half of the image has priority. The left half of the image is the area from the center to the left edge of the image, and the right half of the image is the area from the center to the right edge of the image. Step ST3 may be performed before the vehicle 30 reaches the intersection. In addition, step ST4 may be started from the time when the direction of the vehicle 30 changes at the intersection. In addition, step ST4 may be performed taking into consideration the road information at the point where the vehicle turns at the intersection and the direction of the turn that have been acquired up to that time.

[0050] In such a case, when the driving direction is the direction in which the imaging unit 1 captures an image in a backlit state, in step ST3, it is predicted that the brightness at the planned point where the vehicle 30 will travel will be dark around the sun in the image. When the periphery of the vehicle 30 captured by the imaging unit 1 is located between the imaging unit 1 and the sun, the imaging unit 1 tends to capture an image in a backlit state. In step ST4, the image quality is adjusted so that the periphery of the sun in the image is bright.

[0051] For example, in the image IM4 shown in Fig. 6, a portion P4 of the general region T4 represents the sun and is brighter than the surroundings of the portion P4. The image quality of the image IM4 is adjusted so that the surroundings of the portion P4 become brighter. Note that an example of the image IM4 shown in Fig. 6 represents a road IM4a, a sky IM4b, a building IM4c, etc.

[0052] Also, a portion P5 of the image IM5 shown in FIG. 7 indicates the sun, and is brighter than the surroundings of the portion P5. The surroundings of the portion P5 are too dark and difficult to recognize. The image quality of the image IM5 is adjusted so that the surroundings of the portion P5 become brighter. An example of the image IM5 shown in FIG. 7 indicates a road IM5a, a sky IM5b, a wall IM5c, etc.

[0053] Also, the image IM6 shown in Fig. 8 was captured after the image quality of the image IM5 was adjusted, and its image quality was adjusted. The periphery of the portion P6 of the image IM6 is bright and easy to recognize. An example of the image IM6 shown in Fig. 8 shows a road IM6a, a sky IM6b, a wall IM6c, etc.

[0054] In such a case, when the driving direction is the direction in which the imaging unit 1 captures an image in a front-light state, in step ST3, the brightness at the planned point where the vehicle 30 is to travel is predicted to be bright in the entire image. When the imaging unit 1 is located between the sun and the periphery of the vehicle 30 captured by the imaging unit 1, the imaging unit 1 tends to capture an image in a front-light state. In step ST4, the image quality is adjusted so that the entire image becomes dark.

[0055] As described above, when the information about the surroundings of the vehicle 30 acquired in step ST2 includes the driving direction, the prediction unit 3 predicts the brightness at the location where the vehicle 30 is scheduled to travel, based on the driving direction of the vehicle 30. This makes it possible to predict the brightness at the location where the vehicle 30 is scheduled to travel, for example, according to the route of the vehicle 30 and the position of the sun.

[0056] <Traffic congestion information> The information about the surroundings of the vehicle 30 acquired in step ST2 may include traffic congestion information.

[0057] In such a case, when the traffic congestion information indicates that the vehicle 30 is caught in a traffic jam, in step ST3, it is predicted that the brightness at the planned point where the vehicle 30 is traveling will not fluctuate much because other vehicles will continue to be located around the vehicle 30. In step ST4, the image quality is adjusted so that the license plate numbers of vehicles around the vehicle 30 are given priority in recognition.

[0058] Image IM7 shown in Fig. 9 is an example of an image whose image quality has been adjusted in step ST4. Image IM7 is captured at night. Portion P7 of image IM7 shows the license plate number of vehicle 31 located in front of vehicle 30 (not shown). Portion P7 is brighter than the surrounding area of ​​portion P7, making the license plate number of vehicle 31 easier to recognize. Image IM7 shown in Fig. 9 shows road IM7a, sky IM7b, building IM7c, etc.

[0059] On the other hand, if the information about the vicinity of the vehicle 30 acquired in step ST2 includes traffic congestion information, the traffic congestion information may indicate that the vehicle 30 will leave the traffic congestion. In such a case, in step ST3, it is predicted that the brightness at the planned point where the vehicle 30 is traveling may vary significantly because another vehicle will move away from the vicinity of the vehicle 30. In step ST4, the image quality is adjusted taking into account this variation in brightness.

[0060] When the information on the periphery of the vehicle 30 acquired in the above-mentioned step ST2 includes traffic congestion information, the image shown by the image data may show a moving object moving around the vehicle 30. In such a case, the prediction unit 3 predicts the brightness at the planned point where the vehicle 30 will travel, taking into consideration the influence of other vehicles moving around the vehicle 30, based on the traffic congestion information around the vehicle 30. The image quality adjustment unit 4 adjusts the image quality of the image captured by the imaging unit 1 based on the brightness predicted by the prediction unit 3 so that a specific part of the moving object moving around the vehicle 30 can be recognized. This makes it possible to adjust the image quality taking into consideration the vehicles around the vehicle 30 and the license plates of those vehicles.

[0061] <Traffic information and current time> The information about the surroundings of the vehicle 30 acquired in step ST2 may include traffic congestion information and the current time.

[0062] In such a case, the traffic jam information may indicate that the vehicle 30 is caught in a traffic jam, and the current time may be night. In such a case, in step ST3, it is predicted that the brightness at the planned travel destination of the vehicle 30 will be affected by the headlights and backlights, since another vehicle will continue to be located around the vehicle 30 with its headlights and backlights on. In step ST4, the image quality is adjusted taking into consideration the effect of such headlights and backlights. Specifically, such headlights and backlights may cause the image other than the center, in other words, the periphery of the central part of the image, and the outer edge of the image, to become dark. Therefore, the image quality is adjusted so that the entire image becomes brighter.

[0063] As described above, in each of the above specific examples, steps ST2 to ST4 are performed, and the image quality of the image is adjusted, and then the vehicle 30 arrives at the scheduled location. Since the image quality is adjusted before the vehicle 30 arrives at the scheduled location, the scheduled location can be predicted and the image quality can be adjusted. Furthermore, the image quality adjustment can suppress the occurrence of whiteout in bright areas of the image. Furthermore, the image quality can be adjusted so that dark areas of the image can be recognized.

[0064] (Other embodiments) The in-vehicle device according to the above-mentioned embodiment may have the following hardware configuration. Fig. 10 is a diagram showing an example of the hardware configuration included in the in-vehicle device. As described above in the various embodiments, the processing procedure in the in-vehicle device, the present disclosure may also be embodied as an image quality adjustment method.

[0065] 10 includes a processor 101 and a memory 102, as well as an interface 103. Each component of the in-vehicle device 10 described in the above embodiment is realized by the processor 101 reading and executing a program stored in the memory 102. In other words, this program is a control program for causing the processor 101 to function as the in-vehicle device 10 or a part thereof.

[0066] The above-mentioned program includes a set of instructions (or software code) for making the computer perform one or more functions described in the embodiment when the program is loaded into the computer. The program may be stored in a non-transitory computer-readable medium or a tangible storage medium. By way of example and not limitation, the computer-readable medium or the tangible storage medium includes random-access memory (RAM), read-only memory (ROM), flash memory, solid-state drive (SSD) or other memory technology, CD-ROM, digital versatile disc (DVD), Blu-ray® disk or other optical disk storage, magnetic cassette, magnetic tape, magnetic disk storage or other magnetic storage device. The program may be transmitted on a transitory computer-readable medium or a communication medium. By way of example and not limitation, the transitory computer-readable medium or the communication medium includes electrical, optical, acoustic, or other forms of propagating signals.

[0067] Moreover, the above-mentioned program can be said to be a program for causing the in-vehicle device 10 to execute such an image quality adjustment method.

[0068] The present invention is not limited to the above embodiment, and can be modified as appropriate without departing from the spirit of the present invention. For example, steps ST2 to ST4 of the process by the in-vehicle device 10 described above may be combined to perform steps ST2 to ST4.

[0069] For example, the information on the surroundings of the vehicle 30 acquired in step ST2 may include the driving position, traffic congestion information, the current time, and weather. In such a case, the driving position may be at the exit side of a tunnel, the traffic congestion information may indicate that another vehicle is located ahead of the vehicle 30, and the current time may be daytime. In such a case, in step ST3, although there is a bright area only in the center of the image, it is predicted that the image will be influenced by the other vehicle. In step ST4, the image quality is adjusted taking into account the influence of the other vehicle, as well as the current time and weather.

[0070] Furthermore, for example, the information about the surroundings of the vehicle 30 acquired in step ST2 may include the vehicle's traveling position, traffic congestion information, and the current time.

[0071] In such a case, the driving location may be a downtown area, the traffic congestion information may indicate that no other vehicles are located in front of or behind the vehicle 30, and the current time may be night. In such a case, in step ST3, it is predicted that the brightness at the planned driving location of the vehicle 30 will be affected by the light of surrounding street lights and signs. In step ST4, the image quality is adjusted so that the light of the surrounding street lights and signs is reflected.

[0072] In such a case, the driving position may be a straight country road, the traffic congestion information may indicate that other vehicles are located in front of and behind the vehicle 30, and the current time may be night. In such a case, in step ST3, the brightness at the planned driving point of the vehicle 30 is predicted to be bright in the center of the image and dark around the center of the image. In step ST4, the image quality is adjusted so that the center of the image is dark and the surrounding area of ​​the center of the image is bright. In other words, the light of bicycles and the light of buildings may still occur, but may be ignored.

[0073] Furthermore, the imaging unit 1 may be provided inside the vehicle cabin of the vehicle 30. In such a case, the center of the image related to the image data generated by the imaging unit 1 may indicate the inside of the vehicle cabin, and the peripheral area of ​​the center of the image may indicate the outside of the vehicle cabin. In step ST4, the image quality of only the peripheral area indicating the periphery of the vehicle 30 may be adjusted. [Explanation of symbols]

[0074] 10, 100 In-vehicle equipment 1. Imaging unit 2 Information acquisition section 3. Prediction Department 4. Picture quality adjustment section 20 Navigation devices 30, 31, 32 Cars 101 Processor 102 Memory 103 Interface ST1, ST2, ST3, ST4 steps IM1, IM2, IM3, IM4, IM5, IM6, IM7 Images IM1a, IM2a, IM3a, IM4a, IM5a, IM6a, IM7a Road IM1b, IM2b, IM4b, IM5b, IM6b, IM7b empty IM1c grassland IM2c, IM3c, IM5c, IM6c Wall IM4c, IM7c Building M1 division area T1, T2, T3, T4, T8 General Area WM1, WM1a, WM1b, WM1c, WM1d, WM1e divided area WT1 Weighted Total Area C2, C3, C8 center P4, P5, P6, P7 part

Claims

1. an imaging unit that captures an image of the surroundings of the vehicle and generates image data; an information acquisition unit that acquires information about the surroundings of the vehicle from a navigation device that navigates the vehicle; a prediction unit that predicts brightness at a location where the vehicle is scheduled to travel based on information about the surroundings of the vehicle; and an image quality adjustment unit that adjusts the image quality of the image represented by the image data based on the predicted brightness. In-vehicle device.

2. the image quality adjustment unit starts adjusting the image quality of the image represented by the image data before the vehicle reaches the scheduled location. The in-vehicle device according to claim 1 .

3. When the image represented by the image data shows a moving object moving around the vehicle, the image quality adjustment unit adjusts the image quality of the image captured by the imaging unit based on the predicted brightness so that a predetermined portion of the moving object can be recognized. The in-vehicle device according to claim 1 or 2.

4. In a method for adjusting image quality by an in-vehicle device imaging an area around the vehicle to generate image data; acquiring information about the surroundings of the vehicle from a navigation device that navigates the vehicle; predicting brightness at a location where the vehicle is scheduled to travel based on information about the surroundings of the vehicle; and adjusting the image quality of the image represented by the image data based on the predicted brightness. How to adjust image quality.

5. A computer operating as an in-vehicle device, imaging an area around the vehicle to generate image data; acquiring information about the surroundings of the vehicle from a navigation device that navigates the vehicle; predicting brightness at a location where the vehicle is scheduled to travel based on information about the surroundings of the vehicle; adjusting the image quality of the image represented by the image data based on the predicted brightness. program.

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