Patent Description:
In order to realize automatic driving and prevent traffic accidents, recognition of road signs and pavement markings and vehicle control according to the recognition are important and great interest has been received. In order to recognize road signs and pavement markings, accurate recognition is necessary. Thus, for example, PTL <NUM> discloses a pavement marking recognition device that accurately recognizes a road marking including blurring by judging whether a road marking specified by analyzing an in-vehicle camera image matches a road shape.

<CIT> discloses a vehicle control device with the features in the preamble of present claim <NUM>. Other conventional vehicle control devices are described in <CIT>, <CIT>, <CIT>, and <CIT>.

However, the road shape utilized in the device of PTL <NUM> is used when one recognition result is obtained by image recognition to verify whether this recognition result is correct and, in a case where an incorrect recognition result is obtained at the time point of image recognition, it is impossible to finally make a correct determination even if the road shape is utilized.

Therefore, in a case where the recognition result of the image recognition includes an error, such as when a road sign or a pavement marking is present in a long distance or when shielding or the like due to a three-dimensional object such as a vehicle or a tree has occurred, the pavement marking cannot be accurately recognized.

The purpose of the present invention is to provide: a displayed content recognition device that can accurately determine displayed content for a displayed object on the basis of camera images; and a vehicle control device.

The present invention is defined by the subject-matter of claim <NUM>. The dependent claims relate to preferred modifications.

According to the present invention, it is possible to accurately determine the displayed content for a displayed object on the basis of camera images.

Embodiments of the present invention will be described in detail below.

<FIG> depicts functional blocks illustrating the configuration of a displayed content recognition device according to an embodiment. The displayed content recognition device of the present embodiment includes: a displayed content candidate extraction unit <NUM> that recognizes displayed content for a displayed object (specifically, a road sign and a pavement marking) on the basis of camera images; a travel environment acquisition unit <NUM> that acquires travel environment of a host vehicle; and a displayed content determination unit <NUM> that determines the displayed content on the basis of the candidates for the displayed content extracted by the displayed content candidate extraction unit <NUM> and the travel environment acquired by the travel environment acquisition unit <NUM>.

The displayed content candidate extraction unit <NUM> first specifies an area of an acquired image, specifically, in which portion of the image a road sign or a pavement marking appears. Thereafter, a candidate for displayed content for the road sign or the pavement marking is extracted for the specified area.

In specifying the area, for example, if a target to be recognized is a circular road sign, an edge may be found out in the image such that the Hough transformation using the acquired edge is performed to specify the sign position in the image. Alternatively, the above technique may be replaced with, for example, a maximally stable extremal regions (MSER) algorithm using pixel density information or the like.

In extracting the displayed content candidate for the specified area, first, a likelihood for the displayed content for each recognition target is worked out for the specified area. As a calculation of likelihood, for example, a large number of templates may be prepared in advance for each recognition target such that the correlation between the template and a candidate area is utilized, or a model of the recognition target may be created in a statistical manner on the basis of machine learning such that the correlation with this statistical model is utilized. Next, the displayed content candidate extraction unit <NUM> confirms whether or not the likelihood of each recognition target is equal to or greater than a predetermined threshold value and assigns a recognition target having a likelihood equal to or greater than the threshold value as a candidate for displayed content; then outputs a road sign/pavement marking type corresponding to the likelihood.

The procedure of extracting a displayed content candidate by the displayed content candidate extraction unit <NUM> will be described with reference to <FIG>. For example, when the result illustrated in case <NUM> in <FIG> is acquired using a template or a statistical model, a recognition target having a likelihood equal to or greater than a predetermined threshold value (S201 in <FIG>) is only <NUM>/h maximum speed restriction (S202 in <FIG>), such that <NUM>/h maximum speed restriction and the likelihood thereof are output as an output.

When the result illustrated in case <NUM> in <FIG> is acquired, a recognition target having a likelihood equal to or greater than a predetermined threshold value (S203 in <FIG>) is <NUM>/h maximum speed restriction (S204 in <FIG>) and <NUM>/h maximum speed restriction (S205 in <FIG>), such that <NUM>/h maximum speed restriction and <NUM>/h maximum speed restriction, and respective likelihoods thereof are output as an output.

When the result illustrated in case <NUM> in <FIG> is acquired, since there is no recognition target having a likelihood equal to or greater than a predetermined threshold value (S206 in <FIG>), a candidate for displayed content is not output.

Although the above-described method of extracting the displayed content candidate uses one absolute threshold value, the method may be based on different references. For example, as a relative threshold value, the likelihood of a recognition target having the highest likelihood may be assigned as a reference such that a recognition target whose difference between this value and the likelihood thereof is equal to or smaller than a predetermined value is assigned as a candidate for displayed content.

Although the displayed content candidate extraction unit <NUM> described above outputs the displayed content for a recognition target satisfying the threshold value condition, a candidate not satisfying the threshold value condition may be extracted. That is, in this case, the likelihoods of all the recognition targets and the number of candidates for displayed content satisfying the threshold value condition are output.

The travel environment acquisition unit <NUM> acquires the travel environment around the host vehicle using at least one of a camera image, vehicle speed information, or map information registered in a car navigation system. The travel environment here is information relating to an installation standard for road signs and pavement markings, including expressway, ordinary road, the number of lanes, the number of pedestrians, presence/absence of a median strip, and road shapes such as curve radius, lane width, and shoulder width.

As a method of determining whether the road on which the host vehicle is traveling is an expressway or an ordinary road using a camera image, a gate image installed at an entrance or exit of an expressway may be held in advance as a template such that template matching using this template is performed, or since there are basically no pedestrians on the expressway, information on both of pedestrian recognition and the number of pedestrians may be used to acquire information on whether the road on which the host vehicle is currently traveling is an expressway or an ordinary road.

As a method of acquiring the number of lanes, the median strip, and the number of pedestrians using a camera image, for example, a large number of white line templates, median strip templates, and pedestrian templates may be prepared in advance, respectively, such that the number of lanes, the median strip, and the number of pedestrians are acquired by template matching using these templates, or information on respective items may be acquired by a statistical method using machine learning.

As a method of acquiring a road shape using a camera image, triangulation may be performed using an image acquired at every frame to create a three-dimensional map around the host vehicle, or white line detection may be performed to acquire road shape information on the basis of a result of the white line detection.

Although the above explanation relates to a method of acquiring the travel environment based on a camera image, when the vehicle speed is used, whether the traveling road is an expressway or an ordinary road may be determined using the vehicle speed information up to the present time. According to this method, for example, when the vehicle speed is <NUM>/h or faster for a predetermined time or longer until the present time, it is determined that the traveling road is an expressway and the travel environment information that the current traveling road is an expressway is acquired. In addition, information that the traveling road is an ordinary road may be acquired in a case where this condition is not satisfied.

When the travel environment is acquired using the map information in the car navigation system, the number of lanes written on the map, road shape information and the like are read using the host vehicle position as a reference and used.

The travel environment acquisition unit <NUM> determines displayed content for a road sign or a pavement marking in the image, using the output of the displayed content candidate extraction unit <NUM> and the travel environment information acquired by the travel environment acquisition unit <NUM>. The process of the travel environment acquisition unit <NUM> will be described in detail with reference to <FIG>.

<FIG> illustrates output examples of the displayed content candidate extraction unit <NUM> and the travel environment acquisition unit <NUM>. Case <NUM> in <FIG> represents a case where the displayed content candidate extraction unit <NUM> has extracted <NUM>/h maximum speed limitation and <NUM>/h maximum speed limitation as candidates for displayed content, and the travel environment acquisition unit <NUM> has acquired a travel environment in which the traveling path of the host vehicle is an expressway. At this time, since a <NUM>/h maximum speed restriction sign is not installed on the expressway, the displayed content determination unit <NUM> determines that the road sign captured in the image is an <NUM>/h maximum speed restriction sign.

Case <NUM> in <FIG> denotes a case where the displayed content candidate extraction unit <NUM> has extracted <NUM>/h maximum speed limitation and <NUM>/h maximum speed limitation as candidates for displayed content, and the travel environment acquisition unit <NUM> has acquired a travel environment in which the traveling path of the host vehicle is an ordinary road. At this time, since an <NUM>/h maximum speed restriction sign is not installed on the ordinary road, the displayed content determination unit <NUM> determines that the road sign captured in the image is a <NUM>/h maximum speed restriction sign.

Case <NUM> in <FIG> denotes a case where the displayed content candidate extraction unit <NUM> has extracted <NUM>/h maximum speed limitation and <NUM>/h maximum speed limitation as candidates for displayed content, and the travel environment acquisition unit <NUM> has acquired a travel environment in which the traveling path of the host vehicle is an ordinary road. At this time, since both of <NUM>/h maximum speed limitation and <NUM>/h maximum speed limitation have the possibility of being installed on the ordinary road, the displayed content determination unit <NUM> refers to the likelihoods output by the displayed content candidate extraction unit <NUM> and determines the <NUM>/h maximum speed limitation having a higher likelihood, as displayed content.

As described above, according to the present embodiment, in a case where the recognition result of the image recognition includes an error, such as when a road sign or a pavement marking is present in a long distance or when shielding or the like due to a three-dimensional object such as a vehicle or a tree has occurred, multiple candidates are extracts as displayed content candidates and one of those candidates matching the travel environment is selected, whereby it is possible to recognize the displayed content for the road sign or the pavement marking with high accuracy. In addition, as described above, the present embodiment is characterized in that the recognition result varies according to the travel environment and, when the same road sign or pavement marking is captured by the camera in a different travel environment, a different recognition result is output.

In the present embodiment, the displayed content recognition unit <NUM> selects one candidate that matches the travel environment acquired by the travel environment acquisition unit <NUM>, from among the candidates extracted by the displayed content candidate extraction unit <NUM>. However, this configuration may be changed to a configuration described below, in which the travel environment acquired by the travel environment acquisition unit <NUM> is converted into the likelihood of a road sign or a pavement marking installed around the host vehicle, on the basis of the traffic standard, and a result of the conversion and the result of the displayed content candidate extraction unit <NUM> are combined to determine the displayed content.

Examples of traffic standards are illustrated in <FIG> and <FIG>. <FIG> and <FIG> are regulatory standards for the speed limit in ordinary roads and expressways, respectively. For example, in a case where the travel environment acquisition unit <NUM> has acquired the travel environment information that the current road type is an ordinary road, the number of lanes is two, and the pedestrian traffic volume is large, <FIG> is calculated as likelihoods for displayed content based on the travel environment, using the traffic standard in <FIG>. In addition, when the travel environment acquisition unit <NUM> has acquired information that the current road type is an expressway, the curve radius is <NUM>, the lane width is <NUM>, and the shoulder width is <NUM>, the respective pieces of information relating to the curve radius, the lane width, and the shoulder width are combined on the basis of the traffic standard in <FIG> and <FIG> is calculated as likelihoods for displayed content based on the travel environment. Thereafter, the output of the displayed content candidate extraction unit <NUM> and the result illustrated in <FIG> are subjected to sum or product computation for each item of displayed content, and displayed content with a highest likelihood is employed as a determination result.

With such a procedure, it is possible to consider the likelihoods different for each item of displayed content, on the basis of the acquired travel environment and, even in a situation where a road sign or a pavement marking only partially appear due to occlusion or the like, the displayed content can be determined using the travel environment as a reference.

The displayed content candidate determination unit <NUM> may settle the result in consideration of not only the result of the displayed content candidate extraction unit <NUM> and the result of the travel environment acquisition unit <NUM> but also predetermined priorities. For example, priorities such as those illustrated in <FIG> may be set beforehand such that the priority in <FIG> is added to the likelihood by the displayed content candidate extraction unit <NUM>, or the priority in <FIG> is added to the likelihood calculated from the likelihood by the displayed content candidate extraction unit <NUM> and the travel environment acquired by the travel environment acquisition unit <NUM> to determine displayed content with a highest likelihood.

With this procedure, when safety is taken into consideration as the priority, it is possible to determine displayed content in consideration of safety.

<FIG> is a flowchart representing the operation of the displayed content recognition device of an illustrative example.

In the displayed content recognition device of the illustrative example, the displayed content candidate extraction unit <NUM> firstly performs an image process and extracts a candidate for content for a road sign or a pavement marking in the image (a displayed content candidate extraction process S901 in <FIG>). Thereafter, the travel environment acquisition unit <NUM> acquires the travel environment such as the expressway, the ordinary road, the number of lanes, the number of pedestrians, and the road shape (a displayed content candidate extraction process S902 in <FIG>). Then, the displayed content determination unit <NUM> selects one of the extracted candidates matching the travel environment (a displayed content candidate extraction process S903 in <FIG>).

In <FIG>, the travel environment is always acquired (the displayed content candidate extraction process S902 in <FIG>) when the displayed content candidate is extracted (the displayed content candidate extraction process S901 in <FIG>). As illustrated in <FIG>, however, the travel environment may be acquired only when multiple candidates are extracted in extracting the displayed content candidate.

As illustrated in <FIG>, the displayed content candidate extraction unit <NUM> first performs an image process and extracts a candidate for content for a road sign or a pavement marking in the image (a displayed content candidate extraction process S101 in <FIG>). Thereafter, when the number of candidates is plural as a consequence of confirming the number of candidates, the travel environment acquisition unit <NUM> acquires the travel environment (S103 in <FIG>), and the displayed content determination unit <NUM> determines displayed content for the road sign or the pavement marking on the basis of the outputs of the displayed content candidate extraction unit <NUM> and the travel environment acquisition unit <NUM> (S104 in <FIG>). Meanwhile, when the number of candidates is only one, the displayed content determination unit <NUM> determines displayed content for the road sign or the pavement marking on the basis only of the result of the displayed content candidate extraction unit <NUM> (S104 in <FIG>). In addition, when the number of candidates is zero, displayed content for the road sign or the pavement marking is not determined.

With such a procedure, unneeded processing time can be reduced because it is not necessary to acquire the travel environment in a case where a road sign or a pavement marking can be accurately recognized only by image recognition. Furthermore, with this process, in a case where a sign is present around the host vehicle and the displayed content can be settled only by image recognition, the recognition result does not vary even if the travel environment is varied.

In both of the flowcharts illustrated in <FIG> and <FIG>, the displayed content candidate extraction unit <NUM> first extracts a candidate for content for a road sign or a pavement marking in the image and thereafter the travel environment acquisition unit <NUM> acquires the travel environment. However, as illustrated in <FIG>, a process may be employed such that the travel environment acquisition unit <NUM> acquires the travel environment and thereafter the displayed content candidate extraction unit <NUM> extracts a candidate for content for a road sign or a pavement marking in the image.

In the process illustrated in <FIG>, the travel environment acquisition unit <NUM> firstly acquires the travel environment (a travel environment acquisition process Sill in <FIG>). Next, the displayed content candidate extraction unit <NUM> extracts a candidate for content for a road sign or a pavement marking (a travel environment acquisition process S112 in <FIG>), but at this time, only a candidate satisfying the travel environment acquired by the travel environment acquisition unit <NUM> is extracted. For example, when the travel environment acquisition unit <NUM> has acquired information that the number of pedestrians is very large, it is judged that the host vehicle is traveling on an ordinary road and a maximum speed restriction sign for <NUM>/h or faster installed on the expressway is not extracted. Thereafter, the displayed content determination unit <NUM> selects a candidate extracted as a displayed content candidate, thereby specifying displayed content for the road sign or the pavement marking in the image.

With such a procedure, when the displayed content candidate extraction unit <NUM> extracts candidates by template matching, it is possible to limit the number of times of template matching because the number of candidates to be extracted can be limited, and a shorter processing time can be achieved.

Next, an example useful to understand the present invention will be described.

<FIG> depicts functional blocks illustrating the configuration of a displayed content recognition device according to the present example. The displayed content recognition device of the present example includes: a displayed content candidate extraction unit <NUM> that extracts a candidate for displayed content for a displayed object on the basis of camera images; a travel environment acquisition unit <NUM> that acquires travel environment of a host vehicle; and a vehicle control content designation unit <NUM> that designates vehicle control content on the basis of the candidates for the displayed content extracted by the displayed content candidate extraction unit <NUM> and the travel environment acquired by the travel environment acquisition unit <NUM>.

The travel environment acquisition unit <NUM> uses at least one of a camera image, vehicle speed information, or map information registered in a car navigation system to acquire the travel environment around the host vehicle relating to an installation standard for road signs and pavement markings, including expressway, ordinary road, the number of lanes, the number of pedestrians, presence/absence of a median strip, and road shapes such as curve radius, lane width, and shoulder width.

The vehicle control content designation unit <NUM> receives the outputs of the displayed content candidate extraction unit <NUM> and the travel environment acquisition unit <NUM> as inputs and settles the control content for the vehicle. A method of designating the vehicle control content by the vehicle control content designation unit <NUM> will be described with reference to <FIG> illustrates the current vehicle speed and outputs of the displayed content candidate extraction unit <NUM> and the travel environment acquisition unit <NUM>.

Case <NUM> in <FIG> represents that two candidates, namely, a <NUM>/h maximum speed restriction sign and an <NUM>/h maximum speed restriction sign are extracted by the displayed content candidate extraction unit <NUM> and the host vehicle speed is <NUM>/h when the expressway is acquired as the travel environment. At this time, since the <NUM>/h maximum speed sign is not installed on the expressway, the control content candidate extraction unit determines that the road sign in the image is the <NUM>/h maximum speed sign and settles a brake control signal for lowering the current vehicle speed of <NUM>/h down to <NUM>/h.

Case <NUM> in <FIG> represents that two candidates, namely, a <NUM>/h maximum speed restriction sign and an <NUM>/h maximum speed restriction sign are extracted by the displayed content candidate extraction unit <NUM> and the host vehicle speed is <NUM>/h when information indicating the ordinary road is acquired as the travel environment. At this time, since the <NUM>/h maximum speed sign is not installed on the ordinary road, the control content candidate extraction unit determines that the road sign in the image is the <NUM>/h maximum speed sign and settles a brake control signal for lowering the current vehicle speed of <NUM>/h down to <NUM>/h.

Case <NUM> in <FIG> represents that two candidates, namely, a <NUM>/h maximum speed restriction sign and a <NUM>/h maximum speed restriction sign are extracted by the displayed content candidate extraction unit <NUM> and the host vehicle speed is <NUM>/h when information indicating the ordinary road is acquired as the travel environment. At this time, in the control content candidate extraction unit, since both of <NUM>/h maximum speed limitation and <NUM>/h maximum speed limitation have the possibility of being installed on the ordinary road, the vehicle control content designation unit <NUM> refers to the likelihoods output by the displayed content candidate extraction unit <NUM> and settles a brake control signal for lowering the current vehicle speed of <NUM>/h down to <NUM>/h having a higher likelihood.

As described above, according to the present example, even when the recognition result is ambiguous only by image recognition and the vehicle control signal cannot be settled, the vehicle control signal can be generated using the travel environment.

In the present example, the vehicle control content designation unit <NUM> designates the vehicle control signal on the basis of one candidate that matches the travel environment acquired by the travel environment acquisition unit <NUM>, from among the candidates extracted by the displayed content candidate extraction unit <NUM>. However, this configuration may be changed to a configuration described below, in which the travel environment acquired by the travel environment acquisition unit <NUM> is converted into the likelihood of a road sign or a pavement marking installed around the host vehicle, on the basis of the traffic standard, and a result of the conversion and the result of the displayed content candidate extraction unit <NUM> are combined to designate the vehicle control content.

For example, when the travel environment acquisition unit <NUM> has acquired the travel environment information that the current road type is an ordinary road, the number of lanes is two, and the pedestrian traffic volume is large, <FIG> is calculated as likelihoods for displayed content based on the travel environment, using the traffic standard in <FIG>. In addition, when the travel environment acquisition unit <NUM> has acquired information that the current road type is an expressway, the curve radius is <NUM>, the lane width is <NUM>, and the shoulder width is <NUM>, the respective pieces of information relating to the curve radius, the lane width, and the shoulder width are combined on the basis of the traffic standard in <FIG> and <FIG> is calculated as likelihoods for displayed content based on the travel environment. Thereafter, the output of the displayed content candidate extraction unit <NUM> and the result illustrated in <FIG> are subjected to sum or product computation for each item of displayed content, and the vehicle control content is designated using displayed content with a highest likelihood as a reference.

With such a procedure, it is possible to consider the likelihoods different for each item of displayed content, on the basis of the acquired travel environment and, even in a situation where a road sign or a pavement marking only partially appear due to occlusion or the like, the vehicle control content can be designated using the travel environment as a reference.

The vehicle control content designation unit <NUM> may settle the result on the basis of a predetermined priority. For example, priorities such as those illustrated in <FIG> are set beforehand and, when multiple candidates cannot be distinguished from each other, the vehicle control content is settled on the basis of a candidate with a higher priority.

With such a procedure, when safety is taken into consideration as the priority, it is possible to generate vehicle control signal in consideration of safety.

<FIG> is a flowchart representing the operation of a vehicle control device of the present example.

In the vehicle control device of the present example, the displayed content candidate extraction unit <NUM> firstly performs an image process and extracts a candidate for content for a road sign or a pavement marking in the image (a displayed content candidate extraction process S141 in <FIG>). Thereafter, the travel environment acquisition unit <NUM> acquires the travel environment (a displayed content candidate extraction process S142 in <FIG>). Then, the vehicle control content designation unit <NUM> settles the vehicle control signal on the basis of the results of the displayed content candidate extraction unit <NUM> and the travel environment acquisition unit <NUM> (a vehicle control content designation process S143 in <FIG>).

In <FIG>, the travel environment is always acquired (the displayed content candidate extraction process S142 in <FIG>) when the displayed content candidate is extracted (the displayed content candidate extraction process S141 in <FIG>). As illustrated in <FIG>, however, the travel environment may be acquired only when multiple candidates are extracted in extracting the displayed content candidate.

As illustrated in <FIG>, the displayed content candidate extraction unit <NUM> first performs an image process and extracts a candidate for content for a road sign or a pavement marking in the image (a displayed content candidate extraction process S151 in <FIG>). Next, the number of candidates for content for the road sign or the pavement marking is acquired. Then, on the basis of this number of candidates, it is designated whether the travel environment is to be acquired (S152 in <FIG>). When the number of candidates is plural, the travel environment acquisition unit <NUM> acquires the travel environment (S153 in <FIG>) and the vehicle control content is settled on the basis of the outputs of the displayed content candidate extraction unit <NUM> and the travel environment acquisition unit <NUM> (S154 in <FIG>). Meanwhile, when the number of candidates is only one, the vehicle control content is settled on the basis only of the result of the displayed content candidate extraction unit <NUM> (S154 in <FIG>). When the number of candidates is zero, control content is not settled.

With such a procedure, the processing time can be reduced because it is not necessary to acquire a redundant travel environment in a case where a road sign or a pavement marking can be accurately recognized only by image recognition.

In the process illustrated in <FIG>, the travel environment acquisition unit <NUM> firstly acquires the travel environment (a travel environment acquisition process S161 in <FIG>). Next, the displayed content candidate extraction unit <NUM> extracts a candidate for content for a road sign or a pavement marking (a travel environment acquisition process S162 in <FIG>), but at this time, only a candidate satisfying the travel environment acquired by the travel environment acquisition unit <NUM> is extracted. Thereafter, the vehicle control content designation unit <NUM> designates vehicle control content.

As described above, the vehicle control content designation unit <NUM> performs vehicle control on the basis of displayed content for a road sign or a pavement marking in the image. However, a virtual desired value may be set instead of displayed content for a road sign or a pavement marking in the image such that the vehicle control signal is created so as to be in accordance with the desired value. For example, it is assumed that, the displayed content for a road sign in the image is determined as <NUM>/h maximum speed restriction when the host vehicle speed is <NUM>/h. In addition, at this time, when the travel environment acquisition unit acquires information that there are an enormous number of pedestrians around the host vehicle, for example, <NUM>/h may be set as the desired value for the host vehicle speed such that a brake signal to bring the vehicle speed of <NUM>/h close to <NUM>/h is generated as vehicle control content.

With such a procedure, appropriate vehicle control can be realized according to the surrounding travel environment, instead of depending only on displayed content for road signs and pavement markings.

While the invention of the present application has been described thus far with reference to the embodiment and the example, the invention of the present application is not limited to the above-described embodiments. In particular, in the above embodiment and the example, the recognition of the maximum speed limitation sign among the road signs has been described especially in detail, but the recognition target of the invention of the present application is not limited to the maximum speed limitation sign and other road markings and other road signs are also treated as recognition targets. When these other types are treated as recognition targets, the invention of the present application can be carried out by acquiring the travel environment taking the installation standard into account. For example, a road sign for a pedestrian, a road closed sign for a large-sized automobile or the like, and a pedestrian crossing pavement marking can be used for recognizing displayed content by acquiring pedestrian information as a travel environment.

Claim 1:
A displayed content recognition device comprising:
a displayed content candidate extraction unit (<NUM>) that is adapted to extract candidates for displayed content for a displayed object by template matching on the basis of camera images acquired by a camera that is mounted on a vehicle;
a travel environment acquisition unit (<NUM>) that is adapted to acquire the travel environment of the vehicle; and
a displayed content determination unit (<NUM>) that, when there are multiple candidates for the displayed content, is adapted to determine the displayed content using the travel environment,
wherein:
- the travel environment acquisition unit (<NUM>) is adapted to firstly acquire the travel environment, and
- the device is adapted to then determine which of the candidates satisfy the acquired travel environment, and characterized in that:
- the displayed content candidate extraction unit (<NUM>) is then adapted to perform the template matching limited to the candidates that have been determined to satisfy the acquired travel environment.