Presentation device and presentation method

Abstract

A presentation device (10) comprises: a determination unit (14) that determines, on the basis of a feature amount that is obtained regarding a driver of a vehicle, whether the cognitive function of the driver has deteriorated; an analysis unit (15) that, when the determination unit has determined that the cognitive function of the driver has deteriorated, analyzes the basis of the determination on the basis of the feature amount, and generates information that indicates the basis of the determination; and a presentation unit (16) that presents the determination result by the determination unit that indicates that the cognitive function of the driver has deteriorated, and the basis of the determination that is indicated by the information that is generated by the analysis unit.

Description

Prompting Device and Prompting Method

[0001] The present disclosure relates to a prompting device and a prompting method.

[0002] In recent years, the number of traffic accidents caused by elderly people with declining cognitive functions driving vehicles such as automobiles has been increasing. Therefore, for example, in Patent Document 1, a system that can detect a decline in the cognitive function of a vehicle driver and determine whether driving is permitted has been proposed.

[0003] Japanese Patent Application Laid-Open No. 2019-124975

[0004] In the system described in Patent Document 1, based on information obtained from the driving operation of a vehicle driver, a decline in the cognitive function of the driver is determined, and when it is determined that the cognitive function has declined, this fact is notified. However, in the system described in Patent Document 1, when it is determined that the cognitive function of the driver has declined, only the fact that the cognitive function has declined is notified to the driver. Therefore, there is a problem that the driver may not be fully convinced of the determination result by the system in some cases.

[0005] The present disclosure has been made to solve the above problems, and an object thereof is to provide a technology that can make the driver have a higher sense of acceptance than before with respect to the determination result indicating that the cognitive function has declined.

[0006] The prompting device according to the present disclosure includes a determination unit that determines whether or not the cognitive function of a vehicle driver has declined based on a feature amount obtained regarding the driver, an analysis unit that analyzes the basis of the determination when it is determined by the determination unit that the cognitive function of the driver has declined based on the feature amount, and generates information indicating the basis of the determination, and a prompting unit that prompts the determination result indicating that the cognitive function of the driver has declined by the determination unit and the basis of the determination indicated by the information generated by the analysis unit.

[0007] According to the present disclosure, since it is configured as described above, it is possible to make the driver have a higher sense of acceptance than before with respect to the determination result indicating that the cognitive function has declined.

[0008] This figure shows an example of the configuration of the presentation device according to Embodiment 1. This flowchart shows an example of the operation of the presentation device according to Embodiment 1. This figure illustrates an example of processing by the analysis unit in Embodiment 1. Figure 4A shows an example in which the basis for the reduction judgment is presented as a map showing intersection A and a graph showing the amount of left and right confirmation at intersection A and other intersections, and Figure 4B shows an example in which the basis for the reduction judgment is presented as a graph of the amount of confirmation for each left and right. Figures 5A and 5B show an example of the hardware configuration of the presentation device according to Embodiment 1. This figure shows another example of the configuration of the presentation device according to Embodiment 1. This figure shows an example of the configuration of the presentation device according to Embodiment 2. Figure 8A shows an example in which a map showing intersection A, a graph showing the amount of left and right confirmation at intersection A and an improvement proposal are presented, and Figure 8B shows an example in which a graph showing the amount of confirmation for each left and right and an improvement proposal are presented. This figure shows an example of the configuration of the presentation device according to Embodiment 3. This figure illustrates an example of processing by the improvement evaluation unit in Embodiment 3.

[0009] The embodiments will be described in detail below with reference to the drawings. Embodiment 1. Figure 1 is a diagram showing an example of the configuration of the presentation device 10 according to Embodiment 1. The presentation device 10 is configured to include, for example, a feature quantity calculation unit 11, an accumulation control unit 12, an accumulation unit 13, a determination unit 14, an analysis unit 15, and a presentation unit 16, as shown in Figure 1.

[0010] The feature calculation unit 11 is connected to the vehicle via communication with an imaging device (not shown), an ECU (Electronic Control Unit), a storage device for storing map information, and a GPS (Global Positioning System), all of which are mounted on the vehicle. The imaging device includes a device for imaging the interior of the vehicle and a device for imaging the exterior of the vehicle.

[0011] The feature calculation unit 11 calculates feature quantities related to the vehicle driver in a time series based on imaging information obtained from at least one of an imaging device that images the inside of the vehicle and an imaging device that images the outside of the vehicle, vehicle information obtained from the ECU, map information stored in the storage device, and GPS information obtained from GPS.

[0012] The features obtained regarding the vehicle driver are, for example, features obtained based on the driver's driving actions. Features obtained based on the driver's driving actions include, for example, "amount of left and right checks," "percentage of stopping at stop signs," and "speed of eye movement." These features may also include vehicle location information contained in GPS information (for example, the name of the intersection). In this case, the vehicle information obtained by the ECU may also include information about the vehicle's speed, as well as information about the driver's steering and pedal operations.

[0013] A more detailed explanation of the above-mentioned features will be provided later. The feature calculation unit 11 sequentially outputs the calculated features to the storage control unit 12.

[0014] The storage control unit 12 acquires feature quantities from the feature quantity calculation unit 11. The storage control unit 12 also stores the feature quantities acquired from the feature quantity calculation unit 11 in the storage unit 13.

[0015] The storage control unit 12 may store all the features obtained from the feature calculation unit 11 in the storage unit 13, or it may extract features related to scenes where driving behavior is likely to differ depending on the driver's cognitive function from the features obtained from the feature calculation unit 11 and store them in the storage unit 13. Scenes where driving behavior is likely to differ depending on the driver's cognitive function include, for example, areas near intersections, one-way streets, railway crossings, pedestrian crossings, stop signs, or highways, where a higher level of attention is expected from the driver than on so-called ordinary roads.

[0016] If the storage control unit 12 stores all features obtained from the feature calculation unit 11 in the storage unit 13, the presentation device 10 can store all features obtained from the feature calculation unit 11 in the storage unit 13 without omission, thereby improving the accuracy of the judgment made by the judgment unit 14, which will be described later. On the other hand, if the storage control unit 12 extracts features related to scenes in which driving behavior is likely to differ depending on the driver's cognitive function from the features obtained from the feature calculation unit 11 and stores them in the storage unit 13, the presentation device 10 can suppress the amount of information stored in the storage unit 13. The administrator can appropriately set which of these storage methods to adopt according to the design of the presentation device 10.

[0017] The storage unit 13 stores the feature quantities output from the storage control unit 12. The storage unit 13 is composed of a storage device such as an HDD (Hard Disk Drive) or an SSD (Solid State Drive).

[0018] The determination unit 14 determines whether or not the driver's cognitive function is impaired based on the characteristic quantities obtained regarding the vehicle driver that are stored in the storage unit 13. The method by which the determination unit 14 makes the above determination is not particularly limited, but for example, the determination unit 14 can make the above determination using known methods.

[0019] Furthermore, in order to ensure sufficient judgment accuracy, the determination unit 14 may periodically check whether the number of features stored in the storage unit 13 is equal to or greater than a predetermined threshold, and if the number of features is equal to or greater than the threshold, it may perform the above judgment based on the features stored in the storage unit 13. In this case, the threshold should be set, for example, to a number of features that is considered sufficient to ensure judgment accuracy when determining whether or not there is a decline in the driver's cognitive function, as determined by the administrator of the presentation device 10.

[0020] If the determination unit 14 determines, based on the above determination, that the driver's cognitive function is impaired, it generates information indicating the determination result and outputs the generated information to the analysis unit 15. The determination unit 14 also outputs the feature quantities used when determining that the driver's cognitive function is impaired to the analysis unit 15. However, if the determination unit 14 determines, based on the above determination, that the driver's cognitive function is not impaired, it is not necessarily required to output the information indicating the determination result and the feature quantities used in that determination to the analysis unit 15.

[0021] The analysis unit 15 obtains information from the determination unit 14 indicating that it has determined the driver's cognitive function is impaired. The analysis unit 15 also obtains the feature quantities used when the determination unit 14 determined that the driver's cognitive function was impaired. The analysis unit 15 then analyzes the basis for the determination made by the determination unit 14 when it determined that the driver's cognitive function was impaired, based on the feature quantities obtained from the determination unit 14, and generates information indicating the basis for the determination.

[0022] For example, the analysis unit 15 calculates the contribution of each feature to the judgment result that the driver's cognitive function is impaired. Then, based on the calculated contribution of each feature, the analysis unit 15 identifies the features that contributed to the judgment result and generates information that shows the basis for the judgment by analyzing the identified features. Details of the processing by the analysis unit 15 will be described later.

[0023] The analysis unit 15 outputs to the presentation unit 16 information indicating the judgment result that the driver's cognitive function is impaired, and information indicating the basis for the generated judgment.

[0024] The presentation unit 16 obtains from the analysis unit 15 information indicating the judgment result that the driver's cognitive function is impaired, and information indicating the basis for the judgment. The presentation unit 16 then presents the judgment result indicated by the obtained information, i.e., the judgment result by the judgment unit 14 that the driver's cognitive function is impaired, and the basis for the judgment indicated by the obtained information, i.e., the basis for the judgment indicated by the information generated by the analysis unit 15.

[0025] The recipients of the judgment results and the basis for the judgment by the presentation unit 16 are not particularly limited, but for example, the presentation unit 16 may present the judgment result that the driver's cognitive function is impaired, along with the basis for the judgment, to at least one of the following: the driver, the driver's family, or the vehicle's operations manager. The vehicle's operations manager is, for example, a bus operator if the vehicle is a bus, or a taxi operator if the vehicle is a taxi.

[0026] In the above description, an example was given in which the presentation device 10 includes a feature calculation unit 11, a storage control unit 12, a storage unit 13, a determination unit 14, an analysis unit 15, and a presentation unit 16. However, the feature calculation unit 11, the storage control unit 12, and the storage unit 13 are not essential components of the presentation device 10 and may be provided in an external device or the like that is connected to the presentation device 10 in a communicative manner.

[0027] Next, an example of the operation of the presentation device 10 shown in Figure 1 will be described. Figure 2 is a flowchart illustrating an example of the operation of the presentation device 10.

[0028] As a prerequisite for explaining the operation example of the presentation device 10, it is assumed that the feature calculation unit 11 acquires imaging information obtained from at least one of the imaging devices that image the inside of the vehicle and the imaging device that images the outside of the vehicle, vehicle information obtained from the ECU, and GPS information obtained from the GPS as needed. Furthermore, it is assumed that the feature calculation unit 11 has acquired map information stored in the storage device in advance.

[0029] First, the feature calculation unit 11 calculates feature quantities obtained about the vehicle driver in a time series based on imaging information, vehicle information, and GPS information acquired as needed, as well as map information acquired in advance (step ST1). The feature calculation unit 11 sequentially outputs the calculated feature quantities to the storage control unit 12. Here, the feature quantities obtained about the vehicle driver are assumed to be feature quantities obtained based on the driving actions of the vehicle driver.

[0030] Next, the storage control unit 12 stores the feature quantities obtained from the feature quantity calculation unit 11 in the storage unit 13 (step ST2). In this case, the storage control unit 12 stores all the feature quantities obtained from the feature quantity calculation unit 11 in the storage unit 13.

[0031] Next, the determination unit 14 determines whether or not the driver's cognitive function is impaired based on the feature quantities obtained regarding the vehicle driver that are stored in the storage unit 13 (step ST3). Here, it is assumed that the number of feature quantities stored in the storage unit 13 is equal to or greater than the predetermined threshold mentioned above. Furthermore, it is assumed that the determination unit 14 has determined that the driver's cognitive function is impaired.

[0032] The determination unit 14 generates information indicating that the driver's cognitive function is impaired, and outputs the generated information to the analysis unit 15. The determination unit 14 also outputs to the analysis unit 15 the feature quantities used when determining that the driver's cognitive function is impaired.

[0033] Next, the analysis unit 15 analyzes the basis for the determination made by the determination unit 14 that the driver's cognitive function is impaired, based on the feature quantities obtained from the determination unit 14, and generates information indicating the basis for the determination (step ST4).

[0034] Next, the presentation unit 16 presents the judgment result indicated by the information obtained from the analysis unit 15, that is, the judgment result by the judgment unit 14 that the driver's cognitive function is impaired, and the basis for the judgment indicated by the information obtained from the analysis unit 15, that is, the basis for the judgment indicated by the information generated by the analysis unit 15 (step ST5). The processes of step ST1 and steps ST3 to ST5 will be described in detail below.

[0035] <ST1: Processing by the Feature Calculation Unit 11> An example of processing by the Feature Calculation Unit 11 will be described below.

[0036] The feature calculation unit 11 calculates feature quantities in a time series based on imaging information obtained from an imaging device that images the inside and outside of the vehicle, vehicle information, map information, and GPS information. For example, the feature calculation unit 11 calculates the following values ​​as feature quantities.

[0037] (1) Left and Right Check Amount: This quantity represents how much the driver checks left and right. For example, it represents the range of the driver's head movement when checking left and right. This feature quantity is calculated based on imaging information obtained when an imaging device that images the interior of a vehicle captures images of the driver.

[0038] (2) Percentage of stopping at stop signs This is the percentage of the total number of places where a vehicle needs to stop that are included in the route it travels, at which point the driver actually stopped. This feature is calculated based on, for example, vehicle information, map information, and GPS information.

[0039] (3) Eye movement speed This is the speed at which the driver's eyes move when they shift their gaze. This feature is calculated, for example, based on imaging information obtained when an imaging device that images the interior of a vehicle captures images of the driver.

[0040] (4) Proportion of close-range attention This is the proportion of time the driver spends looking at the area immediately in front of the vehicle, relative to the total driving time during the vehicle's journey. This feature is calculated, for example, based on imaging information obtained by an imaging device that images the interior of the vehicle and the driver, as well as vehicle information. The distance from the vehicle that is considered "nearby" can be set as appropriate by the administrator of the display device 10 according to the design.

[0041] (5) Time spent checking left and right per check This is the time spent checking left and right per check by the driver. This feature is calculated, for example, based on imaging information obtained when an imaging device that images the interior of a vehicle images the driver.

[0042] (6) Number of times the driver checks left and right at intersections. This is the number of times the driver checks left and right when approaching an intersection. This feature is calculated based on, for example, imaging information obtained from an imaging device that images the inside of a vehicle and the driver, map information, and GPS information.

[0043] (7) Time required to find a specific object This is the time required for the driver to find a specific object such as a pedestrian outside the vehicle. This feature quantity is calculated based on, for example, imaging information obtained by an imaging device that images the outside of the vehicle and imaging information obtained by an imaging device that images the inside of the vehicle and captures the driver.

[0044] For example, the feature quantity calculation unit 11 obtains the time when an imaging device that images the outside of the vehicle captures a specific object. Next, the feature quantity calculation unit 11 determines the line-of-sight direction of the driver based on the imaging information obtained by an imaging device that images the inside of the vehicle and captures the driver, and also determines the time when this line-of-sight direction coincides with the direction in which the specific object exists. Then, the feature quantity calculation unit 11 calculates the difference between the two times, and sets the calculated difference as the time required for the driver to find a specific object outside the vehicle.

[0045] (8) Time required from finding a specific object to stepping on the brake pedal This is the time required for the driver to step on the brake pedal after finding a specific object outside the vehicle. This feature quantity is calculated based on, for example, imaging information obtained by an imaging device that images the outside of the vehicle, imaging information obtained by an imaging device that images the inside of the vehicle and captures the driver, and vehicle information.

[0046] For example, the feature quantity calculation unit 11 determines the time when the line-of-sight direction of the driver coincides with the direction in which a specific object exists in the same manner as in (7) above. Also, the feature quantity calculation unit 11 determines the time when the driver actually steps on the brake pedal based on the vehicle information after the time when the line-of-sight direction of the driver coincides with the direction in which a specific object exists. Then, the feature quantity calculation unit 11 calculates the difference between the two times, and sets the calculated difference as the time required for the driver to step on the brake pedal after finding a specific object outside the vehicle.

[0047] Note that the feature amounts exemplified in (1) to (8) above are merely examples, and the feature amount calculation unit 11 may calculate feature amounts other than those described above based on imaging information and the like. For example, the feature amount calculation unit 11 may calculate a feature amount regarding the presence or absence of use of a wiper when the vehicle changes its course. Further, the feature amount calculation unit 11 may calculate any feature amount including the feature amounts exemplified in (1) to (8) above by using known methods.

[0048] <ST3: Processing by Determination Unit 14> An example of the processing by the determination unit 14 will be described.

[0049] The determination unit 14 determines whether or not the cognitive function of the driver has deteriorated based on the feature amount obtained from the driving operation by the driver of the vehicle, which is calculated by the feature amount calculation unit 11.

[0050] For example, the determination unit 14 determines whether or not the cognitive function of the driver has deteriorated based on one or more of the feature amounts (1) to (8) described above. Note that the determination unit 14 can determine whether or not the cognitive function of the driver has deteriorated by using known methods regardless of which feature amount is used. Further, the determination unit 14 can determine whether or not the cognitive function of the driver has deteriorated based on one of the above-described feature amounts, but a more accurate determination can be made by performing the above determination based on a plurality of feature amounts.

[0051] <ST4: Processing by Analysis Unit 15> An example of the processing by the analysis unit 15 will be described while referring to FIG. 3.

[0052] The analysis unit 15 acquires from the determination unit 14 information indicating the determination result that the cognitive function of the driver has deteriorated and the feature amount used by the determination unit 14 for the determination. The analysis unit 15 analyzes the basis for the determination made by the determination unit 14 that the cognitive function of the driver has deteriorated, based on the feature amount acquired from the determination unit 14.

[0053] Specifically, the analysis unit 15 identifies the features obtained from the determination unit 14 that contributed to the determination that the driver's cognitive function was impaired (hereinafter simply referred to as "impairment determination"). For example, the analysis unit 15 calculates the degree to which each feature obtained from the determination unit 14 contributed to the impairment determination (hereinafter simply referred to as "degree of contribution"). Then, based on the calculated degree of contribution, the analysis unit 15 identifies the features that contributed to the impairment determination.

[0054] Here, the analysis unit 15 may calculate the degree of contribution by working backward from, for example, the structure of the Tree-structured classifier, or it may calculate the degree of contribution by setting a threshold for each feature.

[0055] For example, in a Tree-structured classifier, multiple data points located in lower layers branch off from a single data point belonging to a certain layer. Here, each feature is placed at the top-level root and intermediate layer nodes, the leftmost node in the bottom layer has "no cognitive decline" as the final judgment result, and the rightmost node in the bottom layer has "cognitive decline present" as the final judgment result. At the top-level root and intermediate layer nodes, it is determined whether to proceed to the right or left along the branch based on the values ​​of the feature points placed at the respective root and node.

[0056] In this case, moving to the right along the branch means moving in the direction that will ultimately be judged as "cognitive decline present," and moving to the left along the branch means moving in the direction that will ultimately be judged as "no cognitive decline present." Therefore, in this Tree structure, the analysis unit 15 sets the contribution level to be high for the features located at the routes and nodes where the branch is moved to the right, and sets the contribution level to be low for the features located at the routes and nodes where the branch is moved to the left.

[0057] Furthermore, when calculating the degree of contribution by setting a threshold for each feature, for example, the administrator of the presentation device 10 prepares a table in advance that associates the threshold for each feature with the degree of contribution for each feature, and the analysis unit 15 calculates the degree of contribution for each feature by referring to this table.

[0058] Here, Figure 3 shows an example of how the analysis unit 15 calculates the degree of contribution and an example of how features that contributed to the reduction judgment are identified. In the table shown in Figure 3, the leftmost column is the feature, the middle column is the degree of contribution, and the rightmost column is whether the feature contributed to the reduction judgment. The degree of contribution listed in the middle column is expressed on a scale from "0" to "100", with a larger value indicating a greater degree of contribution. A "○" in the rightmost column indicates that the feature contributed to the reduction judgment, and a "×" indicates that the feature did not contribute to the reduction judgment. In this example, the analysis unit 15 determines that a feature contributed to the reduction judgment if the degree of contribution is, for example, 70 or higher, which is a predetermined threshold. The predetermined threshold can be set to any value by the administrator of the presentation device 10 according to the design.

[0059] For example, the analysis unit 15 calculates the degree of contribution of the feature "left / right confirmation amount" to the decrease determination as "90," and compares the calculated degree of contribution with the threshold "70." Since the calculated degree of contribution is greater than or equal to the threshold "70," the analysis unit 15 determines that the feature "left / right confirmation amount" contributed to the decrease determination.

[0060] Furthermore, the analysis unit 15 calculates the degree of contribution of the feature "stop rate during pause" to the decrease determination as "50," and compares the calculated degree of contribution with the threshold "70." Since the calculated degree of contribution is less than the threshold "70," the analysis unit 15 determines that the feature "stop rate during pause" does not contribute to the decrease determination.

[0061] Furthermore, the analysis unit 15 calculates the degree of contribution of the feature "eye movement speed" to the decrease determination as "0," and compares the calculated degree of contribution with the threshold "70." Since the calculated degree of contribution is less than the threshold "70," the analysis unit 15 determines that the feature "eye movement speed" does not contribute to the decrease determination.

[0062] Similarly, the analysis unit 15 calculates the degree to which each of the feature quantities, "proportion of attention paid to nearby objects," "time spent checking left and right per instance," "number of times checking left and right at intersections," "time taken to find a specific object," and "time taken from finding a specific object to pressing the brake pedal," contributed to the decrease in performance, as shown in Figure 3, and compares the calculated degree of contribution with a threshold of "70." Since the calculated degree of contribution for each of the feature quantities is less than the threshold of "70," the analysis unit 15 determines that the aforementioned feature quantities, such as "proportion of attention paid to nearby objects," did not contribute to the decrease in performance.

[0063] Next, the analysis unit 15 generates information indicating the basis for the degradation judgment by analyzing the specific content of the features that it determined contributed to the degradation judgment. The analysis unit 15 generates information indicating the basis for the degradation judgment according to the following two patterns, for example: (1) Extracting and generating the most distinctive scene. (2) Generating by analyzing the features themselves in detail. These will be explained in order below.

[0064] (1) Extract and generate the most distinctive scene. The analysis unit 15 extracts the most distinctive scene from the entire route the vehicle took, based on the features that it determined contributed to the reduction judgment.

[0065] For example, if the feature quantity determined to have contributed to the reduction judgment is "left / right confirmation amount," the analysis unit 15 refers to the "left / right confirmation amount" feature quantity obtained from the judgment unit 14. Then, the analysis unit 15 extracts the scene with the lowest "left / right confirmation amount" out of the entire route the vehicle took as the scene that was most characteristic in terms of "left / right confirmation amount."

[0066] For example, the analysis unit 15, referring to the feature quantity "left / right confirmation amount" obtained from the judgment unit 14, extracts intersection A as the scene that was most characteristic in terms of "left / right confirmation amount" if the "left / right confirmation amount" was the lowest at intersection A. The analysis unit 15 then generates information indicating the basis for the decline judgment, such as a message saying, "A decline in left / right confirmation amount is likely to occur at intersection A. Therefore, we have determined that cognitive function is declining." At this time, the analysis unit 15 may also generate map information showing intersection A, as shown in Figure 4A, and information showing graphs of left / right confirmation amounts at intersection A and other intersections, as information indicating the basis for the decline judgment.

[0067] The information that displays the above message can be generated, for example, using a generation AI. For example, the analysis unit 15 inputs information indicating the "amount of left and right checking" at intersection A and other intersections, and a prompt that reads, "Please generate a message indicating the basis for the judgment of decline regarding the said information." The analysis unit 15 then acquires the information output by the generation AI in response to this input, which displays the message, "A decrease in the amount of left and right checking is likely to occur at intersection A. Therefore, it has been determined that cognitive function is declining."

[0068] Alternatively, the analysis unit 15 can generate information indicating the above-mentioned message according to, for example, pre-created rules. For example, by storing rules such as generating information Y indicating a certain message for a certain input X, the analysis unit 15 can perform rule-based information generation.

[0069] (2) Generated by analyzing the feature itself in detail. For example, if the analysis unit 15 determines that a feature has contributed to the reduction judgment, it will compare each of these features (hereinafter referred to as "individual features") with each other. The analysis unit 15 will then analyze whether there is a bias in the values, such as one of the individual features being extremely small compared to the others.

[0070] The analysis unit 15 then identifies the presence of a bias in values ​​among multiple individual features, such as when one of the individual features is extremely low, as the basis for determining a decline. The analysis unit 15 then generates information explaining the identified basis as information indicating the basis for determining a decline. The information explaining the identified basis may be, for example, information showing some kind of text, or information showing some kind of graph.

[0071] For example, the aforementioned feature quantity, "left-right confirmation quantity," is composed of two individual features, "left-side confirmation quantity" and "right-side confirmation quantity," as shown in Figure 4B. Here, as shown in Figure 4B, if the "right-side confirmation quantity" is extremely small compared to the "left-side confirmation quantity," the analysis unit 15 analyzes that there is a bias in the left-right confirmation quantity. The analysis unit 15 then identifies this bias as the basis for determining a decrease and generates information explaining the identified basis. Here, the analysis unit 15 may, for example, calculate the ratio of one individual feature quantity to another individual feature quantity, and if the calculated ratio is above a predetermined threshold, it may determine that the individual feature quantity is extremely small.

[0072] In the example shown in Figure 4B, the analysis unit 15 generates information indicating the basis for the decline judgment, such as a message stating, "Since the amount confirmed on the right side has decreased, we have determined that cognitive function has declined." At this time, the analysis unit 15 may also generate information indicating the basis for the decline judgment, such as a graph showing the amount confirmed on each side, as shown in Figure 4B.

[0073] In addition to the "amount of left / right check" mentioned above, other examples of features that include multiple individual features include "eye movement speed," "time spent checking left / right per instance," "number of left / right checks at an intersection," "time taken to find a specific object," and "time taken from finding a specific object to pressing the brake pedal."

[0074] "Eye movement speed" may include "speed of eye movement when moving the eyes to the left" and "speed of eye movement when moving the eyes to the right" as individual features. Similarly, "Time spent checking left and right per instance" may include "Time spent checking left per instance" and "Time spent checking right per instance" as individual features. Furthermore, "Number of times checking left and right at an intersection" may include "Number of times checking left at an intersection" and "Number of times checking right at an intersection" as individual features. Additionally, "Time taken to find a specific object" and "Time taken from finding a specific object to pressing the brake pedal" may include, as individual features, cases where the specific object is, for example, a pedestrian and a bicycle.

[0075] In the example shown in Figure 3, the case where the analysis unit 15 determined that only one feature (left / right comparison quantity) contributed to the reduction judgment was explained. However, the analysis unit 15 may determine that multiple features contributed to the reduction judgment. Furthermore, if multiple features are determined to have contributed to the reduction judgment, the analysis unit 15 may analyze the content of all the features determined to have contributed to the reduction judgment and generate information indicating the basis for the reduction judgment, or it may analyze the content of the feature with the highest degree of contribution and generate information indicating the basis for the reduction judgment.

[0076] <ST5: Processing by the presentation unit 16> An example of processing by the presentation unit 16 will be explained.

[0077] The presentation unit 16 presents the determination result from the judgment unit 14, which indicates that the driver's cognitive function is impaired, and the basis for the determination shown by the information generated by the analysis unit 15. Examples of the presentation of the basis for the determination are shown in Figures 4A and 4B described above. For example, the presentation unit 16 presents the determination result that the driver's cognitive function is impaired, along with the basis for the determination, as shown in Figure 4A, a map showing intersection A and a graph showing the amount of left and right checks at intersection A and other intersections. Alternatively, the presentation unit 16 presents the determination result that the driver's cognitive function is impaired, along with the basis for the determination, as shown in Figure 4B, a graph showing the amount of left and right checks.

[0078] Here, the recipients to whom the presentation unit 16 presents the determination result that the driver's cognitive function is impaired, and the basis for that determination, are not particularly limited. However, for example, the presentation unit 16 may present the determination result and the basis for that determination to at least one of the following: the driver, the driver's family, and the vehicle's operations manager.

[0079] Furthermore, there are no particular limitations on the method of presenting the judgment results and the basis for the judgment. For example, when the presentation unit 16 presents the judgment results and the basis for the judgment to the driver, it may communicate with the ECU installed in the vehicle after the end of driving and display the judgment results and the basis for the judgment on a display installed inside the vehicle, thereby presenting them to the driver. Alternatively, the presentation unit 16 may communicate with a mobile terminal held by the driver after the end of driving and display the judgment results and the basis for the judgment on a predetermined application screen installed on the mobile terminal held by the driver, thereby presenting them to the driver.

[0080] Furthermore, when the display unit 16 presents the judgment result and the basis for the judgment to the driver's family, it communicates with a mobile terminal held by the driver's family and displays the judgment result and the basis for the judgment on a predetermined application screen installed on the mobile terminal held by the driver's family, thereby presenting them to the driver's family.

[0081] Furthermore, when the display unit 16 presents the judgment result and the basis for the judgment to the vehicle's operations manager, it is preferable to communicate with a terminal managed by the vehicle's operations manager and display the judgment result and the basis for the judgment on a display connected to the terminal, thereby presenting them to the vehicle's operations manager.

[0082] <Effects of the Presentation Device 10> Next, the effects of the presentation device 10 according to Embodiment 1 will be described. As described above, the presentation device 10 comprises at least a determination unit 14, an analysis unit 15, and a presentation unit 16, and is able to present the basis for the determination that cognitive function is impaired, along with the determination result. Therefore, the presentation device 10 can provide a higher level of acceptance of the determination result that cognitive function is impaired than before. In addition, since the driver can know the basis for the determination that cognitive function is impaired, along with the determination result that cognitive function is impaired, their acceptance of the determination result that cognitive function is impaired will be higher than before.

[0083] Next, with reference to Figure 5, an example of the hardware configuration of the presentation device 10 according to Embodiment 1 will be described. The functions of the feature quantity calculation unit 11, the storage control unit 12, the determination unit 14, the analysis unit 15, and the presentation unit 16 in the presentation device 10 are realized by a processing circuit. The processing circuit may be dedicated hardware as shown in Figure 5A, or it may be a CPU (Central Processing Unit, central processing unit, processing unit, arithmetic unit, microprocessor, microcomputer, processor, or DSP (Digital Signal Processor)) 52 that executes a program stored in the memory 53, as shown in Figure 5B.

[0084] If the processing circuit is dedicated hardware, the processing circuit 51 may be, for example, a single circuit, a composite circuit, a programmed processor, a parallel programmed processor, an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or a combination thereof. The functions of each part, the feature calculation unit 11, the storage control unit 12, the determination unit 14, the analysis unit 15, and the presentation unit 16, may be implemented by the processing circuit 51 individually, or the functions of each part may be implemented together by the processing circuit 51.

[0085] When the processing circuit is a CPU 52, the functions of the feature calculation unit 11, storage control unit 12, determination unit 14, analysis unit 15, and presentation unit 16 are realized by software, firmware, or a combination of software and firmware. The software and firmware are written as programs and stored in memory 53. The processing circuit realizes the functions of each unit by reading and executing the programs stored in memory 53. In other words, the presentation device 10 is equipped with memory for storing programs that, when executed by the processing circuit, result in the execution of each step shown in Figure 2, for example. These programs can also be said to cause the computer to execute the procedures and methods of the feature calculation unit 11, storage control unit 12, determination unit 14, analysis unit 15, and presentation unit 16. Here, the memory 53 includes, for example, non-volatile or volatile semiconductor memory such as RAM (RAM Access Memory), ROM (Read Only Memory), flash memory, EPROM (Erasable Programmable ROM), EEPROM (Electrically EPROM), magnetic disks, flexible disks, optical disks, compact disks, minidiscs, or DVDs (Digital Versatile Discs).

[0086] Furthermore, the functions of the feature calculation unit 11, storage control unit 12, determination unit 14, analysis unit 15, and presentation unit 16 may be partially implemented by dedicated hardware and partially by software or firmware. For example, the feature calculation unit 11 can be implemented by a processing circuit as dedicated hardware, while the storage control unit 12, determination unit 14, analysis unit 15, and presentation unit 16 can be implemented by a processing circuit reading and executing a program stored in memory 53.

[0087] Thus, the processing circuit can realize each of the above-mentioned functions through hardware, software, firmware, or a combination thereof.

[0088] <Other Configuration Examples> In the description of Figure 1 above, an example was described in which the presentation device 10 is configured to include a feature calculation unit 11, an accumulation control unit 12, an accumulation unit 13, a determination unit 14, an analysis unit 15, and a presentation unit 16. However, the presentation device 10 may also be configured to include an acquisition unit 19 instead of the feature calculation unit 11, the accumulation control unit 12, the accumulation unit 13, and the determination unit 14, as shown in Figure 6, for example. The presentation device 10' in this case will be described below.

[0089] The presentation device 10' differs from the presentation device 10 in that it does not determine whether or not the driver's cognitive function is impaired, but instead obtains the determination result that the driver's cognitive function is impaired, along with the driver's characteristic quantities used in that determination, from an external device. In this case, the determination of whether or not the driver's cognitive function is impaired can be made, for example, by an external device other than the presentation device 10' based on the characteristic quantities obtained regarding the driver.

[0090] The acquisition unit 19 acquires information indicating a determination result that the vehicle driver's cognitive function has been determined to be impaired, and characteristic quantities obtained regarding the driver that were used in the determination, from an external device other than the presentation device 10', for example.

[0091] The analysis unit 15 analyzes the basis for the determination that the driver's cognitive function is impaired based on the feature quantities acquired by the acquisition unit 19, and generates information indicating the basis for the determination. The processing content by the analysis unit 15 can be the same as that of the analysis unit 15 included in the presentation device 10 shown in Figure 1.

[0092] The presentation unit 16 presents the judgment result obtained by the acquisition unit 19, which indicates that the driver's cognitive function is impaired, and the basis for the judgment indicated by the information generated by the analysis unit 15. The processing content of the presentation unit 16 can be the same as that of the presentation unit 16 included in the presentation device 10 shown in Figure 1.

[0093] Thus, the presentation device 10' differs from the presentation device 10 in that it does not determine whether or not the driver's cognitive function is impaired, but instead obtains the determination result that the driver's cognitive function is impaired, along with the driver's characteristic quantities used in that determination, from an external device. Even in this case, the presentation device 10' can present the determination result that the driver's cognitive function is impaired, along with the basis for that determination, so, similar to the presentation device 10 shown in Figure 1, it is possible to make the driver feel more convinced by the determination result that their cognitive function is impaired than before. Furthermore, since the driver can know the basis for the determination along with the determination result that their cognitive function is impaired, their sense of conviction regarding the determination result that their cognitive function is impaired will be higher than before.

[0094] As described above, according to this embodiment 1, the presentation device 10 includes a determination unit 14 that determines whether or not the driver's cognitive function is impaired based on characteristic quantities obtained regarding the vehicle driver, an analysis unit 15 that analyzes the basis for the determination based on the characteristic quantities when the determination unit 14 determines that the driver's cognitive function is impaired and generates information indicating the basis for the determination, and a presentation unit 16 that presents the determination result of the determination unit 14 that the driver's cognitive function is impaired and the basis for the determination indicated by the information generated by the analysis unit 15. As a result, the presentation device 10 according to embodiment 1 can provide a higher level of satisfaction with the determination result that cognitive function is impaired than in the conventional version.

[0095] Furthermore, according to this embodiment 1, the presentation device 10 includes an acquisition unit 19 that acquires information indicating a determination result that the vehicle driver's cognitive function has been determined to be impaired, and characteristic quantities obtained regarding the driver that were used in the determination; an analysis unit 15 that analyzes the basis for the determination that the driver's cognitive function is impaired based on the characteristic quantities acquired by the acquisition unit 19 and generates information indicating the basis for the determination; and a presentation unit 16 that presents the determination result that the driver's cognitive function is impaired, acquired by the acquisition unit 19, and the basis for the determination indicated by the information generated by the analysis unit 15. As a result, the presentation device 10 according to embodiment 1 can provide a higher sense of acceptance of the determination result that cognitive function is impaired than in the conventional model.

[0096] Furthermore, the analysis unit 15 calculates the contribution of each feature to the judgment result that the driver's cognitive function is impaired, identifies the feature that contributed to the judgment result based on the calculated contribution of each feature, and generates information indicating the basis for the judgment by analyzing the identified feature. As a result, the presentation device 10 according to Embodiment 1 can accurately identify the feature that contributed to the judgment result and generate information indicating the basis for the judgment according to the identified feature.

[0097] Furthermore, the display unit 16 presents the judgment result indicating that the driver's cognitive function is impaired, along with the basis for the judgment obtained through analysis, to at least one of the driver, the driver's family, or the vehicle's operations manager. This allows at least one of the driver, the driver's family, or the vehicle's operations manager to easily understand the judgment result indicating that the driver's cognitive function is impaired, along with the basis for the judgment.

[0098] Furthermore, the presentation device 10 includes a feature calculation unit 11 that calculates feature quantities in a time series based on imaging information obtained from at least one of an imaging device that images the inside of the vehicle and an imaging device that images the outside of the vehicle, vehicle information, map information, and GPS information, and a storage unit 13 that stores the feature quantities calculated in a time series by the feature calculation unit 11. The determination unit 14 makes a determination based on the feature quantities stored in the storage unit 13 if the number of feature quantities stored in the storage unit 13 is equal to or greater than a threshold. As a result, the presentation device 10 according to Embodiment 1 can ensure the accuracy of the determination when determining whether or not there is a decline in the driver's cognitive function.

[0099] Furthermore, the feature calculation unit 11 calculates feature quantities for at least one of the following based on the imaging information, vehicle information, map information, and GPS information: the amount of left and right checks, the percentage of stopping at stop signs, the speed of eye movement, the percentage of attention paid to nearby objects, the time spent checking left and right each time, the number of times left and right checks are performed at intersections, the time required to find a specific object, and the time required from finding a specific object until the brake pedal is pressed. As a result, the presentation device 10 according to Embodiment 1 can accurately determine whether or not the driver's cognitive function is impaired based on the driver's driving actions.

[0100] Furthermore, the presentation device 10 includes an accumulation control unit 12 that accumulates the feature quantities calculated in a time series by the feature quantity calculation unit 11 in the accumulation unit 13, and the accumulation control unit 12 accumulates all the feature quantities calculated by the feature quantity calculation unit 11 in the accumulation unit 13. As a result, the presentation device 10 according to Embodiment 1 can accumulate all the feature quantities obtained from the feature quantity calculation unit 11 in the accumulation unit 13 without any omissions, and the accuracy of the determination by the determination unit 14 can be improved.

[0101] Furthermore, the presentation device 10 includes an accumulation control unit 12 that accumulates the feature quantities calculated in a time series by the feature quantity calculation unit 11 in the accumulation unit 13. The accumulation control unit 12 extracts feature quantities related to scenes in which driving behavior is likely to differ depending on the driver's cognitive function from the feature quantities calculated by the feature quantity calculation unit 11 and accumulates them in the accumulation unit 13. As a result, the presentation device 10 according to Embodiment 1 can suppress the amount of information accumulated in the accumulation unit 13.

[0102] Embodiment 2. Embodiment 1 described a presentation device that can provide a greater sense of acceptance of a judgment result indicating a decline in cognitive function than conventional devices. Embodiment 2 describes a presentation device that, in addition to the effects of Embodiment 1, can present drivers with suggestions for improving their driving behavior.

[0103] Figure 7 shows an example of the configuration of the presentation device 10b according to Embodiment 2. The presentation device 10b according to Embodiment 2 has an improvement suggestion generation unit 17 added to the presentation device 10 according to Embodiment 1 shown in Figure 1. In other words, the presentation device 10b according to Embodiment 2 is capable of generating and presenting improvement suggestions regarding driving operations to the driver. The other components of the presentation device 10b according to Embodiment 2 are the same as those of the presentation device 10 according to Embodiment 1 shown in Figure 1, so the same reference numerals are used and their descriptions are omitted.

[0104] The improvement proposal generation unit 17 generates information indicating improvement proposals regarding the driver's driving behavior, based on the basis for the determination obtained by the analysis unit 15. The improvement proposal generation unit 17 outputs the generated information to the presentation unit 16.

[0105] The presentation unit 16 acquires the information output from the improvement proposal generation unit 17. Once the presentation unit 16 acquires the information output from the improvement proposal generation unit 17, it presents the improvement proposal indicated by the acquired information.

[0106] An example of processing by the improvement proposal generation unit 17 will be explained with reference to Figure 8. For example, the improvement proposal generation unit 17 generates information indicating improvement proposals using the generation AI.

[0107] For example, let's assume that the analysis unit 15 extracts intersection A as the scene with the most distinctive characteristics in terms of "amount of left and right checking," as explained in "(1) Extract and generate the most distinctive scene" in Embodiment 1. In this case, the improvement plan generation unit 17 obtains information from the analysis unit 15 that indicates the basis for the decline judgment, such as a message saying, "A decrease in the amount of left and right checking is likely to occur at intersection A. Therefore, it has been determined that cognitive function is declining," and also obtains the actual amount of left and right checking at intersection A.

[0108] The improvement plan generation unit 17 inputs the actual amount of left and right checking at intersection A, which it has acquired, into the generating AI. The improvement plan generation unit 17 also inputs a prompt into the generating AI that reads, for example, "The amount of left and right checking at intersection A is XX. Please generate advice regarding driving actions based on this." The value representing the actual amount of left and right checking is embedded in XX.

[0109] The generating AI, in response to the above input, generates and outputs information indicating a message such as, for example, "As shown above, the amount of checking left and right at intersection A is insufficient. In the future, increase the amount of checking left and right at intersection A." The improvement suggestion generation unit 17 acquires the information indicating the above message output from the generating AI as information indicating an improvement suggestion, and outputs the acquired information to the presentation unit 16.

[0110] At this time, the improvement proposal generation unit 17 may, as shown in Figure 8A, obtain from the analysis unit 15 map information showing intersection A generated by the analysis unit 15, and information showing graphs indicating the amount of left and right checks at intersection A and other intersections, and include this obtained information in the information showing the improvement proposal and output it to the presentation unit 16.

[0111] Alternatively, the improvement suggestion generation unit 17 can generate information indicating the above-mentioned message according to, for example, pre-created rules. For example, by storing rules such as generating information Y indicating an improvement suggestion message for input X which is a left / right confirmation amount, the improvement suggestion generation unit 17 can generate rule-based information.

[0112] Furthermore, as explained in "(2) Generated by analyzing the feature quantities themselves in detail" in Embodiment 1, if the analysis unit 15 identifies the presence of a bias in the values ​​of multiple individual feature quantities as the basis for determining a decrease, the improvement proposal generation unit 17 can process it in the same manner as described above.

[0113] In this case, the improvement plan generation unit 17 obtains information from the analysis unit 15 that indicates the basis for the decline judgment, such as a message saying, "We have determined that cognitive function is declining because the amount of checking on the right side has decreased," and also obtains the actual amount of checking on the left and right at intersection A.

[0114] The improvement plan generation unit 17 inputs the actual amount of left and right checking at intersection A, which it has acquired, into the generating AI. The improvement plan generation unit 17 also inputs a prompt into the generating AI that reads, for example, "The amount of left and right checking at intersection A is XX. Please generate advice regarding driving actions based on this." The value representing the actual amount of left and right checking is embedded in XX.

[0115] The generating AI responds to the above input by outputting information that indicates a message, for example, as shown in Figure 8B, such as, "As shown above, the amount of checking on the right side is generally insufficient. In the future, be mindful of checking the right side as well." The improvement suggestion generation unit 17 acquires the information indicating the above message output from the generating AI as information indicating an improvement suggestion, and outputs the acquired information to the presentation unit 16.

[0116] In this case, the improvement proposal generation unit 17 may, as shown in Figure 8B, obtain from the analysis unit 15 information showing graphs of the confirmed amounts for each side generated by the analysis unit 15, include the obtained information in the information showing the improvement proposal, and output it to the presentation unit 16.

[0117] Next, an example of processing by the presentation unit 16 will be described. The presentation unit 16 presents improvement proposals indicated by the information generated by the improvement proposal generation unit 17. The recipients to whom the improvement proposals by the presentation unit 16 are presented are not particularly limited, but for example, the presentation unit 16 may present the improvement proposals to at least one of the following: the driver, the driver's family, and the vehicle's operations manager.

[0118] Furthermore, there are no particular limitations on the method by which the presentation unit 16 presents the improvement proposals. For example, when the presentation unit 16 presents the improvement proposals to the driver, it may communicate with the ECU installed in the vehicle after the end of driving and display the improvement proposals on a display installed inside the vehicle, thereby presenting the improvement proposals to the driver. Alternatively, the presentation unit 16 may communicate with a mobile terminal held by the driver after the end of driving and display the improvement proposals on a predetermined application screen installed on the mobile terminal held by the driver, thereby presenting the improvement proposals to the driver.

[0119] Furthermore, when the presentation unit 16 presents the proposed improvements to the driver's family, it communicates with a mobile device held by the driver's family and displays the proposed improvements on a predetermined application screen installed on the mobile device held by the driver's family, thereby presenting the proposed improvements to the driver's family.

[0120] Furthermore, when the presentation unit 16 presents the proposed improvements to the vehicle's operations manager, it is preferable to communicate with a terminal managed by the vehicle's operations manager and display the proposed improvements on a display or the like connected to the terminal, thereby presenting the proposed improvements to the vehicle's operations manager.

[0121] An example of the improvement proposal presented by the presentation unit 16 is shown in Figure 8. For example, when the presentation unit 16 obtains map information showing intersection A, information showing graphs indicating the amount of left and right checks at intersection A and other intersections, and information showing the improvement proposal (message) from the improvement proposal generation unit 17, it presents the map showing intersection A, the graphs showing the amount of left and right checks at intersection A and other intersections, and the improvement proposal (message) side by side, as shown in Figure 8A.

[0122] Furthermore, when the presentation unit 16 obtains information from the improvement proposal generation unit 17, including a graph showing the amount of confirmation for each side and information showing the improvement proposal (message), it presents the graph showing the amount of confirmation for each side and the improvement proposal (message) side by side, as shown in Figure 8B.

[0123] Next, the effects of the presentation device 10b according to Embodiment 2 will be described. As described above, the presentation device 10b is equipped with an improvement suggestion generation unit 17, and similar to the presentation device 10 according to Embodiment 1, it is possible to give the driver a higher sense of acceptance of the judgment result that cognitive function is impaired than before, and it is also possible to generate and present improvement suggestions regarding driving behavior to the driver. As a result, the driver will be able to understand what to pay particular attention to when it is determined that cognitive function is impaired, and the possibility of the driver causing an accident will be reduced.

[0124] In the above description, the presentation device 10b was described in the case where it is configured by adding an improvement suggestion generation unit 17 to the presentation device 10 shown in Figure 1. However, the presentation device 10b may also be configured by adding an improvement suggestion generation unit 17 to the presentation device 10' shown in Figure 6.

[0125] As described above, according to this second embodiment, the presentation device 10b includes an improvement suggestion generation unit 17 that generates information indicating improvement suggestions regarding the driver's driving behavior according to the basis for the determination obtained by the analysis unit 15, and the presentation unit 16 presents the improvement suggestions indicated by the information generated by the improvement suggestion generation unit 17. As a result, the presentation device 10b according to the second embodiment can generate and present improvement suggestions regarding driving behavior to the driver, in addition to the effects of the first embodiment. This allows the driver to understand what to pay particular attention to when it is determined that their cognitive function is impaired, thereby reducing the likelihood of the driver causing an accident.

[0126] Furthermore, the presentation unit 16 presents the improvement suggestions indicated by the information generated by the improvement suggestion generation unit 17 to at least one of the driver, the driver's family, and the vehicle's operations manager. This allows at least one of the driver, the driver's family, and the vehicle's operations manager to easily understand what specific precautions they should take while driving.

[0127] Embodiment 3. Embodiment 2 described a presentation device that, in addition to the effects of Embodiment 1, can present the driver with suggestions for improving their driving behavior. Embodiment 3 describes a presentation device that, in addition to the effects of Embodiments 1 and 2, can evaluate whether the driver has made improvements to their driving behavior based on the suggested improvements.

[0128] Figure 9 shows an example of the configuration of the presentation device 10c according to Embodiment 3. The presentation device 10c according to Embodiment 3 has an improvement evaluation unit 18 added to the presentation device 10b according to Embodiment 2 shown in Figure 7. In other words, the presentation device 10c according to Embodiment 3 makes it possible to evaluate whether or not the driver has made improvements to the driving operation based on the improvement proposal. The other components of the presentation device 10c according to Embodiment 3 are the same as those of the presentation device 10b according to Embodiment 2 shown in Figure 7, so the same reference numerals are used and their descriptions are omitted.

[0129] The improvement evaluation unit 18 evaluates whether the driver's driving behavior has improved after the improvement proposal is presented by the presentation unit 16.

[0130] If the improvement evaluation unit 18 evaluates that the driver's driving behavior has improved, the presentation unit 16 will display a message to the driver indicating that the driving behavior has been evaluated as having improved.

[0131] An example of the processing performed by the improvement evaluation unit 18 will be explained with reference to Figure 10. After the improvement proposal is presented by the presentation unit 16, the improvement evaluation unit 18 evaluates whether or not the driver's driving behavior has improved.

[0132] The improvement evaluation unit 18 evaluates whether the driver's driving behavior has improved after a predetermined period (for example, one week) has elapsed since the improvement proposal was presented by the presentation unit 16. For example, the improvement evaluation unit 18 refers to the feature quantities accumulated in the storage unit 13 during the period between the presentation of the improvement proposal by the presentation unit 16 and the elapsed time. Based on the referenced feature quantities, the improvement evaluation unit 18 evaluates that the driver's driving behavior has improved if it determines that the feature quantity that triggered the presentation of the improvement proposal has changed to a certain extent in an improvement trend. On the other hand, the improvement evaluation unit 18 evaluates that the driver's driving behavior has not improved if it determines that the feature quantity that triggered the presentation of the improvement proposal has not changed to a certain extent in an improvement trend.

[0133] Furthermore, the administrator of the presentation device 10c can appropriately set, depending on the design, the degree to which the characteristic quantity that triggered the suggestion of improvement changes in order to be evaluated as an improvement in the driver's driving behavior.

[0134] Another method for evaluating improvements is, for example, that the improvement evaluation unit 18 refers to the feature quantities stored in the storage unit 13 during the period between the presentation of the improvement plan by the presentation unit 16 and the elapsed of a predetermined period. Based on the referenced feature quantities, the improvement evaluation unit 18 then performs the same determination as the determination unit 14 performs for the decrease. As a result, if the content in the rightmost column of the table illustrated in Figure 3, that is, the column indicating whether the feature quantity contributed to the decrease determination, changes from "○" to "×", the improvement evaluation unit 18 evaluates that the driver's driving behavior has improved.

[0135] If the improvement evaluation unit 18 evaluates that the driver's driving behavior has improved, it generates information indicating a message that the driver's driving behavior has improved. For example, as shown in Figure 10, the improvement evaluation unit 18 generates information indicating a message that says, "Excellent! The low amount of checking on the right side has been improved." At this time, the improvement evaluation unit 18 may also generate information indicating an increase in the amount of checking on the right side, as shown in Figure 10, based on the feature quantities obtained when referring to the storage unit 13. The improvement evaluation unit 18 may generate information indicating such messages using generation AI, similar to the improvement proposal generation unit 17 described above, or it may generate it according to pre-created rules.

[0136] Furthermore, if the improvement evaluation unit 18 evaluates that the driver's driving behavior has not improved, it may generate information that prompts the driver to improve their driving behavior. For example, if the improvement evaluation unit 18 evaluates that the driver's driving behavior has not improved, it may generate information that says, "You are still not checking the right side enough. Try to increase the amount you check the right side."

[0137] Next, an example of processing by the presentation unit 16 will be described. If the improvement evaluation unit 18 evaluates that the driver's driving behavior has improved, the presentation unit 16 will indicate that the driving behavior has been evaluated as having improved.

[0138] Here, the recipients to whom the presentation unit 16 presents the information that the driving performance has been evaluated as having improved are not particularly limited, but for example, the presentation unit 16 may present the information that the driving performance has been evaluated as having improved to at least one of the following: the driver, the driver's family, and the vehicle's operations manager.

[0139] In particular, by displaying to the driver that the driving performance has been evaluated as improved, the display device 10c allows the driver to realize the value of using the display device 10c, and can also increase motivation to improve driving performance, leading to the continuation of improvement actions. Furthermore, the driver can realize the value of using the display device 10c, and their motivation to improve driving performance will increase, leading to a greater desire to continue improvement actions.

[0140] Furthermore, the method by which the display unit 16 indicates that the driving operation has been evaluated as improved is not particularly limited. For example, when the display unit 16 indicates to the driver that the driving operation has been evaluated as improved, it may communicate with the ECU installed in the vehicle after the end of driving and display the above information on a display installed inside the vehicle to indicate to the driver that the driving operation has been evaluated as improved. Alternatively, the display unit 16 may communicate with a mobile terminal held by the driver after the end of driving and display the above information on a predetermined application screen installed on the mobile terminal held by the driver to indicate to the driver that the driving operation has been evaluated as improved.

[0141] Furthermore, when the display unit 16 displays the above information to the driver's family, it is preferable to communicate with a mobile device held by the driver's family and display the above information on a predetermined application screen installed on the mobile device held by the driver's family, thereby informing the driver's family that the driver's driving performance has been evaluated as having improved.

[0142] Furthermore, when the display unit 16 presents the above information to the vehicle's operations manager, it is preferable to communicate with a terminal managed by the vehicle's operations manager and display the above information on a display connected to the terminal, thereby informing the vehicle's operations manager that the driver's driving performance has been evaluated as having improved.

[0143] An example of a presentation indicating that the operation of the display unit 16 has been evaluated as having improved is shown in Figure 10. For example, if the display unit 16 receives information from the improvement evaluation unit 18 that includes the message, "Excellent! The low amount of confirmation on the right side has been improved," information showing a graph showing an increase in the amount of confirmation on the right side, and information showing a graph showing the amount of confirmation on the right side before it increased, it will present the graph showing the amount of confirmation on the right side before it increased, the graph showing the amount of confirmation on the right side after it increased, and the above message side by side, as shown in Figure 10.

[0144] Furthermore, if the improvement evaluation unit 18 evaluates that the driver's driving behavior has not improved and generates information indicating a message urging the driver to improve their driving behavior, the display unit 16 may display that message.

[0145] Next, the effects of the presentation device 10c according to Embodiment 3 will be described. Similar to the presentation device 10b according to Embodiment 2, the presentation device 10c can provide a higher level of acceptance of the judgment result that cognitive function is impaired, and can generate and present improvement suggestions regarding driving behavior to the driver. Furthermore, as described above, the presentation device 10c is equipped with an improvement evaluation unit 18, which can evaluate whether or not the driver has made improvements to their driving behavior based on the improvement suggestions.

[0146] As a result, the display device 10c can make the driver realize the value of using the display device 10c, and can also increase their motivation to improve their driving behavior, leading to the continuation of improvement behavior. In addition, the driver can realize the value of using the display device 10c, and their motivation to improve their driving behavior will increase, leading to a greater desire to continue improvement behavior.

[0147] As described above, according to this embodiment 3, the presentation device 10c includes an improvement evaluation unit 18 that, after the presentation unit 16 presents an improvement proposal, evaluates whether the driver's driving behavior has improved, and if it is evaluated that the driver's driving behavior has improved, generates information indicating that the driver's driving behavior has been evaluated as having improved. The presentation unit 16 then presents the information generated by the improvement evaluation unit 18 indicating that the driver's driving behavior has been evaluated as having improved. Thus, in addition to the effects of embodiment 2, the presentation device 10c according to embodiment 3 can evaluate whether the driver has made improvements to their driving behavior based on the improvement proposal, and if it is evaluated that the driver has made improvements based on the improvement proposal, it can present that fact.

[0148] Furthermore, the presentation unit 16 displays to at least one of the following: the driver, the driver's family, or the vehicle's operations manager, the information generated by the improvement evaluation unit 18 indicating that the driver's driving performance has been evaluated as having improved. This allows at least one of the following to easily understand that the driver's driving performance has been evaluated as having improved. In particular, when the presentation unit 16 makes the above presentation to the driver, the presentation device 10c can make the driver realize the value of using the presentation device 10c, improve their motivation to improve their driving performance, and encourage them to continue their improvement actions. In addition, the driver can realize the value of using the presentation device 10c, improve their motivation to improve their driving performance, and increase their willingness to continue their improvement actions.

[0149] Furthermore, this disclosure allows for free combination of each embodiment, modification of any component of each embodiment, or omission of any component in each embodiment.

[0150] This disclosure makes it possible to provide a greater sense of acceptance of the judgment result that cognitive function is impaired than before, and is suitable for use in presentation devices and presentation methods.

[0151] 10, 10', 10b, 10c Presentation device, 11 Feature calculation unit, 12 Storage control unit, 13 Storage unit, 14 Judgment unit, 15 Analysis unit, 16 Presentation unit, 17 Improvement proposal generation unit, 18 Improvement evaluation unit, 19 Acquisition unit, 51 Processing circuit, 52 CPU, 53 Memory.

Claims

1. A presentation device comprising: a determination unit that determines whether or not the cognitive function of a vehicle driver is impaired based on characteristic quantities obtained regarding the driver of the vehicle; an analysis unit that analyzes the basis for the determination based on the characteristic quantities when the determination unit determines that the driver's cognitive function is impaired, and generates information indicating the basis for the determination; and a presentation unit that presents the determination result of the determination unit that the driver's cognitive function is impaired, and the basis for the determination indicated by the information generated by the analysis unit.

2. A presentation device comprising: an acquisition unit that acquires information indicating a determination result that the driver of a vehicle has been determined to have impaired cognitive function, and characteristic quantities obtained regarding the driver that were used in the determination; an analysis unit that analyzes the basis for the determination that the driver's cognitive function has impaired based on the characteristic quantities acquired by the acquisition unit and generates information indicating the basis for the determination; and a presentation unit that presents the determination result that the driver's cognitive function has impaired, acquired by the acquisition unit, and the basis for the determination indicated by the information generated by the analysis unit.

3. The presentation device according to claim 1 or 2, characterized in that the analysis unit calculates the contribution of each feature to the judgment result that the driver's cognitive function is impaired, identifies the feature that contributed to the judgment result based on the calculated contribution of each feature, and generates information indicating the basis for the judgment by analyzing the identified feature.

4. The presentation device according to claim 1 or 2, characterized in that the presentation unit presents the judgment result indicating that the driver's cognitive function is impaired, and the basis for the judgment obtained by the analysis, to at least one of the driver, the driver's family, and the vehicle's operations manager.

5. The presentation device according to claim 1, comprising: a feature quantity calculation unit that calculates the feature quantities in a time series based on imaging information obtained by at least one of an imaging device that images the interior of the vehicle and an imaging device that images the exterior of the vehicle, vehicle information of the vehicle, map information, and GPS information; and a storage unit that stores the feature quantities calculated in a time series by the feature quantity calculation unit, wherein the determination unit makes the determination based on the feature quantities stored in the storage unit if the number of feature quantities stored in the storage unit is equal to or greater than a threshold.

6. The presentation device according to claim 5, characterized in that the feature quantity calculation unit calculates, based on the imaging information, the vehicle information, the map information, and the GPS information, a feature quantity for at least one of the following: the amount of left and right checks, the percentage of stopping at a stop sign, the speed of eye movement, the percentage of looking at nearby objects, the time spent checking left and right per instance, the number of times left and right checks are made at an intersection, the time required to find a specific object, and the time required from finding a specific object until pressing the brake pedal.

7. The presentation device according to claim 5 or 6, comprising a storage control unit that stores the feature quantities calculated in a time series by the feature quantity calculation unit in the storage unit, wherein the storage control unit stores all the feature quantities calculated by the feature quantity calculation unit in the storage unit.

8. The presentation device according to claim 5 or 6, further comprising a storage control unit that stores the feature quantities calculated in a time series by the feature quantity calculation unit in the storage unit, wherein the storage control unit extracts feature quantities from the feature quantities calculated by the feature quantity calculation unit that relate to scenes in which the driver's cognitive function is likely to cause differences in driving behavior, and stores them in the storage unit.

9. The presentation device according to claim 1 or 2, further comprising an improvement proposal generation unit that generates information indicating improvement proposals regarding the driver's driving actions in accordance with the basis for the determination obtained by the analysis unit, wherein the presentation unit presents the improvement proposals indicated by the information generated by the improvement proposal generation unit.

10. The presentation device according to claim 9, characterized in that the presentation unit presents the improvement proposal indicated by the information generated by the improvement proposal generation unit to at least one of the driver, the driver's family, and the vehicle's operations manager.

11. The presentation device according to claim 9, further comprising an improvement evaluation unit that, after the presentation unit presents the improvement proposal, evaluates whether the driver's driving behavior has improved, and if it is evaluated that the driver's driving behavior has improved, generates information indicating that the driver's driving behavior has been evaluated as having improved, wherein the presentation unit presents the information generated by the improvement evaluation unit indicating that the driver's driving behavior has been evaluated as having improved.

12. The presentation device according to claim 11, characterized in that the presentation unit presents to at least one of the following: the driver, the driver's family, and the vehicle's operations manager, that the information generated by the improvement evaluation unit indicates that the driver's driving behavior has been evaluated as having improved.

13. A presentation method by a presentation device, comprising the steps of: a determination unit determining whether or not the cognitive function of a vehicle driver is impaired based on characteristic quantities obtained regarding the driver; an analysis unit analyzing the basis for the determination made by the determination unit when the driver's cognitive function is impaired, based on the characteristic quantities, and generating information indicating the basis for the determination; and a presentation unit presenting the determination result by the determination unit that the driver's cognitive function is impaired, and the basis for the determination indicated by the information generated by the analysis unit.

14. A presentation method using a presentation device, comprising the steps of: an acquisition unit acquiring information indicating a determination result that the driver of a vehicle has been determined to have impaired cognitive function, and feature quantities obtained regarding the driver that were used in the determination; an analysis unit analyzing the basis for the determination that the driver's cognitive function has impaired based on the feature quantities acquired by the acquisition unit, and generating information indicating the basis for the determination; and a presentation unit presenting the determination result that the driver's cognitive function has impaired, acquired by the acquisition unit, and the basis for the determination indicated by the information generated by the analysis unit.

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