JP7747211B2 - Vehicle data output method and vehicle data output device - Google Patents

Description

The present invention relates to a vehicle data output method and a vehicle data output device.

For example, Patent Document 1 discloses a technology for failure prediction in which the timing of predicted vehicle malfunctions and the severity of the malfunction corresponding to that timing are stored in a computer in advance, and the timing and parts to be repaired for a vehicle to be repaired are predicted based on the attributes (e.g., model and year) and symptoms (e.g., abnormal noises) of the vehicle.

However, even if the customer (the driver of the vehicle to be repaired) is notified of the predicted failure of the vehicle to be repaired, the vehicle to be repaired is still in the pre-breakdown stage, so there is a risk that the customer will not be able to make a satisfactory decision regarding the predicted failure (failure prediction).

In other words, in order to improve the customer's (subjective) confidence in predicted failures (failure prediction) and to enable the customer to accept predicted failures (failure prediction), further improvements are needed in notifying customers of failure predictions.

The present invention aims to ensure that predicted failures can gain the confidence of drivers (users).

Japanese Patent Application Laid-Open No. 2003-170817

The present invention is characterized in that it arranges in chronological order prediction information regarding predicted failures and prediction information regarding precursor symptoms that are predicted to occur in the process leading up to the predicted failure occurring, and notifies the driver of the vehicle of the process by which the precursor symptoms of the failure progress toward the actual failure as failure prediction information, and when a precursor symptom of a predicted failure is detected, it adds this detection information to the failure prediction information, and further notifies the driver of the stage at which the precursor symptom of the failure was detected in the process of the failure progressing toward the actual failure.

According to the present invention, the driver can grasp the progression of a predicted failure by detecting precursor symptoms, thereby increasing the reliability of the notified failure prediction information.

FIG. 2 is a functional block diagram of the vehicle data output device according to the first embodiment. FIG. 2 is an explanatory diagram showing an example of a failure prediction result stored in a failure prediction result database. FIG. 3 is an explanatory diagram showing an example of driving state information stored in a driving state information database. FIG. 4 is an explanatory diagram showing an example of prediction and detection information stored in a detection information storage unit. FIG. 10 is an explanatory diagram showing an example of displaying failure prediction information when a failure is predicted. FIG. 10 is an explanatory diagram showing an example of display of failure prediction information that is displayed when a precursor symptom that occurs in the process leading up to the predicted failure is detected. 4 is a flowchart showing a processing flow in the vehicle data output device of the first embodiment. FIG. 10 is a functional block diagram of a vehicle data output device according to a second embodiment. FIG. 4 is an explanatory diagram showing an example of information about symptom sensitivity stored in a symptom sensitivity database; 10 is a flowchart showing a processing flow in a vehicle data output device according to a second embodiment. FIG. 10 is a functional block diagram of a vehicle data output device according to a third embodiment.

An embodiment of the present invention will be described in detail below. The vehicle data output device of the embodiment is configured as a cloud system, for example, mainly consisting of a cloud server. The vehicle data output device also includes a plurality of data acquisition devices pre-installed in the vehicle to acquire various data from the vehicle. The vehicle data output device is capable of individually predicting failures for a plurality of vehicles.

1 is a functional block diagram of a vehicle data output device according to a first embodiment. The vehicle data output device is composed of a vehicle 1 (vehicle X) in which an abnormality has been predicted, and a failure prediction information output device 2. The vehicle 1 and the failure prediction information output device 2 can exchange information via a connected car system or an appropriate communication means. The failure prediction information output device 2 can exchange information with multiple vehicles.

The vehicle 1 has a diagnostic unit 3 , a memory 4 , a display unit 5 , a plurality of ECUs (electric control units) 6 , and a vehicle communication unit 7 .

Depending on the type of failure or the urgency of the failure, the diagnosis unit 3 shares the diagnosis with a failure prediction unit 12 (described later) of the failure prediction information output device 2. The diagnosis unit 3 predicts, for example, the time and mileage until the vehicle 1 breaks down.

The display unit 5 is configured, for example, by a display provided in the vehicle 1. Here, the display provided in the vehicle 1 is various display devices provided in the instrument panel. The display unit 5 is capable of displaying information notified (output) from the failure prediction information output device 2.

The memory 4 stores various types of information about the vehicle 1, and is capable of storing, for example, failure prediction information, which will be described later.

The ECU 6 is a control device for various components of the vehicle 1, and receives signals from sensors provided in various parts of the vehicle 1 and performs various controls based on these input signals. The sensor signals input to the ECU 6 can be transmitted to the failure prediction information output device 2 via the vehicle communication unit 7 as vehicle driving data input by the driver of the vehicle 1. In other words, the vehicle driving data is based on the sensor signals input to the ECU 6.

The vehicle communication unit 7 exchanges information (data) such as the vehicle driving data with a communication unit 11 (described later) of the failure prediction information output device 2 .

The failure prediction information output device 2 has a communication unit 11, a failure prediction unit 12, a failure prediction result database 13, a detection information acquisition unit 14, a driving condition information database 15, a detection information storage unit 16 as a storage unit, a symptom prediction unit 17, and a symptom prediction notification content output unit 18 as an output unit.

The communication unit 11 exchanges information (data) such as failure prediction information (described later) with the vehicle communication unit 7 of the vehicle 1 .

The failure prediction unit 12 predicts a failure of the vehicle 1 based on the vehicle driving data acquired while the vehicle is driving. The vehicle driving data is transmitted from the vehicle communication unit 7 of the vehicle 1 to the communication unit 11 of the failure prediction information output device 2 and then to the failure prediction unit 12.

The failure prediction unit 12 continuously performs failure prediction in real time using the vehicle driving data (for example, the rotation speed of the internal combustion engine, the air-fuel ratio of the internal combustion engine, etc.) sent from the vehicle 1 every moment.

The failure prediction unit 12, for example, performs a frequency analysis of the engine speed of the internal combustion engine mounted on the vehicle 1, and predicts a failure when the analysis result transitions from a normal state to an abnormal state obtained when abnormal noise or vibration is generated, assuming that abnormal noise or vibration has been detected. In other words, when making a judgment using the analysis value of the vehicle driving data, the failure prediction unit 12 predicts a failure when, for example, the value of the analysis value becomes equal to or greater than a predetermined threshold value, assuming that abnormal noise or vibration has been detected.

Furthermore, the failure prediction unit 12 estimates which part will fail and the remaining driving distance until the predicted failure occurs when predicting a failure of the vehicle 1. The failing part and the remaining driving distance until the failure occurs are estimated, for example, by referring to past data stored in the failure prediction result database 13.

The failure prediction result database 13 contains, as failure prediction results, information such as, for example, as shown in Figure 2, the contents of the failure prediction for each vehicle including information on the failed parts, the date and time when the failure was predicted (date and time of prediction alert), the estimated mileage from the time when the failure was predicted until the predicted failure occurs (estimated remaining mileage at the time the alert occurs), and the estimated mileage from the current time until the predicted failure occurs (distance that can be driven before failure).

The failure prediction result database 13 includes failed part information (information about vehicle parts). This makes it possible to predict which vehicle parts will fail based on symptoms when predicting a failure. By predicting which parts will fail based on symptoms, the reliability of failure prediction can be improved by comparing the results of failure prediction using the vehicle driving data.

The prediction information, which is information about the failure predicted by the failure prediction unit 12, is output to the detection information acquisition unit 14 and the failure prediction result database 13. The prediction information output to the failure prediction result database 13 is stored in the failure prediction result database 13.

The method for predicting a failure by the failure prediction unit 12 may be a statistical method or a method using machine learning, and is not limited to a specific method.

The detection information acquisition unit 14 uses the vehicle driving data and the like and, while referring to driving state information, detects precursor symptoms that occur in the process leading up to the predicted failure as detection information.

The detection information includes information about the time (date and time) when a precursor symptom of a predicted failure was detected, which makes it possible to predict the time when the predicted failure will occur based on the information about the precursor symptom and the time when the precursor symptom occurred.

The detection information includes information about the mileage at the time when the precursory symptoms of the predicted failure were detected, which makes it possible to predict the mileage until the predicted failure occurs based on the information about the precursory symptoms and the mileage (e.g., total mileage) at the time the precursory symptoms occurred.

The detection information includes information about the vehicle parts in which precursor symptoms of the predicted failure have been detected, which makes it possible to predict the faulty part based on the precursor symptoms and the information about the vehicle parts related to the precursor symptoms.

The driving condition information is information relating to precursor symptoms of predicted failures, and is stored in the driving condition information database 15. The driving condition information database 15 stores, as the driving condition information, for example, as shown in FIG. 3, the causes of each type of failure, as well as information on symptoms and how the symptoms progress. For example, if the type of failure is a duct failure (air leak), the precursor symptoms progress in three stages in this order: abnormal engine noise, vibration, and starting problems of the internal combustion engine. The driving condition information database 15 may be in the form of a map, as shown in FIG. 3.

The detection information acquisition unit 14 also receives the prediction information, which is the result of the failure prediction unit 12. The information input to the detection information acquisition unit 14 is sent to the detection information storage unit 16 as prediction/detection information and stored therein. In other words, the prediction information and the detection information are output from the detection information acquisition unit 14 to the detection information storage unit 16 as prediction/detection information and stored in the detection information storage unit 16. The detection information storage unit 16 is a database that stores the prediction information, which is information about predicted failures, and the detection information in association with each other.

The detection information storage unit 16 has, as the prediction/detection information, for example, failure prediction information for a specific vehicle 1 and the detection information, as shown in Fig. 4. The failure prediction information stored in the detection information storage unit 16 includes, for example, the content of the failure prediction, the date and time when the failure was predicted (date and time of the prediction alert), and the estimated mileage from the time when the failure was predicted until the predicted failure occurs (estimated remaining mileage at the time the alert is generated). The detection information stored in the detection information storage unit 16 includes, for example, each progressing symptom and the estimated remaining mileage until the failure at the time when each progressing symptom is detected.

4 shows a state in which, as prediction/detection information for duct failure, only detection information 1 relating to abnormal engine noise is detected as a precursor symptom after a failure is predicted. Therefore, in FIG. 4, detection information 2 relating to vibration and detection information 3 relating to starting problems are not detected as prediction/detection information for duct failure.

The predicted/detected information stored in the detection information storage unit 16 may be erased when the predicted failure is avoided by repairing the vehicle 1.

The detection information acquisition unit 14 may detect precursor symptoms of a predicted failure using, for example, the same method as the failure prediction unit 12. However, it is important that the driver can sense (feel) the precursor symptoms of a predicted failure as a precursor to the failure. Therefore, when the detection information acquisition unit 14 detects precursor symptoms of a failure using the same method as the failure prediction unit 12, for example, if the target precursor symptom is vibration, it uses a precursor detection threshold value that has a higher vibration level than the threshold value used for failure prediction.

The symptom prediction unit 17 predicts precursor symptoms that will occur in the process leading up to the predicted failure based on the information from the detection information acquisition unit 14 and the above driving condition information in the driving condition information database 15, and outputs the predicted symptoms as prediction information.

The prediction information is information on precursor symptoms predicted from the detection information and the driving condition information stored in the driving condition information database 15. Specifically, the prediction information includes precursor symptoms that are expected to occur in the future and the timing at which they are expected to occur.

The symptom prediction notification content output unit 18 combines the prediction information regarding precursor symptoms predicted by the symptom prediction unit 17 based on the detection information regarding precursor symptoms detected by the detection information acquisition unit 14 with information (prediction information) regarding faulty parts predicted by the failure prediction unit 12, and outputs a prediction of the progression of symptoms leading up to a failure and the current situation as failure prediction information.

That is, when a failure of the vehicle 1 is predicted, the symptom prediction notification content output unit 18 outputs the prediction information and the prediction information as failure prediction information in which the prediction information and the prediction information are arranged in chronological order, for example as shown in Fig. 5. In detail, when a failure of the vehicle 1 is predicted, the symptom prediction notification content output unit 18 outputs the prediction information and the prediction information in chronological order as failure prediction information that can visualize the process by which precursor symptoms of the predicted failure change (progress) toward the time of occurrence of the predicted failure.

Furthermore, when a precursor symptom of the predicted failure is actually detected after a failure of the vehicle 1 has been predicted, the symptom prediction notification content output unit 18 further outputs this detection information, the prediction information, and the prediction information as failure prediction information in which the detection information, the prediction information, and the prediction information are displayed in chronological order, for example, as shown in Fig. 6. In detail, when a precursor symptom of the predicted failure is actually detected after a failure of the vehicle 1 has been predicted, the symptom prediction notification content output unit 18 further outputs this detection information, the prediction information, and the prediction information in chronological order as failure prediction information that can be visualized as to at what stage (position) the detection information was detected in the process in which the precursor symptom of the predicted failure progresses (progresses) toward the time of occurrence of the predicted failure.

Figure 5 is an example of a display of failure prediction information that is displayed on the display unit 5 of the vehicle 1 when a failure is predicted. Figure 5 displays, in chronological order, precursor symptoms that occur from when a failure is predicted to when the failure actually occurs. That is, Figure 5 displays that, when traveling 70 km from the current location where the failure prediction alert was notified, an abnormal noise from the engine (internal combustion engine) is heard as a precursor symptom of a failure, that, when traveling 120 km from the current location where the failure prediction alert was notified, engine (internal combustion engine) vibrations are generated as a precursor symptom of a failure, and that, when traveling 220 km from the current location where the failure prediction alert was notified, a failure that prevents the vehicle from starting due to poor starting will occur.

FIG. 6 shows an example of failure prediction information displayed on the display unit 5 of the vehicle 1 when a precursory symptom that occurs in the process leading up to the predicted failure is detected.

In the display example shown in Fig. 6, similar to the display example of Fig. 5 described above, precursor symptoms occurring from when a failure is predicted to when the failure actually occurs are displayed in chronological order. In more detail, Fig. 6 displays the following: a failure prediction alert has already been notified; an abnormal noise from the engine (internal combustion engine) is currently being heard as a precursor symptom of the failure; engine (internal combustion engine) vibration will occur as a precursor symptom of the failure after traveling 40 km from the current location; and a failure that will prevent the vehicle from starting due to poor starting will occur after traveling 140 km from the current location.

The information output from the symptom prediction notification content output unit 18 is transmitted to the vehicle communication unit 7 of the corresponding vehicle 1 via the communication unit 11 and displayed on the display unit 5 of the vehicle 1 .

The information output from the symptom prediction notification content output unit 18 is notified to the driver via the display unit 5 of the vehicle 1 while driving the vehicle.

FIG. 7 is a flowchart showing the flow of processing in the vehicle data output device of the first embodiment.

In step S10, the vehicle running data is used to predict a failure. The function of step S10 corresponds to the function of the failure prediction unit 12.

In step S20, the predicted precursory symptoms of a failure are referenced using the driving condition information database 15. The function of step S20 corresponds to the function of the symptom prediction unit 17.

In step S30, the driver of the vehicle 1 is notified of information about the predicted failure (failed part) and the predicted progression of precursor symptoms that will occur before the failure. An example of the notification is shown in FIG. 5 above. The function of step S30 corresponds to the function of the symptom prediction notification content output unit 18 and the display unit 5.

In step S40, precursor symptoms occurring in the process leading up to the predicted failure are detected, and the detection information is acquired. After the failure prediction notification is issued, the symptoms progress until the failure occurs, and the detection information, which is the precursor symptoms occurring in the process leading up to the predicted failure, is stored in the detection information storage unit 16. Here, the detection information may be collected from sources other than the vehicle driving data, and it is also possible to use sensor data other than the vehicle driving data, such as data from a vibration sensor or microphone. The function of step S40 corresponds to the function of the detection information acquisition unit 14.

In step S50, the precursory symptoms are determined in reference to the driving condition information database 15. The method for determining the precursory symptoms is not limited to a specific method and may be a statistical method or a method using machine learning. The function of step S50 corresponds to the function of the symptom prediction unit 17.

In step S60, the current precursory symptoms are compared with the prediction results, and the driver is notified of the progression of the symptoms. An example of the notification is shown in Figure 6. The function of step S60 corresponds to the function of the symptom prediction notification content output unit 18 and the display unit 5.

The notification in step S60 may be made when the precursory symptoms occurring in the process leading up to the predicted failure have progressed to the next stage (step). Specifically, if the predicted failure is a duct defect, the notification may be made when an abnormal engine noise is first detected and when vibrations are first generated. In this case, the driver can efficiently grasp the progress of the predicted failure.

In step S70, it is determined whether or not a predicted failure has occurred. If a predicted failure has occurred, the current routine is terminated. If a predicted failure has not occurred, the routine proceeds to step S40. The function of step S70 corresponds to the function of the detection information acquisition unit 14.

In this way, when notifying the driver of vehicle 1 of failure prediction information, the vehicle data output device also notifies the driver of prediction information of precursor symptoms that will occur in vehicle 1 in the process leading up to the predicted failure. Then, when a precursor symptom that actually occurs in vehicle 1 is detected, the vehicle data output device notifies the driver of the detected precursor symptom and notifies the driver that the symptom is progressing according to the prediction information.

Therefore, the driver can grasp the progress of the predicted failure by detecting the precursory symptoms, thereby increasing the reliability of the failure prediction information.

The failure prediction information can be provided at any time after a failure is predicted. That is, the symptom prediction unit 17 constantly updates symptom information relating to precursor symptoms based on the above-mentioned detection information.

Therefore, the driver can actively access the failure prediction information output device 2 using a communication means that can access it, and actively display the failure prediction information on an information display means that can display the failure prediction information, thereby being able to check the progress of the predicted failure at any time (as appropriate).

Another embodiment of the present invention will be described below, in which the same components as those in the above-described embodiment are designated by the same reference numerals and redundant description will be omitted.

A second embodiment of the present invention will be described with reference to Fig. 8. The vehicle data output device of the second embodiment has substantially the same configuration as that of the first embodiment described above, but as shown in Fig. 8, the failure prediction information output device 2 further includes a symptom prediction notification prioritization unit 31 and a symptom sensitivity database 32.

In the vehicle data output device of the second embodiment, the symptom prediction notification prioritization unit 31 prioritizes the precursor symptoms of a predicted failure to be notified to the driver of the vehicle 1. The priority of the precursor symptoms is determined based on the symptom sensitivity database 32.

The symptom sensitivity database 32 stores information on symptom sensitivity in which a sensitivity indicating the degree of ease with which the driver can detect each precursory symptom is assigned, as shown in FIG. 9, for example. Here, the sensitivity is expressed, for example, as a numerical value between 0 and 1. In this case, a sensitivity value of "0" indicates the least sensitivity, and a sensitivity value of "1" indicates the easiest sensitivity. In FIG. 9, the sensitivity of abnormal engine noise is "0.5," and the sensitivity of vibration is "0.8," indicating that vibration is more easily detected by the driver than abnormal engine noise.

The symptom detection database 32 may also take into account vehicle type information. That is, the symptom detection database 32 may classify the detected precursor symptoms based on the ease of detection, taking into account variations (differences) in the ease of detection of precursor symptoms depending on the vehicle type.

This makes it possible to prioritize the ease of detecting precursor symptoms for each vehicle type by using information on symptom sensitivity, which is the ease of detecting precursor symptoms that occur in the process leading up to the predicted failure occurring, and vehicle type information.

In other words, the precursor symptoms that occur in the process leading up to a predicted breakdown can be classified based on how easily they are perceived depending on the vehicle model. As a result, the breakdown prediction results are perceived by the driver as being more reliable, further increasing the driver's trust in them. In other words, by prioritizing the display of symptoms that are easily perceived by the driver depending on the vehicle model, the driver is more likely to trust the breakdown prediction results.

The symptom prediction notification content output unit 18 combines the prediction information regarding precursor symptoms predicted by the symptom prediction unit 17 based on the detection information regarding precursor symptoms detected by the detection information acquisition unit 14 with information on the failed parts predicted by the failure prediction unit 12 (prediction information), compares the prediction of symptom progression to failure with the current situation, and then determines the precursor symptoms to be notified by the symptom prediction notification prioritization unit 31 based on the symptom sensitivity database 32, and outputs the result as failure prediction information. In particular, for symptoms displayed as the first precursor symptoms, priority is given to notifying symptoms that are easily detected by many users.

Specifically, when a failure of the vehicle 1 is predicted, the symptom prediction notification content output unit 18 of the second embodiment does not output as failure prediction information any precursory symptoms in the prediction information that are at a level of sensitivity that the driver cannot currently perceive. Furthermore, even if a precursory symptom of the predicted failure is detected after a failure of the vehicle 1 is predicted, the symptom prediction notification content output unit 18 of the second embodiment does not output as failure prediction information any precursory symptoms that are at a level of sensitivity that the driver cannot currently perceive.

In other words, the symptom prediction notification content output unit 18 of the second embodiment outputs failure prediction information so that the precursor symptom that can be clearly felt by the driver is displayed on the display unit 5 as the first detected precursor symptom.

For example, if a duct failure (air leak) is predicted while the vehicle 1 is running, there is a risk that the initial symptom, an abnormal engine noise, will be blended in with the running noise and will not be detected by the driver. In such a case, the first precursor symptom to be notified to the driver is a vibration that is highly perceptible (loud) and will not be blended in with the running noise while the vehicle is running. In other words, the symptom prediction notification content output unit 18 of the second embodiment does not output abnormal engine noise as a precursor symptom of the predicted failure as failure prediction information.

FIG. 10 is a flowchart showing the flow of processing in the vehicle data output device of the second embodiment.

The flowchart shown in FIG. 10 is substantially the same as that shown in FIG. 7, except that step S51 is added between step S50 and step S60, in which the driver is asked to prioritize precursory symptoms.

Note that steps S10, S20, S30, S40, S50 and S70 in Figure 10 perform the same processing as steps S10, S20, S30, S40, S50 and S70 in Figure 7 described above, so duplicated explanations will be omitted.

In step S51, the precursory symptoms to be notified to the driver are prioritized based on the symptom sensitivity database 32. The function of step S51 corresponds to the function of the symptom prediction notification prioritization unit 31.

In step S60 of FIG. 10, the current precursor symptoms are compared with the prediction results, and the driver is notified of the progression of the symptoms. The precursor symptoms that are currently at a level that the driver can sense are output as failure prediction information.

The vehicle data output device of the second embodiment can achieve substantially the same effects as the vehicle data output device of the first embodiment described above.

In addition, in the vehicle data output device of the second embodiment, the detected precursor symptoms are classified based on ease of detection, and the vehicle data output device of the second embodiment determines the priority of the precursor symptoms to be notified to the driver based on the results of the classification based on ease of detection.

As a result, the vehicle data output device of the second embodiment makes it easier for the driver to detect the precursory symptoms displayed as failure prediction information. Therefore, the results of the failure prediction by the vehicle data output device of the second embodiment are recognized by the driver as being highly reliable. In other words, the vehicle data output device of the second embodiment can further increase the driver's reliability in the failure prediction information by preferentially displaying precursory symptoms that are easier for the driver to detect.

Furthermore, the failure prediction information of the data output device of the second embodiment displays, as the above-mentioned detection information, a precursor symptom that can be clearly felt by the driver as the first detected precursor symptom.

Therefore, the vehicle data output device of the second embodiment can further increase the driver's confidence in the failure prediction information when first notifying the driver of the progression of symptoms by first displaying precursory symptoms that many drivers can sense.

A third embodiment of the present invention will be described with reference to Fig. 11. The vehicle data output device of the third embodiment has substantially the same configuration as the first embodiment described above, except that, as shown in Fig. 11, the failure prediction information output device 2 further includes a symptom prediction notification prioritization unit 31, a symptom sensitivity database 32, and a failure urgency database 41. In other words, the vehicle data output device of the third embodiment has substantially the same configuration as the second embodiment described above, except that the failure prediction information output device 2 further includes a failure urgency database 41.

In the vehicle data output device of the third embodiment, the symptom prediction notification prioritization unit 31 prioritizes the precursor symptoms of a predicted failure to be notified to the driver of the vehicle 1, and determines the level of urgency (degree) of response to the predicted failure from the detected precursor symptoms. The level of urgency is determined based on the failure urgency database 41.

The failure urgency database 41 assigns an urgency level indicating the degree of urgency to each precursor symptom according to the type of failure prediction, for example. Here, the urgency level is expressed, for example, as a numerical value between 0 and 1. In this case, a value of "0" indicates the lowest urgency, and a value of "1" indicates the highest urgency. The display unit 5 displays, for example, a numerical value indicating this urgency level.

The symptom prediction notification content output unit 18 combines the prediction information on precursor symptoms predicted by the symptom prediction unit 17 based on the detection information on precursor symptoms detected by the detection information acquisition unit 14 with information on the failed part predicted by the failure prediction unit 12 (prediction information), compares the prediction of symptom progression to failure with the current situation, and then determines precursor symptoms to be notified by the symptom prediction notification prioritization unit 31 based on the symptom perception database 32, and outputs the determined results as failure prediction information. Furthermore, if the urgency determined by the symptom prediction notification prioritization unit 31 is high, the symptom prediction notification content output unit 18 assigns information indicating a high urgency to the failure prediction information. For example, if the urgency determined by the symptom prediction notification prioritization unit 31 is higher than a predetermined urgency threshold, the symptom prediction notification content output unit 18 determines that the urgency is high and assigns information indicating a high urgency to the failure prediction information.

As a result, if the degree of urgency is high, the display unit 5 displays that the degree of urgency is high.

The vehicle data output device of the third embodiment can achieve substantially the same effects as the vehicle data output devices of the first and second embodiments described above.

The vehicle data output device of the third embodiment determines the urgency of a predicted failure from the detected precursor symptoms, and if the urgency is high, notifies the driver of the vehicle 1.

Therefore, the vehicle data output device of the third embodiment can prompt the driver to have the problem repaired quickly when the degree of urgency is high.

Furthermore, the vehicle data output device of the third embodiment may not output failure prediction information until a precursor symptom that is easily noticeable to the driver is detected when the urgency level is low. For example, when the sensitivity and urgency level of the precursor symptom of the predicted failure are low, the failure prediction information may be output after the precursor symptom that is easily noticeable to the driver is detected.

As a result, the vehicle data output device of the third embodiment can further increase the driver's confidence in the failure prediction information when first notifying the driver of the progression of symptoms.

Furthermore, in the vehicle data output device of the third embodiment, when a failure is predicted, precursor symptoms with low sensitivity and urgency may not be displayed as precursor symptoms predicted to occur in the future.

Although specific embodiments of the present invention have been described above, the present invention is not limited to the above-described embodiments, and various modifications are possible within the scope of the invention.

For example, the failure prediction information output from the symptom prediction notification content output unit 18 may be displayed on a portable device such as a smartphone owned by the driver of the vehicle 1 or the owner of the vehicle 1.

The above-described embodiments relate to a vehicle data output method and a vehicle data output device.

Claims (12)

Predicts vehicle failures based on vehicle driving data acquired while driving,
Predict the precursory symptoms that will occur before a predicted failure occurs,
Prediction information regarding the predicted failure and prediction information regarding precursor symptoms that are predicted to occur in the process leading up to the predicted failure are arranged in chronological order, and the process in which the precursor symptoms of the failure progress toward the occurrence of the failure is notified to the driver of the vehicle as failure prediction information;
When a precursor symptom of a predicted failure is detected, the vehicle data output method adds the detection information to the failure prediction information, and further notifies the driver of the vehicle at what stage in the process of the failure progression the precursor symptom of the failure was detected. The vehicle data output method described in claim 1, wherein the failure prediction information is notified to the driver when a precursor symptom is detected. The vehicle data output method described in claim 1 or 2, wherein the failure prediction information can be provided as needed once a failure is predicted. The vehicle data output method described in claim 1 or 2, wherein the detection information includes information regarding the time when the precursor symptom was detected. The vehicle data output method described in claim 1 or 2, wherein the detection information includes information regarding the mileage at the time the precursory symptom was detected. The vehicle data output method described in claim 1 or 2, wherein the detection information includes information about a vehicle part in which a precursor symptom was detected. The vehicle data output method described in claim 1 or 2, wherein the detected precursor symptoms are classified based on ease of detection, and the priority for notifying the driver is determined based on the classification results. The vehicle data output method described in claim 7, wherein when classifying detected precursor symptoms based on ease of detection, differences in ease of detection of precursor symptoms depending on the vehicle model are taken into consideration. The vehicle data output method described in claim 1 or 2, wherein the failure prediction information displays precursory symptoms that can be felt by the driver as the first detected precursory symptoms as part of the detection information. The vehicle data output method described in claim 1 or 2, wherein the urgency of a predicted failure is determined from the detected precursor symptoms, and if the urgency is high, the failure prediction information includes a notification indicating the high urgency. The vehicle data output method described in claim 1, wherein the display unit displays and notifies the driver at what stage in the process leading up to the occurrence of a failure the failure prediction information and precursory symptoms of the failure were detected. a failure prediction unit that predicts a vehicle failure based on vehicle driving data acquired during driving;
a symptom prediction unit that predicts precursor symptoms that will occur in the process leading up to the predicted failure;
A vehicle data output device having an output unit that arranges in chronological order prediction information regarding predicted failures and prediction information regarding precursor symptoms that are predicted to occur in the process leading up to the predicted failure occurring, and notifies the driver of the vehicle of the process by which the precursor symptoms of the failure progress toward the actual failure as failure prediction information, and when a precursor symptom of the predicted failure is detected, adds the detection information to the failure prediction information and further notifies the driver of the vehicle at what stage in the process by which the precursor symptom of the failure was detected.