JP7814636B2 - Information collection and analysis device and information collection and analysis system - Google Patents

Description

This disclosure relates to technology for collecting and analyzing information on unusual events that occur in a vehicle, including lane departure or LKAS operation.

Patent document 1 describes a system that collects information on uncontrollable locations where the LKAS (Lane Keeping Assist System) does not function, and based on the information on the uncontrollable locations, determines locations where the LKAS frequently does not function as anomalous locations and presents them to the driver.

Japanese Patent Application Laid-Open No. 2004-126888

The system in Patent Document 1 simply determines whether or not there is an unusual point based on the operating status of the LKAS, and is therefore unable to perform a detailed analysis of the causes of lane departures and other unusual points.

This disclosure has been made to solve the above problems and aims to provide technology that enables detailed analysis of the causes of unusual events occurring in vehicles.

The information collection and analysis device disclosed herein comprises an information collection unit that, when a unique event, including lane departure or LKAS operation, occurs in a vehicle capable of autonomous driving control by an autonomous driving control device, collects probe information from the vehicle that links information on the unique event with information on the driving mode that indicates the degree of autonomous driving control at the time the unique event occurs; and an analysis unit that identifies the cause of the unique event as a cause classification based on the driving mode and creates attention-requiring information that links the unique event with the cause classification. The driving modes include a first driving mode in which the driver operates the steering wheel of the vehicle and the autonomous driving control device does not perform steering operations; a second driving mode in which the driver normally operates the steering wheel and the autonomous driving control device intervenes in steering operations and performs LKAS if it detects a risk of lane departure; and a third driving mode in which the autonomous driving control device always controls the steering operation of the vehicle.

The information collection and analysis device disclosed herein can pinpoint the cause of an unusual event based on the operating mode. The objects, features, aspects, and advantages of the present disclosure will become more apparent from the following detailed description and the accompanying drawings.

1 is a block diagram showing a configuration of an information collection and analysis system according to a first embodiment. 4 is a flowchart showing an operation of the probe information generating device. 10 is a flowchart showing an information collection process performed by the information collection and analysis device. FIG. 4 is a diagram showing probe information according to the first embodiment; 10 is a flowchart showing an environment analysis process performed by the information collection and analysis device. FIG. 10 is a diagram illustrating the relationship between a singular point and a general singular point. FIG. 10 is a diagram showing the relationship between environmental cause events and environmental cause classifications. FIG. 10 is a diagram showing caution-requiring information stored in a cause storage unit. FIG. 10 is a block diagram showing the configuration of an information collection and analysis system according to a second embodiment. 10 is a flowchart showing an individual analysis process performed by the information collection and analysis device. FIG. 10 is a diagram illustrating the relationship between individual cause events and individual cause classifications. FIG. 11 is a block diagram showing the configuration of an information collection and analysis system according to a third embodiment. FIG. 10 is a block diagram showing the configuration of an information collection and analysis system according to a fourth embodiment. FIG. 10 is a diagram showing an example of cause information displayed in a vehicle. FIG. 2 is a diagram illustrating a hardware configuration of an information collection and analysis device or a probe information generation device. FIG. 2 is a diagram illustrating a hardware configuration of an information collection and analysis device or a probe information generation device.

<A. First Embodiment>
<A-1. Configuration>
Fig. 1 is a block diagram showing the configuration of an information collection and analysis system 1001 according to embodiment 1. The information collection and analysis system 1001 is configured to include a plurality of vehicles 100, an information collection and analysis device 201, and a wide area communication network 301 that connects each vehicle 100 to the information collection and analysis device 201. Although Fig. 1 shows only one vehicle 100, the information collection and analysis system 1001 includes a plurality of vehicles 100.

The vehicle 100 is configured with a probe information generating device 101, a positioning device 51, a surrounding detection device 52, and an automatic driving control device 53. When an unusual event occurs in the vehicle 100, such as lane departure or LKAS activation, the probe information generating device 101 creates probe information and transmits it to the information collection and analysis device 201.

The positioning device 51 measures the position of the vehicle 100. The positioning device 51 may be composed of a GNSS receiver, or may be a system that corrects the position measured by the GNSS receiver using a vehicle sensor or map matching, etc.

The perimeter detection device 52 detects lane departure by image processing. However, the perimeter detection device 52 only needs to be able to detect lane departure, and the method for doing so is not limited to image processing.

The automatic driving control device 53 controls the automatic driving of the vehicle. The automatic driving control device 53 has at least an LKAS function.

The probe information generating device 101 is configured with a position information acquisition unit 11, a lane departure detection unit 12, a control information acquisition unit 13, a probe information generation unit 14, and a probe information transmission unit 15.

The location information acquisition unit 11 acquires vehicle location information from the positioning device 51 and outputs it to the probe information generation unit 14.

The lane departure detection unit 12 obtains information on whether or not lane departure has occurred from the surrounding detection device 52 and outputs it to the probe information generation unit 14.

The control information acquisition unit 13 acquires, from the automatic driving control device 53, control information indicating the content of the automatic driving control by the automatic driving control device 53 and driving mode information indicating the driving mode of the vehicle 100. The control information includes information on whether the LKAS has functioned. In other words, the control information acquisition unit 13 functions as an information acquisition unit that acquires the driving mode and the operating status of the LKAS. The driving mode indicates the degree of automatic driving control by the automatic driving control device 53, and there are the following three driving modes:

The first driving mode is a mode in which the driver operates the steering wheel and the automatic driving control device 53 does not intervene in the steering operation. The second driving mode is a mode in which the driver operates the steering wheel and, if the automatic driving control device 53 determines that the vehicle 100 is at risk of lane departure, intervenes in the steering operation and executes LKAS. The third driving mode is a mode in which the driver does not operate the steering wheel and the automatic driving control device 53 performs steering control to keep the vehicle within the lane. Note that at least automatic driving level 3 or higher as defined by the SAE (Society of Automotive Engineers) belongs to the third driving mode.

The probe information generation unit 14 recognizes that the vehicle 100 has departed from its lane based on information from the lane departure detection unit 12. Furthermore, the probe information generation unit 14 recognizes that the LKAS has been activated in the vehicle 100 based on control information acquired from the control information acquisition unit 13. In this specification, the lane departure of the vehicle 100 or the activation of the LKAS in the vehicle 100 is referred to as an unusual event. When the probe information generation unit 14 detects that an unusual event has occurred in the vehicle 100, it generates probe information including location information of the unusual point where the unusual event occurred and transmits the generated probe information to the information collection and analysis device 201.

Next, the configuration of the information collection and analysis device 201 will be described. The information collection and analysis device 201 is configured from a server or the like outside the vehicle 100. The information collection and analysis device 201 is configured with an information collection unit 21, a collected information storage unit 22, an analysis unit 23, and a cause storage unit 24.

The information collection unit 21 collects probe information from multiple vehicles 100 and stores it in the collected information storage unit 22.

The analysis unit 23 analyzes the probe information stored in the collected information storage unit 22 at any time and classifies the cause of the unusual event based on the unusual event and the operating mode at the time the unusual event occurred. The causes of the unusual event classified by the analysis unit 23 are called cause classifications. The analysis unit 23 links the unusual event with its cause classification to create caution information.

In this embodiment, the analysis unit 23 classifies the causes of driving environment causal events, which are unique events caused by the driving environment, into environmental cause classifications. Driving environment causal events are not caused by factors specific to the vehicle 100 or its driver, but are caused by the driving environment, such as the shape of the road, and can occur in other vehicles 100 under the same driving environment. The analysis unit 23 links the driving environment causal events with the environmental cause classifications to create environmental caution information, which is caution-required information related to the driving environment causal events.

The cause memory unit 24 includes an environmental cause memory unit 25. The environmental cause memory unit 25 stores the environmental caution information created by the analysis unit 23.

<A-2. Operation>
2 is a flowchart showing the operation of the probe information generating device 101. The operation of the probe information generating device 101 will be described below in accordance with the flow of FIG.

First, in step S101, the probe information generation unit 14 acquires a vehicle ID from an in-vehicle LAN, which is not shown in Figure 1. Here, the vehicle ID is an example of an individual ID for uniquely identifying the vehicle 100 or the driver of the vehicle 100. Instead of the vehicle ID, a personal ID unique to the driver may be used.

After step S101, in step S102, the position information acquisition unit 11 acquires position information of the vehicle 100 from the positioning device 51. After step S102, in step S103, the control information acquisition unit 13 acquires driving mode information indicating the driving mode currently being executed from the automatic driving control device 53.

After step S103, in step S104, the probe information generation unit 14 determines whether a unique event has occurred, including lane departure of the vehicle 100 or activation of the LKAS. The occurrence of lane departure is determined by the lane departure detection unit 12 based on information from the perimeter detection device 52, which performs white line recognition, for example. The LKAS activation state is determined by the control information acquisition unit 13 based on control information acquired from the automatic driving control device 53.

Specific events that can occur in vehicle 100 are divided into three types: A, B, and C. Specific event A is an event in which LKAS does not operate and lane departure occurs. The specific point where specific event A occurs is the lane departure location. Specific event A can occur in the first, second, and third driving modes. Specific event B is an event in which LKAS operates but lane departure occurs. The specific point where specific event B occurs may be the lane departure location or the LKAS operation location. Specific event A can occur in the second and third driving modes. Specific event C is an event in which LKAS operates but lane departure does not occur. The specific point where specific event C occurs is the LKAS operation location. Specific event C can occur in the second driving mode. In the third driving mode, the vehicle 100 is steered by the automatic driving control device 53 and travels near the center of the lane, so the LKAS does not operate.

If an unusual event has occurred in step S104, the processing of the probe information generating device 101 proceeds to step S105. In step S105, the probe information generating unit 14 generates probe information Prove (P, EV, V, M) by associating the position P of the unusual point where the unusual event occurred, the unusual event EV, the vehicle ID V, and the driving mode M. The probe information transmitting unit 15 then transmits the probe information to the information collection and analysis device 201. The probe information may also include the time T at which the unusual event occurred and additional information INFO. The additional information INFO includes information such as driving environment information, weather information, vehicle speed/steering, and brake/accelerator operation.

After step S105, or if no unusual event has occurred in step S104, the processing of the probe information generating unit 14 proceeds to step S106. In step S106, the probe information generating unit 14 determines whether or not the vehicle 100 has finished traveling. If the vehicle 100 has not finished traveling in step S106, the processing of the probe information generating device 101 returns to step S102. If the vehicle 100 has finished traveling in step S106, the processing of the probe information generating device 101 ends.

In the above, it was explained that the probe information generation unit 14 transmits the probe information in step S105, but the transmission timing is not limited to this. For example, the probe information generation unit 14 may transmit multiple pieces of probe information that have been created up to that point together when the vehicle 100 finishes traveling.

Next, the operation of the information collection and analysis device 201 will be described. The information collection and analysis device 201 performs multitasking, including information collection processing and cause analysis processing. However, the processing program for executing each function of the information collection and analysis device 201 may be configured as a single task, or may be configured as three or more tasks. Furthermore, the processing program may be configured in any way, such as multi-process or multi-thread.

Figure 3 is a flowchart of the information collection process by the information collection and analysis device 201. The information collection process by the information collection and analysis device 201 will be described below with reference to Figure 3. First, in step S201, the information collection unit 21 determines whether or not probe information has been received from the vehicle 100, and repeats the process of step S201 until probe information is received. When the information collection unit 21 receives probe information from the vehicle 100 in step S201, it stores the probe information in the collection information storage unit 22 in step S202. Thereafter, the process of the information collection unit 21 returns to step S201.

Figure 4 shows an example of N pieces of probe information stored in the collected information storage unit 22. Each piece of probe information is assigned an information ID to identify it. This information ID is assigned a number in chronological order in which the probe information was collected, for example. Probe information Probe(n) with information ID = n includes at least the position P(n) of the singular point, vehicle ID V(n), singular event EV(n), and driving mode M(n). In the example of Figure 4, probe information Probe(n) includes time T(n) and additional information INFO(n), which will be described later in a modified example.

Figure 5 is a flowchart of the environmental cause analysis process by the information collection and analysis device 201. The environmental cause analysis process by the information collection and analysis device 201 will be explained below with reference to Figure 5. First, in step S301, the analysis unit 23 determines whether or not it is now the timing to analyze, and if it determines that it is now the timing to analyze, it proceeds to the processing of step S302, and if it determines that it is not the timing to analyze, it repeats the processing of step S301.

Here, the analysis timing may be a fixed periodic timing such as once a day or once a week. The analysis timing may also be the timing when the information collection unit 21 obtains special probe information, such as information that a lane departure has occurred without the LKAS being activated. The analysis timing may also be the timing when an analysis instruction is obtained from a server administrator (not shown).

In step S302, the analysis unit 23 loads probe information such as that illustrated in FIG. 4 from the collected information storage unit 22 and extracts general peculiar points Q(m) from the probe information acquired from multiple vehicles 100. Specifically, the analysis unit 23 defines a point where peculiar points frequently occur in multiple vehicles as a general peculiar point Q(m) and counts the number of general peculiar points Q(m) MD. Here, a "point where peculiar points frequently occur" is, for example, a point where a peculiar event occurs Mmin times (e.g., four times) or more within a certain period (e.g., one day) and the occurrence rate is 100 ppm or more. For example, the occurrence rate of a peculiar event at point A is calculated by dividing the number of vehicles 100 where a peculiar event occurred at point A during a certain period TA by the number of vehicles 100 that traveled through point A during period TA. Here, the analysis unit 23 can obtain the number of vehicles 100 that have traveled through the point A during the period TA from a VICS (Vehicle Information and Communication System, registered trademark) or the like.

When defining a general singular point Q(m), the analysis unit 23 considers multiple singular points located within close proximity to be the same point. For example, the analysis unit 23 considers singular points P(m1), P(m2)...P(mKm) that satisfy the conditions |P(m1) - P(mkm)| ≦ THdis and km = 2 or more and Km or less to be the same point. Here, THdis is, for example, 10 m. Note that if the positioning accuracy of the positioning device 51 is high, THdis may be shorter, for example, 5 m. Furthermore, the road including the singular points P(m1), P(m2)...P(mKm) may be divided into sublinks of 10 m increments, and if P(m1), P(m2)...P(mKm) exist within the same sublink, P(m1), P(m2)...P(mKm) may be determined to be the same point.

Next, in step S303, the analysis unit 23 determines whether or not there is a general singular point Q(m). If there is no general singular point Q(m) in step S303, the processing of the analysis unit 23 returns to step S301, and if there is a general singular point Q(m), the processing of the analysis unit 23 proceeds to step S304.

Figure 6 shows a map representation of singular points P(n) and general singular points Q(m). There are eleven singular points P(1), P(2), P(3), P(4), P(5), P(6), P(7), P(8), P(9), P(10), and P(11) on the vehicle's 100 travel route 61. According to the conditions described above, singular points P(3), P(5), P(7), P(10), and P(11) are determined to be the same point and correspond to general singular point Q(1). Furthermore, singular points P(4), P(6), P(8), and P(9) are determined to be the same point and correspond to general singular point Q(2).

The position of a general singular point Q is determined using the positions of multiple singular points P that correspond to the general singular point Q. For example, the position of a general singular point Q may be the average value of the positions of multiple corresponding singular points P. In the example of Figure 6, the position of the singular point P with the smallest ID among the multiple corresponding singular points P is the position of the general singular point. In other words, the position of singular point P(3) is the position of general singular point Q(1), and the position of singular point P(4) is the position of general singular point Q(2).

In step S304, the analysis unit 23 sets the counter m of the general singular point Q(m) for which environmental cause classification is performed to 1.

Next, in step S305, the analysis unit 23 classifies the environmental causes of the general singular point Q(m) based on the singular events and operating modes of the singular point P(mk) corresponding to the general singular point Q(m).

Figure 7 shows example criteria for environmental cause classification. A pattern in which unique event A (lane departure occurs and LKAS does not operate) occurs in the first driving mode (fully manual driving) is designated as pattern A1. Similarly, a pattern in which unique event A occurs in the second driving mode is designated as pattern A2, and a pattern in which unique event A occurs in the third driving mode is designated as pattern A3. Furthermore, a pattern in which unique event B (LKAS operates (semi-activated) but lane departure occurs) occurs in the second driving mode is designated as pattern B2. A pattern in which unique event B occurs in the third driving mode is designated as pattern B3. Furthermore, a pattern in which unique event C (LKAS operates and lane departure is avoided) occurs in the second driving mode is designated as pattern C2.

For example, at a certain general singular point Q(m), if lane departure occurs in the first driving mode (fully manual driving) and the LKAS operates normally in the second driving mode, i.e., if pattern A1 and pattern C2 occur, the general singular point Q(m) is classified as environmental cause classification R1: "a point where driving attention decreases."

Furthermore, if lane departure occurs at a certain general singular point Q(m) in the first driving mode (fully manual driving) and the LKAS is semi-activated in the second driving mode, i.e., if pattern A1 or pattern B2 occurs, the analysis unit 23 determines that the LKAS was unable to keep up with the driver's steering operation and caused the lane departure, and classifies the general singular point Q(m) as a "road shape caution point" under environmental cause classification R2, which is prone to lane departure. Incidentally, semi-activated LKAS refers to a case where the LKAS was activated but did not function, i.e., was unable to keep the vehicle within the lane.

Furthermore, if lane departure occurs at a certain general singular point Q(m) in the first driving mode (fully manual driving) and the LKAS is semi-activated in the third driving mode, i.e., if pattern A1 or pattern B3 occurs, the analysis unit 23 classifies the general singular point Q(m) as environmental cause classification R3: "LKAS non-operating point, possible road surface deterioration." A general singular point Q(m) classified as environmental cause classification R3 is a point where lane departure occurred despite the third driving mode, in which lane departure is less likely to occur than in the second driving mode because the steering wheel is operated by the vehicle, and therefore a decrease in road surface μ due to poor white markings or road surface deterioration is expected.

Furthermore, if the LKAS does not operate and lane departure occurs in the second or third driving mode at a certain general peculiar point Q(m), i.e., if pattern A2 or pattern A3 occurs, the general peculiar point Q(m) is classified as environmental cause classification R4: "LKAS non-operating point." A general peculiar point Q(m) classified as environmental cause classification R4 is highly likely to have poor white lines.

For example, suppose that pattern A1 occurred twice and pattern C2 occurred twice at general singular point Q(2), which includes singular points P(4), P(6), P(8), and P(9). In this case, the analysis unit 23 determines, according to the table in Figure 7, that the cause CAUSE of the singular event at general singular point Q(2) is environmental cause event R, and further analyzes the classification CLASS of environmental cause event R as environmental cause classification R1 (point of reduced driving attention).

For example, suppose that pattern A1 occurred twice, pattern A2 occurred twice, and pattern B3 occurred once at general singular point Q(1), which includes singular points P(3), P(5), P(7), P(10), and P(11). In this case, the analysis unit 23 determines the cause (CAUSE) of the singular event at general singular point Q(1) to be environmental cause event R according to the table in FIG. 7, and further determines the classification CLASS of environmental cause event R as environmental cause classification R3 (LKAS non-operating point, possible road surface deterioration) or environmental cause classification R4 (LKAS non-operating point). Here, the analysis unit 23 may adopt either environmental cause classification R3 or R4, whichever pattern occurred more frequently, or may adopt both. Furthermore, if the number of occurrences of corresponding patterns is the same, the analysis unit 23 may prioritize the environmental cause classification that requires more attention. The order of environmental cause classifications requiring attention is R4, R3, R2, and R1.

Next, in step S306, the analysis unit 23 links the environmental cause event, environmental cause classification, and location information of the general singular point Q (m) to create environmental caution information, and stores the environmental caution information in the environmental cause memory unit 25.

Then, in step S307, the analysis unit 23 increments the counter m of the general singular point Q(m) for which environmental cause classification is performed by 1.

Furthermore, in step S308, the analysis unit 23 determines whether the counter m for the general singular points Q(m) for which environmental cause classification is to be performed is less than or equal to the number MD of general singular points Q. If m≦MD, there are general singular points Q(m) for which environmental cause classification has not yet been performed, so the processing of the analysis unit 23 returns to step S305. If m>MD, environmental cause classification has been completed for all general singular points Q(m), so the processing of the analysis unit 23 returns to step S301.

Figure 8 shows the environmental caution information stored in the environmental cause memory unit 25. The environmental caution information links the ID of the general anomalous point QID(m), the location of the general anomalous point Q(m), the environmental cause event CAUSE(m), and the environmental cause classification CLASS(m).

<A-3. Effects>
As described above, the information collection and analysis system 1001 according to the first embodiment includes the probe information generation device 101 and the information collection and analysis device 201. The information collection and analysis device 201 includes: an information collection unit 21 that, when a unique event including lane departure or LKAS operation occurs in a vehicle 100 capable of automatic driving control by an automatic driving control device 53, collects probe information from the vehicle, in which information on the unique event is linked to information on a driving mode that indicates the degree of automatic driving control at the time the unique event occurs; and an analysis unit 23 that identifies the cause of the unique event as a cause classification based on the driving mode, and creates attention-requiring information that links the unique event with the cause classification. The driving modes include a first driving mode in which the driver operates the steering wheel of the vehicle 100 and the automatic driving control device 53 does not perform any steering operation; a second driving mode in which the driver normally operates the steering wheel and the automatic driving control device 53 intervenes in the steering operation and executes the LKAS if it detects the risk of lane departure; and a third driving mode in which the automatic driving control device 53 always controls the steering operation of the vehicle 100.

With the above configuration, the information collection and analysis device 201 can identify the cause of an unusual event in detail as a cause classification according to the operating mode.

Furthermore, in this embodiment, the probe information includes location information of the singular point where the singular event occurred and an individual ID unique to the vehicle 100 or the driver of the vehicle 100. Then, based on the location information of the singular point and the individual ID, the analysis unit 23 determines whether the singular event is an environmentally caused event caused by the driving environment of the vehicle 100, classifies the driving environment that caused the environmentally caused event as an environmental cause classification based on the driving mode, and creates attention-requiring information that links the environmentally caused event, the environmental cause classification, and the location information of the singular point where the environmentally caused event occurred.

<A-4. Modified Examples>
The probe information may include information on the vehicle model or class of the vehicle 100 as additional information shown in FIG. 4. In this case, the analysis unit 23 of the information collection and analysis device 201 that has collected the probe information can perform environmental cause classification by vehicle model or class. That is, the analysis unit 23 uses probe information collected from vehicles 100 of the same model, vehicle model, or class to determine the environmental cause classification according to the table in FIG. 7. The vehicle model is a classification of the vehicle 100 based on body type, such as sedan or minivan. The vehicle class is a rating of the vehicle 100 based on body size or engine displacement, for example.

In this embodiment, roads are represented by two lines, one going up and one going down. Environmental cause classifications are then categorized separately for up and down. Furthermore, the probe information may include information on the vehicle's 100 lane as additional information, as shown in FIG. 4. In this case, the positioning device 51 or the surroundings detection device 52 has a high-precision map database containing road shape data for each lane, and the function of detecting the vehicle's 100 lane using a high-precision positioning device (HDL) with sub-meter-level positioning accuracy or image processing. This allows the analysis unit 23 of the information collection and analysis device 201, which has collected the probe information, to perform environmental cause classification by lane. That is, the analysis unit 23 uses probe information collected from the same lane to determine the environmental cause classification according to the table in FIG. 7.

The probe information generating device 101 may create probe information for all anomalous events that occur on the vehicle 100 and transmit all of the probe information to the information collection and analysis device 201. Furthermore, the probe information generating device 101 may not transmit to the information collection and analysis device 201 probe information that meets predetermined exclusion conditions. Here, the predetermined exclusion conditions may be, for example, when the vehicle 100 changes lanes, or when the vehicle 100 deviates from its lane or approaches a lane boundary line to avoid approaching another vehicle. Whether or not these exclusion conditions are met is determined based on information about surrounding vehicles or surrounding photographic information detected by the surrounding detection device 52, or driving information about the vehicle 100, such as steering, turn signals, and braking, obtained from an in-vehicle LAN or the like.

In addition, the information necessary to determine whether or not these exclusion conditions are met may be included as additional information in the probe information. In this case, the information collection and analysis device 201 that collected the probe information can determine whether or not the probe information meets the exclusion conditions and discard probe information that meets the exclusion conditions.

The analysis unit 23 may repeatedly classify the environmental causes of the general peculiar point information at regular intervals, such as monthly or annually, and store the environmental caution information of the general peculiar point information as time-series information in the environmental cause storage unit 25. The analysis unit 23 may then further subdivide the environmental cause classification of the general peculiar point information based on changes over time in the environmental cause classification of the general peculiar point information. The analysis unit 23 may also delete old environmental caution information from the environmental cause storage unit 25.

For example, the analysis unit 23 creates environmental caution information for general peculiar point information on a monthly basis, and if a location that was previously a general peculiar point is no longer detected as a peculiar point multiple times, for example, three times in a row, it may determine that the environmental cause has been improved and delete the environmental caution information for that general peculiar point from the environmental cause memory unit 25.

Furthermore, if the rate at which a general singular point is probed as a singular point is increasing, the analysis unit 23 may determine that the environment is gradually deteriorating and add a sub-environmental cause classification such as "environmental deterioration in progress" to the environmental cause classification of the general singular point.

Furthermore, when the rate of increase in the proportion of general anomalous points probed as anomalous points exceeds a predetermined threshold, the analysis unit 23 may add a sub-environmental cause classification such as "Caution required for environmental degradation" or "Sudden environmental degradation" to the environmental cause classification of the general anomalous point. Then, for general anomalous points where the proportion of points probed as anomalous points exceeds a predetermined threshold, the information collection and analysis device 201 may report the general anomalous point to the road administrator as having a poor driving environment.

If lane departure occurs in the second or third driving mode at the same singular point where an environmentally-caused event occurred and the rate at which the LKAS is activated decreases over time, the analysis unit 23 may classify the same singular point as a point where road conditions are deteriorating and LKAS operation is unstable.

The vehicle 100 may be equipped with a DMS (Driver Monitoring System) that detects driver states, such as the driver's inattentiveness, drowsiness, or absentmindedness. The probe information generation unit 14 may then include the driver state as additional information in the probe information. That is, the probe information may include the driver state indicating the driver's driving aptitude status related to inattentiveness or reduced alertness. In this case, the analysis unit 23 of the information collection and analysis device 201 that collects the probe information can add sub-environmental cause classifications that take the driver state into consideration to the environmental cause classification of the general anomalous point. For example, the analysis unit 23 may add sub-environmental cause classifications such as "points where dozing occurs frequently," "points where inattentiveness occurs frequently," and "points where absentmindedness occurs frequently" to the environmental cause classification R1 "points where driving attention is reduced" depending on the driver state.

Furthermore, if the driver's state at a general peculiar point determined to be environmental cause classification R2 "road shape caution point" is appropriate for driving and is not in a distracted, drowsy, or absent-minded state, the analysis unit 23 may add a sub-environmental cause classification of "particular caution required when driving" to the environmental cause classification of the general peculiar point. In other words, the general peculiar point is treated as a point where lane departure may occur even if driving carefully.

<B. Second Embodiment>
<B-1. Configuration>
9 is a block diagram showing the configuration of an information collection and analysis system 1002 according to embodiment 2. Information collection and analysis system 1002 differs from information collection and analysis system 1001 according to embodiment 1 in that it includes an information collection and analysis device 202 instead of information collection and analysis device 201. Information collection and analysis device 202 differs from information collection and analysis device 201 in that it includes an individual cause storage unit 26 instead of environmental cause storage unit 25.

The information collection and analysis device 201 according to the first embodiment analyzed the environmental causes of a singular event that occurred at a general singular point. In contrast, the information collection and analysis device 202 according to the present embodiment analyzes individual causes, which are causes specific to the vehicle 100 or the driver, for a singular event that occurred at a singular point that is not a general singular point.

<B-2. Operation＞
10 is a flowchart showing the individual cause analysis process performed by the information collection and analysis device 202. The individual cause analysis process will be described below with reference to the flow of FIG.

First, steps S401 and S402 are similar to steps S301 and S302 in Figure 5, so their explanation will be omitted.

After step S402, in step S403, the analysis unit 23 sets the counter v of the vehicle ID for which the individual cause is to be analyzed to 1. The vehicle ID is included in the probe information as shown in Figure 4, and the analysis unit 23 classifies the individual causes of the individual cause event by vehicle ID.

In the flow of Figure 10, the analysis unit 23 classifies the individual causes of the individual causal events for all vehicle IDs V = 1, 2, ..., VD. However, the analysis unit 23 may classify the individual causes only for specific vehicle IDs selected using predetermined selection logic. The analysis unit 23 may also determine the order in which the vehicles for which individual causes are analyzed are analyzed, and classify the individual causes for a predetermined number of vehicles each time this process is started.

The timing of the individual cause analysis processing may be the same as or different from the timing of the environmental cause analysis processing described in embodiment 1.

After step S403, in step S404, the analysis unit 23 determines whether an individual singular point among the singular points corresponding to vehicle ID = v conforms to a predetermined rule. An individual singular point is a singular point whose distance from the general singular point Q(m) is equal to or greater than a predetermined threshold. Specifically, the analysis unit 23 extracts VN(v) pieces of probe information Prove(P, EV, V = v, M) corresponding to vehicle ID = v from the collected multiple pieces of probe information Prove(P, EV, V = v, M). Furthermore, the analysis unit 23 extracts DN(v) pieces of probe information Prove of individual singular points whose distance from the general singular point Q(m)|m = 1, 2, ... M is equal to or greater than a threshold from the probe information Prove(P, EV, V = v, M). If the individual singular point matches the predetermined rule, the processing of the analysis unit 23 proceeds to step S405; if not, the processing of the analysis unit 23 proceeds to step S407.

A predetermined rule for individual singular points is, for example, that the number of individual singular points DN(v) is greater than or equal to a predetermined threshold, for example, 3 or greater. Alternatively, a predetermined rule for individual singular points is, for example, that the proportion DN(v)/VN(v) of individual singular points among the singular points corresponding to vehicle ID = v is greater than or equal to a predetermined threshold, for example, 0.1 or greater.

A unique event that occurs at an individual unique point is called an individual cause event. Because an individual cause event occurs at a point where it does not generally occur in other vehicles 100, it is considered to be due to a cause specific to the vehicle 100 or driver in which the individual cause event occurred.

In step S405, the analysis unit 23 classifies the cause of the individual causal event based on the individual causal event and the operating mode at the time the individual causal event occurred. The causes of the individual causal event classified here are referred to as individual cause classifications. In other words, the analysis unit 23 determines the individual cause classification of the individual causal event.

Figure 11 shows an example of criteria for individual cause classification. In Figure 11, the notation for patterns A1 and the like is the same as in Figure 7.

For example, suppose that pattern A1 and pattern C2 occurred at an individual anomalous point for vehicle 100 with vehicle ID = 1. This means that lane departure or LKAS activation occurred for vehicle 100 with vehicle ID = 1 at a point where a typical vehicle would not experience lane departure or LKAS activation. Therefore, the analysis unit 23 determines the individual cause classification for vehicle 100 with vehicle ID = 1 to be U1: "Poor driving skills (of the driver)."

For example, suppose that pattern A1 and pattern B2 occurred at an individual anomalous point for vehicle 100 with vehicle ID = 1. This means that vehicle 100 with vehicle ID = 1 deviated from its lane at a point different from that of a typical vehicle because the LKAS was unable to keep up with the driver's steering operation. Therefore, the analysis unit 23 determines that vehicle 100 with vehicle ID = 1 was driven in a way that could not be controlled by the LKAS, and classifies the individual cause as U2: "vehicle driven recklessly."

For example, suppose that pattern B3 occurred at an individual peculiar point for vehicle 100 with vehicle ID = 1. This means that vehicle 100 with vehicle ID = 1 deviated from its lane in the third driving mode without its LKAS being activated at a point different from that of a typical vehicle. Therefore, the analysis unit 23 determines that there is a problem with vehicle 100 with vehicle ID = 1 and classifies the individual cause as U3: "tire deterioration."

For example, suppose that pattern A1 and pattern A2 occurred at an individual peculiar point for vehicle 100 with vehicle ID = 1. This means that vehicle 100 with vehicle ID = 1 did not activate LKAS in the second driving mode and deviated from its lane at a point different from that of a typical vehicle. Therefore, the analysis unit 23 determines that there is a problem with the LKAS function of vehicle 100 with vehicle ID = 1, and classifies the individual cause as U4: "LKAS function degradation."

For example, suppose that pattern A3 occurred at an individual peculiar point for vehicle 100 with vehicle ID = 1. This means that vehicle 100 with vehicle ID = 1 did not activate LKAS in the third driving mode and deviated from its lane at a point different from that of a typical vehicle. Therefore, the analysis unit 23 determines that there is a problem with the LKAS function of vehicle 100 with vehicle ID = 1, and classifies the individual cause as U4: "LKAS function degradation."

After step S405, in step S406, the analysis unit 23 creates individual attention-requiring information for the vehicle 100 with vehicle ID = v by linking the individual cause event, individual cause classification, and vehicle ID, and stores the individual attention-requiring information in the individual cause memory unit 26.

If the answer is No in step S404, or after step S406, in step S407 the analysis unit 23 increments the counter v of the vehicle ID for which individual cause classification is to be performed by 1. Then, in step S408, the analysis unit 23 determines whether the counter v of the vehicle ID is less than or equal to VD. If the counter v of the vehicle ID is less than or equal to VD in step S408, the analysis unit 23 determines that there is a vehicle 100 for which the individual cause classification has not been determined, and returns to processing in step S404. If the counter v of the vehicle ID is greater than VD in step S408, the analysis unit 23 determines that the individual cause classification has been determined for all vehicles 100, and returns to processing in step S401.

In the above explanation, the analysis unit 23 determined the individual cause classification for each vehicle ID. However, the vehicle ID here is an example of an individual ID that identifies the vehicle 100 or the driver of the vehicle 100. The analysis unit 23 may determine the individual cause classification based on a personal ID that identifies the driver instead of the vehicle ID, or may determine the individual cause classification based on both the vehicle ID and the personal ID. This makes it possible to accurately determine the individual cause classification even when multiple drivers drive the same vehicle 100 or when the same driver drives multiple vehicles 100.

<B-3. Effects>
In the information collection and analysis device 202 according to this embodiment, the probe information includes location information of the specific point where the specific event occurred and an individual ID specific to the vehicle or the vehicle driver. The analysis unit determines whether the specific event is an individual cause event caused by an individual cause specific to the vehicle or the driver based on the location information of the specific point and the individual ID, identifies the individual cause that caused the individual cause event as an individual cause classification based on the driving mode, and creates attention-requiring information linking the individual cause event, the individual cause classification, and the individual ID. Therefore, the information collection and analysis device 202 can accurately classify the cause of the individual cause event based on the driving mode.

Furthermore, in the information collection and analysis device 202, when a singular event occurs for multiple individual IDs at the same singular point, the analysis unit 23 defines the same singular point as a general singular point, and when the proportion of singular points where a singular event occurs for a specific individual ID that differ from general singular points is equal to or exceeds a predetermined threshold, the analysis unit 23 determines that the singular event that occurred for the specific individual ID is an individual cause event. This allows the information collection and analysis device 202 to accurately classify, for a vehicle 100 where a singular event occurs at a point different from that of a general vehicle, the individual cause of the singular event that occurs for the vehicle 100, which is attributable to the vehicle 100 or the driver, based on the driving mode.

<B-4. Modified Examples>
The timing of the individual cause classification process shown in Fig. 10 may be different from the timing of the environmental cause analysis process described in embodiment 1. For example, when the information collection unit 21 acquires probe information about a singular point different from the predefined general singular point Q(m), the analysis unit 23 may perform individual cause classification process on the probe information. In this case, since there is a high possibility that an individual cause event specific to the vehicle 100 or the driver has occurred at the singular point different from the predefined general singular point Q(m), the analysis is performed in a timely manner.

Furthermore, when the information collection unit 21 acquires probe information about different anomalous locations from one vehicle 100 a predetermined number of times or more within a predetermined time period, the analysis unit 23 may perform individual cause classification processing on the probe information. In this case, it becomes possible to detect aggressive driving or driver abnormalities of the vehicle 100 at an early stage.

The information collection and analysis device 202 may also perform individual cause classification processing in response to a request from a third party not shown in FIG. 9, such as the administrator of the vehicle 100. In this case, the request may include the vehicle ID for which individual cause classification is to be performed, and the information collection and analysis device 201 may perform individual cause classification processing for the vehicle with that vehicle ID.

The probe information may also include additional information such as vehicle type, class, mileage, driving time, or intended use. In this case, the information collection and analysis device 202 that collects the probe information may perform individual cause classification processing at a timing that corresponds to this additional information. For example, the analysis unit 23 may shorten the cycle of the individual cause classification processing for buses, trucks, or taxis, since they are always on the move, and may lengthen the cycle of the individual cause classification processing for vehicles that are not used for commuting or business purposes. The analysis unit 23 may also shorten the cycle of the individual cause classification processing for vehicles 100 with long driving distances or driving times.

The analysis unit 23 may repeatedly perform individual cause classification on a driving unit, monthly unit, or yearly unit, and store the attention-requiring information related to the vehicle's individual cause classification as time-series information in the individual cause storage unit 26. The analysis unit 23 may then further subdivide the individual cause classification based on changes in the individual cause classification over time. The analysis unit 23 may also delete old attention-requiring information from the individual cause storage unit 26.

For example, suppose the frequency of probe information transmission from a vehicle 100 that was not previously classified as having individual cause classification U1: "poor driving skill" increases, and then the individual cause classification for that vehicle 100 is classified as having U1: "poor driving skill," and the frequency of probe information transmission from that vehicle 100 exceeds a predetermined threshold. In this case, the analysis unit 23 may add a sub-individual cause classification such as "significant decline in driving skill" to the individual cause classification U1: "poor driving skill" for that vehicle 100. This can be used to determine whether skill has declined due to aging.

In other words, if the number of specific points where an individual causal event occurred in a specific road section for a specific individual ID increases over time, the analysis unit 23 may classify the individual cause of the individual causal event that occurred for a specific individual ID as a decline in the driver's driving skills.

If a vehicle 100 that has not previously been judged as having individual cause classification U2: "rough driving" is judged as having individual cause classification U2: "rough driving" on that day, the analysis unit 23 may add a sub-individual cause classification such as "emotional instability" to the individual cause classification U2: "rough driving" of that vehicle 100.

For example, if the frequency of probe information distribution from a certain vehicle 100 exceeds a predetermined threshold, the analysis unit 23 may add a sub-individual cause classification such as "related to aggressive driving" to the individual cause classification of that vehicle 100. In this case, if that vehicle 100 has previously been judged as individual cause classification U2: "aggressive driving," then that vehicle 100 can be determined to be the perpetrator of aggressive driving. Furthermore, if that vehicle 100 has not previously been judged as individual cause classification U2: "aggressive driving," then that vehicle 100 can be determined to be the victim of aggressive driving.

If the driver of the vehicle 100 is changed due to the sale or purchase of the vehicle 100 or for other reasons, the analysis unit 23 may delete from the individual cause memory unit 26 the attention-requiring information corresponding to the vehicle ID of the vehicle 100, including information regarding cause classifications specific to the driver, such as individual cause classifications U1 and U2.

In addition, when a driver switches to a new vehicle, the analysis unit 23 may inherit the caution information corresponding to the driver's personal ID as caution information corresponding to the vehicle ID of the new vehicle.

The vehicle 100 may be equipped with a DMS that detects the driver's state, such as whether the driver is looking away, drowsy, or absent-minded. The probe information generation unit 14 may then include the driver's state in the probe information as additional information. In this case, the analysis unit 23 of the information collection and analysis device 201 that has collected the probe information can add a sub-individual cause classification that takes into account the driver's state to the vehicle's individual cause classification. For example, the analysis unit 23 may add a sub-individual cause classification such as "frequent distraction" to individual cause classification U1 "poor driving skills" depending on the driver's state.

<C. Third Embodiment>
<C-1. Configuration>
12 is a block diagram showing the configuration of an information collection and analysis system 1003 according to embodiment 3. Information collection and analysis system 1003 differs from information collection and analysis system 1003 according to embodiment 1 in that it includes an information collection and analysis device 203 instead of information collection and analysis device 201. Information collection and analysis device 203 differs from information collection and analysis device 201 in that cause storage unit 24 includes an environmental cause storage unit 25 and an individual cause storage unit 26.

The information collection and analysis device 201 according to the first embodiment analyzed the environmental causes of a peculiar event that occurred at a general peculiar point (environmental cause analysis processing). The information collection and analysis device 202 according to the second embodiment analyzed individual causes, which are causes specific to the vehicle 100 or the driver, for a peculiar event that occurred at a peculiar point that is not a general peculiar point (individual cause analysis processing). When a peculiar event occurs in the vehicle 100, the information collection and analysis device 203 according to the present embodiment determines whether the peculiar event is a driving environment-related cause event caused by the driving environment, or an individual cause event caused by a cause specific to the vehicle 100 or the driver, and performs environmental cause analysis processing for the driving environment-related cause event and individual cause analysis processing for the individual cause event.

<C-2. Operation＞
The operation of the probe information generation device 101 in this embodiment is as described in embodiment 1 with reference to Fig. 2. The information collection process in the information collection and analysis device 203 is as described in embodiment 1 with reference to Fig. 3.

The environmental cause analysis process in the information collection and analysis device 203 is as described in Figure 5 in embodiment 1. In step S306, the caution information including the environmental cause classification is stored in the environmental cause memory unit 25.

Furthermore, the individual cause analysis processing in the information collection and analysis device 203 is generally similar to that described in Figure 10 in embodiment 2. However, the individual cause analysis processing in the information collection and analysis device 203 is performed assuming that the environmental cause analysis processing has already been completed, i.e., that general anomalous point information has already been obtained. That is, in step S401 of Figure 10, if the analysis unit 23 determines that it is time to analyze, it skips the processing of step S402 and executes the processing from step S403 onwards. In step S406, the attention-requiring information including the individual cause classification is stored in the individual cause memory unit 26.

<C-3. Effects>
According to the information collection and analysis device 203 of the third embodiment, when a unique event occurs in a certain vehicle 100, if a unique event also occurs in another vehicle at or near the location where the unique event occurred, the location where the unique event occurred in the vehicle 100 is classified as a general unique location, and the driving environment that caused the unique event at the general unique location is identified as an environmental cause classification based on the driving mode of the vehicle 100 at the time the unique event occurred. Furthermore, according to the information collection and analysis device 203, if the locations where the unique event occurred in the certain vehicle 100 include many locations that are different from general unique locations, it is determined that the vehicle 100 or the driver is the cause of the unique event, and the cause is identified as an individual cause classification based on the driving mode of the vehicle 100 at the time the unique event occurred. In this way, in addition to the effects of the first and second embodiments, the information collection and analysis device 203 makes it possible to determine whether the unique event occurring in the vehicle 100 is an environmentally caused event caused by the driving environment or an individual cause event caused by the vehicle 100 or the driver.

<D. Fourth Embodiment>
<D-1. Configuration>
13 is a block diagram showing the configuration of an information collection and analysis system 1004 according to embodiment 4. Compared to information collection and analysis system 1003 according to embodiment 3, information collection and analysis system 1004 includes a probe information generation device 104 instead of probe information generation device 101, an information collection and analysis device 204 instead of information collection and analysis device 203, and a presentation device 54 in vehicle 100.

In addition to the configuration of the information collection and analysis device 203, the information collection and analysis device 204 includes a distribution unit 27 that distributes caution-requiring information to the wide area communication network 301. The distribution unit 27 retrieves caution-requiring information stored in the cause memory unit 24 as needed and distributes it outside the information collection and analysis device 204. The distribution destination is, for example, the vehicle 100.

The information collection and analysis system 1004 shown in FIG. 13 is configured to distribute both caution-requiring information including environmental cause classifications and caution-requiring information including individual cause classifications, and is the information collection and analysis system 1003 according to embodiment 3 to which a distribution-related configuration has been added. However, the information collection and analysis system 1004 may also be the information collection and analysis system 1001 according to embodiment 1 or the information collection and analysis system 1002 according to embodiment 2 to which a distribution-related configuration has been added.

The probe information generating device 104 includes an information presentation unit 17 in addition to the configuration of the probe information generating device 101. The information presentation unit 17 causes the presentation device 54 mounted on the vehicle 100 to present the cautionary information received by the vehicle communication unit 16 from the information collection and analysis device 204 via the wide area communication network 301. The presentation is made in the form of at least one of display or sound.

The presentation device 54 is, for example, a display device mounted on the vehicle 100. In this case, the information presentation unit 17 controls the display of the cautionary information on the presentation device 54.

<D-2. Operation＞
The distribution unit 27 distributes caution-required information including the environmental cause classification of the general peculiar point at a predetermined timing, such as when an external request is received or during environmental cause analysis. FIG. 14 shows an example in which caution-required information including the environmental cause classification of the general peculiar point distributed from the distribution unit 27 to the vehicle 100 is displayed on the presentation device 54. In the example of FIG. 14 , the current position of the vehicle 100 and a driving route 61 are displayed, and general peculiar points Q(1) and Q(2) are also shown on the driving route 61. Furthermore, the general peculiar point Q(1) is displayed with an environmental cause classification of "LKAS non-operating point," and the general peculiar point Q(2) is displayed with an environmental cause classification of "point of reduced driving attention."

This allows the driver of vehicle 100 to understand that there is an LKAS non-operating point ahead of vehicle 100 and that they should drive with caution.

Note that the location display of general singular points Q(1) and Q(2) as shown in Figure 14 may be performed when the vehicle 100 approaches within a certain distance, such as within 500 m, from the general singular point. Furthermore, when the vehicle 100 approaches within a certain distance from a general singular point of environmental cause classification R4: "LKAS non-operating point" while traveling at the second driving level, a warning regarding the general singular point may be issued by the presentation device 54.

Figure 14 shows an example in which caution information including environmental cause classifications is delivered to vehicle 100, but caution information including environmental cause classifications may also be delivered to road managers. This has the effect of giving road managers an opportunity to encourage road repairs.

The distribution unit 27 distributes the caution information including the individual cause classification at a predetermined timing, for example, when an external request is received or when an individual cause analysis is performed. The recipients of the caution information including the individual cause classification include, for example, the vehicle 100 whose individual cause classification has been determined, the manager of the vehicle 100, a driving enforcement agency, or a driver's license issuing agency.

By delivering the caution information including the individual cause classification to the vehicle 100, the driver can recognize their own poor driving skills, their own reckless driving, or abnormalities in the vehicle's 100 tires or LKAS function. Furthermore, vehicle managers or road enforcement agencies that receive the caution information including the individual cause classification can provide the relevant driver with an opportunity to implement prescribed procedures or processes for safe driving or safe travel. Furthermore, driver's license issuing agencies that receive the caution information including the individual cause classification can provide license renewers with advice such as surrendering their licenses.

<D-3. Effects>
The information collection and analysis device 204 according to the fourth embodiment includes a distribution unit 27 that distributes the caution-required information to the outside of the information collection and analysis device 204. Therefore, the caution-required information created by the information collection and analysis device 204 can be used for driving assistance for the vehicle 100 or road repairs, etc.

<E. Hardware Configuration>
The position information acquisition unit 11, the lane departure detection unit 12, the control information acquisition unit 13, the probe information generation unit 14, the probe information transmission unit 15, the vehicle communication unit 16, and the information presentation unit 17 in the above-mentioned probe information generation devices 101 and 104 are realized by a processing circuit 81 shown in Fig. 15. That is, the processing circuit 81 includes the position information acquisition unit 11, the lane departure detection unit 12, the control information acquisition unit 13, the probe information generation unit 14, the probe information transmission unit 15, the vehicle communication unit 16, and the information presentation unit 17 (hereinafter referred to as "position information acquisition unit 11, etc.") in the probe information generation devices 101 and 104.

Furthermore, the information collection unit 21, collected information storage unit 22, analysis unit 23, cause storage unit 24, and distribution unit 27 in the information collection and analysis devices 201-204 are realized by a processing circuit 81 separate from the processing circuit 81 that realizes the probe information generation devices 101, 104. In other words, this separate processing circuit 81 includes the information collection unit 21, collected information storage unit 22, analysis unit 23, cause storage unit 24, and distribution unit 27 (hereinafter referred to as "information collection unit 21", etc.) in the information collection and analysis devices 201-204.

Each of these processing circuits 81 may be implemented using dedicated hardware or a processor that executes programs stored in memory. Processors include, for example, central processing units, processing units, arithmetic units, microprocessors, microcomputers, and DSPs (Digital Signal Processors).

When the processing circuit 81 is dedicated hardware, the processing circuit 81 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 of these. Each function of the location information acquisition unit 11 or the information collection unit 21 may be realized by multiple processing circuits 81, or the functions of each unit may be combined and realized by a single processing circuit.

When the processing circuit 81 is a processor, the functions of the location information acquisition unit 11, etc. or the information collection unit 21, etc. are realized by a combination of software, etc. (software, firmware, or software and firmware). Software, etc. is written as a program and stored in memory. As shown in FIG. 16, the processor 82 applied to the processing circuit 81 realizes the functions of each unit by reading and executing programs stored in memory 83. That is, the probe information generation devices 101, 104 include a memory 83 for storing a program that, when executed by the processing circuit 81, results in the functions of the location information acquisition unit 11, etc. being executed. The information collection and analysis devices 201-204 also include a memory 83 for storing a program that, when executed by the processing circuit 81, results in the functions of the information collection unit 21, etc. being executed. In other words, this program can be said to cause a computer to execute the procedures or methods of the location information acquisition unit 11, etc. or the information collection unit 21, etc. Here, the memory 83 may be, for example, a non-volatile or volatile semiconductor memory such as a RAM (Random Access Memory), a ROM (Read Only Memory), a flash memory, an EPROM (Erasable Programmable Read Only Memory), or an EEPROM (Electrically Erasable Programmable Read Only Memory), a HDD (Hard Disk Drive), a magnetic disk, a flexible disk, an optical disk, a compact disk, a mini disk, a DVD (Digital Versatile Disk) and its drive device, or any storage medium that will be used in the future.

The above describes a configuration in which the functions of the location information acquisition unit 11, etc. or the information collection unit 21, etc. are realized either by hardware or software, etc. However, this is not limited to this, and a configuration in which part of the location information acquisition unit 11, etc. or the information collection unit 21, etc. is realized by dedicated hardware and another part is realized by software, etc.

As described above, the processing circuit can realize each of the above-mentioned functions using hardware, software, etc., or a combination of these. The collected information storage unit 22 and the cause storage unit 24 are composed of memory 83, but they may be composed of a single memory 83, or each may be composed of a separate memory.

It is possible to freely combine the various embodiments, and to modify or omit any of the embodiments as appropriate. The above description is illustrative in all respects. It is understood that countless variations not illustrated can be envisioned.

11 Position information acquisition unit, 12 Lane departure detection unit, 13 Control information acquisition unit, 14 Probe information generation unit, 15 Probe information transmission unit, 16 Vehicle communication unit, 17 Information presentation unit, 21 Information collection unit, 22 Collected information storage unit, 23 Analysis unit, 24 Cause storage unit, 25 Environmental cause storage unit, 26 Individual cause storage unit, 27 Distribution unit, 51 Positioning device, 52 Surrounding detection device, 53 Automatic driving control device, 54 Presentation device, 61 Travel route, 81 Processing circuit, 82 Processor, 83 Memory, 100 Vehicle, 101, 104 Probe information generation device, 201-204 Information collection and analysis device, 301 Wide area communication network, 1001-1004 Information collection and analysis system.

Claims (17)

an information collection unit that, when a specific event including lane departure or LKAS operation occurs in a vehicle capable of automatic driving control by an automatic driving control device, collects probe information from the vehicle that is linked to information on the specific event and information on a driving mode that indicates the degree of automatic driving control at the time the specific event occurs;
an analysis unit that identifies a cause of the specific event as a cause classification based on the operation mode and creates caution-requiring information that links the specific event with the cause classification;
Equipped with
The operation mode is
a first driving mode in which the driver operates the steering wheel of the vehicle, and the automatic driving control device does not perform the steering wheel operation;
a second driving mode in which the driver normally operates the steering wheel, and when the automatic driving control device detects the risk of lane departure, the automatic driving control device intervenes in the steering wheel operation and executes the LKAS;
a third driving mode in which the automatic driving control device always controls the steering operation of the vehicle;
Information collection and analysis device. the probe information includes location information of a specific point where the specific event occurred and an individual ID unique to the vehicle or the driver of the vehicle;
The analysis unit
determining whether the peculiar event is an environmentally-caused event caused by the vehicle's running environment based on the location information of the peculiar point and the individual ID;
classifying the driving environment that caused the environmentally caused event as an environmental cause classification based on the driving mode;
creating the caution information by linking the environmental cause event, the environmental cause classification, and the location information of the anomalous point where the environmental cause event occurred;
The information collection and analysis device according to claim 1 . the probe information includes location information of a specific point where the specific event occurred and an individual ID unique to the vehicle or the driver of the vehicle;
The analysis unit
determining whether the singular event is an individual cause event caused by an individual cause specific to the vehicle or the driver based on the location information of the singular point and the individual ID;
Identifying the individual causes that caused the individual cause event based on the operation mode as an individual cause classification;
creating the caution information linking the individual cause event, the individual cause classification, and the individual ID;
3. The information collection and analysis device according to claim 1. a distribution unit that distributes the caution-required information to an outside of the information collection and analysis device;
The information collection and analysis device according to claim 1 . The probe information and the caution information include lane-by-lane position information of the specific point where the specific event occurred.
The information collection and analysis device according to claim 2 . the information collection unit collects the probe information, including lane-by-lane position information of the singular point obtained using a high-precision map database having road shape data for each lane, and a high-precision positioning device having sub-meter-level positioning accuracy and outputting the position and driving lane of the vehicle, from the vehicle, the high-precision map database having road shape data for each lane, and the high-precision positioning device.
The information collection and analysis device according to claim 5 . The analysis unit
determining the singular events occurring in the same singular point for a plurality of the individual IDs as the environmentally caused events;
If lane departure occurs in the first driving mode and lane departure does not occur in the second driving mode or the third driving mode at the same specific point where the environmentally caused event occurred, the same specific point is classified as a point where the driver's driving attention is likely to decrease.
The information collection and analysis device according to claim 2 . The analysis unit
determining the peculiar events occurring in the same peculiar point for a plurality of the individual IDs as the environmentally caused events;
If lane departure occurs in the first driving mode at the same specific point where the environmentally caused event occurred, and if LKAS is activated and lane departure occurs in the second driving mode, the same specific point is classified as a point where lane departure is likely to occur due to a road shape.
The information collection and analysis device according to claim 2 . The analysis unit
determining the peculiar events occurring in the same peculiar point for a plurality of the individual IDs as the environmentally caused events;
If the LKAS does not operate and lane departure occurs in the second driving mode at the same specific point where the environmentally caused event occurred, and if lane departure occurs in the third driving mode, the same specific point is classified as a point where the LKAS does not operate.
The information collection and analysis device according to claim 2 . The analysis unit
determining the peculiar events occurring in the same peculiar point for a plurality of the individual IDs as the environmentally caused events;
If a lane departure occurs in the second driving mode or the third driving mode at the same specific point where the environmentally caused event occurred, and the LKAS operation rate at that time decreases over time, the same specific point is classified as a point where road conditions are deteriorating and LKAS operation is unstable.
The information collection and analysis device according to claim 2 . The analysis unit
When the singular event occurs in a plurality of the individual IDs at the same singular point, the same singular point is defined as a general singular point;
When a ratio of the specific points where the specific event occurred in the specific individual ID that is different from the general specific points is equal to or greater than a predetermined threshold, the specific event that occurred in the specific individual ID is determined to be the individual causal event.
The information collection and analysis device according to claim 3 . The analysis unit
In the individual cause event occurring in the specific individual ID, when lane departure occurs in the first driving mode and LKAS is activated in the second driving mode, the individual cause of the individual cause event occurring in the specific individual ID is classified as poor driving skill of the driver.
The information collection and analysis device according to claim 3 . The analysis unit
If the number of specific points where the individual cause event occurred in a specific road section for a specific individual ID increases over time, the individual cause of the individual cause event that occurred in the specific individual ID is classified as a decline in the driver's driving skill.
The information collection and analysis device according to claim 3 . The analysis unit
For a particular individual ID,
When the ratio of the specific point of the first specific event, which is the specific event in which the LKAS does not operate and lane departure occurs in the second driving mode, and the specific point of the second specific event, which is the specific event in which lane departure occurs in the third driving mode, that differs from the general specific point is equal to or greater than a predetermined threshold value,
determining the first specific event and the second specific event as the individual cause events, and classifying the individual causes of the first specific event and the second specific event as deterioration of LKAS function;
The information collection and analysis device according to claim 11. the probe information includes a driver state indicating a driving fitness state related to the driver's inattentiveness or decreased alertness,
The analysis unit assigns a sub-environmental cause classification, based on the driver state, of a dozing-off-the-driver-prone spot or a distracted-driver-prone spot to the specific point of the environmental cause event that has been classified as a spot where the driver's driving attention is likely to decrease.
The information collection and analysis device according to claim 7. the probe information includes a driver state indicating a driving fitness state related to the driver's inattentiveness or decreased alertness,
When the driver state indicates a good driving aptitude state, the analysis unit assigns a sub-environmental cause classification of a point requiring particular driving caution to the singular point of the environmental cause event that has been subjected to the environmental cause classification of a point having a road shape where lane departure is likely to occur.
The information collection and analysis device according to claim 8. a probe information generating device that creates and transmits probe information of a vehicle that can be automatically controlled by an automatic driving control device;
an information collection and analysis device that receives the probe information from the probe information generation device,
The probe information generating device
an information acquisition unit that acquires a driving mode indicating a mode of the automatic driving control by the automatic driving control device and an operating status of the LKAS by the automatic driving control device;
a lane departure detection unit that detects lane departure of the vehicle;
a probe information generating unit configured to generate the probe information including information on the specific event including the lane departure or the LKAS operation when the specific event occurs and information on the driving mode at the time of the specific event occurrence;
a probe information transmitting unit that transmits the probe information to the information collection and analysis device,
The information collection and analysis device
an information collection unit that receives the probe information from the probe information generation device;
an analysis unit that identifies a cause of the specific event as a cause classification based on the operation mode and creates caution-requiring information that links the specific event with the cause classification;
Equipped with
The operation mode is
a first driving mode in which the driver operates the steering wheel of the vehicle, and the automatic driving control device does not perform the steering wheel operation;
a second driving mode in which the driver normally operates the steering wheel, and when the automatic driving control device detects the risk of lane departure, the automatic driving control device intervenes in the steering wheel operation and executes the LKAS;
a third driving mode in which the automatic driving control device always controls the steering operation of the vehicle;
Information collection and analysis system.