Vehicle control system and electronic control device

Abstract

To provide a vehicle control system that enables a vehicle to safely travel by external recognition with an on-vehicle sensor, and an electronic control device.SOLUTION: A vehicle control system 100 includes an electronic control unit 120 mounted on a plurality of vehicles 200 and a server 110 communicably connected thereto. The electronic control unit 120 includes: a recognition unit 121 for recognizing road conditions; a determination unit 122 for determining abnormality; a transmission unit 123 for transmitting abnormality information including a position and time of abnormality determination and a type of determined abnormality to the server 110; and a vehicle control unit 124 for causing the vehicle 200 to travel along a recommended route received from the server 110. The server 110 includes: a recording unit 111 for recording abnormality occurrence information having the received abnormality information associated with map information; an operation unit 112 for calculating a travel difficulty level based on a determination frequency of abnormality; a route selection unit 113 for selecting a route in which total of the travel difficulty level becomes minimum from route candidates in the map information; and a transmission unit 114 for transmitting the selected route as a recommended route.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present disclosure relates to a vehicle control system and an electronic control device. [Background technology]

[0002] 2. Description of the Related Art Conventionally, a danger location information collection system including an on-vehicle device and a danger location information collection device has been known (Patent Document 1, claim 1, paragraph 0005, etc.).

[0003] The vehicle-mounted device includes an operation information acquisition means, a driving information acquisition means, a position information acquisition means, and a control means. The operation information acquisition means acquires information regarding the operation of the vehicle by the driver. The driving information acquisition means acquires information regarding the driving state of the vehicle. The position information acquisition means acquires current position information of the vehicle. When the control means estimates based on the operation information that an operation has been performed to deal with an emergency, it controls the various information to be transmitted to the outside.

[0004] The risk location information collecting device receives the various information from the vehicle-mounted device, and when it determines that the operation is highly likely to have been performed to deal with an emergency, collects the location information as information about the risk location. This determination of the possibility of an emergency is made based on the operation information, the driving information, and traffic rule information for the road on which the vehicle is located and / or information about the situation around the vehicle. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] JP 2007-323281 A Summary of the Invention [Problem to be solved by the invention]

[0006] As described above, the conventional system collects the current position information of the vehicle as information on the danger location when it is determined that the driver's operation of the vehicle is highly likely to have been performed to deal with an emergency. However, in order to safely drive the vehicle by recognizing the outside world using an on-board sensor, information on the danger location based on the driver's operation alone is insufficient.

[0007] The present disclosure provides a vehicle control system and an electronic control device that enable a vehicle to travel safely by recognizing the external environment using on-board sensors. [Means for solving the problem]

[0008] One aspect of the present disclosure is a vehicle control system including electronic control devices mounted on each of a plurality of vehicles and at least one server communicatively connected to each of the electronic control devices, wherein each of the electronic control devices includes a recognition unit that recognizes road conditions based on detection results from an external sensor mounted on each of the vehicles, a determination unit that determines an abnormality including a failure to recognize the road condition by the recognition unit, and a transmission unit that transmits abnormality information including the position of each of the vehicles at the time of the abnormality determination by the determination unit, a time, and the type of the determined abnormality to the server via a communication device mounted on each of the vehicles, and the server includes a recording unit that records abnormality occurrence information in which the abnormality information received from each of the electronic control devices corresponds to each node or link of map information, a calculation unit that calculates a driving difficulty based on the abnormality determination frequency for each of the nodes or links included in the abnormality occurrence information, a route selection unit that selects a safe route with the smallest sum of the driving difficulty from a plurality of route candidates in the map information, and a transmission unit that transmits the safe route to each of the electronic control devices as a recommended route. Effect of the Invention

[0009] According to the above aspect of the present disclosure, it is possible to provide a vehicle control system and an electronic control device that are capable of driving a vehicle safely by recognizing the external environment using an external sensor mounted on the vehicle. [Brief description of the drawings]

[0010] [Figure 1] 1 is a block diagram showing a vehicle control system and an electronic control device according to a first embodiment of the present disclosure. [Diagram 2] 2 is a flow chart showing an example of processing performed by the electronic control device of FIG. 1; [Diagram 3] FIG. 2 is a flow chart showing an example of processing by the server of FIG. 1; [Figure 4] FIG. 4 is a flow diagram showing details of the process of selecting a safe path in FIG. 3; [Diagram 5] FIG. 4 is a diagram showing an example of map information used in the process of selecting a safe route in FIG. 3; [Figure 6] 5 is a graph showing an example of the results of the process of acquiring the abnormality occurrence frequency in FIG. 4. [Figure 7] 5 is a graph showing an example of the results of the process of acquiring the abnormality occurrence frequency in FIG. 4. [Figure 8] FIG. 11 is a block diagram of a server in a vehicle control system according to a third embodiment of the present disclosure. [Figure 9] FIG. 9 is a flow chart showing an example of processing by the server of FIG. 8; [Figure 10] 9 is an example of a display screen by the countermeasure support unit of FIG. 8. [Figure 11] An example of a display screen for the process of displaying the driving difficulty of each node and link in Figure 9. [Figure 12] A graph showing the driving difficulty for each anomaly type for the link selected in Figure 11. [Figure 13] FIG. 9 is a flow chart showing an example of processing by the server of FIG. 8; DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0011] Hereinafter, embodiments of a vehicle control system and an electronic control device according to the present disclosure will be described with reference to the drawings.

[0012] [Embodiment 1] Fig. 1 is a block diagram showing a first embodiment of a vehicle control system and an electronic control device according to the present disclosure. A vehicle control system 100 according to the present embodiment is, for example, an automatic driving assistance system that assists automatic driving of a plurality of vehicles 200. Note that Fig. 1 omits illustration of the plurality of vehicles 200 and shows only one vehicle 200.

[0013] The vehicle control system 100 includes, for example, at least one server 110 and a plurality of electronic control devices 120 (not shown). That is, the vehicle control system 100 may include a plurality of servers 110. Each of the plurality of electronic control devices 120 is mounted on each of the plurality of vehicles 200.

[0014] The server 110 is a computer configured with hardware such as a central processing unit (CPU), memories such as ROM and RAM, a timer, and an input / output unit, etc. The server 110 is communicatively connected to the electronic control device 120 via, for example, an Internet line 300, a communication device 400 such as a wireless base station, a wireless communication line, a communication device 210 mounted on the vehicle 200, an in-vehicle network, etc.

[0015] The server 110 includes, for example, a recording unit 111, a calculation unit 112, a route selection unit 113, and a transmission unit 114. The server 110 may also include a storage unit 115. Each of these units of the vehicle control system 100 represents, for example, each function of the vehicle control system 100 that is realized by the CPU of the vehicle control system 100 executing a program recorded in the memory.

[0016] The electronic control device 120 is, for example, a vehicle control device mounted on the vehicle 200. The electronic control device 120 is, for example, configured with one or more microcontrollers, and includes a CPU, memories such as a ROM and a RAM, a timer, an input / output unit, and the like.

[0017] The electronic control device 120 has, for example, a recognition unit 121, a determination unit 122, a transmission unit 123, and a vehicle control unit 124. Each of these units of the electronic control device 120 represents, for example, each function of the electronic control device 120 that is realized by the CPU of the electronic control device 120 executing a program recorded in a memory.

[0018] Each vehicle 200 is, for example, a gasoline vehicle, a diesel vehicle, a hybrid vehicle, an electric vehicle, a fuel cell vehicle, or the like. In addition to the electronic control device 120, the vehicle 200 is equipped with, for example, a communication device 210, an external sensor 220, a vehicle sensor 230, a navigation system 240, an actuator 250, and an alarm device 260. The vehicle 200 is also equipped with general vehicle components such as a prime mover, a power transmission device, a braking device, a steering device, a running device, a frame, a suspension, an electric device, and a safety device, which are not shown.

[0019] The communication device 210 is an in-vehicle communication device of the vehicle 200, and is connected to the electronic control device 120 via an in-vehicle network. The communication device 210 performs wireless communication with a communication device 400, such as a wireless base station, installed outside the vehicle 200, for example.

[0020] The communication device 400 is connected to the communication device 210 of the vehicle 200 via, for example, a wireless communication line, and is connected to the server 110 via an Internet line 300. The communication device 400 receives information transmitted from the vehicle 200 and transmits it to the server 110, and also receives information transmitted from the server 110 and transmits it to the communication device 210 of the vehicle 200.

[0021] The external sensor 220 is, for example, a sensor that detects objects around the vehicle 200, and is connected to the electronic control device 120 via an in-vehicle network. The external sensor 220 includes, for example, sensors such as a monocular camera, a stereo camera, a millimeter wave radar, a laser radar, and an ultrasonic sensor. In this embodiment, the external sensor 220 includes at least a stereo camera.

[0022] The vehicle sensor 230 is, for example, a sensor that detects physical quantities related to the vehicle 200, and is connected to the electronic control device 120 via an in-vehicle network. The vehicle sensor 230 includes various sensors required for an advanced driver assistance system (ADAS) or an autonomous driving system (ADS), such as a wheel speed sensor, an acceleration sensor, an angular velocity sensor, an angular acceleration sensor, a steering angle sensor, a shift position sensor, an accelerator sensor, and a brake sensor.

[0023] The navigation system 240 includes, for example, a receiver for a global navigation satellite system (GNSS), a storage device in which map information is stored, a display device for displaying the map and information on the route to the destination, and an input device for inputting the destination, etc. The navigation system 240 is connected to the electronic control unit 120 via, for example, an in-vehicle network, and outputs to the electronic control unit 120, information on the current position of the vehicle 200 and the route to the destination.

[0024] The actuator 250 automatically operates the steering device, accelerator, brake, gear shift, etc. of the vehicle 200 based on a control signal output from the electronic control device 120, for example, to realize advanced driving assistance and automatic driving of the vehicle 200. That is, the vehicle 200 is a vehicle equipped with an ADAS or ADS configured by the communication device 210, the external sensor 220, the vehicle sensor 230, the navigation system 240, the electronic control device 120, the actuator 250, etc.

[0025] The notification device 260 is a device that provides information, calls attention, warns, and the like to occupants of the vehicle 200 based on, for example, a control signal output from the electronic control device 120. The notification device 260 includes, for example, a display device and an audio output device. The display device includes, for example, a liquid crystal display device, an organic EL display device, a head-up display, and the like. The audio output device includes, for example, a speaker and a buzzer.

[0026] Hereinafter, the operation of the vehicle control system 100 and the electronic control device 120 of this embodiment will be described with reference to Fig. 2 and Fig. 3. Fig. 2 is a flow diagram showing an example of a process P10 by the electronic control device 120 of Fig. 1. Fig. 3 is a flow diagram showing an example of a process P20 by the server 110 of Fig. 1.

[0027] 2, the electronic control unit 120 first executes process P11 to acquire a departure point and a destination. In this process P11, the electronic control unit 120 acquires the current position of the vehicle 200 from the navigation system 240 as the departure point, for example, by the vehicle control unit 124. In addition, the electronic control unit 120 acquires, for example, by the vehicle control unit 124, position information of the destination input to an input device of the navigation system 240.

[0028] Next, the electronic control unit 120 executes a process P12 for requesting route information from the server 110. In this process P12, the electronic control unit 120 transmits a request for a recommended route from the departure point to the destination to the server 110, for example, by the transmission unit 123 via the communication device 210. Here, information on the departure point and destination of the vehicle 200 included in the request for the recommended route is transmitted in a format that can be collated with spot information included in map information stored in the storage unit 115 of the server 110, for example.

[0029] Specifically, the information on the departure point and destination of the vehicle 200 transmitted from the electronic control device 120 to the server 110 includes, for example, the latitude and longitude of the departure point and the destination. In addition, the request for the recommended route transmitted from the electronic control device 120 to the server 110 includes, for example, the date and time when the vehicle 200 will travel from the departure point to the destination.

[0030] 3, the server 110 first executes process P21 to determine whether or not there is a request for route information from the electronic control unit 120. In process P21, if the route selection unit 113 of the server 110 determines that no request for a recommended route has been received from the electronic control unit 120 of any of the vehicles 200, it determines that there is no request for route information (NO) and executes process P24, which will be described later.

[0031] On the other hand, in this process P21, when the route selection unit 113 of the server 110 determines that a request for a recommended route including a starting point and a destination has been received from, for example, the electronic control unit 120 of at least one vehicle 200, it determines that there is a request for route information (YES). In this case, the server 110 executes a process P22 for selecting a safe route. This process P22 will be described in detail later with reference to FIG. 4.

[0032] After that, the server 110 executes a process P23 in which the server 110 transmits the safe route obtained in the previous process P22 as a recommended route to the electronic control device 120 of the vehicle 200 that has requested the route information. In this process P23, the server 110 transmits the recommended route from the departure point of the vehicle 200 to the communication device 210 of the vehicle 200 via the Internet line 300 and the communication device 400, for example, by the transmission unit 114.

[0033] Next, the electronic control unit 120 of the vehicle 200 executes a process P13 for receiving route information, for example, as shown in Fig. 2. In this process P13, the electronic control unit 120 receives, for example, the recommended route from the departure point to the destination transmitted from the server 110 via the communication device 210 of the vehicle 200 by the vehicle control unit 124. Next, the electronic control unit 120 of the vehicle 200 executes a vehicle driving control process P14 for driving the vehicle 200 along the received recommended route.

[0034] In this process P14, the electronic control unit 120 of the vehicle 200, for example, controls the actuator 250 of the vehicle 200 via the vehicle control unit 124 to drive the vehicle 200 along the recommended route. Furthermore, when the electronic control unit 120 starts process P14, it executes process P15 for recognizing road conditions and process P16 for determining the presence or absence of an abnormality. Note that these processes P15 and P16 may be executed for each control period while the vehicle driving control process P14 is being executed, or may be executed appropriately as necessary.

[0035] In process P15, the electronic control device 120, for example, acquires detection results of the external sensor 220 and the vehicle sensor 230 mounted on the vehicle 200 by the recognition unit 121, and recognizes the road condition based on the detection results. Here, the recognition unit 121 recognizes, as the road condition, for example, an area in which the vehicle 200 can travel, an object that the vehicle 200 should avoid, and a situation in which it is difficult to recognize the road condition.

[0036] More specifically, the recognition unit 121 recognizes an area in which the vehicle 200 can travel by recognizing the road and road edges ahead of the vehicle 200, and dividing lines on the road, for example, based on the detection result of the external sensor 220. In addition, the recognition unit 121 recognizes objects ahead of the vehicle 200 based on the detection result of the external sensor 220, and detects objects that may collide with the vehicle 200 based on the detection result of the vehicle sensor 230, for example.

[0037] Furthermore, the recognition unit 121 recognizes the type of object, such as an automobile, a motorcycle, a pedestrian, a fallen object, or a bump for reducing the vehicle speed, based on the detection result of the external sensor 220. Furthermore, the recognition unit 121 recognizes a situation in which it is difficult to recognize the road condition, based on the detection result of the external sensor 220. Here, the situation in which it is difficult to recognize the road condition includes, for example, a situation in which it is difficult for the camera included in the external sensor 220 to recognize an object due to backlight, heavy rain, snowfall, etc., and an inability to recognize at least one of the left and right lanes due to poor detection of the lane markings by the external sensor 220.

[0038] For example, when at least one of the left and right lanes cannot be recognized or when it becomes difficult to recognize the road conditions, the recognition unit 121 controls the notification device 260 of the vehicle 200 to issue a warning that the lane has not been recognized or that the road conditions have not been recognized to the occupants of the vehicle 200. In addition, when the recognition unit 121 recognizes an object that may collide with the vehicle 200, for example, the recognition unit 121 controls the notification device 260 to issue a warning to the occupants of the vehicle 200 to avoid the collision and outputs information about the object to the vehicle control unit 124.

[0039] Here, when the inter-vehicle distance to the preceding vehicle becomes shorter than a threshold value, or when a collision is predicted based on the position and speed of vehicle 200 and the positions and speeds of other vehicles, recognition unit 121 recognizes those vehicles as objects with which there is a possibility of collision. Also, when a collision is predicted based on the position and speed of vehicle 200 and the position and speed of a pedestrian who has run out onto the road, recognition unit 121 recognizes the pedestrian as an object with which there is a possibility of collision.

[0040] Furthermore, when the recognition unit 121 recognizes, for example, a speed reduction bump, it recognizes the bump as an object that requires deceleration when passing through the vehicle 200. When the vehicle control unit 124 receives, for example, information from the recognition unit 121 about an object that may collide with the vehicle 200 or an object that requires deceleration, the vehicle control unit 124 controls the actuator 250 of the vehicle 200 to perform a steering operation to avoid a collision, activate an automatic emergency brake (AEB), or the like.

[0041] Furthermore, the recognition unit 121 recognizes, as an abnormality, for example, a failure to recognize a road condition, recognition of a road condition different from a steady state, issuance of a warning to the occupants of the vehicle 200, occurrence of collision avoidance control of the vehicle 200, etc. Furthermore, the recognition unit 121 records, for example, the type of the recognized abnormality together with the location and date and time when the abnormality occurred as abnormality information. Here, the location where the abnormality occurred includes, for example, location information of the vehicle 200 acquired from the navigation system 240, and identification information of a link and / or node of map information where the vehicle 200 is located.

[0042] Next, the electronic control unit 120 executes a process P16 for determining the presence or absence of an abnormality, including, for example, a failure to recognize road conditions by the recognition unit 121. In this process P16, the determination unit 122 determines the presence or absence of an abnormality, for example, based on the presence or absence of abnormality information recorded by the recognition unit 121 in the previous process P15. Here, if the determination unit 122 determines that an abnormality exists (YES), the electronic control unit 120 executes a process P17 for transmitting the abnormality information to the server 110, and if the determination unit 122 determines that no abnormality exists (NO), the electronic control unit 120 executes a process P18 for determining whether the vehicle 200 has arrived at the destination.

[0043] In process P17 for transmitting the abnormality information, the electronic control unit 120 transmits the abnormality information to the server 110, for example, by the transmission unit 123, via the communication device 210 mounted on the vehicle 200, the external communication device 400, and the Internet line 300. Here, the server 110 repeatedly executes process P24 for determining whether or not abnormality information has been received from the electronic control unit 120 of each vehicle 200 at a predetermined period, as shown in FIG.

[0044] In this process P24, the server 110 determines, for example, by the recording unit 111, whether or not abnormality information has been received from the electronic control unit 120. If the recording unit 111 determines in this process P24 that abnormality information has not been received from the electronic control unit 120 (NO), the server 110 ends the process P20 shown in Fig. 3 and starts process P20 again. On the other hand, in this process P24, if the electronic control unit 120 determines that the recording unit 111 has received abnormality information from the electronic control unit 120 (YES), it executes process P25 to record the abnormality information.

[0045] In this process P25, the server 110 records, for example, by the recording unit 111, abnormality occurrence information in which the abnormality information received from each electronic control unit 120 is associated with each node N1-N4 or link L1-L7 of the map information. Table 1 below shows an example of abnormality occurrence information recorded in the storage unit 115 by the recording unit 111.

[0046] [Table 1]

[0047] As shown in Table 1, the abnormality occurrence information is, for example, information in which an abnormality type code, date, time, latitude, and longitude as abnormality information are associated with a location code including each link L1-L7 and / or each node N1-N4. Here, the abnormality type code is a code that indicates the type of abnormality.

[0048] For example, as shown in Table 2 below, abnormality type information in which abnormality types and corresponding abnormality type codes are defined in advance is recorded in storage unit 115. Therefore, based on the abnormality type information shown in Table 2, it is possible to identify the abnormality type corresponding to the abnormality type code of the abnormality occurrence information shown in Table 1. Furthermore, in the example shown in Table 2, the abnormality type information includes a weighting coefficient corresponding to the abnormality type. This weighting coefficient is, for example, an index indicating the magnitude of the impact on the safety of vehicle 200.

[0049] [Table 2]

[0050] Furthermore, in the example shown in Table 1, the abnormality occurrence information is information that associates the weather at the position of each vehicle 200 at the time of abnormality determination by the determination unit 122 of each electronic control unit 120 with each link L1-L7 and / or each node N1-N4. For example, if the weather is difficult to detect by the external sensor 220 of the vehicle 200, the recording unit 111 of the server 110 may obtain the weather from a weather database (not shown) based on the date and time of abnormality occurrence included in the abnormality occurrence information and the position information of the vehicle 200 at that time.

[0051] In this way, the recording unit 111 of the server 110 receives abnormality information from, for example, each of the electronic control units 120 of the multiple vehicles 200, and records the abnormality occurrence information as shown in Table 1 in the storage unit 115. This makes it possible to perform statistical processing based on a specific location, a specific time period, a specific type of abnormality, etc., using a large amount of abnormality information included in the abnormality occurrence information recorded in the storage unit 115. As shown in Fig. 3, when the process P25 for recording the abnormality information is completed, the server 110 terminates the process P20 shown in Fig. 3. In addition, the server 110 repeats the process P20 shown in Fig. 3 at a predetermined cycle.

[0052] Hereinafter, the process P22 for selecting a safe route shown in Fig. 3 will be described in detail with reference to Fig. 4 to Fig. 7. Fig. 4 is a flow diagram showing the details of the process P22 for selecting a safe route shown in Fig. 3. Fig. 5 is a diagram showing an example of route candidates CR1, CR2, CR3 for the vehicle 200 based on the map information MI stored in the storage unit 115 of the server 110. The map information MI includes, for example, a road network in which links L1-L7 such as roads are connected via nodes N1-N4 such as intersections.

[0053] When the server 110 starts the process P22 shown in Fig. 4, for example, the route selection unit 113 executes a process P221 in which the route selection unit 113 searches for multiple route candidates CR1, CR2, and CR3 from the map information MI as shown in Fig. 5. The route selection unit 113 acquires information on the departure point S and destination G of the vehicle 200 included in the request for route information received from each electronic control unit 120, for example, and searches the map information MI stored in the storage unit 115 to select multiple route candidates CR1, CR2, and CR3 that can reach the destination G from the departure point S.

[0054] Next, the server 110 executes a process P222 in which, for example, the route selection unit 113 extracts links L1-L7 and / or nodes N1-N4 included in the selected multiple route candidates CR1, CR2, and CR3. In the example shown in Fig. 5, the route selection unit 113 extracts links L1-L5 and / or N1-N4 of route candidate CR1, links L1, L2, L6, and L5 and / or nodes N1, N2, and N4 of route candidate CR2, and links L1, L7, L4, and L5 and / or nodes N1, N3, and N4 of route candidate CR3.

[0055] Next, the server 110 executes a process P223 for acquiring anomaly occurrence frequency, for example, by the route selection unit 113. In this process P223, the route selection unit 113 acquires, for example, a frequency of receiving anomaly information from the electronic control unit 120 of each of the multiple vehicles 200 for each of the links L1-L7 and / or nodes N1-N4 extracted in the previous process P222.

[0056] More specifically, in process P223, the route selection unit 113 extracts abnormality occurrence information that matches driving conditions including, for example, the month, time period, and weather when the vehicle 200 travels from the departure point S to the destination G. Here, the time period used to extract the abnormality occurrence information may be a time period based on the time when the electronic control device 120 of the vehicle 200 transmits a route request, or may be a time period that takes into consideration the required time from the departure point S to the destination G and the predicted passing time of each link or each node. Table 3 below shows an example of the reception frequency of abnormality information for link L6 in FIG. 5 extracted by specifying October, 8:00 a.m., and fine weather as the driving conditions of the vehicle 200.

[0057] [Table 3]

[0058] Table 3 shows that the server 110 received abnormality information of abnormality type code 2 multiple times from each electronic control device 120 mounted on one or more vehicles 200 that traveled along link L6 under the driving conditions of October, between 8 a.m. and sunny weather. The route selection unit 113 calculates the abnormality determination frequency, for example, by counting the number of abnormality occurrence information (number of rows) extracted as in Table 3. In process P223, the route selection unit 113 obtains the abnormality occurrence frequency for each abnormality type for each of links L1-L7 and / or nodes N1-N4 that satisfy the predetermined driving conditions.

[0059] 6 and 7 are graphs showing an example of the results of the process P223 for acquiring the above-mentioned abnormality occurrence frequency. In these graphs, the horizontal axis is an abnormality type code indicating the type of abnormality information, and the vertical axis is the number of times the abnormality information is received, that is, the abnormality occurrence frequency or the abnormality determination frequency. For example, as shown in Table 2, the abnormality type codes 1 to 4 on the horizontal axis of the graph respectively represent automatic emergency braking (AEB) due to pedestrian recognition, lane recognition failure (lane unrecognized), backlight, and obstacle avoidance.

[0060] That is, in the example shown in Fig. 6, the server 110 receives, as abnormality information, abnormality type code 2 indicating an inability to recognize lanes, at a frequency of about 150 times from the electronic control devices 120 of multiple vehicles 200 that traveled along link L6 in October, between 8 a.m., and under clear skies. Also, in the example shown in Fig. 7, the server 110 receives, as abnormality information, abnormality type code 1 indicating the operation of the AEB due to the recognition of a pedestrian, at a frequency of about 79 times from the electronic control devices 120 of multiple vehicles 200 that traveled along link L3 in October, between 8 a.m., and under clear skies.

[0061] Here, it is assumed that the server 110 has not received any abnormality information for links L1, L2, L4, L5, and L7, excluding links L3 and L6, and for nodes N1-N4, under the same driving conditions as for links L3 and L6.

[0062] Among the abnormality types, as shown in Table 3, lane recognition failure (abnormality type code: 2) occurring at the same point on link L6 occurs, for example, when road markings become faded due to wear over time. Lane recognition by the electronic control unit 120 is necessary for the electronic control unit 120 to automatically drive the vehicle 200. Therefore, each electronic control unit 120 of the multiple vehicles 200 transmits lane recognition failure abnormality type code: 2 to the server 110 as abnormality information. Lane recognition failure is an abnormality type for which a large amount of information is collected regardless of the month or time of day.

[0063] Among the anomaly types, AEB due to pedestrian recognition may be a sudden event. However, as shown in Fig. 7, if many AEB due to pedestrian recognition (anomaly type code: 1) occur in the same time period on link L3, the following situation can be assumed. For example, there is a workplace where many employees commute to work facing the road on link L3, and there is a parking lot on the opposite side of the road from the workplace, with no crosswalk between the workplace and the parking lot.

[0064] In such a situation, many pedestrians who are trying to get to their workplaces in the shortest distance cross roads that do not have crosswalks. As a result, as shown in Fig. 7, many AEBs (abnormality type code: 1) due to pedestrian recognition occur during the commute time. In this way, a point on a road where many abnormalities occur due to the environment around the road during a specific time period becomes an obstacle to the safe passage of vehicle 200 during that time period.

[0065] In addition to the above situations, the following situations can be assumed as situations in which many abnormalities occur during a specific time period due to the environment around the road. For example, there may be a store such as a supermarket facing the road, and the number of cars that can be parked in the store's parking lot is less than the number of cars required for the number of customers who are concentrated during a specific time period. In such a case, it is assumed that many obstacle avoidances (abnormality type code: 4) will occur during that specific time period in order to avoid vehicles that are unable to enter the parking lot and are parked on the road.

[0066] More specifically, near such a store, for example, many vehicles park on the road between 5:00 and 6:00 PM when shoppers are concentrated, resulting in many occurrences of obstacle avoidance (abnormality type code: 4) to avoid parked vehicles. On the other hand, for example, between 8:00 AM before the store opens, there are no parked vehicles by shoppers on the road near the store, and no obstacle avoidance to avoid parked vehicles occurs. Similarly, near educational facilities where students are picked up and dropped off at a certain time, it is expected that many vehicles will park on the road during that time, resulting in many occurrences of obstacle avoidance (abnormality type code: 4) to avoid parked vehicles.

[0067] Furthermore, an abnormality caused by a natural phenomenon such as backlighting (abnormality type code: 3) may occur, for example, in a specific location, in a specific season, and at a specific time of day. When backlighting occurs, for example, whiteout occurs in the image of the camera included in the external sensor 220 of the vehicle 200, which may hinder the recognition of objects. By receiving such abnormality information caused by the natural environment from the electronic control devices 120 of the multiple vehicles 200, the server 110 can identify the location, season, and time of day when abnormalities occur frequently.

[0068] Furthermore, the frequency of occurrence of such anomalies is affected by, for example, the weather. More specifically, the number of parked vehicles around the above-mentioned stores and educational facilities is likely to increase or decrease between rainy and sunny weather. Therefore, the number of obstacle avoidances (anomaly type code: 4) for avoiding parked vehicles may be affected by the weather. Furthermore, anomalies caused by natural phenomena such as backlighting only occur in specific weather conditions, such as sunny weather. Therefore, when the anomaly occurrence information includes the weather, it becomes possible to grasp the frequency of anomalies occurring according to the weather.

[0069] 4, the server 110 executes a process P224 for acquiring an average traffic volume, for example. In this process P224, the server 110 acquires the average traffic volume of each link L1-L7 and / or each node N1-N4, for example. More specifically, in this process P224, the route selection unit 113 of the server 110 acquires the average traffic volume stored in the storage unit 115 for each link L1-L7 and / or each node N1-N4 extracted in the above-mentioned process P222.

[0070] In this process P224, the route selection unit 113 acquires, for example, from the storage unit 115, average traffic volumes corresponding to the travel conditions, such as the month, time period, and weather, during which the vehicle 200 that requested the route information travels along the route candidates CR1, CR2, and CR3. As a result, for example, the average traffic volumes of the links L6 and L3 are acquired as 450 vehicles / hour and 1100 vehicles / hour, respectively. Note that the average traffic volumes do not necessarily need to be stored in the storage unit 115, and may be acquired by the route selection unit 113 from outside the server 110 via the Internet line 300.

[0071] Next, the server 110 executes a process P225 for calculating the driving difficulty level of each of the links L1-L7 and / or each of the nodes N1-N4. In this process P225, the calculation unit 112 of the server 110 calculates a driving difficulty level ADD based on the abnormality occurrence frequency for each of the nodes N1-N4 or the links L1-L7 of the abnormality occurrence information, for example, by the following formula (1).

[0072]

number

[0073] In the above formula (1), N is the number of abnormality types, H(n) is the occurrence frequency of the abnormality type, Wn is a weighting coefficient of the abnormality type, and Q is the average traffic volume of each link L1-L7 and / or each node N1-N4 acquired in the previous process P224. That is, the calculation unit 112 calculates a driving difficulty ADD for each node N1-N4 or link L1-L7 in the abnormality occurrence information to be higher as the frequency of abnormality determination by the determination unit 122 of each electronic control unit 120 increases.

[0074] As described above, for example, if the average traffic volumes Q of links L6 and L3 are 450 vehicles / hour and 1100 vehicles / hour, respectively, the driving difficulty ADD can be calculated as follows based on the above formula (1), Table 2, Figures 6 and 7, respectively. Link L6: Running difficulty ADD = (150 x 0.7) / 450 = 0.23 Link L3: Running difficulty ADD = (79 x 0.6) / 1100 = 0.043

[0075] As shown in Table 1, in this embodiment, the abnormality occurrence information includes the weather at the time of abnormality determination associated with each of the nodes N1-N4 or links L1-L7. In this case, the calculation unit 112 may obtain the current weather of each of the nodes N1-N4 or links L1-L7 included in the multiple route candidates CR1, CR2, CR3 in the map information when calculating the driving difficulty level ADD. In this case, the calculation unit 112 may calculate the driving difficulty level ADD based on the abnormality occurrence information in which the weather at the time of abnormality determination matches the current weather.

[0076] Next, the server 110 executes a process P226 to calculate the driving difficulty ADD of each of the route candidates CR1, CR2, and CR3, as shown in Fig. 4. In this process P226, the route selection unit 113 calculates, for example, the sum of the driving difficulty ADD of all the nodes N1-N4 or links L1-L7 included in each of the route candidates CR1, CR2, and CR3. As a result, the driving difficulty ADD of each of the route candidates CR1, CR2, and CR3 can be calculated, for example, as follows:

[0077] Driving difficulty ADD of route candidate CR1 = Driving difficulty ADD(0) of link L1 + Driving difficulty ADD(0) of link L2 + Driving difficulty ADD(0) of link L3 + Driving difficulty ADD(0) of link L4 + Driving difficulty ADD(0) of link L5 = 0.043

[0078] Driving difficulty ADD of route candidate CR2 = Driving difficulty ADD(0) of link L1 + Driving difficulty ADD(0) of link L2 + Driving difficulty ADD(0) of link L6 + Driving difficulty ADD(0.23) of link L5 = 0.23

[0079] Driving difficulty ADD of route candidate CR3 = Driving difficulty ADD(0) of link L1 + Driving difficulty ADD(0) of link L7 + Driving difficulty ADD(0) of link L4 + Driving difficulty ADD(0) of link L5 = 0

[0080] Next, the server 110 executes a process P227 for selecting a safe route, as shown in Fig. 4. In this process P227, the route selection unit 113 of the server 110 selects a safe route with the smallest total driving difficulty ADD from the multiple route candidates CR1, CR2, and CR3 in the map information MI. In this embodiment, as described above, the driving difficulty ADD of the route candidate CR1 is 0.043, the driving difficulty ADD of the route candidate CR2 is 0.23, and the driving difficulty ADD of the route candidate CR3 is 0.

[0081] Therefore, in this process P227, the route selection unit 113 selects the route candidate CR3 as a safe route. After that, the server 110 ends the process P22 shown in Fig. 4, and executes the process P23 of transmitting the recommended route shown in Fig. 3 as described above. In this process P23, the transmission unit 114 transmits the route candidate CR3 selected as the safe route as a recommended route to the electronic control unit 120 of the vehicle 200 that performed the process P12 requesting the route information shown in Fig. 2. After that, the electronic control unit 120 of the vehicle 200 executes the processes P14 to P18 shown in Fig. 2 as described above, and drives the vehicle 200 to the destination along the recommended route.

[0082] The operation of the vehicle control system 100 and the electronic control unit 120 of this embodiment will be described below.

[0083] As described above, the vehicle control system 100 of the present embodiment includes each electronic control device 120 mounted on each of the multiple vehicles 200, and at least one server 110 communicably connected to each electronic control device 120. Each electronic control device 120 includes a recognition unit 121, a determination unit 122, and a transmission unit 123. The recognition unit 121 recognizes the road condition based on the detection result of the external sensor 220 mounted on each vehicle 200. The determination unit 122 determines an abnormality including a failure to recognize the road condition by the recognition unit 121. The transmission unit 123 transmits abnormality information including the position of each vehicle 200 at the time of abnormality determination by the determination unit 122, the time, and the type of abnormality determined to the server 110 via the communication device 210 mounted on each vehicle 200. Meanwhile, the server 110 includes a recording unit 111, a calculation unit 112, a route selection unit 113, and a transmission unit 114. The recording unit 111 records abnormality occurrence information in which the abnormality information received from each electronic control unit 120 is associated with each node N1-N4 or link L1-L7 of the map information MI. The calculation unit 112 calculates a driving difficulty level ADD based on the abnormality determination frequency for each node N1-N4 or link L1-L7 of the abnormality occurrence information. The route selection unit 113 selects a safe route with the smallest total driving difficulty level ADD from multiple route candidates CR1, CR2, CR3 of the map information MI. The transmission unit 114 transmits the selected safe route to each electronic control unit 120 as a recommended route.

[0084] With such a configuration, the vehicle control system 100 of the present embodiment can collect abnormality information including the position, time, and type of determined abnormality of each vehicle 200 from each electronic control unit 120 of the multiple vehicles 200 by at least one server 110. Furthermore, the server 110 records abnormality occurrence information in which the abnormality information collected from each electronic control unit 120 is associated with each node N1-N4 or link L1-L7 of the map information MI. As a result, the server 110 can calculate a driving difficulty ADD based on the abnormality occurrence frequency for each node N1-N4 or link L1-L7 of the abnormality occurrence information, select a route candidate CR3 with a low driving difficulty ADD as a safe route, and transmit the selected safe route to the electronic control unit 120 of the vehicle 200 as a recommended route. Therefore, according to the vehicle control system 100 of the present embodiment, an abnormality determined by the electronic control unit 120 mounted on any vehicle 200 can be used as an experience of automatic driving control in the electronic control unit 120 mounted on the other vehicle 200. This makes it possible to improve the safety of the automatic driving and driving assistance of the vehicle 200 by the electronic control device 120 by avoiding routes on which abnormalities occur frequently, including poor recognition of road conditions, recognition of road conditions different from the steady state, issuance of warnings to the occupants of the vehicle 200, and occurrence of collision avoidance control of the vehicle 200. More specifically, it becomes possible to avoid routes on which obstacle avoidance by parked vehicles and AEB due to pedestrian recognition occur frequently during a specific time period, for example.

[0085] Furthermore, in the vehicle control system 100 of this embodiment, the calculation unit 112 of the server 110 calculates the driving difficulty ADD for each node N1-N4 or link L1-L7 of the abnormality occurrence information to be higher the higher the frequency of abnormality determinations by the determination unit 122 of each electronic control unit 120.

[0086] With this configuration, the server 110 of the vehicle control system 100 of this embodiment selects, as a safe route, the route candidate CR3 for which the electronic control unit 120 has determined anomalies less frequently, from among the route candidates CR1, CR2, and CR3. Furthermore, the server 110 transmits the selected safe route as a recommended route to the electronic control unit 120 of the vehicle 200 that has made the route request. Therefore, according to the vehicle control system 100 of this embodiment, it is possible to improve the safety of the automatic driving and driving assistance of the vehicle 200 by the electronic control unit 120.

[0087] Furthermore, in the vehicle control system 100 of this embodiment, the recording unit 111 of the server 110 records abnormality occurrence information that associates the weather at the position of each vehicle 200 at the time of an abnormality determination by the determination unit 122 of each electronic control unit 120 with each node N1-N4 or link L1-L7. The calculation unit 112 of the server 110 is also able to obtain the current weather of each node N1-N4 or link L1-L7 included in the multiple route candidates CR1, CR2, CR3 of the map information MI when calculating the driving difficulty level ADD, and calculate the driving difficulty level ADD based on the abnormality occurrence information in which the weather at the time of the abnormality determination matches the current weather.

[0088] With this configuration, the server 110 of the vehicle control system 100 of this embodiment can calculate the driving difficulty level ADD of the route candidates CR1, CR2, CR3, even if the occurrence frequency of the abnormality depends on the weather, based on the determination frequency of the abnormality corresponding to the weather when the vehicle 200 is traveling. Therefore, according to the vehicle control system 100 of this embodiment, it is possible to further improve the safety of the automatic driving and driving assistance of the vehicle 200 by the electronic control device 120.

[0089] In addition, in the vehicle control system 100 of this embodiment, when the type of abnormality determined by the determination unit 122 cannot be identified, each electronic control unit 120 can also transmit an image from the camera included in the external sensor 220 together with the abnormality information to the server 110 via the transmission unit 123.

[0090] With such a configuration, the vehicle control system 100 of this embodiment may be able to determine the type of abnormality by analyzing the camera images transmitted from each of the cameras 120 in the server 110. Note that, as examples of cases in which the type of abnormality cannot be identified, there may be cases where the imaging environment of the camera deteriorates and the object cannot be recognized, or where an object is detected jumping out in front of the vehicle 200 but the type of object, such as a pedestrian, bicycle, or animal, is unknown.

[0091] In the vehicle control system 100 of this embodiment, each electronic control unit 120 includes a vehicle control unit 124 that causes each vehicle 200 to travel along the recommended route received from the server 110.

[0092] With this configuration, the vehicle control system 100 of this embodiment can make each vehicle 200 travel along the recommended route by each electronic control device 120 that receives the recommended route from the server 110. Therefore, according to the vehicle control system 100 of this embodiment, it is possible to further improve the safety of the automatic driving and driving assistance of the vehicle 200 by the electronic control device 120.

[0093] The electronic control device 120 of this embodiment is a control device mounted on the vehicle 200. The electronic control device 120 has a recognition unit 121, a determination unit 122, a transmission unit 123, and a vehicle control unit 124. The recognition unit 121 recognizes the road condition based on the detection result of the external sensor 220 mounted on the vehicle 200. The determination unit 122 determines an abnormality including a failure of the recognition unit 121 to recognize the road condition. The transmission unit 123 transmits abnormality information including the position of the vehicle 200 at the time of the abnormality determination by the determination unit 122, the time, and the type of the determined abnormality to the server 110 outside the vehicle 200 via the communication device 210 mounted on the vehicle 200. The vehicle control unit 124 drives the vehicle 200 along the recommended route received from the server 110.

[0094] With this configuration, the electronic control device 120 of this embodiment, like the vehicle control system 100 described above, is able to avoid routes where abnormalities are frequent, thereby improving the safety of the automatic driving and driving assistance of the vehicle 200.

[0095] Although the first embodiment of the vehicle control system and the electronic control device according to the present disclosure has been described above in detail, the vehicle control system and the electronic control device according to the present disclosure are not limited to the above-mentioned embodiment. For example, in the above-mentioned embodiment, an example was described in which the traffic volume is used as a numerical value for normalizing the calculation formula of the driving difficulty ADD, but other numerical values ​​may be used as long as the abnormality determination frequency can be calculated for all passing vehicles.

[0096] Furthermore, the electronic control device 120 of the vehicle 200 may transmit a required time limit to the server 110 at the same time as transmitting a request for route information to the server 110. In this case, the route selection unit 113 of the server 110 can select, for example, from a plurality of route candidates CR1, CR2, CR3, a route that satisfies the required time limit and has the smallest travel difficulty ADD as a safe route.

[0097] Furthermore, in the vehicle control system 100 of this embodiment, when a recommended route includes a caution node or link, for example, N1-N4 or link L1-L7, whose abnormality determination frequency is higher than a predetermined frequency, the transmission unit 114 of the server 110 may transmit the caution node or link to each electronic control unit 120. In this case, the determination unit 122 of each electronic control unit 120 can give a higher priority to determining the type of abnormality determined in the caution node or link with a frequency higher than a predetermined frequency than the priority to determining other types of abnormality.

[0098] More specifically, for example, in a case where the recognition unit 121 of each electronic control device 120 constantly executes a plurality of recognition processes and an upper limit of the processing time for each recognition process is specified, it is conceivable to extend the upper limit of the processing time only when passing through the above-mentioned caution node or link. For example, in the above-mentioned caution node or link where AEB due to pedestrian recognition occurs frequently, by extending the upper limit of the pedestrian recognition processing time, it is possible to perform the pedestrian recognition processing for more objects than usual and prevent pedestrians from going unrecognized.

[0099] [Embodiment 2] Hereinafter, a vehicle control system according to a second embodiment of the present disclosure will be described with reference to Figures 1 to 7 of the first embodiment described above. The vehicle control system 100 of this embodiment differs from the vehicle control system 100 according to the first embodiment described above in the configurations of the recording unit 111 and the calculation unit 112 of the server 110. Other configurations of the vehicle control system 100 of this embodiment are similar to those of the vehicle control system 100 of the first embodiment described above, so similar parts are denoted by the same reference numerals and description thereof will be omitted.

[0100] In the vehicle control system 100 of this embodiment, each electronic control unit 120 mounted on a plurality of vehicles 200 recognizes the road condition based on the detection results of the external sensor 220 and the vehicle sensor 230 by the recognition unit 121 in process P15 shown in Fig. 2, as in the above-described embodiment 1, and records abnormality information. Furthermore, in this embodiment, in process P17 shown in Fig. 2, the electronic control unit 120 transmits abnormality information including the type of the external sensor 220 used to recognize the abnormality in process P15 to the server 110 by the transmission unit 123.

[0101] Furthermore, in the vehicle control system 100 of this embodiment, the recording unit 111 of the server 110 records, for example, abnormality occurrence information in which the types of the external sensors 220 included in the abnormality information are associated with each of the nodes N1-N4 or the links L1-L7, in the storage unit 115. The following Table 4 shows an example of abnormality occurrence information recorded in the storage unit 115 by the recording unit 111 in this embodiment.

[0102] [Table 4]

[0103] In Table 4, the sensor code is a code indicating the type of the external sensor 220. The sensor code is defined in advance for each type of the external sensor 220 and recorded in the storage unit 115, for example, as shown in Table 5 below.

[0104] [Table 5]

[0105] Furthermore, in the vehicle control system 100 of this embodiment, the server 110 executes the following process in process P223 for acquiring the abnormality occurrence frequency shown in Fig. 4. The route selection unit 113 of the server 110 extracts the abnormality occurrence frequency that matches the sensor code indicating the type of the external sensor in addition to the driving conditions including the month, time zone, weather, etc. during which the vehicle 200 travels from the departure point S to the destination G.

[0106] More specifically, as shown in Table 4, for each link L1-L7, the abnormality occurrence frequency is extracted that matches the driving conditions of August, 17:00, and fine weather when the vehicle 200 is traveling, and the sensor code is sensor code: 1 indicating the stereo camera. The other processes of the vehicle control system 100 of this embodiment are similar to those of the vehicle control system 100 of the above-mentioned embodiment 1.

[0107] As described above, in the vehicle control system 100 of this embodiment, the recording unit 111 of the server 110 records abnormality occurrence information in which the type of the external sensor 220 of each vehicle 200 at the time of abnormality determination by the determination unit 122 of each electronic control device 120 is associated with each of the nodes N1-N4 or links L1-L7. In addition, the calculation unit 112 of the server 110 acquires the type of the external sensor 220 of each vehicle 200 at the time of calculating the driving difficulty level ADD, and calculates the driving difficulty level ADD based on the abnormality occurrence information in which the type of the external sensor 220 at the time of abnormality determination matches the type of the external sensor 220 at the time of calculation of the driving difficulty level ADD.

[0108] With such a configuration, the vehicle control system 100 of the present embodiment can improve the safety of the automatic driving and driving assistance of the vehicle 200 by avoiding routes on which abnormalities specific to the type of the external sensor 220 mounted on each vehicle 200 frequently occur. More specifically, for example, when the abnormality type is backlighting, if the type of the external sensor 220 is a camera, it is considered that it will be affected by whiteout, but if the type of the external sensor 220 is a laser radar, it will not be affected. In such a case, according to the vehicle control system 100 of the present embodiment, it is possible to make the vehicle 200 travel along a route suitable for the type of the external sensor 220 mounted on the vehicle 200.

[0109] [Embodiment 3] Finally, with reference to Figs. 1 to 7 and Figs. 8 to 13, a third embodiment of a vehicle control system according to the present disclosure will be described. Fig. 8 is a block diagram showing a configuration of a server 110 of a vehicle control system 100 of the present embodiment. In the vehicle control system 100 of the present embodiment, the server 110 shown in Fig. 8 includes, for example, a countermeasure support unit 116 and a traffic flow simulator 117 in addition to the same configuration as the server 110 of the first embodiment shown in Fig. 1. Since the other configurations of the vehicle control system 100 of the present embodiment are the same as those of the vehicle control system 100 of the first embodiment described above, the same reference numerals are used for the similar parts and the description thereof will be omitted.

[0110] Fig. 9 is a flow diagram showing an example of a process P30 for supporting the creation of measures to reduce the occurrence of abnormalities by the server 110 of Fig. 8. When the server 110 of this embodiment starts the process P30 shown in Fig. 9, the process P31 for accepting input of extraction conditions by the countermeasure support unit 116 is executed.

[0111] Fig. 10 is an example of a display screen by the countermeasure support unit 116 of the server 110 in Fig. 8. The countermeasure support unit 116 includes a display device such as a liquid crystal display device or an organic EL display device, and an input device such as a touch panel, a keyboard, a mouse, etc. As shown in Fig. 10, the countermeasure support unit 116 causes the display device to display a map including each link L1-L7 and / or each node N1-N4, and extraction conditions such as the month, time period, weather, and anomaly type.

[0112] In process P31, the countermeasure support unit 116 accepts input of extraction conditions, for example, via an input device. For example, as shown in Fig. 10, the user of the server 110 scrolls the map displayed on the display device of the countermeasure support unit 116 to select an area on the map where countermeasures to reduce the occurrence of abnormalities are to be implemented, thereby selecting each link L1-L7 and / or each node N1-N4 in the area.

[0113] Furthermore, the user selects extraction conditions such as month, time period, weather, and anomaly type using a scroll bar displayed on the display device, as shown in Fig. 10. The countermeasure support unit 116 accepts the input of the extraction conditions selected by the user and inputs them to the calculation unit 112. Here, it is assumed that "all data" has been input as the extraction conditions for month, time period, weather, and anomaly type. Next, the server 110 executes process P32 to acquire the anomaly occurrence frequency.

[0114] In this process P32, the calculation unit 112 of the server 110 extracts abnormality occurrence information that matches the extraction conditions input from the countermeasure support unit 116 in the previous process P31 from the abnormality occurrence information recorded in the storage unit 115. Furthermore, the calculation unit 112 acquires the abnormality occurrence frequency of each of the links L1-L7 and / or each of the nodes N1-N4 based on the extracted abnormality occurrence information, similar to process P223 shown in Fig. 4 of the above-mentioned first embodiment.

[0115] Next, the server 110 executes a process P33 for acquiring the average traffic volume shown in Fig. 9. In this process P33, the calculation unit 112 of the server 110 acquires the average traffic volume stored in the storage unit 115 for each of the links L1-L7 and / or each of the nodes N1-N4 extracted in the above-mentioned process P32, similar to the process P224 shown in Fig. 4 of the above-mentioned embodiment 1. Here, since the extraction conditions for month, time period, and weather are "all data", the average traffic volumes for all months, time periods, and weather are acquired.

[0116] Next, the server 110 executes a process P34 for calculating the driving difficulty level ADD for each of the links L1-L7 and / or each of the nodes N1-N4 shown in Fig. 9. In this process P34, the calculation unit 112 of the server 110 calculates a driving difficulty level ADD based on the abnormality occurrence frequency for each of the nodes N1-N4 or links L1-L7 of the abnormality occurrence information by the above formula (1), similar to the process P225 shown in Fig. 4 described above, and inputs the driving difficulty level ADD to the countermeasure support unit 116. Thereafter, the server 110 executes a process P35 for displaying the driving difficulty level ADD for each of the links L1-L7 and / or each of the nodes N1-N4.

[0117] Fig. 11 is an example of a display screen of process P35 displaying the driving difficulty ADD of each link L1-L7 and / or each node N1-N4 of Fig. 9. In this process P35, the countermeasure support unit 116 causes the display device to display the driving difficulty of each link L1-L7 and / or each node N1-N4 input from the calculation unit 112 in the previous process P34, as shown in Fig. 11. Here, as shown in Fig. 10, the driving difficulty of each link L1-L7 and / or each node N1-N4 included in the area selected on the map is displayed.

[0118] Fig. 12 is a graph showing the driving difficulty for each anomaly type of the link L6 selected in Fig. 11. On the display screen of the display device of the countermeasure support unit 116 shown in Fig. 11, the user selects each of the links L1-L7 and / or each of the nodes N1-N4 of Fig. 11, for example, using the input device of the countermeasure support unit 116, and clicks or touches an icon labeled "display details". As a result, the driving difficulty for each anomaly type of the selected link L6 can be displayed on the display device of the countermeasure support unit 116, as shown in Fig. 12.

[0119] As described above, the calculation unit 112 of the server 110 causes the display device of the server 110 to display the driving difficulty ADD calculated for each node N1-N4 or link L1-L7 of the abnormality occurrence information extracted using extraction conditions including the time or the type of abnormality. As a result, the vehicle control system 100 of this embodiment can assist in planning measures to reduce the occurrence of abnormalities in each of the links L1-L7 and / or each of the nodes N1-N4.

[0120] More specifically, for example, when a driving difficulty level ADD for a specific link L6 is calculated to be higher than the other links L1-L5 and L7 as shown in Fig. 11, the user selects the link L6 and clicks or presses the "display details" icon. As a result, the countermeasure support unit 116 and the calculation unit 112 display the driving difficulty level ADD for each abnormality type for the selected link L6 as shown in Fig. 12.

[0121] As a result, the user can understand that the driving difficulty level due to non-recognition of lanes, which is abnormality type code: 2, is significantly higher in link L6 than in other abnormality types, as shown in Fig. 12. This allows the user to plan the re-establishment of road boundary lines for link L6 as a measure to reduce the driving difficulty level ADD of link L6, based on the driving difficulty level ADD for each abnormality type displayed on the display device of the countermeasure support unit 116.

[0122] The countermeasure support unit 116 may periodically analyze the abnormality occurrence information recorded in the storage unit 115 to identify extraction conditions that will increase the travel difficulty level ADD of each of the links L1-L7 and / or each of the nodes N1-N4. In this case, when the countermeasure support unit 116 predicts that the conditions of, for example, the month, the time period, and the weather match the extraction conditions identified by the analysis, it displays the travel difficulty level ADD of each of the links L1-L7 and / or each of the nodes N1-N4 based on the extraction conditions on the display device together with the extraction conditions.

[0123] This enables the user to plan measures to reduce the driving difficulty level ADD based on the driving difficulty level ADD of each of the links L1-L7 and / or each of the nodes N1-N4 displayed in the measure support unit 116. For example, as described above, in a specific time period such as the morning rush hour, if a large number of AEBs due to pedestrian recognition occur and the driving difficulty level ADD increases, the user can plan a measure such as installing a crosswalk on the road between the workplace and the parking lot.

[0124] As described above, according to the vehicle control system 100 of this embodiment, the driving difficulty of each selected link L1-L7 and / or each node N1-N4 can be displayed on the display device of the countermeasure support unit 116, and it is possible to support the user in determining the priority of countermeasures and narrowing down the countermeasures. Therefore, according to the vehicle control system 100 of this embodiment, in addition to the effects of the vehicle control system 100 of the above-mentioned embodiment 1, it is possible to support the determination of the priority of infrastructure development for safely performing autonomous driving by the electronic control device 120 of the vehicle 200.

[0125] Fig. 13 is a flow diagram showing an example of a process P40 for verifying a countermeasure for reducing the occurrence of anomalies by the server 110 of Fig. 8. When the server 110 of this embodiment starts the process P40 shown in Fig. 13, the server 110 executes a process P41 for reproducing the traffic flow before the countermeasure for reducing the occurrence of anomalies is implemented by the traffic flow simulator 117.

[0126] Here, the traffic flow simulator 117 inputs parameters such as the traffic volume at a representative point and the right / left turn rate at each intersection to simulate the running behavior of each of the multiple vehicles 200. The traffic flow simulator 117 can be realized using known technology such as the road traffic simulation system described in Japanese Patent Application Laid-Open No. 5-250594.

[0127] In process P41, the known traffic flow before implementing measures to reduce the occurrence of abnormalities is reproduced by adjusting parameters input to the traffic flow simulator 117. More specifically, the parameters input to the traffic flow simulator 117 are adjusted so that the known traffic volume of each of the links L1-L7 and / or each of the nodes N1-N4 matches the traffic volume of each of the links L1-L7 and / or each of the nodes N1-N4 reproduced by the traffic flow simulator 117.

[0128] Next, the server 110 executes a process P42 for calculating a driving difficulty level ADD for each of the links L1-L7 and / or each of the nodes N1-N4 after a countermeasure to reduce the occurrence of abnormalities has been implemented, as shown in Fig. 13. In this process P42, the calculation unit 112 assumes that the frequency of abnormality occurrence for each of the links and / or each of the nodes after the countermeasure has been implemented is zero, for example, and calculates the driving difficulty level ADD for each of the links L1-L7 and / or each of the nodes N1-N4 in the same manner as in processes P31-P34 shown in Fig. 9.

[0129] Next, the traffic flow simulator 117 executes a process P43 for changing the parameters of the right / left turn rate, as shown in Fig. 13. For example, when implementing measures to reduce the frequency of abnormality occurrence in the link L6 shown in Fig. 10, another link L3 is connected to the node N2 connected to this link L6. For example, the traffic flow simulator 117 changes the right / left turn rate from the node N2 to the links L6 and L3 before and after the implementation of the measures in the link L6, for example, as shown in the following Table 6.

[0130] [Table 6]

[0131] As shown in Table 6, for example, as a result of implementing measures such as re-installing lane boundaries in link L6, the driving difficulty of link L6 has changed from 0.23 before and after the implementation of the measures to 0. As a result, the right and left turn rates from node N2 to links L6 and L3, which were 0.2 and 0.8 before and after the implementation of the measures in link L6, have changed to 0.472 and 0.528. Such an increase or decrease in the right and left turn rate can be calculated, for example, based on the inclusion rate of the vehicle 200, which is automatically traveling under the control of the electronic control device 120, in the overall traffic volume at node N2.

[0132] For example, assume that the inclusion rate of autonomous vehicles 200 in the overall traffic volume is 34%, and that before the implementation of the countermeasure, 100% of the vehicles 200 turned right from node N2 to link L3. In this case, the rate of vehicles 200 turning right from node N2 to link L3 in the overall traffic volume is 27.2% (0.8×0.34=0.272). Assuming that these vehicles 200 start turning left to link L6 at node N2 after the implementation of the countermeasure, the right / left turn rate from node N2 to link L6 after the implementation of the countermeasure is 0.2+0.272=0.472.

[0133] As shown in Table 6, before the implementation of the measures, the driving difficulty ADD of link L6 was significantly higher than that of link L3, so the rate of right and left turns onto link L6 was significantly lower than that of link L3. However, after the implementation of the measures, the driving difficulty ADD of link L6 became 0, lower than the driving difficulty ADD of link L3, which was 0.043, so the rate of right and left turns onto link L6 increased and the difference in the rate of right and left turns between link L6 and link L3 decreased and almost disappeared.

[0134] Next, the server 110 executes a process P44 for simulating the traffic flow after the countermeasure is implemented. In this process P44, the traffic flow simulator 117 simulates the traffic flow when the driving difficulty ADD of each link L1-L7 and / or each node N1-N4 of the map information MI is changed using parameters including the right / left turn rate after the countermeasure is implemented, for example, as shown in Table 6.

[0135] Next, the server 110 executes a process P45 to compare the traffic flows of the links L1-L7 and / or the nodes N1-N4 before and after the implementation of the countermeasures. This process makes it possible to confirm the effectiveness of the countermeasures to reduce the occurrence of abnormalities, such as resolving congestion by dispersing traffic flows to the links L6 and L3, and to verify the countermeasures.

[0136] As described above, in the vehicle control system 100 of this embodiment, the server 110 has a traffic flow simulator 117 that simulates traffic flow when the travel difficulty level of each of the nodes or links of the map information MI is changed. With this configuration, the vehicle control system 100 of this embodiment can evaluate the improvement of the degree of congestion on roads, such as traffic jams, in addition to the effects of the vehicle control system 100 of the first embodiment described above.

[0137] The above describes in detail an embodiment of the vehicle control system and electronic control device according to the present disclosure using the drawings, but the specific configuration is not limited to this embodiment, and even if there are design changes, etc., within the scope that does not deviate from the gist of the present disclosure, they are included in the present disclosure. [Explanation of symbols]

[0138] 100 Vehicle Control System 110 Server 111 Recording Department 112 Arithmetic section 113 Route Selection Unit 114 Transmitter 117 Traffic Flow Simulator 120 Electronic control device 121 Recognition part 122 Judgment section 123 Transmitter 124 Vehicle control unit 200 vehicles 210 Communication equipment 220 External Sensor ADD Running difficulty CR1-CR3 pathway candidates L1-L7 Link MI Map Information N1-N4 Nodes

Claims

1. A vehicle control system including an electronic control device mounted on each of a plurality of vehicles, and at least one server communicatively connected to each of the electronic control devices, Each of the electronic control devices has a recognition unit that recognizes road conditions based on detection results of an external sensor mounted on each of the vehicles, a determination unit that determines an abnormality including a failure to recognize the road conditions by the recognition unit, and a transmission unit that transmits abnormality information including a position of each of the vehicles, a time, and a type of the determined abnormality at the time of the abnormality determination by the determination unit to the server via a communication device mounted on each of the vehicles, the server has a recording unit that records abnormality occurrence information in which the abnormality information received from each of the electronic control devices is associated with each node or link of map information; a calculation unit that calculates a driving difficulty level based on a frequency of abnormality determination for each of the nodes or links included in the abnormality occurrence information; a route selection unit that selects a safe route that minimizes the total of the driving difficulty levels from a plurality of route candidates in the map information; and a transmission unit that transmits the safe route to each of the electronic control devices as a recommended route; The recording unit of the server records the abnormality occurrence information in which the type of the external sensor of each of the vehicles at the time of abnormality determination by the determination unit of each of the electronic control devices is associated with each of the nodes or links, a calculation unit of the server acquiring a type of the external sensor of each of the vehicles when calculating the driving difficulty level, and calculating the driving difficulty level based on the abnormality occurrence information in which the type of the external sensor at the time of the abnormality determination matches the type of the external sensor at the time of the calculation of the driving difficulty level.

2. A vehicle control system including an electronic control device mounted on each of a plurality of vehicles, and at least one server communicatively connected to each of the electronic control devices, Each of the electronic control devices has a recognition unit that recognizes road conditions based on detection results of an external sensor mounted on each of the vehicles, a determination unit that determines an abnormality including a failure to recognize the road conditions by the recognition unit, and a transmission unit that transmits abnormality information including a position of each of the vehicles, a time, and a type of the determined abnormality at the time of the abnormality determination by the determination unit to the server via a communication device mounted on each of the vehicles, the server has a recording unit that records abnormality occurrence information in which the abnormality information received from each of the electronic control devices is associated with each node or link of map information; a calculation unit that calculates a driving difficulty level based on a frequency of abnormality determination for each of the nodes or links included in the abnormality occurrence information; a route selection unit that selects a safe route that minimizes the total of the driving difficulty levels from a plurality of route candidates in the map information; and a transmission unit that transmits the safe route to each of the electronic control devices as a recommended route; the transmission unit of the server transmits, when the recommended route includes a caution node or link that is the node or link for which an anomaly determination frequency is higher than a predetermined frequency, the caution node or link to each of the electronic control devices; A vehicle control system characterized in that the judgment unit of each of the electronic control devices gives a higher priority to determining the type of abnormality that is determined at the caution node or link with a frequency higher than the specified frequency than the priority to determining other types of abnormality.

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