DE112022002882T5 - VEHICLE CONTROL SYSTEM AND ELECTRONIC CONTROL DEVICE - Google Patents

Abstract

Provided are a vehicle control system and an electronic control device capable of causing a vehicle to drive safely by detecting the external environment with a vehicle-mounted sensor. A vehicle control system 100 includes a control device 120 mounted in each of a plurality of vehicles 200 and at least one server 110 connected to each electronic control device 120 to enable communication therewith. Each electronic control device 120 includes a detection unit 121 that detects a condition of a road, a determination unit 122 that determines an abnormality, a sending unit 123 that sends to the server 110 abnormal information including a location and a time of the vehicle 200 when the determination unit 122 determines an anomaly and sends the type of the determined anomaly, and a vehicle control unit 124 that causes the vehicle 200 to travel along a recommended route received from the server 110. The server 110 includes a recording unit 111 that records abnormality occurrence information in which the abnormality information received from each electronic control device 120 is associated with each of the nodes or links in map information, a calculation unit 112 that determines a degree of driving difficulty based on a determination frequency of the abnormality for each the nodes or links in the anomaly occurrence information is calculated, a route selection unit 113 that selects a safe route with the smallest overall degree of driving difficulty from a plurality of route candidates in the map information, and a sending unit 114 that sends the safe route to the electronic control device 120 as the recommended one route sends.

Description

Technical area

The present disclosure relates to a vehicle control system and an electronic control device.

Current state of the art

Conventionally, a hazard occurrence location information collection system comprising a vehicle-mounted device a hazard occurrence location information collection device is known (PTL 1, claim 1, paragraph 0005 and the like).

The vehicle-mounted device includes operation information acquisition means, driving information acquisition means, location information acquisition means and control means. The operation information acquisition means acquires information relating to an operation of a vehicle by a driver. The driving information acquiring means acquires information related to a driving state of the vehicle. The location information acquisition means captures current location information of the vehicle. The control means performs control to send the various types of information to the outside when it is estimated that an operation performed to deal with an emergency has occurred based on operation information.

The hazard occurrence location information collection device receives the various types of information from the vehicle-mounted device. Subsequently, the hazard occurrence site information collecting device collects the location information as information related to a hazard occurrence site when it is determined that the operation is most likely to be actually performed to deal with an emergency. The possibility of an emergency is determined based on operating information, driving information, traffic rule information of a road on which the vehicle is located, and/or information regarding a situation surrounding the vehicle.

List of citations

Patent literature

PTL 1: JP 2007-323281 A

Summary of the invention

Technical task

As described above, when it is determined that the operation by the driver in the vehicle is most likely actually performed to deal with an emergency, the conventional system collects the current location information of the vehicle as the information related to the danger occurrence site. However, in order to cause the vehicle to drive safely through external environment detection with a vehicle-mounted sensor, it is not enough to merely provide the information regarding the hazard occurrence location based on the driver's operation.

The present disclosure provides a vehicle control system and an electronic control device capable of causing a vehicle to drive safely by detecting the external environment with a vehicle-mounted sensor.

Technical solution

According to one aspect of the present disclosure, a vehicle control system includes an electronic control device mounted in each of a plurality of vehicles, and at least one server connected to each electronic control device in a manner that enables communication with each electronic control device; wherein each electronic control unit includes a detection unit that detects a condition of a road based on a detection result of an external environment sensor mounted in each of the vehicles, a determination unit that determines an abnormality including a detection error of the condition of the road by the detection unit, and a transmitter a unit that sends anomaly information to the server via a communication device mounted in each of the vehicles, the anomaly information including a location and a time of each of the vehicles when the determination unit determines an anomaly and a type of the determined anomaly, and the server a recording unit that records abnormality occurrence information in which the abnormality information received from each electronic control device is associated with each of the nodes or links in map information, a calculation unit that calculates a degree of driving difficulty based on a determination frequency of the abnormality for each of the nodes or links, the included in the anomaly occurrence information, a route selection unit that selects a safe route with the smallest overall degree of driving difficulty from a plurality of route candidates in the map information, and a sending unit that sends the safe route to each electronic control device as a recommended route.

Advantageous effects of the invention

According to the foregoing aspect of the present disclosure, a vehicle control system and an electronic control device capable of causing a vehicle to drive safely by detecting the external environment with an external environment sensor mounted on the vehicle can be provided.

Brief description of the drawings

• [ 1 ] 1 Fig. 12 is a block diagram showing a vehicle control system and an electronic control device according to Embodiment 1 of the present disclosure.
• [ 2 ] 2 1 is a flowchart showing an example of processing by the electronic control device in FIG 1 .
• [ 3 ] 3 shows a flowchart showing an example of processing by a server in 1 .
• [ 4 ] 4 shows a flowchart depicting details of a process for selecting a safe route in 3 .
• [ 5 ] 5 shows a diagram to illustrate an example of in the process of selecting the safe route in 3 card information used.
• [ 6 ] 6 shows a graph showing an example of a result of a process for detecting an anomaly occurrence frequency in 4 .
• [ 7 ] 7 shows a graph showing the example of a result of a process for detecting the anomaly occurrence frequency in 4 .
• [ 8th ] 8th shows a block diagram of a server in a vehicle control system according to Embodiment 3 of the present disclosure.
• [ 9 ] 9 shows a flowchart showing an example of processing by the server in 8th .
• [ 10 ] 10 shows an example of an action support unit in 8th displayed screen.
• [ 11 ] 11 shows an example of a screen of a process for displaying a level of driving difficulty in each node/link in 9 .
• [ 12 ] 12 shows a graph depicting a level of driving difficulty for each anomaly type in 11 selected connection.
• [ 13 ] 13 shows a flowchart showing an example of processing by the server in 8th .

Description of embodiments

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

[Embodiment 1]

1 Fig. 12 is a block diagram showing a vehicle control system and an electronic control device according to Embodiment 1 of the present disclosure. A vehicle control system 100 in the present embodiment is, for example, an automatic driving support system that supports automatic driving of a plurality of vehicles 200. In 1 the plurality of vehicles 200 is not shown, but only one vehicle 200 is shown.

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 variety of servers 110. Each of the plurality of electronic control devices 120 is mounted in each of the vehicles 200.

The server 110 is, for example, a computer constituted by hardware such as a central processing unit (CPU), a memory such as a ROM and a RAM, a timer, and an input/output unit. The server 110 is connected to the electronic control device 120 in a manner that allows communication with 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 vehicle 200-mounted communication device 210, a vehicle-mounted network and the like.

The server 110 includes, for example, a recording unit 111, a calculation unit 112, a route selection unit 113 and a sending unit 114. The server 110 may further include a storage unit 115. For example, each unit of the vehicle control system 100 represents each function of the vehicle control system 100 executed in such a manner that the CPU of the vehicle control system 100 executes a program recorded in the memory.

The electronic control device 120 is, for example, a vehicle control device mounted in the vehicle 200. The electronic control device 120 is formed by, for example, one or more microcontrollers and includes a CPU, a memory such as a ROM and a RAM, a timer, an input/output unit, and the like.

The electronic control device 120 includes, for example, a recognition unit 121, a determination unit 122, a transmission unit 123 and a vehicle control unit 124. Each unit of the electronic control device 120 represents, for example, each function of the electronic control device 120 executed in such a manner that the CPU of the electronic control device 120 executes a program recorded in memory.

The respective vehicle 200 is, for example, an automobile, such as a gasoline vehicle, a diesel vehicle, a hybrid vehicle, an electric vehicle or a fuel cell vehicle. The vehicle 200 includes, for example, a communication device 210, an external environment sensor 220, a vehicle sensor 230, a navigation system 240, an actuator 250, and an actuator 260 in addition to the electronic control device 120. In addition, the vehicle 200 includes a general configuration of the vehicle; For example, the vehicle 200 includes a prime mover, a power transmission device, a braking device, a steering device, a driving device, a frame, a suspension, an electrical device, a safety device and the like (not shown).

The communication device 210 is a vehicle-mounted communication device of the vehicle 200 and is connected to the electronic control device 120 via a vehicle-mounted network. The communication device 210 performs wireless communication with a communication device 400 such as a wireless base station installed outside the vehicle 200.

The communication device 400 is connected to the communication device 210 of the vehicle 200 via a wireless communication line and is connected to the server 110 via the Internet line 300, for example. The communication device 400 receives information sent from the vehicle 200 and sends the information to the server 110 and receives information sent from the server 110 and sends the information to the communication device 210 of the vehicle 200.

The external environment sensor 220 is, for example, a sensor that detects an object in the surroundings of the vehicle 200 and is connected to the electronic control device 120 via a vehicle-mounted network. The outdoor environment sensor 220 includes, for example, a sensor such as a monocular camera, a stereo camera, a millimeter wave radar, a laser radar, or an ultrasonic sensor. In the present embodiment, the outdoor environment sensor 220 includes at least one stereo camera.

The vehicle sensor 230 is, for example, a sensor that detects a physical quantity related to the vehicle 200 and is connected to the electronic control device 120 via the vehicle-mounted network. The vehicle sensor 230 includes various sensors required for an Advanced Driving Assistance System (ADAS) and an Automatic 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 pedal sensor, and Brake sensor.

The navigation system 240 includes, for example, a Global Navigation Satellite System (GNSS) receiver, a storage device that stores map information, a display device that displays a map and information of a route to a destination, and an input device that inputs the destination. The navigation system 240 is connected to the electronic control device 120 via the vehicle-mounted network, and outputs the current location of the vehicle 200 and the information of the route to the destination, for example, to the electronic control device 120.

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

The notification device 260 is, for example, a device that performs notification, such as providing information, drawing attention, warning an occupant of the vehicle 200, based on 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.

The following is the operation of the vehicle control system 100 and the electronic control device 120 according to the present embodiment with respect to 2 and 3 described. 2 12 is a flowchart showing an example of processing P10 by the electronic control device 120 in FIG 1 . 3 shows a flowchart illustrating an example of processing P20 by the server 110 in 1 .

If the in 2 Processing P10 shown is started, the electronic control device 120 first carries out a process P11 for detecting a place of departure and a destination. In this process P11, the electronic control device 120 causes the vehicle control unit 124 to detect the current location of the vehicle 200 from the navigation system 240, for example, at the departure location. In addition, the electronic control device 120 causes the vehicle control unit 124 to acquire, for example, location information of the destination entered at an input device of the navigation system 240.

Subsequently, the electronic control device 120 executes a process P12 for querying route information from the server 110. In this process P12, the electronic control unit 120 causes the sending unit 123 to send a query of the recommended route from the departure point to the destination to the server 110 via the communication device 210, for example. In this case, information of the departure location and the destination of the vehicle 200 included in the recommended route query is sent in a format that can be matched, for example, with point information contained in the map information stored in the storage unit 115 of the server 110.

The information of the departure location and the destination of the vehicle 200 sent from the electronic control device 120 to the server 110 includes, for example, the latitude and longitude of the departure location and the destination, respectively. In addition, the query of the recommended route sent from the electronic control device 120 to the server 110 includes, for example, the date and time of travel of the vehicle 200 from the departure point to the destination.

Again leads if the in 3 Processing P20 shown is started, the server 110 first carries out a process P21 for determining whether there is a request for the route information from the electronic control device 120. In this process P21, upon determining that the request for the recommended route has not been received from the electronic control device 120 from a vehicle 200, the route selection unit 113 of the server 110 determines that there is no request for route information (NO), and performs a process described below P24 off.

In this process P21, for example, when determining that the query of the route including the departure point and the destination is received by the electronic control device 120 of the at least one vehicle 200, the route selection unit 113 of the server 110 determines that the query of the route information is present (YES). . In this case, the server 110 executes a safe route selection process P22. This process P22 is described in detail below 4 described.

Then, the server 110 executes a process P23 for sending the safe route obtained in the previous process P22 to the electronic control device 120 of the vehicle 200 that has requested the route information as the recommended route. In this process P23, the server 110 causes the sending unit 114 to send the recommended route from the departure point to the destination 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.

Subsequently, the electronic control device 120 of the vehicle 200 executes a process P13 for receiving the route information, for example, as shown in 2 shown. In this process P13, the electronic control device 120 causes the vehicle control unit 124 to receive the recommended route from the departure point to the destination sent from the server 110 via the communication device 210 of the vehicle 200, for example. Subsequently, the electronic control device 120 of the vehicle 200 executes a vehicle travel control process P14 for causing the vehicle 200 to travel along the received recommended route.

In this process P14, in the electronic control device 120 of the vehicle 200, for example, the vehicle control unit 124 controls the actuator 250 of the vehicle 200 to cause the vehicle 200 to travel along the recommended route. In addition, when starting the process P14, the electronic control unit 120 executes a process P15 for detecting the state of a road and a process P16 for determining whether or not an abnormality has occurred. However, the processes P15 and P16 may be executed, for example, for each control cycle while the vehicle travel control process P14 is being executed, or may be executed as necessary, respectively.

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

More specifically, the recognition unit 121 recognizes, for example, a road and a roadside in front of the vehicle 200, a lane mark on the road, and the like based on the detection result of the external environment sensor 220, thereby recognizing an area in which the vehicle 200 can travel. In addition, the detection unit 121 detects, for example, an object in front of the vehicle 200 based on the detection result of the external environment sensor 220, and detects an object having a potential of collision with the vehicle 200 based on the detection result of the vehicle sensor 230.

Further, the recognition unit 121 recognizes, for example, the type of the object, such as an automobile, a two-wheeled vehicle, a pedestrian, a falling object, and a threshold for reducing the speed of the vehicle, based on the detection result of the external environment sensor 220. In addition, the recognition unit 121 recognizes a situation in which it is difficult to recognize the condition of the road based on the detection result of the external environment sensor 220. Here, the situation in which it is difficult to recognize the condition of the road includes, for example, a situation in which it is difficult to recognize an object by the camera arranged in the outdoor environment sensor 220 due to backlight, heavy rain, snowfall or the like, a situation in which it is not possible to detect at least one of left and right lanes due to a detection error of a lane mark by the external environment sensor 220, and the like.

For example, when one of the left and right lanes becomes unrecognizable or when it is difficult to recognize the condition of the road, the recognition unit 121 controls the notification device 260 of the vehicle 200 to issue a lane non-recognition warning or a lane non-recognition warning Road condition to the occupant of the vehicle 200. In addition, for example, when detecting an object with a potential of collision with the vehicle 200, the detection unit 121 controls the notification device 260 to issue a collision avoidance warning to the occupant of the vehicle 200 and gives information of the object to the Vehicle control unit 124.

For example, if the distance of the vehicle to the vehicle in front becomes shorter than a threshold value or if a collision is predicted based on the location and speed of the vehicle 200 and the location and speed of another vehicle, the recognition unit 121 recognizes these vehicles as objects a potential for a collision. In addition, when a collision is predicted based on the location and speed of the vehicle 200 and the location and speed of a pedestrian walking onto the road, the pedestrian is recognized as an object with a potential for collision.

Further, for example, when a threshold for reducing speed is detected, the detection unit 121 recognizes the threshold as an object requiring deceleration when the vehicle 200 passes over it. For example, when information of an object with a potential of collision with the vehicle 200 or an object requiring deceleration is input from the detection unit 121, the vehicle control unit 124 controls the actuator 250 of the vehicle 200 to trigger a steering operation for collision avoidance, a braking operation Reduction of collision damage (AEB) and the like.

In addition, the detection unit 121 detects, for example, as anomalies, a detection error of the road condition, the detection of the road condition different from a normal condition, an occurrence of a warning to the occupant of the vehicle 200, an occurrence of a collision avoidance control of the vehicle 200, and the like. Further, for example, the detection unit 121 records the type of detected abnormality along with the location, date and time at which the abnormality occurred as abnormality information. The location at which the anomaly occurred includes, for example, location information of the vehicle 200 recorded by the navigation system 240, identification information of a connection and/or a node in map information where the vehicle 200 is located.

Subsequently, the electronic control device 120 executes, for example, a process P16 for determining whether or not there is an abnormality including a detection error of the state of the road by the detection unit 121. In this process P16, the determination unit 122 determines whether or not there is an abnormality based on, for example, whether or not there is anomaly information recorded by the detection unit 121 in the previous process P15. When the determination unit 122 determines that an abnormality exists (YES), the electronic control device 120 executes a process P17 for sending the abnormality information to the server 110. When the determination unit 122 determines that there is no abnormality (NO), the electronic control device 120 executes a process P18 for determining the arrival of the vehicle 200 at the destination.

In the process P17 for sending the abnormality information, the electronic control device 120 causes the sending unit 123 to send the abnormality information to the server 110 via the communication device 210 mounted in the vehicle 200, the external communication device 400, and the Internet line 300, for example. This leads, for example, in 3 As shown, the server 110 repeats a process P24 for determining whether the abnormality information has been received from the electronic control device 120 of each vehicle 200 in a predetermined cycle.

In this process P24, the server 110 determines, for example, whether or not the recording unit 111 has received the abnormality information from the electronic control device 120. In this process P24, when it is determined that the recording unit 111 has not received the abnormality information from the electronic control device 120 (NO), the server 110 terminates the in 3 processing shown and restarts processing P20. On the other hand, when it is determined in process P24 that the recording unit 111 has received the abnormality information from the electronic control device 120 (YES), the electronic control device 120 executes a process P25 for recording the abnormality information.

In this process P25, for example, the server 110 causes the recording unit 111 to record abnormal occurrence information in which the abnormal information received from each electronic control device 120 is associated with each of the nodes N1 to N4 or each of the links L1 to L7 in the map information. The following Table 1 shows an example of the abnormal occurrence information recorded in the storage unit 115 by the recording unit 111.
[Table 1] Date time Weather Width length Location code Anomaly type code June 30, 2020 8:00 Sunny 36°30'45'' 140°36'32'' L2 1 August 10, 2020 6:00 p.m Sunny 36°31'41'' 140°37'11'' L7 3 December 10, 2020 6:00 p.m Rainy 36°31'24'' 140°37'2'' L3 4 May 10, 2020 3:00 p.m Cloudy 36°31'12'' 140°36'26'' L6 2 ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮

As shown in Table 1, the anomaly occurrence information is, for example, information in which an anomaly type code, a date, a time, a latitude and a longitude as the anomaly information with a location code including each of the links L1 to L7 and/or each of the Nodes N1 to N4 are connected. The anomaly type code is a code that represents the type of anomaly.

For example, as shown in Table 2 below, abnormality type information in which an abnormality type and an abnormality type code corresponding to the abnormality type are predefined is recorded in the storage unit 115. Therefore, the anomaly type can be set according to the anomaly type code of the anomaly occurrence information shown in Table 1 based on the anomaly type information shown in Table 2. In the example shown in Table 2, the anomaly type information includes a weighting factor corresponding to the anomaly type. This weighting factor is, for example, an index to represent the magnitude of the influence on the safety of the vehicle 200.
[Table 2] Anomaly type Anomaly type code Weighting factor Pedestrian detection/AEB 1 0.6 Failure to recognize the trace 2 0.7 Backlight 3 0.9 Obstacle avoidance 4 0.5 ⋮ ⋮ ⋮

In the example shown in Table 1, the anomaly occurrence information is information in which the weather at the location of each vehicle 200 when the determination unit 122 of each electronic control device 120 determines an anomaly with each of the links L1 to L7 and/or each of the nodes N1 to N4 is connected. For example, if it is difficult to detect the weather by the external environment sensor 220 of the vehicle 200, the recording unit 111 of the server 110 may do so Collect weather from a weather database (not shown) based on the date and time of occurrence of the anomaly included in the anomaly occurrence information and the location information of the vehicle 200 at that time.

For example, as described above, the recording unit 111 of the server 110 receives the abnormality information from the electronic control device 120 of each of the plurality of vehicles 200 and records the abnormality occurrence information in the storage unit 115 as shown in Table 1. Thus, statistical processing can be performed based on a specific location, a specific time period, a specific abnormality type, and the like by using a large number of pieces of abnormality information contained in the abnormality occurrence information recorded in the storage unit 115. As in 3 shown ends when the process P25 of recording the anomaly information is finished, the server 110 the in 3 shown processing P20. Furthermore, the server 110 repeats the in 3 shown processing P20 in a predetermined cycle.

Below is the process P22 for selecting the safe route as in 3 presented in detail in relation to 4 to 7 described. 4 shows a flowchart showing details of the process P22 for selecting the safe route in 3 . 5 shows a diagram showing an example of route candidates CR1, CR2 and CR3 of the vehicle 200 based on 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 connections L1 to L7, such as streets, are connected via nodes N1 to N4, such as intersections.

If the in 4 Process P22 shown is started, for example, the server 110 causes the route selection unit 113 to execute a process P221 for searching for a plurality of route candidates CR1, CR2 and CR3 in the map information MI as in 5 shown. For example, the route selection unit 113 acquires information of a departure point S and a destination G of the vehicle 200 included in the route information query received from each electronic control device 120. The route selection unit 113 then searches the map information MI stored in the storage unit 115 and selects a plurality of route candidates CR1, CR2 and CR3 through which the vehicle 200 can reach the destination G from the departure point S.

Thereafter, the server 110 causes the route selection unit 113 to execute a process P222 for extracting the links L1 to L7 and/or the nodes N1 to N4 included in the plurality of selected route candidates CR1, CR2 and CR3. Im in 5 In the example shown, the route selection unit 113 extracts the connections L1 to L5 and/or the nodes N1 to N4 of the route candidate CR1, the connections L1, L2, L6 and L5 and/or the nodes N1, N2 and N4 of the route candidate CR2 and the connections L1, L7, L4 and L5 and/or the nodes N1, N3 and N4 of the route candidate CR3.

Subsequently, the server 110 causes the route selection unit 113 to execute a process P223 for detecting the abnormality occurrence frequency. In this process P223, the route selection unit 113 acquires, for example, the frequency of receiving the abnormality information from the electronic control device 120 of each of the plurality of vehicles 200 for each of the links L1 to L7 and/or each of the nodes N1 to N4 extracted in the previous process P222.

More specifically, in process P223, for example, the route selection unit 113 extracts the abnormality occurrence information in which a driving condition including a month, a period, the weather, and the like in which the vehicle 200 travels from the departure point S to the destination G is satisfied. Here, the time period used to extract the anomaly occurrence information may be a time period based on the time at which the electronic control device 120 of the vehicle 200 sends the route query, or may be a time period considering the required time from the departure point S to the destination G and the predicted time for be the completion of each connection or node. The following Table 3 shows an example of the reception frequency of the anomaly information in the connection L6 of 5 , which is extracted when October, 8:00 a.m., sunny weather is specified as the driving condition of vehicle 200.
[Table 3] Date time Weather Width length Location code Anomaly type code October 30, 2020 8:00 Sunny 36°31'12'' 140°36'26'' L6 2 October 10, 2020 8:23 Sunny 36°31'12'' 140°36'26'' L6 2 October 4, 2020 8:18 Sunny 36°31'12'' 140°36'26'' L6 2 October 16, 2020 8:15 Sunny 36°31'12'' 140°36'26'' L6 2 ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮

Table 3 shows that the server 110 receives the abnormality information of the abnormality type code 2 from each electronic control device 120 mounted in one or more vehicles 200 traveling on the link L6 under the driving condition of October, 8:00 a.m. and sunny weather a variety of received painting. The route selection unit 113 calculates the anomaly determination frequency by, for example, counting the number of pieces of anomaly occurrence information (the number of rows) extracted as shown in Table 3. In process P223, the route selection unit 113 acquires the abnormality occurrence frequency for each abnormality type for each of the links L1 to L7 and/or each of the nodes N1 to N4 that satisfy a predetermined driving condition, for example.

6 and 7 Fig. 10 shows graphs showing an example of a result of the process P223 for detecting the anomaly occurrence frequency as described above. In these graphs, the horizontal axis represents the anomaly type code for indicating the type of anomaly information, and the vertical axis represents the number of times of receiving the anomaly information, that is, the anomaly occurrence frequency or the anomaly determination frequency. For example, as shown in Table 2, anomaly type codes 1 to 4 on the horizontal axis of the graph each represent collision damage reduction (AEB) braking through pedestrian detection, lane undetectable (lane failure), backlighting, and obstacle avoidance.

That is, in the in 6 In the example shown, the server 110 receives, as anomaly information, the anomaly type code 2 indicating an unrecognizable track at a frequency of about 150 times from the electronic control devices 120 of the plurality of vehicles 200 operating on the link L6 at about 8:00 a.m. in October and drove in sunny weather. Im in 7 In the example shown, the server 110 receives, as anomaly information, the anomaly type code 1 indicating the operation of the AEB by detecting the pedestrian with the frequency of about 79 times from the electronic control devices 120 of the plurality of vehicles 200 operating on the link L3 in October at about 8 :00 a.m. and drove in sunny weather.

In this case, for the connections L1, L2, L4, L5 and L7 and the nodes N1 to N4, excluding the connections L3 and L6, it is assumed that the server 110 does not receive the anomaly information under the same driving conditions as for the connections L3 and L6.

Among the anomaly types as shown in Table 3, an undetectable trace (anomaly type code: 2) occurs at the same point on the link L6, for example, when a lane marking on a road is unclear due to wear over time. The lane detection by the electronic control device 120 is required for the automatic driving of the vehicle 200 by the electronic control device 120. Therefore, the electronic control device 120 sends the unrecognizable lane abnormality type code 2 of each of the plurality of vehicles 200 to the server 110 as abnormality information. The undetectable trace is a type of anomaly in which a large amount of pieces of information are collected regardless of the month or time period.

In addition, it is also conceivable that the AEB due to pedestrian detection is a sudden event among the anomaly types. But if as in 7 If a large number of AEBs (anomaly type code: 1) due to pedestrian detection occur in the same time period in the connection L3, the following situation can be assumed. For example, there may be a situation in which a workplace where many employees go to work faces the L3 road, a situation in which there is a parking lot on the opposite side of the road to the workplace, and a situation in which If there is no crossing aid between the workplace and the parking lot.

In such a situation, a large number of pedestrians who want to go to work cross a road for which no crossing aid is provided by the shortest route at the time of going to work. Thus occurs as in 7 shows a large number of AEBs (anomaly type code: 1) due to pedestrian detection in the time period when the workplace begins. As described above, a point on the road where many abnormalities occur due to the environment around the road in a specific period of time hinders the safe driving of the vehicle 200 in that period.

In addition to the previous situation, the following situation can be assumed as a situation in which many anomalies occur due to the environment around the road in the specific period of time. An example is a store, such as a supermarket, that faces a street; the number of vehicles that can be parked in a store parking lot is less than the number of parked vehicles required for the number of visitors concentrated in the specific time period. In such a case, it can be assumed that a large number of obstacle avoidances (anomaly type code: 4) to avoid vehicles parked on the street without entering the parking lot occur in the specific time period.

More specifically, as described above, for example, near the store, a large number of vehicles are parked on the street from 5:00 in the evening to 6:00 with a concentration of shoppers, so that a large number of obstacle avoidance (Anomaly Type Code: 4) to avoid parked vehicles. On the other hand, for example, no shopper parks his vehicle on the street near the store before the store opens at 8:00 a.m. and no obstacle avoidance occurs to avoid the parked vehicle. Likewise, in the vicinity of an educational institution where pick-up and drop-off of students is concentrated in a specific time period, it can be assumed that a large number of vehicles will be parked on the street in the specific time period and a large number of obstacle avoidances will occur (anomaly type code: 4) to avoid parked vehicles.

Furthermore, an anomaly (anomaly type code: 3) generated by a natural phenomenon such as backlighting may occur, for example, at a specific location, in a specific season and in a specific period of time. For example, when backlighting occurs, overexposure may occur in an image from the camera disposed in the external environment sensor 220 of the vehicle 200 and detection of an object becomes difficult. Also, abnormality information through such a natural environment is received from the server 110 by the electronic control device 120 of each of the plurality of vehicles 200, whereby a location, a season and a period of time with a high frequency of abnormal occurrence can be determined.

In addition, the anomaly occurrence frequency is influenced, for example, by the weather, as described above. In particular, it is conceivable that the number of vehicles parked around a business or an educational institution increases or decreases in rainy weather or in sunny weather, as described above. Therefore, the number of times of obstacle avoidance (anomaly type code: 4) to avoid the parked vehicle may be affected by the weather. In addition, an anomaly caused by a natural phenomenon, such as backlighting, only occurs in specific weather, such as sunny weather, for example. Therefore, when the anomaly occurrence information includes the weather, the anomaly occurrence frequency can be determined according to the weather.

If the process P223 for detecting the abnormality occurrence frequency as in 4 shown ends, the server 110 executes, for example, a process P224 for detecting an average traffic volume. In this process P224, the server 110 determines, for example, the average traffic volume of each of the links L1 to L7 and/or of each of the nodes N1 to N4. More specifically, in this process P224, the route selection unit 113 of the server 110 detects the average traffic volume stored in the storage unit 115, for example, for each of the links L1 to L7 and/or each of the nodes N1 to N4, extracted in the previously described process P222.

In this process P224, the route selection unit 113 acquires from the storage unit 115 the average traffic volume corresponding to, for example, the driving conditions such as month, time period and weather under which the vehicle 200 that has requested the route information travels through the route candidates CR1, CR2 and CR2. Thus, for example, the average traffic volumes of the connection L6 and the connection L3 are determined as 450 vehicles/hour and 1100 vehicles/hour, respectively. The average traffic volume is not necessarily in memory unit 115 stored and the route selection unit 113 can record the average traffic volume from outside the server 110 via the Internet line 300.

Subsequently, the server 110 executes, for example, a process P225 for calculating the degree of driving difficulty of each of the links L1 to L7 and/or each of the nodes N1 to N4. In this process P225, the calculation unit 112 of the server 110 calculates the degree of driving difficulty ADD based on the abnormality occurrence frequency for each of the nodes N1 to N4 or each of the links L1 to L7 of the abnormality occurrence information by, for example, the following expression (1).

[Formula 1] $$ADD={\displaystyle \sum {}_{n=0}^N}\left\{\left(H\left(n\right)\times Wn/Q\right)\right\}$$

In the preceding expression (1), N represents the number of anomaly types, H(n) represents the occurrence frequency of the anomaly type, Wn represents a weighting factor of the anomaly type, and Q represents the average traffic volume of each of the links L1 to L7 and/or each of the represents nodes N1 to N4 that were captured in the previous process. That is, the calculation unit 112 calculates the degree of driving difficulty ADD for each of the nodes N1 to N4 or each of the links L1 to L7 of the abnormality occurrence information to be higher the higher the determination frequency of the abnormality by the determination unit 122 of each electronic control device 120.

$$\begin{array}{l}\text{Verbindung L3}:\text{ Grad der Fahrschwierierigkeit ADD }=(79\\ \times \mathrm{0,6})/1100=\mathrm{0,043}\end{array}$$

For example, as described above, if the average traffic volumes Q of the link L6 and the link L3 are 450 vehicles/hour and 1100 vehicles/hour, respectively, the driving difficulty level ADD may be as follows based on the previous expression (1), Table 2, 6 and 7 , be calculated. $$\begin{array}{l}\text{Connection L6}:\text{Degree of driving difficulty ADD}=\\ \left(150\times 0.7\right)/450=0.23\end{array}$$

$$\begin{array}{l}\text{Connection L3}:\text{Degree of driving difficulty ADD}=(79\\ \times 0.6)/1100=0.043\end{array}$$

As shown in Table 1, in the present embodiment, the anomaly occurrence information includes the weather at the time of anomaly determination associated with each of the nodes N1 to N4 or each of the links L1 to L7. In this case, the calculation unit 112 may detect the current weather in each of the nodes N1 to N4 or each of the links L1 to L7 included in the plurality of route candidates CR1, CR2 and CR3 in the candidate information when calculating the driving difficulty level ADD . In this case, the calculation unit 112 may calculate the degree of driving difficulty ADD based on anomaly occurrence information in which the weather at the time of anomaly determination matches the current weather.

The server then executes 110 as in 4 1 shows a process P226 for calculating the degree of driving difficulty ADD from each of the route candidates CR1, CR2 and CR3. In this process P226, the route selection unit 113 calculates, for example, the sum of the driving difficulty degrees ADD of all the nodes N1 to N4 or all the links L1 to L7 included in each of the route candidates CR1, CR2 and CR3. Thus, the degree of driving difficulty ADD of each of the route candidates CR1, CR2 and CR3 can be calculated, for example, as follows.

Degree of driving difficulty ADD of the route candidate CR1 = Degree of driving difficulty ADD (0) of the connection L1 + Degree of driving difficulty ADD (0) of the connection L2 + Degree of driving difficulty ADD (0.043) of the connection L3 + Degree of driving difficulty ADD (0) of the connection L4 + degree of driving difficulty ADD (0) of connection L5 = 0.043

Degree of driving difficulty ADD of the route candidate CR2 = Degree of driving difficulty ADD (0) of the connection L1 + Degree of driving difficulty ADD (0) of the connection L2 + Degree of driving difficulty ADD (0.23) of the connection L6 + Degree of driving difficulty ADD (0) the connection L5 = 0.23

Degree of driving difficulty ADD of the route candidate CR3 = Degree of driving difficulty ADD (0) of the connection L1 + Degree of driving difficulty ADD (0) of the connection L7 + Degree of driving difficulty ADD (0) of the connection L4 + Degree of driving difficulty ADD (0) of the connection L5 = 0

The server then executes 110 as in 4 a process P227 for selecting a safe route is shown. In this process P227, the route selection unit 113 of the server 110 selects a safe route with the smallest sum of the driving difficulty levels ADD from the plurality of route candidates CR2, CR2 and CR3 in the map information MI. In the present embodiment, as described above, the driving difficulty level ADD of the route candidate CR1 is 0.043, the driving difficulty level ADD of the route candidate CR2 is 0.23, and the driving difficulty level ADD of the route candidate CR3 is 0.

Therefore, in this process P227, the route selection unit 113 selects the route candidate CR3 as the safe route. The server 110 then ends the in 4 Process P22 shown and leads process P23 to send the in 3 the recommended route shown as described above. In the process P23, the sending unit 114 sends the route candidate CR3 selected as the safe route as the recommended route to the electronic control device 120 of the vehicle 200, which executes the process P12 for querying the in 2 route information shown. Subsequently, as described above, the electronic control device 120 of the vehicle 200 carries out the processes P14 to P18 as in 2 shown for causing the vehicle 200 to travel to the destination along the recommended route.

Functions of the vehicle control system 100 and the electronic control device 120 according to the present embodiment will be described below.

As described above, the vehicle control system 100 in the present embodiment includes a control device 120 mounted in each of the plurality of vehicles 200 and at least one server 110 connected to each electronic control device 120 to enable communication with the electronic control device 120. Each electronic control device 120 includes the detection unit 121, the determination unit 122, and the transmission unit 123. The detection unit 121 detects the condition of a road based on the detection result of the external environment sensor 220 mounted in each vehicle 200. The determination unit 122 determines an abnormality including a detection error of the condition of the road by the detection unit 121. The sending unit 123 sends abnormality information including the location and time of each vehicle 200 when the determination unit 122 determines an abnormality and the type of the determined abnormality to the server 110 via the one mounted in each vehicle 200 Communication device 210. The server 110 in turn includes the recording unit 111, the calculation unit 112, the route selection unit 113 and the sending unit 114. The recording unit 111 records abnormality occurrence information in which the abnormality information received from each electronic control device 120 is associated with each of the nodes N1 to N4 or each of the connections L1 to L7 in the map information are connected. The calculation unit 112 calculates the degree of driving difficulty ADD based on the determination frequency of the abnormality for each of the nodes N1 to N4 or each of the links L1 to L7 of the abnormality occurrence information. The route selection unit 113 selects a safe route with the smallest sum of the driving difficulty levels ADD from the plurality of route candidates CR2, CR2 and CR3 in the map information MI. The sending unit 114 sends the selected safe route to each electronic control device 120 as the recommended route.

With such a configuration, in the vehicle control system 100 in the present embodiment, at least one server 110 collects the abnormality information including the location and time of each vehicle 200 and the type of the determined abnormality from the electronic control device 120 of each of the plurality of vehicles 200. Further the server 110 records abnormality occurrence information in which the abnormality information collected by each electronic control device 120 is connected to each of the nodes N1 to N4 or each of the links L1 to L7 in the map information. Thus, for each of the nodes N1 to N4 or each of the links L1 to L7 of the abnormality occurrence information, the server 110 can calculate the degree of driving difficulty ADD based on the abnormality occurrence frequency, select the route candidate CR3 with the lowest degree of driving difficulty ADD as the safe route, and the selected one send safe route to the electronic control device 120 of the vehicle 200 as the recommended route. Therefore, according to the vehicle control system 100 in the present embodiment, the abnormality determined by the electronic control device 120 mounted on a vehicle 200 can also be considered an experience of the automatic driving control in the vehicle Electronic control device 120 mounted on another vehicle 200 can be used. Thereby, it is possible to improve the safety of automatic driving and driving assistance of the vehicle 200 by the electronic control device 120 by avoiding a route on which an abnormality often occurs, the abnormality being a detection error of the state of a road, a detection of the State of a road different from the normal state, an occurrence of a warning to an occupant of the vehicle 200, and an occurrence of a collision avoidance control of the vehicle 200. In particular, for example, a route can be avoided on which avoidance of an obstacle by a parked vehicle or AEB due to pedestrian detection often occurs in a specific time period.

Furthermore, in the vehicle control system 100 of the present embodiment, the calculation unit 112 of the server 110 calculates the degree of driving difficulty ADD for each of the nodes N1 to N4 or each of the links L1 to L7 of the abnormality occurrence information as higher the higher the determination frequency of the abnormality by the determination unit 122 of each electronic control device 120.

With such a configuration, the server 110 of the vehicle control system 100 in the present embodiment selects, as the safe route, the route candidate CR3 with a lower frequency of determination of anomaly by each electronic control device 120 from the route candidates CR1, CR2 and CR3. Further, the server 110 sends the selected safe route to the electronic control device 120 of the vehicle 200 that has requested the route information as the recommended route. Thus, according to the vehicle control system 100 in the present embodiment, the safety of automatic driving and driving assistance of the vehicle 200 by the electronic control device 120 can be improved.

Furthermore, in the vehicle control system 100 of the present embodiment, when the determination unit 122 of each electronic control device 120 determines an abnormality, the recording unit 111 of the server 110 records the abnormality occurrence information in which the weather at the location of each vehicle 200 with each of the nodes N1 to N4 or each of the connections L1 to L7 is connected. Subsequently, the calculation unit 112 of the server 110 can detect the current value at each of the nodes N1 to N4 or on each of the links L1 to L7 included in the plurality of route candidates CR1, CR2 and CR3 of the map information MI when the degree of driving difficulty ADD is calculated, and calculating the degree of driving difficulty ADD based on the anomaly occurrence information in which the weather when an anomaly is determined is consistent with the current weather.

With such a configuration, even considering an anomaly in which the occurrence frequency depends on the weather, the server 110 of the vehicle control system 100 in the present embodiment can ADD the driving difficulty levels of the route candidates CR1, CR2 and CR3 based on the determination frequency of the anomaly according to the weather , if the vehicle travels 200, calculate. Thus, according to the vehicle control system 100 in the present embodiment, the safety of automatic driving and driving assistance of the vehicle 200 by the electronic control device 120 can be further improved.

Furthermore, in the vehicle control system 100 of the present embodiment, when the type of abnormality determined by the determination unit 122 cannot be specified, each electronic control device 120 may use the sending unit 123 to send the image of the camera disposed in the external environment sensor 220 along with the abnormality information to the server 110 cause.

With such a configuration, the vehicle control system 100 in the present embodiment can determine the type of abnormality by analyzing the image of the camera in the server 110 sent from each electronic control device 120. For example, the type of abnormality cannot be specified when the image capture environment of the camera deteriorates and an object cannot be recognized when the type of an object such as a pedestrian, a bicycle or an animal is not known, although the object jumps out in front of the vehicle 200 is detected, and the like.

Furthermore, in the vehicle control system 100 of the present embodiment, each electronic control device 120 includes the vehicle control unit 124, which causes each vehicle 200 to travel along the recommended route received from the server 110.

With such a configuration, in the vehicle control system 100 in the present embodiment, each electronic control device 120 that has received the recommended route from the server 110 can cause each vehicle 200 to travel along the recommended route. Thus, according to the vehicle control system 100 in the present embodiment, the safety of automatic driving and driving assistance of the vehicle 200 by the electronic control device 120 can be further improved.

Furthermore, the electronic control device 120 in the present embodiment is a vehicle 200-mounted control device. The electronic control device 120 includes the detection unit 121, the determination unit 122, the transmission unit 123, and the vehicle control unit 124. The detection unit 121 detects the state of a road based on the detection result of the external environment sensor 220 mounted in the vehicle 200. The determination unit 122 determines an abnormality including an Detection error of the condition of the road by the detection unit 121. The sending unit 123 transmits anomaly information including the location and time of the vehicle 200 when the determination unit 122 determines an anomaly and the type of the determined anomaly to the server 110 outside the vehicle 200 the communication device 210 mounted in each vehicle 200. The vehicle control unit 124 causes the vehicle 200 to travel along the recommended route received from the server 110.

With such a configuration, similarly to the vehicle control system 100 described above, the electronic control device 120 in the present embodiment can improve the safety of automatic driving and driving assistance of the vehicle 200 by avoiding a route on which an abnormality frequently occurs.

Although the vehicle control system and the electronic control device according to Embodiment 1 of the present disclosure have been described in detail above, the vehicle control system and the electronic control device according to the present disclosure are not limited to the above-described embodiment. For example, in the embodiment described above, an example has been described in which the traffic volume is used as a numerical value for normalizing the driving difficulty degree calculation expression ADD; However, other numerical values can also be used, provided the frequency of determination of an anomaly can be calculated for all passing vehicles.

In addition, the restriction of the required time may be sent to the server 110 at the same time as sending the request for route information from the electronic control device 120 of the vehicle 200 to the server 110. In this case, the route selection unit 113 of the server 110 may, for example, select a route that satisfies the required time restriction and has the smallest degree of driving difficulty ADD from the plurality of route candidates CR1, CR2 and CR3 as the safe route.

Furthermore, in the vehicle control system 100 of the present embodiment, for example, if the recommended route includes a node or a link requiring caution and corresponds to N1 to N4 or the links L1 to L7 where the determination frequency is one Anomaly is higher than a predetermined frequency, the sending unit 114 of the server 110 sends the node or connection requiring caution to each electronic control device 120. In this case, the determination unit 122 of each electronic control device 120 may set the priority of determining the type of anomaly detected with a frequency higher than a predetermined frequency at the node or link requiring caution to be higher is than the priority of determining the type of another anomaly.

In particular, for example, if the recognition unit 121 of each electronic control unit 120 normally carries out a plurality of recognition processes and the upper limit of the processing time of each recognition process is defined, it is conceivable to increase the upper limit of the processing time only when the vehicle passes the node or the Connection happens that requires caution. In the node or link described above where caution is required and AEB due to pedestrian detection occurs frequently, by increasing the upper limit of the pedestrian detection processing time, a pedestrian detection process can be performed on more objects than usual , which prevents pedestrians from being detected.

[Embodiment 2]

Below is a vehicle control system according to Embodiment 2 of the present disclosure 1 to 7 described in the previously described embodiment 1. A vehicle control system 100 in the present embodiment differs from the vehicle control system 100 according to the previously described Embodiment 1 in the configurations of the recording unit 111 and the calculation unit 112 of the server 110. Since other configurations of the vehicle control system 100 in the present embodiment are those of the vehicle control system 100 in the previously described Similar to Embodiment 1, similar components are denoted by the same reference numerals and description thereof is omitted.

In the vehicle control system 100 of the present embodiment, each of the electronic control devices 120 mounted in one of a plurality of vehicles 200 causes the detection unit 121 to detect the state of a road based on detection results of the external environment sensor 220 and the vehicle sensor 230 and record abnormality information in the vehicle control system 100 of the present embodiment 2 Process P15 shown is similar to that in the previously described embodiment 1. Furthermore, in the present embodiment, in 2 In the process P17 shown, the electronic control device 120, the sending unit 123 for sending the abnormality information including the type of the external environmental sensor 220 used to detect the abnormality in the process P15 to the server 110.

Furthermore, in the vehicle control system 100 of the present embodiment, the recording unit 111 of the server 110 records, for example, abnormality occurrence information in which the type of the external environment sensor 220 included in the abnormality information is associated with each of the nodes N1 to N4 or each of the links L1 to L7 in the storage unit 115 connected is. The following Table 4 shows an example of the abnormal occurrence information recorded in the storage unit 115 by the recording unit 111 in the present embodiment.
[Table 4] Date time Weather Width length Location code Anomaly type code Sensor code August 20, 2020 5:00 p.m Sunny 36°31'12'' 140°36'26'' L6 2 1 August 10, 2020 17:23 Sunny 36°31'12'' 140°36'26'' L6 2 1 August 4, 2020 5:18 p.m Sunny 36°31'12'' 140°36'26'' L6 2 1 August 16, 2020 5:15 p.m Sunny 36°31'12'' 140°36'26'' L6 2 1 ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮

In Table 4, a sensor code is a code for indicating the type of the outdoor environment sensor 220. For example, as shown in the following Table 5, the sensor code is defined in advance for each type of the outdoor environment sensor 220 and recorded in the storage unit 115.
[Table 5] Outdoor environment sensor type Sensor code Stereo camera 1 Laser radar 2 Millimeter wave radar/monocular camera 3 ⋮ ⋮

Further, in the vehicle control system 100 of the present embodiment, the server 110 performs the following processes in the abnormal occurrence frequency detecting process P223 as shown in 4 shown. For example, the route selection unit 113 of the server 110 extracts the anomaly occurrence frequency with a sensor code corresponding to the sensor code indicating the type of the outdoor environment sensor matches, in addition to the driving condition, including the month, period, weather and the like under which the vehicle 200 travels from the departure point S to the destination G.

Specifically, as shown in Table 4, for each of the links L1 to L7, the abnormality occurrence frequency having driving conditions consistent with the August, 5:00 p.m. and sunny weather driving conditions under which the vehicle 200 is running, and a sensor code has, which corresponds to the sensor code 1 for displaying the stereo camera, extracted. Other processes of the vehicle control system 100 in the present embodiment are similar to those of the vehicle control system 100 in the previously described Embodiment 1.

As described above, in the vehicle control system 100 of the present embodiment, when the determination unit 122 of each electronic control device 120 determines an abnormality, the recording unit 111 of the server 110 records the abnormality occurrence information in which the type of the external environment sensor 220 of each vehicle 200 with each of the nodes N1 to N4 or each of the connections L1 to L7 is connected. In addition, the calculation unit 112 of the server 110 detects the type of the external environment sensor 220 of each vehicle 200 when calculating the degree of driving difficulty ADD, and calculates the degree of driving difficulty ADD based on abnormality occurrence information in which the type of the external environment sensor 220 when a Anomaly is determined to match the type of the external environment sensor 220 when the degree of driving difficulty ADD is calculated.

With such a configuration, the vehicle control system 100 in the present embodiment can improve the safety of automatic driving and driving assistance of the vehicle 200 by avoiding the route on which the abnormality specific to the type of the external environmental sensor 220 mounted in each vehicle 200 often occurs. In particular, when the anomaly type is, for example, backlighting, it is taken into account that there is an influence, for example from overexposed highlights, when the type of the external environmental sensor 220 corresponds to a camera, but there is no influence when the type of the external environmental sensor 220 corresponds to a laser radar . In such a case, according to the vehicle control system 100 in the present embodiment, the vehicle 200 can be made to travel along a route suitable for the type of the external environment sensor 220 mounted in the vehicle 200.

[Embodiment 3]

Finally, a vehicle control system according to Embodiment 3 of the present disclosure is referred to below 1 to 7 and 8 to 13 described. 8th Fig. 12 is a block diagram showing a configuration of a server 110 of a vehicle control system 100 in the present embodiment. In the vehicle control system 100 of the present embodiment, FIG 8th illustrated server 110, for example, a measure support unit 116 and a traffic flow simulator 117 in addition to the configuration similar to the server 110 in the FIG 1 1. Since other configurations of the vehicle control system 100 in the present embodiment are similar to those of the vehicle control system 100 in the previously described Embodiment 1, similar components are denoted by the same reference numerals and description thereof is omitted.

9 12 is a flowchart showing an example of processing P30 to assist in planning an action to reduce the occurrence of an anomaly by the server 110 in FIG 8th . If the in 9 Processing P30 shown is started, the server 110 in the present embodiment executes a process P31 in which the action support unit 116 receives an input of an extraction condition.

10 shows an example of a measure support unit 116 of the server 110 in 8th displayed screen. The action support unit 116 includes, for example, a display device such as a liquid crystal display device or an organic EL display device, and an input device such as a touch screen, a keyboard, or a mouse. As in 10 As shown, the action support unit 116 causes the display device to display a map including each of the links L1 to L7 and/or each of the nodes N1 to N4, and extraction conditions such as a month, a time period, weather, and an anomaly type.

In process P31, the action support unit 116 receives an input of an extraction condition via, for example, the input device. For example, scrolls like in 10 1, a user of the server 110 is shown in the map displayed on the display device of the action support unit 116 to select an area in the map in which the user wants to take an action to reduce the occurrence of an abnormality, thereby making each of the links L1 to L7 and/or each of the nodes N1 to N4 in the selected area can be selected.

Furthermore, for example, choose as in 10 The user selects the extraction conditions, such as the month, the time period, the weather and the anomaly type, by a scroll bar displayed on the display device. The action support unit 116 receives an input of the extraction condition selected by the user and inputs the extraction condition to the calculation unit 112. Here it is assumed that “total data” is entered as the extraction conditions of month, time period, weather and anomaly type. Subsequently, the server 110 executes a process P32 for detecting the abnormality occurrence frequency.

In this process P32, the calculation unit 112 of the server 110 extracts the abnormal occurrence information that matches the extraction condition inputted by the action support unit 116 in the previous process P31 from parts of the abnormal occurrence information recorded in the storage unit 115. Further, the calculation unit 112 detects the anomaly occurrence frequency of each of the links L1 to L7 and/or each of the nodes N1 to N4 based on the extracted anomaly occurrence information similarly to FIG 4 Process P223 shown in Embodiment 1.

The server 110 then executes a process P33 for acquiring an average traffic volume as in 9 shown from. In process P33, the calculation unit 112 of the server 110 detects the average traffic volume stored in the storage unit 115 for each of the connections L1 to L7 and/or each of the nodes N1 to N4, extracted in the process P32, for example similar to in 4 Process P224 shown in Embodiment 1. Since the extraction conditions of month, time period and weather are “aggregate data”, the average traffic volumes of all months, all time periods and all weather are collected.

Subsequently, the server 110 executes a process P34 for calculating the degree of driving difficulty ADD on each of the links L1 to L7 and/or at each of the nodes N1 to N4 as in 9 shown from. In this process P34 calculates similar to the in 4 Illustrated process P225 as described above, the calculation unit 112 of the server 110 calculates the degree of driving difficulty ADD based on the abnormality occurrence frequency for each of the nodes N1 to N4 or each of the links L1 to L7 in the abnormality occurrence information by the foregoing expression (1) and gives the degree of Driving difficulty ADD at the measure support unit 116. Subsequently, the server 110 executes a process P35 for displaying the degree of driving difficulty ADD on each of the links L1 to L7 and/or on each of the nodes N1 to N4.

11 shows an example of a display screen of a process P35 for displaying the level of driving difficulty ADD on each of the links L1 to L7 and/or at each of the nodes N1 to N4 in 9 . In this process P35, the action support unit 116 causes the display device to display the degree of driving difficulty on each of the links L1 to L7 and/or on each of the nodes N1 to N4, which was input by the calculation unit 112 in the previous process P34, as shown in FIG 11 shown. As in 10 Shown is the level of driving difficulty on each of the links L1 to L7 and/or at each of the nodes N1 to N4 included in the area selected on the map.

12 shows a graph depicting a level of driving difficulty for each anomaly type in 11 selected connection L6. On the display screen of the display device of the action support unit 116 as in 11 shown, the user selects each of the connections L1 to L7 and/or each of the nodes N1 to N4 in 11 and, for example, clicks on an icon described as a “detail display” or touches it by using the input device of the action support unit 116. Thus, as in 12 shown, the degree of driving difficulty for each anomaly type in the selected connection L6 is displayed on the display device of the action support unit 116.

As described above, the calculation unit 112 of the server 110 causes the display device of the server 110 to display the degree of driving difficulty ADD calculated for each of the nodes N1 to N4 or each of the links L1 to L7 in the abnormality occurrence information extracted by using the extraction condition the time or type of anomaly. Thus, according to the vehicle control system 100 in the present embodiment, planning a measure to reduce the occurrence of an abnormality on each of the links L1 to L7 and/or on each of the nodes N1 to N4 can be supported.

In particular, for example, chooses as in 11 shown, if the level of driving difficulty ADD is calculated to be higher than that of the other links L1 to L5 and L7 on the specific link L6, the user selects the link L6 and clicks or touches the "Details display" icon. Thus, as in 12 shown, the degree of driving difficulty ADD for each anomaly type in the selected connection L6 is displayed by the action support unit 116 and the calculation unit 112.

Thus, for example, as in 12 shown, the user determines whether the degree of driving difficulty due to the non-detection of the lane corresponding to the anomaly type code 2 is significantly higher than other anomaly types on the link L6. Accordingly, the user can schedule the rerouting of a road boundary line of the link L6 as a measure for reducing the degree of driving difficulty ADD on the link L6 based on the degree of driving difficulty ADD for each anomaly type displayed on the display device of the action support unit 116.

The action support unit 116 may periodically analyze the anomaly occurrence information recorded in the storage unit 115 to set the extraction condition for increasing the driving difficulty level ADD on each of the links L1 to L7 and/or on each of the nodes N1 to N4. For example, in this case, when it is predicted that the conditions of the month, period and weather agree with the extraction conditions determined by the analysis, the action support unit 116 displays the degree of driving difficulty ADD on each of the links L1 to L7 and/or each of the nodes N1 to N4 based on the extraction conditions on the display device along with the extraction conditions.

Thus, the user can plan an action for reducing the level of driving difficulty ADD based on the level of driving difficulty ADD on each of the links L1 to L7 and/or at each of the nodes N1 to N4 as indicated at the action support unit 116. For example, as described above, if a large number of AEBs due to pedestrian detection occur in a specific time period, such as the morning rush hour period, and the degree of driving difficulty ADD increases, the user can take a measure such as arranging a crossing aid on the road between the workplace and parking lot.

As described above, according to the vehicle control system 100 in the present embodiment, the degree of driving difficulty on each of the selected links L1 to L7 and/or on each of the nodes N1 to N4 is displayed on the display device of the action support unit 116, and thus the determination of the driving difficulty can be made Priority of the user's action and the narrowing of the action are supported. Thus, according to the vehicle control system 100 in the present embodiment, in addition to the effects of the vehicle control system 100 in the previously described Embodiment 1, it is possible to support the priority determination of infrastructure for safely performing automatic driving by the electronic control device 120 of the vehicle 200.

13 12 is a flowchart showing an example of processing P40 for checking a measure for reducing the occurrence of an abnormality by the server 110 in FIG 8th . If the in 13 As shown in processing P40, in the present embodiment, the server 110 executes a process P41 for reproducing a traffic flow before performing a measure for reducing the occurrence of an anomaly by the traffic flow simulator 117.

The traffic flow simulator 117 simulates the driving behavior of each of a plurality of vehicles 200 by inputting parameters such as the traffic volume at the representative point and the right/left turn rate at each intersection. The traffic flow simulator 117 can be implemented by using a known method, such as a road traffic simulation system as in JP 5-250594 A disclosed, carried out.

In process P41, the parameters input to the traffic flow simulator 117 are adjusted to reproduce a known traffic flow before taking the action to reduce the occurrence of an anomaly. In particular, the parameters entered on the traffic flow simulator 117 are adjusted so that the known traffic volume of each of the connections L1 to L7 and/or each of the nodes N1 to N4 corresponds to the traffic volume of each of the connections L1 to L7 and/or each of the nodes N1 to N4 as reproduced in the traffic flow simulator 117.

Then leads as in 13 1, the server 110 shows a process P54 for calculating the degree of driving difficulty ADD of each of the links L1 to L7 and/or each of the nodes N1 to N4 after performing the action to reduce the occurrence of an abnormality. In this process P42, the calculation unit 112 calculates the degree of driving difficulty ADD of each of the links L1 to L7 and/or each of the nodes N1 to N4 similarly to, for example, those in FIG 9 illustrated processes P31 to P34 assuming that the anomaly occurrence frequency of each of the links and / or each of the nodes after taking the action is 0.

Then leads as in 13 1, the traffic flow simulator 117 executes a process P43 for changing the right/left turn rate parameter. For example, if on the in 10 In the connection L6 shown, a measure for reducing the frequency of occurrence of anomalies is carried out, another connection L3 is connected to the node N2 connected to the connection L6. For example, the traffic flow simulator 117 changes the rate of right/left turns from node N2 to links L6 and L3 before and after taking the action on link L6 as shown in Table 6 below. [Table 6] Connection before carrying out the measure After carrying out the measure Degree of driving difficulty Rate of turning right/left Degree of driving difficulty Rate of turning right/left L6 0.23 0.2 0 0.472 L3 0.043 0.8 0.043 0.528

For example, as shown in Table 6, by taking an action such as rerouting the lane line on link L6, the driving difficulty level on link L6 changes from 0.23 to 0 before and after taking the action. Thus, 0.2 and 0.8, which are the right/left turn rates from node N2 to link L6 and link L3, change to 0.472 and 0.528 before and after taking the action on link L6. Such an increase or decrease in the right/left turn rate may be calculated, for example, based on a mixing rate of the vehicle 200 automatically traveling under the control of the electronic control device 120 with respect to the total traffic volume at the node N2.

For example, assume that the mixing rate of the vehicle 200, which is driving automatically, with respect to the total traffic volume is 34% and the vehicle 200 turned right from the node N2 to the link L3 at a rate of 100% before taking the action . In this case, the proportion of vehicle 200 turning right from node N2 to connection L3 of the total traffic volume is 27.2% (0.8 × 0.34 = 0.272). Assuming that the vehicle 200 turns left after taking the action at node N2 to link L6, the rate of turning right/left from node N2 to link L6 after taking the action is 0.2 + 0.272 = 0.472 .

As shown in Table 6, since the degree of driving difficulty ADD on the link L6 is significantly higher than the degree of driving difficulty ADD on the link L3 before taking the action, the rate of right/left turning to the link L6 is significantly lower than the rate of turning right/left to connection L3. However, after taking the action, the degree of driving difficulty ADD on the link L6 becomes 0 and becomes lower than 0.043, which is the degree of driving difficulty ADD on the link L3. Thus, the right/left turn rate to link L6 increases and the difference between right/left turn rates of link L6 and link L3 decreases and becomes almost zero.

Subsequently, the server 110 executes a process P44 for simulating a traffic flow after performing the action. In this process P44, the traffic flow simulator 117 simulates the traffic flow when the degree of driving difficulty ADD on each of the links L1 to L7 and/or at each of the nodes N1 to N4 in the map information MI changes, for example, by using the parameters including the rates of turning right/left after performing the action, which is shown in Table 6.

The server 110 then executes a process P45 for comparing the traffic flows of the links L1 to L7 and/or the nodes N1 to N4 before and after taking the action. With this process, for example, an effect of a measure for reducing the occurrence of an anomaly, such as eliminating a traffic congestion by distributing the traffic flow between the link L6 and the link L3, can be checked and the measure can be verified.

As described above, in the vehicle control system 100 of the present embodiment, the server 110 includes the traffic flow simulator 117 that simulates the traffic flow when the degree of driving difficulty changes at each of the nodes or on each of the links in the map information MI. With such a configuration, the vehicle control system 100 in the present embodiment can evaluate the improvement in the degree of complexity of a road, such as a traffic jam, in addition to the effects of the vehicle control system 100 in the previously described Embodiment 1.

Previously, the vehicle control system and the electronic control device according to embodiments of the present disclosure have been described in detail with reference to the drawings. However, the specific configuration is not limited to the embodiments, and design changes and the like without departing from the gist of the present disclosure are included in the present disclosure.

Reference symbol list

100

Vehicle control system

110

server

111

Recording unit

112

Calculation unit

113

Route selection unit

114

Transmitting unit

117

Traffic flow simulator

120

electronic control device

121

Detection unit

122

Determination unit

123

Transmitting unit

124

Vehicle control unit

200

vehicle

210

Communication device

220

external environmental sensor

ADD

Degree of driving difficulty

CR1-CR3

Route candidate

L1-L7

Connection

WED

Card information

N1-N4

node

QUOTES INCLUDED IN THE DESCRIPTION

This list of documents listed by the applicant was generated automatically and is included solely for the better information of the reader. The list is not part of the German patent or utility model application. The DPMA assumes no liability for any errors or omissions.

Cited patent literature

• JP 2007323281 A [0005]
• JP 5250594 A [0119]

Claims (10)

Vehicle control system, comprising: an electronic control device mounted in each of a plurality of vehicles; and at least one server connected to each electronic control device in a manner enabling communication with each electronic control device; whereby any electronic control device a recognition unit that recognizes a condition of a road based on a detection result of an external environment sensor mounted in each of the vehicles, a determination unit that determines an anomaly including a detection error of the condition of the road by the detection unit, and a sending unit that sends anomaly information to the server via a communication device mounted in each of the vehicles, the anomaly information including a location and a time of each of the vehicles when the determining unit determines an anomaly and a type of the determined anomaly, and the server a recording unit that records abnormality occurrence information in which the abnormality information received from each electronic control device is associated with each of the nodes or links in map information, a calculation unit that calculates a degree of driving difficulty based on a determination frequency of the abnormality for each of the nodes or links included in the abnormality occurrence information, a route selection unit that selects a safe route with the lowest overall degree of driving difficulty from a plurality of route candidates in the map information, and a sending unit that sends the safe route to each electronic control device as a recommended route. Vehicle control system Claim 1 , wherein the calculation unit of the server calculates the degree of driving difficulty for each of the nodes or links in the abnormality occurrence information to be higher than the frequency of determination of the abnormality by the determination unit in each electronic control device. Vehicle control system Claim 1 , wherein the recording unit of the server records the anomaly occurrence information in which the weather at a location of each of the vehicles when the determining unit of each electronic control device determines an anomaly is connected to each of the nodes or links, and the computing unit of the server records the current weather from each of the nodes or links included in the plurality of route candidates in the map information when the degree of driving difficulty is calculated, and the degree of driving difficulty based on the anomaly occurrence information in which the weather when the anomaly is determined, matches the current weather. Vehicle control system Claim 1 , wherein the recording unit of the server records the abnormality occurrence information in which a type of the external environment sensor in each of the vehicles when the determination unit of each electronic control device determines an abnormality is connected to each of the nodes or links, and the computing unit of the server records a type of the external environment sensor in each of the vehicles when the degree of driving difficulty is calculated, and the degree of driving difficulty is calculated based on the abnormality occurrence information in which the type of the external environment sensor when the abnormality is determined matches the type of the external environment sensor when the degree of Driving difficulty is calculated. Vehicle control system Claim 1 , wherein the sending unit of the server sends a node or a connection that requires caution to each electronic control device if the recommended route contains a node or a connection that requires caution, which of the nodes or which the is a connection in which the determination frequency of the abnormality is higher than a predetermined frequency, and the determination unit of each electronic control device has a priority of determining the type of abnormality with a frequency higher than the predetermined frequency at the node or on the connection to whom caution is required, sets a particular anomaly so that it is higher than a priority of determining other types of anomaly. Vehicle control system Claim 1 , wherein when the type of the abnormality determined by the determination unit cannot be determined, the electronic control device causes the sending unit to send to the server an image from a camera arranged in the outdoor environment sensor along with the abnormality information. Vehicle control system Claim 1 , wherein the computing unit of the server causes a display device of the server to display the degree of driving difficulty calculated for each of the nodes or links in the abnormality occurrence information extracted by using an extraction condition including the time or the type of the abnormality. Vehicle control system Claim 7 , wherein the server includes a traffic flow simulator that simulates traffic flow as the driving difficulty level of each of the nodes or each of the links in the map information changes. Vehicle control system Claim 1 , wherein each electronic control device includes a vehicle control unit that causes each of the vehicles to travel along the recommended route received from the server. An electronic control device mounted on a vehicle, the electronic control device comprising: a recognition unit that recognizes a state of a road based on a detection result of a vehicle-mounted external environment sensor; a determination unit that determines an anomaly including a detection error of the state of the road by the detection unit; a sending unit that sends anomaly information to a server outside the vehicle via a vehicle-mounted communication device, the anomaly information including a location and a time of the vehicle when the determining unit determines an anomaly and a type of the determined anomaly, and a vehicle control unit that causes the vehicle to travel along a recommended route received from the server.

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