Patent Description:
In recent years, research and development relating to autonomous driving control have been advanced. For example, Patent Document <NUM> discloses a technique that performs risk prediction or the like based on a knowledge base, which stores a logical expression generated using a well-known supervised machine learning method, and utilizes the risk prediction or the like for autonomous driving control of a vehicle. Patent Document <NUM> describes a method for operating a vehicle, comprising detecting a driving style of the driver by monitoring his or her vehicle guidance activity, and correspondingly emulating or mimicking or imitating it. Patent Document <NUM> describes a method for operating a vehicle, the vehicle being controlled in a partially autonomous or autonomous driving mode based on decisions of an artificial neural network structure. Patent Document <NUM> describes an automatic driving device comprising a control program for inputting outside information and vehicle information, and outputting a target control value for a vehicle. Patent Document <NUM> relates to a vehicle traveling controller that performs traveling control of a vehicle.

A model (machine learning model) that is constructed by machine learning varies depending on input learning data, and there is a possibility that each machine learning model may have a unique characteristic. Such a machine learning model is basically considered as desirable in that the model allows optimum control based on learning data provided in constructing the model to be executable. On the other hand, input data (event) that is significantly deviated from learning data provided in constructing the model is not always appropriately dealt with. In view of this point, in a scene where stable control is demanded, or the like, there is a possibility that control based on the machine learning model is not preferable.

An example of an object to be solved by the invention is to provide a technique for enabling stable autonomous driving control.

The above-described object and other objects, features, and advantages will become apparent from preferable embodiments described below and the accompanying drawings.

<FIG> is a diagram illustrating the outline of a control device <NUM> according to the invention. In an example of <FIG>, the control device <NUM> is a device (for example, an electronic control unit (ECU) or the like) that is mounted in a vehicle V. The control device <NUM> can change a first control rule that is changed (optimized) by machine learning, and a second control rule that is a fixed rule without depending on the machine learning. As will be described below in detail, while autonomous driving of the vehicle is being performed using the first control rule based on machine learning, in a case where a predetermined event to be a trigger of changing a control rule of the autonomous driving is detected, the control device <NUM> changes the control rule of the autonomous driving to the second control rule that is the fixed rule according to the event. Information relating to the predetermined event to be the trigger of changing the control rule of the autonomous driving can be output from a sensor device <NUM> that is mounted in the vehicle V or can be acquired from an external device <NUM>. The external device <NUM> is, for example, a device similar to the control device <NUM> that is mounted in another vehicle that is not illustrated, a road-to-vehicle communication device that is provided along a road, or the like.

Hereinafter, an embodiment of the invention will be described referring to the drawings. In all drawings, the same components are represented by the same reference numerals, and description thereof will not be repeated. Except for a case where particular description is provided, each block in a block diagram is not a configuration of a hardware unit but a configuration of a functional unit.

<FIG> is a block diagram conceptually showing the functional configuration of the control device <NUM> in a first embodiment. As shown in <FIG>, the control device <NUM> of the embodiment has an event detection unit <NUM> and a control-rule change unit <NUM>.

The event detection unit <NUM> determines whether or not the predetermined event is detected while the vehicle is performing the autonomous driving using the first control rule based on the machine learning. The predetermined event is the event to be the trigger of changing the control rule at the time of the autonomous driving of the vehicle. The predetermined event can also be expressed as an event indicating a timing at which control of the autonomous driving using the first control rule based on the machine learning is interrupted. A specific example of the predetermined event will be described below. The event detection unit <NUM> can detect the predetermined event on the basis of a result of analysis of outputs from various sensor devices <NUM> mounted in the own vehicle. The event detection unit <NUM> may perform communication with an external device (the control device mounted in another peripheral vehicle or the road-to-vehicle communication device provided along the road) and may detect the predetermined event by way of the external device. The event detection unit <NUM> may detect the predetermined event on the basis of dynamic information included in map data for autonomous driving to be used at the time of the autonomous driving of the vehicle V. Here, the map data for autonomous driving is map data that is called, for example, a "dynamic map", and is data including conventional map information (static information), and information (dynamic information) that varies in real time. The dynamic information includes information that can fluctuate in a comparatively short span (for example, in seconds), such as intelligent transport systems (ITS) look-ahead information (peripheral vehicles, pedestrian information, traffic signal information, and the like), and information (referred to as "quasi-dynamic information") that can fluctuate in a slightly short span (for example, in minutes), such as accident information, congestion information, and narrow area weather information. The static information includes information that can fluctuate in a comparatively long span (for example, in months) , such as road surface information, lane information, and three-dimensional structures, and information (referred to as "quasi-static information") that can fluctuate in a slightly long span (for example, in hours), such as traffic control information, road construction information, and wide area weather information. In the invention, the quasi-static information may be classified into the category of the dynamic information. The static information and the dynamic information included in the map data for autonomous driving are not limited to the examples described herein. In the map data for autonomous driving, various kinds of information that can be utilized for the autonomous driving control of the vehicle can be included.

While the vehicle is performing the autonomous driving using the first control rule based on the machine learning, in a case where an event is detected by the event detection unit <NUM>, the control-rule change unit <NUM> changes the control rule of the autonomous driving of the vehicle to the second control rule that is the fixed rule according to the detected event.

The first control rule that each vehicle uses at the time of the autonomous driving is a rule based on the machine learning as described above. For this reason, in an operation of the vehicle when the first control rule is used, a unique feature according to a learning result of provided learning data can appear. Then, in a case where control at the time of the autonomous driving is performed using the first control rule, there is a possibility that an unpredictable unstable operation is performed due to the unique feature of the first control rule. In regard to this point, in the embodiment, in a case where the predetermined event is detected while the autonomous driving is being performed with the first control rule based on the machine learning, the control rule of the autonomous driving is changed to the second control rule that is the fixed rule according to the detected event. With this, since the operation of the vehicle at the time of the autonomous driving is controlled according to the fixed rule in a case where the predetermined event is detected, it is possible to suppress an unpredictable unstable operation.

In addition, at a place where a plurality of vehicles having the control device <NUM> are gathered, each vehicle performs the autonomous driving using the second control rule as the fixed rule instead of the first control rule based on a machine learning result, whereby it is possible to control movement of each vehicle, and as a result, to expect an effect of improving a traffic environment. For example, a case where an obstacle is present on one lane in a road having three lanes on each side, and each vehicle that is traveling on the lane needs to perform lane change in order to avoid the obstacle is considered. In this case, in a case where each vehicle moves with the first control rule having a characteristic unique to each vehicle, there may occur a problem that the vehicles concentrate on one lane of the remaining two lanes, or the like, resulting in the occurrence or deterioration of congestion. In such a case, the control device <NUM> of the embodiment switches the control rule of the autonomous driving of each vehicle to the fixed rule (in this case, for example, a rule that "as a behavior to avoid the obstacle, a vehicle moves to a lane different from a lane, to which a preceding vehicle moves", or the like) according to the detected event to control each vehicle, whereby it is possible to minimize the amount of congestion by dispersing the vehicles into the remaining two lanes.

Hereinafter, the control device <NUM> of the embodiment will be described in more detail.

Each functional configuration unit of the control device <NUM> may be implemented by hardware (for example, a hard-wired electronic circuit or the like) that implements each functional configuration unit or may be implemented by a combination of hardware and software (example: a combination of an electronic circuit and a program for controlling the same, or the like). Hereinafter, a case where each functional configuration unit of the control device <NUM> is implemented by a combination of hardware and software will be further described.

<FIG> is a diagram illustrating the hardware configuration of the control device <NUM> of the first embodiment. A computer <NUM> is a computer that implements the control device <NUM>. For example, the computer <NUM> is an electronic control unit (ECU) that can control the operation of the vehicle at the time of the autonomous driving. The computer <NUM> may be a computer that is designed dedicatedly in order to implement the control device <NUM> or may be a general-purpose computer.

The computer <NUM> has a bus <NUM>, a processor <NUM>, a memory <NUM>, a storage device <NUM>, an input and output interface <NUM>, and a network interface <NUM>. The bus <NUM> is a data transmission path through which the processor <NUM>, the memory <NUM>, the storage device <NUM>, the input and output interface <NUM>, and the network interface <NUM> transmit and receive data to and from one another. Note that a method of connecting the processor <NUM> and the like to one another is not limited to bus connection. The processor <NUM> is an arithmetic processing device that is implemented using a microprocessor or the like. The memory <NUM> is a main storage device that is implemented using a random access memory (RAM) or the like. The storage device <NUM> is an auxiliary storage device that is implemented using a read only memory (ROM), a flash memory, or the like.

The input and output interface <NUM> is an interface that is provided to connect the computer <NUM> to peripheral equipment. Various analog signals or digital signals to be used in control of the vehicle are input or output to the computer <NUM> through the input and output interface <NUM>. Here, an A/D converter that converts an analog input signal to a digital signal, a D/A converter that converts a digital output signal to an analog signal, and the like are appropriately included in the input and output interface <NUM>.

For example, in <FIG>, the sensor device <NUM> or a drive circuit <NUM> to be used in control of the vehicle are connected to the input and output interface <NUM>. The sensor device <NUM> is light detection and ranging (LIDAR), a millimeter-wave radar, a sonar, a camera, or the like. Though not shown, a plurality of sensor devices <NUM> can be connected to the computer <NUM> through the input and output interface <NUM>. The drive circuit <NUM> is a circuit that is provided to drive various mechanisms, such as a gear, an engine, and a steering mechanism of the vehicle. The control device <NUM> controls the operation of the drive circuit <NUM>, thereby being able to control the operation of the vehicle at the time of the autonomous driving.

The network interface <NUM> is an interface that is provided to connect the computer <NUM> to a communication network. The communication network is, for example, a controller area network (CAN), a local area network (LAN), a wide area network (WAN), or the like. A method in which the network interface <NUM> is connected to the communication network may be wireless connection or may be wired connection. The computer <NUM> performs communication with a control device <NUM> of another vehicle or a road-to-vehicle communication device <NUM> through a wireless LAN, and can acquire information relating to an event to be used in processing of the control device <NUM> from the devices.

The storage device <NUM> stores a program module that implements various functional configuration units of the control device <NUM>. The processor <NUM> reads the program module on the memory <NUM> and executes the program module, thereby implementing the functions of the control device <NUM>. The storage device <NUM> may store the map data for autonomous driving to be used at the time of the autonomous driving of the vehicle V.

A flow of processing that is executed by the control device <NUM> of the embodiment will be schematically described referring to <FIG> is a flowchart illustrating a flow of processing that is executed by the control device <NUM> of the first embodiment.

First, in a case where a traveling mode of the vehicle is switched to an autonomous driving mode (S102: YES), the event detection unit <NUM> is activated, and monitoring processing of the predetermined event is started (S104). Thereafter, in a case where the event detection unit <NUM> detects the predetermined event (S104: YES), the event detection unit <NUM> notifies the control-rule change unit <NUM> that the predetermined event is detected (S106).

The control-rule change unit <NUM> specifies the second control rule corresponding to the event notified in the processing of S106 (S108). The control-rule change unit <NUM> can specify the second control rule corresponding to the event detected by the event detection unit <NUM>, for example, using a table as shown in <FIG>. The table illustrated in <FIG> stores identification information of an event and identification information of the second control rule to be applied with detection of the event in association with each other. For example, the control-rule change unit <NUM> acquires an identifier of the event detected in the processing of S106 from the event detection unit <NUM> and refers to the table of <FIG> on the basis of the identifier of the event, thereby being able to specify the second control rule. Then, the control-rule change unit <NUM> transfers an instruction to apply the second control rule read in the processing of S108 to the ECU or the like that controls the autonomous driving (S110). With this, the operation of the vehicle at the time of the autonomous driving is controlled on the basis of the second control rule. The above description is just illustrative, and the operation of the control-rule change unit <NUM> is not limited to the operation using the table illustrated in <FIG>. For example, in <FIG>, although an example where the different second control rule is associated with each event has been described, the invention is not limited thereto, and the same second control rule may be associated with a plurality of events.

Hereinafter, several specific examples of a more detailed operation will be described.

In the specific example, a case where the event detection unit <NUM> detects "the occurrence of an abnormality in the sensor device <NUM>" as the predetermined event will be described. Here, the abnormality of the sensor device <NUM> means stain of an optical system (lens or the like), internal failure, disturbance (sunlight, rain, fog, snow, the light of an oncoming vehicle, or the like) under a sensing environment, or an abnormality of an output signal from the sensor device <NUM> or communication defect between the sensor device <NUM> and the control device <NUM> due to detection of a signal meaning an error or an unexpected signal.

A case where the event detection unit <NUM> detects the predetermined event on the basis of "the dynamic information included in the map data for autonomous driving" will be described. As described above, the map data for autonomous driving is a digital map in which not only the static information (map information, such as road surface information, lane information, and three-dimensional structures) but also dynamic information (accident information, congestion information, weather information, pedestrian information, traffic signal information, and the like) is incorporated. Specifically, the dynamic information included in the map data for autonomous driving means the above-described dynamic information. For example, the dynamic information is delivered from a server, which manages accident information and the like, to the vehicle V. The vehicle V that receives the accident information and the like stores the accident information and the like in an area indicating the dynamic information in the map data for autonomous driving. Then, the event detection unit <NUM> refers to the dynamic information included in the map data for autonomous driving to be used at the time of the autonomous driving of the vehicle V, thereby being able to detect an event.

The event detection unit <NUM> monitors a signal line that is connected to the sensor device <NUM> through the input and output interface <NUM> and performs measurement of intensity of a signal output from the sensor device <NUM> or analysis of a content of the signal. Then, the event detection unit <NUM> determines whether or not the measured intensity of the signal is lower than a predetermined reference value or whether or not the analyzed signal is a signal meaning an error or an unexpected signal. In this case, the predetermined reference value to be compared is stored in, for example, the memory <NUM> or the storage device <NUM> in advance. In a case where detection is made that the measured intensity of the signal is lower than the predetermined reference value or the analyzed signal is a signal meaning an error or an unexpected signal, the event detection unit <NUM> notifies the control-rule change unit <NUM> to the effect that "an abnormality occurs in the sensor device <NUM>". The control-rule change unit <NUM> specifies the second control rule to be applied when "an abnormality occurs in the sensor device <NUM>" on the basis of the notification from the event detection unit <NUM>. The second control rule in this case is not particularly limited, and is, for example, a rule that "the vehicle is stopped in a predetermined procedure (for example, turning on a hazard lamp and controlling a brake to gradually reduce a speed, or the like)", or the like.

In a case where the intensity of the output signal is lowered due to the abnormality of the sensor device <NUM>, a probability that a control processing unit for the autonomous driving erroneously recognizes circumstances around the vehicle increases, and the operation at the time of the autonomous driving is likely to become unstable. In a case where the abnormality of the sensor device <NUM> is detected, the autonomous driving control is performed according to the second control rule, in which a fixed control operation is defined, whereby it is possible to restrain the operation at the time of the autonomous driving from becoming unstable. In a case where the abnormality of the sensor device <NUM> is detected, the control device <NUM> may be configured to resign control through the autonomous driving and may transfer control authority to a driver, instead of changing the control rule to the second control rule.

In the specific example, a case where the event detection unit <NUM> detects "while the vehicle is traveling on a road having a plurality of lanes on each side, an accident occurs on at least one lane of the plurality of lanes" as the predetermined event will be described.

For example, the event detection unit <NUM> acquires information (for example, position coordinates on the map information, information of a lane on which the accident occurs, and the like) indicating a position of a vehicle in accident by way of the control device <NUM> of another vehicle or the road-to-vehicle communication device <NUM> provided along the road, thereby being able to detect the predetermined event. For example, in a case where the presence of the vehicle in accident is detected using various sensors mounted in the another vehicle, the control device <NUM> of the another vehicle can generate information informing of the presence of the vehicle in accident along with positional information (for example, the position coordinates on the map information, information of the lane on which the accident occurs, and the like) of the vehicle in accident, and can transmit the generated information through vehicle-to-vehicle communication. In this case, the event detection unit <NUM> performs vehicle-to-vehicle communication with the control device <NUM> of the another vehicle, thereby being able to detect the predetermined event by way of the control device <NUM> of the another vehicle. The road-to-vehicle communication device <NUM> can collect information informing of the presence of the vehicle in accident and the positional information of the vehicle in accident from the control device <NUM> of the another vehicle and can broadcast the collected information within a control area of the road-to-vehicle communication device <NUM>. In this case, the event detection unit <NUM> receives information broadcasted from the road-to-vehicle communication device <NUM>, thereby being able to detect the predetermined event by way of the road-to-vehicle communication device <NUM>. In a case where the sensor device <NUM> mounted in the own vehicle is a camera having an image sensor, or the like, image data generated by the camera is analyzed, thereby being able to detect the presence or absence (predetermined event) of the vehicle in accident. Similarly, in a case where the sensor device <NUM> mounted in the own vehicle is LIDAR, the presence or absence (predetermined event) of the vehicle in accident may be detected on the basis of an image that is generated from point group data obtained through laser beam scanning of the LIDAR. For example, the event detection unit <NUM> can discriminate the presence or absence of the vehicle in accident within image data using a convolutional neural network (CNN) constructed using an image of the vehicle in accident as learning data, or the like.

In a case where the event detection unit <NUM> detects the predetermined event, as an example, the control-rule change unit <NUM> changes the control rule to be used at the time of the autonomous driving to a default rule before the first control rule is constructed by the machine learning. Here, the default rule is a control rule in an initial state in which the machine learning is not performed, and in other words, can be referred to as a control rule having no unique characteristic. The autonomous driving is controlled using such a default rule, whereby it is possible to suppress an unpredictable unstable operation due to the unique characteristic resulting from the machine learning.

As another example, with the purpose of controlling the operations among a plurality of vehicles, in a case where the event detection unit <NUM> detects the predetermined event, the control-rule change unit <NUM> may change the control rule to be used at the time of the autonomous driving to a rule (hereinafter, referred to as a "common rule") to be used in common among a plurality of control devices (the control device <NUM>, the control device <NUM> of another vehicle, and control devices mounted in other vehicles that are not illustrated). The common rule can be prepared as, for example, a common rule within a range, such as over the whole world, for each country, for each area, for each vehicle type, or for each vehicle manufacturer. The common rule is stored in the memory <NUM> or the storage device <NUM> in advance, for example, in a format shown in <FIG>. The common rule may be saved in the map data for autonomous driving stored in, for example, the storage device <NUM>, a server device outside the vehicle V, or the like. The rule that is common among a plurality of control devices is used, whereby it is possible to reduce unevenness of the operation at the time of the autonomous driving of each vehicle and to control the operation of each vehicle. A specific example of the common rule is not particularly limited, and is "a vehicle moves to a lane different from a lane to which a preceding vehicle moves", or the like.

In the specific example, a case where the event detection unit <NUM> detects "the presence of an obstacle (falling object, sinking of a road surface, flooding, or the like) on a lane on which the vehicle is traveling" as the predetermined event will be described.

For example, the event detection unit <NUM> acquires information (for example, positional coordinates on the map information, information of the lane on which the obstacle is present, and the like) indicating a position of the obstacle by way of the control device <NUM> of another vehicle or the road-to-vehicle communication device <NUM> provided along the road, thereby being able to detect the predetermined event. For example, in a case where an obstacle is detected using various sensors mounted in the another vehicle, the control device <NUM> of the another vehicle can generate information informing of the presence of the obstacle along with positional information (for example, position coordinates on the map information, information of a lane on which the obstacle is present, and the like) of the obstacle, and can transmit the generated information through vehicle-to-vehicle communication. In this case, the event detection unit <NUM> performs vehicle-to-vehicle communication with the control device <NUM> of the another vehicle, thereby being able to detect the predetermined event by way of the control device <NUM> of the another vehicle. The road-to-vehicle communication device <NUM> can collect information informing of the presence of the obstacle and the positional information of the obstacle from the control device <NUM> of the another vehicle and can broadcast the collected information to within the control area of the road-to-vehicle communication device <NUM>. In this case, the event detection unit <NUM> receives information broadcasted from the road-to-vehicle communication device <NUM>, thereby being able to detect the predetermined event by way of the road-to-vehicle communication device <NUM>. Alternatively, the presence or absence (predetermined event) of an obstacle may be detected using the sensor device <NUM> mounted in the own vehicle. For example, the event detection unit <NUM> can recognize a shape of a road surface or an obstacle on the road surface on the basis of an image generated using the image sensor or a scanning result (distance image) with the LIDAR.

In this case, as in the second specific example, the control-rule change unit <NUM> can change the control rule to be used at the time of the autonomous driving to the default rule before the first control rule is constructed by the machine learning or a rule to be used in common among a plurality of control devices.

In the specific example, a case where the event detection unit <NUM> detects "reception of information indicating an operation to be taken by the vehicle as the second control rule from the control device <NUM> of another vehicle or the road-to-vehicle communication device <NUM>" as the predetermined event will be described.

As an example, first, the control device <NUM> of another vehicle detects the presence or absence of an obstacle on a road surface or a vehicle in accident. For example, the control device <NUM> of the another vehicle can detect an obstacle on a road surface or a vehicle in accident on the basis of an output from the sensor device provided in the another vehicle. The control device <NUM> of the another vehicle can acquire information of an obstacle on a road surface or information relating to a vehicle in accident from the road-to-vehicle communication device <NUM> as well. In a case where an obstacle on a road surface or a vehicle in accident is detected, the control device <NUM> of the another vehicle controls the another vehicle so as to avoid the obstacle or the vehicle in accident and generates information indicating an operation to be taken by a following vehicle. Then, the control device <NUM> of the another vehicle provides, for example, a dedicated identifier to the generated information, and then, transmits the information toward the following vehicle through a communication device. As another example, the road-to-vehicle communication device <NUM> may detect the presence or absence of an obstacle on a road surface or a vehicle in accident on the basis of an output from the sensor device provided in the another vehicle. For example, the road-to-vehicle communication device <NUM> collects information (information of the sensor) relating to the obstacle on the road surface or the vehicle in accident from peripheral vehicles and decides an operation to be taken by each vehicle in a target area using the collected information. The road-to-vehicle communication device <NUM> provides, for example, a dedicated identifier to information indicating the decided operation, and then, broadcasts information within the target area. As an example of a specific instruction, in a case where an obstacle is detected on a center lane of a road having three lanes, the control device <NUM> of the another vehicle or the road-to-vehicle communication device <NUM> can transmit an instruction to the effect that a vehicle having an odd number at the end of a number plate moves to a left lane and a vehicle having an even number at the end of the number place moves to a right lane. In a case where information indicating the operation to be taken by the own vehicle is received from the control device <NUM> of the another vehicle or the road-to-vehicle communication device <NUM>, the event detection unit <NUM> controls the operation at the time of the autonomous driving of the own vehicle using the received information as the second control rule. With the identifier provided to information received from the another vehicle, the event detection unit <NUM> can determine that the received information is "information indicating the operation to be taken by the own vehicle". In the specific example, the operation at the time of the autonomous driving is controlled on the basis of the instruction transmitted from the control device <NUM> of the another vehicle or the road-to-vehicle communication device <NUM>. With this, an unstable operation due to the unique characteristic resulting from the machine learning is restrained.

In the first embodiment, an example where the common rule is used among a plurality of control devices (the control devices <NUM>, the control devices <NUM> of another vehicle, and the control device mounted in a vehicle not shown in the drawing) has been described. The control device <NUM> of the embodiment further includes a configuration for updating the common rule.

<FIG> is a block diagram conceptually showing the functional configuration of the control device <NUM> in the second embodiment. As shown in <FIG>, the control device <NUM> of the embodiment further includes an update information acquisition unit <NUM> and a common rule update unit <NUM>. As shown in <FIG>, in the embodiment, a server device <NUM> that generates information for updating the common rule is connected to perform communication with the control device <NUM>.

The update information acquisition unit <NUM> acquires update information of the common rule from the server device <NUM>. As an example, the server device <NUM> performs learning with a result (for example, variation in the degree of congestion) of the autonomous driving control based on the common rule as a reward and updates the common rule. Specifically, the server device <NUM> acquires information indicating how each vehicle operates according to the common rule and information indicating variation in the degree of congestion from each vehicle, a sensor device provided in the periphery of a road, and the like and evaluates the current common rule. Then, the server device <NUM> updates the common rule on the basis of an evaluation result. For example, it is assumed that a reward "the degree of congestion is deteriorated than a tolerance" is obtained as a result of performing an "operation A" defined by the common rule. In this case, a given penalty or a penalty according to the degree of deterioration is provided to the "operation A", and information for updating the common rule is generated so as to lower the selection priority of the operation A. In addition to the examples described herein, a configuration may be made in which a person in charge of management manually inputs the update information of the common rule to the server device <NUM> and delivers the update information to the control device of each vehicle. The server device <NUM> delivers the update information of the common rule generated in this manner to each vehicle. The server device <NUM> may be configured to deliver a new common rule updated using the update information of the common rule toward the control device of each vehicle. The update information of the common rule delivered from the server device <NUM> may be delivered to the control device <NUM> of each vehicle by way of the road-to-vehicle communication device <NUM>.

The common rule update unit <NUM> updates the common rule stored in the memory <NUM> or the storage device <NUM> using the update information of the common rule acquired by the update information acquisition unit <NUM>.

<FIG> is a diagram illustrating the hardware configuration of the control device <NUM> of the second embodiment. In the embodiment, the storage device <NUM> stores program modules that implement the functions of the update information acquisition unit <NUM> and the common rule update unit <NUM>. The processor <NUM> reads the program modules to the memory <NUM> and executes the program modules, thereby implementing the functions of the update information acquisition unit <NUM> and the common rule update unit <NUM>. In the embodiment, the server device <NUM> is connected through the network interface <NUM>.

A flow of processing that is executed by the control device <NUM> of the second embodiment will be described referring to <FIG> is a sequence diagram illustrating a flow of processing that is executed by the control device <NUM> of the second embodiment.

The server device <NUM> collects log information (for example, information indicating an operation in the common rule selected at the time of the autonomous driving and a time thereof, or the like) of the autonomous driving control based on the common rule from a plurality of control devices mounted in the respective vehicles (S202). The server device <NUM> acquires information indicating variation in the degree of congestion from the road-to-vehicle communication device <NUM> provided along the road along with time information (S204). Then, the server device <NUM> generates the update information of the common rule on the basis of an operation selected by each vehicle according to the current common rule and a result (reward) accompanied by the operation (S206). The server device <NUM> can identify a correspondence relationship of information collected in the processing of S202 and S204 on the basis of the time information. Then, the server device <NUM> delivers the update information of the common rule generated in the processing of S206 to the control device of each vehicle (S208). The server device <NUM> may transmit the update information of the common rule to the road-to-vehicle communication device <NUM> once, and may deliver the update information of the common rule to each vehicle by way of the road-to-vehicle communication device <NUM>.

In each vehicle, the update information of the common rule delivered by the update information acquisition unit <NUM> in S208 is received. The common rule update unit <NUM> updates the current common rule stored in the memory <NUM> or the storage device <NUM> on the basis of the update information of the common rule acquired by the update information acquisition unit <NUM> (S210).

According to the above-described embodiment, it is possible to optimize the common rule stored in each vehicle with the update information of the common rule delivered from the server device <NUM>.

Although the embodiments and examples have been described above referring to the drawings, these are merely illustrative of the invention, and various configurations other than those described above can also be employed.

Claim 1:
A control device (<NUM>) comprising:
an event detection unit (<NUM>) that determines whether or not an event to be a trigger of changing a control rule at the time of autonomous driving of a vehicle is detected while the vehicle is performing the autonomous driving using a first control rule based on machine learning;
a control-rule change unit (<NUM>) that changes the control rule at the time of the autonomous driving of the vehicle to a second control rule according to the event to be the trigger in a case where the event to be the trigger is detected by the event detection unit (<NUM>);
characterised in that
the second control rule is a common rule that is used in common among a plurality of vehicles; and characterised by
an update information acquisition unit (<NUM>) that acquires update information of the common rule; and
a common rule update unit (<NUM>) that updates the common rule on the basis of the update information.