JP7167732B2 - map information system - Google Patents

Description

The present invention relates to driving assistance control that assists driving of a vehicle. In particular, the present invention relates to driving assistance control based on map information.

Patent Literature 1 discloses a route search device that notifies a user of points where automatic driving is difficult. A point where automatic driving is difficult is a point where the sensor detection accuracy does not meet the criteria for acquiring surrounding information necessary for automatic driving. Areas where automatic driving is difficult include, for example, heavy rain areas, frozen road areas, dense fog areas, and areas where white lines and signs cannot be detected by sensors. The route search device predicts an automatic driving difficult point and notifies the user of the predicted automatic driving difficult point.

Patent Literature 2 discloses a vehicle that automatically operates based on map information. By comparing the map information and the sensor detection information, it is determined whether or not the map information is insufficient. When it is determined that the map information is insufficient, the vehicle automatically drives using additional sensor detection information and prompts the user to switch to manual driving.

Patent Literature 3 discloses a technique related to zone driving by an automatic driving system. In the art, a roadgraph contains zones that are associated with specific rules. When the vehicle approaches a zone, the automated driving system notifies the driver of the approaching zone and requests the driver to perform controls (steering, acceleration/deceleration) according to specific rules.

Patent Literature 4 discloses an automatic driving device that executes automatic driving control of a vehicle. The automatic driving device switches the driving state of the vehicle between an automatic driving state and a semi-automatic driving state in consideration of where the vehicle is traveling.

Patent Literature 5 discloses a map update device mounted on a vehicle. The map information includes road geometry and positions of a plurality of landmarks. The map updating device uses sensors to detect landmarks around the vehicle, and uses the detected landmarks to estimate the vehicle position with high accuracy. The map updating device calculates the road shape based on the estimated vehicle position and updates the map information based on the road shape.

Patent Literature 6 discloses a driving support device. The map database stores map data. The map data includes a driving automation level indicating an automation level of automatic driving control associated with each predetermined section of the road. The driving support device generates guidance information according to the current position of the own vehicle and the level of driving automation in front of the vehicle. For example, the driving assistance device displays information on the change point of the driving automation level.

WO2016/139748 U.S. Pat. No. 8,676,430 U.S. Pat. No. 8,825,264 JP 2018-088060 A JP 2007-101690 A WO2017/051478

Consider driving assistance control that assists the driving of a vehicle. The higher the level of driving support control, the less the burden on the driver of the vehicle. Automatically determining the appropriate level of driving assistance control is preferable from the standpoint of driver convenience.

One object of the present invention is to provide a technique capable of automatically determining an appropriate level of driving assistance control for assisting driving of a vehicle.

In one aspect of the invention, a map information system is provided.
The map information system is
a map database containing map information used for driving support control to support driving of a vehicle;
and a driving assistance level determination device that determines an allowable level of the driving assistance control that is allowed when the vehicle travels within a target range.
The map information is associated with an evaluation value that indicates the likelihood of the map information for each position in the absolute coordinate system.
An intervention operation is an operation performed by the driver of the vehicle to intervene in the driving assistance control while the driving assistance control is being executed.
The driving environment information indicating the driving environment of the vehicle includes information indicating that the intervention operation has been performed.
The driving assistance level determination device,
acquiring intervention operation information indicating an intervention operation position, which is a position where the intervention operation is performed, based on the driving environment information;
obtaining the evaluation value for each point or section within the target range based on the map information;
The allowable level is determined for each point or section within the target range based on the evaluation value and the intervention operation position.

According to the present invention, the driving assistance level determination device automatically determines the allowable level of driving assistance control within the target range. In particular, the driving assistance level determination device determines the allowable level based on the evaluation value of the map information. Since the evaluation value of the map information is taken into consideration, the allowable level is appropriately determined. As a result, convenience for the vehicle driver is improved.

Furthermore, the driving assistance level determination device determines the allowable level of driving assistance control based on the intervention operation position. The intervention operation represents the driving intention of the driver. In addition, there is a possibility that an event unfavorable for driving support control exists at the position where the intervention operation was performed. Therefore, by considering the intervention operation position, it becomes possible to more appropriately determine the allowable level at the intervention operation position.

1 is a conceptual diagram for explaining a vehicle according to an embodiment of the invention; FIG. FIG. 2 is a conceptual diagram for explaining an example of multiple driving assistance levels according to the embodiment of the present invention; FIG. 1 is a block diagram schematically showing the configuration of a map information system according to an embodiment of the present invention; FIG. FIG. 4 is a conceptual diagram for explaining an example of map information in the embodiment of the invention; FIG. 2 is a conceptual diagram for explaining an example of a permissible level determination method by the driving assistance level determination device according to the embodiment of the present invention; FIG. 4 is a conceptual diagram for explaining another example of the method of determining the allowable level by the driving assistance level determining device according to the embodiment of the present invention; FIG. 4 is a block diagram schematically showing another example of the configuration of the map information system according to the embodiment of the present invention; FIG. FIG. 10 is a conceptual diagram for explaining still another example of the allowable level determination method by the driving assistance level determination device according to the embodiment of the present invention; FIG. 10 is a conceptual diagram for explaining still another example of the allowable level determination method by the driving assistance level determination device according to the embodiment of the present invention; 1 is a block diagram showing a configuration example of a driving support control device according to an embodiment of the present invention; FIG. 4 is a block diagram showing an example of driving environment information used in the embodiment of the invention; FIG. 1 is a block diagram showing a first configuration example of a database management device according to an embodiment of the present invention; FIG. FIG. 4 is a block diagram showing a second configuration example of the database management device according to the embodiment of the present invention; FIG. 9 is a block diagram showing a third configuration example of the database management device according to the embodiment of the present invention; BRIEF DESCRIPTION OF THE DRAWINGS It is a block diagram which shows the 1st structural example of the driving assistance level determination apparatus which concerns on embodiment of this invention. FIG. 4 is a block diagram showing a second configuration example of the driving assistance level determination device according to the embodiment of the present invention; FIG. 4 is a block diagram showing a third configuration example of the driving assistance level determination device according to the embodiment of the present invention; 6 is a flow chart showing registration of intervention operation information by the database management device according to the embodiment of the present invention; FIG. 4 is a flow chart showing a first example of a method for determining an allowable level of driving support control according to the embodiment of the present invention; FIG. FIG. 7 is a flow chart showing a second example of a method for determining an allowable level of driving support control according to the embodiment of the present invention; FIG. FIG. 9 is a flow chart showing a third example of a method for determining an allowable level of driving support control according to the embodiment of the present invention; FIG. FIG. 9 is a flow chart showing a fourth example of a method for determining an allowable level of driving support control according to the embodiment of the present invention; FIG. 4 is a block diagram showing various examples of map information according to the embodiment of the present invention; FIG. FIG. 2 is a conceptual diagram for explaining an example of stationary object map information according to the embodiment of the present invention; FIG. FIG. 2 is a conceptual diagram for explaining an example of terrain map information according to the embodiment of the present invention; FIG. FIG. 2 is a conceptual diagram for explaining an example of feature map information according to the embodiment of the present invention; FIG. FIG. 4 is a conceptual diagram for explaining an example of self-position estimation according to the embodiment of the present invention; FIG. 4 is a conceptual diagram for explaining an example of self-position estimation according to the embodiment of the present invention; FIG. 2 is a conceptual diagram for explaining an example of track map information according to the embodiment of the present invention; FIG. 4 is a flowchart showing map information update processing by the database management device according to the embodiment of the present invention; FIG. 4 is a conceptual diagram showing an example of display of tolerance levels according to the embodiment of the present invention; FIG. 4 is a conceptual diagram showing another example of display of tolerance levels in the embodiment of the present invention; FIG. 10 is a conceptual diagram showing still another example of display of tolerance levels in the embodiment of the present invention;

Embodiments of the present invention will be described with reference to the accompanying drawings.

1. Overview 1-1. Driving Support Control FIG. 1 is a conceptual diagram for explaining a vehicle 1 according to the present embodiment. The vehicle 1 is equipped with an information acquisition device 20 and a driving support control device 100 .

The information acquisition device 20 acquires various types of information using sensors mounted on the vehicle 1 . The information acquired by the sensors mounted on the vehicle 1 is information indicating the driving environment of the vehicle 1, and is hereinafter referred to as "driving environment information 200". For example, the driving environment information 200 includes vehicle position information indicating the position of the vehicle 1, vehicle state information indicating the state of the vehicle 1, surrounding situation information indicating the situation around the vehicle 1, and the like.

The driving support control device 100 performs driving support control to support driving of the vehicle 1 based on the driving environment information 200 . Typically, driving assistance control includes at least one of steering control, acceleration control, and deceleration control. Such driving assistance controls include autonomous driving control, path-following control, lane tracing assist control, collision avoidance control, and adaptive cruise control. Control (ACC: Adaptive Cruise Control), etc. are exemplified.

Map information MAP is often used in driving support control. The map information MAP provides various information associated with the position. A position is an absolute position and is defined in an absolute coordinate system (latitude, longitude, altitude). The map information MAP is not limited to general road maps and navigation maps, and may be map information MAPs from various viewpoints. For example, map information MAP indicating the positions of stationary objects on the road (eg, guardrails, walls), road surfaces, features (eg, white lines, poles, signboards), etc. can be considered.

In the present embodiment, driving support control is classified into a plurality of levels (stages). The level of driving assistance control is hereinafter referred to as "driving assistance level". High and low comparisons are possible between a plurality of driving assistance levels. The higher the driving assistance level, the more driving operations the driving assistance control device 100 takes on. It can be said that the driving assistance level represents the degree of delegation of the driving of the vehicle 1 by the driver to the driving assistance control device 100 (degree of delegation).

FIG. 2 is a conceptual diagram for explaining an example of multiple driving assistance levels. The driving assistance level LV-A is the lowest and the driving assistance level LV-E is the highest. For example, the contents of the driving support levels LV-A to LV-E are as follows.

[LV-A] Driving support control that does not use map information MAP (eg adaptive cruise control).
[LV-B] Limited driving support control using map information MAP (eg adaptive cruise control + lane keeping support control).
[LV-C] Driving support control using map information MAP. The driving support control device 100 performs steering control. The driver may be hands-off from the steering wheel. A driver is required to monitor the surroundings of the vehicle 1 . The driver performs manual operation as necessary.
[LV-D] Driving support control using map information MAP. The driving support control device 100 performs steering control, acceleration control, and deceleration control. The driver does not have to monitor the surroundings of the vehicle 1 (eyes-off). However, in an emergency, the driving assistance control device 100 issues a "transition demand" requesting the driver to start manual driving. The driver responds to the shift request and starts manual driving within a predetermined time.
[LV-E] Driving support control using map information MAP. The driving support control device 100 performs steering control, acceleration control, and deceleration control. The driver does not have to monitor the circumstances around the vehicle 1 . In an emergency, the driving support control device 100 automatically evacuates the vehicle 1 to a safe place.

Note that the classification of driving support levels is not limited to that shown in FIG. For example, each driving assistance level may be further subdivided into hierarchies. As another example, the driving assistance level classification may match the commonly used automated driving level classification.

The accuracy of driving support control depends on the quality of the map information MAP. As the quality of the map information MAP improves, the precision of the driving support control also increases, making it possible to implement a higher level of driving support control. A map information system that handles map information MAP will be described below.

1-2. Outline of Map Information System FIG. 3 is a block diagram schematically showing the configuration of the map information system 10 according to the present embodiment. The map information system 10 is a system that manages and uses map information MAP. More specifically, the map information system 10 includes a map database MAP_DB, an information acquisition device 20, a database management device 30, and a driving support level determination device 40. The map information system 10 may further include the driving assistance control device 100 described above.

The map database MAP_DB is a collection of map information MAP used for driving support control. The map database MAP_DB may be stored in the storage device of the vehicle 1 or may be stored in an external device outside the vehicle 1 .

The database management device 30 manages the map database MAP_DB. More specifically, the database management device 30 acquires the driving environment information 200 from the information acquisition device 20 and manages the map database MAP_DB based on the driving environment information 200 . Management of the map database MAP_DB includes at least one of generating and updating map information MAP. Management of the map database MAP_DB may include sharing of the map information MAP. Generation and updating of map information MAP will be described in detail in Sections 5 and 6 below.

The database management device 30 may be mounted on the vehicle 1 or may be included in an external device outside the vehicle 1 . Alternatively, the database management device 30 may be distributed between the vehicle 1 and the external device.

The driving assistance level determination device 40 automatically determines the driving assistance level that is permitted when the vehicle 1 travels within the target range. The target range is, for example, a range along the target route on which the vehicle 1 travels. The maximum permitted driving assistance level is hereinafter referred to as "tolerance level ALV". As described above, the higher the quality of the map information MAP is, the higher the precision of the driving support control becomes, making it possible to implement a higher level of driving support control. Therefore, the driving assistance level determination device 40 automatically determines the allowable level ALV of the driving assistance control based on at least the map information MAP.

The driving assistance level determination device 40 may be mounted on the vehicle 1 or may be included in an external device outside the vehicle 1 . Alternatively, the driving assistance level determination device 40 may be distributed between the vehicle 1 and the external device.

The driving support control device 100 performs driving support control based on the driving environment information 200 and the map information MAP. At this time, the driving assistance control device 100 performs driving assistance control at the allowable level ALV determined by the driving assistance level determination device 40 .

A method of determining the allowable level ALV by the driving assistance level determination device 40 will be described in more detail below.

1-3. Determining Acceptance Level Based on Map Information Map information MAP is information associated with a position in an absolute coordinate system (absolute position). According to the present embodiment, the map information MAP is further associated with an "evaluation value P" that indicates the "likelihood" of the map information MAP for each position in the absolute coordinate system. Certainty can also be called accuracy or reliability. The evaluation value P can also be called a score.

FIG. 4 is a conceptual diagram for explaining an example of the map information MAP in this embodiment. In the example shown in FIG. 4, the map information MAP includes basic map information and an evaluation value P. In the example shown in FIG. The basic map information is information associated with the absolute position, and is the main information of the map information MAP. The evaluation value P indicates the likelihood of the basic map information regarding the absolute position. The basic map information and the evaluation value P associated with it constitute one data set.

For example, in the case of the map information MAP indicating the position of the feature, the basic map information is the information itself indicating the position of the feature. The evaluation value P indicates the likelihood that a feature exists at the position indicated by the basic map information. Various examples of map information MAP and evaluation value P will be described in detail in Section 5 below.

In the following description, the higher the likelihood of the map information MAP, the higher the evaluation value P. However, it may be designed such that the higher the "uncertainty" of the map information MAP (the lower the certainty), the higher the evaluation value P. In that case, "evaluation value P is high" should be read as "evaluation value P is low".

The higher the evaluation value P of the map information MAP, the higher the accuracy of the driving assistance control using the map information MAP, making it possible to implement a higher level of driving assistance control. Therefore, in the present embodiment, the allowable level ALV of driving support control is determined in consideration of the evaluation value P of the map information MAP.

FIG. 5 is a conceptual diagram for explaining an example of a method of determining the allowable level ALV. The horizontal axis represents the position within the target range in which the vehicle 1 travels. The vertical axis represents the evaluation value P.

As shown in FIG. 5, a threshold TH is set for each driving assistance level. The threshold TH is the evaluation value P that is the minimum required to perform driving assistance control for each driving assistance level with sufficient accuracy. In other words, the threshold TH is the minimum required evaluation value P to allow each driving assistance level. For example, the threshold TH-C is the minimum required evaluation value P to allow the driving assistance level LV-C. If the evaluation value P is less than the threshold TH-C, the driving assistance level LV-C is not allowed. On the other hand, when the evaluation value P is equal to or greater than the threshold TH-C, the driving assistance level LV-C is allowed.

The allowable level ALV is the highest allowable driving assistance level. For example, at positions between positions K1 and K2, the admissibility level ALV is the driving assistance level LV-D. At positions between positions K3 and K4, the admissibility level ALV is the driving assistance level LV-B. At positions between positions K5 and K6, the admissibility level ALV is the driving assistance level LV-E.

The driving assistance level determination device 40 acquires an evaluation value P for each point within the target range based on the map information MAP (map database MAP_DB). At this time, the driving assistance level determination device 40 may obtain the evaluation value P associated with the map information MAP as it is, or may process the evaluation value P associated with the map information MAP. Further, the driving assistance level determination device 40 compares the evaluation value P with the threshold TH to determine the allowable level ALV for each point within the target range.

As another example, as shown in FIG. 6, the driving assistance level determination device 40 may acquire an evaluation value P for each section within the target range. For example, the average value of the evaluation values P at each of a plurality of points included in a certain section is calculated as the evaluation value P of the section. Then, the driving assistance level determination device 40 compares the evaluation value P with the threshold TH to determine the allowable level ALV for each section within the target range.

In this way, the driving support level determination device 40 acquires the evaluation value P for each point or section within the target range based on the map information MAP. Then, based on the evaluation value P, the driving assistance level determination device 40 determines the allowable level ALV for each point or section within the target range. Specifically, the driving assistance level determination device 40 sets the allowable level ALV at the position where the evaluation value P is less than the threshold TH to the first level LV-1. Further, the driving support level determination device 40 sets the allowable level ALV at the position where the evaluation value P is equal to or greater than the threshold TH to a second level LV-2 higher than the first level LV-1.

A combination of multiple types of map information MAP may be used for driving support control. In this case, evaluation values P for multiple types of map information MAP are used, and multiple allowable levels ALV are obtained for the same point or section. The setting of the threshold TH may be different between multiple types of map information MAPs. The driving assistance level determination device 40 integrates a plurality of allowable levels ALV to determine a final allowable level ALV. For example, the driving assistance level determination device 40 selects the lowest one of the plurality of allowable levels ALV.

As described above, the driving assistance level determination device 40 automatically determines the allowable level ALV of the driving assistance control within the target range. In particular, the driving assistance level determination device 40 determines the allowable level ALV based on the evaluation value P of the map information MAP. This is because the higher the evaluation value P of the map information MAP, the higher the accuracy of the driving support control using the map information MAP. Since the evaluation value P of the map information MAP is considered, the allowable level ALV is appropriately determined. As a result, convenience for the driver of the vehicle 1 is improved. In addition, since inappropriate driving support control is suppressed, safety is improved.

For example, when the evaluation value P of the map information MAP is low, the accuracy of driving support control based on the map information MAP may also be low. In this case, since the allowable level ALV is also automatically lowered, driving support control is performed within a reasonable range. As a result, the driver is prevented from feeling uncomfortable with the driving support control. On the other hand, when the evaluation value P of the map information MAP is high, high-level driving support control can be performed with sufficient accuracy. In this case, since the allowable level ALV is increased, convenience for the driver is improved.

1-4. Determination of Permissible Level Based on Map Information and Intervention Operation Information During execution of driving support control, the driver of the vehicle 1 may perform an “intervention operation”. An intervention operation is an operation performed by the driver to intervene in driving support control. For example, intervention operations in the case of driving assistance control (steering control) at the driving assistance level LV-C include steering operations by the driver. As another example, the intervention operation in the case of driving support control (steering control, acceleration control, and deceleration control) at the driving support level LV-D is at least one of steering operation, accelerator operation, and braking operation by the driver. include. Intervention may include preparatory actions such as gripping the steering wheel or placing a foot on a pedal.

The intervention operation represents the driving intention of the driver. In addition, there is a possibility that an event unfavorable for driving support control exists at the position where the intervention operation was performed. Therefore, by further taking into account the occurrence of an intervention operation, it becomes possible to determine the tolerance level ALV more appropriately.

FIG. 7 is a block diagram schematically showing another example of the configuration of map information system 10 according to the present embodiment. Descriptions that duplicate those in FIG. 3 will be omitted as appropriate.

An intervention operation by the driver of the vehicle 1 is detected by a sensor mounted on the vehicle 1 . That is, the driving environment information 200 acquired by the information acquisition device 20 also includes information indicating that the intervention operation has been performed by the driver. The intervening operation position is the position where the intervening operation was performed. The intervention operation information IOR indicates an intervention operation position.

The driving support level determination device 40 acquires intervention operation information IOR based on the driving environment information 200 . For example, the driving assistance level determination device 40 directly acquires the intervention operation information IOR from the driving environment information 200 . Alternatively, the database management device 30 first acquires the interventional operation information IOR from the driving environment information 200 and registers the interventional operation information IOR in the map database MAP_DB. After that, the driving assistance level determination device 40 acquires the intervention operation information IOR from the map database MAP_DB.

The driving support level determination device 40 holds the intervention operation information IOR, and utilizes the intervention operation information IOR when the vehicle 1 is running thereafter. Specifically, the driving assistance level determination device 40 determines the allowable level ALV of the driving assistance control based on the evaluation value P of the map information MAP and the intervention operation information IOR (intervention operation position).

FIG. 8 is a conceptual diagram for explaining an example of a method of determining the allowable level ALV. The format of FIG. 8 is the same as that of FIGS. 5 and 6 already described. A section between the position KS and the position KE is an intervention operation section in which an intervention operation is performed by the driver. No intervention operation is performed except for the intervention operation section. A position where no intervention operation is performed, that is, a position that is not an intervention operation position is hereinafter referred to as a "normal position".

In the example shown in FIG. 8, the driving assistance level determination device 40 increases the threshold TH at the intervention operation position compared to the normal position. As a result of the increase in threshold TH, the allowable level ALV at the intervention operation position tends to decrease. For example, position K2 is the normal position and position K4 is the intervention position. Although the evaluation value P is the same at the positions K2 and K4, the allowable level ALV (=LV-C) at the position K4 is lower than the allowable level ALV (=LV-D) at the position K2.

However, an increase in the threshold TH does not necessarily lower the allowable level ALV at the intervention operation position. For example, position K1 is the normal position and position K3 is the intervention position. The evaluation value P is the same at the positions K1 and K3. Although the threshold TH-B differs between the positions K1 and K3, the allowable level ALV at each position is the same driving assistance level LV-B.

In this way, the driving assistance level determination device 40 determines the allowable level ALV for each point or section within the target range based on the evaluation value P of the map information MAP and the intervention operation position. Under the condition that the evaluation value P is the same, the allowable level ALV at the intervention operation position is equal to or lower than the allowable level ALV at the normal position. The intervention operation represents the driving intention of the driver. In addition, there is a possibility that an event unfavorable for driving support control exists at the intervention operation position. By considering the intervention operating position, the allowable level ALV at the intervention operating position is determined more appropriately.

FIG. 9 is a conceptual diagram for explaining another example of the method of determining the allowable level ALV. In the example shown in FIG. 9, the driving assistance level determination device 40 corrects the evaluation value P instead of increasing the threshold TH. The corrected evaluation value P is hereinafter referred to as a “corrected evaluation value CP”. More specifically, the driving support level determination device 40 acquires the corrected evaluation value CP by decreasing the evaluation value P for the intervention operation position. On the other hand, for the normal position, the driving support level determining device 40 maintains the evaluation value P as it is and uses it as the corrected evaluation value CP.

Then, the driving support level determination device 40 compares the corrected evaluation value CP instead of the evaluation value P with the threshold value TH. That is, the driving assistance level determination device 40 sets the allowable level ALV at the position where the corrected evaluation value CP is less than the threshold TH to the first level LV-1. Further, the driving assistance level determination device 40 sets the allowable level ALV at the position where the corrected evaluation value CP is equal to or greater than the threshold TH to a second level LV-2 higher than the first level LV-1.

The method shown in FIG. 9 also provides the same effect as the method shown in FIG. That is, under the condition that the evaluation value P is the same, the allowable level ALV at the intervention operation position is equal to or lower than the allowable level ALV at the normal position. By considering the intervention operating position, the allowable level ALV at the intervention operating position is determined more appropriately.

As still another example, the database management device 30 may update the map database MAP_DB (map information MAP) based on the intervention operation information IOR so that the evaluation value P at the intervention operation position is decreased. After updating the map database MAP_DB, the driving assistance level determination device 40 determines the allowable level ALV based on the evaluation value P of the map information MAP (see FIGS. 5 and 6). In this case, since the intervention operation position is reflected in the evaluation value P of the map information MAP, it is not necessary to change the threshold TH (see FIG. 8) or calculate the corrected evaluation value CP (see FIG. 9).

1-5. Effect As described above, the driving assistance level determination device 40 according to the present embodiment automatically determines the allowable level ALV of the driving assistance control within the target range. In particular, the driving assistance level determination device 40 determines the allowable level ALV based on the evaluation value P of the map information MAP. This is because the higher the evaluation value P of the map information MAP, the higher the accuracy of the driving support control using the map information MAP. Since the evaluation value P of the map information MAP is considered, the allowable level ALV is appropriately determined. As a result, convenience for the driver of the vehicle 1 is improved. In addition, since inappropriate driving support control is suppressed, safety is improved.

Furthermore, the driving assistance level determination device 40 may determine the allowable level ALV of the driving assistance control based on the intervention operation position. The intervention operation represents the driving intention of the driver. In addition, there is a possibility that an event unfavorable for driving support control exists at the position where the intervention operation was performed. Therefore, by considering the intervention operation position, it becomes possible to more appropriately determine the allowable level ALV at the intervention operation position.

The driving assistance control device 100 performs driving assistance control at the allowable level ALV determined by the driving assistance level determining device 40 . By performing an appropriate level of driving support control according to the evaluation value P of the map information MAP, it is possible to effectively utilize the map information MAP.

The map database MAP_DB, the database management device 30 and the driving support level determination device 40 may be mounted on the vehicle 1 . In other words, all components of the map information system 10 may be mounted on the vehicle 1 . In this case, the map information system 10 performs all of the acquisition of the driving environment information 200, management of the map database MAP_DB based on the driving environment information 200, determination of the allowable level ALV, and driving support control based on the map database MAP_DB. run automatically with Such a map information system 10 can also be called a "self-learning driving support control system". In particular, when performing automatic driving control as driving support control, such a map information system 10 can also be called a "self-learning automatic driving system."

It can be said that the map database MAP_DB is knowledge useful for driving support control. It can be said that the map information system 10 according to the present embodiment automatically detects, verifies, and accumulates knowledge.

The map information system 10 according to this embodiment will be described in more detail below.

2. Configuration example of map information system 2-1. Configuration Example of Driving Assistance Control Device FIG. 10 is a block diagram showing a configuration example of the driving assistance control device 100 according to the present embodiment. The driving support control device 100 is mounted on the vehicle 1, and includes a surrounding situation sensor 110, a vehicle position sensor 120, a vehicle state sensor 130, a communication device 140, an HMI (Human Machine Interface) unit 150, a traveling device 160, and a control device. 170 is provided.

The surrounding situation sensor 110 detects the surrounding situation of the vehicle 1 . Examples of the surrounding situation sensor 110 include a camera (imaging device), a lidar (LIDAR: Laser Imaging Detection and Ranging), a radar, and the like. The camera captures the situation around the vehicle 1 . The lidar uses laser beams to detect targets around the vehicle 1 . The radar uses radio waves to detect targets around the vehicle 1 .

Vehicle position sensor 120 detects the position and orientation of vehicle 1 . For example, vehicle position sensor 120 includes a GPS (Global Positioning System) sensor. The GPS sensor receives signals transmitted from a plurality of GPS satellites and calculates the position and orientation of the vehicle 1 based on the received signals.

Vehicle state sensor 130 detects the state of vehicle 1 . The state of the vehicle 1 includes the speed (vehicle speed), acceleration, steering angle, yaw rate, and the like of the vehicle 1 . Furthermore, the state of the vehicle 1 also includes driving operations by the driver of the vehicle 1 . Driving operations include accelerator operations, brake operations, and steering operations.

The communication device 140 communicates with the outside of the vehicle 1 . For example, the communication device 140 communicates with an external device outside the vehicle 1 via a communication network. The communication device 140 may perform V2I communication (road-to-vehicle communication) with surrounding infrastructure. The communication device 140 may perform V2V communication (vehicle-to-vehicle communication) with surrounding vehicles.

HMI unit 150 is an interface for providing information to the driver and receiving information from the driver. Specifically, the HMI unit 150 has an input device and an output device. A touch panel, a switch, a microphone, etc. are illustrated as an input device. A display device, a speaker, and the like are exemplified as the output device.

Traveling device 160 includes a steering device, a driving device, and a braking device. The steering device steers the wheels. A driving device is a power source that generates a driving force. An electric motor and an engine are exemplified as the driving device. A braking device generates a braking force.

The control device 170 is a microcomputer with a processor 171 and a storage device 172 . Control device 170 is also called an ECU (Electronic Control Unit). Various processes by the control device 170 are realized by the processor 171 executing the control program stored in the storage device 172 .

For example, the control device 170 acquires necessary map information MAP from the map database MAP_DB. If the map database MAP_DB is installed in the vehicle 1, the control device 170 acquires the necessary map information MAP from the map database MAP_DB. On the other hand, if map database MAP_DB exists outside vehicle 1 , control device 170 acquires necessary map information MAP through communication device 140 . The map information MAP is stored in the storage device 172 and read out as needed for use.

Control device 170 also acquires driving environment information 200 . The driving environment information 200 is stored in the storage device 172 and read out as needed for use.

FIG. 11 shows an example of the driving environment information 200. As shown in FIG. The driving environment information 200 includes surrounding situation information 210 , vehicle position information 220 , vehicle state information 230 and distribution information 240 .

The peripheral situation information 210 indicates the situation around the vehicle 1 . This peripheral situation information 210 is information obtained from the detection result by the peripheral situation sensor 110 . For example, surroundings information 210 includes imaging information obtained by a camera. In addition, the peripheral situation information 210 includes measurement information by rider and radar. Furthermore, the surrounding situation information 210 may include target information regarding targets detected based on imaging information and measurement information. Examples of targets around the vehicle 1 include stationary objects, characteristic objects, surrounding vehicles, pedestrians, and the like. The target information includes the relative position, relative speed, etc. of the detected target with respect to the vehicle 1 . The control device 170 acquires the peripheral situation information 210 based on the detection result by the peripheral situation sensor 110 .

The vehicle position information 220 indicates the position and orientation of the vehicle 1 . Control device 170 acquires vehicle position information 220 from vehicle position sensor 120 . Furthermore, the control device 170 may perform well-known self-position estimation processing (localization) using the target object information included in the surrounding situation information 210 to improve the accuracy of the vehicle position information 220 .

Vehicle state information 230 indicates the state of vehicle 1 . The state of the vehicle 1 includes the speed (vehicle speed), acceleration, steering angle, yaw rate, and the like of the vehicle 1 . Furthermore, the state of the vehicle 1 also includes driving operations by the driver of the vehicle 1 . Driving operations include accelerator operations, brake operations, and steering operations. Control device 170 acquires vehicle state information 230 from vehicle state sensor 130 .

The intervention operation includes at least one of steering operation, accelerator operation, and brake operation by the driver. Vehicle state information 230 also includes information indicating that an intervention operation has been performed by the driver.

Distribution information 240 is information obtained through communication device 140 . The control device 170 acquires the distribution information 240 by communicating with the outside using the communication device 140 . For example, the distribution information 240 includes road traffic information (construction section information, accident information, traffic regulation information, congestion information, etc.) distributed from the infrastructure. The distribution information 240 may include information on surrounding vehicles obtained through V2V communication.

Also, the control device 170 performs driving support control based on the map information MAP and the driving environment information 200 . Examples of driving support control include automatic driving control, path follow-up control, lane keeping support control, collision avoidance control, adaptive cruise control, and the like. For such driving support control, the control device 170 performs vehicle travel control as necessary. Vehicle running control includes steering control, acceleration control, and deceleration control. The control device 170 appropriately operates the traveling device 160 (steering device, driving device, braking device) to perform steering control, acceleration control, and deceleration control. It can be said that the control device 170 and the travel device 160 constitute a "vehicle travel control device" that performs vehicle travel control.

As an example of driving support control, consider a case where the control device 170 performs automatic driving control. Control device 170 generates a travel plan for vehicle 1 based on map information MAP and driving environment information 200 . A travel plan includes a target route to a destination and a local target trajectory (target trajectory within a lane, target trajectory for changing lanes). The travel plan includes a vehicle travel control plan for following the target trajectory, following traffic rules, avoiding obstacles, and the like. The control device 170 performs vehicle travel control so that the vehicle 1 travels according to the travel plan.

2-2. Configuration Example of Information Acquisition Device The information acquisition device 20 acquires the driving environment information 200 . As shown in FIG. 10, the surrounding situation sensor 110, the vehicle position sensor 120, the vehicle state sensor 130, the communication device 140, and the control device 170 constitute the information acquisition device 20. As shown in FIG.

2-3. Configuration example of database management device 2-3-1. First Configuration Example FIG. 12 is a block diagram showing a first configuration example of the database management device 30. As shown in FIG. In the first configuration example, the map database MAP_DB is installed in the vehicle 1 (driving assistance control device 100). More specifically, map database MAP_DB is stored in storage device 180 . Storage device 180 may be the same as storage device 172 of controller 170 . The control device 170 (processor 171 ) manages the map database MAP_DB based on the driving environment information 200 . That is, the control device 170 functions as the database management device 30 .

2-3-2. Second Configuration Example FIG. 13 is a block diagram showing a second configuration example of the database management device 30. As shown in FIG. In the second configuration example, the database management device 30 is realized by an external device 300 outside the vehicle 1 . The external device 300 is, for example, a management server.

More specifically, external device 300 includes storage device 310 , processor 320 and communication device 330 . The storage device 310 stores a map database MAP_DB. The communication device 330 communicates with the communication device 140 on the vehicle 1 side. The processor 320 performs various information processing by executing computer programs stored in the storage device 310 .

The information acquisition device 20 (control device 170 ) of the vehicle 1 transmits the driving environment information 200 to the external device 300 through the communication device 140 . Processor 320 of external device 300 receives driving environment information 200 from information acquisition device 20 through communication device 330 . Processor 320 then manages map database MAP_DB based on driving environment information 200 .

Further, the driving assistance control device 100 (control device 170) of the vehicle 1 requests the necessary map information MAP from the external device 300 through the communication device 140. FIG. Processor 320 of external device 300 reads necessary map information MAP from map database MAP_DB. Processor 320 then provides map information MAP to driving assistance control device 100 through communication device 330 .

2-3-3. Third Configuration Example FIG. 14 is a block diagram showing a third configuration example of the database management device 30. As shown in FIG. In the third configuration example, the map database MAP_DB is stored in the external device 300 as in the second configuration example. On the other hand, database management device 30 is implemented by control device 170 of vehicle 1 . That is, the control device 170 (processor 171) remotely operates the map database MAP_DB on the external device 300 side.

Specifically, control device 170 acquires driving environment information 200 from information acquisition device 20 . Based on the driving environment information 200, the control device 170 registers or updates the map database MAP_DB. At this time, the control device 170 transmits a request signal REQ requesting registration or update to the external device 300 through the communication device 140 . The request signal REQ contains information necessary for registration or update. Processor 320 of external device 300 receives request signal REQ through communication device 330 . The processor 320 then registers or updates the map database MAP_DB in accordance with the request signal REQ.

2-3-4. Fourth Configuration Example The functions of database management device 30 may be distributed between control device 170 (processor 171 ) of vehicle 1 and processor 320 of external device 300 .

The first to fourth configuration examples described above can also be summarized as follows. That is, one processor (processor 171 or processor 320 ) or a plurality of processors (processor 171 and processor 320 ) perform processing as the database management device 30 .

2-4. Configuration example of driving assistance level determination device 2-4-1. First Configuration Example FIG. 15 is a block diagram showing a first configuration example of the driving assistance level determination device 40. As shown in FIG. In the first configuration example, the map database MAP_DB is installed in the vehicle 1 (driving assistance control device 100). More specifically, map database MAP_DB is stored in storage device 180 . Storage device 180 may be the same as storage device 172 of controller 170 . The control device 170 (processor 171 ) functions as the driving assistance level determination device 40 .

Specifically, the control device 170 determines the target range in which the vehicle 1 travels before or during the execution of driving support control. Also, the control device 170 acquires the map information MAP of the target range from the storage device 180 (map database MAP_DB). Further, control device 170 acquires intervention operation information IOR from driving environment information 200 or storage device 180 (map database MAP_DB). Controller 170 then determines the tolerance level ALV within the target range. After that, the control device 170 performs driving support control at the determined allowable level ALV.

2-4-2. Second Configuration Example FIG. 16 is a block diagram showing a second configuration example of the driving assistance level determination device 40. As shown in FIG. In the second configuration example, the driving assistance level determination device 40 is implemented by an external device 300 outside the vehicle 1 . The configuration of the external device 300 is the same as the configuration shown in FIG. 13 above.

The driving assistance control device 100 (control device 170) of the vehicle 1 determines a target range in which the vehicle 1 travels before or during execution of the driving assistance control. The driving assistance control device 100 transmits the target range information to the external device 300 through the communication device 140 . The processor 320 of the external device 300 receives the target range information through the communication device 330 . Alternatively, processor 320 of external device 300 may determine the target range.

The processor 320 of the external device 300 acquires the map information MAP of the target range from the storage device 310 (map database MAP_DB). Processor 320 also acquires intervention operation information IOR from driving environment information 200 or storage device 310 (map database MAP_DB). Processor 320 then determines the tolerance level ALV within the target range. The processor 320 notifies the driving assistance control device 100 of information on the determined allowable level ALV through the communication device 330 .

The driving assistance control device 100 receives information on the determined allowable level ALV through the communication device 140 . The driving assistance control device 100 performs driving assistance control at the notified allowable level ALV.

2-4-3. Third Configuration Example FIG. 17 is a block diagram showing a third configuration example of the driving assistance level determination device 40. As shown in FIG. In the third configuration example, the map database MAP_DB is stored in the external device 300 as in the second configuration example. On the other hand, the driving assistance level determination device 40 is implemented by the control device 170 of the vehicle 1 .

Specifically, the control device 170 determines the target range in which the vehicle 1 travels before or during the execution of driving support control. Control device 170 transmits a request signal REQ requesting provision of map information MAP of the target range to external device 300 through communication device 140 . The request signal REQ may further request provision of interventional operational information IOR.

Processor 320 of external device 300 receives request signal REQ through communication device 330 . The processor 320 reads request information requested by the request signal REQ from the storage device 310 . Processor 320 then sends the request information to controller 170 through communication device 330 .

The control device 170 acquires request information from the external device 300 through the communication device 140 . Control device 170 may acquire intervention operation information IOR from driving environment information 200 . Controller 170 then determines the tolerance level ALV within the target range. Thereafter, the driving assistance control device 100 performs driving assistance control at the determined allowable level ALV.

2-4-4. Fourth Configuration Example The functions of the driving assistance level determination device 40 may be distributed between the control device 170 (processor 171 ) of the vehicle 1 and the processor 320 of the external device 300 .

The first to fourth configuration examples described above can also be summarized as follows. That is, one processor (processor 171 or processor 320 ) or a plurality of processors (processor 171 and processor 320 ) performs processing as driving assistance level determination device 40 .

3. Registration of Intervention Operation Information FIG. 18 is a flowchart showing registration of intervention operation information IOR by the database management device 30 according to the present embodiment. During or after driving support control is being executed, the database management device 30 acquires the driving environment information 200 from the information acquisition device 20 (step S310).

Driving environment information 200 includes vehicle state information 230 . Vehicle state information 230 includes information indicating that an intervention operation has been performed by the driver. Based on the vehicle state information 230, the database management device 30 acquires the position where the intervention operation was performed as the intervention operation position. Then, the database management device 30 registers the intervention operation information IOR indicating the intervention operation position in the map database MAP_DB (step S320).

4. Determination of Permissible Level of Driving Assistance Control Next, a method of determining the permissible level ALV of driving assistance control by the driving assistance level determination device 40 will be described. Various examples are conceivable as a method for determining the allowable level ALV.

4-1. First Example FIG. 19 is a flow chart showing a first example of a method for determining the allowable level ALV. The first example corresponds to the example shown in FIGS. 5 and 6 already described.

In step S410A, the driving assistance level determination device 40 acquires the map information MAP of the target range from the map database MAP_DB.

In step S420, the driving assistance level determination device 40 acquires an evaluation value P for each point or section within the target range based on the map information MAP. In the case of each section, for example, the average value of the evaluation values P at each of the plurality of points included in the section is calculated as the evaluation value P of the section.

In subsequent step S450A, the driving assistance level determination device 40 performs the following determination processing for each point or section within the target range. The driving assistance level determination device 40 compares the evaluation value P with the threshold TH (step S451A). If the evaluation value P is equal to or greater than the threshold TH (step S451A; Yes), the driving assistance level determination device 40 sets the allowable level ALV to a second level LV-2 higher than the first level LV-1 (step S452 ). On the other hand, if the evaluation value P is less than the threshold TH (step S451A; No), the driving assistance level determination device 40 sets the allowable level ALV to the first level LV-1 (step S453).

4-2. Second Example FIG. 20 is a flow chart showing a second example of the method of determining the allowable level ALV. The second example corresponds to the example shown in FIG. 8 already described. Explanations that overlap with the first example will be omitted as appropriate.

In step S410B, the driving assistance level determination device 40 acquires the map information MAP of the target range from the map database MAP_DB. Further, the driving support level determination device 40 acquires intervention operation information IOR from the driving environment information 200 or the map database MAP_DB. Step S420 is the same as in the first example.

In step S430, the driving assistance level determination device 40 increases the threshold TH at the intervention operation position compared to the normal position, which is not the intervention operation position.

Step S450A is the same as in the first example. However, the threshold TH differs between the intervention operation position and the normal position. As a result, under the condition that the evaluation value P is the same, the allowable level ALV at the intervention operation position is equal to or lower than the allowable level ALV at the normal position.

4-3. Third Example FIG. 21 is a flow chart showing a third example of the method of determining the allowable level ALV. The second example corresponds to the example shown in FIG. 9 already described. Explanations that overlap with the first and second examples will be omitted as appropriate.

Steps S410B and S420 are the same as in the second example. In step S440 after step S420, the driving assistance level determination device 40 corrects the evaluation value P to obtain the corrected evaluation value CP. Specifically, the driving support level determination device 40 acquires the corrected evaluation value CP by decreasing the evaluation value P for the intervention operation position. On the other hand, for the normal position, the driving support level determining device 40 maintains the evaluation value P as it is and uses it as the corrected evaluation value CP.

In subsequent step S450B, the driving assistance level determination device 40 performs the following determination processing for each point or section within the target range. Specifically, the driving assistance level determination device 40 compares the corrected evaluation value CP with the threshold TH (step S451B). If the correction evaluation value CP is equal to or greater than the threshold TH (step S451B; Yes), the driving assistance level determination device 40 sets the allowable level ALV to a second level LV-2 higher than the first level LV-1 (step S452). On the other hand, when the correction evaluation value CP is less than the threshold TH (step S451B; No), the driving assistance level determination device 40 sets the allowable level ALV to the first level LV-1 (step S453).

4-4. Fourth Example FIG. 22 is a flow chart showing a fourth example of the method of determining the allowable level ALV. Explanations that overlap with the first example will be omitted as appropriate.

In step S390, the database management device 30 updates the map database MAP_DB (map information MAP) based on the intervention operation information IOR so that the evaluation value P at the intervention operation position is decreased. After that, the same processing as in the first example is performed.

4-5. Fifth Example A combination of multiple types of map information MAP may be used for driving support control. In this case, evaluation values P for multiple types of map information MAP are used, and multiple allowable levels ALV are obtained for the same point or section. The setting of the threshold TH may be different between multiple types of map information MAPs. The driving assistance level determination device 40 integrates a plurality of allowable levels ALV to determine a final allowable level ALV. For example, the driving assistance level determination device 40 selects the lowest one of the plurality of allowable levels ALV.

5. Various Examples of Map Information Next, various examples of the map information MAP according to the present embodiment will be described. The map information MAP includes not only general road maps and navigation maps, but also map information from various viewpoints. In the example shown in FIG. 23, the map information MAP includes stationary map information BG_MAP, terrain map information TE_MAP, feature map information FE_MAP, and track map information TR_MAP. Each map information MAP will be described in detail below.

5-1. Stationary object map information BG_MAP
FIG. 24 is a conceptual diagram for explaining an example of the stationary object map information BG_MAP. The stationary object map information BG_MAP is map information MAP relating to a stationary object and indicates the position of the stationary object. Stationary objects are fixed road structures such as walls and guardrails. A stationary object can also be referred to as a background.

In the example shown in Figure 24, the space around the vehicle 1 is divided into a number of voxels VX. One data set is then created for each voxel VX. Each data set contains the position [X, Y, Z] of the voxel VX, the occupancy R, the evaluation value P, and the evaluation information.

First, the occupation rate R will be explained. As an example, a lidar included in the surroundings sensor 110 is used for stationary object detection. The lidar sequentially outputs (scans) laser beams in a plurality of directions. The distance and direction of the reflection point can be calculated from the reflection state of the laser beam. A lidar point cloud is a set of measurement points (reflection points) measured by a lidar.

If at least one laser beam is reflected at a certain voxel VX, the measurement result value M _i for that voxel VX is set to "1". When all the laser beams incident on a certain voxel VX pass through without being reflected, the measurement result value M _i for that voxel VX is set to "0". A measurement result value M _i =“1” means that some object exists in the voxel VX. On the other hand, the measurement result value M _i =“0” means that there is no object in the voxel VX.

The lidar repeatedly scans the laser beam over time. Therefore, for the same voxel VX, a plurality of temporally continuous measurement result values _Mi are obtained. The occupancy R for voxel VX is defined as the average value of those multiple measurement result values M _i . When the number of measurements is N, the occupancy rate R for the voxel VX is expressed by the following formula (1).

Moreover, each time the vehicle 1 travels on the same road, a new measurement result value Mi for the _voxel VX is obtained, and the occupation ratio R is calculated again. That is, the occupancy rate R is updated.

The evaluation value P indicates the “likelihood” of the stationary object map information BG_MAP. That is, the evaluation value P indicates the probability that a stationary object exists or does not exist at the position [X, Y, Z]. For example, the evaluation value P takes a value in the range of 0-1. The higher the evaluation value P, the higher the possibility that a stationary object exists or does not exist at the position [X, Y, Z].

The evaluation information is information used to calculate the evaluation value P. FIG. For example, the evaluation information includes the number N of measurements. When the number of measurements N is small, the evaluation value P is low, and the evaluation value P increases as the number of measurements N increases.

The evaluation information may include the variance V. The variance V is the variance of the positions of the measurement points (reflection points) included in the voxel VX. For example, if the voxel VX has a wall surface, the laser beam is reflected by the wall surface, and the distribution of measurement points becomes planar. In this case the variance V is relatively small. On the other hand, if an irregular object such as grass or smoke exists in the voxel VX, the distribution of the measurement points becomes three-dimensional and the variance V increases. As the variance V increases, the evaluation value P decreases.

The evaluation information may include the occupation rate R described above. Occupancy R="1" means that there is always some object in voxel VX. Objects that are always present are likely to be stationary. Therefore, it is conceivable to increase the evaluation value P as the occupation ratio R increases.

The database management device 30 generates and updates the stationary object map information BG_MAP based on the driving environment information 200 . Specifically, the driving environment information 200 includes surrounding situation information 210 (rider measurement information) and vehicle position information 220 . The database management device 30 converts the surrounding situation information 210 into an absolute coordinate system based on the position and orientation of the vehicle 1 indicated by the vehicle position information 220 . Then, the database management device 30 generates or updates a data set for each voxel VX based on the surrounding situation information 210 in the absolute coordinate system.

The information that "a stationary object is likely to exist" is useful. For example, such information can be used to remove stationary objects from lidar point clouds and detect non-stationary objects such as pedestrians. The information that "there is a high possibility that there is no stationary object" is also useful. This is because when a target is detected in free space where no stationary object exists, the detected target can be regarded as a non-stationary object. In this way, the stationary object map information BG_MAP can be used for recognizing non-stationary objects, for example. When a non-stationary object is recognized, driving support control to avoid it becomes possible.

5-2. Terrain map information TE_MAP
FIG. 25 is a conceptual diagram for explaining an example of the terrain map information TE_MAP. The terrain map information TE_MAP is map information MAP relating to terrain, and indicates the height (altitude) Z of the road surface at the position [X, Y].

In the example shown in FIG. 25, the area around vehicle 1 is divided into a number of cells. One data set is then created for each cell. Each data set contains the cell position [X, Y], height Z, evaluation value P, and evaluation information.

For example, the lidar included in the surrounding situation sensor 110 is used to calculate the road surface height Z. FIG. Specifically, a road surface point group representing the road surface is extracted from the lidar point group. Furthermore, the road surface point group included in each cell is extracted. Then, the height Z of the road surface at the position [X, Y] is calculated by interpolating the height of each of the extracted road surface point groups. For example, the average value of the heights of the extracted road surface point group is calculated as the height Z. Note that the number of road points used to calculate the height Z and the variance of the respective heights may be used as evaluation information, which will be described later.

Each time the vehicle 1 travels on the same road, the same road surface is repeatedly measured (detected), and the height Z of the same road surface is repeatedly calculated. In this case, the average value or weighted average value of the heights Z calculated so far is used as the height Z. FIG. That is, every time the same road surface is measured, its height Z is updated. In the case of weighted average values, for example, the weight for the latest height Z is set to be the largest.

The evaluation value P indicates the "likelihood" of the terrain map information TE_MAP. That is, the evaluation value P indicates the likelihood that a road surface exists at the position [X, Y] and the height Z indicated by the terrain map information TE_MAP. For example, the evaluation value P takes a value in the range of 0-1.

The evaluation information includes the number of measurements, variance, and the like. The number of measurements includes at least one of the number of calculations of the height Z and the number of road points used to calculate the height Z. The variance includes at least one of the variance of the calculated height Z and the variance of the heights of the road points used to calculate the height Z. For example, when the number of measurements is small, the evaluation value P is low, and the evaluation value P increases as the number of measurements increases. Also, the evaluation value P decreases as the variance increases. As another example, the evaluation value P may decrease as the difference between the height Z and the height Z' of the adjacent position increases.

The database management device 30 generates and updates the terrain map information TE_MAP based on the driving environment information 200 . Specifically, the driving environment information 200 includes surrounding situation information 210 (rider measurement information) and vehicle position information 220 . The database management device 30 converts the surrounding situation information 210 into an absolute coordinate system based on the position and orientation of the vehicle 1 indicated by the vehicle position information 220 . Then, the database management device 30 generates or updates a data set for each cell based on the surrounding situation information 210 in the absolute coordinate system.

The use of the terrain map information TE_MAP is as follows. For example, the road surface can be removed from the lidar point cloud and obstacles (eg, falling objects) on the road surface can be detected. As another example, it is possible to calculate the road surface gradient from information on the height Z, and plan vehicle travel control such as acceleration and deceleration based on the road surface gradient. As still another example, a travel area in which the vehicle 1 can travel can be determined. As still another example, in the case of the driving assistance level LV-E (human-off) illustrated in FIG. 2, it is possible to find an evacuation area for the vehicle 1 to evacuate.

5-3. Feature map information FE_MAP
FIG. 26 is a conceptual diagram for explaining an example of feature map information FE_MAP. The feature map information FE_MAP is map information MAP relating to the feature and indicates the position of the feature. Features include linear objects such as lane markings and curbs, planar objects such as signs and billboards, and cylindrical objects such as poles and telegraph poles.

As an example, consider the feature map information FE_MAP regarding the white line LM. The position of the white line LM is represented by positions [Xs, Ys, Zs] and [Xe, Ye, Ze] of both ends of the white line LM. For example, at least one of a camera and a lidar included in the surroundings sensor 110 is used to calculate the position of the white line LM. Specifically, a road surface image representing the road surface is generated from camera imaging information or lidar measurement information. Subsequently, the white line LM is extracted from the road surface image by binarization processing and edge detection processing. Then, the position of the white line LM is calculated based on the camera imaging information or the lidar measurement information.

Each time the vehicle 1 travels on the same road, the same white line LM is repeatedly measured (detected) and the position of the same white line LM is repeatedly calculated. In this case, the average value or weighted average value of the positions calculated so far is used as the position. That is, each time the same white line LM is measured, its position is updated. In the case of weighted averages, for example, the most recent position is given the highest weight. Whether or not the currently measured white line LM is the same as the known white line LM is determined by whether or not the currently measured white line LM is included in a predetermined range around the known white line LM. .

One data set is created for each white line LM. In the example shown in FIG. 26, the data set includes the position of the white line LM, the evaluation value P, and evaluation information. The same applies to planar objects and cylindrical objects. If the feature is a planar object, the data set may include the center position, width, height, orientation, etc. of the planar object. If the feature is a cylindrical object, the dataset may include the axial center position, height, radius, etc. of the cylindrical object.

The evaluation value P indicates the “likelihood” of the feature map information FE_MAP. That is, the evaluation value P indicates the probability that a feature exists at the position indicated by the feature map information FE_MAP. For example, the evaluation value P takes a value in the range of 0-1.

The evaluation information includes the number of measurements, the distribution of calculated positions, and the like. For example, when the number of measurements is small, the evaluation value P is low, and the evaluation value P increases as the number of measurements increases. Also, the evaluation value P decreases as the variance of the calculated positions increases.

The database management device 30 generates and updates feature map information FE_MAP based on the driving environment information 200 . Specifically, the driving environment information 200 includes surrounding situation information 210 (camera imaging information, rider measurement information) and vehicle position information 220 . The database management device 30 converts the surrounding situation information 210 into an absolute coordinate system based on the position and orientation of the vehicle 1 indicated by the vehicle position information 220 . Then, the database management device 30 generates or updates a data set regarding the feature based on the surrounding situation information 210 in the absolute coordinate system.

Such feature map information FE_MAP is used, for example, for “localization” for increasing the accuracy of the vehicle position information 220 . In the self-position estimation, the position and direction (orientation) of the vehicle 1 are estimated. The method of self-position estimation is well known, and detailed description thereof will be omitted. Driving support control and generation and updating of map information MAP are performed based on highly accurate vehicle position information 220 obtained by self-position estimation.

As shown in FIG. 26, the evaluation information may include errors in self-position estimation. A method for determining the evaluation value P of the feature map information FE_MAP from the viewpoint of error in self-position estimation will be described below. In the following example, the position of the vehicle 1 will be described, but the orientation of the vehicle 1 is also the same.

In the example shown in FIG. 27, there are features F _i (i=1 to 3) around the vehicle 1 . Feature F1 is _a white line, feature F2 is a signboard _, and feature _F3 is a pole. These features F _i are detected based on the surrounding situation information 210 (camera imaging information, lidar measurement information). The measured distance d _i to the feature F _i is also obtained from the surroundings information 210 . Here, the horizontal distance is used as the measured distance _d1 to the white line, and the vertical distance is used as the measured distance _d2 to the signboard. Also, for each measured distance d _i a predetermined measurement error σ _i is assumed.

Assume that the position of the feature Fi is already registered in the feature map information _{FE_MAP} . Self-position estimation is performed based on the position of the feature F _i and the measured distance d _i registered in the feature map information FE_MAP.

FIG. 28 shows the results of self-localization. A position PE is estimated as the position of the vehicle 1 . A band-shaped area B _i in FIG. 28 is defined from the position of the feature F _i registered in the feature map information FE_MAP, the measured distance d _i , and the measurement error σ _i . More specifically, the strip-shaped region B _i is a measured distance d _i from the location of the feature F _i and has a width of 2σ _i . It can be said that the accuracy of the self-position estimation is higher as the overlapping region where the plurality of regions B ₁ to B ₃ overlap is larger. Conversely, if the overlapping area is small, the accuracy of self-position estimation is low and the error in self-position estimation is large.

In order to quantitatively estimate the self-position estimation error EL, consider the distance de _i between the position of the feature F _i registered in the feature map information FE_MAP and the estimated position PE. FIG. 28 illustrates the distance de ₁ between the feature F ₁ and the estimated position PE. The self-position estimation error EL is expressed by the following equation (2), for example.

One factor of the error EL is the positional error of the feature Fi registered in the feature map information _{FE_MAP} . Therefore, the evaluation value P of the feature map information FE_MAP can be determined based on the error EL. For example, the larger the error EL, the lower the evaluation value P, and the smaller the error EL, the higher the evaluation value P.

5-4. Trajectory map information TR_MAP
FIG. 29 is a conceptual diagram for explaining an example of the trajectory map information TR_MAP. The track map information TR_MAP is map information MAP regarding the track TR of the vehicle 1 . More specifically, the track map information TR_MAP indicates the position of the track TR on which the vehicle 1 should travel when there are no obstacles.

The database management device 30 generates and updates the track map information TR_MAP based on the driving environment information 200 or other map information MAP.

Typically, trajectory TR passes through the center of the lane. The database management device 30 acquires the position of the white line LM that defines the lane from the surrounding situation information 210 or the feature map information FE_MAP. Then, the database management device 30 calculates the lane center position from the position of the white line LM, and sets the lane center position as the track TR.

If the lane center position cannot be calculated, the database management device 30 acquires the position of the curb. The position of the curb can be obtained from the surrounding situation information 210, the terrain map information TE_MAP, or the feature map information FE_MAP. Then, the database management device 30 sets a position at a certain distance from the curbstone as the trajectory TR.

Alternatively, the database management device 30 may set the trajectory TR based on the actual trajectory during manual operation. The actual trajectory during manual operation is obtained from the vehicle position information 220 . For example, the database management device 30 sets the average of a plurality of actual trajectories as the trajectory TR. As a result, the trajectory TR becomes closer to the actual trajectory during manual operation. As a result, the driver's sense of discomfort is reduced when the vehicle 1 travels along the track TR.

As shown in FIG. 29, an evaluation value P is associated with each position [X, Y, Z] on the trajectory TR. The evaluation value P indicates the “likelihood (reliability)” of the track map information TR_MAP. For example, the evaluation value P takes a value in the range of 0-1.

The evaluation information may include actual trajectories during manual operation. The evaluation value P is high at a position where the trajectory TR is close to the actual trajectory. The evaluation value P is low at a position where the trajectory TR deviates from the actual trajectory. If there are multiple real trajectories, for example, the average of the multiple real trajectories is compared with the trajectory TR. Alternatively, the evaluation value P is calculated based on the sum of the amounts of deviation between each of the plurality of actual trajectories and the trajectory TR.

The evaluation information may include the lane center position described above. The evaluation value P is high at a position where the trajectory TR is close to the lane center position. The evaluation value P is low at a position where the trajectory TR deviates from the lane center position.

The evaluation information may include the curvature of the trajectory TR. A position with a large curvature has a low evaluation value.

The track map information TR_MAP is used, for example, to create a travel plan for the vehicle 1 . The travel plan includes a target trajectory along which the vehicle 1 travels. The driving assistance control device 100 sets the trajectory TR registered in the trajectory map information TR_MAP as the target trajectory. Then, the driving support control device 100 performs vehicle travel control so that the vehicle 1 follows the target trajectory. When the track map information TR_MAP is used, it is not necessary to detect the white line LM and calculate the lane center position one by one. Therefore, the computational load is reduced. Also, the target trajectory of the section beyond the sensor detection range can be acquired in advance. These are preferable from the viewpoint of the efficiency of driving support control.

5-5. Other Map Information As other map information MAP, traffic signal map information indicating the positions of traffic signals, road marking map information indicating the positions of road markings, and the like can be considered. Examples of road markings include stop lines, stop lines, pedestrian crossings, and the like.

Further, the map information MAP may include the above-described lidar measurement information, camera imaging information, road surface image information, and the like. When acquiring these pieces of information, the database management device 30 registers the acquired information in the map database MAP_DB.

6. Map Information Update Processing The database management device 30 according to the present embodiment updates the map information MAP. The map information update process by the database management device 30 will be described below.

6-1. Basic Flow FIG. 30 is a flowchart showing map information update processing. The processing flow shown in FIG. 30 is repeatedly executed at fixed cycles.

In step S<b>310 , the database management device 30 acquires the driving environment information 200 from the information acquisition device 20 .

In step S<b>320 , the database management device 30 converts the surrounding situation information 210 into an absolute coordinate system based on the position and orientation of the vehicle 1 indicated by the vehicle position information 220 .

In step S<b>330 , the database management device 30 acquires the latest map information MAP based on the driving environment information 200 . In particular, the database management device 30 acquires the latest map information MAP based on the surrounding situation information 210 and the vehicle position information 220 in the absolute coordinate system. The contents of each map information MAP and the evaluation value P are as described in Section 5 above.

In step S340, the database management device 30 updates the existing map information MAP using the latest map information MAP obtained in step S330. At this time, not only the basic map information of the map information MAP but also the evaluation information and the evaluation value P are updated.

The evaluation value P (quality) of the map information MAP is expected to improve each time the vehicle 1 travels on the same road. The higher the evaluation value P of the map information MAP, the higher the level of driving support control that can be performed. By performing an appropriate level of driving support control according to the evaluation value P of the map information MAP, it is possible to effectively utilize the map information MAP.

6-2. First Modification There may be a position where the driving environment information 200 has a large error. For example, noise increases the error in the surroundings information 210 . As another example, at a position where the self-position estimation error EL is large, the error in the vehicle position information 220 also increases. If the map information update process is performed using the driving environment information 200 with a large error, the evaluation value P may rather decrease.

Therefore, the database management device 30 temporarily updates the map information MAP and calculates a temporary evaluation value P. FIG. Furthermore, the database management device 30 extracts positions where the temporary evaluation value P is equal to or less than a predetermined value as exclusion positions. Then, the database management device 30 performs the map information update process again using the driving environment information 200 other than the exclusion position.

Alternatively, the database management device 30 may compare the evaluation values P before and after the map information update process. If the updated evaluation value P is lower than the pre-updated evaluation value P, the database management device 30 cancels the update and restores the map information MAP.

According to the first modification, it is possible to prevent the evaluation value P of the map information MAP from being lowered unnecessarily.

6-3. Second Modification In the second modification, driving environment information 200 unsuitable for map information update processing is eliminated in advance. For example, the driving environment information 200 acquired in a section in which sudden steering occurs frequently is not suitable for map information update processing. As another example, the driving environment information 200 acquired during rainy weather is not suitable for map information update processing.

From this point of view, the database management device 30 calculates the "skill degree ST" indicating the degree to which the driving environment information 200 is suitable for the map information update process. The following are examples of driving environments (factors) for which the degree of fitness ST is calculated to be low.

(a) Lateral acceleration or longitudinal acceleration exceeds a threshold (basis information: vehicle position information 220, vehicle state information 230)
(b) The curvature of the travel locus of the vehicle 1 exceeds the threshold (basis information: vehicle position information 220)
(c) The travel locus of the vehicle 1 becomes discontinuous (basis information: vehicle position information 220)
(d) Rainfall, snowfall (basis information: surrounding situation information 210 (camera imaging information, lidar measurement information))
(e) Nighttime, Backlight (Base information: Surrounding situation information 210 (camera imaging information))
(f) Occurrence of dirt on the camera lens (basis information: peripheral situation information 210 (camera imaging information))
(g) The density of the lidar point cloud is less than the threshold (basis information: surrounding situation information 210 (rider measurement information))
(h) Occurrence of dirt on the rider (basis information: surrounding situation information 210 (rider measurement information))

The database management device 30 calculates the degree of suitability ST based on the basis information. As the degree of each factor increases, the fitness ST decreases. Then, the database management device 30 compares the suitability ST with the suitability threshold and eliminates the driving environment information 200 whose suitability ST is less than the suitability threshold. In other words, the database management device 30 performs the map information update process using the driving environment information 200 whose suitability ST is greater than or equal to the suitability threshold. As a result, it is possible to prevent the evaluation value P of the map information MAP from being lowered unnecessarily.

6-4. Third Modification The database management device 30 may delete a portion of the existing map information MAP with a low evaluation value P. FIG. For example, the database management device 30 extracts, from the existing map information MAP, a portion whose evaluation value P is equal to or less than a predetermined value as a deletion target. Then, the database management device 30 deletes the deletion target from the existing map information MAP. This makes it possible to maintain the quality of the map information MAP at a constant level.

6-5. Fourth Modification The database management device 30 may perform the map information update process only for positions with a low evaluation value P in the existing map information MAP. For example, from the existing map information MAP, the database management device 30 extracts an area whose evaluation value P is equal to or less than a predetermined value as an update target area. Then, the database management device 30 performs map information update processing based on the driving environment information 200 of the update target area. This makes it possible to efficiently improve the evaluation value P of the map information MAP with a small amount of calculation.

6-6. Fifth Modified Example A large change may occur in the real environment at a certain timing. For example, road construction and natural disasters greatly change the shape of roads. If the map information update process is repeatedly performed after such a change timing, it is considered that the evaluation value P in the change occurrence area gradually decreases. Therefore, the database management device 30 accumulates the history of the evaluation value P. FIG. If there is an area in which the evaluation value P has decreased a predetermined number of times in succession, the database management device 30 regards the area as a change occurrence area. Then, the database management device 30 deletes the map information MAP in the change occurrence area. This makes it possible to suppress deterioration of the quality of the map information MAP.

7. Display of Permissible Level of Driving Assistance Control As described above, the driving assistance level determination device 40 determines the permissible level ALV, which is the driving assistance level that is permitted when the vehicle 1 travels within the target range. The driving assistance control device 100 performs driving assistance control at the allowable level ALV determined by the driving assistance level determining device 40 . At this time, the driving assistance control device 100 (control device 170) may display the allowable level ALV on the display device of the HMI unit 150. FIG.

For example, consider a case where the vehicle 1 travels to a destination along a target route. The driving support control device 100 sets a target route for the vehicle 1 to travel. The driving assistance level determining device 40 determines the allowable level ALV along the target route. The driving support control device 100 performs driving support control so that the vehicle 1 travels along the target route. At this time, the driving support control device 100 (control device 170) displays the transition of the allowable level ALV along the target route from the current position or current time on the display device of the HMI unit 150. FIG. Note that it is not always necessary to display all of the allowable levels ALV up to the destination at once. The driving assistance control device 100 may selectively display only the allowable level ALV within a partial range including the current position.

FIG. 31 shows an example of display of the allowable level ALV. The horizontal axis represents time or position along the target route and the vertical axis represents the tolerance level ALV along the target route. In the example shown in FIG. 31, temporal or positional changes in the allowable level ALV are displayed graphically. Icons representing driver actions (eg, eyes-off, hands-off, hands-on) at each tolerance level ALV may be displayed. The driver can easily recognize in advance the level change of the driving support control in the future.

FIG. 32 shows another example of display of the tolerance level ALV. In the example shown in FIG. 32, only the icons representing the passage of time and the driver's actions at each tolerance level ALV are displayed. For example, during the period from time T1 to T2, the allowable level ALV is LV-D. At time T2, the allowable level ALV switches to LV-B. During the period from time T2 to T3, the allowable level ALV is LV-B. At time T3, the allowable level ALV switches again to LV-A. The driver can easily recognize in advance the level change of the driving support control in the future.

FIG. 33 is a conceptual diagram showing still another example of display of the tolerance level ALV. In the example shown in FIG. 33, the driving assistance control device 100 displays a map on the display device. Furthermore, the driving support control device 100 superimposes and displays the transition of the target route and the allowable level ALV on the map. The height of the allowable level ALV can be distinguished by changing the line type and color. For example, in the section from the current position to position P1, the allowable level ALV is LV-D. In the section from position P1 to position P2, the acceptable level is low, LV-C. In the section from position P2 to position P3, the allowable level ALV is further lowered to LV-B. At position P3, the tolerance level ALV goes high and returns to LV-D. An icon representing the driver operation at each allowable level ALV may be displayed. The information shown in FIG. 33 may be displayed together with the information shown in FIG. 31 or FIG.

When a plurality of target route candidates exist, the driving assistance control device 100 may display the plurality of target route candidates along with the transition of the allowable level ALV. The driver selects a desired target route by referring to the transition of the allowable level ALV. Selection of the desired target route is performed, for example, by using the input device of HMI unit 150 . The driving assistance control device 100 performs driving assistance control so that the vehicle 1 travels along the selected target route.

In this way, by displaying the transition of the allowable level ALV along the target route from the current position or current time, the driver can recognize in advance changes in the driving assistance level in the future. Therefore, the driver can respond to changes in the driving assistance level with ample time to spare. This is preferable from the viewpoint of convenience.

1 Vehicle 10 Map Information System 20 Information Acquisition Device 30 Database Management Device 40 Driving Assistance Level Determining Device 100 Driving Assistance Control Device 110 Surrounding Condition Sensor 120 Vehicle Position Sensor 130 Vehicle State Sensor 140 Communication Device 150 HMI Unit 160 Travel Device 170 Control Device 171 Processor 172 Storage device 180 Storage device 200 Driving environment information 210 Surrounding situation information 220 Vehicle position information 230 Vehicle state information 240 Distribution information 300 External device 310 Storage device 320 Processor 330 Communication device IOR Intervention operation information MAP Map information MAP_DB Map database

Claims (10)

a map database containing map information used for driving support control to support driving of a vehicle;
a driving assistance level determination device that automatically determines, from among a plurality of levels, an allowable level of the driving assistance control that is allowed when the vehicle travels within the target range;
The map information includes basic map information associated with an absolute position, and an evaluation value associated with the basic map information and indicating the likelihood of the basic map information,
The intervention operation is an operation performed by the driver of the vehicle to intervene in the driving support control while the driving support control is being executed,
The driving environment information indicating the driving environment of the vehicle includes information indicating that the intervention operation has been performed,
The driving assistance level determination device,
acquiring intervention operation information indicating an intervention operation position, which is a position where the intervention operation is performed, based on the driving environment information;
obtaining the evaluation value for each point or section within the target range based on the map information;
A map information system that determines the allowable level from among the plurality of levels for each point or section within the target range based on the evaluation value and the intervention operation position. The map information system according to claim 1,
The map information system, wherein the allowable level at the intervention operation position is equal to or lower than the allowable level at a normal position other than the intervention operation position under the condition that the evaluation values are the same. The map information system according to claim 2,
The driving assistance level determination device,
setting the tolerance level at a position where the evaluation value is less than a threshold to a first level;
setting the allowable level at a position where the evaluation value is equal to or greater than the threshold to a second level higher than the first level;
A map information system that increases the threshold at the intervention operation position compared to the normal position. The map information system according to claim 2,
The driving assistance level determination device,
obtaining a corrected evaluation value by maintaining the evaluation value at the normal position and decreasing the evaluation value at the intervention operation position;
setting the tolerance level at a position where the correction evaluation value is less than a threshold to a first level;
A map information system, wherein the allowable level at a position where the corrected evaluation value is equal to or greater than the threshold is set to a second level higher than the first level. The map information system according to claim 1,
further comprising a database management device that manages the map database;
The database management device
acquiring the intervention operation information from the driving environment information;
updating the map database so that the evaluation value at the intervention operation position decreases;
The driving assistance level determination device,
setting the tolerance level at a position where the evaluation value is less than a threshold to a first level;
A map information system, wherein the allowable level at a position where the evaluation value is equal to or greater than the threshold is set to a second level higher than the first level. The map information system according to any one of claims 1 to 5,
A map information system, further comprising: a driving support control device that performs the driving support control at the allowable level based on the driving environment information and the map information. The map information system according to claim 6,
Furthermore, comprising a display device mounted on the vehicle,
The driving assistance level determination device determines the allowable level along a target route on which the vehicle travels,
The map information system, wherein the driving support control device displays the transition of the allowable level from a current position or a current time on the display device. The map information system according to claim 7,
The driving support control device superimposes the transition of the target route and the allowable level on a map and displays them on the display device. The map information system according to claim 1,
The driving assistance level determination device,
automatically determining the allowable level from among the plurality of levels by comparing the evaluation value obtained from the map information with a threshold;
At the intervention operation position, the threshold value is increased or the evaluation value is decreased.
Map information system. The map information system according to claim 1,
The evaluation value indicates the probability that a stationary object exists at the absolute position, the probability that a road surface exists at the absolute position, or the probability that a characteristic object exists at the absolute position.
Map information system.

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