Vehicle control device and vehicle control method

Abstract

Provided is a vehicle control device capable of sequentially updating information required for passing narrow roads in a coordinated manner. A vehicle control device is provided with: a passable area candidate determination unit that determines mutual passable areas on a road in which areas that cannot be mutually passed exist; a moving body movement information detection unit that detects movement information of the moving body; a mutual passage pattern estimation unit that estimates at least one or more mutual passage patterns, which are combinations of behavior steps of the vehicle and the moving body, for causing the vehicle and the moving body to pass through the road; a moving body behavior coincidence level evaluation unit that evaluates a coincidence level between a predicted behavior of the moving body predicted on the basis of the movement information and a behavior step of the moving body when the host vehicle and the moving body are staggered in a region that can pass through each other; a mutual passage mode selection unit that selects at least one mutual passage mode on the basis of the level of coincidence; and an automatic driving / aided driving information generation unit that generates information to be used in automatic driving and / or aided driving of the host vehicle that causes the host vehicle and the moving body to pass through each other in the selected mutual passing mode.

Description

Technical Field

[0001] This invention relates to vehicle control devices and methods suitable for assisted or automated driving. Background Technology

[0002] In recent years, in order to achieve comfortable and safe assisted driving and autonomous driving, there has been a desire to develop technologies for coordinated driving in narrow road environments. In particular, a driver assistance system has been proposed that, when encountering oncoming vehicles on narrow roads, coordinates with the oncoming vehicles to pass each other while utilizing available space.

[0003] For example, in the abstract of Patent Document 1, the subject matter is described as "providing an assisted driving system capable of safely and smoothly performing passing maneuvers," and the solution is described as "in the vehicle control device 50, based on the driver's driving history, the driving skills related to the driver's passing maneuver are evaluated, and on a road where the vehicle passes based on vehicle body information and road conditions evaluated in the central control device 10, at least one passing position and the ease of passing at that passing position are estimated. In the central control device 10, based on road information of the road where the vehicle passes, the road conditions required for passing are evaluated, and based on the driver's driving skills evaluated by the vehicle control device 50 and the at least one passing position estimated by passing ease estimation processing and the ease of passing at that passing position, the easiest passing position and passing method are determined, and the determined passing position and passing method are reported to the driver using the reporting unit 70."

[0004] Existing technical documents

[0005] Patent documents

[0006] Patent Document 1: Japanese Patent Application Publication No. 2008-217079 Summary of the Invention

[0007] However, when passing oncoming vehicles on narrow roads, unexpected oncoming vehicle behavior sometimes occurs that is not anticipated by the driver assistance system. For example, sometimes, although the driver assistance system determines the easiest passing position and method based on the driver's driving skill, such as waiting in the left-hand space of the narrow road to allow the oncoming vehicle to pass during this period, the oncoming vehicle may actually be waiting in the right-hand space of the narrow road, urging the vehicle to pass first.

[0008] Even under such circumstances, the driver assistance system in Patent Document 1 does not update the driver's information (the easiest passing position and passing method), so the driver must still rely on their own driving skills to judge the easiest passing position and passing method in the current environment.

[0009] Therefore, the object of the present invention is to provide a vehicle control device and a vehicle control method that can update the information required for coordinated passage through a narrow road based on the actual behavior of surrounding moving objects.

[0010] To address the aforementioned issues, this embodiment provides a vehicle control device, characterized by comprising: a passable area candidate determination unit, which determines one or more passable areas where the vehicle and the mobile body cannot pass each other on a road; a mobile body movement information detection unit, which detects the movement information of the mobile body; a mutual passage mode estimation unit, which estimates at least one mutual passage mode that is a combination of the respective behavior steps of the vehicle and the mobile body for enabling the vehicle and the mobile body to pass through the road; a mobile body behavior consistency level evaluation unit, which evaluates the consistency level between the predicted behavior of the mobile body predicted based on the movement information and the behavior steps of the mobile body when the vehicle and the mobile body pass each other in the passable areas; a mutual passage mode selection unit, which selects at least one mutual passage mode based on the consistency level; and an automatic driving / assisted driving information generation unit, which generates information used in the automatic driving and / or assisted driving of the vehicle for enabling the vehicle and the mobile body to pass each other in the selected mutual passage mode.

[0011] According to the vehicle control device and vehicle control method of the present invention, the information required for coordinated passage through narrow roads can be updated sequentially based on the actual behavior of surrounding moving bodies. Attached Figure Description

[0012] Figure 1 This is a functional block diagram of the vehicle system in Example 1.

[0013] Figure 2 This is a functional block diagram illustrating the function of the processing unit of the vehicle control device in Embodiment 1.

[0014] Figure 3A This is a top view showing an example of mutual passage.

[0015] Figure 3B It is shown in Figure 3A A top view of an example of a predicted mutual passage pattern under mutual passage conditions.

[0016] Figure 4A This is a top view showing other examples of mutual passage.

[0017] Figure 4B It is shown in Figure 4A A top view of an example of a predicted mutual passage pattern under mutual passage conditions.

[0018] Figure 4C It is shown in Figure 4A A top view of other examples of inferred mutual passage patterns under mutual passage conditions.

[0019] Figure 5 This is an example of mutually interacting pattern data groups.

[0020] Figure 6 This is an example of a data set with consistent behavioral levels.

[0021] Figure 7 This is a flowchart of Example 1, which evaluates the consistency level of moving body behavior using a vehicle control device.

[0022] Figure 8 This is an example of the consistency level of moving body behavior calculated by the vehicle control device in Example 1.

[0023] Figure 9 This is a flowchart of Example 2, which evaluates the consistency level of moving body behavior using a vehicle control device.

[0024] Figure 10 This is an example of the consistency level of moving body behavior calculated by the vehicle control device in Example 2.

[0025] Figure 11 This is a flowchart of Example 3, which evaluates the consistency level of moving body behavior using a vehicle control device.

[0026] Figure 12 This is an example of the consistency level of moving body behavior calculated by the vehicle control device in Example 3. Detailed Implementation

[0027] Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings. However, the present invention is not limited to the following embodiments, and various modifications and applications are also included within the scope of the technical concept of the present invention.

[0028] Example 1

[0029] First, use Figures 1 to 8 This invention describes the vehicle control device involved in Embodiment 1.

[0030] Figure 1This is a functional block diagram showing the structure of a vehicle system 1 including the vehicle control device 3 of this embodiment. The vehicle system 1 is mounted on a vehicle 2 (sometimes referred to as "this vehicle V0" to distinguish it from other vehicles) and has the function of providing appropriate assisted driving and driving control based on the condition of the driving road, surrounding vehicles, and other obstacles in the vicinity of the vehicle 2. To achieve this function, the vehicle system 1 includes a vehicle control device 3, an external sensor group 4, a vehicle sensor group 5, a map information management device 6, an actuator group 7, an HMI device group 8, and an external communication device 9, which are connected by an in-vehicle network N. Hereinafter, after describing the general outlines of the external sensor group 4 to the external communication device 9, the vehicle control device 3 of this embodiment will be described in detail.

[0031] The external sensor group 4 is a collection of devices that detect the state of the area surrounding the vehicle V0. The external sensor group 4 includes, for example, camera devices, millimeter-wave radar, lidar (LiDAR), and sonar. The external sensor group 4 detects environmental elements such as obvious obstacles, road markings, signs, and traffic lights within a predetermined range from the vehicle V0, and outputs these detection results to the vehicle control unit 3 via the vehicle network N. "Obvious obstacles" refers to, for example, other vehicles besides the vehicle V0, pedestrians, debris on the road, and curbs. "Road markings" refers to, for example, white lines, crosswalks, and stop lines. Furthermore, the external sensor group 4 also outputs information related to the detected state to the vehicle control unit 3 via the vehicle network N, based on its sensing range and its state.

[0032] The vehicle sensor group 5 is a collection of devices that detect various states of the vehicle V0. Each vehicle sensor detects, for example, the vehicle 2's position information, driving speed, steering angle, accelerator operation amount, brake operation amount, etc., and outputs them to the vehicle control device 3 via the vehicle network N.

[0033] The map information management device 6 is a device that manages and provides digital map information about the vicinity of the vehicle V0. The map information management device 6 may be configured, for example, to include a navigation device. The map information management device 6 is configured to possess digital road map data of a predetermined area including the vicinity of the vehicle V0, and to determine the current position of the vehicle V0 on the map, i.e., the road and lane in which the vehicle V0 is traveling, based on the position information of the vehicle V0 output from the vehicle sensor group 5. Furthermore, the determined current position of the vehicle V0 and the surrounding map data are output to the vehicle control device 3 via the vehicle network N.

[0034] The actuator group 7 is a group of devices that control the steering, braking, accelerator, and other control elements that determine the activity of the vehicle V0. Based on the operation information of the driver's steering wheel, brake pedal, accelerator pedal, etc., and the control command values ​​output from the vehicle control device 3, the actuator group 7 controls the activity of the steering, braking, accelerator, and other control elements, and controls the behavior of the vehicle V0 to perform automatic driving.

[0035] HMI device group 8 is a group of devices used for inputting information from the driver or passenger to vehicle system 1 and for notifying the driver or passenger from vehicle system 1. HMI device group 8 includes displays, speakers, vibrators, switches, etc.

[0036] External communication device 9 is a communication module that wirelessly communicates with the outside of vehicle system 1. External communication device 9 is configured, for example, to communicate with a central system (not shown) that provides / delivers services to vehicle system 1, or the Internet.

[0037] <Vehicle Control Device 3>

[0038] The vehicle control device 3 is the ECU (Electronic Control Unit) installed in vehicle 2.

[0039] The vehicle control device 3, as shown Figure 2 As shown, based on various input information provided from the external sensor group 4, vehicle sensor group 5, map information management device 6, or (not shown) external communication device 9, driving control information for assisted driving or automatic driving of vehicle 2 is generated and output to the actuator group 7, HMI device group 8, and external communication device 9. This vehicle control device 3 has a processing unit 10, a storage unit 30, and a communication unit 40. Each unit will be described in detail below.

[0040] <<Processing Department 10>>

[0041] The processing unit 10 may be configured to include a CPU (Central Processing Unit) as a central processing unit. However, it may also be configured to include a GPU (Graphics Processing Unit), FPGA (Field-Programmable Gate Array), ASIC (Application Specific Integrated Circuit), etc., in addition to a CPU, and may be composed of any one of them.

[0042] The processing unit 10 includes an information acquisition unit 11, a passable area candidate decision unit 12, a mobile body movement information detection unit 13, a mutual passage pattern prediction unit 14, a mobile body behavior consistency level evaluation unit 15, a mutual passage pattern selection unit 16, an automatic driving / assisted driving information generation unit 17, and an information output unit 18, which serve as control functions. The CPU and other components constituting the processing unit 10 implement these control functions by executing a predetermined control program stored in the storage unit 30.

[0043] <<<Information Acquisition Department 11>>>

[0044] The information acquisition unit 11 acquires various types of information from other devices connected to the vehicle control unit 3 via the vehicle network N and stores them in the storage unit 30. For example, it acquires information related to the activities, status, and other actions of the vehicle 2 detected by the vehicle sensor group 5, etc., and stores it in the storage unit 30 as vehicle information data group 31. In addition, it acquires information related to the road environment in which the vehicle 2 is traveling from the map information management device 6, external communication device 9, etc., and stores it in the storage unit 30 as road environment data group 32. Furthermore, it acquires information related to obstacles around the vehicle 2 detected by the external sensor group 4 and the detection area of ​​the external sensor group 4, and stores it in the storage unit 30 as sensor identification data group 33.

[0045] <<<Through the Regional Candidate Decision Department 12>>>

[0046] After determining a moving body facing the vehicle V0, the area candidate decision unit 12 can take into account the road width, the width of the vehicle, the width of the moving body, the presence or absence of obstacles, and determine a wider area (hereinafter referred to as "mutually passable area") where the vehicle V0 and the opposing moving body can easily pass each other, and a narrower area (hereinafter referred to as "mutually impassable area") where the two cannot or have difficulty passing each other.

[0047] Here, the area candidate determination unit 12 can determine, based on the output of the external sensor group 4, the moving body passing by this vehicle V0, the width of the moving body, and whether there are obstacles. Additionally, the area candidate determination unit 12 can determine the width of the currently traveling road based on the road map data from the map information management device 6 and the detection status of the road area obtained from the external sensor group 4. Furthermore, in the following description, the moving body traveling in the opposite direction is assumed to be another vehicle V1, but the moving body passing by this vehicle V0 is not limited to vehicles; it can also be a motorcycle, bicycle, pedestrian, etc.

[0048] Figure 3AThis is a top view showing an example of mutually passable and mutually non-passable areas that can be determined by the area candidate determination unit 12. In this example, suppose vehicle V0 wants to travel from left to right, and other vehicle V1 wants to travel from right to left. As shown in the figure, the road width is narrower on the left and right sides and wider in the center, so the area candidate determination unit 12 can determine the road in the central part as mutually passable area A, and the others as mutually non-passable areas.

[0049] in addition, Figure 4A This is a top view illustrating other examples of mutually passable and mutually non-passable areas that can be determined by the area candidate determination unit 12. Even in this example, let's assume that vehicle V0 wants to travel from left to right, and other vehicle V1 wants to travel from right to left. As shown, the road width is constant, but due to the presence of two utility poles, the wider area that vehicle V0 and other vehicle V1 can pass simultaneously is divided into three. Therefore, the passable area candidate determination unit 12 determines the wider area divided into three as mutually passable areas A, B, and C, and determines the remaining area as mutually non-passable areas.

[0050] <<<Mobile Information Detection Department 13>>>

[0051] The moving body movement information detection unit 13 detects the movement information of the moving body (other vehicle V1) that is about to pass another vehicle based on the output of the external sensor group 4. The movement information of the other vehicle V1 detected here refers to, for example, the other vehicle V1's current position, speed, direction (information related to current movement), turning angle, and predicted trajectory. The predicted trajectory here is a sequence of future trajectory points predicted based on the moving body's past movement information, including information such as the position and orientation where the moving body is most likely to travel within a few seconds. Prediction methods include, for example, linear trajectory points predicted based on past trajectory point information, and nonlinear trajectory points predicted based on a moving body movement model learned using various data from the passing scenario through machine learning (deep learning, etc.).

[0052] <<Mutual Passage Pattern Speculation Section 14>>

[0053] The mutual passage mode prediction unit 14 predicts a mode for the coordinated passage of the vehicle V0 and other vehicles V1 (hereinafter referred to as "mutual passage mode P"). The mutual passage mode is defined by multiple action steps S. Furthermore, "mutually coordinated passage" means that when the vehicle V0 and other vehicles V1 pass through a mutually passable area, they pass each other in a manner that suppresses unnecessary control such as U-turns or reversing of the vehicles.

[0054] Figure 3B It is shown in Figure 3AA top view of an example of a predicted behavior pattern under certain conditions. In behavior step S1 of this behavior pattern P, the vehicle V0 moves to a target reference position p in front of the mutually passable area A. V0，S1 And stop. Additionally, other vehicles V1 move to the target reference position p within the area A where they can pass each other. V1，S1 And stop. In the next action step S2, the vehicle V0 begins to cross the target reference position p of the mutually passable area A. V0，S2 The vehicle V1 began to cross the target reference position p of the mutually passable area A. V1，S2 The driving. Through this behavioral pattern P, Figure 3A Vehicle V0 and other vehicles V1 can pass each other in a coordinated manner.

[0055] Figure 4B It is shown in Figure 4A A top-down view of an example of inferred behavioral patterns under certain circumstances. Figure 4A In the middle, three areas that can pass through each other are set up, but Figure 4B The behavior pattern P is the selected passing pattern within the mutually passable area A. In behavior step S1 of this behavior pattern P, vehicle V0 moves to the target reference position p in front of the mutually passable area A. V0，S1 And stop. Additionally, other vehicles V1 move to the target reference position p within the mutually passable area A. V1，S1 And stop. In the next action step S2, the vehicle V0 begins to cross the target reference position p of the mutually passable area A. V0，S2 The vehicle V1 began to cross the target reference position p of the mutually passable area A. V1，S2 The driving. Through this behavioral pattern P, Figure 4A Vehicle V0 and other vehicles V1 can pass each other in a coordinated manner.

[0056] Figure 4C It is shown Figure 4A A top view of other examples of behavioral patterns under mutually permissive situations. The behavioral pattern P shown here is related to... Figure 4B Similarly, the passing mode performed in the mutually passable area A has action steps S1 to S3. First, in action step S1, the vehicle V0 moves to the target reference position p within the mutually passable area A. V0，S1 And stop. Additionally, other vehicles V1 move to the target reference position p in front of the area A where they can pass each other. V1，S1 And stop. In the next action step S2, vehicle V0 remains stopped. Meanwhile, other vehicles V1 begin to cross the target reference position p of the mutually passable area A. V1，S2The vehicle V0 begins to move beyond the target reference position p in the mutually passable area A during action step S3. V0，S3 The driving. Through this behavioral pattern P, Figure 4A Vehicle V0 and other vehicles V1 can also pass each other in a coordinated manner.

[0057] also, Figure 3B , Figure 4B , Figure 4C This is a simplified example of a behavioral pattern P. In reality, a behavioral pattern P can be broken down into more detailed behavioral steps.

[0058] <<<Evaluation of the Consistency Level of Mobile Behavior 15>>>

[0059] The mobile body behavior consistency level evaluation unit 15 compares the movement information of the mobile body (other vehicle V1) with the nearest target reference position, and calculates the consistency level of the behavior of the mobile body (other vehicle V1) and the mutual passage pattern. The details of the calculation method of the consistency level here will be described later. In this embodiment, the consistency level value is assumed to be a value of 0 to 1, and the closer it is to 1, the higher the consistency level of the two.

[0060] <<<Mutual Passage Mode Selection Section 16>>>

[0061] The mutual passage mode selection unit 16 is used when there are multiple mutual passage modes predicted by the mutual passage mode prediction unit 14 (see reference). Figure 4B , Figure 4C Under this condition, the mutual passage mode with the highest consistency level calculated by the moving body behavior consistency level evaluation unit 15 is selected as the highest priority mutual passage mode (hereinafter referred to as "passing mode").

[0062] <<<Autonomous Driving / Assisted Driving Information Generation Department 17>>>

[0063] The autonomous driving / assisted driving information generation unit 17 converts the information corresponding to the passing mode selected by the mutual passing mode selection unit 16 into control information for the actuator group 7 and HMI device group 8 and sends it. For example, in the case of autonomous driving, it generates a driving trajectory corresponding to the next action steps of the vehicle V0 included in the passing mode, and determines the actuator control command values ​​of the vehicle V0 to track the driving trajectory. In addition, in the case of assisted driving, it reports the recommended target position of the vehicle V0 to the driver via the display of the HMI device group 8.

[0064] <<<Information Output Department 18>>>

[0065] The information output unit 18 outputs various information to other devices connected to the vehicle control unit 3 via the vehicle network N. For example, it outputs control command values ​​contained in the autonomous driving data group 38 to the actuator group 7 to control the driving of the vehicle V0.

[0066] Additionally, for example, the planned trajectory, including the sensor identification data group 33, the mutual passage mode data group 35, and the driver assistance data group 37 described later, is output to the HMI device group 8 to provide information to the occupants of the vehicle V0. Thus, in the vehicle V0 operating in autonomous driving mode, the occupants can be informed how the vehicle system 1 interprets the surrounding driving environment (display of sensor identification data group 33) and what kind of driving is planned (display of mutual passage mode data group 35 and driver assistance data group 37).

[0067] <<Storage Department 30>>

[0068] The storage unit 30 is configured to include, for example, storage devices such as HDD (Hard Disk Drive), flash memory, ROM (Read Only Memory), and RAM (Random Access Memory). The storage unit 30 stores the program processed by the processing unit 10, the data required for that processing, etc. Furthermore, it serves as the main storage when the processing unit 10 executes the program, and is also used for temporarily storing data required for program computation.

[0069] In this embodiment, as information for implementing the functions of the vehicle control device 3, vehicle information data group 31, road environment data group 32, sensor identification data group 33, area candidate data group 34, mutual passage mode data group 35, behavior consistency level data group 36, assisted driving data group 37, and autonomous driving data group 38 are stored in the storage unit 30. Each data group will be described in detail below.

[0070] <<<Vehicle Information Data Group 31>>>

[0071] Vehicle information data group 31 refers to the collection of data related to the actions of the vehicle V0 detected by the vehicle sensor group 5, etc. The data related to the actions of the vehicle V0 refers to information indicating the activities and status of the vehicle V0, such as the vehicle V0's position, speed, steering angle, accelerator input, brake input, and driving path.

[0072] <<<Road Environment Data Group 32>>>

[0073] Road environment data group 32 refers to the collection of data related to the road environment surrounding this vehicle V0.

[0074] Road environment related data refers to information relating to the roads surrounding vehicle V0, including the road on which vehicle V0 travels. This includes, for example, information relating to the shape and attributes (direction of travel, speed limits, driving regulations, etc.) of the lanes surrounding vehicle V0, traffic light information, traffic information relating to the traffic conditions (average speed, etc.) of each road and lane, and statistical knowledge information based on past events. Static information such as the shape and attributes of roads / lanes is included, for example, in map information obtained from map information management device 6, etc.

[0075] <<<Sensor Identification Data Set 33>>>

[0076] Sensor identification data group 33 refers to the collection of data related to the detection information or detection status detected by the external sensor group 4. Detection information refers to, for example, information determined by the external sensor group 4 based on its sensing information related to environmental elements such as obstacles, road markings, signs, and traffic lights around the vehicle V0, or the sensing information itself (LiDAR, RADAR point cluster information, camera images, parallax images from stereo cameras, etc.) sensed by the external sensor group 4 around the vehicle V0. Detection status refers to information indicating the area detected by the sensor and its accuracy, such as a grid map like an OGM.

[0077] <<<Regional Candidate Data Group 34>>>

[0078] Regional candidate data group 34 refers to the set of mutually interoperable regions that can be determined by the regional candidate decision-making unit 12. Figure 3A In the example, only region A can be used as a candidate data group 34, in which... Figure 4A In the example, regions A, B, and C can be used as candidate data groups for regions 34.

[0079] <<<Mutual Passage Pattern Data Group 35>>>

[0080] The mutual passage pattern data group 35 is a data group that collects mutual passage patterns inferred by the mutual passage pattern inference unit 14. Figure 5 This is an example of a mutual passage pattern data group 35. The mutual passage pattern data group 35 illustrated here has a data column 35a representing the overall action sequence (sequential numbering), a data column 35b representing the vehicle ID, a data column 35c representing the action steps for the vehicle ID, a data column 35d representing the target reference position of each action step, a data column 35e representing the end condition of the action step, and a data column 35f representing the area A through which the object can pass.

[0081] <<<Behavioral Consistency Level Data Group 36>>>

[0082] Behavioral consistency level data cluster 36 is a data cluster that aggregates the behavioral consistency levels of each mutual pattern candidate, calculated by the mobile body behavioral consistency level evaluation unit 15. Figure 6 Example of data construction.

[0083] <<<Assisted Driving Data Group 37>>>

[0084] The assisted driving data group 37 refers to the data group used to track the driving trajectory, which is determined by the autonomous driving / assisted driving information generation unit 17 and is for the driving behavior steps currently being executed in the driving behavior plan of the vehicle V0, including control command values ​​of the HMI device group 8 output to the vehicle V0.

[0085] <<<Autonomous Driving Data Group 38>>>

[0086] The autonomous driving data group 38 refers to the data group used to track the driving trajectory, which is determined by the autonomous driving / assisted driving information generation unit 17 for the currently executed behavior steps in the driving behavior plan of the vehicle V0, including the control command values ​​of the actuator group 7 output to the vehicle V0.

[0087] <<Communication Department 40>>

[0088] The communication unit 40 has a communication function with other devices connected via the vehicle network N. The communication function of the communication unit 40 is used when the information acquisition unit 11 acquires various information from other devices via the vehicle network N, and when the information output unit 18 outputs various information to other devices via the vehicle network N.

[0089] The communication unit 40 is configured, for example, to include a network interface card (NIC) conforming to communication standards such as IEEE 802.3 and CAN (Controller Area Network). Within the vehicle system 1, the communication unit 40 transmits and receives data between the vehicle control device 3 and other devices according to various protocols.

[0090] In addition, Figure 1 In this paper, the communication unit 40 and the processing unit 10 are described separately, but a portion of the processing of the communication unit 40 may also be performed in the processing unit 10. For example, the hardware device portion of the communication processing may be located in the communication unit 40, while other components such as the device driver group and communication protocol processing may be located in the processing unit 10.

[0091] <Flowchart>

[0092] Next, use Figure 7 and Figure 8This explains the method for calculating the level of behavior consistency implemented by the behavior consistency evaluation unit 15 of the vehicle control device 3.

[0093] First of all, Figure 7 In step St1 of the flowchart, the moving body behavior consistency level evaluation unit 15 obtains the mutual passage pattern candidates predicted by the mutual passage pattern prediction unit 14. Hereinafter, the process of obtaining... Figure 4B and Figure 4C The mutual passage mode is considered as a candidate for the mutual passage mode.

[0094] Next, in step St2, the moving body behavior consistency level evaluation unit 15 obtains the target reference position p of the oncoming vehicle (other vehicle V1) in step S1 from each mutual passage pattern candidate. V1，S1 .

[0095] In step St3, the moving body behavior consistency level evaluation unit 15 obtains the movement information (current position, movement speed, movement direction, turning angle, predicted trajectory, etc.) of the oncoming vehicle (other vehicle V1) from the moving body movement information detection unit 13.

[0096] In step St11, the moving body behavior consistency level evaluation unit 15 calculates the target reference positions p up to those obtained in step St2, based on the current positions of the oncoming vehicles (other vehicles V1) obtained in step St3. V1，S1 The distance.

[0097] In step St12, the moving body behavior consistency level evaluation unit 15 calculates the distance the oncoming vehicle (other vehicle V1) travels from its current position to each target reference position p. V1，S1 The estimated velocity is calculated here. Furthermore, the estimated velocity is approximately proportional to the length of the distance calculated in step St11.

[0098] In step St13, the moving body behavior consistency level evaluation unit 15 determines whether the predicted velocity calculated in step St12 is 0. If the necessary condition is met, the process proceeds to step St14; otherwise, it proceeds to step St15.

[0099] In step St14, the moving body behavior consistency level evaluation unit 15 calculates the consistency level using the formula illustrated. Furthermore, in this formula, "distance" refers to the distance calculated in step St11, "maximum distance" refers to a parameter pre-set to match the system, and "direction factor" refers to the distance based on the target reference position p. V1，S1 The coefficient is determined by whether the vehicle is in front of or behind other vehicles V1. Therefore, the closer the distance is to the maximum distance, the smaller the consistency level calculated in this step becomes. Furthermore, although in Figure 7Not shown in the diagram, the consistency level is set to 0 when the distance is greater than the maximum distance.

[0100] Furthermore, the orientation factor at the target reference position p V1，S1 The value is, for example, 1 when the target is in front of other vehicles V1, and 0.8 when it is behind them. Therefore, if the target reference position p V1，S1 When in front of other vehicles V1, the consistency level is higher compared to when behind them.

[0101] In step St15, the moving body behavior consistency level evaluation unit 15 determines the target reference position p. V1，S1 Is it in front of an oncoming vehicle (other vehicle V1)? And is the velocity vector of the oncoming vehicle (other vehicle V1) pointing forward? If the necessary conditions are met, proceed to step St16; if the necessary conditions are not met, proceed to step St17.

[0102] In step St16, the consistency level evaluation unit 15 calculates the consistency level using the formula illustrated. Furthermore, in this formula, "oncoming vehicle speed" refers to the speed of the oncoming vehicle (other vehicle V1) obtained in step St3, and "estimated speed" refers to the estimated speed of the oncoming vehicle (other vehicle V1) calculated in step St12. Therefore, the closer the measured speed of the oncoming vehicle (other vehicle V1) is to the estimated speed, the higher the consistency level becomes.

[0103] In step St17, the moving body behavior consistency level evaluation unit 15 determines the target reference position p. V1，S1 Is it behind an oncoming vehicle (other vehicle V1)? And is the velocity vector of the oncoming vehicle (other vehicle V1) pointing backward? If the necessary conditions are met, proceed to step St16; otherwise, end the process.

[0104] In step St18, the moving body behavior consistency level evaluation unit 15 evaluates the consistency level as 0. Furthermore, this step is achieved when the opposing vehicle (other vehicle V1) moves towards the target reference position p. V1，S1 The situation of traveling in the opposite direction.

[0105] Through the above processing, the vehicle control device 3 can calculate the level of consistency with the behavior of each of the multiple mutual passage mode candidates based on the estimated speed of the oncoming vehicle (other vehicle V1).

[0106] Figure 8 According to Figure 7 This is an example of a flowchart calculating the consistency level. In this diagram, the target reference position p1 is... Figure 4B Target reference position pV1，S1 Equivalent, target reference position p2 and Figure 4C Target reference position p V1，S1 The figures clearly show that the distance from the current position of other vehicles V1 to the target reference position p1 is relatively long, while the distance to the target reference position p2 is relatively short. Therefore, in step St12, the estimated velocity of other vehicles V1 when they move to the target reference position p1 and stop is calculated to be greater than the estimated velocity of other vehicles V1 when they move to the target reference position p2 and stop. Thus, if the current velocity vector of other vehicles V1 is large, a relatively large consistency level (e.g., 0.8) is calculated for the mutual passing pattern including the distant target reference position p1, and a relatively small consistency level (e.g., 0.2) is calculated for the mutual passing pattern including the nearby target reference position p2.

[0107] Therefore, based on the observed speeds of other vehicles V1, the mobile body behavior consistency level evaluation unit 15 calculates, as follows: Figure 8 Under consistent conditions, it is possible to predict the actions of other vehicles' V1. Figure 4B The behavioral steps, therefore, are mutually selected through the pattern selection unit 16. Figure 4B The mutual passage mode, the autonomous driving / assisted driving information generation unit 17 to achieve Figure 4B Various types of information are generated through patterns.

[0108] The vehicle control device of this embodiment, as described above, can update the information required for coordinated passage through narrow roads according to the actual speed of oncoming vehicles. By enabling coordinated passage between vehicle V0 and other vehicles V1, the time required for vehicle V0 and other vehicles V1 to pass each other in a passable area can be shortened, thus achieving smooth traffic flow. When the vehicle control device 3 is used in the automatic driving of vehicle V0, the passenger comfort of vehicle V0 can be improved. Furthermore, when the vehicle control device 3 is used in the assisted driving of vehicle V0, both passenger comfort and driver operability can be improved.

[0109] Example 2

[0110] Next, use Figure 9 and Figure 10 Embodiment 2 of the present invention will be described. In Embodiment 1, the level of consistency with each mutual passage mode was evaluated based on the speed of the moving body; however, in this embodiment, the level of consistency with each mutual passage mode is evaluated based on the trajectory of the moving body. Furthermore, the following descriptions regarding similarities with Embodiment 1 will be omitted.

[0111] Figure 9This is a flowchart of the mobile body behavior consistency level evaluation in this embodiment. Steps S1 to S3 in this diagram are the same as those in Embodiment 1. Figure 7 The steps are the same, so the explanation is omitted.

[0112] In step St21, the moving body behavior consistency level evaluation unit 15 estimates the current position of the oncoming vehicle (other vehicle V1) obtained in step St3 to the respective target reference positions p obtained in step St2. V1，S1 A smooth path. Various techniques can be used in the path estimation method in this step; for example, a simple spline can be used to connect the current position of other vehicles V1 and the target reference position p. V1，S1 This can be used to make inferences. Furthermore, if there are obstacles on the inferred path, it can be modified to a path that avoids those obstacles.

[0113] In step St22, the moving body behavior consistency level evaluation unit 15 calculates the root mean square error (hereinafter referred to as "RMSE") of the predicted trajectory obtained in step St3 and the inferred path obtained in step St21 for each target reference position.

[0114] In step St23, the moving body behavior consistency level evaluation unit 15 determines whether the RMSE calculated in step St22 is above a predetermined RMSE threshold. If the necessary condition is met, the process proceeds to step St24; otherwise, it proceeds to step St25. Furthermore, the RMSE threshold is a parameter preset to match the system (e.g., 0.5).

[0115] In step St24, the consistency level evaluation unit 15 evaluates the consistency level as 0.

[0116] In step St25, the consistency level evaluation unit 15 of the moving body behavior calculates the consistency level using the calculation formula shown in the figure.

[0117] Through the above processing, the vehicle control device 3 can calculate the level of consistency with the behavior of each of the multiple mutual passage mode candidates based on the predicted trajectory of the oncoming vehicle (other vehicle V1).

[0118] Figure 10 According to Figure 9 This is an example of a flowchart calculating the consistency level. The target reference position p1 in the above diagram is... Figure 4B Target reference position p V1，S1 Equivalent, target reference position p2 and Figure 4C Target reference position p V1，S1Comparatively, when another vehicle V1 moves from its current position to the target reference position p1, a path is predicted that it will move in a straight line and then turn left, while when it moves to the target reference position p2, a path is predicted that it will turn right quickly. In contrast, the predicted trajectory of the other vehicle V1 obtained in step St3 is to move in a straight line and then turn left. Therefore, as shown in the figure below, a relatively large consistency level (e.g., 0.8) is calculated for the mutual passing pattern including the distant target reference position p1, and a relatively small consistency level (e.g., 0.1) is calculated for the mutual passing pattern including the nearby target reference position p2.

[0119] Therefore, the consistency level evaluation unit 15 calculates as follows: Figure 10 Under consistent conditions, it is possible to predict the actions of other vehicles' V1. Figure 4B The behavioral steps, therefore, are mutually selected through the pattern selection unit 16. Figure 4B The mutual passage mode, the autonomous driving / assisted driving information generation unit 17 to achieve Figure 4B Various types of information are generated through patterns.

[0120] The vehicle control device of this embodiment described above can update the information required for coordinated passage through narrow roads based on the actual trajectories of oncoming vehicles.

[0121] Example 3

[0122] Next, use Figure 11 and Figure 12 This section describes Embodiment 3 of the present invention. In Embodiment 1, the level of behavioral consistency with each mutual passage mode was evaluated based on the speed of the moving body; in Embodiment 2, the level of behavioral consistency with each mutual passage mode was evaluated based on the trajectory of the moving body; however, in this embodiment, the level of behavioral consistency with each mutual passage mode is evaluated based on the steering angle of the moving body. Furthermore, repeated descriptions of the commonalities with Embodiments 1 and 2 will be omitted below.

[0123] Figure 11 This is a flowchart of the mobile body behavior consistency level evaluation in this embodiment. Steps S1 to S3 in this diagram are the same as those in Embodiment 1. Figure 7 The steps are the same, so the explanation is omitted.

[0124] Step St31 involves estimating the target reference position p. V1，S1 The processing of the unobstructed path is the same as step St21 in Example 2.

[0125] In step St32, the moving body behavior consistency level evaluation unit 15 evaluates the target reference positions p according to the prediction made in step St31. V1，S1 The path is calculated to each target reference position p.V1，S1 The steering angle α required for turning while driving.

[0126] In step St33, the moving body behavior consistency level evaluation unit 15 determines whether the steering angle α calculated in step St32 is greater than the maximum steering angle of the oncoming vehicle (the steering angle at which the sharpest turn is possible within the standard). If the necessary condition is met, the process proceeds to step St34; otherwise, it proceeds to step St35. Furthermore, meeting the necessary condition for this step means that the oncoming vehicle (other vehicle V1) is moving towards the target reference position p... V1，S1 You must make a U-turn at least once while driving.

[0127] In step St34, the consistency level evaluation unit 15 evaluates the consistency level as 0.

[0128] In step St35, the consistency level evaluation unit 15 of the moving body behavior calculates the consistency level using the calculation formula shown in the figure.

[0129] Through the above processing, the vehicle control device 3 can calculate the level of consistency with the behavior of each of the multiple mutual passage mode candidates based on the estimated steering angle of the oncoming vehicle (other vehicle V1).

[0130] Figure 12 According to Figure 11 This is an example of a consistency level calculated using a flowchart. When another vehicle V1 moves from its current position to the target reference position p3, a roughly straight path is predicted, while when another vehicle V1 moves from its current position to the target reference position p4, a sharp left turn path is predicted. Therefore, the consistency level for the mutual passage pattern moving roughly in a straight line to the target reference position p3 is calculated as a relatively large 0.9, while the consistency level for the mutual passage pattern moving sharply left to the target reference position p4 is calculated as a relatively small 0.1.

[0131] Therefore, the consistency level evaluation unit 15 calculates as follows: Figure 12 Under the condition of consistent level, it can be predicted that other vehicles V1 will adopt the behavior steps of driving towards the target reference position p3. Therefore, the mutual passage mode selection unit 16 selects a mutual passage mode that includes the behavior steps of driving towards the target reference position p3, and the automatic driving / assisted driving information generation unit 17 generates various information in a way that realizes the selected mutual passage mode.

[0132] The vehicle control device of this embodiment described above can update the information required for coordinated passage through narrow roads according to the steering angle of oncoming vehicles.

[0133] Furthermore, in the above embodiments, the consistency level was calculated using different methods for each embodiment. However, the consistency level calculation methods of each embodiment can also be combined, and the selected mutual passage mode can be determined based on the sum and average of the consistency levels calculated using different methods.

[0134] Example 4

[0135] Next, the vehicle control device 3 according to Embodiment 4 of the present invention will be described. Furthermore, repeated descriptions of commonalities with the above embodiments will be omitted. In the above embodiments, it is assumed that the driver follows the mutual passage pattern suggested by the vehicle control device 3 via the HMI device group 8 during assisted driving. However, in practice, drivers sometimes do not follow the suggested mutual passage pattern. Therefore, in the vehicle control device 3 of this embodiment, when the driver does not follow the initially suggested mutual passage pattern, or is unable to follow the initially suggested mutual passage pattern, an alternative mutual passage pattern can be suggested.

[0136] As an example of a situation where a driver does not follow the recommended passing pattern, consider a situation where the driver's low driving skills make it difficult to perform the initially recommended passing pattern. Therefore, in this embodiment, if the vehicle control device 3 does not perform the recommended passing pattern within a predetermined time (e.g., 10 seconds), it suggests a simpler alternative passing pattern.

[0137] Furthermore, as another example of a situation where the driver does not follow the recommended passing pattern, consider a situation where the driver's driving skills are low, making it impossible to implement the passing pattern including reversing the vehicle (V0). Therefore, in this embodiment, if the initially selected passing pattern includes reversing, the vehicle control device 3 recommends a second-best passing pattern that does not include reversing from the beginning.

[0138] According to the embodiment described above, even drivers with low driving skills can be advised on feasible mutual passage modes.

[0139] (Symbol Explanation)

[0140] 1: Vehicle system; 2: Vehicle; 3: Vehicle control device; 10: Processing unit; 11: Information acquisition unit; 12: Passable area candidate decision unit; 13: Mobile body movement information detection unit; 14: Mutual passage pattern inference unit; 15: Mobile body behavior consistency level evaluation unit; 16: Mutual passage pattern selection unit; 17: Automated driving / assisted driving information generation unit; 18: Information output unit; 30: Storage unit; 31: Vehicle information data group; 32: Road environment data group; 33: Sensor identification data group; 34: Area candidate data group; 35: Mutual passage pattern data group; 36: Behavior consistency level data group; 37: Assisted driving data group; 38: Automated driving data group; 40: Communication unit; 4: External sensor group; 5: Vehicle sensor group; 6: Map information management device; 7: Actuator group; 8: HMI device group; 9: External communication device; N: Vehicle network; V0: This vehicle; V1: Other vehicles.

Claims

1. A vehicle control device characterized by comprising: Possessing: a non-passing region candidate decision unit that decides one or more mutual passing regions in which the host vehicle and the mobile body can pass each other in a road in which the host vehicle and the mobile body cannot pass each other in a non-mutual passing region; a mobile body movement information detection unit that detects movement information of the mobile body; a mutual passing pattern estimation unit that estimates at least one mutual passing pattern for passing the host vehicle and the mobile body on the road as a combination of behavior steps of the host vehicle and the mobile body respectively; a mobile body behavior consistency level evaluation unit that evaluates a consistency level of a predicted behavior of the mobile body based on the movement information and a behavior step of the mobile body when the host vehicle and the mobile body pass each other in the mutual passing region; a mutual passing pattern selection unit that selects at least one mutual passing pattern based on the consistency level; and an autonomous driving / assisted driving information generation unit that generates information used in autonomous driving and / or assisted driving of the host vehicle for passing the host vehicle and the mobile body in the selected mutual passing pattern.

2. The vehicle control device according to claim 1, wherein the movement information includes information related to a speed of the mobile body, the behavior step includes information related to a target position of the mobile body, the mobile body behavior consistency level evaluation unit evaluates the consistency level of the predicted behavior of the mobile body and the behavior step based on a distance from the target position of the behavior step of the mobile body and an approach speed in the mutual passing pattern.

3. The vehicle control device according to claim 1, wherein the movement information includes information related to a past trajectory point series of the mobile body, the behavior step includes information related to a target position of the mobile body, the mobile body movement information detection unit predicts a future trajectory point series of the mobile body based on the past trajectory point series of the mobile body, the mobile body behavior consistency level evaluation unit estimates a path of the mobile body traveling toward the target position of the behavior step of the mobile body in the mutual passing pattern, and evaluates the consistency level based on a size of a position error of the estimated path and the future trajectory point series.

4. The vehicle control device according to claim 1, wherein the behavior step includes information related to a target position of the mobile body, the mobile body behavior consistency level evaluation unit estimates a path of the mobile body traveling toward the target position of the behavior step of the mobile body in the mutual passing pattern, and evaluates the consistency level based on a size of a steering angle required in the traveling of the estimated path.

5. The vehicle control device according to claim 1, wherein the autonomous driving / assisted driving information generation unit prompts a driver of the mutual passing pattern selected by the mutual passing pattern selection unit via an HMI device.

6. The vehicle control device according to claim 5, wherein The mutual passing mode selection section selects another mutual passing mode in a case where the driver does not follow the mutual passing mode prompted via the HMI device.

7. The vehicle control device according to claim 5, wherein The mutual passing mode selection section selects another mutual passing mode in a case where the initially selected mutual passing mode includes a behavior step of reversing.

8. A vehicle control method executed by a vehicle control device, characterized by, The vehicle control method includes: The area candidate decision step decides one or more mutual passing areas in which the host vehicle and the moving body can pass each other in a road in which the host vehicle and the moving body cannot pass each other; The moving body movement information detection step detects movement information of the moving body; The mutual passing mode estimation step estimates at least one or more mutual passing modes for passing the road by the host vehicle and the moving body as a combination of behavior steps of the host vehicle and the moving body; The moving body behavior consistency level evaluation step evaluates a consistency level of a predicted behavior of the moving body predicted from the movement information and a behavior step of the moving body when the host vehicle and the moving body pass each other in the mutual passing area; The mutual passing mode selection step selects at least one mutual passing mode according to the consistency level; and The automatic driving / assistance driving information generation step generates information used in automatic driving and / or assistance driving of the host vehicle for passing the road by the host vehicle and the moving body in the selected mutual passing mode.

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