Vehicle control method and position reliability calculation method

Abstract

The position information of the target vehicle is acquired on the basis of the measurement results of a sensor mounted on the target vehicle, and includes the horizontal position and the vertical position of the target vehicle. A position reliability calculation method for calculating reliability of position information of a target vehicle includes: (A) acquiring, as a sensor-based height, a height of a representative point at a horizontal position of the target vehicle on the basis of a vertical position included in the position information; (B) acquiring the height of a representative point at the horizontal position of the target vehicle as the height based on a height map showing the correspondence of the latitude, longitude, and height of the road surface; and (C) calculating the reliability such that the reliability becomes lower as the deviation between the sensor-based height and the map-based height becomes larger.

Description

Technical Field

[0001] This disclosure relates to vehicle control utilizing vehicle location information. Additionally, this disclosure relates to techniques for calculating the reliability of vehicle location information. Background Technology

[0002] U.S. Patent Application Publication No. 2018 / 0154723 discloses a road displacement map that represents the correspondence between road surface displacement (road surface unevenness) and position. Vibration reduction control is achieved by utilizing such a road displacement map. Specifically, based on the road displacement map, the road surface displacement at a predetermined position in front of the vehicle is identified in advance. The control amount of the active suspension is pre-calculated based on the pre-identified road surface displacement. Then, by controlling the active suspension at the moment the wheel passes through that predetermined position, vehicle vibration can be effectively suppressed. Summary of the Invention

[0003] Vehicle control utilizing vehicle location information is considered. Typically, vehicle location information is obtained from measurements taken by sensors mounted on the vehicle. If the accuracy of the vehicle location information is low, the accuracy of vehicle control using this information may also decrease. Therefore, when performing vehicle control using location information, it is preferable to determine the reliability of this location information. Thus, it is desirable to calculate the reliability of the vehicle location information.

[0004] One object of this disclosure is to provide a technique for calculating the reliability of vehicle location information.

[0005] Another object of this disclosure is to provide a technology that can take into account the reliability of vehicle location information in order to control the vehicle using that location information.

[0006] The first aspect relates to vehicle control methods for the controlled vehicle. Vehicle control methods include the following processes: Based on the measurement results of the sensors mounted on the target vehicle, position information including the horizontal and vertical positions of the target vehicle is obtained; The reliability of the location information is calculated; and Location-based vehicle control is performed with consideration of the reliability of location information. This location-based vehicle control is the control of an object vehicle that utilizes location information. Calculating the reliability of location information involves the following processes: Based on the vertical position contained in the location information, the height of a representative point at the horizontal position of the target vehicle is obtained as the sensor-based height. Based on the height mapping that shows the correspondence between latitude, longitude, and altitude of the road surface, the height of a representative point at the horizontal position of the target vehicle is obtained as the height based on the mapping; and Reliability is calculated in such a way that the greater the deviation between the sensor-based height and the mapping-based height, the lower the reliability.

[0007] The second aspect involves a method for calculating the reliability of the location information of a vehicle using a computer. The location information is obtained based on the measurement results of sensors mounted on the target vehicle, and includes the horizontal and vertical positions of the target vehicle. The location reliability calculation method includes the following processing: Based on the vertical position contained in the location information, the height of a representative point at the horizontal position of the target vehicle is obtained as the sensor-based height. Based on the height mapping that shows the correspondence between latitude, longitude, and altitude of the road surface, the height of a representative point at the horizontal position of the target vehicle is obtained as the height based on the mapping; and Reliability is calculated in such a way that the greater the deviation between the sensor-based height and the mapping-based height, the lower the reliability.

[0008] Based on the first aspect, the reliability of the vehicle's location information can be considered when controlling the vehicle using that location information.

[0009] Based on the second aspect, the reliability of the vehicle's location information can be calculated. Attached Figure Description

[0010] The features, advantages, and technical and industrial significance of exemplary embodiments of the present invention will be described with reference to the accompanying drawings, in which the same reference numerals denote the same parts, wherein: Figure 1 This is a schematic diagram showing an example of the configuration of a vehicle according to the embodiment. Figure 2 This is a conceptual diagram illustrating an example of the suspension configuration involved in the embodiment. Figure 3 This is a flowchart illustrating an example of the unsprung displacement calculation process involved in the embodiment. Figure 4 This is a block diagram illustrating an example of the configuration of a vehicle control system according to an embodiment. Figure 5 This is a block diagram illustrating an example of driving environment information involved in an implementation method. Figure 6 This is a block diagram illustrating an example configuration of the mapping management system according to the implementation method. Figure 7 This is a conceptual diagram used to illustrate the unsprung displacement mapping involved in the implementation method. Figure 8 This is a flowchart that summarizes the mapping generation / update process involved in the implementation method. Figure 9 This is a conceptual diagram illustrating the predictive control using unsprung displacement mapping involved in the implementation method. Figure 10 This is a flowchart illustrating the predictive control using unsprung displacement mapping involved in the implementation method. Figure 11 This is a conceptual diagram used to illustrate the method for calculating the location reliability involved in the implementation method. Figure 12 This is a flowchart illustrating the processing related to location utilization vehicle control involved in the implementation method. Figure 13 (a) is a conceptual diagram illustrating an example of predictive control corresponding to location reliability involved in the implementation. Figure 13 (b) is a conceptual diagram illustrating another example of predictive control corresponding to location reliability involved in the implementation. Figure 13 (c) is a conceptual diagram illustrating another example of predictive control corresponding to location reliability involved in the implementation. Figure 14 (a) is a conceptual diagram illustrating another example of predictive control corresponding to location reliability involved in the implementation. Figure 14 (b) is a conceptual diagram illustrating another example of predictive control corresponding to location reliability involved in the implementation. Figure 14 (c) is a conceptual diagram illustrating another example of predictive control corresponding to location reliability involved in the implementation. Figure 15 This is a conceptual diagram illustrating an example of autonomous driving control corresponding to position reliability involved in the implementation method. Figure 16 This is a flowchart illustrating the mapping generation / update process that takes into account location reliability in the implementation. Detailed Implementation

[0011] The embodiments of this disclosure will be described with reference to the accompanying drawings. 1. Suspension and vertical motion parameters

[0012] Figure 1This is a schematic diagram showing an example configuration of the vehicle 1 according to this embodiment. The vehicle 1 includes wheels 2 and suspension 3. The wheels 2 include a left front wheel 2FL, a right front wheel 2FR, a left rear wheel 2RL, and a right rear wheel 2RR. Suspension 3FL, 3FR, 3RL, and 3RR are respectively provided for these left front wheel 2FL, right front wheel 2FR, left rear wheel 2RL, and right rear wheel 2RR. In the following description, unless otherwise specified, each wheel will be referred to as wheel 2, and each suspension will be referred to as suspension 3.

[0013] Figure 2 This is a conceptual diagram showing an example of the configuration of suspension 3. Suspension 3 is configured to connect the unsprung structure 4 and the sprung structure 5 of vehicle 1. The unsprung structure 4 includes a wheel 2. Suspension 3 includes a spring 3S, a shock absorber 3D, and an actuator 3A. The spring 3S, shock absorber 3D, and actuator 3A are arranged in parallel between the unsprung structure 4 and the sprung structure 5. The spring constant of spring 3S is K. The damping coefficient of shock absorber 3D is C. The damping force of shock absorber 3D can also be variable. Actuator 3A applies a vertical control force Fc between the unsprung structure 4 and the sprung structure 5.

[0014] Here, the terms are defined. "Road displacement Zr" is the vertical displacement of the road surface RS. "Unsprung displacement Zu" is the vertical displacement of the unsprung structure 4. "Sprung displacement Zs" is the vertical displacement of the sprung structure 5. "Unsprung velocity Zu'" is the vertical velocity of the unsprung structure 4. "Sprung velocity Zs'" is the vertical velocity of the sprung structure 5. "Unsprung acceleration Zu"" is the vertical acceleration of the unsprung structure 4. "Sprung acceleration Zs"" is the vertical acceleration of the sprung structure 5. Furthermore, the signs of each parameter are positive when pointing upwards and negative when pointing downwards.

[0015] Wheel 2 moves on the road surface RS. In the following description, the parameters related to the vertical motion of wheel 2 are referred to as "vertical motion parameters". Examples of vertical motion parameters include the road surface displacement Zr, unsprung displacement Zu, unsprung velocity Zu', unsprung acceleration Zu", sprung displacement Zs, sprung velocity Zs', and sprung acceleration Zs". Vertical motion parameters can also be referred to as "road surface displacement related parameters" that are related to the road surface displacement Zr.

[0016] As an example, the following description considers the case where the vertical motion parameter is the unsprung displacement Zu. In general, "unsprung displacement" will be replaced with "vertical motion parameter" in the following description.

[0017] Figure 3 This is a flowchart illustrating an example of unsprung displacement calculation.

[0018] In S11, the spring acceleration Zs” is detected by the spring acceleration sensor 22 installed on the spring structure 5. In S12, the spring displacement Zs is calculated by performing a second-order integral on the spring acceleration Zs”.

[0019] In S13, the relative displacement between the sprung structure 5 and the unsprung structure 4, i.e., the stroke ST (=Zs-Zu), is obtained. For example, the stroke ST is detected by a stroke sensor installed on the suspension 3. As another example, the stroke ST can also be estimated based on the sprung acceleration Zs” by an observer constructed based on a single-wheel two-degree-of-freedom model.

[0020] In S14, the time-series data of the sprung displacement Zs is filtered to suppress the effects of sensor drift, etc. Similarly, in S15, the time-series data of the stroke ST is filtered. For example, the filter is a bandpass filter that allows signal components of a specific frequency band to pass through. The specific frequency band can be set to include the sprung resonant frequency of vehicle 1. For example, the specific frequency band is 0.3Hz to 10Hz.

[0021] In S16, the difference between the sprung displacement Zs and the stroke ST is calculated as the unsprung displacement Zu.

[0022] It can also replace S14 and S15 to filter the time series data of the unsprung displacement Zu calculated in S16.

[0023] As another example, the unsprung acceleration Zu can also be detected by the unsprung acceleration sensor, and the unsprung displacement Zu can be calculated from the unsprung acceleration Zu. 2. Vehicle control system 2-1. Example of composition

[0024] Figure 4 This is a block diagram illustrating an example configuration of the vehicle control system 10 according to this embodiment. The vehicle control system 10 is applied to a vehicle 1 to control the vehicle 1. For example, the vehicle control system 10 is mounted on the vehicle 1. As another example, the vehicle control system 10 may also be distributed between the vehicle 1 and a remote device. The vehicle control system 10 includes a vehicle status sensor 20, an identification sensor 30, a position sensor 40, a communication device 50, a driving device 60, and a control device 70.

[0025] Vehicle status sensor 20 is mounted on vehicle 1 to detect the status of vehicle 1. Vehicle status sensor 20 includes a vehicle speed sensor (wheel speed sensor) 21 for detecting the vehicle speed V of vehicle 1, a sprung acceleration sensor 22 for detecting the sprung acceleration Zs", etc. Vehicle status sensor 20 may also include a travel sensor 23 for detecting the travel ST. Vehicle status sensor 20 may also include an unsprung acceleration sensor. In addition, vehicle status sensor 20 includes a lateral acceleration sensor, a yaw rate sensor, a steering angle sensor, etc.

[0026] The identification sensor 30 is mounted on the vehicle 1 to identify (detect) the surrounding conditions of the vehicle 1. Examples of identification sensors include cameras, LIDAR (Laser Imaging Detection and Ranging), and radar.

[0027] The position sensor 40 is mounted on the vehicle 1 and includes a positioning device for detecting the position and orientation of the vehicle 1. For example, the position sensor 40 includes GNSS (Global Navigation Satellite System). For example, the position sensor 40 includes RTK-GNSS.

[0028] The communication device 50 communicates with the outside of the vehicle 1.

[0029] The running gear 60 includes a steering device 61, a drive device 62, a braking device 63, and a suspension 3 mounted on the vehicle 1 (see reference). Figure 2 The steering device 61 steers the wheels 2. For example, the steering device 61 includes an electric power steering (EPS) device. The drive device 62 is the power source that generates driving force. Examples of drive devices 62 include an engine, an electric motor, a hub motor, etc. The braking device 63 generates braking force.

[0030] The control device 70 is a computer that controls the vehicle 1. The control device 70 may be mounted on the vehicle 1 or partially included in a remote device. The control device 70 includes one or more processors 71 (hereinafter simply referred to as processors 71) and one or more storage devices 72 (hereinafter simply referred to as storage devices 72). The processor 71 performs various processes. For example, the processor 71 includes a CPU (Central Processing Unit). The processor 71 may also be referred to as a processing circuitry. The storage device 72 stores various information required for the processes performed by the processor 71. Examples of storage devices 72 include volatile memory, non-volatile memory, HDD (Hard Disk Drive), SSD (Solid State Drive), etc. The control device 70 may also include one or more ECUs (Electronic Control Units).

[0031] The vehicle control program 80 is a computer program used to control vehicle 1, and is executed by processor 71. The vehicle control program 80 is stored in storage device 72. Alternatively, the vehicle control program 80 may also be recorded in a computer-readable recording medium. The functions of control device 70 are realized by processor 71 executing the vehicle control program 80. 2-2. Driving Environment Information

[0032] Figure 5 This is a block diagram illustrating an example of driving environment information 90 representing the driving environment of vehicle 1. The driving environment information 90 is stored in storage device 72. The driving environment information 90 includes map information 91, vehicle status information 92, surrounding conditions information 93, and location information 94.

[0033] Map information 91 contains a typical navigation map. Map information 91 may also show lane configurations, road shapes, etc. Map information 91 may also include location information for white lines, traffic lights, signs, landmarks, etc. Map information 91 is obtained from a map database. Alternatively, the map database may be mounted on vehicle 1 or stored on an external management server. In the latter case, control device 70 communicates with the management server to obtain the required map information 91.

[0034] Map information 91 also includes "Unsprung displacement mapping 200". Details about unsprung displacement mapping 200 will be described later.

[0035] Vehicle status information 92 indicates the status of vehicle 1. Control device 70 obtains vehicle status information 92 from vehicle status sensor 20. For example, vehicle status information 92 includes vehicle speed V, sprung acceleration Zs", travel ST, lateral acceleration, yaw rate, steering angle, etc. Vehicle speed V can also be calculated based on the vehicle position detected by position sensor 40. Control device 70 can also... Figure 3 The method shown is used to calculate the unsprung displacement Zu. In this case, the vehicle status information 92 also includes the unsprung displacement Zu calculated by the control device 70.

[0036] Surrounding conditions information 93 is information showing the conditions around vehicle 1. Control device 70 uses recognition sensor 30 to recognize the conditions around vehicle 1 and obtain surrounding conditions information 93. For example, surrounding conditions information 93 includes image information captured by a camera. As another example, surrounding conditions information 93 includes point cloud information obtained by LIDAR.

[0037] The surrounding environment information 93 also includes "object information" related to objects around vehicle 1. Examples of objects include pedestrians, bicycles, other vehicles (vehicles ahead, parked vehicles, etc.), road features (white lines, curbs, guardrails, walls, median strips, roadside structures, etc.), signs, poles, obstacles, etc. The object information shows the relative position and relative speed of the object relative to vehicle 1. For example, by analyzing image information obtained from a camera, objects can be identified and their relative positions calculated. Alternatively, objects can also be identified and their relative positions and speeds obtained from point cloud information obtained from LIDAR.

[0038] Location information 94 indicates the position and orientation of vehicle 1. Position includes horizontal and vertical positions. For example, horizontal position is defined by latitude and longitude. Vertical position is defined by altitude (elevation). Altitude can be exemplified by elevation, geoid height, ellipsoidal height, etc. Control device 70 obtains location information 94 based on measurements from position sensors 40 such as GNSS. Alternatively, control device 70 can obtain location information 94 through dead reckoning. Yet another example, control device 70 can obtain high-precision location information 94 through known self-location estimation processing (Localization) utilizing object information and map information 91. 2-3. Vehicle Control

[0039] The control device 70 performs vehicle driving control to control the movement of the vehicle 1. Vehicle driving control includes steering control, drive control, and braking control. The control device 70 performs vehicle driving control by controlling the driving devices 60 (steering device 61, drive device 62, and braking device 63). The control device 70 can also perform driver assistance control based on driving environment information 90 to assist the driving of the vehicle 1. Examples of driver assistance controls include lane keeping control, collision avoidance control, and automatic driving control.

[0040] Furthermore, the control device 70 controls the suspension 3. Typically, the control device 70 controls the suspension 3 to perform damping control to suppress the vibrations of the vehicle 1. For example, the control device 70 controls the actuator 3A to generate a vertical control force Fc between the unsprung structure 4 and the sprung structure 5 (see reference). Figure 2 As another example, the control device 70 can also provide variable control over the damping force of the damper 3D. The damping control includes "predictive control" as described later. 3. Mapping Management System 3-1. Example of composition

[0041] Figure 6 This is a block diagram illustrating an example configuration of the mapping management system 100 according to this embodiment. The mapping management system 100 is a computer that manages various types of map information. The management of map information includes the generation, updating, provision, and distribution of map information. Typically, the mapping management system 100 is a management server in the cloud. The mapping management system 100 may also be a distributed system in which multiple servers perform distributed processing.

[0042] The mapping management system 100 includes a communication device 110. The communication device 110 is connected to a communication network NET. For example, the communication device 110 communicates with multiple vehicles 1 via the communication network NET.

[0043] The mapping management system 100 also includes one or more processors 120 (hereinafter simply referred to as processors 120) and one or more storage devices 130 (hereinafter simply referred to as storage devices 130). The processors 120 perform various information processing tasks. For example, the processors 120 include a CPU. The processors 120 may also be referred to as processing circuitry. The storage devices 130 store various map information. Additionally, the storage devices 130 store various information required for the processing performed by the processors 120. Examples of storage devices 130 include volatile memory, non-volatile memory, HDDs, SSDs, etc.

[0044] Mapping management program 140 is a computer program for mapping management, executed by processor 120. Mapping management program 140 is stored in storage device 130. Alternatively, mapping management program 140 may also be recorded on a computer-readable recording medium. The functions of mapping management system 100 are implemented by executing mapping management program 140 by processor 120.

[0045] The processor 120 communicates with the vehicle control system 10 of the vehicle 1 via the communication device 110. The processor 120 collects various information from the vehicle control system 10 and generates and updates map information based on the collected information. Additionally, the processor 120 distributes map information to the vehicle control system 10. Furthermore, the processor 120 provides map information in response to requests from the vehicle control system 10. 3-2. Unsprung displacement mapping

[0046] One of the map information managed by the mapping management system 100 is "Unsprung Displacement Mapping (Up-Down Motion Parameter Mapping) 200". Unsprung Displacement Mapping 200 is a map related to unsprung displacement Zu (up-down motion parameter), showing the correspondence between unsprung displacement Zu (up-down motion parameter) and position. Unsprung Displacement Mapping 200 is stored in storage device 130.

[0047] Figure 7 This is a conceptual diagram used to illustrate the unsprung displacement mapping 200. The XY plane represents the horizontal plane. For example, the absolute coordinate system on the horizontal plane is defined by the latitude and longitude directions, and the horizontal position is defined by latitude and longitude. The unsprung displacement mapping 200 at least represents the correspondence between the horizontal position (X,Y) and the unsprung displacement Zu. In other words, the unsprung displacement mapping 200 represents the unsprung displacement Zu as at least a function of the horizontal position (X,Y).

[0048] The road area can be divided into a grid on a horizontal plane. That is, the road area can be divided into multiple unit areas M on a horizontal plane. A unit area M is, for example, a square. The length of one side of a square is, for example, 10 cm. The unsprung displacement map 200 represents the correspondence between the position of a unit area M and its unsprung displacement Zu. The position of a unit area M can be defined by its representative position (e.g., the center position) or by its range (latitude range, longitude range). The unsprung displacement Zu of a unit area M is, for example, the average value of the unsprung displacement Zu obtained within that unit area M. The smaller the unit area M, the higher the resolution of the unsprung displacement map 200. 3-3. Mapping Generation / Update Processing

[0049] Processor 120 collects information from multiple vehicles 1 via communication device 110. Then, processor 120 generates and updates the unsprung displacement map 200 based on the information collected from the multiple vehicles 1. An example of the map generation / update process is described in more detail below.

[0050] The position in the unsprung displacement map 200 is the position through which the wheel 2 passes. The position of each wheel 2 is calculated based on the position information 94 mentioned above. Specifically, the relative positional relationship between the reference point of the vehicle position in vehicle 1 and each wheel 2 is known information. Based on this relative positional relationship and the vehicle position shown by the position information 94, the position of each wheel 2 can be calculated.

[0051] Unsprung displacement Zu passes Figure 3 The method shown is used for calculation. That is, by using the vehicle state sensor 20 mounted on vehicle 1, the sprung displacement Zs and stroke ST can be obtained. For convenience, these sprung displacements Zs and stroke ST are referred to as "sensor-based information". The unsprung displacement Zu is calculated based on this sensor-based information.

[0052] For example, while vehicle 1 is in motion, the control unit 70 of the vehicle control system 10 calculates the unsprung displacement Zu in real time based on sensor-based information. Additionally, the control unit 70 associates the wheel positions at the same time with the unsprung displacement Zu. Then, the control unit 70 sends the combined time-series data of the wheel positions and the unsprung displacement Zu to the mapping management system 100. The processor 120 of the mapping management system 100 generates and updates the unsprung displacement mapping 200 based on the time-series data of the wheel positions and the unsprung displacement Zu.

[0053] As another example, the control unit 70 of the vehicle control system 10 associates the wheel positions at the same time with sensor-based information. Then, the control unit 70 sends a set of time-series data of the wheel positions and time-series data of the sensor-based information to the mapping management system 100. The processor 120 of the mapping management system 100 calculates the unsprung displacement Zu based on the received sensor-based information. Further, the processor 120 generates and updates the unsprung displacement mapping 200 based on the time-series data of the wheel positions and the unsprung displacement Zu.

[0054] Furthermore, since there is no processing time constraint when calculating the unsprung displacement Zu in the mapping management system 100, a zero-phase filter can be used for filtering. By utilizing the zero-phase filter, "phase shift" can be prevented.

[0055] Figure 8 This is a flowchart that summarizes the mapping generation / update process involved in this embodiment.

[0056] In S100, the processor 120 of the mapping management system 100 obtains "mapping update information" from the vehicle 1 (vehicle control system 10) via the communication device 110. The mapping update information includes time-series data of the position (wheel position) of the vehicle 1. In addition, the mapping update information includes time-series data of sensor-based information (e.g., sprung displacement Zs, stroke ST) required to calculate the unsprung displacement Zu. Alternatively, the mapping update information may also include time-series data of the unsprung displacement Zu calculated by the control device 70 of the vehicle control system 10.

[0057] In S200, the processor 120 of the mapping management system 100 generates / updates the unsprung displacement mapping 200 based on the mapping update information. 3-4. Variations

[0058] The vehicle control system 10 of vehicle 1 can also maintain a database of unsprung displacement mapping 200 and generate / update its own unsprung displacement mapping 200. That is, the mapping management system 100 can be included in the vehicle control system 10. 4. Predictive control using unsprung displacement mapping

[0059] The control unit 70 of the vehicle control system 10 communicates with the mapping management system 100 via the communication device 50. The control unit 70 obtains the unsprung displacement mapping 200 of the region containing the current position of the vehicle 1 from the mapping management system 100. The unsprung displacement mapping 200 is stored in the storage device 72. Then, the control unit 70 performs "predictive control" as a type of vibration damping control based on the unsprung displacement mapping 200.

[0060] Figure 9 This is a conceptual diagram used to illustrate predictive control. Figure 10 This is a flowchart illustrating predictive control. (Refer to...) Figure 9 as well as Figure 10 This section explains the concept of predictive control.

[0061] In step S31, the control device 70 obtains the current position P0 of each wheel 2. The relative positional relationship between the reference point of the vehicle position in vehicle 1 and each wheel 2 is known information. Based on this relative positional relationship and the vehicle position shown by the position information 94, the position of each wheel 2 can be calculated.

[0062] In S32, the control device 70 calculates the predicted passing position Pf of wheel 2 after a prediction time tp. The prediction time tp is, for example, set to be more than the time required for calculation and communication processing until the actuator 3A of suspension 3 is activated. The prediction time tp can be fixed or variable depending on the situation. The prediction distance Lp is given by the product of the prediction time tp and the vehicle speed V. The predicted passing position Pf is the position forward by a prediction distance Lp from the current position P0. As a variation, the control device 70 can also calculate the expected driving route based on the vehicle speed V and the steering angle of wheel 2, and calculate the predicted passing position Pf based on the expected driving route.

[0063] In S33, the control device 70 reads the predicted unsprung displacement Zu at position Pf from the unsprung displacement map 200.

[0064] In S34, the control device 70 calculates the target control force Fc_t of the actuator 3A of the suspension 3 based on the predicted unsprung displacement Zu at position Pf. The target control force Fc_t is calculated, for example, as follows.

[0065] With spring-loaded structure 5 (see Figure 2 The relevant equation of motion is represented by the following equation (1). (Equation 1) m·Zs″=C(Zu′-ZS′)+K(Zu-ZS)-Fc…(1)

[0066] In equation (1), m is the mass of the spring-loaded structure 5, C is the damping coefficient of the damper 3D, K is the spring constant of the spring 3S, and Fc is the vertical control force Fc generated by the actuator 3A. Assuming that the vibration of the spring-loaded structure 5 is completely canceled by the control force Fc (Zs”=0, Zs′=0, Zs=0), the control force Fc is expressed by equation (2). (Equation 2) Fc=C·Zu′+K·Zu…(2)

[0067] The control force Fc that at least brings about vibration reduction is represented by the following formula (3). (Equation 3) Fc=α·C·Zu′+β·K·Zu…(3)

[0068] In equation (3), the gain α is greater than 0 and less than 1, and the gain β is also greater than 0 and less than 1. Without omitting the differential terms in equation (3), the control force Fc that at least provides vibration reduction is represented by equation (4). (Equation 4) Fc=β·K·Zu…(4)

[0069] The control device 70 calculates the target control force Fc_t according to the above formula (3) or formula (4). That is, the control device 70 substitutes the predicted unsprung displacement Zu at position Pf into formula (3) or formula (4) to calculate the target control force Fc_t.

[0070] In S35, the control device 70 controls the actuator 3A in such a way that a target control force Fc_t is generated at the moment when the wheel 2 passes the predicted passing position Pf. The moment when the wheel 2 passes the predicted passing position Pf is known from the prediction time tp.

[0071] By utilizing the predictive control of the unsprung displacement mapping 200 as described above, the vibration of vehicle 1 (sprung structure 5) can be effectively suppressed. 5. Reliability of location information

[0072] In the following description, for convenience, the object controlled by the vehicle control system 10 (control device 70) will be referred to as "object vehicle 1T". Furthermore, vehicle control utilizing the position information 94 of the object vehicle 1T will be referred to as "position-utilizing vehicle control". The aforementioned predictive control utilizing the position information 94 to calculate the position of the wheels 2 is an example of position-utilizing vehicle control.

[0073] If the accuracy of the position information 94 of the target vehicle 1T is low, the accuracy of vehicle control based on position may decrease. Therefore, when performing vehicle control based on position, it is preferable to determine how reliable the position information 94 of the target vehicle 1T can be. Thus, it is desirable to calculate the reliability of the position information 94 of the target vehicle 1T. Hereinafter, the reliability of the position information 94 of the target vehicle 1T will be referred to as "position reliability R".

[0074] By mastering the location reliability R (94), it's possible to appropriately adjust the vehicle control functionality based on the location reliability R. For example, when the location reliability R is low, suppressing vehicle control might be considered. However, if the accuracy (reliability) of the location reliability R itself is low, the accuracy or effectiveness of vehicle control might decrease. For instance, if the location reliability R is actually low but incorrectly judged as high, the accuracy of vehicle control might decrease. Conversely, if the location reliability R is actually high but incorrectly judged as low, vehicle control might be unnecessarily suppressed, failing to achieve its full effect. Therefore, it is desirable to improve the calculation accuracy (reliability) of the location reliability R itself.

[0075] Figure 11 This is a conceptual diagram used to illustrate the calculation method of the location reliability R involved in this embodiment.

[0076] As described above, the position information 94 of the target vehicle 1T is obtained based on the measurement results of the position sensor 40 mounted on the target vehicle 1T. The position information 94 includes the horizontal and vertical positions of the target vehicle 1T. For example, the horizontal position is defined by latitude and longitude. The vertical position is defined by altitude (elevation). Altitude can be exemplified by elevation, geoid height, ellipsoidal height, etc.

[0077] More precisely, the horizontal and vertical positions of the target vehicle 1T refer to the horizontal and vertical positions of the "vehicle reference point" that moves together with the target vehicle 1T. The vehicle reference point of the target vehicle 1T is arbitrary. For example, the vehicle reference point of the target vehicle 1T can be the center point of the target vehicle 1T or the mounting position of the position sensor 40. The design value (default value) of the relative height of the vehicle reference point from the road surface is provided as known information. Furthermore, the relative height of the vehicle reference point from the road surface can be corrected from the design value by considering factors such as the suspension 3 travel ST, vehicle body tilt (roll angle, pitch angle), and tire flex. In either case, the relative height of the vehicle reference point from the road surface can be obtained. The control device 70 can convert the vertical position of the target vehicle 1T into the road surface height based on the relative height of the vehicle reference point from the road surface. Similarly, the control device 70 can also convert the road surface height into the vertical position of the target vehicle 1T based on the relative height of the vehicle reference point from the road surface.

[0078] The control device 70 of the vehicle control system 10 obtains the height of a "representative point P" at the horizontal position of the target vehicle 1T. For example, the representative point P is the road surface. As another example, the representative point P can also be a point at a certain height above the road surface. As yet another example, the representative point P can also be the vehicle reference point of the target vehicle 1T. As described above, the height of the road surface and the vertical position of the vehicle reference point can be converted to each other. Therefore, as long as the height of the road surface or the vertical position of the vehicle reference point is known, the height of the representative point P can be calculated.

[0079] According to this embodiment, the control device 70 obtains the height of a representative point P at the horizontal position of the target vehicle 1T using two different methods.

[0080] The first method uses the position information 94 of the target vehicle 1T. Based on the vertical position of the target vehicle 1T (vehicle reference point) contained in the position information 94, the control device 70 obtains the height of a representative point P at the horizontal position of the target vehicle 1T. As described above, the height of the representative point P can be calculated as long as the vertical position of the vehicle reference point is known. Hereinafter, the height of the representative point P obtained based on the position information 94 will be referred to as the "sensor-based height Hsen".

[0081] The second method uses a height map 400 that shows the correspondence between latitude, longitude, and altitude of the road surface (ground). Altitude can be exemplified by elevation, geoid height, ellipsoidal height, etc. For example, map data issued by the Geospatial Information Authority of Japan can be used as the height map 400. The height map 400 is a type of map information 91, pre-stored in the storage device 72. The control device 70 reads the road surface height at the horizontal position of the target vehicle 1T from the height map 400. Furthermore, the control device 70 calculates the height of the representative point P based on the road surface height read from the height map 400. Hereinafter, the height of the representative point P obtained based on the height map 400 will be referred to as the "mapped height Hmap".

[0082] Next, the control device 70 calculates the height deviation ΔH (=|Hsen-Hmap|) between the sensor-based height Hsen and the mapping-based height Hmap at a representative point P at the same horizontal position. Then, the control device 70 calculates the position reliability R of the position information 94 based on the height deviation ΔH. The smaller the height deviation ΔH, the higher the position reliability R. Conversely, the larger the height deviation ΔH, the lower the position reliability R.

[0083] As explained above, according to this embodiment, the position reliability R of the position information 94 of the target vehicle 1T can be calculated. In particular, by referring to the height mapping 400 as the forward solution data, the position reliability R of the position information 94 can be calculated with high accuracy.

[0084] As a comparative example, we also consider the case where a position estimation algorithm from GNSS or similar sources estimates and outputs the reliability of its own position estimation process. However, the reliability of such a position estimation process is only estimated based on the internal parameters within the position estimation process, and not on the forward solution data. The accuracy of the reliability estimated by the position estimation algorithm is lower than the accuracy of the position reliability R obtained by referring to the height mapping 400 as the forward solution data. 6. Location-based vehicle control considering position reliability

[0085] Figure 12 This is a flowchart illustrating the processing related to vehicle control using location as described in this embodiment. In S40, the control device 70 of the vehicle control system 10 acquires the location information 94 of the target vehicle 1T. In S50, the control device 70 calculates the location reliability R of the location information 94. The method for calculating the location reliability R is as described in section 5 above.

[0086] In S60, the control device 70 performs position utilization vehicle control considering the position reliability R. More specifically, the control device 70 flexibly adjusts the "degree" of position utilization vehicle control based on the position reliability R. The degree of position utilization vehicle control is represented, for example, by the gain of position utilization vehicle control. The higher the gain of position utilization vehicle control, the higher the degree of position utilization vehicle control.

[0087] For example, the lower the position reliability R, the less the control device 70 controls the vehicle's position utilization. Conversely, the higher the position reliability R, the more the control device 70 controls the vehicle's position utilization. Generally, consider a first position reliability R1 and a second position reliability R2 (R1 > R2) that is lower than the first position reliability R1. The control device 70 makes the degree of vehicle utilization control in the case of the second position reliability R2 lower than the degree of vehicle utilization control in the case of the first position reliability R1. This suppresses inappropriate vehicle utilization control when the position reliability R is low. Furthermore, it enables effective vehicle utilization control when the position reliability R is high.

[0088] The following is a specific example of using vehicle control to determine the position reliability R. 6-1. First Case: Predictive Control

[0089] In the first example, the vehicle position control utilizes predictive control. In predictive control, position information 94 is used to obtain the position of wheel 2. It is assumed that if the position reliability R of position information 94 is low, the accuracy of wheel 2's position will be low. If the accuracy of wheel 2's position is low, the unsprung displacement Zu read from the unsprung displacement map 200 may deviate from the actual unsprung displacement Zu at wheel 2's position. This will lead to a reduction in the effectiveness of predictive control, and depending on the situation, may result in increased vibration rather than reduced vibration.

[0090] Therefore, the control device 70 flexibly adjusts the gain of the predictive control based on the position reliability R of the position information 94. For example, the gain of the predictive control is β in the above equation (4).

[0091] Figure 13 of (a), Figure 13 (b) and Figure 13 (c) is a conceptual diagram illustrating various examples of predictive control corresponding to position reliability R. The horizontal axis represents position reliability R, and the vertical axis represents the gain of the predictive control. Figure 13 In (a), as the position reliability R decreases, the gain of the predictive control monotonically decreases. Figure 13 In (b), as the position reliability R decreases, the gain of the predictive control decreases in stages. Figure 13 In (c), predictive control is performed when the position reliability R is above the threshold Rth, and no predictive control is performed (gain = 0) when the position reliability R is below the threshold Rth. This is generalized as follows: Consider a first position reliability R1 and a second position reliability R2 (R1 > R2) that is lower than the first position reliability R1. The control device 70 makes the gain of the predictive control in the case of the second position reliability R2 lower than the gain of the predictive control in the case of the first position reliability R1. Therefore, it is possible to suppress inappropriate predictive control when the position reliability R is low. Furthermore, predictive control can be effectively implemented when the position reliability R is high.

[0092] Vibration reduction control can also be a combination of predictive control and feedback control. In the case of a combination of predictive control and feedback control, the control force Fc is represented by, for example, the following equation (5). Equation (5) is equivalent to adding a feedback term related to feedback control to the right side of the above equation (4). γ is the gain of the feedback control. (Equation 5) Fc=β·K·Zu+γ·Zs′…(5)

[0093] Figure 14 of (a), Figure 14 (b) and Figure 14 (c) illustrates various examples of combinations of predictive control and feedback control. Figure 14 In (a), as the position reliability R decreases, the gain of the predictive control monotonically decreases, while the gain of the feedback control monotonically increases. Figure 14 In (b), as the position reliability R decreases, the gain of the predictive control decreases in stages, while the gain of the feedback control increases monotonically. Figure 14 In (c), predictive control is performed when the position reliability R is above the threshold Rth, and feedback control is performed instead of predictive control when the position reliability R is below the threshold Rth. This is generally described as follows: The control device 70 makes the gain of the predictive control in the case of the second position reliability R2 lower than the gain of the predictive control in the case of the first position reliability R1. Furthermore, the control device 70 makes the gain of the feedback control in the case of the second position reliability R2 higher than the gain of the feedback control in the case of the first position reliability R1. Therefore, it is possible to suppress inappropriate predictive control when the position reliability R is low, and to compensate for the effect of vibration reduction control through feedback control.

[0094] A combination of predictive control and rear preview control can also be used. In rear preview control, it is assumed that the front and rear wheels of the vehicle 1T pass through the same position. First, when the front wheels pass through the first position, [the system]... Figure 3The method shown calculates the unsprung displacement Zu in real time. For convenience, this unsprung displacement Zu is referred to as the front wheel unsprung displacement Zu_f. Then, at the moment the rear wheel passes the first position, the previously calculated front wheel unsprung displacement Zu_f is used for predictive control, instead of the unsprung displacement Zu registered in the unsprung displacement map 200. In the post-predictive control, it is not necessary to read the unsprung displacement Zu from the unsprung displacement map 200, and therefore position information 94 is not utilized. That is, the post-predictive control is not affected by the position reliability R. Therefore, the gain of the post-predictive control can also be made equal to... Figure 14 of (a), Figure 14 (b) and Figure 14 The gain of the feedback control shown in (c) changes similarly. Therefore, even with low position reliability R, the effect of the vibration reduction control can be compensated for by back-forward control. 6-2. Second example: Automated driving control

[0095] The control device 70 of the vehicle control system 10 can also perform automated driving control of the controlled vehicle 1T. Here, automated driving refers to automatically performing at least a portion of the steering, acceleration, and deceleration of the vehicle 1T independently of the driver's operation. As an example, level 3 or higher automated driving can be performed. The control device 70 generates a driving plan based on the driving environment information 90. Examples of driving plans include maintaining the current driving lane, changing lanes, making left and right turns, and avoiding collisions with objects. More specifically, the driving plan includes a path plan and a speed plan. The path plan is a set of target positions of the vehicle 1T. The speed plan is a set of target speeds for each target position. The combination of the path plan and the speed plan is also called the target trajectory. That is, the target trajectory includes the target position and target speed of the vehicle 1T. The control device 70 performs vehicle driving control by causing the vehicle 1T to follow the target trajectory.

[0096] Figure 15 This is a conceptual diagram illustrating an example of autonomous driving control corresponding to position reliability R. The horizontal axis represents position reliability R, and the vertical axis represents the level of autonomous driving control (autonomous driving level). Figure 15 As shown, the level of automatic driving control decreases as the position reliability R decreases. This is generally explained below. Consider a first position reliability R1 and a second position reliability R2 (R1 > R2) that is lower than the first position reliability R1. The control device 70 sets the level of automatic driving control in the case of the second position reliability R2 to be lower than the level of automatic driving control in the case of the first position reliability R1. This suppresses inappropriate automatic driving control when the position reliability R is low. Furthermore, a higher level of automatic driving control can be implemented when the position reliability R is high. 7. Mapping Update

[0097] Figure 16 This is a flowchart illustrating the mapping generation / update process considering location reliability R. In S400, the mapping management system 100 (refer to...) Figure 6 The location information 94 of the target vehicle 1T is obtained. In S500, the mapping management system 100 calculates the location reliability R of the location information 94. The calculation method of the location reliability R is as described in section 5 above. In addition, the height mapping 400 is pre-stored in the storage device 130 of the mapping management system 100.

[0098] In S600, the mapping management system 100 generates / updates the unsprung displacement mapping 200 considering the position reliability R. For example, if the position reliability R is less than a threshold, the mapping management system 100 does not generate / update the unsprung displacement mapping 200 associated with that position. Only when the position reliability R is above the threshold does the mapping management system 100 generate / update the unsprung displacement mapping 200 associated with that position. This suppresses the reduction in the accuracy of the unsprung displacement mapping 200.

Claims

1. A vehicle control method, wherein the controlled object is a vehicle, comprising the following processing: Based on the measurement results of the sensors mounted on the target vehicle, position information including the horizontal and vertical positions of the target vehicle is obtained; Calculate the reliability of the location information; and Location-based vehicle control is performed considering the reliability of the location information; the location-based vehicle control is the control of the target vehicle utilizing the location information. Calculating the reliability of the location information includes the following processes: Based on the vertical position contained in the location information, the height of a representative point at the horizontal position of the target vehicle is obtained as the sensor-based height. Based on the height mapping showing the correspondence between latitude, longitude, and altitude of the road surface, the height of the representative point at the horizontal position of the target vehicle is obtained as the height based on the mapping; and The reliability is calculated in such a way that the greater the deviation between the sensor-based height and the mapping-based height, the lower the reliability.

2. The vehicle control method according to claim 1, wherein, It also includes the following process: making the degree of vehicle control of the position when the reliability is a second reliability that is lower than the first reliability less than the degree of vehicle control of the position when the reliability is the first reliability.

3. The vehicle control method according to claim 1, wherein, The location is determined using vehicle control, including predictive control. The predictive control includes the following processes: Obtain a vertical motion parameter mapping that shows the correspondence between vertical motion parameters and position related to the vertical motion of the vehicle's wheels; Based on the position information, the vertical motion parameters at the position of the wheels of the target vehicle are read from the vertical motion parameter mapping; as well as The target vehicle is controlled based on the vertical motion parameters read from the vertical motion parameter mapping. The gain of the predictive control when the reliability is a second reliability that is lower than the first reliability is set to be lower than the gain of the predictive control when the reliability is the first reliability.

4. The vehicle control method according to claim 1, wherein, The location utilizes vehicle control, including the autonomous driving control of the target vehicle. When the reliability is a second reliability that is lower than the first reliability, the level of the autonomous driving control is set to be lower than the level of the autonomous driving control when the reliability is the first reliability.

5. A method for calculating location reliability, which uses a computer to calculate the reliability of the location information of a vehicle, wherein, The location information is obtained based on the measurement results of sensors mounted on the target vehicle, and includes the horizontal and vertical positions of the target vehicle. The location reliability calculation method includes the following processing: Based on the vertical position contained in the location information, the height of a representative point at the horizontal position of the target vehicle is obtained as the sensor-based height. Based on the height mapping showing the correspondence between latitude, longitude, and altitude of the road surface, the height of the representative point at the horizontal position of the target vehicle is obtained as the height based on the mapping; and The reliability is calculated in such a way that the greater the deviation between the sensor-based height and the mapping-based height, the lower the reliability.

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