VEHICLE POSITION ESTIMATION METHOD AND VEHICLE CONTROL SYSTEM

Abstract

To improve accuracy in estimating a position of a vehicle utilizing a map.SOLUTION: Time-series data on parameters relevant to vertical motion of a wheel of a vehicle are obtained during travelling of the vehicle. A parameter of a circumference of the vehicle is obtained as a reference parameter, from a parameter map representing a correspondence relation between the parameters and a position. On the basis of contrast between the time-series data on the parameters and time-series data on the reference parameter, the position of the vehicle is estimated. While, road surface roughness representing roughness in a lateral direction of a road surface around the vehicle and a lateral position of the vehicle in the road are recognized, using a recognition sensor mounted on the vehicle. When the road surface roughness less than a threshold, components of the lateral position at the estimated position of the vehicle are replaced at the lateral position recognized using the recognition sensor, so that the position of the vehicle is determined.SELECTED DRAWING: Figure 19

Description

[Technical field]

[0001] The present disclosure relates to a technique for estimating a vehicle position using a map. [Background technology]

[0002] Patent Document 1 discloses a road surface displacement map that indicates the correspondence between road surface displacement (road surface unevenness) and position. Vibration suppression control is performed by utilizing such a road surface displacement map. Specifically, the road surface displacement at a predetermined position in front of the vehicle is recognized in advance from the road surface displacement map. The control amount of the active suspension is calculated in advance according to the recognized road surface displacement. Then, the active suspension is controlled at the timing when the wheel passes the predetermined position, thereby effectively suppressing the vibration of the vehicle.

[0003] Patent Document 2 discloses a reference map that indicates the correspondence between the vertical movement of the wheels and the position. By using such a reference map, vehicle localization is performed. Specifically, while the vehicle is traveling, time series data of the vertical movement of the wheels is detected by using an on-board sensor. The wheel position, i.e., the vehicle position, is identified by comparing the detected time series data of the vertical movement of the wheels with the vertical movement indicated in the reference map. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] US Patent Application Publication No. 2018 / 0154723 [Patent Document 2] US Patent Application Publication No. 2019 / 0079539 Summary of the Invention [Problem to be solved by the invention]

[0005] There is room for improvement in the vehicle position estimation method disclosed in Patent Document 2. For example, when there are few road surface irregularities in a lateral direction intersecting the traveling direction of the vehicle, the estimation accuracy of the lateral position of the vehicle may be reduced.

[0006] One object of the present disclosure is to provide a technique capable of improving the accuracy of vehicle position estimation using a map. [Means for solving the problem]

[0007] The first aspect relates to a vehicle position estimation method. The vehicle position estimation method is acquiring time series data of a parameter related to vertical movement of the wheels of the vehicle while the vehicle is traveling; A process of acquiring parameters of the surroundings of the vehicle as reference parameters from a parameter map that represents a correspondence relationship between the parameters and positions; estimating a vehicle position of the vehicle based on a comparison between the time series data of the parameters and the time series data of the reference parameters; A process of recognizing road surface roughness representing roughness in a lateral direction of a road surface around the vehicle using a recognition sensor mounted on the vehicle; Recognizing the lateral position of a vehicle on a road using a recognition sensor; If the road surface roughness is less than a threshold value, a process of replacing a lateral position component in the estimated vehicle position with a lateral position recognized using a recognition sensor; Includes.

[0008] The second aspect relates to a vehicle position estimation method. The vehicle position estimation method is acquiring time series data of a parameter related to vertical movement of the wheels of the vehicle while the vehicle is traveling; A process of acquiring parameters of the surroundings of the vehicle as reference parameters from a parameter map that represents a correspondence relationship between the parameters and positions; A process of estimating a vehicle position of the vehicle based on a comparison between the time series data of the parameter and the time series data of the reference parameter. Includes. The parameters are calculated based on sensor-based information obtained by sensors mounted on the vehicle. The process of acquiring the time-series data of the parameter while the vehicle is traveling includes a first filtering process of applying a first filter to the sensor-based information or the time-series data of the parameter. The parameters in the parameter map are also calculated through a first filtering process using the first filter.

[0009] The third aspect relates to a vehicle position estimation method. The vehicle position estimation method is acquiring time series data of a parameter related to vertical movement of the wheels of the vehicle while the vehicle is traveling; A process of acquiring parameters of the surroundings of the vehicle as reference parameters from a parameter map that represents a correspondence relationship between the parameters and positions; A process of estimating a vehicle position of the vehicle based on a comparison between the time series data of the parameter and the time series data of the reference parameter. Includes. The parameter map represents the correspondence between parameters, positions, and vehicle speeds. The process of acquiring the reference parameters includes a process of acquiring the reference parameters corresponding to the vehicle speed from a parameter map.

[0010] The fourth aspect relates to a vehicle position estimation method. The vehicle position estimation method is acquiring time series data of a parameter related to vertical movement of the wheels of the vehicle while the vehicle is traveling; A process of acquiring parameters of the surroundings of the vehicle as reference parameters from a parameter map that represents a correspondence relationship between the parameters and positions; A process of estimating a vehicle position of the vehicle based on a comparison between the time series data of the parameter and the time series data of the reference parameter. Includes. The effective frequency band of the parameters is inversely proportional to the vehicle speed. The process of estimating the vehicle position includes comparing the time series data of the parameter with the time series data of the reference parameter in a common effective frequency band where the effective frequency band of the parameter and the effective frequency band of the reference parameter overlap.

[0011] The fifth aspect relates to a vehicle control system. The vehicle control system includes one or more processors. The one or more processors acquiring time series data of a parameter related to vertical movement of the wheels of the vehicle while the vehicle is traveling; A process of acquiring parameters of the surroundings of the vehicle as reference parameters from a parameter map that represents a correspondence relationship between the parameters and positions; estimating a vehicle position of the vehicle based on a comparison between the time series data of the parameters and the time series data of the reference parameters; A process of recognizing road surface roughness representing roughness in a lateral direction of a road surface around the vehicle using a recognition sensor mounted on the vehicle; Recognizing the lateral position of a vehicle on a road using a recognition sensor; If the road surface roughness is less than a threshold value, a process of replacing a lateral position component in the estimated vehicle position with a lateral position recognized using a recognition sensor; is configured to run Effect of the Invention

[0012] According to the present disclosure, the accuracy of vehicle position estimation using a map is improved. [Brief description of the drawings]

[0013] [Figure 1] 1 is a schematic diagram illustrating an example of the configuration of a vehicle according to an embodiment. [Diagram 2] 1 is a conceptual diagram showing a configuration example of a suspension according to an embodiment; [Diagram 3] 5 is a flowchart illustrating an example of an unsprung displacement calculation process according to the embodiment. [Figure 4]1 is a block diagram showing an example of the configuration of a vehicle control system according to an embodiment; [Diagram 5] 4 is a block diagram showing an example of driving environment information according to an embodiment. FIG. [Figure 6] 1 is a block diagram showing an example of a configuration of a map management system according to an embodiment; [Figure 7] FIG. 4 is a conceptual diagram for explaining an unsprung displacement map according to the embodiment. [Figure 8] FIG. 4 is a conceptual diagram illustrating an example of an unsprung displacement map according to the embodiment. [Figure 9] 5 is a conceptual diagram for explaining the relationship between the sampling results of vertical movement parameters by a sensor and the vehicle speed. FIG. [Figure 10] FIG. 10 is a conceptual diagram showing another example of the unsprung displacement map in the embodiment. [Figure 11] FIG. 10 is a conceptual diagram showing yet another example of the unsprung displacement map in the embodiment. [Figure 12] 11 is a conceptual diagram for explaining preview control using an unsprung displacement map according to an embodiment. FIG. [Figure 13] 10 is a flowchart showing a preview control using an unsprung displacement map according to an embodiment. [Figure 14] 10 is a conceptual diagram for explaining an overview of a self-position estimation process using an unsprung displacement map according to an embodiment. FIG. [Figure 15] 10 is a conceptual diagram for explaining an overview of a self-position estimation process using an unsprung displacement map according to an embodiment. FIG. [Figure 16] 4 is a flowchart illustrating an outline of a self-position estimation process using an unsprung displacement map according to an embodiment. [Figure 17] 11 is a flowchart showing an example of step S200 and step S300 of a self-position estimation process according to the embodiment. [Figure 18] 6A to 6C are conceptual diagrams for explaining a lateral position correction process according to the embodiment. [Figure 19] 10 is a flowchart illustrating an example of a lateral position correction process according to an embodiment. [Figure 20] 10 is a flowchart showing another example of the lateral position correction process according to the embodiment. [Figure 21] 5 is a flowchart showing a map update process according to the embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0014] Embodiments of the present disclosure will be described with reference to the accompanying drawings.

[0015] 1. Suspension and vertical movement parameters FIG 1 is a schematic diagram showing an example of the configuration of a vehicle 1 according to this embodiment. The vehicle 1 is equipped with wheels 2 and suspensions 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. Suspensions 3FL, 3FR, 3RL, and 3RR are provided for the left front wheel 2FL, the right front wheel 2FR, the left rear wheel 2RL, and the right rear wheel 2RR, respectively. In the following description, unless there is a particular need to distinguish between them, each wheel will be referred to as a wheel 2, and each suspension will be referred to as a suspension 3.

[0016] FIG. 2 is a conceptual diagram showing an example of the configuration of a suspension 3. The suspension 3 is provided to connect between an unsprung structure 4 and a sprung structure 5 of a vehicle 1. The unsprung structure 4 includes a wheel 2. The suspension 3 includes a spring 3S, a damper (shock absorber) 3D, and an actuator 3A. The spring 3S, the damper 3D, and the actuator 3A are provided in parallel between the unsprung structure 4 and the sprung structure 5. The spring 3S has a spring constant K. The damper 3D has a damping coefficient C. The damping force of the damper 3D may be variable. The actuator 3A applies a control force Fc in the vertical direction between the unsprung structure 4 and the sprung structure 5.

[0017] Here, the terms are defined. "Road surface 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. The sign of each parameter is positive when pointing upward and negative when pointing downward.

[0018] The wheel 2 moves on the road surface RS. In the following description, parameters related to the vertical motion of the wheel 2 are referred to as "vertical motion parameters". Examples of the vertical motion parameters include the above-mentioned road surface displacement Zr, unsprung displacement Zu, unsprung velocity Zu', unsprung acceleration Zu'', sprung displacement Zs, sprung velocity Zs', sprung acceleration Zs'', etc. The vertical motion parameters can also be said to be "road surface displacement related parameters" related to the road surface displacement Zr.

[0019] As an example, in the following description, a case where the vertical movement parameter is the unsprung displacement Zu will be considered. When generalizing, the "unsprung displacement" in the following description should be read as the "vertical movement parameter".

[0020] FIG. 3 is a flowchart illustrating an example of the unsprung displacement calculation process.

[0021] In step S11, the sprung acceleration Zs'' is detected by the sprung acceleration sensor 22 installed in the sprung structure 5. In step S12, the sprung displacement Zs is calculated by integrating the sprung acceleration Zs'' twice.

[0022] In step S13, a stroke H (=Zs-Zu), which is the relative displacement between the sprung structure 5 and the unsprung structure 4, is acquired. For example, the stroke H is detected by a stroke sensor installed in the suspension 3. As another example, the stroke H may be estimated based on the sprung acceleration Zs'' by an observer configured based on a single-wheel two-degree-of-freedom model.

[0023] In step S14, in order to suppress the influence of sensor drift and the like, a filtering process is performed on the time series data of the sprung displacement Zs. Similarly, in step S15, a filtering process is performed on the time series data of the stroke H. For example, the filter is a band-pass filter that passes signal components in a specific frequency band. The specific frequency band may be set so as to include the sprung resonance frequency of the vehicle 1. For example, the specific frequency band is 0.3 to 10 Hz.

[0024] In step S16, the difference between the sprung displacement Zs and the stroke H is calculated as the unsprung displacement Zu.

[0025] Instead of steps S14 and S15, a filtering process may be performed on the time series data of the unsprung displacement Zu calculated in step S16.

[0026] As yet another example, the unsprung acceleration Zu'' may be detected by an unsprung acceleration sensor, and the unsprung displacement Zu may be calculated from the unsprung acceleration Zu''.

[0027] 2.Vehicle Control System 2-1. Configuration example 4 is a block diagram showing an example of the configuration of a vehicle control system 10 according to this embodiment. The vehicle control system 10 is mounted on a vehicle 1 and controls the vehicle 1. The vehicle control system 10 includes a vehicle state sensor 20, a recognition sensor 30, a position sensor 40, a communication device 50, a driving device 60, and a control device 70.

[0028] The vehicle state sensor 20 detects the state of the vehicle 1. The vehicle state sensor 20 includes a vehicle speed sensor (wheel speed sensor) 21 that detects the vehicle speed V of the vehicle 1, a sprung acceleration sensor 22 that detects the sprung acceleration Zs'', and the like. The vehicle state sensor 20 may include a stroke sensor 23 that detects the stroke H. The vehicle state sensor 20 may include an unsprung acceleration sensor. In addition, the vehicle state sensor 20 includes a lateral acceleration sensor, a yaw rate sensor, a steering angle sensor, and the like.

[0029] The recognition sensor 30 recognizes (detects) the surrounding situation of the vehicle 1. Examples of the recognition sensor include a camera, a LIDAR (Laser Imaging Detection and Ranging), and a radar.

[0030] The position sensor 40 detects the position and orientation of the vehicle 1. For example, the position sensor 40 includes a Global Navigation Satellite System (GNSS).

[0031] The communication device 50 communicates with the outside of the vehicle 1 .

[0032] The traveling device 60 includes a steering device 61, a driving device 62, a braking device 63, and a suspension 3 (see FIG. 2). The steering device 61 steers the wheels 2. For example, the steering device 61 includes an electric power steering (EPS) device. The driving device 62 is a power source that generates a driving force. Examples of the driving device 62 include an engine, an electric motor, and an in-wheel motor. The braking device 63 generates a braking force.

[0033] The control device 70 is a computer that controls the vehicle 1. The control device 70 includes one or more processors 71 (hereinafter simply referred to as processor 71) and one or more storage devices 72 (hereinafter simply referred to as storage devices 72). The processor 71 executes various processes. For example, the processor 71 includes a CPU (Central Processing Unit). The storage device 72 stores various information required for processing by the processor 71. Examples of the storage device 72 include a volatile memory, a non-volatile memory, an HDD (Hard Disk Drive), and an SSD (Solid State Drive). The control device 70 may include one or more ECUs (Electronic Control Units).

[0034] The vehicle control program 80 is a computer program for controlling the vehicle 1, and is executed by the processor 71. The vehicle control program 80 is stored in the storage device 72. Alternatively, the vehicle control program 80 may be recorded in a computer-readable recording medium. The processor 71 executes the vehicle control program 80 to realize the functions of the control device 70.

[0035] 2-2. Driving environment information 5 is a block diagram showing an example of driving environment information 90 indicating the driving environment of the vehicle 1. The driving environment information 90 is stored in the storage device 72. The driving environment information 90 includes map information 91, vehicle state information 92, surrounding situation information 93, and position information 94.

[0036] The map information 91 includes a general navigation map. The map information 91 may indicate lane layout, road shape, etc. The map information 91 may include position information of white lines, traffic lights, signs, landmarks, etc. The map information 91 is obtained from a map database. The map database may be mounted on the vehicle 1 or may be stored in an external management server. In the latter case, the control device 70 communicates with the management server to obtain the necessary map information 91.

[0037] The map information 91 further includes an "unsprung displacement map 200." The unsprung displacement map 200 will be described in detail later.

[0038] The vehicle state information 92 is information indicating the state of the vehicle 1. The control device 70 acquires the vehicle state information 92 from the vehicle state sensor 20. For example, the vehicle state information 92 includes the vehicle speed V, the sprung acceleration Zs'', the stroke H, the lateral acceleration, the yaw rate, the steering angle, and the like. The vehicle speed V may be calculated from the vehicle position detected by the position sensor 40. The control device 70 may calculate the unsprung displacement Zu by the method shown in FIG. 3. In that case, the vehicle state information 92 also includes the unsprung displacement Zu calculated by the control device 70.

[0039] The surrounding situation information 93 is information indicating the situation around the vehicle 1. The control device 70 recognizes the situation around the vehicle 1 using the recognition sensor 30, and acquires the surrounding situation information 93. For example, the surrounding situation information 93 includes image information captured by a camera. As another example, the surrounding situation information 93 includes point cloud information obtained by a LIDAR.

[0040] The surrounding situation information 93 further includes "object information" regarding objects around the vehicle 1. Examples of objects include pedestrians, bicycles, other vehicles (preceding vehicles, parked vehicles, etc.), road configurations (white lines, curbs, guardrails, walls, median strips, roadside structures, etc.), signs, poles, obstacles, etc. The object information indicates the relative position and relative speed of the object with respect to the vehicle 1. For example, by analyzing image information obtained by a camera, it is possible to identify the object and calculate the relative position of the object. It is also possible to identify the object and obtain the relative position and relative speed of the object based on point cloud information obtained by LIDAR.

[0041] The position information 94 is information indicating the position and direction of the vehicle 1. The control device 70 acquires the position information 94 from the detection result by the position sensor 40. In addition, the control device 70 may acquire highly accurate position information 94 by a well-known self-position estimation process (Localization) using object information and map information 91.

[0042] 2-3.Vehicle control The control device 70 executes vehicle driving control to control the driving of the vehicle 1. The vehicle driving control includes steering control, drive control, and braking control. The control device 70 executes vehicle driving control by controlling the driving device 60 (the steering device 61, the drive device 62, and the braking device 63). The control device 70 may execute driving assistance control to assist the driving of the vehicle 1 based on the driving environment information 90. Examples of driving assistance control include lane keeping control, collision avoidance control, and automatic driving control.

[0043] Furthermore, the control device 70 controls the suspension 3. Typically, the control device 70 performs vibration suppression control to suppress vibration of the vehicle 1 by controlling the suspension 3. 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 FIG. 2). As another example, the control device 70 may variably control the damping force of the damper 3D. The vibration suppression control includes a "preview control" to be described later.

[0044] 3. Map Management System 3-1. Configuration example 6 is a block diagram showing an example of the configuration of a map management system 100 according to this embodiment. The map management system 100 is a computer that manages various types of map information. The management of map information includes the generation, update, provision, distribution, etc. of the map information. Typically, the map management system 100 is a management server on a cloud. The map management system 100 may be a distributed system in which multiple servers perform distributed processing.

[0045] The map 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 a number of vehicles 1 via the communication network NET.

[0046] The map management system 100 further includes one or more processors 120 (hereinafter simply referred to as processor 120) and one or more storage devices 130 (hereinafter simply referred to as storage device 130). The processor 120 executes various types of information processing. For example, the processor 120 includes a CPU. The storage device 130 stores various types of map information. The storage device 130 also stores various types of information required for processing by the processor 120. Examples of the storage device 130 include volatile memory, non-volatile memory, HDD, SSD, etc.

[0047] The map management program 140 is a computer program for map management, and is executed by the processor 120. The map management program 140 is stored in the storage device 130. Alternatively, the map management program 140 may be recorded in a computer-readable recording medium. The processor 120 executes the map management program 140 to realize the functions of the map management system 100.

[0048] 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. The processor 120 also distributes the map information to the vehicle control system 10. The processor 120 also provides the map information in response to a request from the vehicle control system 10.

[0049] 3-2.Unsprung displacement map One of the map information managed by the map management system 100 is an “unsprung displacement map (vertical movement parameter map) 200.” The unsprung displacement map 200 is a map related to unsprung displacement Zu (vertical movement parameter). The unsprung displacement map 200 is stored in the storage device 130.

[0050] 7 is a conceptual diagram for explaining the unsprung displacement map 200. The absolute coordinate system in the horizontal plane is defined by, for example, latitude and longitude. The position in the horizontal plane is defined by, for example, latitude LAT and longitude LON. The unsprung displacement map 200 represents the correspondence between the position (LAT, LON) and the unsprung displacement Zu. In other words, the unsprung displacement map 200 represents the unsprung displacement Zu as a function of the position (LAT, LON).

[0051] The road area may be divided into a mesh shape on a horizontal plane. That is, the road area may be divided into a plurality of unit areas M on a horizontal plane. The unit area M is, for example, a square. The length of one side of the square is, for example, 10 cm. The unsprung displacement map 200 represents the correspondence between the position of the unit area M and the unsprung displacement Zu. The position of the unit area M may be defined by a representative position (e.g., a central position) of the unit area M, or may be defined by the range of the unit area M (latitude range, longitude range). The unsprung displacement Zu of the unit area M is, for example, an average value of the unsprung displacement Zu acquired within the unit area M. The smaller the unit area M is, the higher the resolution of the unsprung displacement map 200 is.

[0052] The processor 120 collects information from a large number of vehicles 1 via the communication device 110. Then, the processor 120 generates and updates the unsprung displacement map 200 based on the information collected from the large number of vehicles 1. Various examples of a method for generating the unsprung displacement map 200 are considered, as follows.

[0053] 3-2-1. First example The position of each wheel 2 is calculated based on the above-mentioned position information 94. Specifically, the relative positional relationship between the reference point of the vehicle position on the vehicle 1 and each wheel 2 is known information. Based on the relative positional relationship and the vehicle position indicated by the position information 94, the position of each wheel 2 can be calculated.

[0054] The unsprung displacement Zu is calculated by the method shown in Fig. 3. That is, the sprung displacement Zs and the stroke H are obtained by using the vehicle condition sensor 20 mounted on the vehicle 1. For convenience, the sprung displacement Zs and the stroke H are called "sensor-based information". The unsprung displacement Zu is calculated based on this sensor-based information. Here, as described above, in order to suppress the influence of sensor drift and the like, a filtering process is performed on the sensor-based information or the time-series data of the unsprung displacement Zu.

[0055] For example, while the vehicle 1 is traveling, the control device 70 of the vehicle control system 10 calculates the unsprung displacement Zu in real time. However, when calculating the unsprung displacement Zu in real time, a zero-phase filter cannot be used due to processing time restrictions. A filter (online filter) that the control device 70 can use in real time is hereinafter referred to as a "first filter." The control device 70 performs a "first filtering process" in which a first filter is applied to the sensor-based information or the time-series data of the unsprung displacement Zu. The unsprung displacement Zu calculated through this first filtering process is hereinafter referred to as a "first unsprung displacement Zu1." With regard to the time-series data of the first unsprung displacement Zu1, a "phase shift" occurs due to the first filtering process using the first filter. Specifically, a "phase lead" occurs due to the high-pass filter, and a "phase lag" occurs due to the low-pass filter.

[0056] The control device 70 of the vehicle control system 10 associates the position of the wheel 2 with the first unsprung displacement Zu1 at the same timing. Then, the control device 70 transmits a set of time series data of the position of the wheel 2 and time series data of the first unsprung displacement Zu1 to the map management system 100. The processor 120 of the map management system 100 generates an unsprung displacement map 200 representing the correspondence between the position and the first unsprung displacement Zu1 based on the information received from the vehicle control system 10. The unsprung displacement map 200 representing the correspondence between the position and the first unsprung displacement Zu1 is hereinafter referred to as the "first unsprung displacement map 210". It can be said that the first unsprung displacement map 210 is the unsprung displacement map 200 generated through a first filtering process using a first filter.

[0057] Instead of the vehicle control system 10, the map management system 100 may perform the first filtering process. In this case, the control device 70 of the vehicle control system 10 associates the position of the wheel 2 with the sensor-based information or the unsprung displacement Zu (before the filtering process) at the same timing. Then, the control device 70 transmits a set of the time series data of the position of the wheel 2 and the time series data of the sensor-based information or the unsprung displacement Zu (before the filtering process) to the map management system 100. The processor 120 of the map management system 100 performs a first filtering process that applies a first filter to the time series data of the sensor-based information or the unsprung displacement Zu. Through such a first filtering process, the first unsprung displacement Zu1 is calculated. The processor 120 generates a first unsprung displacement map 210 based on the position of the wheel 2 and the first unsprung displacement Zu1.

[0058] 3-2-2. Second example When performing filtering processing in the map management system 100, since there is no restriction on processing time, a zero-phase filter can be used. By using a zero-phase filter, it is possible to prevent "phase shift". The filtering processing in which a zero-phase filter is applied to the time series data of the sensor-based information or the unsprung displacement Zu is hereinafter referred to as a "second filtering processing". The unsprung displacement Zu calculated through the second filtering processing is hereinafter referred to as a "second unsprung displacement Zu2". The unsprung displacement map 200 representing the correspondence between the position and the second unsprung displacement Zu2 is hereinafter referred to as a "second unsprung displacement map 220". It can be said that the second unsprung displacement map 220 is the unsprung displacement map 200 generated through the second filtering processing using a zero-phase filter.

[0059] The control device 70 of the vehicle control system 10 associates the position of the wheel 2 with the sensor-based information or the unsprung displacement Zu (before filtering processing) at the same timing. Then, the control device 70 transmits a set of time series data of the position of the wheel 2 and time series data of the sensor-based information or the unsprung displacement Zu (before filtering processing) to the map management system 100. The processor 120 of the map management system 100 performs a second filtering process that applies a second filter to the time series data of the sensor-based information or the unsprung displacement Zu. Through such second filtering process, the second unsprung displacement Zu2 is calculated. The processor 120 generates a second unsprung displacement map 220 based on the position of the wheel 2 and the second unsprung displacement Zu2.

[0060] Fig. 8 is a conceptual diagram showing an example of the unsprung displacement map 200. In the example shown in Fig. 8, the unsprung displacement map 200 includes both a first unsprung displacement map 210 and a second unsprung displacement map 220. In this case, as will be described later, the first unsprung displacement map 210 and the second unsprung displacement map 220 are used depending on the application.

[0061] As another example, the unsprung displacement map 200 may include only one of the first unsprung displacement map 210 and the second unsprung displacement map 220.

[0062] 3-2-3.Third example The unsprung displacement map 200 may represent the correspondence relationship between the unsprung displacement Zu and the position before filtering processing.

[0063] 3-2-4. Fourth Example FIG. 9 is a conceptual diagram for explaining the relationship between the sampling result of the vertical movement parameter by the sensor and the vehicle speed V. The horizontal axis represents the position on the route that the wheel 2 has passed. The vertical axis represents the road surface displacement Zr as an example of the vertical movement parameter. Even if the wheel 2 passes through the exact same route, the sampling result by the sensor changes according to the vehicle speed V. Specifically, when traveling at low speed, fine unevenness of the road surface RS is detected (extracted). That is, when traveling at low speed, data with a short wavelength and high spatial frequency is detected. On the other hand, when traveling at high speed, fine unevenness of the road surface RS is not detected (extracted). That is, when traveling at high speed, data with a long wavelength and low spatial frequency is detected, and data with a short wavelength and high spatial frequency is difficult to detect. In this way, the vehicle speed V is inversely proportional to the detectable spatial frequency.

[0064] Therefore, in the fourth example, the unsprung displacement map 200 is generated taking into consideration the vehicle speed V. Specifically, the control device 70 of the vehicle control system 10 associates the position of the wheel 2 with the vehicle speed V when the wheel 2 passes through that position, and transmits a set of the position and the vehicle speed V to the map management system 100. The processor 120 of the map management system 100 generates the unsprung displacement map 200 based on the position and the vehicle speed V. That is, the unsprung displacement map 200 represents the correspondence relationship between the position, the vehicle speed V, and the unsprung displacement Zu.

[0065] FIG. 10 shows an example of the unsprung displacement map 200 according to the fourth example. In the example shown in FIG. 10, the unsprung displacement map 200 shows the correspondence between the position and the unsprung displacement Zu for each vehicle speed range. In other words, a different unsprung displacement map 200 is prepared for each vehicle speed range. For example, the unsprung displacement map 200-1 is an unsprung displacement map 200 used when the vehicle speed V belongs to a first vehicle speed range. The unsprung displacement map 200-2 is an unsprung displacement map 200 used when the vehicle speed V belongs to a second vehicle speed range different from the first vehicle speed range.

[0066] Fig. 11 shows another example of the unsprung displacement map 200 according to the fourth example. In the example shown in Fig. 11, the position is a position of a unit area M (see Fig. 7). The unsprung displacement map 200 shows the correspondence relationship between the vehicle speed V (or vehicle speed range) and the unsprung displacement Zu for each position of the unit area M.

[0067] 3-2-5. Fifth Example The first to third examples may be combined with the fourth example. That is, the first unsprung displacement map 210 may represent the correspondence relationship between the position, the vehicle speed V, and the first unsprung displacement Zu1. Similarly, the second unsprung displacement map 220 may represent the correspondence relationship between the position, the vehicle speed V, and the second unsprung displacement Zu2.

[0068] 4. Preview control using unsprung displacement map The control device 70 of the vehicle control system 10 communicates with the map management system 100 via the communication device 50. The control device 70 acquires an unsprung displacement map 200 of an area including the current position of the vehicle 1 from the map management system 100. The unsprung displacement map 200 is stored in the storage device 72. Then, the control device 70 executes "preview control", which is a type of vibration suppression control, based on the unsprung displacement map 200.

[0069] Fig. 12 is a conceptual diagram for explaining preview control, and Fig. 13 is a flowchart showing preview control. Preview control will be explained with reference to Figs.

[0070] In step S31, the control device 70 acquires the current position P0 of each wheel 2. The relative positional relationship between the reference point of the vehicle position on the vehicle 1 and each wheel 2 is known information. Based on the relative positional relationship and the vehicle position indicated by the position information 94, the position of each wheel 2 can be calculated.

[0071] In step S32, the control device 70 calculates a predicted passing position Pf of the wheel 2 after the preview time tp. The preview time tp is set to, for example, a time required for calculation processing and communication processing required to operate the actuator 3A of the suspension 3 or more. The preview time tp may be fixed or may be variable depending on the situation. The preview distance Lp is given by the product of the preview time tp and the vehicle speed V. The predicted passing position Pf is a position that is the preview distance Lp ahead of the current position P0. As a modified example, the control device 70 may calculate a predicted traveling route based on the vehicle speed V and the steering angle of the wheel 2, and calculate the predicted passing position Pf based on the predicted traveling route.

[0072] In step S33, the control device 70 reads out the unsprung displacement Zu at the predicted passing position Pf from the unsprung displacement map 200. Preferably, the control device 70 uses the second unsprung displacement map 220 (see Section 3-2-2) without the “phase shift” as a reference map, and reads out the second unsprung displacement Zu2 at the predicted passing position Pf from the second unsprung displacement map 220.

[0073] In step S34, the control device 70 calculates a target control force Fc_t of the actuator 3A of the suspension 3 based on the unsprung displacement Zu (second unsprung displacement Zu2) at the predicted passing position Pf. The target control force Fc_t is calculated, for example, as follows.

[0074] The equation of motion for the sprung structure 5 (see FIG. 2) is expressed by the following equation (1).

[0075]

number

[0076] In equation (1), m is the mass of the sprung 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. If the vibration of the sprung structure 5 is completely canceled by the control force Fc (Zs''=0, Zs'=0, Zs=0), the control force Fc is expressed by the following equation (2).

[0077]

number

[0078] The control force Fc that provides at least the vibration damping effect is expressed by the following formula (3).

[0079]

number

[0080] In formula (3), the gain α is greater than 0 and less than or equal to 1, and the gain β is also greater than 0 and less than or equal to 1. When the differential term in formula (3) is omitted, a control force Fc that provides at least a vibration damping effect is expressed by the following formula (4).

[0081]

number

[0082] 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 calculates the target control force Fc_t by substituting the unsprung displacement Zu (second unsprung displacement Zu2) at the predicted passing position Pf into formula (3) or formula (4).

[0083] In step S35, the control device 70 controls the actuator 3A to generate a target control force Fc_t at the timing when the wheel 2 passes through the predicted passing position Pf. The timing when the wheel 2 passes through the predicted passing position Pf can be known from the preview time tp.

[0084] The preview control using the unsprung displacement map 200 described above makes it possible to effectively suppress vibration of the vehicle 1 (sprung structure 5). In particular, by using the second unsprung displacement Zu2 without phase shift obtained from the second unsprung displacement map 220, the target control force Fc_t required for vibration suppression can be generated with high accuracy at the predicted passing position Pf. In other words, the accuracy of the preview control is improved.

[0085] 5. Self-location estimation process using unsprung displacement map Next, a description will be given of the self-location estimation process (Localization) using the unsprung displacement map 200. The self-location estimation process is a process in which the control device 70 of the vehicle control system 10 estimates the vehicle location of the vehicle 1.

[0086] FIG. 14 is a conceptual diagram for explaining an overview of the self-position estimation process using the unsprung displacement map 200. As shown in FIG.

[0087] The actual trajectory TR_A is the actual trajectory (path) traveled by the wheel 2. The unsprung displacement Zu on the actual trajectory TR_A is hereinafter referred to as the "detected unsprung displacement Zu_d" or the "detected parameter." The time series data of the detected unsprung displacement Zu_d along the actual trajectory TR_A is acquired in real time by the method shown in FIG. 3.

[0088] On the other hand, the virtual trajectory TR_I is a trajectory that is virtually set in the vicinity of the vehicle 1. The virtual trajectory TR_I is set based on the approximate vehicle position and traveling direction obtained from the position information 94. Hereinafter, a plurality of virtual trajectories TR_I are set. As an example, three types of virtual trajectories TR_I[1] to TR_I[3] are shown in FIG. 14. The unsprung displacement Zu on the virtual trajectory TR_I is hereinafter referred to as the "reference unsprung displacement Zu_ref" or the "reference parameter." Time series data of the reference unsprung displacement Zu_ref along each virtual trajectory TR_I is acquired from the unsprung displacement map 200 (reference map).

[0089] FIG. 15 shows an example of time series data of the detected unsprung displacement Zu_d and time series data of multiple reference unsprung displacements Zu_ref. The horizontal axis is position or time. Conversion between the space domain and the time domain is possible by using the vehicle speed V.

[0090] For example, a comparison is made between the time series data of the detected unsprung displacement Zu_d and the time series data of a plurality of reference unsprung displacements Zu_ref. Then, one virtual trajectory TR_I in which the correlation between the time series data of the detected unsprung displacement Zu_d and the time series data of the reference unsprung displacement Zu_ref is highest is selected. In the example shown in FIG. 15, the correlation between the time series data of the detected unsprung displacement Zu_d and the time series data of the reference unsprung displacement Zu_ref[2] is highest, so the virtual trajectory TR_I[2] is selected. The position of the selected virtual trajectory TR_I[2] is estimated as the position of the wheel 2, and the vehicle position is calculated from the position of the wheel 2. The relative positional relationship between the reference point of the vehicle position on the vehicle 1 and each wheel 2 is known information.

[0091] FIG. 16 is a flowchart outlining the self-position estimation process using the unsprung displacement map 200.

[0092] In step S100, the control device 70 acquires time-series data of the detected unsprung displacement Zu_d in real time by the method shown in FIG.

[0093] In step S200, the control device 70 acquires the reference unsprung displacement Zu_ref around the vehicle 1 from the unsprung displacement map 200 (reference map).

[0094] In step S300, the control device 70 estimates the vehicle position based on a comparison between the time series data of the detected unsprung displacement Zu_d and the time series data of the reference unsprung displacement Zu_ref.

[0095] FIG. 17 is a flowchart showing an example of steps S200 and S300.

[0096] In step S210, the control device 70 sets a plurality of virtual trajectories TR_I of the wheels 2 around the vehicle 1. The approximate position and traveling direction of the vehicle 1 are obtained from the position information 94. The control device 70 sets a plurality of virtual trajectories TR_I of the wheels 2 based on the approximate position and traveling direction of the vehicle 1.

[0097] In step S220, the control device 70 acquires time-series data of the reference unsprung displacement Zu_ref along each virtual trajectory TR_I from the unsprung displacement map 200 (reference map).

[0098] In step S310, the control device 70 compares the time series data of the detected unsprung displacement Zu_d with the time series data of the reference unsprung displacements Zu_ref. Then, the control device 70 selects one virtual trajectory TR_I from the multiple virtual trajectories TR_I that has the highest correlation between the time series data of the detected unsprung displacement Zu_d and the time series data of the reference unsprung displacement Zu_ref.

[0099] In step S320, the control device 70 estimates the vehicle position based on the position of the selected one of the virtual trajectories TR_I.

[0100] The present inventors have studied a method for further improving the accuracy of the self-position estimation process using the unsprung displacement map 200. Hereinafter, the improvement of the estimation accuracy of the vehicle position will be described from various viewpoints.

[0101] 5-1. First example of improving estimation accuracy As described above, in step S100, the control device 70 acquires the time series data of the detected unsprung displacement Zu_d in real time by the method shown in FIG. 3. At this time, due to the constraint of the processing time, it is not possible to use a zero-phase filter. Therefore, the control device 70 acquires the time series data of the detected unsprung displacement Zu_d through the first filtering process using the first filter. As a result, a "phase shift" occurs in the time series data of the detected unsprung displacement Zu_d.

[0102] If the second unsprung displacement map 220 (see Section 3-2-2) is used as the reference map in step S200, the second unsprung displacement Zu2 without phase shift is read out as the reference unsprung displacement Zu_ref. If the second unsprung displacement Zu2 is used as is in step S300, the time series data of the detected unsprung displacement Zu_d with phase shift and the time series data of the reference unsprung displacement Zu_ref with no phase shift are compared. This leads to a decrease in the estimation accuracy of the vehicle position.

[0103] Therefore, in the first example, in step S200, the control device 70 uses the first unsprung displacement map 210 (see Section 3-2-1) as a reference map. The first unsprung displacement Zu1 in the first unsprung displacement map 210 is calculated through a first filtering process using a first filter, similar to the case of step S100. Therefore, the phases of the time series data of the detected unsprung displacement Zu_d and the time series data of the reference unsprung displacement Zu_ref (first unsprung displacement Zu1) compared in step S300 match. As a result, it becomes possible to estimate the vehicle position with high accuracy.

[0104] 5-2. Second example of improving estimation accuracy In the second example, the second unsprung displacement map 220 (see Section 3-2-2) is used as a reference map in step S200. However, as described above, if the second unsprung displacement Zu2 is used as is, a phase mismatch occurs.

[0105] Therefore, the control device 70 reads out the second unsprung displacement Zu2 from the second unsprung displacement map 220 as the reference unsprung displacement Zu_ref, and then applies a first filter to the time series data of the read reference unsprung displacement Zu_ref. That is, the control device 70 performs a first filtering process on the time series data of the reference unsprung displacement Zu_ref (second unsprung displacement Zu2) acquired from the reference map. In step S300, the time series data of the reference unsprung displacement Zu_ref after the first filtering process is compared with the time series data of the detected unsprung displacement Zu_d. As a result, the phases of the two match, making it possible to estimate the vehicle position with high accuracy.

[0106] As a modified example, the control device 70 may obtain the amount of phase shift caused by the first filter in advance, and correct the position of the reference unsprung displacement Zu_ref (second unsprung displacement Zu2) obtained from the reference map by the amount of phase shift.

[0107] 5-3. Third example of improving estimation accuracy When the unsprung displacement map 200 indicates the correspondence relationship between the unsprung displacement Zu before the filtering process and the position (see Section 3-2-3), the same process as in the second example is performed. The control device 70 reads out the unsprung displacement Zu before the filtering process from the unsprung displacement map 200 as the reference unsprung displacement Zu_ref. Then, the control device 70 performs a first filtering process on the time series data of the read reference unsprung displacement Zu_ref, or corrects the position by the amount of phase shift.

[0108] 5-4. Fourth example of improving estimation accuracy 9, even if the wheel 2 moves along the exact same path, the sampling result by the sensor changes according to the vehicle speed V. In other words, the frequency band of the unsprung displacement Zu changes according to the vehicle speed V. If the vehicle speed V when the unsprung displacement map 200 is created is significantly different from the vehicle speed V at step S100, the detected unsprung displacement Zu_d and the reference unsprung displacement Zu_ref of different frequency bands are compared in step S300. This leads to a decrease in the estimation accuracy of the vehicle position.

[0109] Therefore, in the fourth example, the vehicle position estimation process is performed taking into account the vehicle speed V as well. For this purpose, an unsprung displacement map 200 (see Section 3-2-4, Figs. 10 and 11) generated taking into account the vehicle speed V as well is used as a reference map. The reference map represents the correspondence between the position, the vehicle speed V, and the unsprung displacement Zu. In step S200, the control device 70 obtains the reference unsprung displacement Zu_ref according to the current vehicle speed V from the reference map. This causes the effective frequency bands of the detected unsprung displacement Zu_d and the reference unsprung displacement Zu_ref to match, improving the estimation accuracy of the vehicle position.

[0110] 10, a different unsprung displacement map 200 is prepared for each vehicle speed range. In step S200, the control device 70 selects, as a reference map, the unsprung displacement map 200 for the vehicle speed range to which the current vehicle speed V belongs. Then, the control device 70 obtains the reference unsprung displacement Zu_ref from the selected reference map.

[0111] 11, the unsprung displacement map 200 represents the correspondence relationship between the vehicle speed V (or vehicle speed range) and the unsprung displacement Zu for each position of the unit area M. In step S200, the control device 70 selects an entry of the unit area M to which the virtual trajectory TR_I belongs. Then, the control device 70 obtains the reference unsprung displacement Zu_ref corresponding to the current vehicle speed V from the selected entry.

[0112] 5-5. Fifth example of improving estimation accuracy In the fifth example, the effective frequency band of the unsprung displacement Zu, which depends on the vehicle speed V, is considered. As described above, when the vehicle is traveling at a low speed, data with a short wavelength and high spatial frequency is detected. On the other hand, when the vehicle is traveling at a high speed, data with a long wavelength and low spatial frequency is detected. In other words, the vehicle speed V is inversely proportional to the detectable spatial frequency.

[0113] As an example, the unsprung displacement map 200 was generated based on data when the vehicle speed V was 50 kph, but the current vehicle speed V is 100 kph. Also, the filter setting is 0.3 to 10 Hz. From the perspective of the current vehicle speed V (= 100 kph), the effective frequency band of the reference unsprung displacement Zu_ref obtained from the unsprung displacement map 200 is 0.6 to 20 Hz. That is, data on the low frequency side is missing. Therefore, a common effective frequency band (= 0.6 to 10 Hz) where the effective frequency band (= 0.3 to 10 Hz) of the detected unsprung displacement Zu_d and the effective frequency band (= 0.6 to 20 Hz) of the reference unsprung displacement Zu_ref overlap is extracted. Then, in the common effective frequency band (= 0.6 to 10 Hz), the time series data of the detected unsprung displacement Zu_d and the time series data of the reference unsprung displacement Zu_ref are compared. This suppresses the influence of the difference in vehicle speed V.

[0114] As an opposite example, consider a case where the unsprung displacement map 200 is generated based on data when the vehicle speed V is 100 kph, but the current vehicle speed V is 50 kph. When viewed from the current vehicle speed V (= 50 kph), the effective frequency band of the reference unsprung displacement Zu_ref obtained from the unsprung displacement map 200 is 0.15 to 5 Hz. In other words, data on the high frequency side is missing. Therefore, a common effective frequency band (= 0.3 to 5 Hz) where the effective frequency band (= 0.15 to 5 Hz) of the detected unsprung displacement Zu_d and the effective frequency band (0.3 to 10 Hz) of the reference unsprung displacement Zu_ref overlap is extracted. Then, in the common effective frequency band (= 0.3 to 5 Hz), the time series data of the detected unsprung displacement Zu_d and the time series data of the reference unsprung displacement Zu_ref are compared. This suppresses the influence of the difference in vehicle speed V.

[0115] In this way, the effective frequency band of the unsprung displacement Zu is inversely proportional to the vehicle speed V. The lower the vehicle speed V, the higher the effective frequency band, and the higher the vehicle speed V, the lower the effective frequency band. In step S300, the control device 70 recognizes the effective frequency band of the detected unsprung displacement Zu_d based on the current vehicle speed V. The control device 70 also recognizes the effective frequency band of the reference unsprung displacement Zu_ref obtained from the unsprung displacement map 200. The effective frequency band of the reference unsprung displacement Zu_ref is recognized based on the vehicle speed V when the unsprung displacement Zu registered in the unsprung displacement map 200 is calculated. The vehicle speed V when the unsprung displacement Zu registered in the unsprung displacement map 200 is calculated may be registered in the unsprung displacement map 200. Furthermore, the control device 70 recognizes a common effective frequency band where the effective frequency band of the detected unsprung displacement Zu_d and the effective frequency band of the reference unsprung displacement Zu_ref overlap. Then, the control device 70 compares the time series data of the detected unsprung displacement Zu_d with the time series data of the reference unsprung displacement Zu_ref in the common effective frequency band. For example, the control device 70 uses a band pass filter or the like to extract components of the detected unsprung displacement Zu_d and the reference unsprung displacement Zu_ref in the common effective frequency band and compares them.

[0116] 5-6. Sixth example of improving estimation accuracy When there are few road surface irregularities in the lateral direction intersecting the traveling direction of the vehicle 1, there is a risk of reducing the accuracy of estimating the lateral position of the vehicle 1. Therefore, in the sixth example, a "lateral position correction process" is performed to correct the lateral position of the vehicle 1 as necessary.

[0117] 18 is a conceptual diagram for explaining the lateral position correction process. The XY coordinate system is a vehicle coordinate system fixed to the vehicle 1. The X direction represents the traveling direction of the vehicle 1, and the Y direction represents the lateral direction of the vehicle 1. The X direction and the Y direction intersect. The traveling direction of the vehicle 1 is obtained from the position information 94.

[0118] "Lateral position Dy" is the Y-direction position of the vehicle 1 on the road. A reference line WL extending along the road is considered as a reference for the lateral position Dy. Examples of the reference line WL include a white line and a curb. The lateral position Dy is the Y-direction position of the vehicle 1 based on the reference line WL. In other words, the lateral position Dy corresponds to the Y-direction distance between the reference line WL and the vehicle position (or the position of the wheels 2). Since the relative positional relationship between the reference point of the vehicle position and each wheel 2 is known, the vehicle position and the position of the wheels 2 are treated as equivalent.

[0119] The lateral position Dy is obtained from surrounding situation information 93 (see FIG. 5) indicating the recognition result by a recognition sensor 30 (e.g., a camera, LIDAR) mounted on the vehicle 1. Specifically, a reference line WL such as a white line or a curb is recognized by using the recognition sensor 30, and the relative position of the reference line WL with respect to the vehicle 1 is calculated. For example, the reference line WL can be identified and the relative position of the reference line WL can be calculated by analyzing image information obtained by a camera. Also, a curb can be identified and the relative position of the curb can be obtained based on point cloud information obtained by LIDAR. Then, the lateral position Dy is obtained from the relative position of the reference line WL with respect to the vehicle 1.

[0120] "Road surface roughness Ry" represents the roughness in the Y direction of the road surface RS around the vehicle 1. In other words, the road surface roughness Ry represents the variation in the Y direction of the road surface displacement Zr around the vehicle 1. In further other words, the road surface roughness Ry represents the degree to which the road surface displacement Zr changes in the Y direction. The road surface roughness Ry is also obtained from the surrounding situation information 93 indicating the recognition result by the recognition sensor 30 mounted on the vehicle 1. For example, the road surface displacement Zr of the road surface RS around the vehicle 1 is measured by LIDAR. The road surface roughness Ry is calculated based on the variance in the Y direction of the measured road surface displacement Zr. The larger the variance in the Y direction of the road surface displacement Zr, the larger the road surface roughness Ry. For example, as shown in FIG. 18, the road surface roughness Ry at a position Xa in front of the vehicle 1 is calculated.

[0121] For convenience, the vehicle position estimated by the above steps S100 to S300 is referred to as the "temporary vehicle position." The "temporary lateral position" is a component equivalent to the lateral position Dy in the temporary vehicle position. In other words, the temporary lateral position is the Y direction position of the temporary vehicle position based on the reference line WL. The temporary lateral position corresponds to the Y direction distance between the temporary vehicle position and the reference line WL. The position (LAT, LON) of the reference line WL in the absolute coordinate system is registered in the map information 91 (see FIG. 5). The temporary lateral position can be calculated based on the map information 91 and the temporary vehicle position.

[0122] When the road surface unevenness in the Y direction is small, that is, when the road surface roughness Ry is small, the estimation accuracy of the tentative lateral position of the tentative vehicle position estimated by steps S100 to S300 may be low. On the other hand, the accuracy of the lateral position Dy recognized by the recognition sensor 30 is relatively high. Therefore, when the road surface roughness Ry is less than a predetermined threshold value Ry_th, the tentative lateral position of the tentative vehicle position is replaced by the lateral position Dy recognized by the recognition sensor 30. That is, the estimated vehicle position is corrected based on the lateral position Dy, and the final estimated vehicle position is determined. This is the lateral position correction process. This lateral position correction process makes it possible to improve the estimation accuracy of the vehicle position even when the road surface roughness Ry is small.

[0123] 19 is a flowchart showing an example of the lateral position correction process. The control device 70 of the vehicle control system 10 performs the lateral position correction process (step S400) following the above steps S100 to S300.

[0124] In step S410, the control device 70 recognizes the road surface roughness Ry using the recognition sensor 30. In step S420, the control device 70 recognizes the lateral position Dy using the recognition sensor 30. In step S430, the control device 70 compares the road surface roughness Ry with a threshold value Ry_th. If the road surface roughness Ry is less than the threshold value Ry_th (step S430; Yes), the control device 70 corrects the estimated vehicle position by replacing the tentative lateral position of the tentative vehicle position with the lateral position Dy (step S450). On the other hand, if the road surface roughness Ry is equal to or greater than the threshold value Ry_th (step S430; No), the control device 70 adopts the tentative vehicle position as the estimated vehicle position as it is (step S460).

[0125] 20 is a flowchart showing another example of the lateral position correction process. Instead of executing step S420 before step S430, the control device 70 executes step S440 between step S430 and step S450. The content of step S440 is the same as that of step S420. That is, the control device 70 calculates the lateral position Dy only when the road surface roughness Ry is less than the threshold value Ry_th (step S430; Yes).

[0126] As shown in FIG. 18, the road roughness Ry recognized by the recognition sensor 30 is typically the road roughness Ry at a position Xa ahead of the vehicle 1. The position Xa does not coincide with the tentative vehicle position. However, since road surface structures such as ruts extend in the X direction, it can be assumed that the road roughness Ry continues for a certain distance in the X direction. Therefore, in step S430, the road roughness Ry at the position Xa may be compared with a threshold value Ry_th. As a modified example, the position Xa may be aligned with the tentative vehicle position. That is, the road roughness Ry at the current tentative vehicle position may be obtained from time-series data of the road roughness Ry obtained in the past, and the road roughness Ry at the tentative vehicle position may be compared with the threshold value Ry_th.

[0127] As another modification, in step S410, when ruts on the road surface RS are detected by the recognition sensor 30, the road surface roughness Ry may be automatically set to a value equal to or greater than the threshold value Ry_th. In other words, when ruts on the road surface RS are detected by the recognition sensor 30, the road surface roughness Ry may be regarded as being equal to or greater than the threshold value Ry_th (step S430; No).

[0128] The above-described lateral position correction process makes it possible to improve the accuracy of estimating the vehicle position even when there are few bumps and grooves on the road surface in the lateral direction.

[0129] 5-7. Seventh example of improving estimation accuracy As long as no contradiction occurs, it is possible to combine two or more of the first to sixth examples described above. By combining them, the estimation accuracy of the vehicle position is further improved.

[0130] Effects As described above, according to this embodiment, the accuracy of the self-position estimation process using the unsprung displacement map 200 is improved. That is, the estimation accuracy of the vehicle position is improved.

[0131] For example, it is possible to estimate the vehicle position with high accuracy even in an area where the GNSS cannot cover (e.g., a tunnel) or an area where the position estimation accuracy by the GNSS is low. Furthermore, the method according to the present embodiment can be a substitute for the expensive GNSS.

[0132] The vehicle position (position information 94) estimated by the self-position estimation process according to this embodiment is used for various vehicle controls. For example, the vehicle position is used for driving assistance control that assists the driving of the vehicle 1. Examples of driving assistance control include lane keeping control, collision avoidance control, and automatic driving control. The vehicle position is also used for the above-mentioned preview control (see FIGS. 12 and 13). As the estimation accuracy of the vehicle position is improved, the accuracy of vehicle control that uses the vehicle position is also improved.

[0133] 6. Map update process FIG. 21 is a flowchart showing the map update process according to this embodiment.

[0134] In step S500, the control device 70 of the vehicle control system 10 transmits map update information to the map management system 100 via the communication device 50. The map update information includes time series data of the position information 94 indicating the vehicle position obtained by the self-position estimation process described in Section 5. The map update information also includes time series data of sensor-based information (e.g., sprung displacement Zs, stroke H) required to calculate the unsprung displacement Zu. Alternatively, the map update information may include time series data of the unsprung displacement Zu calculated by the control device 70. Furthermore, the map update information may include time series data of the vehicle speed V. Furthermore, the map update information may include information indicating the contents of the self-position estimation process. For example, the contents of the self-position estimation process include whether or not the estimated vehicle position has been corrected based on the lateral position Dy, that is, whether step S450 or step S460 has been performed.

[0135] In step S600, the processor 120 of the map management system 100 receives the map update information via the communication device 110. The processor 120 updates the unsprung displacement map 200 based on the received map update information.

[0136] There is a case where high-precision position information has already been registered as position information in the unsprung displacement map 200. In that case, the processor 120 updates the position information in the unsprung displacement map 200 by combining the registered high-precision position information with the position information 94 included in the map update information. At this time, the weight for the position information 94 included in the map update information may be reduced. For example, when the weight for the registered high-precision position information is 1, the weight for the position information 94 included in the map update information is set to 0.5.

[0137] Furthermore, when the estimated vehicle position is corrected based on the lateral position Dy (step S450), the position estimation accuracy may be lower than when no correction is required (step S460). Therefore, the weight for the position information 94 when step S450 is performed may be set smaller than the weight when step S460 is performed.

[0138] As another example, only the unsprung displacement map 200 in areas where high-precision position information has not yet been registered may be updated. [Explanation of symbols]

[0139] 1 vehicle 2 wheels 3. Suspension 3A Actuator 10 Vehicle Control System 20 Vehicle condition sensor 30 Recognition Sensor 40 Position Sensor 50 Communication Equipment 60 Running gear 70 Control device 80 Vehicle Control Program 90 Driving Environment Information 93 Surrounding Area Information 94 Location information 100 Map Management System 110 Communication equipment 120 processors 130 Storage device 140 Map Management Program 200 Unsprung Displacement Map 210 1st unsprung displacement map 220 Second unsprung displacement map Dy Horizontal position Ry Road roughness WL reference line Zu Unsprung Displacement Zu_d Detected unsprung displacement Zu_ref Reference unsprung displacement

Claims

1. acquiring time series data of a parameter related to vertical movement of a wheel of the vehicle while the vehicle is traveling; acquiring the parameters of the surroundings of the vehicle as reference parameters from a parameter map that represents a correspondence relationship between the parameters and positions; estimating a vehicle position of the vehicle based on a comparison between the time series data of the parameter and the time series data of the reference parameter; A process of recognizing road surface roughness representing roughness in a lateral direction of a road surface around the vehicle using a recognition sensor mounted on the vehicle; using the recognition sensor to recognize the lateral position of the vehicle on a road; if the road surface roughness is less than a threshold, replacing the lateral position component in the estimated vehicle position with the lateral position recognized using the recognition sensor; Includes Vehicle position estimation method.

2. 2. A vehicle position estimation method according to claim 1, comprising: If the road surface roughness is equal to or greater than the threshold, adopting the estimated vehicle position. Vehicle position estimation method.

3. 3. A vehicle position estimation method according to claim 1, further comprising: The process of recognizing road surface roughness includes: A process of measuring a road surface displacement around the vehicle using the recognition sensor; A process of calculating the road surface roughness based on the variance of the road surface displacement in the lateral direction; Includes Vehicle position estimation method.

4. A vehicle position estimation method according to any one of claims 1 to 3, comprising: The process of estimating the vehicle position includes: establishing a plurality of virtual trajectories of the wheels around the vehicle; obtaining the time series data of the reference parameter along each of the plurality of virtual trajectories from the parameter map; selecting one virtual trajectory from the plurality of virtual trajectories, which has the highest correlation between the time series data of the parameter and the time series data of the reference parameter; estimating the vehicle position based on a position of the one selected virtual trajectory; Includes Vehicle position estimation method.

5. A vehicle position estimation method according to any one of claims 1 to 4, comprising: The parameters are calculated based on sensor-based information obtained by a sensor mounted on the vehicle; The process of acquiring the time series data of the parameter while the vehicle is traveling includes a first filtering process of applying a first filter to the sensor-based information or the time series data of the parameter; The parameters in the parameter map are also calculated through the first filtering process using the first filter. Vehicle position estimation method.

6. A vehicle position estimation method according to any one of claims 1 to 4, comprising: The parameters are calculated based on sensor-based information obtained by a sensor mounted on the vehicle; The process of acquiring the time series data of the parameter while the vehicle is traveling includes a first filtering process of applying a first filter to the sensor-based information or the time series data of the parameter; the parameters in the parameter map are calculated by applying a zero-phase filter to the sensor-based information or time series data of the parameters; The process of acquiring the reference parameters includes performing the first filtering process on the time-series data of the reference parameters acquired from the parameter map; In the process of estimating the vehicle position, the time series data of the reference parameters after the first filtering process is used. Vehicle position estimation method.

7. A vehicle position estimation method according to any one of claims 1 to 6, comprising: the parameter map represents a correspondence relationship between the parameters, the position, and the vehicle speed; The process of acquiring the reference parameters includes a process of acquiring the reference parameters corresponding to the vehicle speed from the parameter map. Vehicle position estimation method.

8. A vehicle position estimation method according to claim 7, further comprising the steps of: the parameter map represents the correspondence relationship between the parameters and the positions for each vehicle speed range; The process of acquiring the reference parameters includes: selecting the parameter map for the vehicle speed range to which the vehicle speed of the vehicle belongs; obtaining the reference parameters from the selected parameter map; Includes Vehicle position estimation method.

9. A vehicle position estimation method according to any one of claims 1 to 6, comprising: The effective frequency band of the parameter is inversely proportional to the vehicle speed, The process of estimating the vehicle position includes comparing the time series data of the parameter with the time series data of the reference parameter in a common effective frequency band where the effective frequency band of the parameter and the effective frequency band of the reference parameter overlap. Vehicle position estimation method.

10. One or more processors; The one or more processors: acquiring time series data of a parameter related to vertical movement of a wheel of the vehicle while the vehicle is traveling; acquiring the parameters of the surroundings of the vehicle as reference parameters from a parameter map that represents a correspondence relationship between the parameters and positions; estimating a vehicle position of the vehicle based on a comparison between the time series data of the parameter and the time series data of the reference parameter; A process of recognizing road surface roughness representing roughness in a lateral direction of a road surface around the vehicle using a recognition sensor mounted on the vehicle; using the recognition sensor to recognize the lateral position of the vehicle on a road; if the road surface roughness is less than a threshold, replacing the lateral position component in the estimated vehicle position with the lateral position recognized using the recognition sensor; configured to run Vehicle control system.

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