Processing system, processing device, processing method, and processing program

Abstract

To provide a processing system improving the drivability of a host vehicle.SOLUTION: A processor in a processing system for making the traveling of a host vehicle follow a target raceway to control, is configured to execute estimating a host state amount of the host vehicle by dead reckoning based on internal information on the host vehicle, estimating the host state amount by map matching based on external information on the host vehicle and map information, obtaining an estimated state amount giving an estimated correction amount based on the host state amount by the map matching to the host state amount by the dead reckoning, and adjusting a control parameter for controlling the traveling of the host vehicle on the basis of a deviation of the estimated state amount with respect to a target state amount indicated by the target raceway, according to the estimated correction amount.SELECTED DRAWING: Figure 5

Description

[Technical field]

[0001] The present disclosure relates to a technique for controlling the traveling of a host vehicle to follow a target trajectory. [Background technology]

[0002] The technology disclosed in Patent Document 1 estimates the state quantity of the host vehicle by dead reckoning based on the internal information of the host vehicle and by map matching based on the external information and map information of the host vehicle. Therefore, in the technology disclosed in Patent Document 1, an estimation result is obtained in which an error based on the state quantity by map matching is applied as a correction amount to the state quantity by dead reckoning. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2008-249639 A Summary of the Invention [Problem to be solved by the invention]

[0004] For example, when the host vehicle moves from an inhibiting area where map matching is inhibited to an allowable area where map matching is allowed, errors in the state quantities due to map matching are accumulated in the inhibiting area. As a result, the correction quantities based on the state quantities due to map matching change suddenly as the host vehicle moves to the allowable area. In a case where the estimation result is used to control the running of the host vehicle to follow a target trajectory, such a sudden change in the correction quantities may disturb the behavior of the host vehicle due to the running control, thereby deteriorating drivability.

[0005] An object of the present disclosure is to provide a processing system for improving the drivability of a host vehicle. Another object of the present disclosure is to provide a processing device for improving the drivability of a host vehicle. Yet another object of the present disclosure is to provide a processing method for improving the drivability of a host vehicle. Yet another object of the present disclosure is to provide a processing program for improving the drivability of a host vehicle. [Means for solving the problem]

[0006] The technical means of the present disclosure for solving the problems will be described below. Note that the claims and the reference characters in parentheses in this section indicate the corresponding relationship with the specific means described in the embodiments described later in detail, and do not limit the technical scope of the present disclosure.

[0007] A first aspect of the present disclosure is A processing system having a processor (12) for controlling a host vehicle (2) to follow a target trajectory, The processor Estimating a host state quantity, which is a state quantity of the host vehicle, by dead reckoning based on internal information of the host vehicle; Estimating a host state quantity by map matching based on external information of a host vehicle and map information; Obtaining an estimated state quantity by applying an estimated correction amount based on the host state quantity obtained by map matching to the host state quantity obtained by dead reckoning; and adjusting, in accordance with the estimated correction amount, a control parameter for controlling the running of the host vehicle based on the deviation of the estimated state quantity from the target state quantity represented by the target trajectory.

[0008] A second aspect of the present disclosure is A processing device having a processor (12) for controlling a host vehicle (2) to follow a target trajectory, The processor Estimating a host state quantity, which is a state quantity of the host vehicle, by dead reckoning based on internal information of the host vehicle; Estimating a host state quantity by map matching based on external information of a host vehicle and map information; Obtaining an estimated state quantity by applying an estimated correction amount based on the host state quantity obtained by map matching to the host state quantity obtained by dead reckoning; and adjusting, in accordance with the estimated correction amount, a control parameter for controlling the running of the host vehicle based on the deviation of the estimated state quantity from the target state quantity represented by the target trajectory.

[0009] A third aspect of the present disclosure is A processing method executed by a processor (12) for controlling a host vehicle (2) to follow a target trajectory, comprising: Estimating a host state quantity, which is a state quantity of the host vehicle, by dead reckoning based on internal information of the host vehicle; Estimating a host state quantity by map matching based on external information of a host vehicle and map information; Obtaining an estimated state quantity by applying an estimated correction amount based on the host state quantity obtained by map matching to the host state quantity obtained by dead reckoning; and adjusting, in accordance with the estimated correction amount, a control parameter for controlling the running of the host vehicle based on the deviation of the estimated state quantity from the target state quantity represented by the target trajectory.

[0010] A fourth aspect of the present disclosure is A processing program stored in a recording medium (10) for controlling a host vehicle (2) to follow a target trajectory, the processing program including instructions to be executed by a processor (12), The command is, A host state quantity, which is a state quantity of the host vehicle, is estimated by dead reckoning based on internal information of the host vehicle; Estimating a host state quantity by map matching based on external information and map information of a host vehicle; Acquiring an estimated state quantity by applying an estimated correction amount based on the host state quantity obtained by map matching to the host state quantity obtained by dead reckoning; The method includes adjusting, in accordance with the estimated correction amount, a control parameter for controlling the running of the host vehicle based on the deviation of the estimated state quantity from the target state quantity represented by the target trajectory.

[0011] In these first to fourth aspects, an estimated state quantity is obtained by applying an estimated correction amount based on the host state quantity by map matching to the host state quantity by dead reckoning. Therefore, according to the first to fourth aspects, a control parameter for controlling the running of the host vehicle based on the deviation of the estimated state quantity from the target state quantity represented by the target trajectory is adjusted according to the estimated correction amount. With this, even if the estimated correction amount changes suddenly, the running control can be applied to the host vehicle so as to follow the control parameter adapted to the suddenly changed estimated correction amount. Therefore, it is possible to suppress a situation in which the behavior of the host vehicle is disturbed by the running control and improve drivability. [Brief description of the drawings]

[0012] [Figure 1] 1 is a block diagram showing an overall configuration of an embodiment; [Diagram 2] FIG. 2 is a schematic diagram showing a driving environment of a host vehicle to which an embodiment is applied. [Diagram 3] 1 is a block diagram showing a functional configuration of a processing system according to an embodiment. [Figure 4] FIG. 11 is a schematic diagram for explaining deviation acquisition according to an embodiment. [Diagram 5] 1 is a flow chart illustrating a processing method according to one embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0013] Hereinafter, one embodiment of the present disclosure will be described with reference to the drawings.

[0014] The processing system 1 of one embodiment shown in Fig. 1 is a system for controlling the running of a host vehicle 2 to follow a target trajectory Tt in which a target state quantity is defined in a time series as shown in Fig. 2. Here, from a viewpoint centered on the host vehicle 2, the host vehicle 2 can also be said to be an ego-vehicle.

[0015] The host vehicle 2 is provided with an autonomous driving mode, the level of which is determined according to the degree of manual intervention by the occupant in the driving task. The autonomous driving mode may be realized by autonomous driving control, such as conditional driving automation, high driving automation, or full driving automation, in which the system performs all driving tasks when activated. The autonomous driving mode may also be realized by advanced driving assistance control, such as driving assistance or partial driving automation, in which the occupant performs some or all driving tasks. The autonomous driving mode may be realized by either one of the autonomous driving control and the advanced driving assistance control, or by a combination of them, or by switching between them.

[0016] The host vehicle 2 is equipped with a sensor system 5 and a map database 7 shown in Fig. 1. The sensor system 5 acquires sensor information usable by the processing system 1 by detecting the external and internal environments of the host vehicle 2. To this end, the sensor system 5 is configured to include an external sensor 50 and an internal sensor 52.

[0017] The external sensor 50 acquires external information usable by the processing system 1 from the external world that is the surrounding environment of the host vehicle 2. The external sensor 50 may acquire the external information by detecting a target object present in the external world of the host vehicle 2. The target detection type external sensor 50 is at least one of a camera, a LiDAR (Light Detection and Ranging / Laser Imaging Detection and Ranging), a radar, a sonar, etc. The external sensor 50 may acquire the external information by receiving a positioning signal from a satellite of a Global Navigation Satellite System (GNSS) present in the external world of the host vehicle 2. The positioning type external sensor 50 is, for example, a GNSS receiver, etc.

[0018] The internal sensor 52 acquires internal information usable by the processing system 1 from the internal world, which is the internal environment of the host vehicle 2. The internal sensor 52 may acquire the internal information by detecting a specific physical amount of motion in the internal world of the host vehicle 2. The internal sensor 52 of the physical amount detection type is at least one of a vehicle speed sensor, an inertial sensor, a steering angle sensor, etc. The internal sensor 52 may acquire the internal information by detecting a specific state of an occupant in the internal world of the host vehicle 2. The internal sensor 52 of the occupant detection type is at least one of a driver status monitor (registered trademark), a biological sensor, a seating sensor, an actuator sensor, an in-vehicle equipment sensor, etc.

[0019] The map database 7 stores map information that can be used by the processing system 1. The map database 7 includes at least one type of non-transitory tangible storage medium, such as a semiconductor memory, a magnetic medium, or an optical medium. The map database 7 may be a database of a locator that estimates state quantities including the position of the host vehicle 2. The map database 7 may be a database of a navigation unit that navigates the driving route of the host vehicle 2. The map database 7 may be a combination of multiple types of these databases.

[0020] The map database 7 acquires and stores the latest map information, for example, by communication with an external center. Here, the map information is digitized in two dimensions or three dimensions as information representing the driving environment of the host vehicle 2. In particular, as the three-dimensional map data, digital data of a high-precision map is preferably adopted. The map information may include road information representing at least one of the following: the position, shape, and road surface condition of the road itself. The map information may include marking information representing at least one of the following: the positions and shapes of signs and dividing lines attached to the road. The map information may include structure information representing at least one of the positions and shapes of buildings and traffic lights facing the road.

[0021] The processing system 1 is connected to a sensor system 5 and a map database 7 via at least one of, for example, a LAN (Local Area Network) line, a wire harness, an internal bus, and a wireless communication line. The processing system 1 is configured to include at least one dedicated computer.

[0022] The dedicated computer constituting the processing system 1 may be a driving control ECU (Electronic Control Unit) that controls the driving of the host vehicle 2. The dedicated computer constituting the processing system 1 may be a navigation ECU that navigates the driving route of the host vehicle 2. The dedicated computer constituting the processing system 1 may be a locator ECU that estimates the state quantity of the host vehicle 2. The dedicated computer constituting the processing system 1 may be an actuator ECU that controls a driving actuator of the host vehicle 2, such as a steering actuator 9 (see FIG. 3 described later). The dedicated computer constituting the processing system 1 may be an HCU (Human Machine Interface Control Unit (HMI)) that controls the presentation of information in the host vehicle 2. The dedicated computer constituting the processing system 1 may be a computer other than the host vehicle 2 that constitutes, for example, an external center or a mobile terminal capable of communicating with the host vehicle 2.

[0023] The dedicated computer constituting the processing system 1 has at least one memory 10 and one processor 12. The memory 10 is at least one type of non-transitory tangible storage medium, such as a semiconductor memory, a magnetic medium, or an optical medium, that non-temporarily stores computer-readable programs and data. The processor 12 includes at least one type of core, such as a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a RISC (Reduced Instruction Set Computer)-CPU, a DFP (Data Flow Processor), or a GSP (Graph Streaming Processor).

[0024] In the processing system 1, the processor 12 executes a plurality of instructions included in a processing program stored in the memory 10 in order to control the traveling of the host vehicle 2 to follow the target trajectory Tt. In this way, the processing system 1 constructs a plurality of function blocks for controlling the traveling of the host vehicle 2 to follow the target trajectory Tt. The plurality of function blocks constructed in the processing system 1 include an estimation block 100 and a following control block 110 shown in FIG.

[0025] The estimation block 100 has a dead reckoning block 101, a map matching block 102, and a fusion block 103 as sub-function blocks. Specifically, the dead reckoning block 101 estimates a host state quantity Zd, which is a state quantity of the host vehicle 2, by dead reckoning based on the internal field information acquired by the internal field sensor 52. At this time, the dead reckoning block 101 estimates a coordinate position Xd in the longitudinal direction, a coordinate position Yd in the lateral direction, and a yaw angle θd as an attitude angle as the host state quantity Zd ​​representing the traveling state of the host vehicle 2. Thus, examples of the internal field information used to estimate the host state quantity Zd ​​include the vehicle speed and angular velocity, which are the motion physical quantities of the host vehicle 2. At this time, the estimation of the host state quantity Zd ​​may also use positioning information acquired as external field information by the external field sensor 50 of a positioning type.

[0026] The map matching block 102 estimates a host state quantity Zm, which is a state quantity of the host vehicle 2, by map matching based on the external environment information acquired by the external environment sensor 50 and the map information read out from the map database 7. At this time, the map matching block 102 estimates a coordinate position Xm in the vertical direction, a coordinate position Ym in the horizontal direction, and a yaw angle θm as an attitude angle as the host state quantity Zm representing the traveling state of the host vehicle 2. Therefore, examples of the external environment information used to estimate the host state quantity Zm include a point cloud image observed by a LiDAR or an imaging radar. A point cloud map is used as the map information used to estimate the host state quantity Zm.

[0027] The fusion block 103 monitors the driving area in which the host vehicle 2 is driving. At this time, the fusion block 103 identifies, with respect to the driving area of ​​the host vehicle 2, an allowable area Ap in which map matching by the map matching block 102 is allowed and an inhibiting area Ai in which the map matching is inhibited, as shown in Fig. 2. Here, the allowable area Ap means a driving area in which map matching is allowed because it is possible to normally acquire both a point cloud image and a point cloud map. On the other hand, the inhibiting area Ai means a driving area in which map matching is inhibited because it is difficult to normally acquire at least one of a point cloud image and a point cloud map, for example, because there is no observable feature or the map information is not up to date.

[0028] The fusion block 103 shown in FIG. 3 fuses the host state amount Zd estimated by the dead reckoning block 101 and the host state amount Zm estimated by the map matching block 102 to obtain an estimated state amount Ze. At this time, the fusion block 103 obtains the coordinate position Xe in the longitudinal direction, the coordinate position Ye in the lateral direction, and the yaw angle θe as the attitude angle as the estimated state amount Ze representing the traveling state of the host vehicle 2. Therefore, in the allowable area Ap, the estimated state amount Ze given by the Kalman filter with the estimated correction amount ΔZ based on the host state amount Zm by the map matching block 102 for the host state amount Zd by the dead reckoning block 101 is calculated according to Equations 1 and 2. Here, K in Equation 2 is the Kalman gain in the Kalman filter. On the other hand, in the inhibition area Ai, the host state amount Zd by the dead reckoning block 101 is output as it is as the estimated state amount Ze without the estimated correction amount ΔZ.

number

number

[0029] The tracking control block 110 has, as sub-function blocks, a target trajectory acquisition block 111, a feedforward block 112, a deviation acquisition block 113, a feedback block 114, and a target steering angle acquisition block 115. Specifically, the target trajectory acquisition block 111 acquires a target trajectory Tt as a future driving trajectory that specifies a time-series change in a target state quantity Zt in the host vehicle 2 in the autonomous driving mode. At this time, the target trajectory acquisition block 111 sets a curvature κt of the target trajectory Tt, a coordinate position Xt in the vertical direction, a coordinate position Yt in the horizontal direction, and a yaw angle θt as an attitude angle as the target state quantity Zt represented by the target trajectory Tt, as shown in Fig. 4.

[0030] The feedforward block 112 shown in Fig. 3 converts the curvature κt acquired by the target trajectory acquisition block 111 through feedforward control using the vehicle speed V of the host vehicle 2 as a control gain to acquire a feedforward yaw rate γff. The deviation acquisition block 113 acquires deviations Δe and Δθ shown in Fig. 4 based on the coordinate positions Xt, Yt and yaw angle θt acquired by the target trajectory acquisition block 111 and the coordinate positions Xe, Ye and yaw angle θe acquired by the fusion block 103. Here, the deviation Δe is a position deviation Δe related to the lateral position of the host vehicle 2. On the other hand, the deviation Δθ is an attitude deviation Δθ related to the yaw angle among the attitude angles of the host vehicle 2.

[0031] The feedback block 114 converts the position deviation Δe acquired by the deviation acquisition block 113 into a yaw rate γie by integral (I) control using a feedback control gain Kie as a control parameter for controlling the traveling of the host vehicle 2 (hereinafter also simply referred to as a control parameter). The feedback block 114 converts the position deviation Δe acquired by the deviation acquisition block 113 into a yaw rate γpe by proportional (P) control using a feedback control gain Kpe as a control parameter. The feedback block 114 converts the attitude deviation Δθ acquired by the deviation acquisition block 113 into a yaw rate γpθ by proportional (P) control using a feedback control gain Kpθ as a control parameter. The feedback block 114 combines the converted yaw rates γie, γpe, and γpθ by addition operation to acquire a feedback yaw rate γfb.

[0032] In the allowable area Ap identified by the fusion block 103, the feedback block 114 selects at least one of the feedback control gains Kie, Kpe, and Kpθ, and performs adjustment according to the estimated correction amount ΔZ acquired by the fusion block 103. At this time, the feedback block 114 sets the selected gain as an adjustment target according to the estimated correction amount ΔZ so that the tracking of the driving control to the target state amount Zt in the host vehicle 2 decreases as the estimated correction amount ΔZ increases. Here, the gain to be adjusted according to the estimated correction amount ΔZ is a parameter related to steering control, which will be described later, among the driving control in the allowable area Ap.

[0033] Therefore, in the feedback block 114, at least one of the feedback control gains Kie, Kpe that weight the rate of change of the steering control with respect to the position deviation Δe in the permissible area Ap (i.e., the reflection rate of the position deviation Δe) may be adjusted as a position control gain corresponding to the estimated correction amount ΔZ. In this case, the position control gain is adjusted to be smaller so that the tracking ability of the steering control decreases as the estimated correction amount ΔZ increases. Also, in the feedback block 114, the feedback control gain Kpθ that weights the rate of change of the steering control with respect to the attitude deviation Δθ in the permissible area Ap (i.e., the reflection rate of the attitude deviation Δθ) may be adjusted as a posture control gain corresponding to the estimated correction amount ΔZ. In this case, the posture control gain is adjusted to be smaller so that the tracking ability of the steering control decreases as the estimated correction amount ΔZ increases.

[0034] Here, in the permissible area Ap, it is particularly preferable that the feedback control gain Kpe, which is the position control gain of the proportional control, and the feedback control gain Kpθ, which is the attitude control gain of the proportional control, are adjusted to be compatible with at least the estimated correction amount ΔZ. However, in the permissible area Ap, the gains Kie, Kpe, and Kpθ that are not selected as the gains to be adjusted according to the estimated correction amount ΔZ are adjusted to be constant values ​​previously obtained by adaptation. Also, the feedback control gains Kie, Kpe, and Kpθ in the inhibition area Ai are adjusted to be constant values ​​previously obtained by adaptation. The gain adjustment in the feedback block 114 described above may be realized using at least one of, for example, a map, a table, and a function formula stored in the memory 10.

[0035] The target steering angle acquisition block 115 converts the target yaw rate γt, which is the sum of the feedforward yaw rate γff acquired by the feedforward block 112 and the feedback yaw rate γfb acquired by the feedback block 114, into a target steering angle δt. At this time, the target steering angle acquisition block 115 calculates the target steering angle δt according to Equation 3 based on a two-wheel model. Here, A in Equation 3 is the stability factor of the host vehicle 2. L in Equation 3 is the wheelbase of the host vehicle 2. V in Equation 3 is the vehicle speed of the host vehicle 2.

number

[0036] The target steering angle δt thus acquired by the target steering angle acquisition block 115 is provided to the steering actuator 9 of the host vehicle 2, whereby the actual steering angle δr is adjusted by feedback control toward the target steering angle δt. As a result, the running state of the host vehicle 2, particularly the steering state, is controlled to follow the target trajectory Tt.

[0037] The flow of a processing method (hereinafter, referred to as the processing flow) in which the processing system 1 controls the host vehicle 2 to follow the target trajectory Tt by cooperation of the above-described blocks 100 and 110 will be described below with reference to Fig. 5. This processing flow is repeatedly executed during startup of the host vehicle 2. Note that each "S" in this processing flow refers to multiple steps executed by multiple commands included in the processing program.

[0038] In S101 of the processing flow, the dead reckoning block 101 estimates a host state quantity Zd ​​of the host vehicle 2 by dead reckoning based on the internal world information acquired by the internal world sensor 52. In S102 of the processing flow, the map matching block 102 estimates a host state quantity Zm of the host vehicle 2 by map matching based on the external world information acquired by the external world sensor 50 and the map information read out from the map database 7.

[0039] In S103 of the processing flow, the fusion block 103 monitors the driving area of ​​the host vehicle 2. As a result, if it is determined that the driving area is an allowed area Ap, an allowed flag is stored in the memory 10. On the other hand, if it is determined that the driving area is an inhibited area Ai, an inhibited flag is stored in the memory 10.

[0040] In S104 of the processing flow, the fusion block 103 obtains an estimated state quantity Ze by fusing the host state quantity Zd ​​estimated in S101 and the host state quantity Zm estimated in S102. At this time, if an allowance flag is stored in the memory 10, an estimated state quantity Ze is obtained by applying an estimated correction amount ΔZ based on the host state quantity Zm by S102 to the host state quantity Zd ​​by S101. On the other hand, if an inhibition flag is stored in the memory 10, the host state quantity Zd ​​by S101 is obtained as the estimated state quantity Ze excluding the estimated correction amount ΔZ based on the host state quantity Zm by S102.

[0041] In S105 of the process flow, the target trajectory acquisition block 111 acquires a target trajectory Tt of the host vehicle 2 in the autonomous driving mode. In S106 of the process flow, the feedforward block 112 acquires a feedforward yaw rate γff by conversion from the curvature κt of the target state quantity Zt represented by the target trajectory Tt acquired in S105. In S107 of the process flow, the deviation acquisition block 113 acquires a position deviation Δe and an attitude deviation Δθ. At this time, the deviations Δe and Δθ are acquired based on the coordinate positions Xt, Yt and the yaw angle θt of the target state quantity Zt represented by the target trajectory Tt acquired in S105, and the coordinate positions Xe, Ye and the yaw angle θe of the estimated state quantity Ze acquired in S104.

[0042] In S108 of the processing flow, the feedback block 114 converts the deviations Δe, Δθ acquired in S107 into yaw rates γie, γpe, γpθ using feedback control gains Kie, Kpe, Kpθ, and acquires a feedback yaw rate γfb by combining them. At this time, if an allowable flag is stored in the memory 10, the feedback block 114 selects at least one of the feedback control gains Kie, Kpe, Kpθ, and executes adjustment according to the estimated correction amount ΔZ acquired in S104. On the other hand, the gains in the case of non-selection even if an allowable flag is stored in the memory 10, and the feedback control gains Kie, Kpe, Kpθ in the case of an inhibition flag stored in the memory 10 are adjusted to be constant values ​​obtained in advance by adaptation.

[0043] In S109 of the processing flow, the target steering angle acquisition block 115 acquires the target steering angle δt by converting the target yaw rate γt, which is the sum of the feedforward yaw rate γff acquired in S106 and the feedback yaw rate γfb acquired in S108. The target steering angle δt acquired in S109 is given to the steering actuator 9, whereby the running state of the host vehicle 2, particularly the steering state, is controlled to follow the target trajectory Tt.

[0044] (Action and effect) The effects of the present embodiment described above will be described below.

[0045] In this embodiment, an estimated state quantity Ze is acquired by applying an estimated correction amount ΔZ based on a host state quantity Zm by map matching to a host state quantity Zd ​​by dead reckoning. Therefore, according to this embodiment, a control parameter for controlling the traveling of the host vehicle 2 based on the deviations Δe, Δθ of the estimated state quantity Ze from the target state quantity Zt represented by the target trajectory Tt is adjusted according to the estimated correction amount ΔZ. According to this, even if the estimated correction amount ΔZ suddenly changes, the traveling control can be applied to the host vehicle 2 so as to follow the control parameter adapted to the suddenly changed estimated correction amount ΔZ. Therefore, it is possible to suppress a situation in which the behavior of the host vehicle 2 is disturbed by the traveling control, and to improve drivability.

[0046] According to this embodiment, the control parameters are adjusted so that the tracking of the driving control with respect to the target state quantity Zt decreases as the estimated correction amount ΔZ increases. As a result, even if the deviations Δe, Δθ of the estimated correction amount ΔZ from the given estimated state quantity Ze increase rapidly with a sudden increase in the estimated correction amount ΔZ, the tracking of the driving control with respect to the target state quantity Zt can be relaxed. Therefore, it is possible to contribute to high drivability by suppressing the behavior disturbance of the host vehicle 2.

[0047] According to this embodiment, at least one of the position control gains Kie and Kpe, which weight the rate of change of the driving control with respect to the position deviation Δe with respect to the lateral position of the host vehicle 2 among the control parameters, i.e., the feedback control gains, may be adjusted to be smaller as the estimated correction amount ΔZ increases. This allows the target state amount Zt to be less susceptible to follow-up due to changes in the driving control even if the position deviation Δe between the estimated state amount Ze and the estimated correction amount ΔZ increases rapidly with a sudden increase in the estimated correction amount ΔZ. This makes it possible to suppress disturbances in the behavior of the host vehicle 2 and improve the reliability of high drivability.

[0048] According to this embodiment, the attitude control gain that weights the rate of change of the driving control with respect to the attitude deviation Δθ, which is related to the yaw angle as the attitude angle of the host vehicle 2 among the control parameters, i.e., the feedback control gain Kpθ, is adjusted to be smaller as the estimated correction amount ΔZ increases. As a result, even if the attitude deviation Δθ between the estimated state amount Ze and the estimated correction amount ΔZ increases rapidly with a sudden increase in the estimated correction amount ΔZ, the tracking due to the change in the driving control can be mitigated for the target state amount Zt. Therefore, it is possible to improve the reliability of high drivability by suppressing the behavior disturbance of the host vehicle 2.

[0049] According to this embodiment, in an allowable area Ap where map matching is allowed, an estimated state quantity Ze with an estimated correction amount ΔZ applied is acquired, whereas in an inhibiting area Ai where map matching is inhibited, an estimated state quantity Ze without the estimated correction amount ΔZ is acquired. According to this, even if the estimated correction amount ΔZ suddenly changes as the host vehicle 2 moves from the inhibiting area Ai to the allowable area Ap as shown in Fig. 2, it is possible to provide the host vehicle 2 with driving control according to control parameters adapted to the suddenly changed estimated correction amount ΔZ. Therefore, it is possible to suppress behavior disturbance of the host vehicle 2 caused by the possibility of map matching and improve drivability.

[0050] (Other embodiments) Although one embodiment has been described above, the present disclosure should not be construed as being limited to the embodiment described above, and can be applied to various embodiments within the scope not departing from the gist of the present disclosure.

[0051] In a modified example, the dedicated computer constituting the processing system 1 may have at least one of a digital circuit and an analog circuit as a processor. Here, the digital circuit is at least one of ASIC (Application Specific Integrated Circuit), FPGA (Field Programmable Gate Array), SOC (System on a Chip), PGA (Programmable Gate Array), CPLD (Complex Programmable Logic Device), etc. Furthermore, such a digital circuit may have a memory that stores a program.

[0052] In a modified example, the monitoring of the travel area by the fusion block 103 and S103 may be omitted. In this modified example, the gain adjustment by the feedback block 114 and S108 may be performed in accordance with the case where the travel area is the permissible area Ap, that is, the case where the permissible flag is stored in the memory 10. In the modified example, the blocks 112 to 115 and S106 to S109 may be modified so as to perform, for example, vehicle speed control or acceleration / deceleration control instead of or in addition to steering control as the travel control of the host vehicle 2.

[0053] In addition to the embodiments described above, the processing system 1 according to the above-mentioned embodiments and modifications may be implemented as a processing device (e.g., a processing ECU, etc.) that is entirely mounted on the host vehicle 2. Furthermore, the above-mentioned embodiments and modifications may be implemented as a semiconductor device (e.g., a semiconductor chip, etc.) having at least one each of the processor 12 and memory 10 of the processing system 1. [Explanation of symbols]

[0054] 1: Processing system, 2: Host vehicle, 7: Map database, 10: Memory, 12: Processor, 50: External sensor, 52: Internal sensor, 100: Estimation block, 101: Dead reckoning block, 102: Map matching block, 103: Fusion block, 110: Tracking control block, 111: Target trajectory acquisition block, 113: Deviation acquisition block, 114: Feedback block, 115: Target steering angle acquisition block, Ai: Inhibition area, Ap: Tolerance area, Kie, Kpe, Kpθ: Feedback control gains, Tt: Target trajectory, Zd, Zm: Host state quantity, Ze: Estimated state quantity, Zt: Target state quantity, ΔZ: Estimated correction quantity, Δe: Position deviation, Δθ: Attitude deviation

Claims

1. A processing system having a processor (12) for controlling a host vehicle (2) to follow a target trajectory, comprising: The processor, estimating a host state quantity, which is a state quantity of the host vehicle, by dead reckoning based on internal information of the host vehicle; estimating the host state quantity by map matching based on external information and map information of the host vehicle; obtaining an estimated state quantity by applying an estimated correction amount based on the host state quantity obtained by the map matching to the host state quantity obtained by the dead reckoning; and adjusting a control parameter for controlling traveling of the host vehicle based on a deviation of the estimated state quantity from a target state quantity represented by the target trajectory in accordance with the estimated correction amount.

2. Adjusting the control parameters includes: The processing system according to claim 1 , further comprising: adjusting the control parameters such that the tracking ability of driving control with respect to the target state quantity decreases as the estimated correction amount increases.

3. The control parameters are: a position control gain that weights a rate of change of cruise control in response to a position deviation, the deviation being relative to a lateral position of the host vehicle; Adjusting the control parameters includes: The processing system according to claim 2 , further comprising adjusting the position control gain to be smaller as the estimated correction amount becomes larger.

4. The control parameters are: an attitude control gain that weights a rate of change of a cruise control with respect to an attitude deviation, the deviation being the deviation with respect to an attitude angle of the host vehicle; Adjusting the control parameters includes: The processing system according to claim 2 , further comprising adjusting the attitude control gain to be smaller as the estimated correction amount becomes larger.

5. The obtaining of the estimated state quantity includes: acquiring the estimated state quantity to which the estimated correction amount is applied in an allowable area where the map matching is allowed; 5. The processing system according to claim 1, further comprising: acquiring the estimated state quantity from which the estimated correction quantity is removed in an inhibition area where the map matching is inhibited.

6. A processing device having a processor (12) for controlling a host vehicle (2) to follow a target trajectory, The processor, estimating a host state quantity, which is a state quantity of the host vehicle, by dead reckoning based on internal information of the host vehicle; estimating the host state quantity by map matching based on external information and map information of the host vehicle; obtaining an estimated state quantity by applying an estimated correction amount based on the host state quantity obtained by the map matching to the host state quantity obtained by the dead reckoning; and adjusting a control parameter for controlling traveling of the host vehicle based on a deviation of the estimated state quantity from a target state quantity represented by the target trajectory in accordance with the estimated correction amount.

7. A processing method executed by a processor (12) for controlling a host vehicle (2) to follow a target trajectory, comprising: estimating a host state quantity, which is a state quantity of the host vehicle, by dead reckoning based on internal information of the host vehicle; estimating the host state quantity by map matching based on external information and map information of the host vehicle; obtaining an estimated state quantity by applying an estimated correction amount based on the host state quantity obtained by the map matching to the host state quantity obtained by the dead reckoning; and adjusting a control parameter for controlling traveling of the host vehicle based on a deviation of the estimated state quantity from a target state quantity represented by the target trajectory in accordance with the estimated correction amount.

8. A processing program stored in a recording medium (10) for controlling a host vehicle (2) to follow a target trajectory, the processing program including instructions to be executed by a processor (12), The instruction: A host state quantity, which is a state quantity of the host vehicle, is estimated by dead reckoning based on internal information of the host vehicle; estimating the host state quantity by map matching based on external information and map information of the host vehicle; acquiring an estimated state quantity by applying an estimated correction amount based on the host state quantity obtained by the map matching to the host state quantity obtained by the dead reckoning; and adjusting a control parameter for controlling traveling of the host vehicle based on a deviation of the estimated state quantity from a target state quantity represented by the target trajectory, in accordance with the estimated correction amount.

Images