Control device for autonomous driving vehicle

Abstract

This control device for an autonomous vehicle causes the vehicle to travel in an autonomous manner on the basis of a target trajectory calculated on the basis of the current position of the vehicle on map data estimated by map data, a behavior detection sensor, and an external detection sensor, the target trajectory being calculated on the basis of the relative position of the vehicle on a travel road. The present invention estimates the current position of a vehicle by correcting the position of the vehicle on map data, finds the amount of correction at that time, predicts the change in the amount of correction by means of the map data and an external detection sensor, and changes the characteristics of travel control by changing the control gain in the control input when the vehicle is automatically driven on the basis of the change in the amount of correction.

Description

Technical Field

[0001] The present invention relates to a control device for a vehicle capable of automatically driving based on the behavior of the vehicle recognized by a sensor or the like and information acquired from the outside. Background Art

[0002] Japanese Patent Laid-Open No. 2019-207190 discloses a self-position estimation device, the purpose of which is to stabilize the estimation accuracy of the self-position even when traveling in a section where the number of lanes changes. The self-position estimation device disclosed in Japanese Patent Laid-Open No. 2019-207190 estimates the position of the vehicle based on image data obtained by an on-board camera, data of the state quantity of the vehicle obtained from sensors, etc., GPS data, and map data. In the self-position estimation device disclosed in Japanese Patent Laid-Open No. 2019-207190, when an section where the number of lanes increases or decreases, i.e., a laneless section, is identified based on image data, the use of map data is limited compared to the case where the laneless section is not identified, in other words, the weighting of the map data is reduced to correct the estimated position of the vehicle. In Japanese Patent Laid-Open No. 2019-207190, by such a structure, it is possible to suppress the reduction of the estimation accuracy of the self-position in a laneless section such as a road section where the road segment data is not correctly prepared, so the estimation accuracy of the self-position can be stabilized.

[0003] When determining the position of the vehicle, the respective components of the longitudinal direction (front-to-back direction), lateral direction (left-to-right direction), and yaw direction (or azimuth) are determined. That is, for each component, as in the device of Japanese Patent Application Laid-Open No. 2019-207190, the position of the vehicle inferred based on the state quantity of the vehicle and map data, etc., and the position of the vehicle on the actual driving road measured by a camera, etc. are obtained at predetermined intervals, and they are matched to infer the position of the vehicle on the map data. The vehicle is controlled in a manner that assists the vehicle's travel along the target track based on the inferred position of the vehicle. When the position of the vehicle is inferred by correcting the position of the vehicle inferred in this way based on the measured position of the vehicle to infer the position of the vehicle, it is sometimes constructed as data that changes the weight according to each component.

[0004] For example, when estimating the lateral position of the vehicle, the information recognized by the camera mounted on the vehicle is weighted, and when estimating the position of the vehicle in the yaw direction, the information related to the behavior of the vehicle detected by various sensors installed on the vehicle is weighted. In such a case, for example, when there is a deviation between the map data and the actual travel road, the recognition of the difference between the actual vehicle position and the center position or the target position on the travel road may differ for each component.

[0005] Specifically, with respect to the lateral direction, the information recognized by the camera is weighted, so that the occurrence of such a difference can be detected relatively early. On the other hand, in the yaw direction, when such a difference occurs, the information based on the behavior of the vehicle is weighted. Therefore, with respect to the yaw direction, the above-mentioned difference may be detected as smaller due to errors in map data and data related to the behavior of the vehicle. That is, compared to relatively accurate detection of the deviation of the vehicle from the center position or target position on the road in the lateral direction, the deviation may not be accurately detected in the yaw direction. When the behavior of the vehicle in the lateral and yaw directions of the vehicle is changed, the operation amount related to the steering of the vehicle is changed. Therefore, the calculation results of the operation amount related to the steering of the vehicle are different in the lateral and yaw directions, and there is a possibility that the position of the vehicle will not immediately return to the center position or target position of the road. As a result, the behavior of the vehicle may produce behavior that the passengers do not want. Summary of the invention

[0006] The present invention is completed with a focus on the above-mentioned technical issues, and its purpose is to provide a control device for an autonomous driving vehicle, which can suppress the behavior of the vehicle from becoming behavior that the passengers do not want when assisting the driving of the vehicle based on the behavior of the vehicle and the external conditions of the vehicle.

[0007] In order to achieve the above-mentioned object, the present invention provides a control device for an autonomous driving vehicle, which comprises: a plurality of behavior detection sensors that detect parameters related to the behavior of the vehicle; and a plurality of external detection sensors that detect objects existing around a driving road on which the vehicle is driving.

[0008] The control device of the autonomous driving vehicle is configured to estimate the current position of the vehicle on the map data based on pre-stored map data, detection results of the plurality of behavior detection sensors, and detection results of the plurality of external detection sensors, calculate a target trajectory based on the estimated current position of the vehicle on the map data, and drive the vehicle autonomously based on the target trajectory.

[0009] The control device of the autonomous driving vehicle includes a controller that estimates the current position on the map data and performs driving control of the vehicle based on a control input set in such a way that the vehicle performs the autonomous driving driving along the target track.

[0010] The controller corrects the position of the vehicle on the map data according to the relative position of the vehicle on the travel road obtained based on the detection results of the plurality of external detection sensors, thereby estimating the current position of the vehicle on the map data.

[0011] calculating a correction amount of the position of the vehicle on the map data when the current position of the vehicle is estimated,

[0012] predicting the correction amount in the predetermined travel route based on the map data and data related to the predetermined travel route detected by at least one of the plurality of external detection sensors, and

[0013] When the travel control is executed based on the control input, the characteristics of the travel control are changed in such a way that a control gain in the control input is changed according to the correction amount and the predicted correction amount.

[0014] Furthermore, in the present invention, the controller may be configured to change the characteristics of the travel control so that the control gain becomes larger as the correction amount and the predicted correction amount become larger.

[0015] In addition, in the present invention, the controller may be configured to calculate the correction amount in the longitudinal, lateral and yaw directions of the vehicle, respectively, and

[0016] Based on the correction amounts for the longitudinal direction, the lateral direction, and the yaw direction and the predicted correction amounts, the characteristics of the travel control are changed so as to change the magnitude of the control gain, thereby causing the vehicle to travel in an automated driving manner.

[0017] In addition, in the present invention, the controller may be configured to: execute the driving control for the autonomous driving driving, and during the autonomous driving driving, use the feedforward value set based on the predetermined driving road as the control input to perform the autonomous driving driving,

[0018] When the characteristic of the travel control is changed, the value of the feedforward is changed based on the correction amount and the predicted correction amount.

[0019] Further, in the present invention, the controller may be configured to: set the feedforward value based on the curvature of the target track in the predetermined driving road according to the current position of the vehicle on the map data, and calculate the control input regarding the steering angle of the vehicle based on the feedforward value,

[0020] When changing the characteristics of the travel control, if the correction made when estimating the current position of the vehicle on the map data includes a correction in the longitudinal direction of the vehicle, the feedforward value is changed to an appropriate value based on the estimated current position.

[0021] According to the control device of the automatic driving vehicle of the present invention, the position data of the vehicle based on the map data and the behavior detection sensor of the vehicle is corrected according to the relative position of the vehicle based on the detection result of the external detection sensor of the vehicle, such as the relative distance between the vehicle and the white line, thereby estimating the current position of the vehicle. The vehicle is controlled to drive automatically according to the estimated current position of the vehicle. When the vehicle is driven automatically, the correction amount when the position data is corrected according to the measured relative position and the correction amount predicted in the predetermined driving road based on the pre-stored map data and the external detection sensor are calculated. In addition, the driving control when the vehicle is driven automatically is performed based on the control input to the mechanism for driving the vehicle. And when the driving control is performed, the characteristics of the driving control of the automatic driving are changed in a manner that the control gain in the driving control is changed based on the correction amount and the predicted correction amount. That is, the control input for driving the vehicle automatically is changed according to the change of the correction amount of the position data and the predicted correction amount. Since the control gain in the driving control of the vehicle is changed in this way, the reduction of the following performance of the vehicle when the vehicle is driven automatically based on the target track can be suppressed, and the occurrence of undesirable behavior of the passenger can be suppressed.

[0022] In addition, the automatic driving is performed in a manner that the larger the correction amount and the predicted correction amount are, the more the control gain in the driving control is increased. That is, since the control gain is increased when the correction amount is large, the situation where the position of the vehicle is offset relative to the center position of the driving road and the target position can be suppressed. In addition, even if the position of the vehicle is offset, the vehicle can be driven automatically in a manner that quickly reduces the offset. In addition, when the feedforward value is used in the driving control, the feedforward value is changed according to the correction amount. The feedforward value set based on the curvature of the target track is used to calculate the control input about the steering angle of the vehicle. In the case of a correction to the longitudinal direction of the vehicle, the feedforward value is changed to an appropriate value based on the corrected position of the vehicle, that is, the estimated current position. Therefore, when the vehicle is driven along the target track, it is possible to suppress the vehicle from deviating from the target track, or even if it deviates, the vehicle can be quickly returned to the target track. That is, the vehicle's tracking performance to the target track can be improved.

[0023] Features, advantages, and technical and industrial significance of embodiments of the present invention will be described below with reference to the accompanying drawings, wherein like reference numerals represent like elements. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 This is a diagram showing an example of a vehicle equipped with the control device for an autonomous driving vehicle according to an embodiment of the present invention.

[0025] Figure 2This is a block diagram for explaining the functional structure of the control device of the autonomous driving vehicle in the embodiment of the present invention.

[0026] Figure 3 This is a flowchart showing an example of control executed by the control device of the automatically driven vehicle in the embodiment of the present invention.

[0027] Figure 4 This is an explanatory diagram for explaining position data of a vehicle on the map data and the current position of the vehicle on the map data.

[0028] Figure 5 This is an explanatory diagram for explaining a case where map data is different from an actual road. DETAILED DESCRIPTION

[0029] Hereinafter, the present invention will be described based on the illustrated embodiment. In addition, the embodiment described below is only an example of a case where the present invention is embodied, and does not limit the present invention.

[0030] Figure 1 An automatic driving vehicle (hereinafter, simply referred to as a vehicle) 1 equipped with a control device in an embodiment of the present invention is shown. The vehicle 1 in the embodiment of the present invention is a vehicle that can not only travel by manual operation, but also can travel automatically by controlling the driving force, braking force, steering control amount, etc. in a manner that does not require the driver to perform driving operations. In addition, the vehicle 1 is configured to be able to recognize the driving environment, monitor the surrounding conditions, etc. based on the surrounding information obtained from the outside.

[0031] The vehicle 1 performs automatic driving based on data related to the behavior of the vehicle 1 and data obtained from the outside. For example, the vehicle 1 obtains data related to the inferred or measured position of the vehicle 1, and based on the data related to the position of the vehicle 1, map data, etc., the driving force, braking force, steering control amount and other operation quantities are calculated so that the vehicle 1 can travel along a predetermined target track to control the vehicle 1, thereby performing automatic driving. The vehicle 1 in the embodiment of the present invention is configured to accurately estimate the current position of the vehicle 1 that serves as a reference when calculating the operation quantities of each device for changing the behavior of the vehicle 1, so that the vehicle 1 can perform automatic driving along the target track. In addition, the vehicle 1 can be any vehicle that can perform driving based on automatic driving, for example, it can be an existing general vehicle such as an engine vehicle, a hydrogen car, a hybrid electric vehicle, a fuel cell electric vehicle, etc.

[0032] exist Figure 1 An example of such a vehicle 1 is shown in FIG. Figure 1 The illustrated vehicle 1 includes a driving force source 2 , a brake device 3 , wheels 4 , a steering device 5 , a detection unit 6 , and an ECU (electronic control unit) 7 .

[0033] The driving force source 2 outputs torque for driving the vehicle 1. The driving force source 2 may include, for example, a conventionally known engine (internal combustion engine) or a motor generator, or may include both of them.

[0034] The brake device 3 is the same as a conventionally known brake device, and is, for example, provided at each front and rear wheel 4 of the vehicle 1. An example of the brake device 3 is a friction brake such as a disc brake, a drum brake, or a powder brake, which is configured to generate a braking force in a direction of stopping the rotation of each wheel 4 by generating a friction force through hydraulic pressure, electromagnetic force, or the like.

[0035] The steering device 5 changes the heading angle to adjust the traveling direction of the vehicle 1. The steering device 5 is the same as a conventionally known steering device, for example, a rack and pinion electric power steering device provided with an electric assist mechanism.

[0036] The detection unit 6 is a device or apparatus for obtaining various data and information required for controlling the vehicle 1. The detection unit 6 includes an internal sensor 8 for obtaining data related to the driving of the vehicle 1 itself, and an external sensor 9 for obtaining information outside the vehicle 1. The internal sensor 8 includes: an acceleration sensor 8a for detecting the acceleration of the vehicle 1; a gyro sensor 8b for detecting the change of the posture and orientation of the vehicle 1, such as the yaw angle (azimuth angle) as an angular velocity; and a vehicle speed sensor 8c for detecting the vehicle speed based on the rotation speed of the wheel, etc. The external sensor 9 includes: a vehicle-mounted camera 9a for obtaining a captured image; and a LiDAR 9b for obtaining information of an object based on the data of the reflected light of the laser, etc. In addition, the internal sensor 8 is equivalent to the behavior detection sensor in the embodiment of the present invention, and the external sensor 9 is equivalent to the external detection sensor in the embodiment of the present invention.

[0037] ECU7 is equivalent to the controller in the embodiment of the present invention, and is mainly composed of a microcomputer composed of a processor (CPU), storage elements (RAM, ROM), and input-output devices (input-output interface). ECU7 is configured to use data from various sensors 8, 9 installed in the vehicle 1 or external input, pre-stored data, etc., to perform calculations according to a prescribed program, and output the results of the calculations as control command signals. For example, ECU7 loads the program stored in the recording medium into the working area of ​​the storage unit through the processor and executes it, thereby performing various controls realized by the execution of the program, thereby executing functions consistent with the prescribed purpose. In addition, if Figure 1 as well as Figure 2 As shown, the ECU 7 includes a vehicle position estimation unit 10 , a target trajectory calculation unit 11 , and a tracking control unit 12 .

[0038] The vehicle position estimation unit 10 estimates the current position of the vehicle 1 on the map data based on the changes in the behavior of the vehicle 1 and the detection results of external information. The vehicle position estimation unit 10 detects and calculates the state quantities of the vehicle 1, such as the vehicle speed, acceleration, yaw rate, etc., based on the various sensors 8 and 9 mentioned above. The vehicle position estimation unit 10 obtains the position data of the vehicle 1 on the map data based on the detected state quantities of the vehicle 1, the estimation results of the past vehicle positions, the stored map data, etc. That is, on the map data, it is estimated at which position of the road the vehicle 1 is currently traveling, and if it is a curved road, at what degree of curvature or yaw rate the vehicle 1 is traveling on the curved road. In addition, the vehicle position estimation unit 10 obtains the relative position of the vehicle 1 on the actual traveling road based on the vehicle-mounted camera 9a, LiDAR 9b, etc. as the external sensor 9. For example, the vehicle position estimation unit 10 measures the relative position of the lane in which the vehicle 1 is traveling, the position of the vehicle 1 in the driving lane, etc. based on the relative distance between the vehicle 1 and the white line in the driving lane, the relative distance between the vehicle 1 and surrounding vehicles or signs, etc., which are identified by the vehicle-mounted camera 9a.

[0039] The vehicle position estimation unit 10 is configured to obtain the difference between the relative position of the vehicle 1 measured in this way and the position of the vehicle 1 estimated on the map data. For example, when the vehicle 1 is traveling on a curved road, the estimated position of the vehicle 1 on the map data is traveling along the center of the driving lane, while the measured relative position of the vehicle 1 is traveling close to a dividing line (e.g., a lane center line) to the right of the center of the driving lane. In such a case, the vehicle position estimation unit 10 is configured to obtain a correction amount for the position data when estimating the current position of the vehicle on the map data based on the difference between the position data of the vehicle 1 on the estimated map data and the actual position of the vehicle 1 measured.

[0040] In addition, the vehicle position estimation unit 10 calculates the difference between the estimated position data of the vehicle 1 and the actually measured position for each component of the longitudinal direction (front-rear direction), the lateral direction (left-right direction), and the yaw direction of the vehicle 1. The vehicle position estimation unit 10 changes the parameter weighted for each component according to the characteristics of these components, etc., so that the position of the vehicle 1 can be appropriately estimated. For example, the vehicle position estimation unit 10 estimates the position by weighting the data based on the detection value of the internal sensor 8 provided in the vehicle 1 for the yaw direction of the vehicle 1. That is, the vehicle position estimation unit 10 obtains the yaw angle of the vehicle 1 on the map data mainly based on the behavior of the vehicle 1 such as the speed, acceleration, and steering amount. In addition, the vehicle position estimation unit 10 estimates the position on the map data by weighting the data based on the position of the vehicle 1 actually measured by the external sensor 9 for the lateral direction of the vehicle 1. That is, the vehicle position estimation unit 10 is configured to estimate the position on the map data based on the behavior of the vehicle 1 in the lateral direction of the vehicle 1 in the same manner as the yaw direction, and to estimate the position by weighting the data based on the actually measured relative position of the vehicle 1. The vehicle position estimation unit 10 estimates the current position of the vehicle 1 on the map data by repeatedly executing the above-mentioned calculation at every predetermined period.

[0041] The target trajectory calculation unit 11 calculates the target trajectory and target curvature of the vehicle 1 based on the current position of the vehicle 1 on the map data obtained by the vehicle position estimation unit 10 and the information related to the driving road on which the vehicle 1 is traveling or the driving road on which it is scheduled to travel, that is, the scheduled driving road. The information related to the driving road is, for example, the stored map data and the information such as white lines, signs, and other vehicles on the driving road detected by the external sensor 9. Based on this information, the target trajectory calculation unit 11 calculates the target trajectory of the vehicle 1 so that the vehicle 1 can travel comfortably to the passengers by automatic driving, such as in the form of the vehicle 1 traveling at an appropriate speed in the center of the driving lane or the vehicle 1 entering the curvature along the curvature of the curved road on the map data. In addition, when the current position of the vehicle 1 on the map data is different from the current target trajectory or target curvature of the vehicle 1, the target trajectory calculation unit 11 calculates the driving trajectory of the vehicle 1 that can quickly return to such a target trajectory or curvature. In addition, the white lines, signs, and other vehicles correspond to the objects existing around the vehicle in the embodiment of the present invention.

[0042] For example, the target trajectory calculation unit 11 is configured to obtain lane, dividing line, and road boundary line information based on map data, and calculate the target trajectory based on the target position of the vehicle 1 based on the center line of the driving lane. At this time, when it is detected that the current position of the vehicle 1 on the map data deviates from the target trajectory, the target trajectory calculation unit 11 calculates the driving trajectory in which the current position of the vehicle 1 on the map data gradually becomes consistent with the position on the currently set target trajectory. That is, the target trajectory is calculated based on the longitudinal, lateral, and yaw deviations from the current position of the vehicle 1 on the map data, i.e., the current target trajectory, the size of the control input, the moving time of the vehicle 1, the allowable deviation, and other parameters, as well as the evaluation function and constraints in the optimal control.

[0043] The tracking control unit 12 is configured to perform driving control of the vehicle 1, and to make the vehicle 1 automatically drive by combining feedback and feedforward based on the data obtained from the vehicle position estimation unit 10 and the target trajectory calculation unit 11. That is, the tracking control unit 12 calculates the operation amount and control input of each device such as the driving force source 2, the braking device 3, the steering device 5, etc. that control the behavior of the vehicle 1, such as the driving force, braking force, and steering amount. The tracking control unit 12 obtains data on the correction amount of the position data on the map data based on the relative position of the vehicle 1 on the driving road or the actual position for each of the longitudinal, lateral, and yaw directions of the vehicle 1 from the vehicle position estimation unit 10. In addition, the tracking control unit 12 obtains the target trajectory, curvature, etc. of the vehicle 1 calculated in the target trajectory calculation unit 11. Based on the obtained data, the tracking control unit 12 calculates the operation amount and control input of each device that controls the behavior of the vehicle 1, which is required for the vehicle 1 to travel according to the target trajectory and curvature from the current position on the map data.

[0044] For example, when the operation amount related to the steering angle of the vehicle 1 is obtained based on the target track, the tracking control unit 12 calculates the target steering angle and calculates the steering torque used to obtain the target steering angle. The target steering angle is calculated using the target curvature, target yaw angle and target lateral deviation obtained based on the shape of the target track, and the control gain predetermined for these parameters. As an example, the target steering angle is calculated based on the value obtained by multiplying the target curvature, the deviation of the target yaw angle from the actual yaw angle, the deviation of the target lateral deviation from the actual lateral deviation, and the integral value of the deviation of the target lateral deviation from the actual lateral deviation by the control gain. Such a calculation of the target steering angle is configured to be performed at a predetermined period, and the calculation result is sent to the ECU 7 each time to control the steering mechanism. In this way, based on the operation amount calculated by the tracking control unit 12, each device that controls the behavior of the vehicle 1 is controlled.

[0045] Next, an example of the control executed by the ECU 7 of the vehicle 1 configured in this way will be described. Figure 3 As shown, in S1, the correction amount when correcting the position data of the vehicle 1 on the map data based on the actual measured position of the vehicle 1 on the travel road is calculated. In S1, as described above, when the position data of the vehicle 1 on the map data estimated mainly based on the state amount of the vehicle 1 deviates from the relative position of the vehicle 1 actually measured mainly based on the external sensor 9, the position data is corrected based on the relative position to obtain the current position of the vehicle 1 on the map data. In S1, the correction amount for each of the longitudinal, lateral and yaw directions of the vehicle 1 at this time is obtained.

[0046] After the correction amount of the position data of the vehicle 1 is calculated, the processing enters S2 to calculate the correction amount of the predicted position data. In S2, based on the direction of travel of the vehicle 1 and the shape of the planned travel path obtained from the map data and the data of the external sensor 9, it is predicted that the correction amount will increase or decrease. For example, when there is a straight road ahead in the direction of travel, it is predicted that the correction amount will decrease. On the contrary, when there is a curved road ahead in the direction of travel of the vehicle 1, or when the vehicle 1 is traveling on a temporary road due to road construction, etc., it is predicted that the deviation between the map data and the actual travel road will increase during the planned travel. In such a case, it is predicted that the correction amount of the position data will increase. In S2, the change of the correction amount is predicted mainly based on the map data, the direction of travel obtained from the external sensor 9, and the shape of the road in the planned travel path.

[0047] After predicting the change in the correction amount of the position data of the vehicle 1, the processing enters S3 to change the characteristics of the tracking control when executing the automatic driving. In S3, for each component of the longitudinal, lateral and yaw directions of the vehicle 1, the weight of the parameter when calculating the operation amount for making the vehicle 1 automatically drive is changed according to the correction amount calculated in S1 and the correction amount predicted in S2. Specifically, in S3, in each of the above-mentioned directions of the vehicle 1, the larger the correction amount of the position data of the vehicle 1 on the map data based on the actual position of the vehicle 1 on the driving road and the predicted correction amount, the larger the control gain, that is, the feedback gain, for the deviation in each direction based on the corrected position data. In addition, as needed, control is performed in a manner that the value of the feedforward used to calculate the control input about the steering angle of the vehicle 1 is close to an appropriate value or a target value based on the driving road.

[0048] For example, in the lateral direction of the vehicle 1, when the correction amount of the position data of the vehicle 1 calculated in S1 is small and the correction amount of the predicted position data of the vehicle 1 is small, it is difficult for the position of the vehicle 1 to deviate from the center position or the target position of the driving road. That is, when the correction amount is small, the difference between the position data or the estimated position of the vehicle 1 inferred based on the state quantity of the vehicle 1 and the map data, etc., and the actual measured position of the vehicle 1 on the driving road measured by the external sensor 9 is small. That is, since the own position of the vehicle 1 can be inferred with high precision, as a result, it is difficult for the position of the vehicle 1 to deviate from the center position or the target position on the driving road. Therefore, the driving characteristics in the tracking control are not deliberately changed. That is, the current driving characteristics are maintained and the automatic driving of the vehicle 1 based on the tracking control is continued.

[0049] On the contrary, in the lateral direction of the vehicle 1, when the correction amount of the position data of the vehicle 1 is large and the predicted correction amount of the position data of the vehicle 1 is large, the feedback gain for the lateral direction of the vehicle 1 is increased to perform the vehicle tracking control. When both the correction amount of the position data of the vehicle 1 and the predicted correction amount are large, the vehicle 1 is likely to deviate from the center position of the travel road or the target position mainly in the lateral direction and the yaw direction of the vehicle 1. Therefore, it is configured to increase the feedback gain in the lateral direction or the yaw direction in advance.

[0050] In addition, when the longitudinal direction of the vehicle 1 is corrected, the correction is performed by increasing such a feedback gain, and the feedforward value set based on the curvature of the target track on the predetermined driving road is changed according to the current position of the vehicle 1 on the map data, thereby changing the operation amount. For example, when the vehicle 1 approaches a curved road in the left direction, in the case where it is detected that the position data on the map data is backward relative to the actual measured position of the vehicle 1 on the driving road, the position data of the vehicle 1 is corrected forward in the longitudinal direction and rightward in the lateral direction based on the relative position. As a result, the position data of the vehicle 1 on the map data is corrected to a position that matches the actual measured position of the vehicle on the driving road, and the current position of the vehicle 1 on the map data is in a state where it is right relative to the driving road.

[0051] In such a case, in order to quickly move the vehicle 1 to the target position such as the center of the driving lane, the curvature feedforward of the vehicle 1 is increased to the left turning side, thereby increasing the feedback gain for the lateral deviation. That is, by increasing the feedback gain, the deviation of the vehicle 1 relative to the center position and the target position of the driving road can be quickly detected, and the deviation can be reflected in the operation amount of each device. Moreover, by correcting the position of the vehicle 1 on the map data forward, the feedforward value is changed to a value consistent with the corrected current position of the vehicle 1. That is, by increasing the feedforward to the left turning side based on the curvature of the target track on the driving road corresponding to the corrected current position of the vehicle 1, the change in the operation amount caused by such feedback control can be avoided, and the feedforward value can be made close to the appropriate value or target value along the driving road. Therefore, the actual position of the vehicle 1 is controlled in a manner that quickly returns to the center position and the target position.

[0052] Thus, in S3, as the correction amount of the estimated position of the vehicle 1 and the predicted correction amount are larger in any one of the longitudinal, lateral and yaw directions, the feedback gain in that direction is increased, thereby changing the characteristics of the tracking control of the vehicle 1. In addition, the value of the feedforward is changed as needed. Then, based on the characteristics of the tracking control after such a change, the driving force, braking force and steering control amount of the vehicle 1 are controlled, thereby executing the automatic driving of the vehicle 1. In addition, when the correction amount and the predicted correction amount in the longitudinal direction are large, there is a high possibility that the lateral and yaw directions will also be offset, so it can also be configured to increase the feedback gain based on the deviation in each direction.

[0053] In the control device of the automatic driving vehicle 1 thus configured, the current position of the vehicle 1 on the map data is estimated based on the position data of the vehicle 1 on the map data estimated from the behavior of the vehicle 1 and the measured position of the vehicle 1 on the driving road measured relatively by recognizing objects around the vehicle 1. The target track that the vehicle 1 should travel is determined based on the current position of the vehicle 1 thus determined, the map data, the white line of the driving lane detected by the external sensor 9, the road shape, etc., and the vehicle 1 is controlled based on the operation amount of the driving force source 2, the brake device 3, the steering device 5, etc. required for the vehicle 1 to automatically drive along the target track. At this time, when it is predicted that the difference between the position data of the vehicle 1 on the map data and the measured position of the vehicle 1 on the driving road will become larger, the ECU 7 is configured to increase the feedback gain to control the vehicle 1. In addition, it is configured to make the value of the feedforward close to an appropriate value as needed.

[0054] For example, when the vehicle 1 is traveling on a road that curves to the left, the vehicle speed changes, or the direction of the vehicle 1 changes toward the curve. Figure 4As shown, when approaching such a curved road, there is a case where the position data of the vehicle 1 on the map data is estimated to be located at the rear side relative to the measured position on the driving road, that is, as shown by the vehicle 1 at the position of the position data, there is a case where it is estimated to be at the rear side and the left side relative to the actual driving road although it is traveling along the target track on the map data. In this case, the position data of the vehicle 1 is corrected to the front direction and the right direction according to the distance from the object such as the white line obtained by the external sensor 9, and the current position of the vehicle on the map data is obtained. That is, the current position of the vehicle 1 on the map data is obtained based on the measured relative position of the vehicle 1. The current position of the vehicle 1 on the map data thus obtained is close to the right side of the driving lane. Therefore, the driving control of the vehicle 1 is controlled in such a way that the vehicle 1 approaches the center of the driving lane and increases the turning amount to the left. Then, the driving force or braking force of the vehicle 1 required for the vehicle 1 to return to the target track and travel, as well as the steering angle, are calculated, and each device is controlled based on the calculation result.

[0055] At this time, in the embodiment of the present invention, the driving characteristics in the tracking control of the vehicle 1 are changed. That is, as described above, the feedback gain is increased as the correction amount between the estimated position and the measured position of the vehicle 1 and the predicted correction amount become larger. In addition, at this time, the value of the feedforward is changed to not hinder the feedback or to be close to an appropriate value or a target value. In the case of the above example, the larger the correction amount of the position data of the vehicle 1 based on the measured relative position of the vehicle 1 in the lateral direction, the larger the feedback gain in the lateral direction in the driving control of the vehicle 1. In addition, the feedforward based on the curvature in the target track of the driving road is increased toward the left turning side, in other words, it is changed to a value close to an appropriate value for the driving road. It should be noted that at this time, the position of the yaw direction of the vehicle 1 is mainly obtained based on the parameters corresponding to the behavior of the vehicle 1, so the correction amount may be detected as small. Therefore, the ECU 7 is configured to continue the calculation without making a special change to the feedback gain in the yaw direction. That is, the behavior of the vehicle 1 can be controlled in a manner that the deviation between the center position, the target position and the actual position of the vehicle 1 on the driving road is quickly reduced or the occurrence of such a deviation is suppressed.

[0056] In addition, if Figure 5 As shown, depending on the travel road, there is a case where the map data cannot accurately reflect the actual travel road due to the accuracy of the map data, construction, etc. In such a case, the difference between the position data of the vehicle 1 and the measured position is likely to increase. As a result, the position of the vehicle 1 is likely to be offset from the center position of the travel road or the target position. Specifically, Figure 5As shown in FIG. 1 , although the vehicle is traveling at the center of the driving road on the map data (on the map), it may gradually approach the right side of the driving road on the actual driving road (on the actual road). In contrast, in the above-mentioned embodiment, as the correction amount of the position data of the vehicle 1 relative to the actual measured position and the predicted correction amount become larger, the feedback gain is increased, and the feedforward value is made close to an appropriate value as needed. Therefore, in the case where there is a deviation between the position data of the vehicle on the map data and the relative position of the actually measured vehicle, the operation amount of each device that controls the behavior of the vehicle 1 can be set in a manner to reduce the deviation.

[0057] Therefore, for example, even if the driving position of the vehicle 1 deviates from the target track due to the difference between the position data of the vehicle 1 on the map data and the relative position of the vehicle 1 on the driving road, it is possible to suppress the vehicle 1 from causing a sudden deceleration or a change in the steering amount when returning the driving position of the vehicle 1 to the target track. That is, the driving position of the vehicle 1 can be quickly returned to the target track. In addition, it is possible to suppress the occurrence of behaviors that the passengers do not want in the behavior of the vehicle 1.

[0058] The above is an explanation of the embodiments of the present invention, but the present invention is not limited to the above examples and can be appropriately changed within the scope of achieving the purpose of the present invention. For example, in the automatic driving of the vehicle 1 based on tracking control, it is also possible to use model predictive control or the like to obtain the control input that minimizes the evaluation function to perform optimal control. In such a case, in the above control, when the correction amount of the estimated position based on the actual position of the vehicle 1 and the predicted correction amount become larger, the weighting of the parameters related to the lateral deviation and yaw angle deviation as the track following item is increased to perform tracking control.

Claims

1. A control device for an autonomous driving vehicle, wherein: have: a plurality of behavior detection sensors that detect parameters related to the behavior of the vehicle; and a plurality of external detection sensors that detect objects existing around a road on which the vehicle is traveling, The control device of the autonomous driving vehicle is configured to estimate the current position of the vehicle on the map data based on pre-stored map data, detection results of the plurality of behavior detection sensors, and detection results of the plurality of external detection sensors, calculate a target trajectory based on the estimated current position of the vehicle on the map data, and drive the vehicle autonomously based on the target trajectory. The control device of the autonomous driving vehicle includes a controller that estimates the current position on the map data and performs driving control of the vehicle based on a control input set in such a way that the vehicle performs the autonomous driving driving along the target track. The controller corrects the position of the vehicle on the map data based on the relative position of the vehicle on the travel road obtained based on the detection results of the plurality of external detection sensors, thereby estimating the current position of the vehicle on the map data. calculating a correction amount of the position of the vehicle on the map data when the current position of the vehicle is estimated, predicting the correction amount in the scheduled travel route based on the map data and data related to the scheduled travel route on which the vehicle is scheduled to travel detected by at least one of the plurality of external detection sensors, and When the travel control is executed based on the control input, the characteristics of the travel control are changed in such a way as to change the control gain in the control input according to the correction amount and the predicted correction amount.

2. The control device for an autonomous driving vehicle according to claim 1, wherein: The controller is configured to change the characteristics of the travel control so that the control gain increases as the correction amount and the predicted correction amount increase.

3. The control device for an autonomous driving vehicle according to claim 1 or 2, wherein: The controller is configured to calculate the correction amount for the longitudinal, lateral and yaw directions of the vehicle respectively, and The characteristics of the travel control are changed so as to change the magnitude of the control gain based on the correction amounts and the predicted correction amounts for each of the longitudinal direction, the lateral direction, and the yaw direction, thereby causing the vehicle to perform automatic driving travel.

4. The control device for an autonomous driving vehicle according to claim 1 or 2, wherein: The controller is configured to: execute the driving control for the automatic driving driving, wherein the automatic driving driving is performed by using a feedforward value set based on the predetermined driving road as the control input, When the characteristic of the travel control is changed, the value of the feedforward is changed based on the correction amount and the predicted correction amount.

5. The control device for an autonomous driving vehicle according to claim 4, wherein: The controller sets the value of the feedforward based on the curvature of the target track in the scheduled travel road according to the current position of the vehicle on the map data, and calculating the control input regarding the steering angle of the vehicle based on the feedforward value, When the travel control characteristic is changed, if the correction made when estimating the current position of the vehicle on the map data includes a correction in the longitudinal direction of the vehicle, the feedforward value is changed to an appropriate value based on the estimated current position.

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