Control device of automatic driving vehicle

Abstract

To provide a control device of an automatic driving vehicle that can suppress behavior of a vehicle from changing into behavior not intended by a person on board, when supporting running of the vehicle on the basis of behavior of the vehicle and an external situation of the vehicle.SOLUTION: A control device, which enables a vehicle to run by automatic driving on the basis of a target track calculated based on a current position on map data on the vehicle presumed by the map data, a behavior detection sensor and an external detection sensor, presumes the current position of the vehicle by correcting a position of the vehicle on the map data, in accordance with a relative position of the vehicle on a running road, and determines a correction amount for the position (a step S1), predicts variation of the correction amount using the map data and the external detection sensor (a step S2), and changes control gain in control-input at the time of making the vehicle run by automatic driving, in accordance with the change of the correction amount, so as to change characteristics of the running control (a step S3).SELECTED DRAWING: Figure 3

Description

[Technical field]

[0001] The present invention relates to a control device for a vehicle capable of autonomous driving based on the behavior of the vehicle recognized by sensors or the like and information acquired from an external source. [Background technology]

[0002] Patent Document 1 discloses a self-location estimation device that aims to stabilize the estimation accuracy of the self-location even when traveling through a section where the number of lanes changes. The self-location estimation device of Patent Document 1 estimates the position of the vehicle based on image data from an on-board camera, data on the state quantity of the vehicle acquired from a sensor, GPS data, and map data. The self-location estimation device of Patent Document 1 is configured to limit the use of map data, that is, to reduce the weighting of map data and correct the estimated position of the vehicle, when recognizing a laneless section, which is a section where the number of lanes increases or decreases, based on image data, compared to when the laneless section is not recognized. Patent Document 1 claims that such a configuration can suppress the estimation accuracy of the self-location from decreasing in a laneless section where road link data is not accurately created, thereby stabilizing the self-location estimation accuracy. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2019-207190 A Summary of the Invention [Problem to be solved by the invention]

[0004] When identifying the vehicle position, each of the components in the vertical direction (forward / backward direction), horizontal direction (left / right direction), and yaw direction (or azimuth angle) is identified. That is, for each component, as in the device of Patent Document 1, the vehicle position estimated based on the vehicle state quantity and map data, etc., and the vehicle position on the actual driving road actually measured by a camera, etc. are acquired at a predetermined period, and the vehicle position on the map data is estimated by matching them. The vehicle is controlled so as to support the vehicle's driving along the target trajectory from the estimated vehicle position. When the vehicle position estimated in this way is corrected based on the actually measured vehicle position to estimate the vehicle position, the data to be weighted according to each component may be changed.

[0005] For example, when estimating the lateral position of the vehicle, the information recognized by a camera mounted on the vehicle may be weighted, and when estimating the yaw position of the vehicle, the information related to the behavior of the vehicle detected by various sensors provided on the vehicle may be weighted. In such a case, for example, if there is a deviation between the map data and the actual driving path, the perception of the difference between the center position or the target position on the driving path and the actual vehicle position may differ for each component.

[0006] Specifically, in the lateral direction, the information recognized by the camera is weighted, so that the occurrence of such a difference is 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, in the yaw direction, the above-mentioned difference may be detected as small due to errors in the map data and data related to the behavior of the vehicle. That is, while the deviation of the vehicle from the center position on the road or the target position is detected relatively correctly in the lateral direction, the deviation may not be detected correctly in the yaw direction. When changing the behavior of the vehicle in the lateral and yaw directions of the vehicle, the operation amount related to the steering of the vehicle is changed in both cases. Therefore, the calculation result of the operation amount related to the steering of the vehicle differs between the lateral direction and the yaw direction, so that the position of the vehicle may not immediately return to the center position or the target position of the road. As a result, the vehicle may behave in a manner unintended by the occupant.

[0007] The present invention has been made with a focus on the above-mentioned technical problems, and aims to provide a control device for an autonomous vehicle that is capable of preventing the vehicle's behavior from becoming unintended by the occupants when assisting the vehicle's driving based on the vehicle's behavior and the vehicle's external conditions. [Means for solving the problem]

[0008] In order to achieve the above object, the present invention provides a control device for an autonomous vehicle, the control device comprising a plurality of behavior detection sensors which detect parameters related to the behavior of a vehicle, and a plurality of external detection sensors which detect objects existing around a road on which the vehicle is traveling, and configured to estimate a 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 autonomously drive the vehicle based on the target trajectory, the control device comprising: a controller which estimates the current position on the map data, and executes driving control of the vehicle based on a control input set so that the vehicle autonomously drives along the target trajectory; the controller estimates the current position of the vehicle on the map data by correcting a position of the vehicle on the map data in accordance with a relative position of the vehicle on the driving path determined based on the detection results of the plurality of external detection sensors, calculates a correction amount for the position of the vehicle on the map data when the current position of the vehicle is estimated, predicts the correction amount on the planned driving path based on the map data and data related to a planned driving path along which the vehicle is scheduled to travel detected by at least one of the plurality of external detection sensors, and changes a characteristic of the driving control so as to change a control gain in the control input in accordance with the correction amount and the predicted correction amount when executing the driving control based on the control input.

[0009] In the present invention, the controller may be configured to change the characteristic of the driving control so that the control gain increases as the correction amount and the predicted correction amount increase.

[0010] In addition, in the present invention, the controller may be configured to calculate the correction amount for each of the longitudinal, lateral, and yaw directions of the vehicle, and to change the characteristics of the driving control so as to change the magnitude of the control gain based on the correction amount for each of the longitudinal, lateral, and yaw directions and the predicted correction amount, thereby causing the vehicle to drive autonomously.

[0011] In addition, in the present invention, the controller may be configured to execute the driving control for driving the autonomous vehicle by using a feedforward value set based on the planned driving route as the control input, and may be configured to change the feedforward value based on the correction amount and the predicted correction amount when changing the characteristics of the driving control.

[0012] Furthermore, in the present invention, the controller may be configured to set the feedforward value based on a curvature of the target trajectory on the planned driving route from the current position of the vehicle on the map data, calculate the control input for the steering angle of the vehicle based on the feedforward value, and when changing the characteristics of the driving 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, change the feedforward value to an appropriate value based on the estimated current position. Effect of the Invention

[0013] According to the control device for an autonomous vehicle of the present invention, the current position of the vehicle is estimated by correcting the position data of the vehicle based on the map data and the vehicle behavior detection sensor according to the detection result of the external detection sensor of the vehicle, for example, the relative position of the vehicle based on the relative distance between the vehicle and the white line, and the vehicle is controlled to run autonomously according to the estimated current position of the vehicle. When the vehicle is run autonomously, a correction amount when correcting the position data according to the actually measured relative position and a predicted correction amount on the planned running route based on the map data and the external detection sensor stored in advance are calculated. In addition, the running control when the vehicle is run autonomously is executed based on a control input to a mechanism for running the vehicle. Then, when the running control is executed, the characteristic in the running control of the autonomous running is changed so as to change the control gain in the running control based on the correction amount and the predicted correction amount. That is, the control input for running the vehicle autonomously is changed according to the change in the correction amount of the position data and the predicted correction amount. By changing the control gain in the vehicle's driving control in this manner, it is possible to suppress a decrease in the vehicle's tracking performance when the vehicle is driven automatically based on a target trajectory, and ultimately to suppress unintended behavior by the occupants.

[0014] In addition, the system is configured to increase the control gain in the driving control as the correction amount and the predicted correction amount increase, thereby executing automatic driving. That is, since the control gain is increased when the correction amount is large, it is possible to suppress the occurrence of deviation of the vehicle's position from the center position or the target position on the driving path. In addition, even if the position of the vehicle is deviated, the vehicle can be made to drive automatically so as to quickly reduce the deviation. In addition, when a feedforward value is used for the driving control, the feedforward value is changed according to the correction amount. The feedforward value set based on the curvature of the target trajectory is used to calculate a control input for the steering angle of the vehicle, and when the vehicle is corrected in the longitudinal direction, the feedforward value is changed to an appropriate value based on the corrected vehicle position, that is, the estimated current position. Therefore, when the vehicle is made to drive along the target trajectory, it is possible to suppress the vehicle from deviating from the target trajectory, or, even if the vehicle deviates, to quickly return the vehicle to the target trajectory. In other words, it is possible to improve the vehicle's ability to follow the target trajectory. [Brief description of the drawings]

[0015] [Figure 1] FIG. 1 is a diagram illustrating an example of a vehicle equipped with a control device for an autonomous driving vehicle according to an embodiment of the present invention. [Diagram 2] 2 is a block diagram for explaining the functional configuration of a control device for an autonomous driving vehicle in an embodiment of the present invention. FIG. [Diagram 3] 4 is a flowchart illustrating an example of control executed by a control device for an autonomous driving vehicle in an embodiment of the present invention. [Figure 4] FIG. 2 is an explanatory diagram for explaining position data on map data of a vehicle and a current position of the vehicle on the map data. [Diagram 5] FIG. 11 is an explanatory diagram for explaining a case where map data differs from actual roads; DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0016] The present invention will be described below based on the embodiments shown in the drawings. Note that the embodiments described below are merely examples of the present invention and are not intended to limit the present invention.

[0017] 1 shows an autonomous vehicle (hereinafter simply referred to as vehicle) 1 equipped with a control device according to an embodiment of the present invention. Vehicle 1 according to the embodiment of the present invention is a vehicle that can be driven not only by manual operation but also by autonomous driving by controlling the driving force, braking force, steering amount, etc. without the driver having to perform driving operation. Vehicle 1 is also configured to be capable of recognizing the driving environment and monitoring the surrounding situation based on surrounding information acquired from the outside.

[0018] The vehicle 1 performs automatic driving based on data related to the behavior of the vehicle 1 and data acquired from the outside. For example, the vehicle 1 acquires data related to the estimated or measured position of the vehicle 1, and controls the vehicle 1 by calculating operation amounts such as driving force, braking force, and steering amount so that the vehicle 1 can run along a predetermined target trajectory based on the data related to the position of the vehicle 1 and map data, 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, which is a reference when determining the operation amount of each device for changing the behavior of the vehicle 1, and to enable the vehicle 1 to perform automatic driving along a target trajectory. Note that the vehicle 1 may be any vehicle capable of running by automatic driving, and may be, for example, an existing general vehicle such as an engine vehicle, a hydrogen vehicle, a hybrid vehicle, or a fuel cell vehicle.

[0019] An example of such a vehicle 1 is shown in Fig. 1. The vehicle 1 shown in Fig. 1 includes a driving force source 2, a braking device 3, wheels 4, a steering device 5, a detection unit 6, and an ECU (electronic control unit) 7.

[0020] The driving force source 2 outputs torque for propelling 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.

[0021] The brake device 3 is a device similar to a conventionally known brake device, and is provided, for example, on each of the front and rear wheels 4 of the vehicle 1. An example of the brake device 3 is a friction brake such as a disc brake, drum brake, or powder brake, and is configured to generate a friction force by hydraulic pressure or electromagnetic force, thereby generating a braking force in a direction that stops the rotation of each wheel 4.

[0022] The steering device 5 changes the azimuth angle to adjust the traveling direction of the vehicle 1. The steering device 5 is a device similar to a conventionally known steering device, such as a rack-and-pinion type electric power steering device provided with an electric assist mechanism.

[0023] The detection unit 6 is a device or apparatus for acquiring various data and information required for controlling the vehicle 1. The detection unit 6 includes an internal sensor 8 for acquiring data related to the traveling of the vehicle 1 itself and an external sensor 9 for acquiring 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 in the attitude and direction of the vehicle 1, for example, the yaw angle (azimuth angle) as an angular velocity, and a vehicle speed sensor 8c for detecting the vehicle speed from the rotation speed of the wheels. The external sensor 9 includes an on-board camera 9a for acquiring an image and a LiDAR 9b for acquiring information on an object based on data on the reflected light of a laser beam. The internal sensor 8 corresponds to a behavior detection sensor in the embodiment of the present invention, and the external sensor 9 corresponds to an external detection sensor in the embodiment of the present invention.

[0024] The ECU 7 corresponds to a controller in the embodiment of the present invention, and is mainly composed of a microcomputer including a processor (CPU), a storage element (RAM or ROM), and an input / output device (input / output interface). The ECU 7 is configured to perform calculations according to a predetermined program using various sensors 8 and 9 provided in the vehicle 1, data input from the outside, and pre-stored data, and to output the results of the calculations as control command signals. For example, the ECU 7 executes functions that meet a predetermined purpose by having the processor load a program stored in a recording medium into a working area of ​​the storage unit and execute the program, and perform various controls through the execution of the program. In addition, the ECU 7 includes a vehicle position estimation unit 10, a target trajectory calculation unit 11, and a trace control unit 12, as shown in FIG. 1 and FIG. 2.

[0025] The vehicle position estimation unit 10 estimates the current position of the vehicle 1 on the map data based on the detection results of changes in the behavior of the vehicle 1 and external information. The vehicle position estimation unit 10 detects and calculates the state quantities of the vehicle 1, such as the vehicle speed, acceleration, and yaw rate, based on the various sensors 8 and 9 described 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, and the like. That is, the vehicle position estimation unit 10 estimates the position of the vehicle 1 on the road on which the vehicle 1 is currently traveling on the map data, and if the road is a curved road, the curvature or yaw rate at which the vehicle 1 is traveling on the curved road. The vehicle position estimation unit 10 also acquires the relative position of the vehicle 1 on the actual road based on the external sensor 9, such as the on-board camera 9a and LiDAR 9b. For example, the vehicle position estimation unit 10 measures the relative position of the vehicle 1, such as the lane in which the vehicle 1 is traveling and where in the traveling lane the vehicle 1 is traveling, based on the relative distance between the vehicle 1 and a white line in the traveling lane recognized by the onboard camera 9a, the relative distance between the vehicle 1 and surrounding vehicles, or the relative distance between the vehicle 1 and signs, etc.

[0026] The vehicle position estimation unit 10 is configured to determine the difference between the relative position of the vehicle 1 measured in this way and the estimated position of the vehicle 1 on the map data. For example, when the vehicle 1 travels on a curved road, the estimated position of the vehicle 1 may travel along the center of the travel lane on the map data, whereas the actually measured relative position of the vehicle 1 may travel closer to a dividing line (e.g., a roadway center line) on the right side of the center of the travel lane. In such a case, the vehicle position estimation unit 10 is configured to determine the amount of correction of the position data when estimating the current position of the vehicle on the map data, based on the difference between the estimated position data of the vehicle 1 on the map data and the actually measured actual position of the vehicle 1.

[0027] The vehicle position estimation unit 10 also calculates the difference between the estimated position data of the vehicle 1 and the actually measured position for each of the components in the vertical direction (front-rear direction), horizontal direction (left-right direction), and yaw direction of the vehicle 1. The vehicle position estimation unit 10 changes the parameters for weighting each component according to the characteristics of the components so that the position of the vehicle 1 can be appropriately estimated. For example, the vehicle position estimation unit 10 estimates the position of the vehicle 1 in the yaw direction by weighting data based on the detection value of the internal sensor 8 provided in 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. The vehicle position estimation unit 10 also estimates the position on the map data in the lateral direction of the vehicle 1 by weighting data based on the position of the vehicle 1 actually measured by the external sensor 9. 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 for the lateral direction of the vehicle 1 in the same manner as for the yaw direction, and estimate the position by weighting 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 predetermined intervals.

[0028] The target trajectory calculation unit 11 calculates a target travel trajectory and a 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 information on the planned travel path, which is the travel path on which the vehicle 1 is traveling or the travel path on which the vehicle 1 is scheduled to travel. The information on the travel path is, for example, information on the stored map data and white lines, signs, other vehicles, etc. on the travel path detected by the external sensor 9. Based on the information, the target trajectory calculation unit 11 calculates a target trajectory on the map data for the vehicle 1 to travel in the center of the travel lane at an appropriate speed, or for the vehicle 1 to enter with a curvature that follows the curvature of a curved road, so that the vehicle 1 can travel autonomously in a comfortable manner for the passengers. In addition, when the current position of the vehicle 1 on the map data differs from the current target trajectory or target curvature of the vehicle 1, the target trajectory calculation unit 11 calculates a travel trajectory that allows the vehicle 1 to quickly return to such a target trajectory or curvature. The white lines, signs, and other vehicles correspond to objects present around the vehicle in the embodiment of the present invention.

[0029] For example, the target trajectory calculation unit 11 is configured to obtain information on lanes, division lines, road boundary lines, etc. based on map data, and calculate a target trajectory based on the target position of the vehicle 1 based on the center line of the driving lane. At that time, if 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 obtains a driving trajectory such that the current position of the vehicle 1 on the map data gradually matches the position on the currently set target trajectory. In other words, the target position of the vehicle 1 with respect to the elapsed time from the current position of the vehicle 1 on the map data, that is, the deviation amount in the longitudinal direction, lateral direction, and yaw direction from the current target trajectory, the magnitude of the control input, the moving time of the vehicle 1, the allowable deviation amount, and other parameters, as well as the evaluation function and constraint conditions in optimal control, are obtained.

[0030] The trace control unit 12 is configured to execute driving control of the vehicle 1, and based on data acquired from the vehicle position estimation unit 10 and the target trajectory calculation unit 11, etc., the trace control unit 12 performs automatic driving of the vehicle 1 by combining feedback and feedforward. That is, the trace control unit 12 calculates the operation amount and control input of each device such as the driving force source 2, the brake device 3, and the steering device 5 that control the behavior of the vehicle 1 such as the driving force, braking force, and steering amount. The trace control unit 12 acquires data on the relative position of the vehicle 1 on the road or the correction amount of the position data on the map data based on the actual measured position for each of the longitudinal, lateral, and yaw directions of the vehicle 1 from the vehicle position estimation unit 10. The trace control unit 12 also acquires the target trajectory, curvature, etc. of the vehicle 1 calculated by the target trajectory calculation unit 11. Based on the acquired data, the trace control unit 12 calculates the operation amount and control input of each device that controls the behavior of the vehicle 1, which are necessary for the vehicle 1 to travel from the current position on the map data according to the target trajectory and curvature.

[0031] For example, when the amount of operation related to the steering angle of the vehicle 1 is calculated based on the target trajectory, the trace control unit 12 calculates the target steering angle and calculates the steering torque for obtaining the target steering angle. The target steering angle is calculated using the target curvature, the target yaw angle, and the target lateral deviation calculated based on the shape of the target trajectory and the control gains determined in advance for these parameters. As an example, the target steering angle is calculated based on the values ​​obtained by multiplying the target curvature, the deviation between the target yaw angle and the actual yaw angle, the deviation between the target lateral deviation and the actual lateral deviation, and the integral value of the deviation between the target lateral deviation and the actual lateral deviation by the control gains. The calculation of the target steering angle is executed at predetermined intervals, and the calculation result is transmitted to the ECU 7 each time to control the steering mechanism. In this way, each device that controls the behavior of the vehicle 1 is controlled based on the amount of operation calculated by the trace control unit 12.

[0032] Next, an example of control executed by the ECU 7 of the vehicle 1 configured as above will be described. As shown in Fig. 3, in step S1, a correction amount is calculated when correcting the position data of the vehicle 1 on the map data based on the actual position of the vehicle 1 on the road. In step S1, as described above, if the position data of the vehicle 1 on the map data estimated mainly based on the state quantity of the vehicle 1 is shifted from the relative position of the vehicle 1 actually measured mainly based on the external sensor 9, the position data is corrected according to the relative position to obtain the current position of the vehicle 1 on the map data. In step S1, correction amounts for the longitudinal direction, lateral direction, and yaw direction of the vehicle 1 at that time are acquired.

[0033] After the correction amount of the position data of the vehicle 1 is calculated, the process proceeds to step S2, where the predicted correction amount of the position data is calculated. In step S2, based on the traveling direction of the vehicle 1 and the shape of the planned traveling route acquired from the map data and data from the external sensor 9, etc., the correction amount is predicted to change, such as to become larger or smaller. For example, when the road ahead in the traveling direction is a straight road, the correction amount is predicted to become smaller. On the other hand, when the road ahead in the traveling direction of the vehicle 1 is a curved road or when the vehicle 1 is scheduled to travel on a temporary road due to road surface construction, etc., it is predicted that the deviation between the map data and the actual traveling route will become larger. In such a case, the correction amount of the position data is predicted to become larger. In step S2, the change in the correction amount is predicted mainly based on the traveling direction acquired from the map data and the external sensor 9 and the road shape of the planned traveling route.

[0034] After the change in the correction amount of the position data of the vehicle 1 is predicted, the process proceeds to step S3, and the characteristics of the trace control when performing automatic driving are changed. In step S3, the weight of the parameters when calculating the operation amount for automatically driving the vehicle 1 is changed according to the correction amount calculated in step S1 and the correction amount predicted in step S2 for each component of the longitudinal direction, lateral direction, and yaw direction of the vehicle 1. Specifically, in step S3, 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 road and the predicted correction amount are, in each of the above-mentioned directions of the vehicle 1, the larger the control gain for the deviation in each direction based on the corrected position data, that is, the feedback gain is made. In addition, the feedforward value for calculating the control input for the steering angle of the vehicle 1 is controlled to approach an appropriate value or a target value based on the road, as necessary.

[0035] For example, in the lateral direction of the vehicle 1, when the correction amount of the position data of the vehicle 1 calculated in step S1 is small and the correction amount of the predicted position data of the vehicle 1 is small, the position of the vehicle 1 is unlikely to deviate from the center position or the target position on the road. In other words, when the correction amount is small, the difference between the position data or the estimated position of the vehicle 1 estimated 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 actual road measured by the external sensor 9 is small. In other words, since the self-position of the vehicle 1 can be estimated with high accuracy, as a result, the position of the vehicle 1 is unlikely to deviate from the center position or the target position on the road. Therefore, the driving characteristics in the trace control are not particularly changed. In other words, the current driving characteristics are maintained and the automatic driving of the vehicle 1 by the trace control is continued.

[0036] Conversely, when the amount of correction of the position data of the vehicle 1 is large in the lateral direction of the vehicle 1 and the predicted amount of correction of the position data of the vehicle 1 is large, the feedback gain for the lateral direction of the vehicle 1 is increased and vehicle trace control is executed. When both the amount of correction of the position data of the vehicle 1 and the predicted amount of correction are large, the vehicle 1 is likely to deviate from the center position or the target position on the road, mainly in the lateral direction and yaw direction of the vehicle 1. For this reason, the feedback gain is configured to be increased in advance in the lateral direction or yaw direction.

[0037] Furthermore, when correcting the longitudinal direction of the vehicle 1, in addition to correcting by increasing such feedback gain, the operation amount is changed by changing the feedforward value set based on the curvature of the target trajectory on the planned driving route from the current position of the vehicle 1 on the map data. For example, when the vehicle 1 approaches a curved road to the left, if it is detected that the position data on the map data is behind the actual measured position of the vehicle 1 on the driving route, the position data of the vehicle 1 is corrected forward in the longitudinal direction and to the right 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 corresponds to the actual measured position of the vehicle on the driving route, and the current position of the vehicle 1 on the map data is shifted to the right of the driving route.

[0038] In such a case, in order to move the vehicle 1 quickly to a target position such as the center of the travel lane, the curvature feedforward of the vehicle 1 is increased to the left turning side, and the feedback gain for the lateral deviation is increased. That is, by increasing the feedback gain, the deviation of the vehicle 1 from the center position of the travel path or the target position can be detected quickly, and the deviation can be reflected in the operation amount of each device. Then, the position of the vehicle 1 on the map data is corrected forward, and the feedforward value is changed to a value corresponding to 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 trajectory on the travel path corresponding to the corrected current position of the vehicle 1, it is possible to not hinder the change in the operation amount due to such feedback control, and to make the feedforward value closer to an appropriate value or target value along the travel path. Therefore, the actual position of the vehicle 1 is controlled to return quickly to the center position or the target position.

[0039] In this way, in step S3, the larger the correction amount and the predicted correction amount of the estimated position of the vehicle 1 in any one of the longitudinal, lateral, and yaw directions, the larger the feedback gain in that direction is changed to change the characteristics of the trace control of the vehicle 1. In addition, the feedforward value is changed as necessary. Then, based on the characteristics of the trace control changed in this way, the driving force, braking force, and steering amount of the vehicle 1 are controlled, thereby executing automatic driving of the vehicle 1. Note that, when the correction amount and the predicted correction amount in the longitudinal direction are large, there is a high possibility that deviations have also occurred in the lateral and yaw directions, so that the feedback gain based on the deviation in each direction may be configured to be large.

[0040] In the control device for the autonomous vehicle 1 configured in this manner, 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 based on the behavior of the vehicle 1, and the actual position of the vehicle 1 on the roadway measured relatively by recognizing objects around the vehicle 1. A target trajectory along which the vehicle 1 should travel is obtained based on the current position of the vehicle 1 thus obtained, the white lines of the travel lane, the road shape, etc. detected by the map data and the external sensor 9, and the vehicle 1 is controlled according to the operation amount of the driving force source 2, the brake device 3, the steering device 5, etc. required to automatically drive the vehicle 1 along the target trajectory. At that time, when it is predicted that the difference between the position data of the vehicle 1 on the map data and the actual position of the vehicle 1 on the roadway will become large, the ECU 7 is configured to increase the feedback gain to control the vehicle 1. Also, the ECU 7 is configured to bring the feedforward value closer to an appropriate value as necessary.

[0041] For example, when the vehicle 1 travels on a road that curves to the left, the vehicle speed changes, and the direction of the vehicle 1 changes toward the direction of the curve. As shown in FIG. 4, when the vehicle 1 approaches such a curved road, the position data of the vehicle 1 on the map data may be estimated to be located behind the actual measured position on the road, that is, as shown by the vehicle 1 at the position of the position data, the vehicle 1 travels along the target track on the map data, but is estimated to be behind and to the left of the actual road. In that case, the position data of the vehicle 1 is corrected forward and to the right according to the distance to an object such as a white line based on the external sensor 9, and the current position of the vehicle on the map data is obtained. In other words, the current position of the vehicle 1 on the map data is obtained based on the actually measured relative position of the vehicle 1. The current position of the vehicle 1 on the map data obtained in this way is shifted to the right side of the travel lane. Therefore, the travel control of the vehicle 1 is controlled so as to increase the amount of turning to the left while moving the vehicle 1 to the center of the travel lane. Then, the driving force or braking force, steering angle, and the like of the vehicle 1 required for the vehicle 1 to return to the target trajectory and travel are calculated, and each device is controlled based on the results of the calculation.

[0042] At this time, in the embodiment of the present invention, the driving characteristics in the trace 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 increase. In addition, at that time, the feedforward value is changed so as not to interfere with the feedback or to approach 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 lateral feedback gain in the driving control of the vehicle 1 is. Then, the feedforward based on the curvature in the target trajectory of the travel path is increased to the left turning side, that is, changed to approach an appropriate value for the travel path. At this time, since the position is obtained mainly based on parameters corresponding to the behavior of the vehicle 1, there is a possibility that the correction amount is detected as small for the yaw direction of the vehicle 1. Therefore, the ECU 7 is configured to continue the calculation without making any particular change to the feedback gain in the yaw direction. In other words, the behavior of the vehicle 1 can be controlled so that the deviation between the center position or target position on the road and the actual position of the vehicle 1 is quickly reduced, or the occurrence of such deviation is suppressed.

[0043] Also, as shown in FIG. 5, depending on the road, the map data may not accurately reflect the actual road due to the accuracy of the map data or construction work being carried out. In such a case, there is a high possibility that the difference between the position data of the vehicle 1 and the measured position increases. As a result, there is a high possibility that the position of the vehicle 1 will travel at a position that is deviated from the center position of the road or the target position. Specifically, as shown in FIG. 5, although the vehicle 1 travels at the center position of the road on the map data (on the map), there is a possibility that the vehicle will gradually move to the right side of the road on the actual road (on the actual road). In response to this, in the above-mentioned embodiment, the feedback gain is increased as the correction amount and the predicted correction amount of the position data of the vehicle 1 to the measured position increase, and the feedforward value is made closer to an appropriate value as necessary. Therefore, when a deviation occurs between the vehicle's position data on the map data and the measured relative position of the vehicle, the operation amount of each device that controls the behavior of the vehicle 1 can be set so as to reduce the deviation.

[0044] Therefore, for example, even if the running position of the vehicle 1 deviates from the target track due to a difference between the position data of the vehicle 1 on the map data and the relative position of the vehicle 1 on the road, it is possible to prevent the vehicle 1 from suddenly decelerating or changing the steering amount when returning the running position of the vehicle 1 to the target track. That is, it is possible to quickly return the running position of the vehicle 1 to the target track. In addition, it is possible to prevent the behavior of the vehicle 1 from occurring unintended by the passenger.

[0045] Although the embodiment of the present invention has been described above, the present invention is not limited to the above-mentioned examples, and may be appropriately modified within the scope of achieving the object of the present invention. For example, for the automatic driving of the vehicle 1 based on the trace control, an optimal control that obtains a control input that minimizes an evaluation function, such as model predictive control, may be used. In such a case, in the above-mentioned control, when the correction amount of the estimated position based on the actual measured position of the vehicle 1 and the predicted correction amount become large, it is preferable to configure the trace control to be executed by increasing the weighting of the parameters related to the lateral deviation and yaw angle deviation, which are the trajectory tracking terms. [Explanation of symbols]

[0046] 1 vehicle 6. Detection section 7 ECU 8 Internal Sensors 9 External Sensors 10 Vehicle position estimation unit 11 Target trajectory calculation section 12 Trace control section

Claims

1. A plurality of behavior detection sensors for detecting parameters related to the behavior of a vehicle; a plurality of external detection sensors for detecting objects present around a road on which the vehicle is traveling; A control device for an autonomous vehicle configured to estimate a current position of the vehicle on the map data based on pre-stored map data, detection results of a plurality of the behavior detection sensors, and detection results of a plurality of the external detection sensors, calculate a target trajectory based on the estimated current position of the vehicle on the map data, and autonomously drive the vehicle based on the target trajectory, a controller that estimates the current position on the map data and executes driving control of the vehicle based on a control input set so that the vehicle automatically drives along the target trajectory; The controller: a current position of the vehicle on the map data is estimated by correcting a position of the vehicle on the map data in accordance with a relative position of the vehicle on the roadway determined based on the detection results of the plurality of external detection sensors; 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 for a planned travel route on which the vehicle is to travel based on the map data and data related to the planned travel route detected by at least one of the plurality of external detection sensors; When the driving control is executed based on the control input, a characteristic of the driving control is changed so as to change a control gain in the control input in accordance with the correction amount and the predicted correction amount. A control device for an autonomous vehicle.

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

3. The vehicle control device according to claim 1 or 2, The controller: Calculating the correction amount for each of the longitudinal direction, the lateral direction, and the yaw direction of the vehicle; and changing a characteristic of the driving control so as to change a magnitude of the control gain based on the correction amount and the predicted correction amount for each of the longitudinal direction, the lateral direction, and the yaw direction, thereby causing the vehicle to automatically drive. A control device for an autonomous vehicle.

4. The vehicle control device according to claim 1 or 2, The controller is configured to execute the driving control for causing the vehicle to travel in an autonomous driving manner by using a feedforward value set based on the planned driving route as the control input, When changing the characteristics of the driving control, the feedforward value is changed based on the correction amount and the predicted correction amount. A control device for an autonomous vehicle.

5. The vehicle control device according to claim 4, The controller: setting the feedforward value based on a curvature of the target trajectory on the planned travel route from the current position of the vehicle on the map data; Calculating the control input for a steering angle of the vehicle based on the feedforward value; When changing the characteristics of the driving control, if a correction in a longitudinal direction of the vehicle is included in a correction for estimating the current position of the vehicle on the map data, the feedforward value is changed to an appropriate value based on the estimated current position. A control device for an autonomous vehicle.

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