Control device of automatic driving vehicle

Abstract

To provide a control device of an automatic driving vehicle that can reduce loads by predetermined calculation, when supporting running of a vehicle on the basis of control-input obtained by repeatedly executing the predetermined calculation.SOLUTION: In a control device of an automatic driving vehicle, which is configured to determine control-input for enabling a vehicle to run by automatic driving along a target track determined based on a presumed current position of the vehicle, by repeatedly performing predetermined calculation, a controller presumes the current position of the vehicle by correcting a position of the vehicle on map data, on the basis of a relative position on a running road of the vehicle (a step S1), and when it is determined that the presumption of the current position is not correct on the basis of the correction amount for the position of the vehicle (YES in a step S2), adjusts a predetermined parameter in predetermined calculation so that the number of times of the calculation reduces, in accordance with the correction amount (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. In the self-location estimation device of Patent Document 1, when a laneless section where the number of lanes increases or decreases is recognized based on image data, the use of map data is restricted, that is, the weighting of the map data is reduced, and the estimated position of the vehicle is corrected, compared to when the laneless section is not recognized. Then, the device is configured to provide assistance for automatic driving based on the estimated position of the vehicle. 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 relative position of the vehicle on the actual driving road measured by a camera, etc. are acquired at a predetermined period, and the vehicle position on the map data is estimated by matching them. As in Patent Document 1, in the case of a vehicle capable of autonomous driving, a mechanism for driving the vehicle, such as a drive unit, is controlled so that the vehicle drives along a target trajectory from the estimated self-position.

[0005] In such an autonomous driving, for example, the vehicle may be controlled to travel along a target trajectory in the lane in which the vehicle is currently traveling by periodically repeating calculations based on the data such as the movement of the vehicle in each direction, map data, and the estimated position acquired as described above, and the vehicle may be autonomously driven. In such a control, from the viewpoint of vehicle safety, a constraint may be imposed such that when the actual vehicle position deviates from the target trajectory by a predetermined amount or more, a control input to a mechanism for driving the vehicle is changed so that the vehicle is quickly returned to the target position. For example, the calculation formula may include a term that adds a calculation when the deviation of the vehicle position becomes a predetermined amount or more.

[0006] On the other hand, when the vehicle position is estimated as described above, the estimated vehicle position may deviate significantly due to errors in the map data or weighting of the parameters used to estimate the position differing in each direction. Therefore, although there is a deviation between the actual position of the vehicle and the target position on the target trajectory, the deviation is not immediately reflected in the control input, and the deviation becomes even larger. In such a case, it may be necessary to significantly correct the estimated position of the vehicle, and the behavior of the vehicle on the map data based on the correction of the estimated position may become unnatural as the behavior of the actual vehicle. As a result, the number of calculations increases in the calculations that are repeatedly performed, which may lead to an increase in the load on the control device. In addition, if such a deviation violates the above-mentioned constraints, the calculation may not be performed correctly, or the load due to the increase in the number of calculations may further increase. Or, since the constraints are suddenly violated, the vehicle behavior may be such that the vehicle behavior gives the passengers an uncomfortable feeling due to the control that tries to quickly return the vehicle to the target trajectory.

[0007] The present invention has been made with attention to the above-mentioned technical problems, and aims to provide a control device for an autonomous vehicle that is capable of reducing the load caused by calculations when assisting the vehicle's driving based on control inputs obtained by repeatedly executing specified calculations. [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 the vehicle, and a plurality of external detection sensors which detect data related to objects existing around a road on which the vehicle is traveling, the control device being configured to estimate a current position of the vehicle on the map data based on pre-stored map data, the detection data of the plurality of behavior detection sensors, and the detection data of the plurality of external detection sensors, calculate a target trajectory based on the estimated current position of the vehicle on the map data, calculate a control input for autonomously driving the vehicle along the target trajectory by repeatedly performing predetermined calculations, and execute driving control of the vehicle based on the control input, the control device being configured to estimate the current position of the vehicle, and the vehicle being detected by the plurality of external detection sensors, the control device being configured to estimate a current position of the vehicle on the map data, and the detection data of ... The vehicle navigation system further includes a controller that executes the driving control for driving along a target trajectory, the controller estimates the current position of the vehicle on the map data by correcting the position of the vehicle on the map data based on a relative position of the vehicle on the road determined based on the detection data 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 on the map data is estimated, determines whether the estimation of the current position of the vehicle on the map data is accurate based on the correction amount, and if it is determined that the estimation of the current position of the vehicle on the map data is not accurate, adjusts a predetermined parameter in the predetermined calculation in accordance with the correction amount so as to reduce the number of calculations.

[0009] In addition, in the present invention, determining whether the estimation of the current position of the vehicle on the map data is accurate may be configured to include determining whether the correction amount is greater than a predetermined correction amount that can be determined to be a possible behavior of the vehicle.

[0010] In addition, in the present invention, the controller has a prediction model for predicting the behavior of the vehicle up to a predetermined time ahead, and a constraint is imposed on the specified calculation in the driving control such that, when a deviation between the estimated current position of the vehicle and a predicted position of the vehicle up to the predetermined time ahead based on the prediction model, and a target position on the target trajectory, becomes larger than a predetermined deviation, the control input is changed so as to reduce the deviation, and the constraint is configured to change the control input by adding a calculation when the deviation in the specified calculation becomes larger than the predetermined deviation, and the adjustment of the parameters may be configured to include relaxing the constraint.

[0011] Furthermore, in the present invention, the specified calculation in the driving control may be configured to be repeatedly executed until a predetermined convergence judgment threshold for determining whether the control input is an appropriate value is satisfied, and the adjustment of the parameter may be configured to include increasing the convergence judgment threshold. Effect of the Invention

[0012] 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 of the vehicle on the map data and the data based on the behavior detection sensor of the vehicle 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. Then, a target trajectory is set according to the estimated current position of the vehicle, and driving control is executed so that the vehicle automatically drives along the target trajectory. The driving control is performed according to the operation amount to the mechanism for driving the vehicle, which is obtained based on the control input calculated by repeatedly performing a predetermined calculation. Also, during the driving control, it is determined that the estimation of the current position of the vehicle on the map data is accurate based on the correction amount when estimating the current position. If it is determined that the estimation of the current position is inaccurate, a predetermined parameter in the predetermined calculation is configured to be adjusted according to the correction amount so that the number of calculations is reduced. That is, if the number of calculations in the predetermined calculation increases due to the inaccuracy of the estimation of the current position of the vehicle on the map data, the predetermined calculation is adjusted so that the number of calculations is reduced according to the correction amount. Therefore, the load on the control device due to the increase in the number of calculations can be reduced.

[0013] In addition, the determination that the estimation of the current position of the vehicle on the map data is accurate includes that the correction amount is greater than a predetermined correction amount that can determine that the behavior of the vehicle is possible. The predetermined parameter is adjusted by relaxing the constraint imposed on the predetermined calculation to reduce the deviation of the position of the vehicle, or relaxing a predetermined convergence judgment threshold that is set to calculate an appropriate control input in the predetermined calculation. For example, when a predetermined condition is satisfied, the constraint is made less likely to be violated, the problem is changed to one that does not include the constraint itself and the predetermined calculation is performed, or the control itself is temporarily switched to a rule-based driving control such as PID control, or the convergence judgment threshold is increased. Therefore, it is possible to suppress an increase in the number of calculations due to a violation of the constraint, an increase in the number of calculations due to a small convergence judgment threshold, which increases the load on the ECU, or an inability to find a solution to the calculation in the predetermined optimal control problem. Furthermore, it is possible to suppress the occurrence of behavior unintended by the occupant in the behavior of the vehicle. [Brief description of the drawings]

[0014] [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. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0015] 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.

[0016] 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. The vehicle 1 according to the embodiment of the present invention is capable of autonomous driving by controlling the driving force, braking force, steering amount, etc., without the driver having to perform driving operation, as well as driving by manual operation. The 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.

[0017] 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.

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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.

[0022] 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 attitude and direction of the vehicle 1, for example, a change in 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 4. 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 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.

[0023] 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 a control command signal. 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. 2. The vehicle position estimation unit 10, the target trajectory calculation unit 11, and the trace control unit 12 are each composed of a different microcomputer.

[0024] The vehicle position estimation unit 10 estimates the current position of the vehicle 1 on the map data based on detection data such as changes in the behavior of the vehicle 1 and external information. The vehicle position estimation unit 10 detects and calculates state quantities of the vehicle 1, such as vehicle speed, acceleration, and yaw rate, based on the various sensors 8 and 9 described above. The vehicle position estimation unit 10 obtains position data of the vehicle 1 on the map data based on the detected state quantities of the vehicle 1, past estimation results of the vehicle 1 position, stored map data, and the like. That is, the vehicle position estimation unit 10 estimates, on the map data, where the vehicle 1 is currently traveling on the road, and if the road is a curved road, what curvature or yaw rate the vehicle 1 is traveling on the curved road. The vehicle position estimation unit 10 also acquires the actual relative position of the vehicle 1 on the road based on the external sensors 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 1, or the relative distance between the vehicle 1 and signs, etc.

[0025] 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 the dividing line (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 1 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.

[0026] 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.

[0027] 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, white lines and signs on the travel path detected by the external sensor 9, other vehicles 1, etc. 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 automatically 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. Such white lines, signs, and other vehicles 1 correspond to objects present around the vehicle 1 in the embodiment of the present invention.

[0028] 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.

[0029] The trace control unit 12 is configured to execute driving control of the vehicle 1, and automatically drives the vehicle 1 based on data acquired from the vehicle position estimation unit 10 and the target trajectory calculation unit 11. 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, which 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 correction amount of the position data on the map data based on the actual measured position of the vehicle 1 on the road for each of the longitudinal direction, lateral direction, and yaw direction of the vehicle 1, from the vehicle position estimation unit 10. In addition, the trace control unit 12 acquires the target trajectory, curvature, etc. of the vehicle 1 calculated in 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.

[0030] 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.

[0031] The trace control unit 12 also includes an optimum calculation unit 12a. The optimum calculation unit 12a calculates an optimum control input to the device used for driving control based on a predetermined optimum control problem, taking into consideration the characteristics (dynamic characteristics) of the vehicle 1, including the performance of the device. The predetermined optimum control problem is also subject to a constraint expressed by an inequality determined to determine whether the control input is equal to or greater than a predetermined upper limit value or equal to or less than a predetermined lower limit value, which is set based on the characteristics and safety of the vehicle 1. The optimum calculation unit 12a repeatedly performs calculations to find an optimum solution based on an evaluation function and constraint conditions for the predetermined optimum control problem. The optimum calculation unit 12a repeats calculations until the solution falls below a predetermined convergence judgment value, which is a predetermined convergence judgment threshold value for determining that the control input is optimum, and determines that the optimum solution has been found when the solution becomes smaller than the convergence judgment value. The optimization calculation unit 12a performs such optimization calculations at predetermined control cycles, and, for example, when determining a target steering angle of the vehicle 1, the target steering angle is determined by solving a mathematical equation based on the lateral and yaw deviations of the vehicle 1, constraints on the steering angle speed, and weighting of these parameters and integrated values ​​over a specified period of time.

[0032] The formula includes constraint conditions according to the characteristics, performance, or behavior of the vehicle 1. Such constraint conditions allow for highly accurate trace control. For example, the optimal control problem for determining the above-mentioned target steering angle includes constraint conditions on the lateral deviation, and when the lateral deviation exceeds a predetermined deviation, the target steering angle is determined so that the exceeded value is minimized. Specifically, the constraint conditions are set by a predetermined state equation based on the current steering angle, the running trajectory, and the movement of the vehicle 1, and an inequality based on the amount of excess of the lateral deviation. The optimal calculation unit 12a is configured to determine control inputs to the driving force source 2, the brake device 3, the steering device 5, and the like, by repeating the optimization calculation including the constraint conditions configured in this way for each control period. The deviations in the lateral, yaw, and longitudinal directions of the vehicle 1 are deviations between the predicted position of the vehicle 1 until a predetermined time ahead based on the estimated current position of the vehicle 1 and a prediction model predicting the behavior of the vehicle 1 until a predetermined time ahead, and the target position on the target trajectory. That is, it includes the deviation between the current position of the vehicle 1 and the target position, and the deviation between a predicted position for a predetermined time ahead based on a prediction model of the vehicle 1 and a target position at the predicted position. The predetermined optimal control problem corresponds to a predetermined calculation in the embodiment of the present invention. Furthermore, the optimal control used for such driving control may be so-called model predictive control configured to determine an optimal operation while predicting the movement of the vehicle 1 using a model.

[0033] 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, the correction amount for each of the longitudinal direction, lateral direction, and yaw direction of the vehicle 1 at that time is acquired at a predetermined cycle.

[0034] After the correction amount of the position data of the vehicle 1 is obtained, the process proceeds to step S2, where it is determined whether or not there is a problem with the estimation of the current position of the vehicle 1. That is, in step S2, it is determined that the estimation of the current position of the vehicle is accurate based on the correction amount of the position data calculated in step S1. Specifically, in step S2, it is determined that the behavior of the vehicle 1 in the current cycle is corrected to the behavior of the vehicle 1 up to the immediately previous cycle based on the correction amount calculated in step S1. In other words, in step S2, it is determined that the deviation of the position data of the vehicle 1 is a deviation caused by a calculation for estimating the position of the vehicle 1. Such a determination is made, for example, based on whether the correction amount in each direction calculated in step S1 is equal to or greater than a predetermined correction amount.

[0035] When estimating the current position of the vehicle 1, the parameters to be referred to and the weighting of the parameters differ depending on each direction (each component) of the vehicle 1. For example, as described above, the position of the vehicle 1 in the yaw direction may be calculated based on the behavior of the vehicle 1, such as the speed, acceleration, and steering amount, and the position in the lateral direction may be calculated by referring to data based on the relative position of the vehicle 1 actually measured by the external sensor 9, etc., in addition to the behavior of the vehicle 1. In such a case, if an error or disturbance in the map data causes a deviation between the actual position of the vehicle 1 on the road on which the vehicle 1 is traveling and the position on the map data on the road on which the vehicle 1 is traveling, there is a possibility that a difference in detection of the deviation may occur in each direction of the vehicle 1.

[0036] For example, when entering a curved road, the position of the vehicle 1 on the map data may be detected behind the actual position due to the above-mentioned errors and disturbances. In such a case, the lateral position of the vehicle 1 is estimated from data based on the behavior of the vehicle 1 and the actually measured relative position of the vehicle 1, so such a deviation can be detected relatively early. On the other hand, the position of the vehicle 1 in the yaw direction is estimated from data based on the behavior of the vehicle 1, so there is a possibility that the detection of such a deviation in the lateral direction of the vehicle 1 may be delayed. In other words, since the relative position is not referenced for the yaw direction, or the relative position has a small weight, it is estimated that the yaw angle is changing along the road on the map data, and a delay in the detection of such a deviation occurs.

[0037] The steering angle operation amount of the vehicle 1 is obtained using the deviation between the target position of the vehicle 1 on the map data and the estimated position and predicted position in the lateral and yaw directions of the vehicle 1. Therefore, if the deviation in the yaw direction is small, the steering angle operation amount may not be large enough, and the lateral deviation of the vehicle 1 may become large. In such a case, the correction amount when correcting the position of the vehicle 1 on the map data may be a large correction amount that cannot occur as the actual behavior of the vehicle 1. If it is determined that the correction amount calculated in step S1 is larger than a predetermined correction amount that can occur as the actual behavior of the vehicle 1, it is determined in step S2 that the correction is due to the estimation of the current position of the vehicle 1. In this way, if it is determined in step S2 that the correction is not due to the estimation of the current position of the vehicle 1 because the correction amount is equal to or less than the predetermined correction amount, the flow chart is temporarily terminated without executing the subsequent control.

[0038] On the other hand, if it is determined that the correction is due to estimation of the position of the vehicle 1 because the correction amount is larger than the predetermined correction amount, the process proceeds to step S3, where parameters in the optimal control problem are adjusted according to the correction amount. In step S3, parameters that lead to an increase in the number of calculations in the predetermined optimal control problem because the correction amount is larger than the predetermined correction amount are adjusted. For example, in step S3, constraint conditions related to the state quantities of the vehicle 1 and convergence judgment values ​​in the predetermined optimal control problem are changed.

[0039] When changing the constraints on the state quantities of the vehicle 1, the constraint conditions set for the deviations in the lateral, yaw, and longitudinal directions of the vehicle 1 are relaxed according to the correction amount calculated in step S1. For example, when it is determined that the current position of the vehicle 1 has not been correctly estimated in the lateral direction, the constraints are expanded by increasing a predetermined lateral deviation for determining that the constraints have been violated. Alternatively, the weight of an evaluation function related to the constraints in the optimal control problem is reduced, for example, the weight of an evaluation function related to a slack variable constituting the constraints in the optimal control problem is reduced. Alternatively, the optimal control problem for the lateral deviation is changed to an optimal control problem that does not include a constraint, and an optimization calculation is performed. Alternatively, instead of using optimal control, the driving control is switched to a rule-based driving control such as a conventionally known PID control. Note that the constraints in the optimal control problem may be relaxed by a conventionally known minimum time method or constraint relaxation method. In addition, when relaxing the constraints, the relaxation is performed in consideration of hard constraints and soft constraints.

[0040] Moreover, when changing the convergence judgment value in the predetermined optimal control problem, the convergence judgment value is relaxed according to the correction amount calculated in step S1. For example, when it is determined that the current position of the vehicle 1 in the lateral direction is not correctly estimated, the convergence judgment value in the optimal control problem for obtaining the target steering angle is increased. In this way, in step S3, the predetermined parameter in the optimal control problem is changed according to the correction amount, and this flowchart is temporarily terminated.

[0041] 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 at predetermined intervals 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 road that is actually 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 and the white lines of the travel lane and the road shape detected by the map data and the external sensor 9. In order to make the vehicle 1 autonomously travel along the target trajectory, the operation amounts of the driving force source 2, the brake device 3, the steering device 5, and the like are obtained by solving a predetermined optimal control problem that takes into account the constraints on the vehicle 1, and the vehicle 1 autonomously travels according to the operation amounts.

[0042] In such driving control, a correction amount for estimating the current position of the vehicle 1 is calculated. At that time, based on the calculated correction amount, it is determined whether or not the current position of the vehicle 1 can be accurately estimated. In other words, it is determined whether or not the current position of the vehicle 1 on the map data has changed in a way that would not occur as the actual behavior of the vehicle 1. If it is determined that such a change has occurred, the constraint conditions and the convergence judgment value are relaxed in the predetermined optimal control problem. That is, while taking into consideration the effect on the driving control, parameters that lead to an increase in the number of calculations due to such a change are adjusted. For example, if a large correction amount that would not occur as the actual behavior of the vehicle 1 is obtained, the number of calculations required to calculate an optimal solution in the predetermined optimal control problem increases accordingly. Furthermore, if such a change conflicts with a constraint condition, the number of calculations based on the constraint condition increases. Alternatively, the number of calculations required for a solution to reach a convergence judgment value in the predetermined optimal control problem increases.

[0043] In the control device according to the embodiment of the present invention, when such a change is detected, the constraint is expanded, the problem is changed to one that does not include the constraint, the control itself is switched to rule-based control such as PID control, or the convergence determination value is increased. That is, the parameters that lead to an increase in the number of calculations due to the occurrence of such a change are adjusted. Therefore, it is possible to suppress an increase in the load on the ECU 7 due to an increase in the number of calculations, or a failure to find a solution to a calculation in a predetermined optimal control problem. Furthermore, it is possible to suppress the occurrence of behavior of the vehicle 1 that is not intended by the occupant.

[0044] 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 modified as appropriate within the scope of achieving the object of the present invention. For example, when relaxing the constraint conditions, it may be configured to be executed when it is determined that there is a high possibility of violating the constraint based on the correction amount in the position data of the vehicle 1. In other words, the change in the correction amount is predicted by comparing the change in the correction amount in the previous cycle with the correction amount in the current cycle. Then, when it is expected that the constraint will be violated if the change continues, it may be configured to expand the constraint or switch to a driving control without the constraint to execute the driving control. [Explanation of symbols]

[0045] 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 12a Optimal Calculation 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 data relating to 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 data from a plurality of the behavior detection sensors, and detection data from a plurality of the external detection sensors, calculate a target trajectory based on the estimated current position of the vehicle on the map data, calculate a control input for autonomously driving the vehicle along the target trajectory by repeatedly performing predetermined calculations, and execute driving control of the vehicle based on the control input, a controller that estimates the current position of the vehicle and executes the travel control for the vehicle to travel along the target trajectory; The controller: estimating the current position of the vehicle on the map data by correcting a position of the vehicle on the map data based on a relative position of the vehicle on the roadway determined based on the detection data 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 on the map data is estimated; determining whether the estimation of the current position of the vehicle on the map data is accurate based on the correction amount; When it is determined that the estimation of the current position of the vehicle on the map data is not accurate, a predetermined parameter in the predetermined calculation is adjusted in accordance with the correction amount so as to reduce the number of calculations. A control device for an autonomous vehicle.

2. The control device for an autonomous vehicle according to claim 1, Determining whether the estimation of the current position of the vehicle on the map data is accurate includes determining whether the correction amount is greater than a predetermined correction amount that can be determined to be a possible behavior of the vehicle. A control device for an autonomous vehicle.

3. The control device for an autonomous vehicle according to claim 1 or 2, The controller has a prediction model for predicting a behavior of the vehicle up to a predetermined time ahead, a constraint is imposed on the predetermined calculation in the traveling control such that, when a deviation between the estimated current position of the vehicle and a predicted position of the vehicle up to the predetermined time ahead based on the prediction model, and a target position on the target trajectory, becomes larger than a predetermined deviation, the control input is changed so as to reduce the deviation; the constraint is configured to modify the control input by adding an operation when the deviation in the given operation becomes greater than the given deviation; Adjusting the parameters includes relaxing the constraints. A control device for an autonomous vehicle.

4. The control device for an autonomous vehicle according to claim 1 or 2, The predetermined calculation in the driving control is configured to be repeatedly executed until a predetermined convergence determination threshold for determining whether the control input is an appropriate value is satisfied, The parameter adjustment includes increasing the convergence determination threshold. A control device for an autonomous vehicle.

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