Autonomous vehicle control device

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 that is capable of autonomous driving based on the behavior of the vehicle recognized by sensors and the like and information acquired from the outside. [Background technology]

[0002] Patent Document 1 discloses a self-location estimation device that aims to stabilize the estimation accuracy of the vehicle's own location even when traveling through a section where the number of lanes changes. The self-location estimation device of Patent Document 1 estimates the vehicle's location based on image data from an onboard camera, data on the vehicle's state variables acquired from sensors, GPS data, and map data. When the self-location estimation device of Patent Document 1 recognizes a laneless section, where the number of lanes increases or decreases based on the image data, it limits the use of map data, i.e., reduces the weighting of the map data, to correct the estimated vehicle's location compared to when the laneless section is not recognized. The device is then configured to provide assistance for autonomous driving based on the estimated vehicle's location. Patent Document 1 claims that this configuration can prevent a decrease in the estimation accuracy of the vehicle's own location in a laneless section where road link data is not accurately created, thereby stabilizing the accuracy of the self-location estimation. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 2019-207190 Summary of the Invention [Problem to be solved by the invention]

[0004] When identifying the vehicle's position, the longitudinal (front-rear), lateral (left-right), and yaw (or azimuth) components are identified. That is, for each component, as in the device of Patent Document 1, the vehicle's position estimated based on the vehicle's state variables and map data, etc., and the relative position of the vehicle on the actual road measured by a camera, etc., are acquired at predetermined intervals, and the vehicle's 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, mechanisms for the vehicle's driving, such as a drive unit, are controlled so that the vehicle drives from its estimated self-position along a target trajectory.

[0005] In such 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 data such as the vehicle's movement in each direction, map data, and estimated position acquired as described above, thereby autonomously driving the vehicle. In such control, from the viewpoint of vehicle safety, a constraint may be imposed such that, if the actual vehicle position deviates from the target trajectory by more than a predetermined amount, a control input to a mechanism for driving the vehicle is changed so as to quickly return the vehicle 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 more than a predetermined amount.

[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 different weightings of parameters used for position estimation in each direction. Therefore, although there is a deviation between the actual vehicle position 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, the estimated vehicle position may need to be significantly corrected, and the vehicle behavior on the map data based on the correction of the estimated position may become unnatural compared to the actual vehicle behavior. As a result, the number of calculations performed repeatedly may increase, which may lead to an increase in the load on the control device. Furthermore, if such deviation violates the above-mentioned constraints, the calculation may not be performed correctly, or the load may further increase due to the increase in the number of calculations. Alternatively, if the constraints are suddenly violated, the control that attempts to quickly return the vehicle to the target trajectory may result in the vehicle behavior being uncomfortable for the occupants.

[0007] The present invention has been made with an eye on the above-mentioned technical problems, and aims to provide a control device for an autonomous vehicle that can reduce the load caused by calculations when assisting the vehicle's driving based on control inputs obtained by repeatedly executing predetermined 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 that detect parameters related to the behavior of the vehicle, and a plurality of external detection sensors that detect data related to objects present 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, determine a target trajectory based on the estimated current position of the vehicle on the map data, determine 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 configured to estimate the current position of the vehicle and detect the target trajectory based on the estimated current position of the vehicle on the map data, determine 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 vehicle navigation system includes a controller that executes the driving control for driving along a target trajectory, and 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 the relative position of the vehicle on the roadway 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 according to 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 used to determine whether the behavior of the vehicle is likely to occur.

[0010] Furthermore, in the present invention, the controller has a prediction model for predicting the behavior of the vehicle up to a predetermined time in the future, and the predetermined calculation in the driving control is subject to a constraint such that, if a deviation between the estimated current position of the vehicle and a predicted position of the vehicle up to the predetermined time in the future based on the prediction model, and a target position on the target trajectory, becomes larger than a predetermined deviation, the control input is changed to reduce the deviation, and the constraint is configured to change the control input by adding a calculation when the deviation in the predetermined calculation becomes larger than the predetermined deviation, and 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 determination 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 determination threshold. [Effects 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 map data and data based on the vehicle's behavior detection sensor in accordance with detection results from the vehicle's external detection sensor, for example, the relative position of the vehicle based on the relative distance between the vehicle and a white line. A target trajectory is then set based on the estimated current position of the vehicle, and cruise control is executed so that the vehicle autonomously drives along the target trajectory. The cruise control is performed based on an operation amount for a mechanism for driving the vehicle, which is calculated based on a control input calculated by repeatedly performing a predetermined calculation. Furthermore, during cruise control, it is determined whether the current position of the vehicle on the map data is accurately estimated based on the correction amount used when estimating the current position. If it is determined that the current position is not accurately estimated, a predetermined parameter in the predetermined calculation is adjusted in accordance with the correction amount so as to reduce the number of calculations. In other words, if the number of calculations in the predetermined calculation increases due to an inaccurate estimation of the current position of the vehicle on the map data, the number of calculations is adjusted in accordance with the correction amount. Therefore, the load on the control device due to an increase in the number of calculations can be reduced.

[0013] Furthermore, determining whether the estimation of the vehicle's current position on the map data is accurate involves determining whether the correction amount is greater than a predetermined correction amount that can determine whether the vehicle's behavior is likely. The predetermined parameters are adjusted by relaxing constraints imposed on the predetermined calculation to reduce deviations in the vehicle's position, or by relaxing a predetermined convergence threshold that is set to calculate appropriate control inputs in the predetermined calculation. For example, when a predetermined condition is satisfied, the system may make it less likely to violate the constraints, change the problem to one that does not include the constraints, perform the predetermined calculation, temporarily switch the control itself to rule-based driving control such as PID control, or increase the convergence threshold. This prevents an increase in the number of calculations due to a constraint violation, or an increase in the load on the ECU due to a small convergence threshold, or an inability to find a solution to the calculation of the predetermined optimal control problem. Furthermore, it also prevents the vehicle from behaving in a way that is unintended by the occupant. [Brief explanation of the drawings]

[0014] [Figure 1] 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. [Figure 2] 1 is a block diagram illustrating the functional configuration of a control device for an autonomously driven vehicle according to an embodiment of the present invention. FIG. [Figure 3] 3 is a flowchart illustrating an example of control executed by a control device for an autonomously driven vehicle according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[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 specific embodiments 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 not only running by manual operation, but also running autonomously by controlling the driving force, braking force, steering amount, etc. without the driver having to perform any driving operation. The vehicle 1 is also configured to be able to recognize the driving environment and monitor the surrounding conditions based on surrounding information acquired from the outside.

[0017] The vehicle 1 performs autonomous driving based on data related to the behavior of the vehicle 1 and data acquired from an external source. For example, the vehicle 1 acquires data related to the estimated or measured position of the vehicle 1, and performs autonomous driving by calculating operation amounts such as driving force, braking force, and steering amount to control the vehicle 1 so that the vehicle 1 can travel along a predetermined target trajectory based on the data related to the position of the vehicle 1 and map data. The vehicle 1 in the embodiment of the present invention is configured to accurately estimate the current position of the vehicle 1, which serves as a reference when determining the operation amounts of each device for changing the behavior of the vehicle 1, and to enable the vehicle 1 to travel autonomously along a target trajectory. Note that the vehicle 1 may be any vehicle capable of autonomous 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 changes in the attitude and orientation of the vehicle 1, such as the yaw angle (azimuth angle), as angular velocity, and a vehicle speed sensor 8c for detecting the vehicle speed from the rotational speed of the wheels 4, etc. The external sensor 9 includes an on-board camera 9a for acquiring captured images and a LiDAR 9b for acquiring information about 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 the controller in the embodiment of the present invention and is mainly configured with a microcomputer including a processor (CPU), memory elements (RAM and ROM), and input / output devices (input / output interfaces). The ECU 7 is configured to perform calculations according to a predetermined program using data input from various sensors 8 and 9 provided on the vehicle 1, external data, and pre-stored data, and to output the results of the calculations as control command signals. For example, the ECU 7 executes functions consistent with a predetermined purpose by having a processor load a program stored on a recording medium into a working area of ​​the memory and execute the program, and then performs various controls through the execution of the program. As shown in FIG. 2, the ECU 7 also includes a vehicle position estimation unit 10, a target trajectory calculation unit 11, and a trace control unit 12. The vehicle position estimation unit 10, the target trajectory calculation unit 11, and the trace control unit 12 are each configured with 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 calculates the 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's position, stored map data, and the like. That is, the vehicle position estimation unit 10 estimates, based on the map data, the position of the vehicle 1 on the road it is currently traveling on, and, if the road is curved, 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 actual relative position of the vehicle 1 on the road based on external sensors 9, such as an on-board camera 9a and a 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 the position in the traveling lane where the vehicle 1 is traveling, based on the relative distance between the vehicle 1 and the 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.

[0025] The vehicle position estimation unit 10 is configured to determine the difference between the relative position of the vehicle 1 measured in this manner and the estimated position of the vehicle 1 on the map data. For example, when the vehicle 1 is traveling on a curved road, the estimated position of the vehicle 1 on the map data may indicate that the vehicle 1 is traveling along the center of the traveling lane, whereas the actually measured relative position of the vehicle 1 may indicate that the vehicle 1 is traveling closer to a dividing line (roadway center line) to the right of the center of the traveling lane. In such a case, the vehicle position estimation unit 10 is configured to determine the amount of correction to 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 longitudinal (front-rear), lateral (left-right), and yaw directions of the vehicle 1. The vehicle position estimation unit 10 changes the weighting parameters for each component depending on the characteristics of the components so as to appropriately estimate the position of the vehicle 1. 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 values ​​of the internal sensor 8 provided on the vehicle 1. In other words, the vehicle position estimation unit 10 calculates 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 of the vehicle 1 in the lateral direction on the map data 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 in the lateral direction of the vehicle 1, as in the yaw direction, and to 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 calculations at predetermined intervals.

[0027] The target trajectory calculation unit 11 calculates a target driving trajectory and a target curvature for 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 about the planned driving trajectory, which is the driving trajectory on which the vehicle 1 is currently driving or the driving trajectory on which the vehicle 1 is scheduled to drive. The information about the driving trajectory includes, for example, stored map data, white lines and signs on the driving trajectory detected by the external sensor 9, and information about other vehicles 1. Based on this information, the target trajectory calculation unit 11 calculates a target trajectory on the map data for the vehicle 1 to drive in the center of the driving lane at an appropriate speed, or for the vehicle 1 to enter a curved road at a curvature that follows the curvature of the curved road, so that the vehicle 1 can drive autonomously in a comfortable manner for the occupants. Furthermore, if the current position of the vehicle 1 on the map data differs from the current target trajectory or target curvature for the vehicle 1, the target trajectory calculation unit 11 calculates a driving 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 acquire information on lanes, division lines, and road boundary lines based on map data, and calculate a target trajectory based on the target position of the vehicle 1 relative to the center line of the driving lane. 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 calculates a driving trajectory that gradually brings the current position of the vehicle 1 on the map data toward a position on the currently set target trajectory. In other words, the target trajectory is calculated based on parameters such as the target position of the vehicle 1 relative to the elapsed time from the current position of the vehicle 1 on the map data, i.e., the deviation amounts in the longitudinal, lateral, and yaw directions from the current target trajectory, the magnitude of the control input, the travel time of the vehicle 1, and the allowable deviation amount, as well as an evaluation function and constraints in optimal control.

[0029] The trace control unit 12 is configured to execute driving control of the vehicle 1 and causes the vehicle 1 to drive automatically 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 amounts and control inputs 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 from the vehicle position estimation unit 10 regarding the correction amount of position data on the map data based on the actual measured position of the vehicle 1 on the road, for each of the longitudinal, lateral, and yaw directions of the vehicle 1. The trace control unit 12 also acquires the target trajectory and curvature 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 amounts and control inputs of each device that controls the behavior of the vehicle 1, which are necessary for the vehicle 1 to travel from its current position on the map data in accordance with the target trajectory and curvature.

[0030] For example, when determining the amount of operation related to the steering angle of the vehicle 1 based on the target trajectory, the trace control unit 12 calculates the target steering angle and then calculates the steering torque required to achieve that target steering angle. The target steering angle is calculated using the target curvature, target yaw angle, and target lateral deviation determined based on the shape of the target trajectory and predetermined control gains for those parameters. As an example, the target steering angle is calculated based on 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 of the deviation between the target lateral deviation and the actual lateral deviation, multiplied by the control gains. This calculation of the target steering angle is performed at predetermined intervals, and the calculation result is transmitted to the ECU 7 each time, thereby controlling 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 takes into account the characteristics (dynamic characteristics) of the vehicle 1, including the performance of the devices used for driving control, and calculates an optimum control input to the devices based on a predetermined optimum control problem. The predetermined optimum control problem is also subject to constraints expressed as inequalities that are set to determine whether the control input is above a predetermined upper limit or below a predetermined lower limit, which are set based on the characteristics and safety of the vehicle 1. The optimum calculation unit 12a repeatedly performs calculations to find an optimum solution to the predetermined optimum control problem based on an evaluation function and constraint conditions. The optimum calculation unit 12a repeats calculations until the solution falls below a predetermined convergence judgment value, which is a predetermined convergence judgment threshold for determining whether the control input is optimal. When the solution becomes smaller than the convergence judgment value, it is determined that an optimum solution has been found. The optimization calculation unit 12a performs such optimization calculations at predetermined control cycles, and for example, when determining the 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, weighting of these parameters, and integrated values ​​over a predetermined time period.

[0032] The formula also includes constraints based on the characteristics, performance, or behavior of the vehicle 1. Such constraints enable highly accurate trace control. For example, the optimal control problem for determining the target steering angle described above includes a constraint on lateral deviation. If the lateral deviation exceeds a predetermined deviation, the target steering angle is determined so as to minimize the deviation. Specifically, the constraints are set by a predetermined state equation based on the current steering angle, the traveling trajectory, and the movement of the vehicle 1, and an inequality based on the amount of excess lateral deviation. The optimization 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 an optimization calculation including the constraints configured in this manner for each control period. The lateral, yaw, and longitudinal deviations of the vehicle 1 are deviations between the target position on the target trajectory and the predicted position of the vehicle 1 for a predetermined time period based on the estimated current position of the vehicle 1 and a prediction model that predicts the behavior of the vehicle 1 for a predetermined time period. That is, it includes the deviation between the current position of the vehicle 1 and the target position, and the deviation between the predicted position for a predetermined time ahead based on a prediction model of the vehicle 1 and the target position at that predicted position. The predetermined optimal control problem corresponds to the 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, which is configured to determine the 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 measured 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, which is estimated mainly based on the state quantities of the vehicle 1, deviates from the relative position of the vehicle 1, which is actually measured mainly based on the external sensor 9, the position data is corrected in accordance with the relative position to determine the current position of the vehicle 1 on the map data. In step S1, correction amounts for each of the longitudinal, lateral, and yaw directions of the vehicle 1 at that time are acquired at predetermined intervals.

[0034] After the correction amount for the position data of vehicle 1 is calculated, the process proceeds to step S2, where it is determined whether there is a problem with the estimation of the current position of vehicle 1. That is, in step S2, it is determined whether the estimation of the current position of vehicle 1 is accurate based on the correction amount for the position data calculated in step S1. Specifically, in step S2, it is determined based on the correction amount calculated in step S1 that the behavior of vehicle 1 in the current cycle compared to the behavior of vehicle 1 up to the immediately previous cycle is a correction that will occur as the actual behavior of vehicle 1. Conversely, in step S2, it is determined whether the deviation in the position data of vehicle 1 is a deviation that has occurred due to calculations for estimating the position of 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 referenced and the weighting of those 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 is 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 an external sensor 9, etc., in addition to the behavior of the vehicle 1. In such a case, if an error in the map data, disturbance, or the like causes a deviation between the actual position of the vehicle 1 on the roadway and the position of the vehicle 1 on the map data, there is a possibility that the deviation will be detected differently in each direction of the vehicle 1.

[0036] For example, when entering a curved road, the position of vehicle 1 on the map data may be detected as being further behind its actual position due to errors or disturbances such as those described above. In such cases, the lateral position of vehicle 1 is estimated from data based on the behavior of vehicle 1 and the actually measured relative position of vehicle 1, so such a deviation can be detected relatively early. On the other hand, the position of vehicle 1 in the yaw direction is estimated from data based on the behavior of vehicle 1, so there is a possibility that the detection of such a deviation with respect to the lateral direction of vehicle 1 will be delayed. In other words, because the relative position is not referenced in the yaw direction, or the relative position is given little weight, it is estimated that the yaw angle is changing along the road on the map data, causing a delay in the detection of such a deviation.

[0037] The steering angle operation amount of the vehicle 1 is calculated using the deviation between the target position of the vehicle 1 on the map data and the estimated and predicted positions of the vehicle 1 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, resulting in a large deviation in the lateral direction of the vehicle 1. In such a case, the correction amount used to correct the position of the vehicle 1 on the map data may be too large to be actual behavior of the vehicle 1. If it is determined that the correction amount calculated in step S1 is larger than the predetermined correction amount that could be actual behavior of the vehicle 1, it is determined in step S2 that the correction is due to an estimation of the current position of the vehicle 1. If it is determined in step S2 that the correction is not due to an estimation of the current position of the vehicle 1 because the correction amount is equal to or smaller 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 an estimation of the position of the vehicle 1, for example 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 determination 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 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, if it is determined that the current position of the vehicle 1 has not been correctly estimated in the lateral direction, the constraint is widened by increasing the predetermined lateral deviation for determining whether the constraint conditions have been violated. Alternatively, the weight of the evaluation function related to the constraint conditions in the optimal control problem is reduced, for example, the weight of the evaluation function related to the slack variable constituting the constraint conditions 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 constraint conditions, and an optimization calculation is performed. Alternatively, instead of using optimal control, the system is configured to switch to rule-based cruise control such as conventionally known PID control to perform cruise control. Note that the system may be configured to relax the constraint conditions in the optimal control problem using conventionally known minimum time methods, constraint relaxation methods, and the like. Furthermore, when relaxing the constraint conditions, the system is configured to relax them taking into account hard constraints, soft constraints, and the like.

[0040] Furthermore, when changing the convergence criterion value in a predetermined optimal control problem, the convergence criterion 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 has not been correctly estimated in the lateral direction, the convergence criterion value in the optimal control problem for calculating 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] The control device for the autonomously driven vehicle 1 configured in this manner estimates the current position of the vehicle 1 on the map data 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 roadway measured relatively by recognizing objects around the vehicle 1. A target trajectory along which the vehicle 1 should travel is determined based on the current position of the vehicle 1 determined in this way 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 autonomously drive the vehicle 1 along the target trajectory, a predetermined optimal control problem is solved taking into account constraints on the vehicle 1 to determine the operation amounts of the driving force source 2, the brake device 3, the steering device 5, etc., and the vehicle 1 drives autonomously according to the operation amounts.

[0042] In such cruise control, a correction amount for estimating the current position of the vehicle 1 is calculated. At that time, it is determined whether the current position of the vehicle 1 has been accurately estimated based on the calculated correction amount. In other words, it is determined whether the current position of the vehicle 1 on the map data has changed in a way that would not occur in the actual behavior of the vehicle 1. If it is determined that such a change has occurred, the constraint conditions and the convergence criterion value in the predetermined optimal control problem are relaxed. That is, while taking into account the impact on cruise control, parameters are adjusted so that such a change would lead to an increase in the number of calculations. For example, if a large correction amount that would not occur in 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 violates a constraint, the number of calculations based on the constraint increases. Alternatively, the number of calculations required for a solution to the predetermined optimal control problem to reach the convergence criterion value increases.

[0043] In the control device according to the embodiment of the present invention, when such a change is detected, the control device performs changes such as widening the constraint, changing the problem to one that does not include the constraint, switching the control itself to rule-based control such as PID control, or increasing the convergence criterion value. That is, the control device adjusts parameters that lead to an increase in the number of calculations due to such a change. This prevents an increase in the load on the ECU 7 due to an increase in the number of calculations and prevents the calculation from being unable to find a solution for a predetermined optimal control problem. Furthermore, it also prevents the vehicle 1 from behaving in a way that is unintended by the occupant.

[0044] Although the embodiments of the present invention have been described above, the present invention is not limited to the above examples and may be modified as appropriate within the scope of achieving the object of the present invention. For example, the constraint conditions may be relaxed when it is determined that there is a high possibility of violating the constraints based on the correction amount in the position data of the vehicle 1. In other words, a change in the correction amount may be predicted by comparing the change in the correction amount in the previous cycle with the correction amount in the current cycle. Then, if it is predicted that the constraints will be violated if the change continues, the constraints may be widened or the driving control may be switched to one without constraints to execute driving control. [Explanation of symbols]

[0045] 1 vehicle 6. Detection unit 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 the 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 autonomously driven 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, determine a target trajectory based on the estimated current position of the vehicle on the map data, determine 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 the position of the vehicle on the map data based on the relative positions of the vehicle on the roadway determined based on the detection data of the plurality of external detection sensors; calculating 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; 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 the behavior of the vehicle up to a predetermined time in the future; a constraint is imposed on the predetermined calculation in the travel 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 predetermined 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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