Rudder angle control device

Abstract

This improves the path-following performance of steering angle control in response to changes in road surface conditions. [Solution] The steering angle control device includes a model prediction control unit 161c that acquires a vehicle state quantity indicating the motion state of the vehicle based on sensor values ​​from an on-board sensor, predicts the behavior of the vehicle based on the vehicle state quantity, and calculates a steering angle instruction value for the steering actuator using the prediction result; a disturbance estimation unit that estimates the disturbance component of the steering angle included in the vehicle state quantity and calculates a first steering angle correction amount based on the estimation result; and a cant compensation unit 161b that calculates a steering angle that compensates for the cant angle of the road surface based on the vehicle state quantity and calculates a second steering angle correction amount based on the calculation result. The model prediction control unit 161c corrects the steering angle included in the vehicle state quantity used to predict vehicle behavior based on a third steering angle correction amount obtained by combining the first steering angle correction amount and the second steering angle correction amount. The model prediction control unit 161c also outputs the steering angle instruction value corrected based on the third steering angle correction amount to the steering actuator.

Description

Technical Field

[0001] The present invention relates to a steering angle control device for controlling the steering angle of a vehicle.

Background Art

[0002] In recent years, efforts have been actively made to provide access to a sustainable transportation system that takes into account people in vulnerable positions among traffic participants. In order to achieve this, research and development on driving support technologies have been focused on further improving traffic safety and convenience. As a device of this type, conventionally, a device has been known that determines auxiliary torque to be added to a steering system so as to remove disturbance torque corresponding to changes in road surface conditions (see Patent Document 1). In the device described in Patent Document 1, auxiliary torque is determined by feedback control based on the deviation between the steering torque acting on the steering wheel and the steering torque detected by a steering torque sensor provided in the middle of the steering system.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] However, since the degree of change in road surface conditions varies depending on the location, if the auxiliary torque is determined by feedback control as in the device described in Patent Document 1, there is a possibility that high responsiveness to changes in road surface conditions cannot be obtained.

Means for Solving the Problems

[0005] A steering angle control device according to one aspect of the present invention includes: an acquisition unit that acquires vehicle state quantities indicating the motion state of the vehicle based on sensor values ​​of an on-board sensor; a prediction unit that predicts the behavior of the vehicle based on the vehicle state quantities acquired by the acquisition unit; a steering angle instruction unit that uses the prediction results obtained by the prediction unit to calculate a steering angle instruction value for the vehicle's steering actuator so as to maintain a driving state in which the vehicle travels along a target path; a first correction amount calculation unit that estimates the disturbance component of the vehicle's steering angle based on the vehicle state quantities and calculates a first steering angle correction amount based on the estimation results; and a second correction amount calculation unit that calculates a steering angle that compensates for the road surface cant angle, which is part of the disturbance component, based on the vehicle state quantities and calculates a second steering angle correction amount based on the calculation results. The prediction unit corrects the vehicle's steering angle included in the vehicle state quantities used to predict the vehicle's behavior based on a third steering angle correction amount obtained by combining the first steering angle correction amount and the second steering angle correction amount. The steering angle instruction unit outputs the steering angle instruction value corrected based on the third steering angle correction amount to the steering actuator. [Effects of the Invention]

[0006] According to the present invention, the path-following performance of steering angle control in response to changes in road surface conditions can be improved. [Brief explanation of the drawing]

[0007] [Figure 1] A diagram illustrating the configuration of a vehicle control system having a steering angle control device according to an embodiment of the present invention. [Figure 2] A block diagram showing the main components of the rudder angle control device. [Figure 3A] A block diagram illustrating the flow of rudder angle control. [Figure 3B] A block diagram illustrating the details of the rudder angle control unit in Figure 3A. [Figure 3C] A diagram illustrating fusion rudder angle disturbances. [Figure 4] A flowchart showing an example of the arithmetic processing performed in the controller's arithmetic unit, as shown in Figure 2. [Figure 5A] A diagram showing an example of a driving scene of the vehicle. [Figure 5B] This figure shows the steering angle correction amount corresponding to the driving scene in Figure 5A. [Figure 6A] A diagram showing other examples of the vehicle's driving scenes. [Figure 6B] This figure shows the steering angle correction amount corresponding to the driving scene in Figure 6A. [Modes for carrying out the invention]

[0008] Embodiments of the present invention will now be described with reference to the drawings. One embodiment of the present invention controls the steering angle of the vehicle's steering device (e.g., power steering device) so that the vehicle follows a target path (which may also be called a target trajectory). The steering angle control device is applicable to both manually driven vehicles equipped with ADAS (Advanced driver-assistance systems) and vehicles with automatic driving functions, i.e., autonomous vehicles, but for the sake of explanation, the case of application to an autonomous vehicle will be used as an example below.

[0009] Furthermore, in this embodiment, the vehicle equipped with the steering angle control device may be referred to as "the vehicle itself" to distinguish it from other vehicles. The vehicle itself may be an engine-powered vehicle having an internal combustion engine as its driving source, an electric vehicle having a drive motor as its driving source, or a hybrid vehicle having both an engine and a drive motor as its driving sources. The vehicle itself can be driven not only in an automated driving mode that does not require driver operation, but also in a manual driving mode with driver operation.

[0010] First, the general configuration of a vehicle related to autonomous driving will be described. Figure 1 is a block diagram illustrating the configuration of a vehicle control system 100 of a vehicle having a steering angle control device according to an embodiment of the present invention. As shown in Figure 1, the vehicle control system 100 mainly comprises a controller 10, a group of external sensors 1 and 2, an input / output device 3, a positioning unit 4, a map database 5, a navigation device 6, a communication unit 7, and an actuator AC for driving.

[0011] External sensor group 1 is a collective term for multiple sensors (external sensors) that detect external conditions, which are information about the surroundings of the vehicle. External sensor group 1 includes, for example, a lidar that measures the distance from the vehicle to surrounding obstacles by measuring scattered light from the vehicle's omnidirectional illumination; a radar that detects other vehicles and obstacles around the vehicle by emitting electromagnetic waves and detecting reflected waves; and a camera mounted on the vehicle that has an image sensor such as a CCD or CMOS sensor to capture images of the area around the vehicle (front, rear, and sides).

[0012] Internal sensor group 2 is a collective term for multiple sensors (internal sensors) that detect the vehicle's driving status. Internal sensor group 2 includes, for example, a vehicle speed sensor that detects the vehicle's speed, acceleration sensors that detect the vehicle's acceleration in the longitudinal direction (direction of travel) and the acceleration in the lateral direction (lane width direction) (lateral acceleration), a rotation speed sensor that detects the rotation speed of the driving power source, and a yaw rate sensor that detects the rotational angular velocity of the vehicle's center of gravity around the vertical axis. Sensors that detect the driver's operations in manual driving mode, such as accelerator pedal operation, brake pedal operation, and steering wheel operation, are also included in internal sensor group 2.

[0013] Input / output device 3 is a general term for devices that receive commands from the driver or output information to the driver. Input / output device 3 includes, for example, various switches that the driver uses to input commands by operating control members, a microphone that the driver uses to input commands by voice, a display that provides information to the driver via displayed images, and a speaker that provides information to the driver by voice.

[0014] The positioning unit (GNSS unit) 4 has a positioning sensor that receives positioning signals transmitted from positioning satellites. Positioning satellites are artificial satellites such as GPS satellites and quasi-zenith satellites. The positioning unit 4 uses the positioning information received by the positioning sensor to measure the current position (latitude, longitude, altitude) of the vehicle.

[0015] The map database 5 is a device that stores general map information used in the navigation device 6, and is composed of, for example, a magnetic disk or a semiconductor element. The map information includes the position information of roads, the information of road shapes (such as curvature), and the position information of intersections and branch points. Note that the map information stored in the map database 5 is different from the high-precision map information stored in the storage unit 12 of the controller 10.

[0016] The navigation device 6 is a device that, for example, searches for a route on the road to a destination input by a driver and performs driving guidance along the searched route. The input of the destination and the driving guidance along the searched route are performed via the input / output device 3. The search for the route is performed based on the current position of the host vehicle measured by the positioning unit 4, the position of the input destination, and the map information stored in the map database 5. It is also possible to measure the current position of the host vehicle using the detection values of the external sensor group 1, and the route may be searched based on this current position and the high-precision map information stored in the storage unit 12.

[0017] The communication unit 7 communicates with various servers (not shown) via a network including a wireless communication network typified by the Internet network or a mobile phone network, and periodically or at an arbitrary timing acquires map information, driving history information, traffic information, etc. from the server. Not only acquire the driving history information, but it is also possible to transmit the driving history information of the host vehicle to the server via the communication unit 7. The network includes not only a public wireless communication network but also a closed communication network provided for each predetermined management area, such as a wireless LAN, Wi-Fi (registered trademark), Bluetooth (registered trademark), etc. The acquired map information is output to the map database 5 and the storage unit 12, and the map information is updated.

[0018] Actuator AC is a driving actuator for controlling the running of the host vehicle. When the driving power source is an engine, actuator AC includes a throttle actuator for adjusting the opening degree (throttle opening) of the throttle valve of the engine. When the driving power source is a driving motor, the driving motor is included in actuator AC. A brake actuator for operating the braking device of the host vehicle and a steering actuator for driving the steering device are also included in actuator AC.

[0019] Controller 10 is constituted by an electronic control unit (ECU). More specifically, controller 10 includes a computer having an arithmetic unit 11 such as a CPU (microprocessor), a storage unit 12 such as a ROM and a RAM, and other peripheral circuits (not shown) such as an I / O interface. Although a plurality of ECUs with different functions such as an engine control ECU, a driving motor control ECU, and a braking device ECU can be provided separately, in FIG. 1, for the sake of convenience, controller 10 is shown as an aggregation of these ECUs.

[0020] The storage unit 12 stores highly accurate and detailed map information for autonomous driving. The highly accurate map information includes road position information, road shape (such as curvature) information, road gradient information, intersection and branch point position information, type and position information of lane lines such as white lines, number of lanes (driving lanes) information, lane width and position information for each lane (information on the center position of the lane and the boundary lines of the lane positions), position information of landmarks (traffic signals, signs, buildings, etc.) as marks on the map, and road surface profile information such as road surface unevenness. The highly accurate map information stored in the storage unit 12 may include highly accurate map information acquired from outside the host vehicle via the communication unit 7, or highly accurate map information created by the host vehicle itself using the detection values of the external sensor group 1 or the detection values of the external sensor group 1 and the internal sensor group 2. The storage unit 12 may store information such as various control programs and thresholds used in the programs.

[0021] The calculation unit 11 has a functional configuration that includes a vehicle position recognition unit 13, an external environment recognition unit 14, an action plan generation unit 15, and a driving control unit 16.

[0022] The vehicle position recognition unit 13 recognizes the vehicle's position on the map (vehicle position) based on the vehicle's position information obtained by the positioning unit 4 and the map information in the map database 5. The vehicle position may also be recognized using high-precision map information stored in the storage unit 12 and surrounding information of the vehicle detected by the external sensor group 1, thereby enabling high-precision recognition of the vehicle's position. Furthermore, the vehicle's position can also be recognized by calculating the vehicle's movement information (direction of movement, distance traveled) based on the detection values ​​of the internal sensor group 2. When the vehicle's position can be measured by sensors installed on or beside the road, the vehicle position can also be recognized by communicating with those sensors via the communication unit 7.

[0023] The external environment recognition unit 14 recognizes the external conditions around the vehicle based on signals from the external sensor group 1, including cameras, lidars, and radars. For example, it recognizes the position, speed, and acceleration of surrounding vehicles (vehicles in front and behind) traveling around the vehicle, the position of surrounding vehicles stopped or parked around the vehicle, and the position and state of other objects, and creates landmark information. Other objects include signs, traffic lights, roads, buildings, guardrails, utility poles, billboards, pedestrians, and bicycles. Markings on the road surface, such as lane markings (white lines, etc.) and stop lines, are also included as other objects (roads). The state of other objects includes the color of traffic lights (red, blue, yellow), and the speed and direction of pedestrians and bicycles. Some of the stationary objects among the other objects constitute landmarks that serve as indicators of location on a map, and the external environment recognition unit 14 also recognizes the position and type of these landmarks.

[0024] The action plan generation unit 15 generates a driving trajectory (target route) for the vehicle from the present time to a predetermined time in advance, based on, for example, the route searched by the navigation device 6, the high-precision map information stored in the memory unit 12, the vehicle's position recognized by the vehicle position recognition unit 13, and the external conditions recognized by the external environment recognition unit 14. If there are multiple possible target route candidates on the route searched by the navigation device 6, the action plan generation unit 15 selects the optimal route from among them that complies with laws and regulations and satisfies criteria such as efficient and safe driving, and generates the selected route as the target route. The action plan generation unit 15 then generates an action plan corresponding to the generated target route. The action plan generation unit 15 generates various action plans corresponding to driving modes such as overtaking to pass a preceding vehicle, changing lanes, following a preceding vehicle, lane keeping to maintain the lane, decelerating, or accelerating. When generating a target route, the action plan generation unit 15 first determines the driving mode and then generates the target route based on the driving mode.

[0025] In autonomous driving mode, the driving control unit 16 controls each actuator AC so that the vehicle travels along the target path generated by the action plan generation unit 15. For example, in autonomous driving mode, the driving control unit 16 considers the driving resistance determined by the road gradient, etc., and calculates the required driving force to obtain the target acceleration per unit time calculated by the action plan generation unit 15. Then, it provides feedback control to the actuator AC so that the actual acceleration detected by, for example, the internal sensor group 2 becomes the target acceleration. In other words, it controls the drive actuator AC so that the vehicle travels at the target speed and target acceleration.

[0026] Furthermore, in automatic driving mode, the driving control unit 16 calculates the optimal steering angle for the vehicle to follow a target route based on vehicle state quantities observed by the internal sensor group 2, etc. Then, it outputs a steering angle instruction signal corresponding to the calculated steering angle to control the steering actuator AC.

[0027] In manual driving mode, the driving control unit 16 controls each actuator AC in accordance with driving commands (such as steering operations) from the driver acquired by the internal sensor group 2.

[0028] By the way, in lane keeping driving, the steering actuator (hereinafter also referred to as the steering actuator) is controlled so that the vehicle travels through a target position in the lane width direction. However, if disturbances such as changes in the slope of the road surface (changes in the gradient in the lane width direction) or strong crosswinds occur, the position of the vehicle in the lane width direction may deviate from the target position, or the orientation of the vehicle may deviate from the direction of travel. Therefore, in this embodiment, the steering angle control device is configured as follows to correct the deviations in the position and orientation of the vehicle that occur during driving.

[0029] Figure 2 is a block diagram showing the main components of the steering angle control device 50 according to this embodiment. This steering angle control device 50 is configured, for example, as part of the functions of the controller 10 in Figure 1. The controller 10 is connected to a camera 1a, a steering angle sensor 2a, a steering angular velocity sensor 2b, a steering torque sensor 2c, an IMU (Inertial Measurement Unit) 2d, a navigation device 6, and a steering actuator AC1. The steering angle sensor 2a, steering angular velocity sensor 2b, steering torque sensor 2c, and IMU 2d constitute part of the internal sensor group 2 in Figure 1.

[0030] Camera 1a is a monocular camera having an image sensor and constitutes part of the external sensor group 1 in Figure 1. Camera 1a may also be a stereo camera. Camera 1a is mounted, for example, at a predetermined position on the front of the vehicle and continuously captures images of the space in front of the vehicle to acquire images (camera images) of objects. Objects include lane markings on the road. Alternatively, or in conjunction with camera 1a, objects may be detected by radar, lidar, or the like.

[0031] The steering angle sensor 2a detects the rotation angle (steering angle) of the steering shaft connected to a steering wheel (not shown), for example. The steering angular velocity sensor 2b detects the rotational angular velocity (steering angular velocity) of the steering shaft. Steering angular velocity may also be simply called rudder angular velocity.

[0032] The steering torque sensor 2c detects the steering operation performed by the driver, more specifically, the steering torque acting on the steering wheel. For example, the steering angle detected by the steering angle sensor 2a when the steering wheel is rotated counterclockwise from the neutral position is defined as a positive value, and the steering angle detected by the steering angle sensor 2a when the steering wheel is rotated clockwise from the neutral position is defined as a negative value.

[0033] The IMU2d detects the translational and rotational motion of the vehicle in three axes (X, Y, and Z). The X axis corresponds to the vehicle's longitudinal direction, the Y axis corresponds to the vehicle's leftward direction, and the Z axis corresponds to the vehicle's vertical upward direction.

[0034] The controller 10 has the following functional configurations, which are handled by the calculation unit 11 (Figure 1): a path error calculation unit 131, a target calculation unit 141, a track calculation unit 151, a target path calculation unit 152, and a rudder angle control unit 161. In addition, as described above, the controller 10 has a storage unit 12.

[0035] The route error calculation unit 131 may constitute part of the vehicle position recognition unit 13. The target calculation unit 141 may constitute part of the external environment recognition unit 14. The road calculation unit 151 and the target route calculation unit 152 may constitute part of the action plan generation unit 15. The steering angle control unit 161 may constitute part of the driving control unit 16.

[0036] The route error calculation unit 131 compares the position and orientation of the vehicle recognized by the vehicle position recognition unit 13 with the target route set by the target route calculation unit 152 (described later), and calculates the lateral position deviation and azimuth deviation of the vehicle relative to the target route directly beside the vehicle.

[0037] The route error calculation unit 131 first recognizes the position and shape of the lane markings from the camera image of camera 1a, and recognizes the lane based on the recognition result. Next, the route error calculation unit 131 compares the recognized position, angle, and shape of the lane with the target route set by the target route calculation unit 152 (described later), and calculates the amount of deviation between the position of the vehicle and the position on the target route directly beside the vehicle (center of the lane) as the route lateral position deviation. The route error calculation unit 131 also calculates the amount of deviation between the direction of the vehicle and the azimuth angle of the target route directly beside the vehicle as the route azimuth angle deviation.

[0038] The route error calculation unit 131 may calculate the route lateral position deviation and route azimuth angle deviation using the vehicle's position and orientation recognized based on the high-precision map information stored in the memory unit 12 and the surrounding information of the vehicle detected by the external sensor group 1, or the vehicle's position and orientation measured by the positioning unit 4.

[0039] The target calculation unit 141 calculates information indicating targets present around the vehicle. Based on signals input from the external sensor group 1, including the camera 1a, lidar, and radar, the target calculation unit 141 recognizes targets including moving objects such as other vehicles, bicycles, and pedestrians, as well as stationary objects (which may also be called terrain features) such as guardrails and signs, and outputs target information indicating the recognized targets.

[0040] The route calculation unit 151 calculates (searches for) a route (referred to as a base route) based on the current position of the vehicle measured by the positioning unit 4, the location of the destination entered by the driver, and the map information stored in the map database 5. The calculation of the base route is the same as the route search performed by the navigation device 6. The route calculation unit 151 may also obtain a route set by the navigation device 6 as the base route from the navigation device 6.

[0041] The target path calculation unit 152 sets target positions in the lane width direction that the vehicle should traverse on the base path calculated by the path calculation unit 151 and the external environment recognized by the external environment recognition unit 14. When the vehicle is, for example, maintaining its lane, the target path calculation unit 152 repeatedly sets target positions along the direction of travel. As a result, a target path (a trajectory obtained by connecting the target positions) is generated along the base path.

[0042] Furthermore, if the external environment recognition unit 14 recognizes an obstacle such as a utility pole or a parked vehicle in front of the vehicle's direction of travel, the target route calculation unit 152 uses the obstacle information output from the obstacle calculation unit 141 to set a target position such that the distance between the vehicle and the obstacle in the lane width direction does not fall below a certain distance when the vehicle passes to the side of the obstacle. In addition, if the driver gives an instruction to change lanes via the turn signal (not shown), the target route calculation unit 152 sets a target position such that the vehicle's driving position gradually moves along the direction of travel towards the center of the target lane.

[0043] The steering angle control unit 161 controls the steering angle of the vehicle via the steering actuator AC1 so that the vehicle follows the target path, for example, when the lane keeping assist function is enabled. Specifically, the steering angle control unit 161 calculates the steering angle required to make the vehicle's position follow the target path.

[0044] The steering angle control unit 161 acquires the target path generated by the target path calculation unit 152. The steering angle control unit 161 also acquires design information about the vehicle (hereinafter referred to as vehicle characteristic information) from the storage unit 12. This vehicle characteristic information includes the vehicle mass [kg] and yaw moment of inertia [kgm] of the vehicle. 2 This includes the distance between the center of gravity G and the front axle [m], the distance between the center of gravity G and the rear axle lr [m], the equivalent cornering power of one front wheel [N / rad], the equivalent cornering power of one rear wheel [N / rad], the stability factor [-], etc.

[0045] Furthermore, the steering angle control unit 161 acquires vehicle state variables. These vehicle state variables include the vehicle's speed (vehicle body speed), yaw angular velocity (yaw rate), steering angle (steering angle of the front wheels), gravitational acceleration, steering angular velocity, and vehicle orientation, which are obtained from the output values ​​of the sensors constituting the internal sensor group 2, or from calculations using the sensor output values. In addition, the vehicle state variables also include path lateral position deviation, path azimuth angle deviation, and yaw angular velocity deviation.

[0046] The vehicle orientation is calculated, for example, based on the direction of the target path and the vehicle's length direction (sometimes called the longitudinal direction) as recognized from the camera image of camera 1a, etc. The lateral path deviation is, as described above, the amount of deviation of the vehicle's position from the target path in the lane width direction. The azimuth angle deviation is the angle of deviation of the vehicle's orientation (vehicle orientation) relative to the target path. The yaw angular velocity deviation is the deviation between the yaw angular velocity detected by the IMU 2d included in the internal sensor group 2 and the target yaw angular velocity (= vehicle speed × path curvature). The path curvature is the curvature of the target path in front of the vehicle's direction of travel and is calculated, for example, by the steering angle control unit 161.

[0047] The steering angle control unit 161 estimates state variables that cannot be observed using the internal sensor group 2 using a state estimation model described later. Specifically, the steering angle control unit 161 estimates the steering angle disturbance δd [rad] and the vehicle slip angle β [rad], and calculates the effective front wheel steering angle δ^ [rad] excluding the steering angle disturbance δd. "^" indicates that it is an estimated value. The relationship between the front wheel steering angle δ, the effective front wheel steering angle δ^, and the steering angle disturbance δd is expressed by the equation δ = δ^ + δd. The steering angle disturbance δd is a disturbance component included in the vehicle's steering angle (observed value), and refers to a steering angle that does not affect the vehicle's behavior, such as counter-steering for the cant angle. The cant angle is the gradient in the lane width direction (road surface transverse gradient). Counter-steering caused by crosswinds and misalignment of the steering device's midpoint may be included in the steering angle disturbance δd. The vehicle body slip angle β is the angle of deviation between the direction of the vehicle's speed and the direction of the vehicle's body.

[0048] The steering angle control unit 161 inputs the acquired vehicle state variables and vehicle characteristic information into a driving simulation model (hereinafter referred to as the prediction model). Using the prediction model, the steering angle control unit 161 calculates the optimal steering angle (optimal steering angle sequence described later) for the vehicle's future driving position to follow the target path through model predictive control. Model predictive control is a conventional technique. Model predictive control is one of the control methods that calculates the optimal control input using predictive estimation of the controlled object. Model predictive control uses a prediction model and an optimizer that evaluates the operation of the prediction model to calculate the optimal control input. The prediction model is a model for mimicking the controlled object.

[0049] The steering angle control unit 161 extracts the steering angle to be indicated for a foreseeable time ahead from the above optimal steering angle sequence, adds the steering angle disturbance (fusion steering angle disturbance δ12 described later) that was excluded in advance, outputs the target steering angle as the steering angle instruction value, and controls the steering angle by the steering actuator AC1.

[0050] Furthermore, when the steering angle control unit 161 is performing steering angle control based on the target steering angle, if steering torque is detected by the steering torque sensor 2c, it may determine that the driver has performed a steering operation (an instruction to change the steering angle has been given) and may interrupt the steering angle control based on the steering angle instruction value.

[0051] Furthermore, the steering angle control unit 161 may continue steering angle control based on the target steering angle unless a large steering torque that clearly indicates the driver's intention to cancel path-following driving is detected by the steering torque sensor 2c while performing steering angle control based on the target steering angle; in other words, unless a steering operation that changes the steering angle by more than a predetermined value is performed.

[0052] Figure 3A is a block diagram illustrating the flow of steering angle control by the steering angle control unit 161. Figure 3A shows the target calculation unit 141, the track calculation unit 151, the target path calculation unit 152, the steering angle control unit 161, the steering actuator AC1, and the vehicle body of the vehicle 101, as illustrated in Figure 2.

[0053] Figure 3B is a block diagram illustrating the details of the rudder angle control unit 161 shown in Figure 3A. The rudder angle control unit 161 includes an estimation unit 161a, a cant compensation unit 161b, and a model prediction control unit 161c.

[0054] The estimation unit 161a models vehicle state variables that are influenced by factors such as the steering angular velocity δ' [rad / s] of the front wheels, and uses these factors, the observed vehicle state variables, and the previous estimate to calculate estimated values ​​of the steering angle disturbance δd and the vehicle slip angle β using a Kalman filter. In general, the vehicle state variables actually observed contain noise components (observation noise) on a stationary basis, and also contain noise components (process noise) that represent the uncertainty of the model. By repeating the estimation calculation using the Kalman filter, the estimation with the least error is performed, taking into account the uncertainty of the observed values ​​and the model. Hereinafter, the estimated value of the steering angle disturbance δd will be called the estimated steering angle disturbance δ1.

[0055] The cant compensation unit 161b observes the cant angle. Specifically, the cant compensation unit 161b calculates the cant angle at the vehicle's driving position based on the lateral component of gravitational acceleration (hereinafter referred to as lateral gravitational acceleration) included in the vehicle state variables. Furthermore, the cant compensation unit 161b predicts the effect of the cant angle (deviation in the orientation of the vehicle relative to the direction of travel) through feedforward compensation and calculates a steering angle amount (hereinafter referred to as cant compensation steering angle) that cancels out this effect.

[0056] The model prediction control unit 161c comprises a prediction calculation unit 161c1 and a target steering angle calculation unit 161c2. The prediction calculation unit 161c1 performs a model prediction calculation using the target path and observed vehicle state variables of the vehicle 101 as input. By performing the model prediction calculation, the prediction calculation unit 161c1 predicts the speed and direction of travel (predicted path) of the vehicle 101 for the next few seconds (predicted horizon) on the model at predetermined calculation intervals (e.g., several milliseconds to several seconds), and performs a process (solution search calculation) to calculate the optimal steering angle sequence for the next few seconds (predicted horizon). The optimal steering angle sequence includes the optimal steering angle predicted at each step according to the sample time. The sample time for each step of the predicted horizon may be adjustable. The prediction calculation unit 161c1 outputs the calculation result of the model prediction calculation (optimal steering angle sequence) to the target steering angle calculation unit 161c2.

[0057] As shown in Figure 3B, the model prediction control unit 161c excludes the estimated steering angle disturbance δ1 calculated by the estimation unit 161a and the cant compensation steering angle δ2 calculated by the cant compensation unit 161b from the steering angle (actual steering angle) included in the vehicle state variables as input values ​​for the model prediction calculation. In this way, in the model prediction calculation of the prediction calculation unit 161c1, the steering angle obtained by excluding the estimated steering angle disturbance δ1 and the cant compensation steering angle δ2 from the actual steering angle (hereinafter referred to as the effective steering angle) is used as the input value instead of the actual steering angle. δ12 in Figure 3B represents the value obtained by combining the estimated steering angle disturbance δ1 and the cant compensation steering angle δ2 (referred to as the fusion steering angle disturbance). The model prediction control unit 161c excludes the estimated steering angle disturbance δ1 and the cant compensation steering angle δ2 from the actual steering angle by subtracting the fusion steering angle disturbance δ12 from the actual steering angle.

[0058] The target rudder angle calculation unit 161c2 receives the optimal rudder angle sequence calculated by the prediction calculation unit 161c1 and the fusion rudder angle disturbance δ12 as input. The target rudder angle calculation unit 161c2 extracts the rudder angle to be indicated for a predetermined forecast time tp from the optimal rudder angle sequence. The target rudder angle calculation unit 161c2 corrects the extracted rudder angle by adding the fusion rudder angle disturbance δ12. In this way, the target rudder angle that takes into account the effect of the rudder angle disturbance δd is calculated. The target rudder angle calculation unit 161c2 outputs the calculated target rudder angle to the steering actuator AC1 as the rudder angle instruction value for the next few milliseconds.

[0059] Figure 3C is a diagram illustrating the fusion rudder angle disturbance δ12 shown in Figure 3B. As shown in Figure 3C, the estimated rudder angle disturbance δ1 output from the estimation unit 161a and the cant-compensated rudder angle δ2 output from the cant compensation unit 161b are merged via filter CF. Filter CF is a complementary filter designed so that the sum of the gains (amplification ratios) of the low-pass filter (LPF) and the high-pass filter (HPF) is 1 over the entire frequency range. The time constant of the complementary filter CF is set according to the required responsiveness (responsiveness of rudder angle control to cant changes).

[0060] The estimated rudder angle disturbance δ1 output from the estimation unit 161a is input to a low-pass filter (LPF) according to the gain of the frequency component (rate of change over time) of the estimated rudder angle disturbance δ1, as shown in characteristic f1. On the other hand, the cant-compensated rudder angle δ2 output from the cant-compensation unit 161b is input to a high-pass filter (HPF) according to the gain of the frequency component (rate of change over time) of the cant-compensated rudder angle δ2, as shown in characteristic f2. The filtered estimated rudder angle disturbance δ1 and the cant-compensated rudder angle δ2 are merged (added) as shown in Figure 3C to obtain the fusion rudder angle disturbance δ12.

[0061] Figure 4 is a flowchart showing an example of calculation processing performed by the arithmetic unit 11 of the controller 10 in Figure 2 according to a predetermined program. The processing shown in this flowchart is repeatedly executed, for example, when the vehicle 101 is driving in automatic driving mode. It is also repeatedly executed when the vehicle 101 is driving in manual driving mode and, for example, when the lane keeping assist function, which is one of the driving assistance functions, is enabled, i.e., when lane keeping driving is in progress.

[0062] In step S1, the controller 10 acquires the target route of its own vehicle 101. More specifically, it acquires route information indicating the target route generated by the target route calculation unit 152. In step S2, the controller 10 acquires vehicle state quantities from the output values ​​of the sensors constituting the internal sensor group 2, or by calculations using the output values ​​of the sensors.

[0063] In step S3, the controller 10 calculates the steering angle correction amount (fusion steering angle disturbance δ12). Specifically, the controller 10 estimates the disturbance component of the steering angle (actual steering angle) included in the vehicle state variables and obtains the estimation result as the estimated steering angle disturbance δ1. The controller 10 also calculates the steering angle that compensates for the cant angle (cant compensation steering angle) δ2, which is part of the disturbance component of the actual steering angle. The controller 10 combines the estimated steering angle disturbance δ1 and the cant compensation steering angle δ2 via a complementary filter CF to calculate the fusion steering angle disturbance δ12.

[0064] In step S4, the controller 10 performs a model prediction calculation using the target path acquired in step S1 and the vehicle state variables acquired in step S2 as inputs to calculate the optimal steering angle sequence. When performing the model prediction calculation, the controller 10 corrects (subtracts) the steering angle (actual steering angle) included in the vehicle state variables as input values ​​by the fusion steering angle disturbance δ12 calculated in step S3.

[0065] In step S5, the controller 10 extracts the steering angle to be instructed for the forecast time ahead from the calculation results (optimal steering angle sequence) of the model prediction calculation, and calculates the target steering angle by adding the fusion steering angle disturbance δ12 calculated in step S3 to the extracted steering angle. The controller 10 outputs the target steering angle to the steering actuator AC1 as the steering angle instruction value for the next few milliseconds.

[0066] In step S6, the controller 10 determines whether or not to terminate the process. For example, if the automatic driving mode is deactivated, the controller 10 determines step S6 to be positive and terminates the process shown in Figure 4. On the other hand, if the automatic driving mode is to be continued, the controller 10 determines step S6 to be negative and returns to step S1, and repeats the process described above.

[0067] The effects of this embodiment will be explained with reference to Figures 5A to 6B. Figure 5A schematically shows the state of the vehicle 101 traveling in lane LN as viewed from the rear. As shown in Figure 5A, the gradient in the lane width direction, i.e., the cant, at the travel position at time t0 is α1%. On the other hand, the cant at the travel position at time t1, which is earlier than time t0, is -α1%.

[0068] Figure 5B shows the steering angle correction amount corresponding to the driving scene in Figure 5A. Characteristic f11 shows the estimated steering angle disturbance δ1. Characteristic f12 shows the cant compensation steering angle δ2. Characteristic f13 shows the fusion steering angle disturbance δ12 obtained by combining the estimated steering angle disturbance δ1 shown by characteristic f11 and the cant compensation steering angle δ2 shown by characteristic f12.

[0069] The estimated steering angle disturbance δ1, calculated using a Kalman filter, has high accuracy (data reliability) in steady-state conditions where there is no change in cant, but its responsiveness to changes in cant deteriorates in the transient region of cant (time t0~t1). In the example in Figure 5B, the response of the estimated steering angle disturbance δ1 to the change in cant is delayed by time d. On the other hand, the cant-compensated steering angle δ2, calculated based on the observed cant angle by feedforward compensation, has high responsiveness to changes in cant, as shown in Figure 5B. However, even in steady state, if the cant angle is not zero (the road surface has a gradient in the lane width direction), an offset occurs in the cant-compensated steering angle δ2 to compensate for that cant angle. In the example in Figure 5B, the steady-state cant-compensated steering angle δ2 is shifted to the positive side (upward in the figure) compared to the estimated steering angle disturbance δ1.

[0070] By merging two steering angle correction amounts with these characteristics (estimated steering angle disturbance δ1 and cant compensation steering angle δ2) via the complementary filter CF in Figure 3C, a steering angle correction amount (fusion steering angle disturbance δ12) with high responsiveness to cant changes and high accuracy is obtained, as shown in characteristic f13. Using such a steering angle correction amount, as shown in Figure 3B, the robustness to cant during path-following driving can be improved by correcting the input value (actual steering angle) and output value (the steering angle to be indicated for a predetermined foreseeable time from the optimal steering angle sequence). As a result, the vehicle 101 can accurately follow the target path in the transient region of cant and in steady state where there is no cant change.

[0071] Figure 6A shows vehicle 101 sequentially traveling along curved section IN1, straight section IN2, and curved section IN3 of the circuit course RD. The cant near the apex of the curved section of circuit course RD is α21%. On the other hand, the cant of the straight section is α22 (<α21)%.

[0072] Figure 6B shows the steering angle correction amount corresponding to the driving scene in Figure 6A. Characteristic f21 shows the estimated steering angle disturbance δ1. Characteristic f22 shows the cant compensation steering angle δ2. Characteristic f23 shows the fusion steering angle disturbance δ12 obtained by combining the estimated steering angle disturbance δ1 shown by characteristic f21 and the cant compensation steering angle δ2 shown by characteristic f22. As shown in characteristic f23, even when driving on a road with a gradual change in cant, such as the circuit course RD, a steering angle correction amount (fusion steering angle disturbance δ12) with high responsiveness and accuracy in the transient cant region (times t20~t21, t22~t23) can be obtained. As a result, regardless of the speed of the change in cant angle in the transient cant region, the vehicle 101 can accurately follow the target path.

[0073] This embodiment can provide the following effects and advantages. (1) The steering angle control device 50 includes a model prediction control unit 161c that acquires a vehicle state quantity indicating the motion state of the vehicle 101 based on sensor values ​​from an on-board sensor, predicts the behavior of the vehicle 101 based on the acquired vehicle state quantity, and uses the prediction result to calculate a steering angle instruction value for the steering actuator AC1 so that the vehicle 101 maintains a driving state in which it drives along a target path; an estimation unit 161a that estimates the disturbance component of the steering angle included in the vehicle state quantity and calculates an estimated steering angle disturbance δ1 as a first steering angle correction amount based on the estimation result; and a cant compensation unit 161b that calculates a steering angle that compensates for the cant angle of the road surface, which is part of the disturbance component, based on the vehicle state quantity, and calculates a cant compensation steering angle δ2 as a second steering angle correction amount based on the calculation result. The model prediction control unit 161c corrects the steering angle (actual steering angle) included in the vehicle state variables used to predict vehicle behavior based on a fusion steering angle disturbance δ12, which is a third steering angle correction amount obtained by combining the estimated steering angle disturbance δ1 and the cant compensation steering angle δ2. The model prediction control unit 161c also acts as a steering angle indicator and outputs the steering angle indicator value corrected based on the fusion steering angle disturbance δ12 to the steering actuator AC1. The disturbance component of the steering angle included in the vehicle state variables includes at least one of the steering angle that compensates for the cant angle of the road surface (corrective steering) and the steering angle that compensates for the midpoint misalignment of the steering device of the vehicle 101 (corrective steering). This improves the responsiveness of steering angle control to changes in road surface conditions. As a result, the vehicle 101 can drive appropriately along the target path even in driving scenes where the cant changes within the same lane, or in driving scenes where the vehicle crosses road surfaces with different cants due to lane changes, etc. Furthermore, even when traveling in a lane with a constant cant over a predetermined distance, the vehicle 101 can travel appropriately along the target path. In this way, the vehicle 101 can accurately follow the target path both in the transient cant region and in the steady state where there is no change in cant.

[0074] (2) The model prediction control unit 161c changes the ratio of merging the estimated steering angle disturbance δ1 and the cant compensation steering angle δ2 based on the degree of change in the cant angle with respect to time at the driving position of the vehicle 101. Specifically, the model prediction control unit 161c merges the estimated steering angle disturbance δ1 and the cant compensation steering angle δ2 such that the proportion of the cant compensation steering angle δ2 included in the fusion steering angle disturbance δ12 increases as the degree of change in the cant angle with respect to time increases. This allows the steering angle to be appropriately controlled according to the degree of change in cant.

[0075] (3) The estimation unit 161a estimates the disturbance component of the steering angle included in the vehicle state quantity using a Kalman filter based on the vehicle state quantity obtained based on the sensor value of the on-board sensor. This improves the accuracy of tracking the target path in steady state when there is no change in cant.

[0076] (4) The cant compensation unit 161b calculates the steering angle to compensate for the cant angle based on the vehicle state quantity using feedforward compensation. This improves the accuracy of following the target path in the transient region of cant.

[0077] The above embodiment can be modified into various forms. Modifications will be described below. In the above embodiment, the model prediction control unit 161c, which acts as the acquisition unit, acquires the vehicle speed, yaw angular velocity (yaw rate), front wheel steering angle, acceleration, steering angular velocity, vehicle orientation, path lateral position deviation, vehicle path azimuth deviation, yaw angular velocity deviation, vehicle slip angle, etc., as vehicle state quantities. However, the acquisition unit may acquire other state quantities as vehicle state quantities.

[0078] Furthermore, in the above embodiment, the estimation unit 161a estimates the disturbance component of the steering angle included in the vehicle state variables using a Kalman filter based on the vehicle state variables. However, the estimation unit 161a may also estimate the disturbance component of the steering angle included in the vehicle state variables using a state estimation method other than a Kalman filter.

[0079] Furthermore, in the above embodiment, the estimated rudder angle disturbance δ1 calculated by the estimation unit 161a, which is the first correction amount calculation unit, and the cant-compensated rudder angle δ2 calculated by the cant compensation unit 161b, which is the second correction amount calculation unit, are merged via a complementary filter CF to calculate the fusion rudder angle disturbance δ12. However, the method for calculating the fusion rudder angle disturbance δ12 is not limited to this. That is, the estimated rudder angle disturbance δ1 and the cant-compensated rudder angle δ2 may be merged using means other than a complementary filter.

[0080] The above description is merely an example, and the present invention is not limited by the embodiments and modifications described above, as long as they do not impair the features of the present invention. [Explanation of Symbols]

[0081] 1 External sensor group, 2 Internal sensor group, 6 Navigation device, 10 Controller, 11 Calculation unit, 12 Memory unit, 13 Vehicle position recognition unit, 14 External environment recognition unit, 15 Action plan generation unit, 16 Driving control unit, 50 Steering angle control device, 100 Vehicle control system, 101 Own vehicle, 131 Path error calculation unit, 141 Target calculation unit, 151 Track calculation unit, 152 Target path calculation unit, 161 Steering angle control unit, 161a Estimation unit, 161b Cant compensation unit, 161c Model prediction control unit, 161c1 Prediction calculation unit, 161c2 Target steering angle calculation unit, AC1 Steering actuator

Claims

1. An acquisition unit that acquires vehicle state quantities indicating the vehicle's motion state based on sensor values ​​from on-board sensors, A prediction unit predicts the behavior of the vehicle based on the vehicle state quantities acquired by the acquisition unit, A steering angle instruction unit calculates a steering angle instruction value for the steering actuator of the vehicle, using the prediction results obtained by the prediction unit, so as to maintain a driving state in which the vehicle travels along a target path. A first correction amount calculation unit estimates the disturbance component of the steering angle of the vehicle based on the vehicle state variables and calculates a first steering angle correction amount based on the estimation result, The system includes a second correction amount calculation unit that calculates a steering angle that compensates for the road surface cant angle, which is part of the disturbance component, based on the aforementioned vehicle state quantities, and calculates a second steering angle correction amount based on the calculation results, The prediction unit corrects the steering angle of the vehicle included in the vehicle state quantity used to predict the behavior of the vehicle, based on a third steering angle correction amount obtained by combining the first steering angle correction amount and the second steering angle correction amount. The steering angle control device is characterized in that the steering angle instruction unit outputs the steering angle instruction value, corrected based on the third steering angle correction amount, to the steering actuator.

2. In the steering angle control device according to claim 1, The steering angle control device is characterized in that the prediction unit changes the ratio of combining the first steering angle correction amount and the second steering angle correction amount based on the degree of change of the cant angle with respect to time.

3. In the steering angle control device according to claim 1, The steering angle control device is characterized in that the prediction unit combines the first steering angle correction amount and the second steering angle correction amount such that the proportion of the second steering angle correction amount included in the third steering angle correction amount increases as the degree of change of the cant angle with respect to time increases.

4. In the steering angle control device according to claim 1, The steering angle control device is characterized in that the first correction amount calculation unit estimates the disturbance component using a Kalman filter based on the vehicle state quantity.

5. In the steering angle control device according to claim 1, The steering angle control device is characterized in that the second correction amount calculation unit calculates a steering angle that compensates for the cant angle based on the vehicle state quantity by feedforward compensation.

6. In the steering angle control device according to any one of claims 1 to 5, The steering angle control device is characterized in that the disturbance component includes at least a steering angle that compensates for the cant angle and a steering angle that compensates for the midpoint misalignment of the vehicle's steering device.

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