Rudder angle control device and rudder angle control method

Abstract

If the vehicle's position deviates from the target path while traveling along it, the driver must appropriately correct the steering. [Solution] A steering angle control device that controls the steering angle of a vehicle to follow a target path includes a prediction unit 161B1 that predicts the behavior of the vehicle, including the deviation of the vehicle's position and direction from the target path, up to a predetermined time in advance, based on a vehicle state quantity indicating the vehicle's motion state and information indicating the target path, and an optimization unit 161B2 that optimizes the target steering angle using the prediction results obtained by the prediction unit 161B1. The optimization unit 161B2 performs optimization using an evaluation function that includes a first element aimed at suppressing deviation and a second element aimed at suppressing the amount of change in the steering angle.

Description

[Technical Field]

[0001] The present invention relates to a steering angle control device that controls the steering angle of a vehicle based on a vehicle model that models the motion state of the vehicle. [Background technology]

[0002] A technique is known for controlling the steering actuators of a vehicle so that the vehicle travels along a target trajectory (see Patent Document 1). [Prior art documents] [Patent Documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2019-167039 [Overview of the project] [Problems that the invention aims to solve]

[0004] In the technology described in Patent Document 1, at least one of the braking / driving actuator and the steering actuator is controlled so that the vehicle travels according to a target trajectory identified by a trajectory identification unit. Although the method of making the vehicle travel along the target trajectory is disclosed, the method of correcting the steering when the vehicle's position deviates from the target trajectory is not disclosed. Therefore, there is room for improvement in order to follow the target path. Following a target route improves traffic safety while minimizing the decline in traffic flow. This, in turn, can contribute to the development of a sustainable transportation system. [Means for solving the problem]

[0005] A first aspect of the present invention is a steering angle control device that controls the steering angle of a vehicle to follow a target path, comprising: a prediction unit that predicts the behavior of the vehicle, including deviations in the position and direction of the vehicle from a target path, up to a predetermined time in advance, based on vehicle state quantities indicating the motion state of the vehicle and information indicating the target path; and an optimization unit that optimizes the target steering angle using the prediction results obtained by the prediction unit, wherein the optimization unit performs optimization using an evaluation function that includes a first element aimed at suppressing deviations and a second element aimed at suppressing the amount of change in the steering angle. A second aspect of the present invention is a steering angle control method for controlling the steering angle of a vehicle to follow a target path, which involves predicting the behavior of the vehicle, including deviations in the vehicle's position and direction from the target path, up to a predetermined time in advance, based on a vehicle state quantity indicating the vehicle's motion state and information indicating the target path, and optimizing the target steering angle using the prediction results obtained from the prediction, wherein the optimization is performed using an evaluation function that includes a first element aimed at suppressing deviations and a second element aimed at suppressing the amount of change in the steering angle. [Effects of the Invention]

[0006] According to the present invention, if the position of the vehicle traveling along a target path deviates from the target path, it becomes possible to appropriately correct the steering. [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 4A] A schematic diagram showing an example of a predicted path that follows the target path. [Figure 4B] A schematic diagram showing the relationship between a two-wheeled vehicle model and a target path according to the embodiment. [Figure 5A] A diagram illustrating the categories for changing weight values. [Figure 5B] A diagram illustrating the combinations of weight values. [Figure 6A] A flowchart illustrating an example of the arithmetic processing performed by the arithmetic unit based on the program. [Figure 6B] A flowchart illustrating an example of the process for determining weight values. [Modes for carrying out the invention]

[0008] Embodiments of the invention will be described below with reference to the drawings. A steering angle control device according to 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 can be applied to, for example, a vehicle with an autonomous driving function, i.e., an autonomous vehicle. The steering angle control device according to this embodiment is applicable to both manually driven vehicles with driver assistance functions and autonomous vehicles, but for the sake of explanation, the following example will be given to the case where it is applied to an autonomous vehicle. 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 with an internal combustion engine as its driving source, an electric vehicle with a drive motor as its driving source, or a hybrid vehicle with 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.

[0009] <Vehicle Configuration> First, the general configuration of the vehicle related to autonomous driving will be described. Figure 1 is a block diagram illustrating the configuration of the vehicle control system 100 of the vehicle having a steering angle control device according to the embodiment. As shown in Figure 1, the vehicle control system 100 mainly comprises a controller 10, a group of external sensors 1 and 2, an internal sensor group 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.

[0010] 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).

[0011] 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.

[0012] 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.

[0013] 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.

[0014] The map database 5 is a device that stores general map information used in the navigation device 6, and is composed of, for example, magnetic disks and semiconductor elements. The map information includes road location information, road shape information (curvature, etc.), and location information of intersections and junctions. 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.

[0015] The navigation device 6 is a device that, for example, searches for a route along the road to a destination entered by the driver and provides driving guidance along the searched route. The input of the destination and driving guidance along the searched route are performed via the input / output device 3. The route search is performed based on the current position of the vehicle measured by the positioning unit 4, the position of the entered destination, and the map information stored in the map database 5. The current position of the vehicle can also be measured using the detection values ​​of the external sensor group 1, and the route may be searched based on this current position and high-precision map information stored in the storage unit 12.

[0016] The communication unit 7 communicates with various servers (not shown) via a network including wireless communication networks such as the Internet and mobile phone networks, and obtains map information, driving history information, and traffic information from the servers periodically or at arbitrary times. In addition to obtaining driving history information, the communication unit 7 may also transmit its own vehicle's driving history information to the server. The network includes not only public wireless communication networks but also closed communication networks established for each predetermined management area, such as 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.

[0017] Actuator AC is a drive actuator used to control the movement of the vehicle. When the drive source is an engine, actuator AC includes a throttle actuator that adjusts the opening degree (throttle opening) of the engine's throttle valve. When the drive source is a drive motor, the drive motor is included in actuator AC. Brake actuators that operate the vehicle's braking system and steering actuators that drive the steering system are also included in actuator AC.

[0018] The controller 10 is comprised of an electronic control unit (ECU). More specifically, the controller 10 includes a computer having an arithmetic unit 11 such as a CPU (microprocessor), a storage unit 12 such as ROM and RAM, and other peripheral circuits (not shown) such as an I / O interface. Although it is possible to provide multiple ECUs with different functions, such as an ECU for engine control, an ECU for drive motor control, and an ECU for braking system, for convenience, Figure 1 shows the controller 10 as a collection of these ECUs.

[0019] The memory unit 12 stores high-precision, detailed map information for autonomous driving. The high-precision map information includes road location information, road shape information (curvature, etc.), road gradient information, intersection and branching point location information, type and location information of lane markings such as white lines, number of lanes (driving lanes), lane width and location information for each lane (information on the center position of the lane and the boundary lines of the lane positions), location information of landmarks (traffic lights, signs, buildings, etc.) as markers on the map, and road surface profile information such as road surface irregularities. The high-precision map information stored in the memory unit 12 may include high-precision map information acquired from outside the vehicle via the communication unit 7, or it may include high-precision map information created by the vehicle itself using detection values ​​from the external sensor group 1 or detection values ​​from the external sensor group 1 and the internal sensor group 2. The memory unit 12 may also store information such as various control programs and threshold values ​​used in the programs. 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.

[0020] 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 movement information (direction of movement, distance traveled) can be calculated based on the detection values ​​of the internal sensor group 2, and the vehicle's position can be recognized accordingly. Furthermore, when the vehicle's position can be measured by sensors installed on or beside the road, the vehicle's position can also be recognized by communicating with those sensors via the communication unit 7.

[0021] The external environment recognition unit 14 recognizes the external conditions around the vehicle based on signals from the external sensor group 1, such as 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 that are stopped or parked around the vehicle, and the position and state of other objects, and creates target 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 in other objects (roads). The state of other objects includes the color of traffic lights (red, blue, yellow), the speed and direction of pedestrians and cyclists, etc. Some of the stationary objects among the other objects constitute landmarks that serve as indicators of location on the map, and the external environment recognition unit 14 also recognizes the location and type of these landmarks.

[0022] The action plan generation unit 15 generates a driving trajectory (target trajectory) 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 trajectories on the route searched by the navigation device 6, the action plan generation unit 15 selects the optimal trajectory from among them that complies with laws and regulations and satisfies criteria such as efficient and safe driving, and generates the selected trajectory 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.

[0023] 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. 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. 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.

[0024] Incidentally, in lane-keeping driving, steering actuators (which can also be called steering actuators) are controlled so that the vehicle travels through a target position in the lane width direction. However, if disturbances such as changes in road surface slope (changes in gradient in the lane width direction) or strong crosswinds occur, the vehicle's position in the lane width direction may deviate from the target position, or the vehicle's orientation may deviate from the direction of travel. Therefore, in this embodiment, the steering angle control device is configured as follows to eliminate the positional deviation and orientation deviation of the vehicle that occur during driving.

[0025] <Steering angle control device> Figure 2 is a block diagram showing the main components of a steering angle control device 50 according to an 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, a navigation device 6, and a steering actuator AC1.

[0026] 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.

[0027] The steering angle sensor 2a detects, for example, the rotation angle (steering angle) of the steering shaft connected to a steering wheel (not shown). 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. The steering torque sensor 2c detects the steering operation 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. The steering angle sensor 2a, steering angular velocity sensor 2b, and steering torque sensor 2c described above constitute a part of the internal sensor group 2 shown in Figure 1. The internal sensor group 2 may also include an IMU (Inertial Measurement Unit) that detects the vehicle's three-axis translational and rotational motion.

[0028] 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. 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.

[0029] <Path error> 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. 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. It 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. 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.

[0030] <Target> 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.

[0031] <Base Track> 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.

[0032] <Target Path> 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. 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.

[0033] <Steering angle control> When, for example, the vehicle is traveling while maintaining the lane, the steering angle control unit 161 controls the steering angle via the steering actuator AC1 so that the host vehicle travels following the target route. Specifically, the steering angle control unit 161 calculates the steering angle necessary to make the position of the host vehicle follow the target route.

[0034] <Acquisition of Vehicle State Quantities> First, the steering angle control unit 161 acquires the target route generated by the target route calculation unit 152. Further, the steering angle control unit 161 acquires the vehicle mass m [kg] of the host vehicle, the yaw moment of inertia I Z [kgm 2 , the distance l f [m] between the center of gravity G and the front wheel axle, the distance l r [m] between the center of gravity G and the rear wheel axle, the equivalent cornering power K f [N / rad] of one front wheel, the equivalent cornering power K r [N / rad] of one rear wheel, the stability factor A [-], etc. from the storage unit 12 as design information regarding the host vehicle. These may be referred to as vehicle characteristic information regarding the motion characteristics. Further, some of these correspond to the respective symbols in FIG. 4B described later. The steering angle control unit 161 further obtains the vehicle speed (body speed) V [m / s], the yaw angular velocity (yaw rate) γ [rad / s], the steering angle δ f [rad] of the front wheels, the gravitational acceleration g [m / s 2 , the steering angular velocity δ f ´ [rad / s], the vehicle body orientation, the above route lateral position deviation e [m], the route azimuth angle deviation Δψ [rad] of the vehicle body, the yaw angular velocity deviation Δγ [rad / s], etc. as vehicle state quantities from the output values of the sensors constituting the internal sensor group 2 or by calculation using the output values of the sensors.

[0035] The above “´” indicates time differentiation. That is, the steering angular velocity δ f ´ is dδ fThis is synonymous with / dt. The vehicle orientation is calculated, for example, based on the direction of the target path's extension and the vehicle's length direction (sometimes called the longitudinal direction) recognized from the camera image of camera 1a, etc. The lateral path deviation e[m] 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 Δψ[rad] is the angle of deviation of the vehicle's orientation (vehicle orientation) relative to the target path. The yaw angular velocity deviation Δγ[rad / s] is the deviation between the yaw angular velocity γ[rad / s] detected by the yaw rate sensor included in the internal sensor group 2 and the target yaw angular velocity (=vehicle speed V[m / s] × path curvature κ[rad / m]). The path curvature κ[rad / m] 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. Next, the rudder angle control unit 161 estimates state variables that cannot be observed using the internal sensor group 2 using the state estimation model (equations (1) and (2) described later), which will be explained in detail later. In this embodiment, the rudder angle disturbance δ d In addition, the vehicle body slip angle β [rad] is estimated, and the steering angle disturbance δ is also estimated. d Effective front wheel steering angle δ excluding f Calculate ^[rad]. "^" indicates an estimated value. Front wheel steering angle δ f and the effective steering angle δ of the front wheels f ^, rudder angle disturbance δ d The relationship is δ f =δ f ^+δ d It is expressed by the following equation. Here, the rudder angle disturbance δ d This refers to a steering angle that does not affect the vehicle's behavior, such as a counter-steering motion against a cant angle φ [rad]. The cant angle φ is the gradient in the lane width direction (road surface transverse gradient). Counter-steering caused by crosswinds or misalignment of the steering gear's midpoint is called a steering angle disturbance δ. d It may be included in this. The vehicle slip angle β [rad] is the angle of deviation between the direction of the vehicle's velocity and the direction of the vehicle's body. Note that the above steering angle velocity δ f ' may be a sensor value from the steering angular velocity sensor 2b, or a value calculated based on the sensor value from the steering angle sensor 2a. Also, the longitudinal component of the vehicle speed V is V x [m / s], the transverse component is Vy [m / s], gravitational acceleration g[m / s 2 The transverse component of ] is g y [m / s 2 ]

[0036] <Optimal rudder angle sequence> Next, the steering angle control unit 161 uses the acquired vehicle state quantity and the above m and l values. f , the above l r , the above K f , the above K r The above A, etc., are input into a driving simulation model (hereinafter referred to as the prediction model). In this embodiment, the prediction model (equations (7) and (8) described in detail later) is used to calculate the optimal steering angle (optimal steering angle sequence) so that the future driving position of the vehicle follows the target path through model predictive control. Details of the prediction model and model predictive control will be described later.

[0037] <Steering angle instruction value> The rudder angle control unit 161 extracts the rudder angle to be indicated for a predicted time ahead from the above optimal rudder angle sequence and excludes the rudder angle disturbance δ in advance. d The target rudder angle, including the added factor, is output as a rudder angle instruction value, and the rudder angle is controlled by the steering actuator AC1. 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. 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.

[0038] <Flow of steering angle control> Figure 3A is a block diagram illustrating the flow of steering angle control by the steering angle control unit 161. Of the configurations exemplified in Figure 2, 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 are shown. Figure 3B is a block diagram illustrating the details of the steering angle control unit 161 shown in Figure 3A. The steering angle control unit 161 includes a vehicle state estimation unit 161A using a Kalman filter, a model prediction control unit 161B, and a target steering angle calculation unit 161C. The vehicle state estimation unit 161A using a Kalman filter includes a state estimation model 161A1. Furthermore, the model prediction control unit 161B includes a prediction unit 161B1 and an optimization unit 161B2.

[0039] <Model-based predictive control> Refer to Figure 3B to explain model predictive control. Model predictive control is a conventional technology. Model predictive control is a control method that calculates the optimal control input using predictive estimation of the controlled object. Model predictive control uses a predictive model and an optimizer. The predictive model is a model that mimics the controlled object. In this embodiment, a prediction unit 161B1, which combines a vehicle model and a path deviation model, is used as the predictive model. Furthermore, an optimization unit 161B2, which evaluates the operation of the prediction unit 161B1 and calculates the optimal control input, is used as the optimizer.

[0040] Figure 4A illustrates the position of the vehicle 101, the target path Tr, and the predicted path Pr. Figure 4B is a schematic diagram showing the relationship between the two-wheeled model and the target path Tr according to the embodiment. Each symbol in the figures represents the vehicle speed V (vehicle longitudinal speed V) of the vehicle 101. x Vehicle lateral speed V y ), center of gravity G, yaw angular velocity γ, vehicle path azimuth deviation Δψ, vehicle path lateral position deviation e, center of gravity G - front wheel axle distance l f , center of gravity G - rear wheel axle distance l r , front wheel steering angle δ f Target path Tr, vehicle slip angle β, front wheel slip angle β f , rear wheel slip angle β r Corresponds to symbol 2F. yf The above Kf [N / rad] and the above β f This indicates the front wheel cornering force [N], which is the product of [rad]. Symbol 2F yr The above K r [N / rad] and the above β r This shows the rear wheel cornering force [N], which is the product of [rad]. In this embodiment, the motion model of the vehicle 101 is represented by an equivalent two-wheel model in which the two front wheels and two rear wheels of the vehicle 101 are moved to the central axis of the vehicle body.

[0041] The vehicle state estimation unit 161A in Figure 3B uses the state estimation model 161A1 to estimate the steering angle disturbance δ based on the vehicle state quantity. d The vehicle body slip angle β [rad] and other parameters are estimated. Equation (1) is the state equation for state estimation model 161A1. Equation (2) is the output equation (which may also be called the observation equation) for state estimation model 161A1. Equations (1) and (2) are examples of continuous-time state estimation models using a steering angle velocity input vehicle model (two-wheeled model) as the vehicle model. x' = A S x + B S u ……… (1) y = C S x ……… (2) However, the coefficient matrix in the formula is as follows:

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[0042] The state estimation model 161A1 uses, for example, the steering angular velocity δ of the front wheels. f We model the vehicle state variables that are influenced by factors such as [rad / s], and use these factors, the observed vehicle state variables, and the previous estimate to calculate the steering angle disturbance δ d The system also calculates estimated values ​​for the vehicle body slip angle β [rad] and yaw angular velocity γ [rad / s]. In general, the observed vehicle state variables contain stationary noise components (observation noise), as well as noise components representing model uncertainty (process noise). By repeatedly performing estimation calculations using the state estimation model 161A1, the system takes into account the observed values ​​and model uncertainty to obtain the estimation with the smallest error.

[0043] The model prediction control unit 161B takes the target path Tr and the observed or estimated vehicle state quantities of the vehicle 101 as input and performs a model prediction calculation. At predetermined calculation intervals (e.g., several milliseconds to several seconds), it predicts the speed and direction of travel (predicted path Pr) of the vehicle 101 for the next few seconds (predicted horizon) on the model, and performs a process (solution search calculation) to find the optimal steering angle sequence for the next few seconds (predicted horizon). In this embodiment, the input x to the prediction model is information indicating the state of the vehicle 101 (Δθ, e), and the input u is information indicating the input of the vehicle 101 (front wheel steering angle δ). f ) and input w is information indicating the curvature κ of the target path Tr. The rudder angle δ of input u f This is the actual rudder angle δ acquired by the internal sensor group 2. f The steering angle disturbance δ estimated by the vehicle state estimation unit 161A d Excluding the above estimated value δ f Enter ^.

[0044] <Explanation of the predictive model> Equation (3) is the state equation of the prediction model of the prediction unit 161B1. Equation (4) is the output equation of the prediction model of the prediction unit 161B1. Equations (3) and (4) are examples of continuous-time prediction models using a path deviation system that includes a steering angle input vehicle model (referred to as a steady-state two-wheel model) representing a steady state as a vehicle model. x' = A C x + B C u +W C w ……… (3) y = C C x ……… (4) However, the coefficient matrix in the formula is as follows:

number

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[0045] When discretizing the state and output equations in equations (3) and (4) above, applying the first-order hold assumption yields equations (7) and (8). Equations (7) and (8) are first-order hold discrete-time prediction models. By using a first-order hold, it becomes possible to maintain accuracy of the curvature κ of the target path even when using longer sample times, compared to the zero-order hold case. The first-order hold assumption is the idea that the state changes linearly from the state at the previous sample time to the state at the next sample time.

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[0046] According to the above equations (7) and (8), by repeatedly obtaining the vehicle state quantity at the next step k + 1 based on the vehicle state quantity at step k on the model, the vehicle state quantity at the Hp steps ahead (k = Hp) of the current time (k = 0) can be predicted. More specifically, the prediction unit 161B1 inputs the vehicle state quantity of the host vehicle 101 at the current time, which is observed or calculated, into the state equation, and predicts the vehicle state quantity corresponding to each of step k = 1 to step k = Hp.

[0047] The optimization unit 161B2 represents each of a plurality of elements used for evaluation (in the embodiment, the path lateral position deviation e, the path azimuth angle deviation Δθ, and the front wheel steering angle difference Δδ f ) as a function, and outputs the sum of the outputs of each function as the evaluation function J. Also, the constraint conditions to be satisfied are set. <00^00428> The following equation (9) is an example of the evaluation function J of the optimization unit 161B2. Also, the following equation (10) is an example of the constraint condition.

number

[0049] In this embodiment, the evaluation function J is defined by multiple elements. Specifically, it includes a function f1 for the lateral position deviation e of the vehicle's path, a function f2 for the azimuth angle deviation Δθ of the velocity vector's path, and the steering angle difference Δδ of the front wheels. f The function f3 is a function of the same nature as the function f1, f2, and f3. The evaluation function J is a function composed of these three elements. The optimization unit 161B2 finds the value of the evaluation function J that is determined by the sum of these three functions f1, f2, and f3, which is the smallest value.

[0050] The three functions f1, f2, and f3 will be explained in more detail. The optimization unit 161B2 calculates the result of multiplying the square of the path lateral position deviation e(k) at each step from the next step k=1 to the next Hp step (k=Hp) by the weight value We, and sets this as the function f1 with respect to the path lateral position deviation e. Furthermore, the optimization unit 161B2 calculates the result of multiplying the square of the path azimuth angle deviation Δθ(k) at each step from the next step k=1 to the next Hp step (k=Hp) by the weight value WΔθ, and sets this as a function f2 with respect to the path azimuth angle deviation Δθ. Furthermore, the optimization unit 161B2 calculates the difference in rudder angle (Δδ) at each step from the next step k=1 to the next Hp step (k=Hp). f (k)-Δδ f (k-1)) squared and the weight value WΔδ f The result of multiplication with is calculated, and the rudder angle difference Δδ f Let f3 be the function for this.

[0051] The model prediction control unit 161B, having the configuration described above, repeats the loop processing by the prediction unit 161B1 and the optimization unit 161B2 multiple times for each calculation cycle to determine the optimal rudder angle sequence that satisfies the constraints and minimizes the evaluation function J. In other words, the optimal rudder angle sequence u(1) to u(Hp) for each step from the next step k=1 to the next Hp step (k=Hp) is determined as a candidate for rudder angle instruction values.

[0052] The target steering angle calculation unit 161C calculates the steering angle disturbance δ estimated by the vehicle state estimation unit 161A. d The optimal rudder angle sequence u(1)~u(Hp) determined by the model prediction control unit 161B is input, and the rudder angle to be indicated for a predetermined predicted time tp is extracted from the optimal rudder angle sequence, and the rudder angle disturbance δ that was excluded in advance is used. d The target rudder angle, including the specified value, is output to the steering actuator AC1 as the rudder angle instruction value for the next few milliseconds. In this embodiment, the rudder angle corresponding to the predicted time tp is obtained from the optimal rudder angle sequence u(1) to u(Hp) by linear interpolation.

[0053] <Change in weight values> Figure 5A illustrates the categories for changing the weight values ​​in equation (9) above, which is used as an example of an evaluation function. By changing the weight values, multiple elements used in the evaluation (in this embodiment, the path lateral position deviation e, the path azimuth angle deviation Δθ, and the front wheel steering angle difference Δδ) can be changed. f Within that context, it becomes possible to set an element that takes precedence over other elements. Here, function group (1) is assumed to be a driver burden reduction function similar to lane keeping assist, function group (2) is assumed to be an accident reduction function similar to road departure prevention, and function group (3) is assumed to be an autonomous driving function for urban areas.

[0054] For example, the weight value We can be combined with other weight values ​​WΔθ and WΔδ f By making it greater than this, steering angle control becomes possible that converges the path lateral position deviation e as quickly as possible within the range in which the vehicle 101 can respond (the range of the constraint conditions by equation (10) above). Similarly, the weight value WΔθ is the same as the other weight values ​​We and WΔδ fBy making it greater than this, steering angle control becomes possible that converges the path azimuth deviation Δθ as quickly as possible within the range in which the vehicle 101 can respond (the range of the constraint conditions by equation (10) above). For the two examples above, the weight value WΔδ f By making this larger than the other weight values ​​We and WΔθ, the rudder angle difference Δδ f This enables steering angle control that minimizes steering input (in other words, minimizes steering input). Because steering input is reduced, vehicle 101 will not turn, resulting in steering angle control that does not follow the target path. Furthermore, a weight value for emergency avoidance, which is the opposite of the control that reduces steering input and maximizes steering input, can also be set. Specifically, the weight value WΔδ f By setting this to zero, vehicle 101 can prioritize avoiding danger above all else in its steering angle control, rather than the impact on the occupants (such as ride comfort).

[0055] <Changes based on driving scene> In this embodiment, the scene in which the vehicle 101 is driving is determined, and predetermined weight values ​​We, WΔθ, and WΔδ are assigned to each determined scene. f Apply the combinations to the evaluation function in equation (9) above.

[0056] Referring to Figure 5A, an example of changing weight values ​​for different driving scenes will be explained. In this embodiment, the driving state of the vehicle 101 is classified into normal driving and emergency avoidance. Emergency avoidance refers to a situation where the external environment recognition unit 14, etc., determines that there is a very high possibility that the vehicle 101 will collide with an obstacle if it continues on its current course, and the emergency avoidance flag is set. The emergency avoidance flag is reset when the external environment recognition unit 14, etc., determines that there is no possibility (sufficiently low) that the vehicle 101 will collide with an obstacle if it continues on its current course. In this embodiment, driving scenes are further classified into two categories. The first is when, using the high-precision map information stored in the memory unit 12, the vehicle's position recognized by the vehicle position recognition unit 13, and vehicle state quantities observed by the internal sensor group 2, etc., it is determined that the driving scene falls within the operational design domain where autonomous driving functions can be provided. This is called an "urban area." The second is a scene where the vehicle drives on a general road or expressway that does not fall within the operational design domain, and this is called an "expressway / major local road." An expressway refers to, for example, a road exclusively for motor vehicles that meets a predetermined standard. This also includes scenes where the vehicle drives through facilities (JCTs) that connect expressways.

[0057] For example, the steering angle control unit 161 uses high-precision map information stored in the memory unit 12, the vehicle's position recognized by the vehicle position recognition unit 13, and vehicle state quantities observed by the internal sensor group 2, etc., to determine that the driving scene falls within an operational design domain where autonomous driving functions can be provided, and if it determines that the driving scene is an "urban area," it determines that the driving scene is an "urban area." Furthermore, if it is determined that the driving scene does not fall under the operational design domain, and if there are lanes recognized by the external environment recognition unit 14, etc., the driving scene is determined to be "expressway / major local road".

[0058] As described above, when the steering angle control unit 161 determines the driving state and driving scene of its own vehicle 101, it determines the weight values ​​We, WΔθ, and WΔδ of each function group corresponding to the determined state and scene. f The combination is applied to the evaluation function in equation (9) above. In Figure 5A, in region R53, which corresponds to normal driving on "expressways / major local roads", the steering angle control unit 161 applies the combination of weight values ​​of function group (1). Referring to Figure 5B, an example of the weight values ​​for function group (1) is explained. For example, the weight values ​​We and WΔθ determine the basic path-following performance. When the absolute value of the path lateral position deviation e is less than or equal to a first predetermined length (e.g., 0.1 [m]), the weight value table We and WΔθ is determined based on the centering performance near the center of the lane. Furthermore, when the absolute value of the path lateral position deviation e is greater than or equal to a second predetermined length (e.g., 0.8 [m]), the weight value table We and WΔθ is determined based on the convergence performance from a state significantly offset from the target path. Furthermore, if the absolute value of the path lateral position deviation e is greater than or equal to the first predetermined length and less than or equal to the second predetermined length, the weight values ​​We and WΔθ shall be linearly interpolated. Also, the weight value WΔδ f This is determined to reduce corrective steering to the extent that it does not impair the path-following performance of function group (1). By switching the weight values ​​based on the lateral deviation e of the path, steering angle control is achieved that balances centering performance and convergence performance. In other words, by prioritizing the weight value We near the center of the lane and prioritizing the weight value WΔθ when significantly offset from the target path, steering angle control is achieved that balances centering performance and convergence performance.

[0059] The steering angle control unit 161 applies a combination of weight values ​​from function group (2) in area R51, which corresponds to emergency avoidance on "expressways / major local roads". Although not shown in the figure, it has a separate weight value table for function group (2) from the weight value table for function group (1) exemplified in Figure 5B. For example, the steering angle control unit 161 has predetermined weight values ​​We, WΔθ, and WΔδ for emergency avoidance. f Combinations of (for example, We=1, WΔθ=1, WΔδ regardless of path lateral position deviation e) f Apply (=0) to the evaluation function in equation (9) above. The rudder angle control unit 161 may further execute a separately configured emergency avoidance control system. The weight value WΔθ is kept as small as possible, and the weight value WΔδ f Setting this to 0 results in rudder angle control that converges the lateral deviation e of the path as quickly as possible, which is necessary for emergency avoidance. In other words, in an emergency, the rudder angle control will quickly turn the rudder to avoid danger, such as a collision.

[0060] The steering angle control unit 161 applies a combination of weight values ​​from function group (3) in the region R52 corresponding to "urban areas". Although not shown in the figure, it has a separate weight value table for function group (3) in addition to the weight value tables for function group (1) and function group (2). For example, when the vehicle speed V is less than or equal to a predetermined value (e.g., 100 [km / h]), the weight values ​​WΔθ, WΔδ f By making it smaller compared to function group (1), the steering angle control prioritizes centering performance compared to function group (1).

[0061] In Figure 5A, in region R52, which corresponds to "urban area," the steering angle control unit 161 applies the combination of weight values ​​of function group (3) regardless of whether the driving state is "normal driving" or "emergency avoidance." The weight values ​​for emergency avoidance in region R52 are We, WΔθ, and WΔδ. f The reason for not using this combination is as follows: Specifically, the autonomous driving function of function group (3) interferes significantly with the accident reduction function of the conventional function group (2). Therefore, function group (3) is planned to include accident reduction functions similar to road departure prevention. As a result, function group (3) does not depend on conventional driving conditions. For example, when overtaking a parked vehicle on the shoulder of the road, the driver may temporarily cross the center line. Conventionally, in this scenario, the state would transition from "normal driving" to "emergency avoidance," but function group (3) understands the scene and sets a target path Tr to avoid the parked vehicle, thus not depending on the conventional driving state.

[0062] <Changes to the seam area> In the joint regions J1 to J3 located at the boundary between the aforementioned regions R51 to R53, the rudder angle control unit 161 uses predetermined weight values ​​We, WΔθ, and WΔδ for the joint regions J1 to J3. f The combination of these may also be applied to the evaluation function in equation (9) above.

[0063] The transition regions J1 to J3 correspond to cases where the steering angle control unit 161 determines a different driving scene or driving state from the previous one. In the transition regions J1 to J3, the weight values ​​We, WΔθ, and WΔδ are respectively determined. f The combination of weight values ​​is applied to the evaluation function in equation (9) above in such a way that the combination is gradually changed, in other words, to create a fade effect. The period for creating the fade effect is, for example, longer than the calculation period of processing by the prediction unit 161B1 and the optimization unit 161B2. This makes it possible to avoid a sudden change in rudder angle control when regions 51-53 in Figure 5A change, in other words, to make the change in control less noticeable to the crew.

[0064] Weight values ​​We, WΔθ, and WΔδ when applying the fade effect f The rate of change of each element (which may also be called the slope of the fade) can be kept constant, or the rate of change of each element can be varied. Note that the weight values ​​We, WΔθ, and WΔδ of function group (1) f The weight values ​​We, WΔθ, and WΔδ of the functional group (2) are obtained from the combinations. f When changing to this combination (corresponding to the joint region J2), it is not necessary to apply a fade effect. By quickly changing the weight value combination to the one used for emergency avoidance, it becomes possible to quickly transition to rudder angle control that simply turns the rudder to avoid danger. Conversely, the weight values ​​We, WΔθ, and WΔδ of function group (2) f From the combinations, the weight values ​​We, WΔθ, and WΔδ of the function group (1) f When changing to this combination (corresponding to the transition region J2), a fade effect may be applied. By gradually changing the combination of weight values, it becomes less likely for the occupants to notice that the steering angle control has changed suddenly.

[0065] <Explanation of the flowchart> Figure 6A is a flowchart showing an example of calculation processing performed by the calculation 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 function, which is one of the driving assistance functions, is enabled, i.e., when lane keeping driving is in progress.

[0066] In step S10, the controller 10 obtains the route information as the target path described above using the rudder angle control unit 161 and proceeds to step S20. In step S20, the controller 10 acquires the vehicle state quantities and other information described above using the steering angle control unit 161 and proceeds to step S30. In step S30, the controller 10 performs the aforementioned model predictive control calculation using the rudder angle control unit 161 and proceeds to step S40. In step S40, the controller 10, using the steering angle control unit 161, calculates the target steering angle as described above, outputs a steering angle instruction value, and performs steering angle control, then proceeds to step S50. As a result, the steering actuator AC1 is controlled based on the steering angle instruction value.

[0067] In step S50, the controller 10 determines whether or not to terminate the process. If, for example, the automatic driving mode is deactivated, the controller 10 affirms step S50 and terminates the process shown in Figure 6A. If, for example, the automatic driving mode is to be continued, the controller 10 negates step S50 and returns to step S10, repeating the process described above.

[0068] Figure 6B is a flowchart showing an example of the process for determining weight values. The process shown in Figure 6B is included in the process of step S30 in Figure 6A and is executed as part of the process by the prediction unit 161B1 and the optimization unit 161B2.

[0069] In step S300, the steering angle control unit 161 determines whether or not the driving scene falls under the above-described function group (3). If the above-described driving scene falls under "urban area", the steering angle control unit 161 affirms step S300 and proceeds to step S310. If the driving scene does not fall under "urban area", the steering angle control unit 161 negates step S300 and proceeds to step S320. The driving scene in which step S300 is negated falls under "highway / major local road".

[0070] In step S310, the rudder angle control unit 161 completes the processing shown in Figure 6B by setting the weight values ​​to be applied to the evaluation function of equation (9) above as the weight values ​​of the function group (3). More specifically, the rudder angle control unit 161 uses the predetermined weight values ​​We, WΔθ, and WΔδ of the function group (3). f Apply the combinations to the evaluation function in equation (9) above.

[0071] If step S300 is rejected, the steering angle control unit 161 proceeds to step S320, in which case it determines whether the above-mentioned driving state corresponds to "emergency avoidance". If the steering angle control unit 161 determines that the emergency avoidance flag is present, it affirms step S320 and proceeds to step S330. If the steering angle control unit 161 determines that the emergency avoidance flag is absent, it rejects step S320 and proceeds to step S340. The driving state when step S320 is rejected corresponds to "normal driving".

[0072] In step S330, the rudder angle control unit 161 completes the processing shown in Figure 6B by setting the weight values ​​to be applied to the evaluation function of equation (9) above as the weight values ​​of the function group (2). More specifically, the rudder angle control unit 161 uses the predetermined weight values ​​We, WΔθ, and WΔδ of the function group (2). f Apply the combinations to the evaluation function in equation (9) above.

[0073] In step S340, which proceeds if step S320 is deemed negative, the rudder angle control unit 161 terminates the processing shown in Figure 6B by setting the weight values ​​to be applied to the evaluation function of equation (9) above as the weight values ​​of the function group (1). More specifically, the rudder angle control unit 161 uses the predetermined weight values ​​We, WΔθ, and WΔδ of the function group (1). f Apply the combinations to the evaluation function in equation (9) above.

[0074] According to the embodiments described above, the following effects and advantages are achieved. (1) The steering angle δ of the vehicle 101 is set to follow the target path Tr. f The steering angle control device 50 that controls the steering includes a prediction unit 161B1 that predicts the behavior of the vehicle 101, including the deviation (e, Δθ) of the vehicle 101 relative to the target path Tr, up to a predetermined time in advance, based on vehicle state quantities indicating the motion state of the vehicle 101 and information indicating the target path Tr, and an optimization unit 161B2 that optimizes the target steering angle using the prediction results obtained by the prediction unit 161B1. The optimization unit 161B2 includes a first element aimed at suppressing the deviation (e, Δθ) and a steering angle δ fOptimization is performed using a second element aimed at minimizing the change in , and an evaluation function (equation (5)) that includes . With this configuration, it is possible to achieve both the effect of including the first element in the evaluation function (reducing deviation from the target path Tr by turning the steering wheel) and the effect of including the second element in the evaluation function (reducing the effects caused by turning the steering wheel (such as deterioration of ride comfort due to lateral G-forces)), thereby enabling appropriate steering correction when the position of the vehicle 101 deviates from the target path Tr while driving. The above vehicle state variables include the vehicle speed V and the steering angle δ of the front wheels, which are acquired by the internal sensor group 2 of the vehicle 101. f , steering angular velocity δ of the front wheels f In addition to first vehicle state quantities such as yaw angular velocity γ, the steering angle disturbance δ, which is not acquired by the internal sensor group 2 of the vehicle 101, is estimated by the vehicle state estimation unit 161A. d This also includes a second vehicle state variable, such as the slip angle β.

[0075] (2) In the steering angle control device 50 described in (1) above, the optimization unit 161B2 includes the path lateral position deviation e, which indicates the deviation between the target path Tr and the position of the vehicle 101, and the path azimuth angle deviation Δθ, which indicates the deviation in the direction in which the vehicle 101 is moving relative to the target path Tr, as first elements in the evaluation function (equation (9)), and the steering angle difference Δδ for each calculation period f =( δ f (k)-δ f (k-1)) is included as the second element in the evaluation function (Equation (9)). With this configuration, it becomes possible to suppress both the lateral deviation e and azimuth deviation Δθ, which are deviations from the target path Tr, and the effects caused by steering.

[0076] (3) In the steering angle control device 50 described in (1) above, the optimization unit 161B2 includes the path lateral position deviation e, which indicates the deviation between the target path Tr and the position of the vehicle 101, and the path azimuth angle deviation Δθ, which indicates the deviation in the direction in which the vehicle 101 is moving relative to the target path Tr, as first elements in the evaluation function, and the steering angle velocity δ for each calculation cycle f ´ =(δ f (k)-δf (k-1) / t s This is included as the second element in the evaluation function. With this configuration, it becomes possible to suppress both the lateral deviation e and azimuth deviation Δθ, which are deviations from the target path Tr, and the effects caused by steering.

[0077] (4) In the steering angle control device 50 described in (1) above, the optimization unit 161B2 sets weight values ​​We, WΔθ, and WΔδ for the first and second elements in accordance with the driving scene and driving state on the target path Tr. f Optimization is performed using an evaluation function (Equation (9)) with a modified combination of the elements. With this configuration, for example, depending on whether the vehicle 101 is driving normally or in an emergency avoidance situation, it becomes possible to prioritize control that reduces the corrective steering angle, rather than insisting on moving the vehicle closer to the center of the lane, or to prioritize steering and following the target path Tr at all costs.

[0078] (5) In the steering angle control device 50 described in (4) above, the optimization unit 161B2 optimizes the weight values ​​We, WΔθ, and WΔδ for the first element and the second element, respectively, in the case of normal driving on general roads including expressways ("expressway / major local road" normal driving), emergency avoidance driving on general roads ("expressway / major local road" emergency avoidance), and urban driving including intersections on general roads ("urban area"). f Optimization is performed using an evaluation function (Equation (9)) with a modified combination of the elements. With this configuration, it becomes possible to perform appropriate steering angle control for each driving scenario in which the vehicle 101 is driving. For example, in the "urban area" scenario, the weight values ​​WΔθ and WΔδ are compared to the normal driving scenario on the "highway / major local road". f By making a small change, centering performance is prioritized compared to normal driving on "expressways / major local roads." As a result, for example, when overtaking a parked vehicle on the shoulder, it becomes possible to drive with a small lateral deviation e to match the target path Tr that avoids the parked vehicle, allowing for a smooth overtaking maneuver.

[0079] (6) In the steering angle control device 50 described in (4) above, the optimization unit 161B2 corresponds to the path lateral position deviation e, which indicates the difference between the target path Tr and the position of the vehicle 101, and assigns weight values ​​We, WΔθ, and WΔδ to the first and second elements. f Change the combination. With this configuration, it is possible to achieve both centering performance in driving scenes where the deviation from the target path Tr is small, and convergence performance towards the target path Tr in driving scenes where the deviation from the target path Tr is large.

[0080] (7) In the steering angle control device 50 described in (4) above, the optimization unit 161B2 sets the weight values ​​We, WΔθ and WΔδ for the first element and the second element. f When changing the combination, the change is made over a period longer than the calculation cycle. This configuration allows for gradual changes to the weight combinations without causing discomfort to the occupants of vehicle 101. As a result, changes in control can be made less noticeable to the occupants.

[0081] (8) In the steering angle control device 50 described in (4) to (7) above, the optimization unit 161B2 further optimizes the weight values ​​We, WΔθ, and WΔδ for the first and second elements during emergency avoidance driving. f If you need to change the combination, do so promptly. With this configuration, in the event of an emergency evasive maneuver, the control system quickly changes to one that allows for a large rudder turn, enabling faster steering maneuvers compared to a system that changes control gradually.

[0082] One of the control objectives of steering angle control is to bring the path azimuth angle deviation Δθ, which is the deviation angle of the vehicle's direction of travel (velocity vector) relative to the target path Tr, closer to zero. Using the path azimuth angle deviation Δψ, which is the deviation angle of the vehicle's orientation relative to the target path Tr, and the vehicle slip angle β, the relationship Δθ = Δψ + β holds. Conventionally, control was performed to bring Δθ closer to zero by adopting a state equation of a vehicle model that included Δψ and β as state variables. In contrast, in this embodiment, the path deviation model is expressed in a steady state, and β is not treated as a state. As a result, Δθ'=Δψ' can be treated, and the elements corresponding to the vehicle model in the conventional state equation are incorporated into the state equation (3) of the path deviation system according to this embodiment (coefficient matrix Bc). As a result, control that brings Δθ closer to 0 can be performed even without a vehicle model that includes Δψ and β as state variables. Since we decided not to include Δψ and β as state variables in the vehicle model, the size of the coefficient matrix in equation (3) above is extremely compact compared to conventional state equations. This reduces the number of vehicle state variables handled in the model prediction calculation, thereby lowering the computational load on the prediction unit 161B1, and making it possible to implement the system in even lower-spec ECUs, etc. Furthermore, by modeling the steady-state relationship, it becomes possible to ensure continuity with control in the extremely low-speed range (e.g., automatic parking control) compared to when a steady state is not considered. Moreover, the accuracy of predicting the future behavior of the vehicle 101 is guaranteed even when the path deviation model is represented in a steady state, so the performance of following the target trajectory is not impaired.

[0083] The above embodiment can be modified into various forms. Modifications will be described below. (Variation 1) In the above embodiment, the weight values ​​We, WΔθ, and WΔδ for the first and second elements, defined for each driving scene, are exemplified: f The values ​​shown are merely examples. Also, the weight values ​​We, WΔθ, and WΔδ are used for emergency avoidance. f The values ​​of are merely examples. In other words, the weight values ​​We, WΔθ, and WΔδ applied to the evaluation function (Equation (5)) are just one example. f The value can be changed as needed.

[0084] (Modification 2) In the above embodiment, the vehicle state variables are the vehicle speed (vehicle body speed) V [m / s], the yaw angular velocity (yaw rate) γ [rad / s], and the steering angle δ of the front wheels. f [rad], gravitational acceleration g[m / s 2 ], steering angular velocity δf '[rad / s], vehicle orientation, path lateral position deviation e[m], vehicle path azimuth angle deviation Δψ[rad], yaw angular velocity deviation Δγ[rad / s], steering angle disturbance δ d While examples such as the vehicle body slip angle β [rad] have been given, other state variables may also be added.

[0085] (Variation 3) The state equation and output equation exemplified are examples only, and the state estimation model and prediction model are not limited to those exemplified in the embodiment and may be modified as appropriate.

[0086] 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]

[0087] 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 Road calculation unit, 152 Target path calculation unit, 161 Steering angle control unit, 161A Vehicle state estimation unit, 161B Model prediction control unit, 161B1 Prediction unit, 161B2 Optimization unit, 161C Target steering angle calculation unit, AC1 Steering actuator

Claims

1. A steering angle control device that controls the steering angle of a vehicle so that it follows a target path, A prediction unit predicts the behavior of the vehicle, including deviations in the vehicle's position and direction from the target path, up to a predetermined time in advance, based on vehicle state quantities indicating the vehicle's motion state and information indicating the target path. The system includes an optimization unit that optimizes the target steering angle using the prediction results obtained by the prediction unit, The optimization unit performs the optimization using an evaluation function that includes a first element aimed at suppressing the deviation and a second element aimed at suppressing the amount of change in the steering angle. A steering angle control device characterized by the following features.

2. In the steering angle control device according to claim 1, The optimization unit includes in the evaluation function a lateral position deviation indicating the deviation between the target path and the vehicle's position, and an azimuth angle deviation indicating the deviation in the direction the vehicle is moving relative to the target path, as the first element, and includes in the evaluation function the steering angle difference for each calculation cycle as the second element. A steering angle control device characterized by the following features.

3. In the steering angle control device according to claim 1, The optimization unit includes in the evaluation function a lateral position deviation indicating the deviation between the target path and the vehicle's position, and an azimuth angle deviation indicating the deviation in the direction the vehicle is moving relative to the target path, as the first element, and includes in the evaluation function the steering angular velocity for each calculation cycle as the second element. A steering angle control device characterized by the following features.

4. In the steering angle control device according to claim 1, The optimization unit performs the optimization using the evaluation function, which modifies the combination of weight values ​​for the first and second elements in accordance with the driving scene and driving state on the target path. A steering angle control device characterized by the following features.

5. In the steering angle control device according to claim 4, The optimization unit performs the optimization using the evaluation function, which has been modified in which the combination of weight values ​​for the first and second elements has been changed, for at least normal driving on general roads including expressways, emergency avoidance driving on general roads, and urban driving including intersections on general roads. A steering angle control device characterized by the following features.

6. In the steering angle control device according to claim 4, The optimization unit changes the combination of weight values ​​for the first element and the second element in accordance with the lateral position deviation. A steering angle control device characterized by the following features.

7. In the steering angle control device according to claim 4, When the optimization unit changes the combination of weight values ​​for the first and second elements, it takes a longer time to make the change than the calculation cycle in the prediction unit. A steering angle control device characterized by the following features.

8. In the steering angle control device according to any one of claims 4 to 7, Furthermore, the optimization unit promptly changes the combination of weight values ​​for the first and second elements if necessary during emergency evasive driving. A steering angle control device characterized by the following features.

9. A steering angle control method for controlling the steering angle of a vehicle so that it follows a target path, Based on the vehicle state quantities indicating the vehicle's motion state and the information indicating the target path, predict the vehicle's behavior, including deviations in the vehicle's position and direction relative to the target path, up to a predetermined time in advance. Using the prediction results obtained from the above prediction, the target rudder angle is optimized, The optimization is performed using an evaluation function that includes a first element aimed at suppressing the deviation and a second element aimed at suppressing the amount of change in the steering angle. A steering angle control method characterized by the following.

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