Vehicle control device

Abstract

To provide a vehicle control device which can perform automatic driving control suitable for a road and road circumference conditions.SOLUTION: A vehicle control device includes: a target track arithmetic section 31 which calculates a target track in automatic driving; a target steering angle arithmetic section 32 which calculates a target steering angle on the basis of the target track; a correction gain arithmetic section 33 which calculates a correction gain to be incorporated into calculation of the target steering angle in the target steering angle arithmetic section 32; and a steering control section 2B which controls a steering device 2A on the basis of the target steering angle. The vehicle control device further includes: a road surface state determination section 34 which determines a road surface state of a road on which an own vehicle travels; and an allowance degree arithmetic section 35 which calculates an allowance degree in a right and left direction of the own vehicle on the basis of at least one of a road width, a distance from the own vehicle to a road end, and a road end condition in the road on which the own vehicle travels. The correction gain arithmetic section 33 calculates the correction gain on the basis of a determination result of the road surface state determination section 34 and an arithmetic result of the allowance degree arithmetic section 35.SELECTED DRAWING: Figure 4

Description

[Technical Field]

[0001] The present invention relates to a vehicle control device. [Background technology]

[0002] As a technology for a vehicle control device capable of performing autonomous driving, for example, Japanese Patent Application Laid-Open Publication No. 2018-154304 discloses a technology that corrects the gain of feedforward control for the curvature of a target route when a sudden disturbance is predicted to be applied to the host vehicle. According to this device, if the disturbance is determined to be a crosswind, the gain is corrected in a direction that makes the vehicle more likely to follow the target route than usual, and if the disturbance is determined to be a rut, the gain is corrected in a direction that makes the vehicle much less likely to follow the target route than usual. This allows the vehicle's driving state to be adjusted in advance in preparation for the disturbance, and the effects of the disturbance are mitigated. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2018-154304 Summary of the Invention [Problem to be solved by the invention]

[0004] There are various possible conditions for the road and its surroundings on which the vehicle travels. For example, in mines and other places, most roads are unpaved and have ruts and other imperfections. There are also various possible cases for the road width and road edge (left and right edges). For example, the left or right edge of the road may be a cliff. The control of the above-mentioned device does not take into account such conditions for the road and its surroundings, and there is room for improvement in the device. An object of the present invention is to provide a vehicle control device capable of performing automatic driving control suited to the conditions of the road and its surroundings. [Means for solving the problem]

[0005] The vehicle control device of the present invention is a vehicle control device comprising: a steering device that steers wheels; a target trajectory calculation unit that calculates a target trajectory based on map data, the position of the host vehicle, and a destination during autonomous driving; a target steering angle calculation unit that calculates a target steering angle based on the target trajectory calculated by the target trajectory calculation unit; a correction gain calculation unit that calculates a correction gain to be incorporated into the calculation of the target steering angle by the target steering angle calculation unit; and a steering control unit that controls the steering device based on the target steering angle, and further comprises: a road surface condition determination unit that determines the road surface condition of the road on which the host vehicle is traveling; and a margin calculation unit that calculates a margin of safety in the lateral direction of the host vehicle based on at least one of the road width, the distance from the host vehicle to the road edge, and the road edge situation on the road on which the host vehicle is traveling, and the correction gain calculation unit calculates the correction gain based on the determination result of the road surface condition determination unit and the calculation result of the margin calculation unit. [Effects of the Invention]

[0006] According to the present invention, the correction gain incorporated into the calculation of the target steering angle is calculated based on the road surface condition and the margin of freedom in the lateral direction of the host vehicle. In other words, according to the present invention, the correction gain can be changed depending on whether there is margin of freedom in the lateral direction of the host vehicle or not. For example, the wider the road, the greater the margin, and if there is a cliff on the left or right edge of the road, the margin of freedom can be said to be small. According to the present invention, the magnitude of the correction gain can be adjusted according to the calculated margin of freedom, and the strength of the tracking ability to the target trajectory can be adjusted. In this way, according to the present invention, automatic driving control suitable for the conditions of the road and its surroundings is possible. [Brief explanation of the drawings]

[0007] [Figure 1] 1 is a configuration diagram of a vehicle control device according to an embodiment of the present invention; [Figure 2] FIG. 10 is a schematic diagram illustrating an example of a situation in which the margin is large in this embodiment. [Figure 3] FIG. 10 is a schematic diagram illustrating an example of a situation in which the margin is small in this embodiment. [Figure 4]FIG. 2 is a conceptual diagram illustrating functions of an autonomous driving ECU according to the present embodiment. [Figure 5] 5 is a flowchart showing an example of a flow of calculation of a correction gain in feedback control according to the present embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0008] Hereinafter, as a mode for carrying out the present invention, a vehicle control device 1 which is one embodiment of the present invention will be described in detail with reference to the drawings. In addition to the following embodiment, the present invention can be carried out in various forms with various changes and improvements based on the knowledge of those skilled in the art.

[0009] Vehicle control device 1 of this embodiment includes a steering system 2, an automatic driving ECU 3, and various sensors including a periphery monitoring device 53. Steering system 2 includes a steering device 2A and a steering ECU (corresponding to a "steering control unit") 2B. Steering device 2A steers left and right front wheels 10A, which are steered wheels. Steering device 2A is an electric power steering system, and includes a steering knuckle 21, a steering rod 22, a steering wheel 23, which is a steering operation member, a steering shaft 24, a motion conversion mechanism 25, a steering actuator 26, an operation angle sensor 27, and an operation force sensor 28.

[0010] A pair of steering knuckles 21 each rotatably holds front wheels 10A. Both ends of steering rod 22 are connected to steering knuckles 21 via tie rods 22a. Steering shaft 24 rotates integrally with steering wheel 23. Motion conversion mechanism 25 is a rack-and-pinion mechanism that converts the rotational motion of steering shaft 24 into linear movement of steering rod 22 in the left-right direction. Steering actuator 26 is configured to apply a force (hereinafter also referred to as "axial force") to steering rod 22 that moves steering rod 22 in the left-right direction. Steering actuator 26 includes a steering motor 261 that applies an axial force to steering rod 22.

[0011] The operation angle sensor 27 is a sensor for detecting a steering operation angle (hereinafter also simply referred to as "operation angle"), which is the amount of operation of the steering wheel 23. The operation force sensor 28 is a sensor for detecting the amount of twist of a torsion bar (not shown) provided on the steering shaft 24 caused by the steering operation of the driver, that is, a sensor for detecting operation torque as the operation force applied to the steering wheel 23 by the driver.

[0012] Steering ECU 2B is an electronic control unit equipped with a CPU, memory, etc., and controls steering device 2A. Steering ECU 2B sets a control current value (hereinafter also referred to as an "assist current value") to be supplied to steering motor 261 based on the operation torque and operation direction (and further, for example, vehicle speed information) detected by operating force sensor 28, and supplies a control current corresponding to the assist current value to steering motor 261. Steering ECU 2B is equipped with a drive circuit that drives steering motor 261. The actual steering angle, which is the actual steering angle (steered amount) of front wheels 10A, is determined based on the detection value of rotation angle sensor 261a provided in steering motor 261. Furthermore, the assist current value is detected by current sensor 261b provided in steering motor 261.

[0013] The autonomous driving ECU 3 is an electronic control unit equipped with a CPU, memory, etc., and performs control related to autonomous driving of the vehicle. The autonomous driving ECU 3 determines the position of the vehicle based on the detection results of the periphery monitoring device 53 and map data. Details of the autonomous driving ECU 3 will be described later. The periphery monitoring device 53 is composed of multiple sensors, and is configured to include, for example, a camera that captures images of the periphery of the vehicle, and a millimeter-wave radar and / or LiDAR that measures the distance between the vehicle and objects around the vehicle. The periphery monitoring device 53 can also be said to be a device that measures the distance between the vehicle and objects around the vehicle in order to determine the position of the vehicle.

[0014] During autonomous driving, steering ECU 2B sets an assist current value based on the target steering angle (command value) received from autonomous driving ECU 3, and supplies a control current corresponding to the assist current value to steering motor 261. Steering ECU 2B determines the assist current value (axial force, torque) so as to reduce the difference between the target steering angle and the actual steering angle. Steering device 2A is operated by steering motor 261 to which a control current is applied, even if steering wheel 23 is not operated. During autonomous driving, front wheels 10A are steered according to an assist current value based on a target trajectory and a target steering angle, without the operation of steering wheel 23. At this time, autonomous driving ECU 3 also calculates a target operation angle of steering wheel 23 corresponding to the target steering angle.

[0015] In this way, steering ECU 2B controls steering device 2A in accordance with the target steering angle received from automatic driving ECU 3 during automatic driving, and in accordance with the driver's operation of steering wheel 23 during manual driving. Automatic driving ECU 3 sets a target trajectory for automatic driving in map data, and transmits a target steering angle based on that target trajectory to steering ECU 2B during automatic driving.

[0016] (various sensors) The vehicle is equipped with various sensors, such as a longitudinal acceleration sensor 51 that detects acceleration in the longitudinal direction of the vehicle, a lateral acceleration sensor 52 that detects acceleration in the lateral direction of the vehicle, a periphery monitoring device 53 that monitors the periphery of the vehicle, a yaw rate sensor 54 that detects the yaw rate of the vehicle, a roll rate sensor 55 that detects the roll rate of the vehicle, a pitch rate sensor 56 that detects the pitch rate of the vehicle, a wheel speed sensor 57 that detects the wheel speed, and a vertical acceleration sensor 58 that detects acceleration in the vertical direction of the vehicle. Vehicle speed can be calculated based on the detection result of the wheel speed sensor 57, for example. For example, sensors 51, 52, 54, 55, 56, and 58 can each be considered sensors that detect vehicle behavior states.

[0017] (Details of the autonomous driving ECU) The autonomous driving ECU 3 includes a target trajectory calculation unit 31, a target steering angle calculation unit 32, a correction gain calculation unit 33, a road surface condition determination unit 34, and a margin calculation unit 35. The target trajectory calculation unit 31 calculates a target trajectory based on map data, the position of the host vehicle, and a destination. The map data is stored in the autonomous driving ECU 3 or another storage medium from which the autonomous driving ECU 3 can acquire data via communication. The position of the host vehicle may be determined based on the detection results of the periphery monitoring device 53 and the map data, or may be determined based on GPS data. The destination is an arbitrary point on the map data that is set in advance by the user.

[0018] During autonomous driving, target steering angle calculation unit 32 calculates a target steering angle based on the target trajectory calculated by target trajectory calculation unit 31. In other words, target steering angle calculation unit 32 calculates a target steering angle corresponding to the position of the host vehicle so that the traveling trajectory of the host vehicle follows (tracks) the target trajectory. Target steering angle calculation unit 32 executes feedforward control and feedback control. Target steering angle calculation unit 32 calculates a command value αt for the target steering angle based on the FF target angle αff calculated based on the target trajectory by feedforward control and the FB target angle αfb calculated by feedback control.

[0019] In feedforward control, target steering angle calculation unit 32 calculates FF target angle αff based on curvature k of the target trajectory. Target steering angle calculation unit 32 calculates FF target angle αff, for example, by multiplying curvature k by FF correction gain Gff (αff=k×Gff). Note that the correction gain can also be considered a coefficient.

[0020] In feedback control, target steering angle calculation unit 32 calculates FB target angle αfb based on the difference between the target vehicle state and the current vehicle state so as to bring the current vehicle state closer to the target vehicle state. Specifically, target steering angle calculation unit 32 calculates FB target angle αfb based on, for example, a lateral deviation δ which is the deviation of the left and right position of the host vehicle from the target trajectory, and a yaw angle deviation θy based on the target trajectory.

[0021] As an example, the FB target angle αfb includes a proportional control term αp for the lateral deviation δ1 of the host vehicle relative to the target trajectory when the host vehicle travels a predetermined distance at the current steering angle, an integral control term αi for the current lateral deviation δ2 of the host vehicle relative to the target trajectory, a proportional control term αy for the yaw angle deviation θy of the host vehicle relative to the target trajectory, and a differential control term αr for the yaw angle deviation θy of the host vehicle relative to the target trajectory (αfb = αp + αi + αy + αr). The proportional control term αp is a value obtained by multiplying the lateral deviation δ1 by the FB correction gain Gp (αp = Gp × δ1). The integral control term αi is a value obtained by multiplying the integral value of the lateral deviation δ2 by the FB correction gain Gi (αi = Gi × ∫δ2dt). The proportional control term αy is a value obtained by multiplying the yaw angle deviation θy by the FB correction gain Gy (αy = Gy × θy). The differential control term αr is a value obtained by multiplying the differential value of the yaw angle deviation θy by the FB correction gain Gr (αr=Gr×dθy / dt).

[0022] Target steering angle calculation unit 32 calculates a command value αt for steering ECU 2B regarding the target steering angle by adding FF target angle αff and FB target angle αfb (αt=αff+αfb). Steering ECU 2B controls steering device 2A based on command value αt for the target steering angle. Note that the calculation of FF target angle αff and FB target angle αfb described above is an example, and calculations different from the above may also be used.

[0023] As described above, correction gain calculation unit 33 calculates at least one of FF correction gain Gff and FB correction gains Gp, Gi, Gy, Gr (hereinafter collectively referred to as "Gfb") to be incorporated into the calculation of the target steering angle (command value αt) in target steering angle calculation unit 32. Correction gain calculation unit 33 of this embodiment calculates FB correction gain Gfb under predetermined conditions. FF correction gain Gff is set to a predetermined value. The calculation of FB correction gain Gfb will be described later.

[0024] The road surface condition determination unit 34 determines the road surface condition of the road on which the vehicle is traveling. The road surface can be said to be the surface that makes up the road. A road can be said to have some kind of partition (for example, white lines, embankments, guardrails, cliffs, etc.) that can determine the road width at the left and right ends in the vehicle's traveling direction. The term "road" includes unpaved roads and paved roads.

[0025] Road surface condition determination unit 34 stores, as a reference value, a control amount that serves as a reference for steering ECU 2B with respect to the actual steering angle, which is set based on the target trajectory or target steering angle. The control amount is the current value (assist current value) of the control current that steering ECU 2B supplies to steering device 2A. The reference value is set in advance in accordance with the target trajectory or target steering angle, and can be said to be an assist current value that serves as a model for the actual steering angle. In other words, the reference value corresponds to the assist current value with respect to the actual steering angle when the vehicle is traveling on a flat road surface.

[0026] Road surface condition determination unit 34 detects the control amount of steering ECU 2B corresponding to the actual steering angle of front wheels 10A. Road surface condition determination unit 34 stores, for example, time-series data of the actual steering angle and time-series data of the assist current value. This allows the actual steering angle and the assist current value to be associated with each other based on time (hours). During autonomous driving, if the deviation from reference, which is the deviation of the actual control amount from a reference value, exceeds a predetermined threshold, road surface condition determination unit 34 determines that the road surface is bad (uneven road, ruts, etc.).

[0027] The steering ECU 2B sets the assist current value, for example by feedback control, so as to reduce the difference between the actual steering angle and the target steering angle. Therefore, in a situation where it is difficult to reduce the difference between the actual steering angle and the target steering angle due to unevenness of the road surface, the steering ECU 2B increases the assist current value and the steering assist force. For example, when the vehicle attempts to turn over a rut, the road resistance to steering increases compared to when the vehicle is turning on a paved road, and the assist current value for the actual steering angle increases. In other words, when achieving the target trajectory and target steering angle, differences occur in the assist current value and the axial force depending on the road surface condition. Based on this difference, it is determined whether the road surface is rough.

[0028] Furthermore, differences appear in the detected values ​​of various sensors (for example, the rotation angle sensor 261a, the yaw rate sensor 54, and the current sensor 261b) and the calculated values ​​based on the detected values ​​depending on whether the road surface is uneven. The road surface condition determination unit 34 may use this difference to determine whether the road surface is rough.

[0029] The road surface condition determination unit 34 may also determine the condition of the road surface based on the wheel speed of each of the wheels 10A and 10B. For example, the road surface condition determination unit 34 may determine that the road surface is bad when the number of times that the amount of change in wheel speed exceeds a predetermined threshold within a predetermined time period is equal to or greater than a count threshold.

[0030] The road surface condition determination unit 34 may also determine whether the road surface is bad based on the detection result of the periphery monitoring device 53. The road surface condition determination unit 34 may also determine whether the road surface is bad based on road surface condition data acquired by wireless communication from a central control device (not shown) configured by a server or the like that stores road surface information.

[0031] In this way, the road surface condition determination unit 34 determines the road surface condition based on at least one of, for example, the control amount (assist current value) of the steering device 2A by the steering ECU 2B, the detection results of the vehicle behavior state (yaw rate, etc.) by various sensors, the wheel speed, the detection results of the surrounding monitoring device 53, and road surface condition data obtained from an external storage device (for example, a server of a control system) via wireless communication.

[0032] (Margin calculation) The margin calculation unit 35 calculates the margin of safety in the left-right direction of the vehicle based on at least one of the road width, the distance from the vehicle to the road edge, and the road edge conditions of the road on which the vehicle is traveling. As an example, the margin of safety increases as the road width increases, and decreases when an oncoming vehicle is present. The margin of safety also varies depending on the road edge conditions, i.e., the conditions of the left and right edges of the road. For example, if there is a protective measure (e.g., embankment or guardrail) at the road edge, the vehicle is deemed relatively safe, and the margin of safety increases or remains unchanged. On the other hand, if there is no protective measure at the road edge and there is a dangerous terrain (e.g., a cliff), the vehicle is deemed relatively dangerous, and the margin of safety decreases. Regarding the road edge conditions, the lower the risk, the greater the margin of safety. The protective measure can be considered a means provided at the road edge to prevent the vehicle from going off the road.

[0033] The margin calculation unit 35 calculates the road width or the distance from the vehicle to the road edge (hereinafter also referred to as "road width, etc.") based on, for example, the detection results of the periphery monitoring device 53. The margin calculation unit 35 increases the margin as the calculated road width, etc. increases. The margin calculation unit 35 may calculate the road width, etc. based on map data and GPS data, or based on road surface condition data acquired from a server.

[0034] The margin calculation unit 35 also determines the road edge condition based on, for example, the detection results of the perimeter monitoring device 53. The margin calculation unit 35 may determine the road edge condition based on map data including topographical information and protective measure information, and / or road surface condition data acquired from a server. The margin calculation unit 35 changes the margin depending on the determined road edge condition (for example, the presence or absence of protective measures, the type of protective measures, the topography, etc.).

[0035] Furthermore, the margin calculation unit 35 determines the presence or absence of an oncoming vehicle based on, for example, the detection result of the periphery monitoring device 53. The margin calculation unit 35 determines the presence or absence of an oncoming vehicle based on, for example, image data of the area ahead of the vehicle captured by a camera of the periphery monitoring device 53. If it is determined that an oncoming vehicle is present, the margin calculation unit 35 reduces the margin. If it is determined that no oncoming vehicle is present, the margin calculation unit 35 does not change or increases the margin. The margin calculation unit 35 may further determine the size and type (e.g., automobile, heavy machinery, etc.) of the oncoming vehicle and calculate the margin according to the determination result. For example, the margin calculation unit 35 may reduce the margin the larger the oncoming vehicle is, and may reduce the margin if the oncoming vehicle is heavy machinery compared to when the oncoming vehicle is an automobile.

[0036] The margin calculation unit 35 calculates the current margin M based on margins ±M1 based on the calculation results of road width etc., margins ±M2 based on the determination results of road edge conditions, and margins ±M3 based on the presence and type of oncoming vehicle. For example, the margin calculation unit 35 calculates the current margin M by adding margins ±M1 to ±M3 to the initial value of the margin. Alternatively, the margin calculation unit 35 may, for example, calculate the sum of margins ±M1 to ±M3 as margin M at any time.

[0037] (correction gain calculation) The correction gain calculation unit 33 calculates a correction gain based on the determination result of the road surface condition determination unit 34 and the calculation result of the margin calculation unit 35. More specifically, the correction gain calculation unit 33 calculates at least one of an FF correction gain Gff which is a correction gain incorporated in the calculation of the FF target angle αff and an FB correction gain Gfb which is a correction gain incorporated in the calculation of the FB target angle αfb based on the determination result of the road surface condition determination unit 34 and the calculation result of the margin calculation unit 35.

[0038] In this embodiment, when the road surface condition determination unit 34 determines that the road surface is bad, the correction gain calculation unit 33 calculates the FB correction gain based on the margin M calculated by the margin calculation unit 35. When the road surface is not determined to be bad, the correction gain calculation unit 33 sets the FB correction gain to a predetermined value (for example, an initial value) regardless of the margin M.

[0039] In a situation where the road surface is determined to be a bad road (hereinafter also referred to as a "bad road situation"), the correction gain calculation unit 33 calculates and sets at least one of the FB correction gains Gfb so that the tracking ability to the target trajectory becomes weaker as the margin of safety M increases. That is, in a bad road situation, the correction gain calculation unit 33 reduces at least one of the FB correction gains Gfb as the margin of safety M increases. In other words, in a bad road situation, the correction gain calculation unit 33 calculates and sets at least one of the FB correction gains Gfb so that the tracking ability to the target trajectory becomes stronger as the margin of safety M decreases. That is, in a bad road situation, the correction gain calculation unit 33 increases at least one of the FB correction gains Gfb as the margin of safety M decreases. At least one of the FB correction gains Gfb becomes smaller than its initial value as the margin of safety M increases, and becomes larger than its initial value as the margin of safety M decreases. The correction gain calculation unit 33 pre-stores, for example, a map showing the relationship between the margin of safety M and each FB correction gain Gfb.

[0040] For example, as shown in Figure 2, in rough road conditions, if the road is wide, the road edge is made up of embankments, and there are no oncoming vehicles, the FB correction gain will be relatively small, and the ability to follow the target trajectory will be relatively weak. On the other hand, as shown in Figure 3, in rough road conditions, if the road is narrow, there is a cliff with no protective measures on one side of the road, and there are oncoming vehicles, the FB correction gain will be relatively large, and the ability to follow the target trajectory will be relatively strong. If the margin M is different, the FB correction gain (i.e., the ability to follow) will be different even on roads with similar rough surface conditions (for example, even on roads with the same ruts).

[0041] As an overview of autonomous driving, as shown in Figure 4, the autonomous driving ECU 3 calculates a target trajectory from map data, an estimated position of the vehicle, and a destination. The autonomous driving ECU 3 calculates a FF target angle αff based on the target trajectory. The autonomous driving ECU 3 determines the road surface condition and calculates a margin of safety in accordance with recognition of the surrounding situation based on information from various sensors, etc. The autonomous driving ECU 3 calculates a FB correction gain Gfb based on the road surface condition and margin of safety M. The autonomous driving ECU 3 calculates a FB target angle αfb based on the FB correction gain Gfb, lateral deviation δ, yaw angle deviation θy, etc. The autonomous driving ECU 3 calculates a command value αt for the target steering angle, which is the sum of the FF target angle αff and the FB target angle αfb, and transmits the information of the command value αt to the steering ECU 2B.

[0042] To explain an example of the calculation of the FB target angle αff during autonomous driving by the autonomous driving ECU 3, as shown in FIG. 5, first, it is determined whether the road surface is rough (S1). If it is determined that the road surface is rough (S1: Yes), a margin of safety M is calculated based on various road-related data (S2). Next, an FB correction gain Gfb is calculated based on the calculated margin of safety M (S3). Then, the FB target angle αfb is calculated based on the FB correction gain Gfb determined taking the margin of safety M into account, the lateral deviation δ, and the yaw angle deviation θy (S4). On the other hand, if it is not determined that the road surface is rough, i.e., if it is determined that the road surface is not rough (S1: No), the FB correction gain is set to a predetermined value (e.g., an initial value or a normal value) regardless of the margin of safety M (S5). Then, the FB target angle αfb is calculated based on the FB correction gain, lateral deviation δ, and yaw angle deviation θy that are set to the predetermined values ​​(S4).

[0043] The calculation of the correction gain based on the margin M may be applied to the calculation of the FF correction gain Gff. That is, the correction gain calculation unit 33 may be configured to calculate the margin M in a bad road condition and calculate the FF correction gain Gff based on the calculated margin M. The calculation of the correction gain based on the margin M may also be performed when the road surface is not bad. In this case, for example, the margin calculation unit 35 calculates the margin M regardless of the determination result of the road surface condition determination unit 34, and the correction gain calculation unit 33 calculates at least one of the FF correction gain Gff and the FB correction gain Gfb based on the margin M. The margin calculation unit 35 may also change the margin M depending on the determination result of the road surface condition determination unit 34.

[0044] (Effects of this embodiment) According to this embodiment, the correction gain incorporated into the calculation of the target steering angle is calculated based on the road surface condition and the margin M of the vehicle in the left-right direction. In other words, according to this embodiment, the correction gain can be changed depending on whether there is margin or not in the left-right direction of the vehicle. For example, the margin M can be said to be larger the wider the road, and if there is a cliff at the left or right end of the road, the margin M can be said to be small. According to this embodiment, the magnitude of the correction gain can be adjusted according to the calculated margin M, and the strength of the tracking ability to the target trajectory can be adjusted. In other words, according to this embodiment, automatic driving control that is suitable for the conditions of the road and its surroundings is possible.

[0045] For example, as shown in Figure 2, when the road condition is rough but the margin of error M is large, the correction gain can be reduced to avoid excessive tracking control. This allows the vehicle to travel without going against the ruts, suppressing the impact when going over the ruts, preventing a decrease in passenger comfort on rough road conditions and preventing excessive impact on transported goods. In addition, it is possible to suppress an increase in the assist current value for going over the ruts, thereby suppressing an increase in power consumption.

[0046] Furthermore, for example, as shown in FIG. 3, in a situation where the road condition is bad and the margin M is small, it is possible to increase the correction gain and prioritize tracking to the target trajectory. This allows the vehicle to travel along the target trajectory even if it has to go against ruts, enabling driving with a higher priority on safety. In this way, by incorporating the concept of margin M into the calculation of the target steering angle, it is possible to achieve performance (emphasis on comfort and energy saving, or emphasis on safety) that is appropriate for the situation in automatic driving of the vehicle. Correction gain calculation unit 33 can also be considered gain change means that changes the initial correction gain or the correction gain set by another calculation based on margin M.

[0047] (others) The present invention is not limited to the above embodiment. For example, in the above embodiment, two patterns of whether or not the road surface is bad are exemplified as the determination result of the road surface condition by the road surface condition determination unit 34. However, three or more patterns of stepwise determination results may be set, such as a flat road, a slightly bad road, and a very bad road. In this case, the autonomous driving ECU 3 may calculate the margin or correction gain so that it changes stepwise in accordance with the determination result. Furthermore, the steering system 2 may be a steer-by-wire system. Furthermore, the autonomous driving ECU 3 may have a function or configuration (e.g., a drive circuit) that controls the steering device 2A without going through the steering ECU 2B. Furthermore, GPS data may be used to estimate the position of the host vehicle. [Explanation of symbols]

[0048] 1...vehicle control device, 2A...steering device, 2B...steering control unit, 31...target trajectory calculation unit, 32...target steering angle calculation unit, 33...correction gain calculation unit, 34...road surface condition determination unit, 35...margin calculation unit, 53...periphery monitoring device

Claims

1. A steering device for steering the wheels; a target trajectory calculation unit that calculates a target trajectory based on map data, a vehicle position, and a destination in an autonomous driving mode; a target steering angle calculation unit that calculates a target steering angle based on the target trajectory calculated by the target trajectory calculation unit; a correction gain calculation unit that calculates a correction gain to be incorporated into the calculation of the target steering angle by the target steering angle calculation unit; a steering control unit that controls the steering device based on the target steering angle; A vehicle control device comprising: a surroundings monitoring device including a camera for measuring a distance between the host vehicle and an object around the host vehicle; a road surface condition determination unit that determines the road surface condition of the road on which the vehicle is traveling; a margin calculation unit that determines the distance from the host vehicle to the road edge on the road on which the host vehicle is traveling, the presence and type of oncoming vehicle, and the road edge condition based on the detection results of the perimeter monitoring device, the map data, and / or road surface condition data acquired from an external storage device via wireless communication, and calculates the margin of the host vehicle in the lateral direction based on these determination results; Furthermore, the road surface condition determination unit determines whether the road surface is uneven, the margin calculation unit calculates the margin when the road surface condition determination unit determines that the road surface is an uneven road, The roadside conditions include a condition where there is a protective measure at the roadside and a condition where there is no protective measure at the roadside and there is a dangerous terrain; the margin calculation unit decreases the margin relative to an initial value of the margin as the distance from the host vehicle to the road edge is smaller than a value corresponding to the initial value, increases the margin relative to an initial value of the margin as the distance from the host vehicle to the road edge is greater than a value corresponding to the initial value, decreases the margin in accordance with the type of oncoming vehicle when an oncoming vehicle is present, increases the margin or leaves the margin unchanged when no oncoming vehicle is present, decreases the margin when the margin calculation unit determines that the road edge situation is a situation where there is no protective means at the road edge and there is dangerous terrain, and increases the margin or leaves the margin unchanged when the margin calculation unit determines that there is protective means at the road edge, When the road surface condition determination unit determines that the road surface is an uneven road, the correction gain calculation unit sets the correction gain so that the smaller the margin is compared to the initial value, the stronger the ability to follow the target trajectory, and sets the correction gain so that the larger the margin is compared to the initial value, the weaker the ability to follow the target trajectory. Vehicle control device.

2. The margin calculation unit calculates a first margin (±M1) based on the calculation result of the distance from the vehicle to the road edge, a second margin (±M2) based on the result of determining the road edge condition, and a third margin (±M3) based on the presence or absence of an oncoming vehicle and the type of oncoming vehicle, and calculates the margin by adding the first margin, the second margin, and the third margin to the initial value. The vehicle control device according to claim 1 .

3. the road surface condition determination unit determines whether the road surface is uneven based on at least one of the control amount of the steering device by the steering control unit, the wheel speed, the detection result of the vehicle behavior state, the detection result of the periphery monitoring device, and the road surface condition data. The vehicle control device according to claim 1 or 2.

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