Driving assistance device, monitoring device, driving assistance method, and program

Abstract

To provide a driving support device, a driving support method, and a program which can appropriately determine validity of steering avoidance.SOLUTION: A driving support device comprises: a recognition part which recognizes an object existing around a movable body; a first driving support part which performs a first control to avoid contact with the movable body and the recognized object through steering operation; a second driving support part which performs a second control to avoid contact with the movable body and the recognized object through braking operation; and a management part which derives an estimated value of a yaw rate generated in the movable body based on reference information including at least a plurality of kinds of information different from measured value of the yaw rate outputted from a yaw rate sensor mounted on the movable body, and suppresses the first control when deviation between the measured value and the estimated value is larger than a reference value.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a driving assistance device, a monitoring device, a driving assistance method, and a program. [Background technology]

[0002] 2. Description of the Related Art Conventionally, there is known a device that determines that the vehicle behavior is unstable when the difference between a target yaw rate and an actual yaw rate (measured by a yaw rate sensor) is large (Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] International Publication No. 2018 / 230376 Summary of the Invention [Problem to be solved by the invention]

[0004] In the above-mentioned conventional technology, it is not determined whether the actual yaw rate itself is appropriate or not, so that there are cases where the appropriateness of steering to avoid an accident cannot be appropriately determined.

[0005] The present invention has been made in consideration of the above circumstances, and one of its objectives is to provide a driving assistance device, a driving assistance method, and a program that are capable of appropriately determining the appropriateness of steering avoidance. [Means for solving the problem]

[0006] A driving assistance device, a monitoring device, a driving assistance method, and a program according to the present invention employ the following configuration. (1): A driving assistance device according to one embodiment of the present invention includes a recognition unit that recognizes objects present in the vicinity of a moving body, a first driving assistance unit that performs a first control to avoid contact between the moving body and the recognized object by steering, a second driving assistance unit that performs a second control to avoid contact between the moving body and the recognized object by braking, and a management unit that derives an estimate of a yaw rate generated in the moving body based on reference information including at least multiple types of information different from a measured yaw rate output by a yaw rate sensor mounted on the moving body, and suppresses the first control when a deviation between the measured value and the estimated value is greater than a reference value.

[0007] (2): In the aspect of (1) above, the management unit derives the estimated value by inputting the reference information collected in the moving body into a trained model that has been trained to output an estimated value of the yaw rate when the reference information is input.

[0008] (3): In the aspect of (2) above, the management unit selects one or more trained models that output a value close to the measured value from among a plurality of trained models trained under different conditions, and derives the estimated value based on the output of the selected one or more trained models.

[0009] (4): In the above aspect (3), the condition includes at least one of a state condition that indicates a state of the moving object and an environmental condition that indicates an environment in which the moving object is placed.

[0010] (5): In any of the above aspects (1) to (4), the reference information includes at least a portion of output information of a steering angle sensor mounted on the moving body, output information of a speed sensor mounted on the moving body, and control information output by a movement control device of the moving body.

[0011] (6) In any one of the above aspects (1) to (5), the reference information includes time-series information spanning control cycles at multiple points in time.

[0012] (7): A monitoring device according to another aspect of the present invention includes a derivation unit that derives an estimated value of a yaw rate occurring in a moving body based on reference information including at least multiple types of information different from a measured yaw rate output by a yaw rate sensor mounted on the moving body, and a comparison unit that outputs predetermined information when the deviation between the measured value and the estimated value is greater than a reference value.

[0013] (8): A driving assistance method according to another aspect of the present invention includes a computer that recognizes an object present in the vicinity of a moving body, performs a first control to avoid contact between the moving body and the recognized object by steering, performs a second control to avoid contact between the moving body and the recognized object by braking, derives an estimate of the yaw rate generated in the moving body based on reference information including at least multiple types of information different from a measured yaw rate output by a yaw rate sensor mounted on the moving body, and suppresses the first control when a deviation between the measured value and the estimated value is greater than a reference value.

[0014] (9): A program according to another aspect of the present invention causes a computer to recognize objects present in the vicinity of a moving body, to perform a first control for steering to avoid contact between the moving body and the recognized object, to perform a second control for braking to avoid contact between the moving body and the recognized object, to derive an estimate of the yaw rate occurring in the moving body based on reference information including at least multiple types of information different from a measured yaw rate output by a yaw rate sensor mounted on the moving body, and to suppress the first control when the deviation between the measured value and the estimated value is greater than a standard. Effect of the Invention

[0015] According to the above aspects (1) to (9), the appropriateness of steering avoidance can be appropriately determined. [Brief description of the drawings]

[0016] [Figure 1]FIG. 1 is a configuration diagram mainly showing a driving assistance device 100. [Diagram 2] FIG. 4 is a diagram showing an example of a situation in which the first control and the second control are selectively performed. [Diagram 3] FIG. 13 is a diagram showing an example of the contents of processing performed by a derivation unit 152. [Figure 4] 13 is a flowchart showing an example of the flow of processing executed by a management unit 150. [Diagram 5] 13 is a flowchart showing another example of the flow of the process executed by the management unit 150. [Figure 6] FIG. 1 is a diagram showing an example of the relationship between a trained model 172(i, j, k) and the environment during training. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0017] Hereinafter, with reference to the drawings, an embodiment of the driving assistance device, the monitoring device, the driving assistance method, and the program of the present invention will be described. The driving assistance device and the monitoring device are mounted on a moving body. The moving body is, for example, a four-wheeled vehicle, a two-wheeled vehicle, a micromobility, a robot, or the like that moves by itself, or a portable device such as a smartphone that is placed on a moving body that moves by itself or is carried by a person. In the following description, the moving body is a four-wheeled vehicle, and the moving body is referred to as "vehicle M". The monitoring device may operate independently or may be included in the driving assistance device. In the following description, the monitoring device is included in the driving assistance device.

[0018] The driving assistance device selectively performs a first control for avoiding contact between a moving body and an object by steering, and a second control for avoiding contact between a moving body and an object by braking, or performs one or both simultaneously depending on the situation.

[0019] "Avoiding by steering" may mean, for example, (1) indirectly avoiding contact with the object by communicating the steering direction to the driver by voice or image, (2) avoiding contact with the object by applying a reaction force or vibration to the steering wheel (steering operator) against steering in a direction toward the object, as is done in lane keeping control known as LKAS (Lane Keeping Assist System), or (3) a driving assistance device performing autonomous steering control on the steering device to avoid contact with the object (so-called automatic driving). In the following description, it is assumed that the first control is performed by the method of (1).

[0020] "Avoiding by braking" may mean, for example, either (A) using manual driving as a basis under normal circumstances, and performing intervention control to output braking force to a brake device or the like only when there is a possibility of contact with an object, or (B) a driving assistance device performing autonomous braking control on a brake device or the like to avoid contact with an object (so-called automatic driving). In the following explanation, it is assumed that the second control is performed by the method of (A).

[0021] First Embodiment The first embodiment will be described below. FIG. 1 is a configuration diagram mainly showing a driving support device 100. In addition to the driving support device 100, a camera 10, a radar device 12, a LIDAR (Light Detection and Ranging) 14, an object recognition device 16, a vehicle sensor 40, a control device 50, an HMI (Human Machine Interface) 60, a brake device 70, and the like are mounted on a vehicle M. These devices and equipment are connected to each other by a multiplex communication line such as a CAN (Controller Area Network) communication line, a serial communication line, a wireless communication network, and the like. Note that the configuration shown in FIG. 1 is merely an example, and a part of the configuration may be omitted, or another configuration may be added. For example, the vehicle M may also be mounted with a driving operator such as an accelerator pedal or a brake pedal, a driving force output device such as an engine or a motor, and a configuration required for manual driving such as a steering device, but illustration and detailed description of these will be omitted.

[0022] The camera 10 is, for example, a digital camera using a solid-state imaging element such as a CCD (Charge Coupled Device) or a CMOS (Complementary Metal Oxide Semiconductor). The camera 10 is attached to any location of the vehicle M. When capturing an image of the front, the camera 10 is attached to the top of the front windshield, the back of the rearview mirror, or the like. The camera 10, for example, periodically and repeatedly captures images of the periphery of the vehicle M. The camera 10 may be a stereo camera.

[0023] The radar device 12 emits radio waves such as millimeter waves around the vehicle M and detects radio waves reflected by an object (reflected waves) to detect at least the position (distance and direction) of the object. The radar device 12 is attached to an arbitrary location of the vehicle M. The radar device 12 may detect the position and speed of an object by an FM-CW (Frequency Modulated Continuous Wave) method.

[0024] The LIDAR 14 irradiates light (or electromagnetic waves with a wavelength close to that of light) around the vehicle M and measures the scattered light. The LIDAR 14 detects the distance to the target based on the time between light emission and light reception. The irradiated light is, for example, a pulsed laser light. The LIDAR 14 is attached to an arbitrary location of the vehicle M.

[0025] The object recognition device 16 performs sensor fusion processing on some or all of the detection results from the camera 10, the radar device 12, and the LIDAR 14 to recognize the position, type, speed, etc. of the object. The object recognition device 16 outputs the recognition result to the driving assistance device 100. The object recognition device 16 may output the detection results from the camera 10, the radar device 12, and the LIDAR 14 directly to the driving assistance device 100. In addition, the object recognition device 16 does not need to be installed in the vehicle M.

[0026] The vehicle sensor 40 includes at least a yaw rate sensor 42 that detects the angular velocity around the vertical axis of the vehicle M. The "vertical axis" is an axis relative to the body of the vehicle M that would be the vertical axis if no pitch or roll occurs. The yaw rate sensor 42 is provided near the center of gravity of the vehicle M as viewed from above.

[0027] The vehicle sensor 40 may further include a steering angle sensor that detects the steering angle of the vehicle M (which may be the angle of the steering wheels or the operating angle of the steering wheel), a speed sensor that detects the speed of the vehicle M, a steering torque sensor that detects the torque applied to the steering wheel of the vehicle M, etc.

[0028] The control device 50 is a control device that controls a driving force output device such as an engine or a motor. The control device 50 also outputs control information (e.g., control information indicating a target driving force) to be given to the driving force output device to the driving assistance device 100. The control device 50 is an example of a "mobility control device" in the claims.

[0029] The HMI 60 presents various information to an occupant of the vehicle M and accepts input operations by the occupant. The HMI 60 includes a speaker, a display device (including a HUD (Head Up Display)), a buzzer, a touch panel, switches, keys, and the like.

[0030] The braking device 70 includes a brake control device, a brake actuator, and the like. The brake actuator may be, for example, an electronically controlled brake actuator, and may be capable of outputting a braking force regardless of the amount of operation of the brake pedal. The braking device 70 outputs a braking force to the wheels of the vehicle M based on the amount of operation performed on the brake pedal, or autonomously outputs a braking force to the wheels of the vehicle M in response to an instruction from the driving assistance device 100. In addition, the brake control device of the braking device 70 also outputs control information (for example, control information indicating a target braking force) to be given to the brake actuator to the driving assistance device 100. The braking device 70 may be another example of a "mobility control device" in the claims.

[0031] The driving support device 100 includes, for example, a recognition unit 110, a first driving support unit 120, a second driving support unit 130, and a management unit 150. The management unit 150 includes a derivation unit 152 and a comparison unit 154. These components are realized by, for example, a hardware processor such as a CPU (Central Processing Unit) executing a program (software). Some or all of these components may be realized by hardware (including circuitry) such as an LSI (Large Scale Integration), an ASIC (Application Specific Integrated Circuit), an FPGA (Field-Programmable Gate Array), or a GPU (Graphics Processing Unit), or may be realized by cooperation between software and hardware. The program may be stored in advance in a storage device (a storage device having a non-transient storage medium) such as an HDD (Hard Disk Drive) or a flash memory, or may be stored in a removable storage medium (a non-transient storage medium) such as a DVD or a CD-ROM, and may be installed by mounting the storage medium in a drive device. The management unit 150 is an example of a "monitoring device" in the claims. The illustrated configuration is merely an example, and the management unit 150 may be included in the first driving support unit. The driving support device 100 further includes a storage unit 170. The storage unit 170 may be a storage medium that stores the above-mentioned program, or may be another storage medium. The storage unit 170 stores a trained model 172. The storage unit 170 is, for example, a HDD, a flash memory, a RAM (Random Access Memory), or the like.

[0032] The recognition unit 110 recognizes the position, speed, acceleration, and other states of objects around the vehicle M based on information input from the camera 10, the radar device 12, and the LIDAR 14 via the object recognition device 16. The position of the object is recognized as a position on a virtual plane seen from above, with a representative point of the vehicle M (such as the center of gravity or the center of the drive shaft) as the origin, and is used for control. The position of the object may be represented by a representative point such as the center of gravity or a corner of the object, or may be represented by a represented area. The "state" of the object may include the acceleration or jerk of the object, or the "behavioral state" (for example, whether or not the object is changing lanes or is about to change lanes).

[0033] Further, the recognition unit 110 recognizes, for example, the lane in which the vehicle M is traveling (driving lane). For example, the recognition unit 110 recognizes the driving lane based on the position of the road dividing line around the vehicle M recognized from the image captured by the camera 10. Note that the recognition unit 110 may recognize the driving lane by recognizing a road boundary (road boundary) including not only the road dividing line but also the road dividing line, the road shoulder, the curb, the median strip, the guard rail, and the like. When recognizing the driving lane, the recognition unit 110 recognizes the position and attitude of the vehicle M with respect to the driving lane. For example, the recognition unit 110 may recognize the deviation of the reference point of the vehicle M from the center of the lane and the angle with respect to the line connecting the lane centers in the traveling direction of the vehicle M as the relative position and attitude of the vehicle M with respect to the driving lane. Alternatively, the recognition unit 110 may recognize the position of the reference point of the vehicle M with respect to either side end of the driving lane (road dividing line or road boundary) as the relative position of the vehicle M with respect to the driving lane.

[0034] The first driving support unit 120 performs a first control for steering the vehicle M to avoid contact with the object recognized by the recognition unit 110. As the first control, the first driving support unit 120 generates a target trajectory that can avoid contact with the object, and outputs a voice such as "Turn the steering wheel to the right (left)" to a speaker until the steering angle is along the target trajectory, thereby guiding the driver's driving operation. The first driving support unit 120 may also perform a similar guidance by, for example, displaying an image indicating a direction on the HUD. If the steering angle is not along the target trajectory within a predetermined time or a predetermined distance after starting the guidance, the first driving support unit 120 decides to stop the first control and transmits information indicating that to the second driving support unit 130. Note that the target trajectory that can avoid contact with the object may be within the own lane (the lane in which the vehicle M is currently located) or may involve changing lanes to another lane. In the latter case, the conditions for continuing the first control may be set stricter than in the former case.

[0035] The second driving support unit 130 performs a second control for avoiding contact between the vehicle M and the object recognized by the recognition unit 110 by braking. As the second control, the second driving support unit 130 determines a target stop position for stopping the vehicle M with a certain margin of distance from the object with which it is difficult to avoid contact by steering, and determines a braking force (brake torque) based on the deceleration calculated on the assumption that the deceleration is constant and the speed of the vehicle M, and outputs the determined braking force to the brake device 70. The second driving support unit 130 may also use engine braking or regenerative braking of the motor. The second driving support unit 130 may start the second control when information to stop the first control is obtained, or may execute the second control regardless of the operating state of the first driving support unit 120. In the latter case, the second driving support unit 130 judges whether or not the object is "an object with which the vehicle M has difficulty in avoiding contact by steering" based on indicators such as the amount of overlap (lap amount) between the predicted path of the vehicle M and the object, TTC (Time To Collision), etc. Even if the vehicle M is able to pass beside the object by the first control, the autonomous deceleration control may be performed in addition just to be sure.

[0036] 2 is a diagram showing an example of a situation in which the first control and the second control are selectively performed. In the diagram, OB is an object (here, a parked vehicle), and K is a "target trajectory capable of avoiding contact with the object." As shown in the diagram, if the driver of the vehicle M does not follow the guidance and does not steer to the right, the second driving support unit 130 automatically stops the vehicle M.

[0037] The management unit 150 judges whether or not an abnormality occurs in the measuring means such as the yaw rate sensor 42, and suppresses the first control when it is judged that an abnormality occurs. "Suppressing the first control" may mean stopping the first control, or may mean stopping the first control when it is judged that an abnormality occurs multiple times in succession, or stopping only the first control that involves a lane change to another lane. In the following description, "suppressing the first control" means stopping the first control.

[0038] The derivation unit 152 of the management unit 150 derives an estimated value Yr* of the yaw rate generated in the vehicle M based on reference information including at least a plurality of types of information different from the measured value Yr of the yaw rate output by the yaw rate sensor 42. The reference information includes, for example, at least a portion of the output information θ of the steering angle sensor, the output information V of the speed sensor, and the control information output by the control device 50 or the brake device 70. The reference information may also include information on the weight carried by the vehicle M, such as the number of passengers (which may be an estimated value or a value detected by an in-vehicle camera or the like) and the remaining energy. Furthermore, the reference information may include time-series information spanning a control cycle at a plurality of points in time. The control cycle is a time unit that serves as a reference when the management unit 150 performs repeated processing.

[0039] FIG. 3 is a diagram showing an example of the contents of the processing of the derivation unit 152. For example, the derivation unit 152 derives the estimated value Yr* of the yaw rate by inputting the output information θ(k), θ(k-1), ... θ(kn) of the steering angle sensor, the output information V(k), V(k-1), ..., V(km) of the speed sensor, and the control information into the learned model 172. θ is the steering angle, and V is the speed. (k) indicates a value collected in the current control cycle, (k-1) indicates a value collected in the control cycle one control cycle before, (kn) indicates a value collected in the control cycle n times before, and (km) indicates a value collected in the control cycle m times before. Similarly, the control information may be time-series information spanning multiple control cycles. When the time-series information is not input to the learned model 172, it is preferable that the learned model 172 is a recursive model such as an RNN (Recurrent Neural Network).

[0040] The trained model 172 is, for example, a deep neural network (DNN), a radial basis function network (RBFN), or other network-type model. The trained model 172 is trained to output an estimated yaw rate value Yr* when reference information is input. In this learning process, for example, reference information collected in an experimental vehicle of the same model as the vehicle M is used as learning data, and measurements by a yaw rate sensor that is installed in the experimental vehicle and confirmed to be in a normal state are used as teacher data.

[0041] The comparison unit 154 suppresses the first control when the deviation between the measurement value Yr of the yaw rate sensor 42 and the estimated value Yr* of the yaw rate derived by the derivation unit 152 is larger than a reference value. "The deviation is larger than a reference value" means, for example, that the absolute value of the difference is larger than a threshold value.

[0042] 4 is a flowchart showing an example of the flow of processing executed by the management unit 150. The processing of this flowchart is repeatedly executed for each control cycle.

[0043] First, the management unit 150 acquires the reference information and the measurement value Yr of the yaw rate sensor 42 (step S200). The reference information is stored in the storage unit 170 as time-series information and is used in subsequent processes. Next, the derivation unit 152 derives the estimated value Yr* of the yaw rate by inputting the reference information acquired in the current or previous control cycle to the learned model 172 (step S202). Next, the comparison unit 154 determines whether the deviation between the measurement value Yr of the yaw rate sensor 42 and the estimated value Yr* of the yaw rate derived by the derivation unit 152 is greater than a reference value (step S204). If the deviation between the measurement value Yr of the yaw rate sensor 42 and the estimated value Yr* of the yaw rate is greater than a reference value, the comparison unit 154 notifies at least the first driving support unit 120 to suppress the first control (step S206).

[0044] The management unit 150 may simply repeat the processing of the flowchart shown in FIG. 4 for each control cycle, or may adjust the frequency of the judgment process, etc., based on a history of a determination that the deviation between the measurement value Yr of the yaw rate sensor 42 and the estimated yaw rate value Yr* is greater than a reference value.

[0045] 5 is a flowchart showing another example of the flow of processing executed by the management unit 150. The processing of this flowchart is repeatedly executed in parallel with the processing of the flowchart of FIG.

[0046] First, the management unit 150 determines whether or not the deviation between the measurement value Yr of the yaw rate sensor 42 and the estimated value Yr* of the yaw rate derived by the derivation unit 152 is determined to be greater than a reference value in the process of step S204 in FIG. 4 (step S300).

[0047] When it is determined that the deviation between the measurement value Yr of the yaw rate sensor 42 and the estimated value Yr* of the yaw rate derived by the derivation unit 152 is larger than the reference value, the management unit 150 determines whether the determination result is the same as the previous time (step S302). When it is determined that the determination result is the same as the previous time, the management unit 150 counts up the first counter (for example, increments by 1) (step S304). Next, the management unit 150 determines whether the value of the first counter is equal to or greater than a first reference value C1 (step S306). The first reference value C1 is, for example, a value of several times to several tens of times. When it is determined that the value of the first counter is equal to or greater than the first reference value C1, the management unit 150 stops the first control, reduces the frequency (determination frequency) of the determination process of steps S200 to S204 in FIG. 4, and resets (sets to zero) the value of the second counter (step S308). For example, the management unit 150 reduces the frequency of judgment to every few seconds to every few minutes, whereas the judgment process is normally performed every few milliseconds. In this state, the judgment process is performed on the deviation between the measured value Yr of the yaw rate sensor 42 and the estimated value Yr* of the yaw rate derived by the derivation unit 152, but the first control is not performed regardless of the judgment result. This is because the deviation was large several times in succession, and it is estimated that there is a high probability that a failure or the like has occurred in the yaw rate sensor 42 or related devices.

[0048] Furthermore, the management unit 150 judges whether the value of the first counter is equal to or greater than a second reference value C2 that is greater than the first reference value C1 (step S310). If it is judged that the value of the first counter is equal to or greater than the second reference value C2, the management unit 150 causes the HMI 60 to output information prompting repair of the yaw rate sensor 42 or related devices (step S312).

[0049] When it is determined that the deviation between the measurement value Yr of the yaw rate sensor 42 and the estimated value Yr* of the yaw rate derived by the derivation unit 152 is equal to or smaller than the reference value, the management unit 150 determines whether the determination result is the same as the previous time (step S314). When it is determined that the determination result is the same as the previous time, the management unit 150 counts up the second counter (for example, increments by 1) (step S316). Next, the management unit 150 determines whether the value of the second counter is equal to or larger than the third reference value C3 (step S318). When it is determined that the value of the second counter is equal to or larger than the third reference value C3, the management unit 150 determines whether the first control is stopped and the determination frequency is decreasing by the process of step S308 (step S320). When a positive determination result is obtained in the process of step S320, the management unit 150 restores the first control and returns the determination frequency to the original value (step S322). Then, the management unit 150 resets the values ​​of the first counter and the second counter (step S324). Through this process, the first control can be temporarily suspended while the vehicle M is traveling on a road with a bad road surface condition for a certain period of time, and then the first control can be resumed after it is sufficiently confirmed that the road condition has returned to a normal state.

[0050] If it is determined in step S302 or S314 that the determination result is not the same as the previous time, the process of step S324 is performed. If a negative determination result is obtained in step S320, the management unit 150 ends one cycle of the process of this flowchart.

[0051] According to the first embodiment described above, an estimated value Yr* of the yaw rate occurring in the vehicle M is derived based on reference information including at least multiple types of information different from the measured yaw rate Yr output by the yaw rate sensor 42 mounted on the vehicle M, and the first control is suppressed when the deviation between the measured value and the estimated value is greater than a reference value, so that the appropriateness of steering avoidance can be appropriately determined and vehicle control can be performed.

[0052] <Second embodiment> The second embodiment will be described below. In the first embodiment, one model is used as the trained model 172, but in the second embodiment, a plurality of trained models trained under different conditions are used.

[0053] The trained model used in the second embodiment is denoted as trained model 172(i, j, k). Each of the parameters i, j, and k indicates a condition when the trained model was trained, and is a natural number with an upper limit. In the following description, it is assumed that there are three conditions, but the number of conditions may be any number.

[0054] The conditions include at least one of state conditions that represent the state of the vehicle M and environmental conditions that represent the environment in which the vehicle M is placed. The state conditions include, for example, tire air pressure, accumulated mileage, and the like. This is because tire air pressure directly affects the turning sensitivity to steering operations, and accumulated mileage indirectly affects the degree of turning by inducing suspension fatigue. The environmental conditions include, for example, road surface conditions. This is because when road surface conditions deteriorate (road surface μ decreases) due to rainfall, unpaved roads, freezing, and the like, the degree of turning relative to the steering angle changes.

[0055] 6 is a diagram showing an example of the relationship between trained models 172(i, j, k) and the environment at the time of training. As shown in the figure, trained model 172(1, 1, 1) is trained under the conditions (tire pressure: high, cumulative mileage: short, road surface μ: high), trained model 172(2, 1, 1) is trained under the conditions (tire pressure: medium, cumulative mileage: short, road surface μ: high), and trained models 172(i, j, k) are prepared for all parameter combinations in the same manner.

[0056] The management unit 150 of the second embodiment selects one or more trained models 172(i, j, k) that output a value close to the measurement value Yr of the yaw rate sensor 42, and derives an estimated value based on the output of the selected one or more trained models 172(i, j, k). Specifically, the management unit 150 of the second embodiment may select a small number of trained models 172(i, j, k) that are known to output a value close to the measurement value Yr of the yaw rate sensor 42 based on the processing result of the previous time or earlier, derive an estimated value, and use it for the judgment, or may derive an estimated value Yr* of the yaw rate using some or all of the trained models 172(i, j, k), and select a value close to the measurement value Yr of the yaw rate sensor 42 from among them and use it for the judgment.

[0057] As an example, the management unit 150 defines a space consisting of a plurality of conditions, and, for example, allows switching up to adjacent conditions, and judges that an abnormality exists when switching of more conditions is required. For example, when the learned model 172(2,2,2) was selected in the previous process, the management unit 150 allows switching to the learned models 172(1,2,2), (3,2,2), (2,1,2), (2,3,2), (2,2,1), and (2,2,3), and when the deviation between the estimated value Yr* of the yaw rate derived by the derivation unit 152 using any of the allowed learned models 172(i,j,k) and the measured value Yr of the yaw rate sensor 42 is equal to or less than a reference value, the management unit 150 may judge that "the deviation is equal to or less than a reference value," and when not, may judge that "the deviation is greater than a reference value."

[0058] In the above description, any one of the trained models 172(i, j, k) is selected, and a determination is made as to the deviation between the estimated yaw rate value Yr* derived by the selected trained model 172(i, j, k) and the measured value Yr of the yaw rate sensor 42. However, the management unit 150 may determine the weighted sum of the estimated yaw rate values ​​Yr*(1), Yr*(2), Yr*(3), ... derived by any multiple trained models 172(i, j, k) as the estimated yaw rate value Yr*. In this case, the range of the trained models 172(i, j, k) that are allowed to be switched may be determined based on, for example, the distance from the center of gravity position indicated by the weight of the weighted sum.

[0059] According to the second embodiment described above, in addition to achieving the same effects as the first embodiment, the appropriateness of steering avoidance can be appropriately determined using an appropriate trained model 172(i, j, k) corresponding to the aging of the vehicle M, and vehicle control can be performed accordingly.

[0060] The above-described embodiment can be expressed as follows. A storage device storing a program; a hardware processor; The hardware processor executes the program stored in the storage device, Recognizes objects around the moving object, performing a first control for avoiding contact between the moving body and the recognized object by steering; performing a second control for avoiding contact between the moving body and the recognized object by braking; deriving an estimated value of a yaw rate generated in the moving body based on reference information including at least a plurality of types of information different from a measured value of a yaw rate output by a yaw rate sensor mounted on the moving body, and suppressing the first control when a deviation between the measured value and the estimated value is larger than a reference value; A driving assistance device configured as above.

[0061] The above describes the form for carrying out the present invention using an embodiment, but the present invention is not limited to such an embodiment, and various modifications and substitutions can be made within the scope that does not deviate from the gist of the present invention. [Explanation of symbols]

[0062] 100 Driving support device 110 Recognition part 120 First Driving Support Division 130 2nd Driving Support Division 150 Management Department (Monitoring Device) 152 Derivation part 154 Comparison Section 170 Storage section 172 trained models

Claims

1. A recognition unit that recognizes objects present around the moving object; a first driving support unit that performs a first control to avoid contact between the moving body and the recognized object by steering; a second driving support unit that performs a second control to avoid contact between the moving body and the recognized object by braking; a management unit that derives an estimated value of a yaw rate generated in the moving body based on reference information including at least a plurality of types of information different from a measured value of a yaw rate output by a yaw rate sensor mounted on the moving body, and suppresses the first control when a deviation between the measured value and the estimated value is larger than a reference value; the management unit selects one or more trained models that output a value close to the measurement value from among a plurality of trained models trained under different conditions as a trained model trained to output the estimated value when the reference information is input, and derives the estimated value based on an output of the one or more selected trained models when the reference information collected in the mobile object is input to the one or more selected trained models. Driving assistance device.

2. The condition includes at least one of a state condition that represents a state of the moving object and an environmental condition that represents an environment in which the moving object is placed. The driving assistance device according to claim 1.

3. the reference information includes at least a part of output information of a steering angle sensor mounted on the moving body, output information of a speed sensor mounted on the moving body, and control information output by a movement control device of the moving body; A driving assistance device according to claim 1 or 2.

4. The reference information includes time-series information spanning control cycles at multiple points in time. A driving assistance device according to any one of claims 1 to 3.

5. The method further comprises a comparison unit that outputs predetermined information when a deviation between the measured value and the estimated value is greater than a reference value. A driving assistance device according to any one of claims 1 to 4.

6. The computer Recognizes objects around the moving object, performing a first control for avoiding contact between the moving body and the recognized object by steering; performing a second control for avoiding contact between the moving body and the recognized object by braking; Deriving an estimated value of a yaw rate occurring in the moving body based on reference information including at least a plurality of types of information different from a measured value of a yaw rate output by a yaw rate sensor mounted on the moving body; suppressing the first control when a deviation between the measured value and the estimated value is greater than a reference value; The computer, selecting one or more trained models that output a value close to the measurement value from among a plurality of trained models trained under different conditions as a trained model trained to output an estimated value when the reference information is input, and deriving the estimated value based on an output of the one or more selected trained models when the reference information collected in the mobile object is input to the one or more selected trained models; Driving assistance methods.

7. On the computer, Recognize objects around the moving object, performing a first control for avoiding contact between the moving body and the recognized object by steering; performing a second control for avoiding contact between the moving body and the recognized object by braking; Deriving an estimated value of a yaw rate occurring in the moving body based on reference information including at least a plurality of types of information different from a measured value of a yaw rate output by a yaw rate sensor mounted on the moving body; suppressing the first control when a deviation between the measured value and the estimated value is greater than a reference value; The computer includes: selecting one or more trained models that output a value close to the measured value from among a plurality of trained models trained under different conditions as a trained model trained to output an estimated value when the reference information is input, and causing the selected one or more trained models to derive the estimated value based on an output of the selected one or more trained models when the reference information collected in the mobile object is input; program.

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