Yaw rate sensor anomaly detection device

Abstract

To provide a highly practical yaw rate anomaly detection device. [Solution] In a yaw rate sensor abnormality determination device 10 mounted on a vehicle and used to determine abnormalities in a yaw rate sensor 30 provided by the vehicle, an abnormality determination unit 50 is provided that compares the detected yaw rate γd detected by the yaw rate sensor with the estimated yaw rate estimated based on the vehicle's behavior (Gy, vw) and operation (δ) to determine an abnormality detected by the yaw rate sensor. In addition, a determination prohibition unit 52 is provided that, based on images from a camera 38 that captures images of the front of the vehicle, prohibits the determination if it determines that the abnormality determination by the abnormality determination unit is inappropriate.

Description

Technical Field

[0001] The present invention relates to a yaw rate sensor abnormality determination device that determines an abnormality of a yaw rate sensor mounted on a vehicle.

Background Art

[0002] The yaw rate of a vehicle is used, for example, for vehicle stability control (VSC control) to ensure proper turning of the vehicle, control of rear wheel steering of a four-wheel steering vehicle, etc. Therefore, it is desirable to appropriately determine an abnormality of a yaw rate sensor that detects the yaw rate. In this regard, for example, there is a technique described in the following patent document. This technique uses the lower of the yaw rate estimated from the vehicle speed and the minimum turning radius of the vehicle and the yaw rate estimated from the lateral acceleration generated in the vehicle as a threshold yaw rate for determination, and when the yaw rate detected by the yaw rate sensor exceeds the threshold yaw rate, it is determined that the yaw rate sensor is abnormal.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] According to the technology described in the above-mentioned patent document, for example, when a vehicle turns on a road surface with extremely low surface friction coefficient (μ), such as ice, the yaw rate estimated from the lateral acceleration is extremely low, and therefore, that yaw rate is always recognized as the threshold yaw rate. As a result, when performing a four-wheel drift turn on such a road surface, the yaw rate sensor will be judged as abnormal even though it is not abnormal. The technology described in the above-mentioned patent document still has this problem. If the abnormality of the yaw rate sensor can be properly determined, the practicality of the yaw rate sensor abnormality determination device will be improved. The present invention has been made in view of such circumstances, and aims to provide a highly practical yaw rate abnormality determination device. [Means for solving the problem]

[0005] To solve the above problems, the yaw rate sensor abnormality determination device of the present invention is A yaw rate sensor abnormality detection device that is mounted on a vehicle and determines abnormalities in the yaw rate sensor provided by that vehicle, An abnormality determination unit that compares the detected yaw rate detected by the yaw rate sensor with the estimated yaw rate estimated based on the vehicle's behavior and operation to determine an abnormality detected by the yaw rate sensor, Based on the image from the camera capturing the area in front of the vehicle, if the abnormality determination unit determines that the abnormality determination is inappropriate, the determination prohibition unit prohibits that determination. It is equipped with. [Effects of the Invention]

[0006] According to the yaw rate sensor abnormality determination device of the present invention (hereinafter sometimes abbreviated as "this abnormality determination device"), the determination prohibition unit prevents the abnormality determination unit from determining an abnormality of the yaw rate sensor when the vehicle is turning on a road surface with extremely low road surface friction coefficient, such as ice. This prevents misjudgments, that is, situations in which the yaw rate sensor is determined to be abnormal even though it is not. (Aspects of the Invention)

[0007] The "estimated yaw rate" used in the "abnormality determination unit" of this abnormality detection device is not particularly limited, as long as it is estimated based on the vehicle's behavior and operation. For example, various types can be used, such as an operation-dependent estimated yaw rate estimated based on the amount of operation of an operating member such as a steering wheel and the vehicle speed, a lateral acceleration-dependent estimated yaw rate estimated based on the lateral acceleration occurring in the vehicle, and a wheel speed difference-dependent estimated yaw rate estimated based on the peripheral speed (or rotational speed) of the left and right wheels. The number of estimated yaw rates used is not limited to just one, but may be two or more.

[0008] Anomaly detection can be performed, for example, as follows: The estimated yaw rate can be determined to be abnormal if the difference between the detected yaw rate and either the estimated yaw rate based on lateral acceleration or the estimated yaw rate based on wheel speed difference is greater than the first set difference, and the difference between the estimated yaw rate based on lateral acceleration and the estimated yaw rate based on wheel speed difference is smaller than the second set difference. In short, the detection by the yaw rate sensor can be determined to be abnormal if the two estimated yaw rates are roughly the same, but there is a certain difference between either of them and the detected yaw rate. This makes it possible to appropriately determine abnormalities in the detection by the yaw rate sensor.

[0009] When images from a camera are repeatedly acquired at set time intervals (for example, several milliseconds to tens of milliseconds), the "determination prohibition unit" of this abnormality detection device may make a determination according to the following method, for example. Specifically, for example, while the vehicle is turning, the previous image and the current image are compared, and if the number of pixels whose pixel value has changed (hereinafter sometimes referred to as "value-changing pixels") is less than or equal to a set number, the determination of whether or not there is an abnormality detected by the yaw rate sensor should be prohibited. For example, when a vehicle is traveling on a normal road surface, pixels corresponding to lane markings that demarcate the driving lane, course, etc., move, so the number of value-changing pixels becomes considerably large. On the other hand, when traveling on ice, compacted snowfields, etc. (hereinafter sometimes referred to as "ice, etc."), the above-mentioned lane markings do not exist, and the number of value-changing pixels becomes small. The above method utilizes this phenomenon. The "pixel value" mentioned above can be any value representing brightness. For example, the grayscale value obtained by converting each pixel that makes up the image, that is, the gradation value divided into predetermined steps (for example, 256 steps), can be used.

[0010] When employing the above method, it is desirable for the judgment prohibition unit to exclude non-road surface areas from the camera image and determine whether detection by the yaw rate sensor is inappropriate. Simply put, the upper part of the image contains buildings, trees, distant scenery, clouds in the sky, etc., which become noise in the above judgment. Specifically, since their positions constantly change in the left-right direction within the image during vehicle turns, images containing them will have a considerably large number of pixels with changing values. Taking this into consideration, for example, it would be advisable to use the lower half of the image.

[0011] Furthermore, when employing the above method, even on surfaces such as ice or snowfields, some minute changes in the road surface exist. Therefore, in order to eliminate the influence of these changes, it is desirable to change the "number of setting pixels" for the number of value-changing pixels according to the height of the yaw rate. More specifically, for example, if the yaw rate is high, the number of setting pixels should be set to a large value, and if the yaw rate is low, the number of setting pixels should be set to a small value. Note that the yaw rate used here may be the detected yaw rate or any of the estimated yaw rates mentioned above. However, even if the value of the yaw rate used is an abnormal value, it is desirable to change the number of setting pixels to a value that allows the judgment prohibition unit to make an appropriate judgment. [Brief explanation of the drawing]

[0012] [Figure 1] This is a schematic diagram showing the configuration of a vehicle equipped with the yaw rate sensor abnormality detection device of the embodiment. [Figure 2] This is a conceptual diagram to explain how to judge the appropriateness of anomaly detection using a camera. [Figure 3] This is a conceptual diagram illustrating the influence of changes in the road surface on the assessment of the appropriateness of anomaly detection by cameras. [Figure 4] This is a flowchart of the abnormality detection permission / prohibition processing program executed in the yaw rate sensor abnormality detection device of the embodiment. [Figure 5] This is a flowchart of the abnormality detection processing program executed in the yaw rate sensor abnormality detection device of the embodiment. [Figure 6] This is a flowchart of an anomaly detection subroutine, which is part of an anomaly detection processing program. [Modes for carrying out the invention]

[0013] Hereinafter, as an embodiment for carrying out the present invention, a yaw rate sensor abnormality detection device, which is an embodiment of the present invention, will be described in detail with reference to the figures. In addition to the embodiments described below, the present invention can be carried out in various forms by making various changes and improvements based on the knowledge of those skilled in the art, starting with the forms described in the section [Embodiments of the Invention] above. [Examples]

[0014] [A] Vehicle configuration equipped with a yaw rate sensor anomaly detection device As schematically shown in Figure 1, the vehicle on which the yaw rate sensor abnormality determination device 10 of the embodiment of the present invention (hereinafter sometimes abbreviated as "this yaw rate sensor abnormality determination device" or "this abnormality determination device") 10 is installed is a vehicle having two front wheels 12f on the left and right, and two rear wheels 12r on the left and right. In this vehicle, the two front wheels 12f are steerable wheels, and the vehicle has a steering device 14 for steering these front wheels 12f. The steering device 14 is a so-called electric power steering device and is composed of operating members such as a steering wheel 16, a steering column 18, and a steering actuator 20. In the following description, when it is not necessary to distinguish between the front wheels 12f and the rear wheels 12r, they may be collectively referred to as wheels 12.

[0015] This vehicle is a four-wheel drive vehicle, and although not shown in the diagram, it is equipped with a drive system for driving each wheel 12. In addition, each wheel 12 is provided with a brake system 22, which is an electric brake system, and these brake systems 22 are controlled by a brake electronic control unit (hereinafter sometimes referred to as "brake ECU") 24. As will be explained in more detail later, this vehicle also has a VSC controller 26 installed because it performs so-called vehicle stability control (hereinafter sometimes referred to as "VSC control").

[0016] The vehicle is equipped with various sensors, including the yaw rate sensor 30 which is the object of the abnormality determination by the present abnormality determination device 10. Specifically, a steering angle sensor 32 for detecting the steering angle δ which is the operation amount of the steering wheel 16, a lateral acceleration sensor 34 for detecting the lateral acceleration Gy generated in the vehicle, a wheel speed sensor 36 for detecting the wheel speed vw of each wheel 12 (actually the rotational speed, but hereinafter will be treated as the circumferential speed), etc. are provided. In addition, the vehicle is also equipped with a camera 38 for monitoring the front. The vehicle is equipped with a CAN (Controllable Area Network or Car Area Network) 40, and those sensors 30, 32, 34, 36, the camera 38, and the present abnormality determination device 10, the steering device 14, the brake ECU 24, and the VSC controller 26 are each connected to the CAN 40.

[0017] Here, the VSC control executed by the VSC controller 26 will be briefly described. Since the VSC control is a general control, briefly speaking, in the turning of the vehicle, when the vehicle is not turning along the appropriate turning line, it is the control for correcting the turning by the brake device 22. Specifically speaking, the VSC controller 26 specifies an appropriate yaw rate γr (a kind of "operation-dependent estimated yaw rate") which is the yaw rate γ that the vehicle should achieve in turning, based on the vehicle speed v calculated from the wheel speed vw of each wheel 12 and the steering angle δ of the steering wheel 16, and generates a braking force in the brake device 22 based on the difference between the appropriate yaw rate γr and the detected yaw rate γd which is the yaw rate γ detected by the yaw rate sensor 30. More specifically, the VSC controller 26 issues a command to the brake ECU 24 to apply a braking force to the outer turning wheel 12 when the detected yaw rate γd is high, and to apply a braking force to the inner turning wheel 12 when the detected yaw rate γd is low, according to the difference. The VSC control is an example of the usage mode of the detected yaw rate γd detected by the yaw rate sensor 30, but the detected yaw rate γd is an important parameter used in various controls of the vehicle.

[0018] As can be understood from the above description, the detection yaw rate γd should be properly detected, and the present abnormality determination device 10 determines an abnormality of the yaw rate sensor 30, that is, an abnormality in the detection of the detection yaw rate γd by the yaw rate sensor 30. The present abnormality determination device 10 is mainly configured by a computer, and as will be described in detail later, based on the functions of the abnormality determination device 10, it has two functional parts, an abnormality determination unit 50 and a determination prohibition unit 52.

[0019] [B] Process for determining an abnormality of the yaw rate sensor The process for determining whether the yaw rate sensor 30 is abnormal, that is, whether the detection of the detection yaw rate γd by the yaw rate sensor 30 is abnormal, is performed by the above-described abnormality determination unit 50. If this process is called the abnormality determination process, then briefly speaking, the abnormality determination process is performed by comparing the detection yaw rate γd with an estimated yaw rate estimated based on the behavior and operation of the vehicle. Hereinafter, the abnormality determination process will be described in detail.

[0020] In the abnormality determination process by the present abnormality determination device 10, as the estimated yaw rate, two types are adopted: a lateral acceleration-dependent estimated yaw rate γg estimated based on the lateral acceleration occurring in the vehicle, and a wheel speed difference-dependent estimated yaw rate γv estimated based on the difference between the wheel speeds vw of the left and right wheels 12. The abnormality determination unit 50 estimates the lateral acceleration-dependent estimated yaw rate γg according to the following formula based on the lateral acceleration Gy detected by the lateral acceleration sensor 34 and the vehicle speed v of the vehicle specified from the wheel speeds vw of each of the four wheels 12, γg = Gy / v Based on the right wheel speed vwr which is the wheel speed vw of the right wheel 12 on the right side, the left wheel speed vwl which is the wheel speed vw of the left wheel 12 on the left side, and the tread Tr (see FIG. 1) which is the distance between the left and right wheels 12, the wheel speed difference-dependent estimated yaw rate γv is estimated according to the following formula. γv = (vwr - vwl) / Tr Incidentally, for the right wheel speed vwr, the left wheel speed vwl, and the tread Tr, the average of the front wheel 12f side and the rear wheel side 12r may be adopted.

[0021] The abnormality detection unit 50 compares the detected yaw rate γd with the lateral acceleration-based estimated yaw rate γg and the wheel speed difference-based estimated yaw rate γv, and determines whether the difference between them exceeds the first set difference, which is the first yaw rate threshold difference Δγa, according to the following equation. Incidentally, the first yaw rate threshold difference Δγa should be set to a value that clearly indicates that an abnormality has occurred in the yaw rate sensor 30. |γd-γg|>Δγa |γd-γv|>Δγa

[0022] On the other hand, the abnormality determination unit 50 determines whether the difference between the lateral acceleration-based estimated yaw rate γg and the wheel speed difference-based estimated yaw rate γv is smaller than the second set difference, which is the second yaw rate threshold difference Δγb, according to the following equation. Incidentally, the second yaw rate threshold difference Δγb should be set to a value that allows the lateral acceleration-based estimated yaw rate γg and the wheel speed difference-based estimated yaw rate γv to be considered to be substantially the same. |γg-γv|<Δγb

[0023] The abnormality detection unit 50 determines that the detection of the detected yaw rate γd by the yaw rate sensor 30 is abnormal if the yaw rate γg estimated based on lateral acceleration and the yaw rate γv estimated based on wheel speed difference are substantially the same (not significantly different), and the detected yaw rate γd is considerably different from at least one of the yaw rate γg estimated based on lateral acceleration and the yaw rate γv estimated based on wheel speed difference, and this condition continues for a certain period of time, i.e., beyond the set time Ct0. Incidentally, the set time Ct0 can be set to, for example, a few seconds to several tens of seconds.

[0024] [C] Processing to determine whether to prohibit abnormality detection As described above, an abnormality in the yaw rate sensor 30 is detected. However, even if there is no abnormality in the yaw rate sensor 30, the above conditions may be met, and it may be determined that there is an abnormality. In other words, depending on where the vehicle is traveling, that is, on the road surface the vehicle is traveling on, it may be determined that there is an abnormality even if there is no abnormality. More specifically, for example, when the vehicle is traveling on a road surface with a relatively high surface friction coefficient (μ), such as a public road or a race track, the determination based on the above conditions is appropriate. On the other hand, when the vehicle is turning on a road surface with a considerably low surface friction coefficient, such as ice, compacted snow, or dry sand, especially when the vehicle is turning on a road surface with an extremely low surface friction coefficient, such as ice, even if there is no abnormality in the yaw rate sensor 30, the above conditions may be met, and the yaw rate sensor 30 may be determined to be abnormal.

[0025] In light of the above, if the vehicle is traveling in a location where it would be inappropriate to determine an abnormality in the yaw rate sensor 30, that is, if the vehicle is traveling in such a location, the determination of the abnormality is prohibited. The process of prohibiting the determination of an abnormality, in other words, the process of deciding whether to allow or prohibit the determination of an abnormality by the abnormality determination unit 50 (hereinafter sometimes referred to as the "abnormality determination allow / prohibit determination process") is performed by the determination prohibition unit 52. The abnormality determination allow / prohibit determination process will be explained in detail below with reference to a conceptual diagram.

[0026] Figure 2(a) schematically shows an image captured by a camera 38 that monitors the front of the vehicle. This image is acquired when the vehicle is, for example, driving on a circuit. The image is acquired repeatedly at a set time interval (for example, a few milliseconds to tens of milliseconds). The judgment prohibition unit 52 compares the image acquired this time (hereinafter sometimes referred to as the "current image") with the image acquired last time (hereinafter sometimes referred to as the "previous image") to determine whether to allow or prohibit the abnormality determination by the abnormality determination unit 50. Since this determination is made by estimating the condition of the ground (road surface) on which the vehicle is driving, the part of the image above the horizon HL, simply put, the part that is not the road surface, becomes so-called noise in the determination. Therefore, in the abnormality determination allow / prohibition processing by the judgment prohibition unit 52, that part is excluded from the determination by trimming.

[0027] The judgment prohibition unit 52 first converts the current image to grayscale. More specifically, it converts the brightness value of each pixel in the current image to a gradation value (for example, a gradation value of 256) and stores that value as the pixel value K of each pixel. For all pixels, it compares the current pixel value Kc, which is the pixel value K of the current image, with the previous pixel value Kp, which is the pixel value K of the previous image that has already been stored, and determines the pixel value difference D of each pixel according to the following formula. |Kc-Kp|=D Then, the judgment prohibition unit 52 determines a binarized value B for each pixel, which is binarized to 0 or 1, based on the pixel value difference D, using the difference threshold Dth as shown in the following equation. D > Dth → B = 1 D≦Dth → B=0 In short, by comparing the previous image with the current image, the binarized value B of pixels whose pixel value K has changed significantly is determined to be 1, and the binarized value B of pixels whose value has not changed much is determined to be 0. The binarized image schematically looks like Figure 2(b). Pixels corresponding to the lane markings DL on both sides of the driving lane are pixels with a binarized value B of 1, and are shown in white in the figure.

[0028] In contrast, Figure 2(c) shows an image acquired by camera 38 when the vehicle is driving, for example, on ice. There are no lane markings DL on the ice, and the image of the ice is, simply put, extremely flat (with no visual difference overall). Therefore, as shown in Figure 2(d), in the binarized image, most pixels have a binarization value B of 0. Since the road surface on which such a binarized image is acquired can be estimated to be a road surface with a considerably small road surface μ, it is undesirable to perform the above-mentioned abnormality judgment on the yaw rate sensor 30 when driving on such a road surface. Therefore, the judgment prohibition unit 52 decides whether to allow or prohibit the abnormality judgment by the following process.

[0029] The judgment prohibition unit 52 calculates the total binarized value ΣB of the entire binarized image by summing the binarized values ​​B of each pixel. If this total binarized value ΣB exceeds the judgment threshold ΣBth, the unit allows the abnormality judgment, assuming that the vehicle is traveling in a location (road surface) where the possibility of a false abnormality judgment is low. If this total binarized value ΣB is less than or equal to the judgment threshold ΣBth, the unit prohibits the abnormality judgment, assuming that the vehicle is traveling in a location (road surface) where the possibility of a false abnormality judgment is high. In short, if the number of value-change pixels (pixels whose pixel value has changed between the previous image and the current image) is less than or equal to a set number, the unit prohibits the determination of whether or not there is an abnormality detected by the yaw rate sensor 30.

[0030] However, even when driving on ice or similar surfaces, it may be determined that the vehicle is driving in a location where the possibility of an abnormality detection being incorrect is low. Schematically, as shown in Figure 3(a), if there are small changes (unevenness, undulations, etc.) UE on the road surface, when the vehicle is turning at a low yaw rate γ, the binarized image will not detect these changes UE, as shown in Figure 3(b). However, when the vehicle is turning at a high yaw rate γ, these changes UE will be detected, as shown in Figure 3(c). Therefore, the judgment prohibition unit 52 sets a judgment threshold ΣBth based on the yaw rate γ. Specifically, as shown in the graph in Figure 3(d), the higher the yaw rate γ, the larger the judgment threshold ΣBth is set to. Note that instead of setting the judgment threshold ΣBth based on the yaw rate γ, it may also be set as a fixed value independent of the yaw rate γ. In this case, the yaw rate γ used may be the detected yaw rate γd, or one of the estimated yaw rates mentioned above—the appropriate yaw rate γr, the yaw rate estimated based on wheel speed difference γv, or the yaw rate estimated based on lateral acceleration γg—may be used. It is desirable to set a judgment threshold ΣBth so that even if the adopted yaw rate γ is an abnormal value, the abnormality judgment does not result in a false positive.

[0031] [D] Flow of processing by the anomaly detection device The aforementioned judgment prohibition unit 52 and abnormality judgment unit 50 are functional units that are realized in this abnormality judgment device 10 by the execution of the abnormality judgment permission / prohibition judgment processing program and the abnormality judgment processing program, respectively, whose flowcharts are shown in Figures 4 and 5. Below, the flow of the abnormality judgment permission / prohibition judgment processing and the abnormality judgment processing described above will be briefly explained in accordance with the flowcharts. Incidentally, these programs are executed repeatedly in parallel with each other at short time intervals (several milliseconds to tens of milliseconds).

[0032] i) Abnormality detection / permission decision processing In the processing according to the abnormality detection permission / prohibition processing program, first, in step 1 (hereinafter sometimes abbreviated as "S1"; the same applies to other steps), an image of the front of the vehicle captured by camera 38 is acquired. Subsequently, in S2, the portion of the image above the horizon is removed by cropping, and in S3, the remaining image after cropping is converted to grayscale, that is, the pixel value of each pixel is graded, and that image is stored as the current image.

[0033] In the next step, S4, the current image and the previous image are compared. More specifically, as explained earlier, for each pixel, the absolute value of the difference between the current pixel value Kc (the pixel value of the current image) and the previous pixel value Kp (the pixel value of the previous image) is identified as the pixel value difference D. After identifying the pixel value difference D, in S5, the grayscale current image is designated as the previous image. In other words, the previous image is updated.

[0034] In the following step S6, the binarized sum ΣB is reset to 0. In the next step S7, the pixel value difference D of a pixel with a value of 1 is compared with the difference threshold Dth. If the pixel value difference D is greater than the difference threshold Dth, in S8 the binarized value B of that pixel is set to 1. If it is less than or equal to the difference threshold Dth, in S9 the binarized value B of that pixel is set to 0. Then, in S10, the binarized value B of that pixel is added to the binarized sum ΣB. Based on the determination in S11, the process from S7 to S10 is repeated until it has been performed for all pixels.

[0035] In the following step S12, the yaw rate γ is obtained as explained earlier, and in S13, a map as shown in Figure 3(d) is referenced, and the decision threshold ΣBth is set based on that yaw rate γ.

[0036] Then, in S14, the binarized sum value ΣB and the judgment threshold ΣBth are compared. If the binarized sum value ΣB is greater than the judgment threshold ΣBth, then in S15, the execution of the abnormality judgment for the yaw rate sensor 30 is permitted. If the binarized sum value ΣB is less than or equal to the judgment threshold ΣBth, then it is determined that the abnormality judgment is inappropriate, and in S16, the execution of the abnormality judgment for the yaw rate sensor 30 is prohibited. After S15 or S16, one execution of this abnormality judgment permission / prohibition judgment processing program ends.

[0037] As explained earlier, it is also possible to use a fixed decision threshold ΣBth that is independent of the yaw rate γ, without performing the S12 and S13 processes.

[0038] ii) Anomaly detection process In the process following the abnormality detection processing program, it is first determined whether the prerequisites for executing the abnormality detection process are met. Specifically, in S21, it is determined whether the execution of VSC control is permitted. If the execution of VSC control is not permitted, in S22, it is determined whether the abnormality detection is prohibited by the abnormality detection permission / prohibition process described above. If VSC control is permitted, the abnormality detection process is permitted. Also, even if VSC control is not permitted, if the abnormality detection is not prohibited by the abnormality detection permission / prohibition process, the abnormality detection process is permitted. On the other hand, if the abnormality detection is prohibited by the abnormality detection permission / prohibition process, the abnormality detection process is prohibited.

[0039] In the processing according to this program, a vehicle speed vj for determining whether the preconditions for executing the abnormality determination process are met is used. If the preconditions are met, in S23 the vehicle speed vj for determining whether If the vehicle speed v does not exceed the vehicle speed vj used for determining permission, the abnormality determination process is not permitted, and in S27, the time counter Ct, which will be explained later, is reset.

[0040] Furthermore, if multiple modes are set for VSC control, such as normal mode and sport mode, the abnormality detection process may be allowed only in normal mode, for example, as shown in parentheses in S21. Also, although the processing according to this program uses the permission of VSC control as a prerequisite for the abnormality detection process, it is not necessary to make the permission of VSC control a prerequisite for the abnormality detection process. In other words, the processing in S21 may be omitted. Moreover, it is not necessary to use the vehicle speed vj for the determination permission decision, and the abnormality detection processing subroutine in S26 may be executed directly if the prerequisite is met, and the processing in S27 may be executed directly if the prerequisite is not met.

[0041] In the process following the abnormality detection processing subroutine shown in the flowchart in Figure 6, first, in S31, the detected yaw rate γd detected by the yaw rate sensor 30 is obtained. Then, in S32 and S33, the wheel speed difference-based estimated yaw rate γv and the lateral acceleration-based estimated yaw rate γg are calculated, respectively.

[0042] In the following S34, it is determined whether at least one of the absolute values ​​of the difference between the detected yaw rate γd and the wheel speed difference-based estimated yaw rate γv, and the absolute value of the difference between the detected yaw rate γd and the lateral acceleration-based estimated yaw rate γg exceeds the first yaw rate threshold difference Δγa described above. In S35, it is determined whether the absolute value of the difference between the wheel speed difference-based estimated yaw rate γv and the lateral acceleration-based estimated yaw rate γg is smaller than the second yaw rate threshold difference Δγb described above. If at least one of the absolute values ​​of the difference between the detected yaw rate γd and the wheel speed difference-based estimated yaw rate γv, and the absolute value of the difference between the detected yaw rate γd and the lateral acceleration-based estimated yaw rate γg exceeds the first yaw rate threshold difference Δγa, and the absolute value of the difference between the wheel speed difference-based estimated yaw rate γv and the lateral acceleration-based estimated yaw rate γg is smaller than the second yaw rate threshold difference Δγb, then the yaw rate sensor 30 is estimated to be in an abnormal state, i.e., the sensor is estimated to be in an abnormal state. In order to confirm the duration of this state, in S36, the time counter Ct is incremented by a count-up value ΔCt corresponding to the execution pitch of the program.

[0043] On the other hand, if neither the absolute value of the difference between the detected yaw rate γd and the wheel speed difference-based estimated yaw rate γv nor the absolute value of the difference between the detected yaw rate γd and the lateral acceleration-based estimated yaw rate γg exceeds the first yaw rate threshold difference Δγa, or if the absolute value of the difference between the wheel speed difference-based estimated yaw rate γv and the lateral acceleration-based estimated yaw rate γg is greater than or equal to the second yaw rate threshold difference Δγb, then in S37, the time counter Ct is reset and the processing of the subroutine ends.

[0044] In S38, if it is determined that the time counted by the time counter Ct has exceeded the set time Ct0, then in S39, it is determined that the yaw rate sensor 30 is abnormal, that is, the detected yaw rate γd detected by the yaw rate sensor 30 is abnormal. If the time counted by the time counter Ct has not exceeded the set time Ct0, then it is not determined that the yaw rate sensor 30 is abnormal, and the processing of the subroutine ends. [Explanation of symbols]

[0045] 10: Yaw rate sensor anomaly detection device 30: Yaw rate sensor 32: Steering angle sensor 34: Lateral acceleration sensor 36: Wheel speed sensor 38: Camera 50: Anomaly detection unit 52: Detection prohibition unit Gy: Lateral acceleration γ: Yaw rate γd: Detected yaw rate γv: Estimated yaw rate based on wheel speed difference γg: Estimated yaw rate based on lateral acceleration Δγa: First yaw rate threshold difference [First setting difference] Δγb: Second yaw rate threshold difference [Second setting difference] vw: Wheel speed v: Vehicle speed HL: Horizon DL: Lane marking UE: Road surface change K: Pixel value Kc: Current pixel value Kp: Previous pixel value D: Pixel value difference Dth: Difference threshold B: Binarized value ΣB: Binarized sum ΣBth: Judgment threshold

Claims

1. A yaw rate sensor abnormality detection device that is mounted on a vehicle and determines abnormalities in the yaw rate sensor provided by that vehicle, An abnormality determination unit that compares the detected yaw rate detected by the yaw rate sensor with the estimated yaw rate estimated based on the vehicle's behavior and operation to determine an abnormality detected by the yaw rate sensor, Based on the image from the camera capturing the area in front of the vehicle, if the abnormality determination unit determines that the abnormality determination is inappropriate, the determination prohibition unit prohibits that determination. A yaw rate sensor anomaly detection device equipped with the following features.

2. The image from the aforementioned camera is repeatedly acquired at set time intervals. The yaw rate sensor abnormality determination device according to claim 1, wherein the determination prohibition unit is configured to compare the previous image with the current image and prohibit the determination of abnormality if the number of pixels whose pixel value has changed is less than or equal to a set number.

3. The yaw rate sensor abnormality determination device according to claim 2, wherein the determination prohibition unit is configured to change the setting number according to the height of the yaw rate.

4. The yaw rate sensor abnormality determination device according to claim 1, wherein the determination prohibition unit is configured to exclude portions of the camera image that are not road surfaces and to determine whether the determination of the abnormality becomes inappropriate.

5. The yaw rate sensor abnormality determination device according to claim 1, wherein the abnormality determination unit adopts, as the estimated yaw rate, a lateral acceleration-based estimated yaw rate estimated based on the lateral acceleration occurring in the vehicle and a wheel speed difference-based estimated yaw rate estimated based on the difference in peripheral speed between the left and right wheels, and determines that the detection by the yaw rate sensor is abnormal when the difference between the detected yaw rate and either the lateral acceleration-based estimated yaw rate or the wheel speed difference-based estimated yaw rate is greater than a first set difference, and the difference between the lateral acceleration-based estimated yaw rate and the wheel speed difference-based estimated yaw rate is smaller than a second set difference.

Images