Vehicle positioning anomaly checking method and vehicle computer

Abstract

A vehicle positioning abnormality checking method, executed on a processing device, includes obtaining a plurality of sets of driving data, wherein each of the driving data includes at least three vehicle positions, a steering wheel angle, and a vehicle speed, executing an error calculation procedure on each of the driving data to obtain a plurality of pieces of error data, and outputting an abnormality notification in response to a sum of the error data being greater than a tolerance value. An embodiment of the present invention further provides a driving computer.

Description

Technical Field

[0001] This invention relates to a method for detecting vehicle positioning anomalies and an on-board computer. Background Technology

[0002] Typically, car repairs or maintenance are only performed when a vehicle's mileage exceeds 5,000 kilometers, a warning light illuminates on the dashboard, or after an accident. Currently, many car manufacturers are selling electric vehicles, and autonomous vehicles are under development. However, regardless of whether it's a regular gasoline car, an electric car, or an autonomous vehicle, maintenance is only required under these conditions. In other words, drivers usually only discover abnormalities in their vehicles when they are taken to a repair shop or involved in an accident, leaving the vehicle's safety inadequate. Summary of the Invention

[0003] In view of the above, one or more embodiments of the present invention provide a vehicle positioning anomaly detection method and vehicle computer to solve the above problems.

[0004] A vehicle positioning anomaly detection method according to an embodiment of the present invention is executed in a processing device. The method includes: acquiring multiple sets of driving data, wherein each of these driving data includes at least three vehicle positions, steering wheel angles, and vehicle speeds; performing an error calculation program on each of these driving data to obtain multiple error data. After performing the error calculation program on all driving data, the method further includes: outputting an anomaly notification in response to the sum of these error data exceeding a tolerance value.

[0005] A vehicle computer according to an embodiment of the present invention includes a controller area network (Controller Area Network) and a processing device. The Controller Area Network is used to acquire multiple sets of driving data from a vehicle, wherein each set of driving data includes at least three vehicle positions, steering wheel angles, and vehicle speeds. The processing device is connected to the Controller Area Network and is used to acquire the driving data from the Controller Area Network and to perform an error calculation procedure on each set of vehicle driving data to obtain multiple error data points. After performing the error calculation procedure on all driving data, the processing device outputs an anomaly notification in response to the sum of these error data points exceeding a tolerance value.

[0006] In summary, the vehicle alignment anomaly detection method and vehicle computer according to one or more embodiments of the present invention can realize the function of real-time self-detection of the vehicle, and output an anomaly notification when it is determined that the four-wheel alignment of the vehicle is abnormal, so that drivers and car manufacturers can detect the four-wheel alignment anomaly before the vehicle is brought in for maintenance. Therefore, it can bring the benefit of improved safety to the field of autonomous vehicles.

[0007] The foregoing description of the contents of this disclosure and the following description of the embodiments are intended to demonstrate and explain the spirit and principles of the present invention, and to provide a further explanation of the claims of the present invention. Attached Figure Description

[0008] Figure 1 This is a block diagram of a vehicle computer according to an embodiment of the present invention.

[0009] Figure 2 This is a flowchart illustrating a vehicle positioning anomaly detection method according to an embodiment of the present invention.

[0010] Figure 3 It is a drawing Figure 2 The detailed flowchart of step S3.

[0011] Figure 4 This is a schematic diagram illustrating an error calculation procedure according to an embodiment of the present invention.

[0012] Figure 5(a) shows the normal driving direction of a vehicle and the direction of the steering wheel, while Figure 5(b) shows the abnormal driving direction of a vehicle and the direction of the steering wheel.

[0013] Figure 6 It is a drawing Figure 3 The detailed flowchart of step S31.

[0014] Figure 7 It is a drawing Figure 3 A detailed flowchart of an embodiment of step S33.

[0015] Figure 8 It is a drawing Figure 3 A detailed flowchart of another embodiment of step S33.

[0016] Figure 9 This is a block diagram of a vehicle computer and other automotive components connected to the vehicle computer, according to another embodiment of the present invention.

[0017] Figure 10 This is a flowchart illustrating a vehicle positioning anomaly detection method according to another embodiment of the present invention.

[0018] Figure 11 This is a flowchart illustrating a vehicle positioning anomaly detection method according to another embodiment of the present invention.

[0019] The reference numerals in the attached figures are explained as follows:

[0020] A1, A2: Vehicle Computer

[0021] 11: Controller Area Network

[0022] 12: Processing device

[0023] 13: Positioning component

[0024] 14: Steering torque angle sensor

[0025] 15: Speed ​​sensor

[0026] P0: First vehicle position

[0027] P1: Second vehicle position

[0028] P2: Target Location

[0029] P2': Predicted location

[0030] a1, a2: Direction of movement

[0031] a2': Offset direction

[0032] S1, S3, S5, S7, S31, S33, S35, S311, S313, S315, S317, S319, S331a, S331b, S333a, S3 33b, S335a, S335b, S337b, S01a, S01b, S03a, S03b, S05a, S05b, S07a, S09a, S011a: Steps Detailed Implementation

[0033] The following detailed description of the features and advantages of the present invention in the embodiments is sufficient to enable anyone skilled in the art to understand the technical content of the present invention and implement it accordingly. Based on the disclosure, claims, and drawings in this specification, anyone skilled in the art can easily understand the related objectives and advantages of the present invention. The following embodiments are further detailed in illustrating the points of view of the present invention, but are not intended to limit the scope of the present invention in any way.

[0034] Please refer to Figure 1 , Figure 1 This is a block diagram illustrating a vehicle computer according to an embodiment of the present invention. The vehicle computer A1 can be applied to various automobiles, especially autonomous vehicles. The vehicle computer A1 includes a controller area network (CAN Bus) 11 and a processing device 12. The controller area network 11 can be electrically connected to the processing device 12 or communicatively connected to the processing device 12. The processing device 12 can be a microcontroller, a programmable logic device, or a special application integrated circuit, etc., and one or more embodiments of the present invention are not limited thereto. The controller area network 11 is used to acquire vehicle driving data, and the processing device 12 is used to determine whether the four-wheel alignment of the vehicle is abnormal based on the driving data.

[0035] For a more detailed explanation of the techniques for determining whether a vehicle's four-wheel alignment is abnormal, please refer to [link / reference needed]. Figure 1 and Figure 2 ,in Figure 2 This is a flowchart illustrating a vehicle positioning anomaly detection method according to an embodiment of the present invention. The vehicle positioning anomaly detection method is performed on... Figure 1 The processing device 12.

[0036] like Figure 2 As shown, the vehicle positioning anomaly detection method includes: Step S1: Obtaining multiple sets of driving data, wherein each of these driving data includes at least three vehicle positions, steering wheel angles, and vehicle speeds; Step S3: Executing an error calculation program on each of these driving data to obtain multiple error data; Step S5: Determining whether the sum of these error data is greater than the tolerance value; If the determination result of step S5 is "yes", proceeding to step S7: Outputting an anomaly notification; and if the determination result of step S5 is "no", proceeding to step S1.

[0037] In step S1, the controller area network 11 obtains the aforementioned multiple sets of driving data from the vehicle. The processing device 12 obtains this driving data through the controller area network 11, wherein this driving data is preferably generated in a time sequence. The controller area network 11 can communicate with a satellite positioning system to obtain the vehicle's position, and the steering wheel angle and vehicle speed can be obtained in the following ways. Specifically, the data frame of the controller area network can have 11 bits of identification code (ID) information and 8 bytes of data information, wherein different identification codes correspond to different vehicle information. For example, referring to Table 1 below, Table 1 shows the data format of the controller area network, identification code 0xB6 represents the vehicle's driving speed, and identification code 0x10B represents the vehicle's steering wheel angle. Therefore, the processing device 12 can determine, according to Table 1, that the data in the data frame corresponding to identification code 0xB6 from the 24th to the 39th bit is the vehicle's driving speed data, and according to Table 1, determine that the data in the data frame corresponding to identification code 0x10B from the 8th to the 23rd bit is the vehicle's steering wheel angle data. It should also be noted that Table 1 only illustrates the data format of the controller area network as an example to clearly explain how the steering wheel angle and vehicle speed are obtained from driving data.

[0038] Table 1

[0039]

[0040] Next, in step S3, the processing device 12 performs an error calculation procedure on each of these driving data sets to obtain multiple error data points. After obtaining the error data corresponding to each set of driving data, in step S5, the processing device 12 determines whether the sum of these error data sets is greater than a tolerance value to determine whether the vehicle's four-wheel alignment has deviated. The tolerance value is a numerical value used to indicate the maximum allowable deviation distance and can be set based on the vehicle's total driving distance. For example, assuming the total driving distance determined based on the vehicle's position in these driving data sets is 100 meters, the tolerance value can be set to 3; assuming the total driving distance determined based on the vehicle's position in these driving data sets is 500 meters, the tolerance value can be set to 5. In other words, the larger the total driving distance, the larger the tolerance value can be, but one or more embodiments of the present invention do not limit the specific setting method of the tolerance value.

[0041] If the judgment result of step S5 is "no", it means that the four-wheel alignment of the vehicle is normal, and the processing device 12 can execute step S1 again to continuously monitor the four-wheel alignment status of the vehicle.

[0042] If the judgment result of step S5 is "yes", it indicates that the vehicle's four-wheel alignment may be abnormal or faulty, causing the cumulative error between the predicted position based on parameters such as the steering wheel angle and the actual target position obtained from the satellite positioning system to reach the tolerance value. Therefore, in step S7, the processing device 12 outputs an abnormality notification, wherein the abnormality notification indicates that the vehicle's four-wheel alignment may be abnormal. The processing device 12 may output an abnormality notification in the following ways: controlling the vehicle's lights to flash through the controller area network 11, and / or outputting an abnormality notification to a cloud database or server accessible to the user or the vehicle manufacturer. One or more embodiments of the present invention do not limit the way the processing device 12 outputs an abnormality notification.

[0043] For a more detailed explanation of the error calculation procedure, please refer to [the relevant documentation]. Figure 3 and Figure 4 ,in Figure 3 It is a drawing Figure 2 The detailed flowchart of step S3 (i.e., the flowchart of the error calculation procedure) Figure 4 This is a schematic diagram illustrating an error calculation procedure according to an embodiment of the present invention.

[0044] like Figure 3 As shown, Figure 2 Step S3 includes: Step S31: Obtain the direction of movement and the target position based on at least three vehicle positions; Step S33: Obtain the predicted position based on the direction of movement, vehicle speed and steering wheel angle; and Step S35: Determine the difference between the predicted position and the target position as one of multiple error data, wherein the predicted position and the target position correspond to the same timestamp.

[0045] In step S31, the processing device 12 obtains the movement direction a1 and the target position P2 based on the first vehicle position P0, the second vehicle position P1, and the third vehicle position P2. The first vehicle position P0, the second vehicle position P1, and the target position P2 are the vehicle's positioning positions, and the movement direction a1 is the vehicle's driving direction. It should be noted that the first vehicle position P0 corresponds to the first timestamp, the second vehicle position P1, vehicle speed, and steering wheel angle correspond to the second timestamp, and the target position P2 corresponds to the third timestamp, with the second timestamp being later than the first timestamp and the third timestamp being later than the second timestamp. In short, the first vehicle position P0, the second vehicle position P1, and the target position P2 can be data generated in chronological order. Furthermore, the processing device 12 can also obtain the movement direction and target position based on three or more vehicle positions. For example, the processing device 12 can obtain the movement direction a1 based on two or more vehicle positions and use the vehicle position corresponding to the latest timestamp among the three or more vehicle positions as the target position P2.

[0046] In step S33, the processing device 12 obtains the predicted position P2' based on the moving direction a1, vehicle speed and steering wheel angle. The predicted position P2' is the position of the vehicle predicted by the processing device 12 at the third timestamp.

[0047] In step S35, the processing device 12 determines the difference between the predicted position P2' and the target position P2 as a piece of error data. The error data can be the error vector (directional distance) from the predicted position P2' to the target position P2 or the error vector (directional distance) from the target position P2 to the predicted position P2'.

[0048] Taking Figure 5(a) and Figure 5(b) as examples, Figure 5(a) shows the normal vehicle driving direction and steering wheel direction, and Figure 5(b) shows the abnormal vehicle driving direction and steering wheel direction. The solid arrows indicate the vehicle driving direction, and the dashed arrows indicate the driving direction indicated by the steering wheel angle.

[0049] In Figure 5(a), although the steering wheel direction is slightly deflected to the left and right, the vectors of the left and right deflections cancel each other out, so that the steering wheel direction is still approximately the same as the vehicle's driving direction. Therefore, the processing device 12 can determine that the error data between multiple consecutive predicted positions and the corresponding multiple target positions is not greater than the tolerance value. Figure 2 The judgment result of step S5 is "No". In Figure 5(b), the steering wheel direction continues to point to the right, and the processing device 12 may therefore determine multiple consecutive predicted positions deviating to the right, but the corresponding multiple target positions indicate that the vehicle is traveling in a straight direction, causing the processing device 12 to determine that the error data between the multiple consecutive predicted positions and the corresponding multiple target positions is greater than the tolerance value. Figure 2 The result of step S5 is "yes".

[0050] This enables vehicles to perform real-time self-diagnosis and output an anomaly notification when an abnormality is detected in the four-wheel alignment. This allows drivers and manufacturers to detect abnormalities even before the vehicle undergoes maintenance. Therefore, it can bring improved safety benefits to the field of autonomous vehicles.

[0051] Please continue to refer to this. Figure 6 ,in Figure 6 It is a drawing Figure 3 The detailed flowchart of step S31 is shown below. Figure 6 As shown, step S31 may include: step S311: determining that one or more timestamps corresponding to the steering wheel angles do not have a corresponding vehicle position among the multiple timestamps; step S313: determining the previous timestamp that is closest to the timestamp and has a corresponding vehicle position; step S315: determining the next timestamp that is closest to the timestamp and has a corresponding vehicle position; step S317: performing interpolation on the vehicle position corresponding to the previous timestamp and the vehicle position corresponding to the next timestamp to obtain an interpolated position; and step S319: using the interpolated position as one of the vehicle positions.

[0052] Since the report rate of a satellite positioning system is not necessarily equal to the report rate of steering wheel angle and / or vehicle speed—for example, the report rate of a commonly used satellite positioning system is approximately 1 Hz, while the report rate of steering wheel angle and / or vehicle speed is approximately 10 Hz—if the report rate of the satellite positioning system is less than the report rate of steering wheel angle and / or vehicle speed, the processing device 12 can determine in step S311 that one or more of the multiple timestamps corresponding to these steering wheel angles in the driving data do not correspond to a vehicle location.

[0053] For each timestamp where no corresponding vehicle location exists, processing device 12 executes steps S313 to S319. In steps S313 and S315, processing device 12 determines the preceding and following timestamps closest to the timestamp and containing a corresponding vehicle location. In steps S317 and S319, processing device 12 performs interpolation on the vehicle locations of the preceding and following timestamps to obtain interpolated positions, and uses these interpolated positions as the vehicle location for that timestamp, thereby filling in the missing vehicle locations in the satellite positioning system. In short, in steps S317 and S319, processing device 12 can determine the preceding and following timestamps adjacent to the current timestamp, and perform interpolation on the vehicle locations of the preceding and following timestamps to obtain interpolated positions as the missing vehicle locations for that timestamp. It should also be noted that... Figure 6Step S313 is illustrated as being executed before step S315, but step S313 may also be executed after step S315, or step S313 may be executed simultaneously with step S315.

[0054] Please continue to refer to this. Figure 7 ,in Figure 7 It is a drawing Figure 3 A detailed flowchart of an embodiment of step S33. (See attached flowchart.) Figure 7 As shown, step S33 may include: step S331a: determining the offset direction relative to the movement vector based on the steering wheel angle; step S333a: determining the predicted distance based on the time difference between the second vehicle position and the target position and the vehicle speed; and step S335a: using the second vehicle position as a reference point, determining the predicted position based on the predicted distance and the offset direction.

[0055] Similarly Figure 4 For example, in step S331a, the processing device 12 determines, based on the movement direction a1 from the first vehicle position P0 to the second vehicle position P1, that the vehicle will travel from the second vehicle position P1 along direction a2 without the steering wheel being turned, and using the movement direction a2 as the straight line direction, turns the movement direction a2 to an offset direction a2' relative to the movement direction a1 / a2 according to the steering wheel angle. In step S333a, the processing device 12 determines the predicted distance of the vehicle from the second vehicle position P1 based on the time difference between the second vehicle position P1 and the target position P2 and the vehicle speed. In step S335a, the processing device 12 uses the second vehicle position P1 as a reference point and determines the predicted position P2' of the vehicle based on the offset direction a2' and the predicted distance.

[0056] Please continue to refer to this. Figure 8 ,in Figure 8 It is a drawing Figure 3 A detailed flowchart of another embodiment of step S33. (See attached flowchart.) Figure 8 As shown, step S33 may include: step S331b: obtaining the forward azimuth angle based on the position of the first vehicle and the position of the second vehicle; step S333b: determining the offset direction relative to the direction of movement based on the steering wheel angle and the forward azimuth angle; step S335b: determining the predicted distance based on the time difference between the position of the second vehicle and the position of the target vehicle and the vehicle speed; and step S337b: using the position of the second vehicle as a reference point, determining the predicted position based on the predicted distance and the offset direction. Figure 8 Step S335b can be combined with Figure 7 The steps in S333a are the same, so they will not be repeated here. Figure 8 Detailed implementation of step S335b.

[0057] Similarly Figure 4For example, in step S331b, the processing device 12 can obtain the forward azimuth angle based on the first vehicle position P0 and the second vehicle position P1, where the forward azimuth angle represents the angle from the first vehicle position P0 to the second vehicle position P1. Specifically, the processing device 12 can obtain the forward azimuth angle using the following formula (1):

[0058]

[0059] Where θ is the forward azimuth angle (clockwise from the north); Let P0 be the latitude of the first vehicle's position. Let Δλ be the latitude of the second vehicle position P1; Δλ is the difference between the longitude of the first vehicle position P0 and the longitude of the second vehicle position P1.

[0060] In steps S333b and S335b, the processing device 12 uses the sum of the steering wheel angle and the forward azimuth angle θ as the offset direction a2', and obtains the predicted distance.

[0061] In step S337b, the processing device 12 can determine the predicted position P2' based on the predicted distance and offset direction a2' (denoted by α in equations (2) and (3)) using the following formulas (2) and (3):

[0062]

[0063]

[0064] in λ1 is the latitude of the target position P2; λ2 is the longitude of the target position P2; λ1 is the longitude of the second vehicle position P1; α is the offset direction a2'; and α is the angle. d is the predicted distance, and R is the Earth's radius.

[0065] Accordingly, the processing device 12 can obtain a more accurate predicted position P2'.

[0066] Please refer to Figure 9 ,in Figure 9 This is a block diagram of a vehicle computer and other automotive components connected to the vehicle computer, according to another embodiment of the present invention. The vehicle computer A2 includes a controller area network 11, a processing unit 12, a positioning component 13, a steering torque angle sensor 14, and a speed sensor 15, wherein the controller area network 11 and the processing unit 12 of the vehicle computer A2 can be respectively connected to… Figure 1 The vehicle's computer A1 has the same controller area network 11 and processing unit 12, while the positioning component 13, steering torque angle sensor 14, and speed sensor 15 are selectively configured elements. Furthermore, as... Figure 9As shown, the controller area network 11 can be electrically connected to the steering torque angle sensor 14 to obtain the steering wheel angle, and the controller area network 11 can be electrically or communicatively connected to the speed sensor 15 to obtain the vehicle speed.

[0067] The positioning component 13 can be electrically or communicatively connected to the controller area network 11, and connected to the processing device 12 via the controller area network 11. The positioning component 13 can be a component of the aforementioned satellite positioning system, such as a Global Navigation Satellite System (GNSS), Global Positioning System (GPS), GLONASS (Russian Global Navigation Satellite System), Galileo, or BeiDou Navigation Satellite System (BDS). The processing device 12 can obtain the vehicle position through the positioning of the positioning component 13, and then determine whether the amount of data obtained (vehicle position) is sufficient to execute the vehicle positioning anomaly detection method described in one or more of the above embodiments.

[0068] For a more detailed explanation of the embodiment in which the processing device 12 determines whether the amount of data is sufficient, please also refer to... Figure 9 and Figure 10 ,in Figure 10 This is a flowchart illustrating a vehicle positioning anomaly detection method according to another embodiment of the present invention. Figure 10 As shown, during execution Figure 2 Before step S1 or step S3, the processing device 12 may further execute: step S01a: obtaining the starting position from the vehicle's positioning component; step S03a: obtaining the sampling position from the positioning component, wherein the sampling time point corresponding to the sampling position differs from the sampling time point corresponding to the starting position by a sampling time interval; step S05a: obtaining the sampling distance between the starting position and the sampling position; step S07a: determining whether the sampling distance reaches a preset distance; if the determination result of step S07a is "yes", execute step S09a: obtaining at least three vehicle positions for each of these driving data based on the starting position, the sampling position, and multiple positions between the starting position and the sampling position; and if the determination result of step S07a is "no", execute step S011a: obtaining another sampling position from the positioning component, wherein the sampling time point corresponding to the other sampling position differs from the sampling time point corresponding to the sampling position by a sampling time interval.

[0069] In step S01a, the processing device 12 obtains positioning information from the vehicle's positioning component 13 as the starting position. In step S03a, the processing device 12 obtains another positioning information from the vehicle's positioning component 13 as a sampling position, wherein the sampling time point corresponding to the sampling position differs from the sampling time point corresponding to the starting position by a sampling time interval, which depends on the reporting speed of the aforementioned satellite positioning system. The sampling time point is the time point at which the positioning component 13 generates the starting position / sampling position. In other words, the faster the reporting speed of the satellite positioning system, the shorter the sampling time interval. One or more embodiments of the present invention do not limit the actual value of the sampling time interval. In short, in steps S01a and S03a, the processing device 12 uses the first positioning information as the starting position, and the positioning information obtained after the first positioning information is used as the sampling position.

[0070] Next, the processing device 12 executes a distance determination procedure, which includes steps S05a and S07a. In step S05a, the processing device 12 calculates the distance between the starting position and the sampling position (hereinafter referred to as the first sampling position) as the sampling distance and executes the distance determination procedure. In step S07a, the processing device 12 determines whether the sampling distance is equal to or greater than a preset distance, wherein the preset distance is, for example, 500 meters or 1000 meters, but is not limited to one or more embodiments of the present invention.

[0071] If the sampling distance is equal to or greater than the preset distance, it means that the number of data points corresponding to the starting position, the first sampling position, and multiple positions between the starting position and the first sampling position is sufficient to determine a predictable position for reference. Therefore, in step S09a, the processing device 12 can obtain the at least three vehicle positions for each of these driving data points based on the starting position, the first sampling position, and the positions between the starting position and the first sampling position. Next, the processing device 12 can execute step S03a again.

[0072] If the sampling distance is not equal to or greater than the preset distance, it means that the starting position, the first sampling position, and the position between the starting position and the first sampling position corresponding to this sampling distance are still insufficient to determine a predictable position with reference value. Therefore, in step S011a, the processing device 12 can obtain another sampling position (hereinafter referred to as the second sampling position) from the positioning component 13. The sampling time point corresponding to the second sampling position is also different from the sampling time point of the first sampling position by the sampling time interval, and the sampling time point corresponding to the second sampling position is later than the sampling time point of the first sampling position. Then, the processing device 12 can execute the distance judgment procedure again.

[0073] In short, if the processing device 12 determines in step S07a that the sampling distance has reached the preset distance, the processing device 12 will use all the currently obtained positions as the vehicle position of the driving data; if the processing device 12 determines in step S07a that the sampling distance has not reached the preset distance, the processing device 12 will continue to obtain the sampling position from the self-positioning component 13 until the distance between the starting position and the currently obtained sampling position reaches the preset distance.

[0074] For a more detailed explanation of another embodiment of the processing device 12 determining whether the amount of data is sufficient, please refer to the following: Figure 1 or Figure 9 and Figure 11 ,in Figure 11 This is a flowchart illustrating a vehicle positioning anomaly detection method according to another embodiment of the present invention. Figure 11 As shown, during execution Figure 3 Before step S1 or step S3, the processing device 12 may further perform: step S01b: obtaining the start time point of the corresponding starting position; step S03b: calculating the driving distance based on the start time point and vehicle speed; and step S05b: in response to the driving distance reaching a preset distance, obtaining at least three vehicle positions for each of these driving data based on the starting position, the end position of the corresponding driving distance, and multiple positions between the starting position and the end position.

[0075] In step S01b, the processing device 12 can obtain the start time point corresponding to the starting position. The start time point is the time point when the satellite positioning system generates the starting position, wherein the processing device 12 can obtain the starting position from the positioning component 13. In step S03b, the processing device 12 can calculate the driving distance of the vehicle from the starting position based on the start time point and the vehicle speed obtained from the controller area network 11. In step S05b, the processing device 12 can continuously calculate the driving distance of the vehicle based on the current positioning position of the vehicle, and after determining that the driving distance is equal to or greater than a preset distance, it can obtain at least three vehicle positions for each of these driving data, based on the starting position, the ending position of the corresponding driving distance, and multiple positions between the starting position and the ending position.

[0076] It should also be noted that, assuming that the positions between the starting position and the sampling position (or the ending position) are the first position to the fourth position, the processing device 12 can take the starting position, the first position and the second position as three vehicle positions in one set of driving data, and take the first position, the second position and the third position as at least three vehicle positions in another set of driving data, and so on; or, the processing device 12 can take the starting position, the first position and the second position as three vehicle positions in one set of driving data, and take the third position, the fourth position and the sampling position (or the ending position) as at least three vehicle positions in another set of driving data.

[0077] In summary, the vehicle positioning anomaly detection method and vehicle computer according to one or more embodiments of the present invention can realize the function of real-time self-detection of the vehicle and output an anomaly notification when it is determined that the four-wheel alignment of the vehicle is abnormal, so that drivers and vehicle manufacturers can detect the four-wheel alignment anomaly before the vehicle is brought in for maintenance. Therefore, it can bring the benefit of improved safety to the field of autonomous vehicles. In addition, by determining whether the amount of data is sufficient before determining the predicted position, it is possible to ensure that the predicted position has sufficient reference value, so as to avoid the vehicle computer sending anomaly notifications erroneously.

[0078] While the present invention has been disclosed above with reference to the foregoing embodiments, it is not intended to limit the invention. Any modifications and refinements made without departing from the spirit and scope of the invention are within the scope of patent protection of the present invention. For a description of the scope of protection defined in the present invention, please refer to the appended claims.

Claims

1. A method for detecting vehicle positioning anomalies, executed in a processing device, the method comprising: Acquire multiple sets of driving data, each of which includes at least three vehicle positions, a steering wheel angle, and a vehicle speed; An error calculation program is executed for each of the driving data to obtain multiple error data points; as well as In response to the sum of the multiple error data points exceeding a tolerance value, an anomaly notification is output. The error calculation procedure includes: A predicted position is obtained based on a direction of movement, the vehicle speed, and the steering wheel angle; and The difference between the predicted location and a target location is determined as one of the multiple error data points, wherein the predicted location and the target location correspond to the same timestamp. The determination of the difference between the predicted location and the target location of the vehicle includes: Determine an error vector between the predicted position and the target position; and The error vector is used as the multiple error data.

2. The vehicle positioning anomaly detection method as described in claim 1, wherein the error calculation program further includes: The direction of movement is obtained based on the positions of at least three vehicles; as well as Select one of the at least three vehicle locations as the target location.

3. The vehicle positioning anomaly detection method as described in claim 2, wherein the at least three vehicle positions include a first vehicle position, a second vehicle position, and the target position; the first vehicle position corresponds to a first timestamp; the second vehicle position, the vehicle speed, and the steering wheel angle correspond to a second timestamp; and the predicted position and the target position correspond to a third timestamp; and The third timestamp is later than the second timestamp, and the second timestamp is later than the first timestamp.

4. The vehicle positioning anomaly detection method as described in claim 2, wherein the at least three vehicle positions include a first vehicle position, a second vehicle position following the first vehicle position, and the target position, the direction of movement is from the first vehicle position to the second vehicle position, and obtaining the predicted position based on the direction of movement, the vehicle speed, and the steering wheel angle includes: Determine the offset direction relative to the direction of movement based on the steering wheel angle; A predicted distance is determined based on the time difference between the second vehicle's position and the target position, as well as the vehicle's speed; and Using the position of the second vehicle as a reference point, the predicted position is determined based on the predicted distance and the offset direction.

5. The vehicle positioning anomaly detection method as described in claim 2, wherein the at least three vehicle positions include a first vehicle position, a second vehicle position following the first vehicle, and the target position, the direction of movement is from the first vehicle position to the second vehicle position, and obtaining the predicted position based on the direction of movement, the vehicle speed, and the steering wheel angle includes: A forward azimuth angle is obtained based on the positions of the first vehicle and the second vehicle; Determine an offset direction relative to the direction of movement based on the steering wheel angle and the forward azimuth angle; A predicted distance is determined based on the time difference between the position of the second vehicle and the position of the target vehicle, and the vehicle speed; and Using the position of the second vehicle as a reference point, the predicted position is determined based on the predicted distance and the offset direction.

6. The vehicle positioning anomaly detection method as described in claim 5, wherein determining the offset direction relative to the direction of movement based on the steering wheel angle and the forward azimuth angle includes: The offset direction is the sum of the steering wheel angle and the forward azimuth angle.

7. The vehicle positioning anomaly detection method of claim 2, wherein before performing the error calculation procedure on each of the vehicle's driving data, the method further comprises: The starting point position is obtained from a positioning component of the vehicle; The positioning component obtains a sampling position, wherein the sampling time point corresponding to the sampling position differs from the sampling time point corresponding to the starting position by a sampling time interval. Execute a distance determination program, wherein the distance determination program includes: Obtain a sampling distance between the starting position and the sampling position; and Determine whether the sampling distance reaches a preset distance; If the sampling distance reaches the preset distance, based on the starting position, the sampling position, and multiple positions between the starting position and the sampling position, obtain at least three vehicle positions for each of the driving data; and If the sampling distance does not reach the preset distance, another sampling position is obtained from the positioning component to execute the distance judgment procedure again, wherein the sampling time point corresponding to the other sampling position differs from the sampling time point corresponding to the sampling position by the sampling time interval.

8. The vehicle positioning anomaly detection method of claim 2, wherein before performing the error calculation procedure on each of the vehicle's driving data, the method further comprises: Obtain the start time point corresponding to the start point location; Calculate the distance traveled based on the start time and vehicle speed; as well as In response to the travel distance reaching a preset distance, the at least three vehicle positions for each of the travel data are obtained based on the starting position, an ending position corresponding to the travel distance, and multiple positions between the starting position and the ending position.

9. The vehicle positioning anomaly detection method as described in claim 2, wherein obtaining the direction of movement based on the at least three vehicle positions includes: Determine that none of the timestamps corresponding to the steering wheel angle are timestamps that correspond to the vehicle position; as well as Perform the following for each of the one or more timestamps: Determine the previous timestamp that is closest to the current timestamp and has a corresponding vehicle location; Determine the next timestamp that is closest to the current timestamp and has a corresponding vehicle location; Perform interpolation between the vehicle location corresponding to the previous timestamp and the vehicle location corresponding to the next timestamp to obtain an interpolated location; and The interpolated position is used as one of the vehicle positions.

10. An in-vehicle computer, comprising: A controller area network for acquiring multiple sets of driving data, each of which includes at least three vehicle positions, a steering wheel angle, and a vehicle speed; and A processing device, connected to the controller area network, is configured to obtain the driving data from the controller area network, and execute an error calculation program on each of the vehicle driving data to obtain multiple error data points, and output an anomaly notification in response to the sum of the multiple error data points exceeding a tolerance value. The error calculation procedure includes: A predicted position is obtained based on a direction of movement, the vehicle speed, and the steering wheel angle; and The difference between the predicted location and a target location is determined as one of the multiple error data points, wherein the predicted location and the target location correspond to the same timestamp. The processing device performs the following steps to determine the difference between the predicted position and the target position of the vehicle: Determine an error vector between the predicted position and the target position; and The error vector is used as the multiple error data.

11. The vehicle computer of claim 10, wherein the error calculation program comprises: The direction of movement is obtained based on the positions of at least three vehicles; as well as Select one of the at least three vehicle locations as the target location.

12. The vehicle computer of claim 11, wherein the at least three vehicle positions include a first vehicle position, a second vehicle position, and the target position, the first vehicle position corresponding to a first timestamp, the second vehicle position, the vehicle speed, and the steering wheel angle corresponding to a second timestamp, and the predicted position and the target position corresponding to a third timestamp; and The third timestamp is later than the second timestamp, and the second timestamp is later than the first timestamp.

13. The vehicle computer of claim 11, wherein the at least three vehicle positions include a first vehicle position, a second vehicle position following the first vehicle, and the target position, the direction of movement is a vector from the first vehicle position to the second vehicle position, and the processing device performs the operation of obtaining the predicted position based on the direction of movement, the vehicle speed, and the steering wheel angle, comprising: Determine the offset direction relative to the direction of movement based on the steering wheel angle; A predicted distance is determined based on the time difference between the position of the second vehicle and the position of the target vehicle, and the vehicle speed; and Using the position of the second vehicle as a reference point, the predicted position is determined based on the predicted distance and the offset direction.

14. The vehicle computer of claim 11, wherein the at least three vehicle positions include a first vehicle position, a second vehicle position following the first vehicle, and the target position, and the processing device performs the operation of obtaining the predicted position based on the direction of movement, the vehicle speed, and the steering wheel angle, comprising: A forward azimuth angle is obtained based on the positions of the first vehicle and the second vehicle; Determine an offset direction relative to the direction of movement based on the steering wheel angle and the forward azimuth angle; A predicted distance is determined based on the time difference between the position of the second vehicle and the position of the target vehicle, and the vehicle speed; and Using the position of the second vehicle as a reference point, the predicted position is determined based on the predicted distance and the offset direction.

15. The vehicle computer of claim 14, wherein the processing device performs the function of determining the offset direction relative to the direction of movement based on the steering wheel angle and the forward azimuth angle, comprising: The processing device uses the sum of the steering wheel angle and the forward azimuth angle as the offset direction.

16. The vehicle computer of claim 11, further comprising a positioning component connected to the processing device, the positioning component being used to obtain a starting position, wherein before executing the error calculation procedure on each of the driving data of the vehicle, the processing device further performs: The starting point position is obtained from the positioning component of the vehicle; The positioning component obtains a sampling position, wherein the sampling time point corresponding to the sampling position differs from the sampling time point corresponding to the starting position by a sampling time interval. Execute a distance determination program, wherein the distance determination program includes: Obtain a sampling distance between the starting position and the sampling position; and Determine whether the sampling distance reaches a preset distance; If the sampling distance reaches the preset distance, based on the starting position, the sampling position, and multiple positions between the starting position and the sampling position, obtain at least three vehicle positions for each of the driving data; and If the sampling distance does not reach the preset distance, another sampling position is obtained from the positioning component to execute the distance judgment procedure again, wherein the sampling time point corresponding to the other sampling position differs from the sampling time point corresponding to the sampling position by the sampling time interval.

17. The vehicle computer of claim 11, wherein before executing the error calculation procedure on each of the vehicle's driving data, the processing device further performs: Obtain the start time point corresponding to the start point location; Calculate a travel distance based on the start time and vehicle speed; and In response to the travel distance being equal to or greater than a preset distance, the at least three vehicle positions for each of the travel data are obtained based on the starting position, an ending position corresponding to the travel distance, and multiple positions between the starting position and the ending position.

18. The vehicle computer of claim 11, wherein the processing device performs the task of obtaining the direction of movement based on the at least three vehicle positions, comprising: Determine that none of the timestamps corresponding to the steering wheel angle are timestamps that correspond to the vehicle position; as well as Perform the following for each of the one or more timestamps: Determine the previous timestamp that is closest to the current timestamp and has a corresponding vehicle location; Determine the next timestamp that is closest to the current timestamp and has a corresponding vehicle location; Perform interpolation between the vehicle location corresponding to the previous timestamp and the vehicle location corresponding to the next timestamp to obtain an interpolated location; and The interpolated position is used as one of the vehicle positions.

Images