TW202303462A - Fault diagnosis system and method for state of radar system of roadside units - Google Patents

Abstract

The present disclosure provides a fault diagnosis method for the state of a radar system of roadside units, which includes steps as follows. The feature extraction is performed on the radar information of the radar system of the roadside units to extract the feature information; the weight training is performed on the feature information and preset error to obtain the weighted feature information; the weighted feature information is classified and diagnosed to determine whether the radar system of roadside units has the preset error.

Description

System and method for error diagnosis of state of radar system of roadside unit

The present invention relates to a system and its method, and in particular to an error diagnosis system and an error diagnosis method for the status of a radar system of a roadside unit.

With the advent of the 5G generation, smart transportation has begun to be widely discussed, and roadside units for monitoring will also be deployed in large numbers. The radar systems of the existing roadside units mostly rely on manual adjustment and setting.

However, it is impractical to rely solely on human monitoring for a large number of radar systems deployed in smart transportation.

The present invention proposes an error diagnosis system and error diagnosis method for the state of the radar system of the roadside unit, which improves the problems of the prior art.

In an embodiment of the present invention, the error diagnosis system for the state of the radar system of the roadside unit proposed by the present invention includes a storage device and a processor. The storage device stores at least one instruction, and the processor is electrically connected to the storage device. The processor is used to access and execute at least one instruction to: perform feature extraction on the radar information of the radar system of the roadside unit to extract feature information; perform weight training on the feature information and default errors to obtain weight feature information; The feature information is classified and diagnosed to determine whether there is a preset error in the radar system of the roadside unit.

In an embodiment of the present invention, the processor is used to access and execute at least one instruction: collect radar information of the radar system of the roadside unit using radar detection points and radar tracking points, The feature information extracted through feature extraction includes complex features. The complex features include: the number of complex detection points, the maximum value of the complex detection points at the x coordinate, the maximum value of the complex detection points at the y coordinate, and the complex detection points The average value of the x-coordinate, the average value of the multiple detection points at the y-coordinate, the minimum value of the multiple detection points at the x-coordinate, the minimum value of the multiple detection points at the y-coordinate, the standard deviation of the multiple detection points at the x-coordinate , the standard deviation of the complex detection points at the y coordinate, the quadratic dynamic difference of the complex detection points at the x coordinate, the quadratic dynamic difference of the complex detection points at the y coordinate, the number of the complex tracking points, the complex tracking points at The average value of the x-coordinate, the maximum value of the complex tracking points at the x-coordinate, the minimum value of the complex tracking points at the x-coordinate, the standard deviation of the complex tracking points at the x-coordinate, the quadratic dynamic difference of the complex tracking points at the x-coordinate, complex The average value of the tracking points at the y coordinate, the maximum value of the multiple tracking points at the y coordinate, the minimum value of the multiple tracking points at the y coordinate, the standard deviation of the multiple tracking points at the y coordinate, the quadratic dynamic difference of the multiple tracking points at the y coordinate value and track features extracted from multiple track points at multiple time points.

In one embodiment of the present invention, the preset error is one or more preset error states of the radar system of the roadside unit, and the processor is used to access and execute at least one instruction to: execute a weight training algorithm (attention), so as to combine the features Information and one or more preset error states for weight training.

In an embodiment of the present invention, the weight training algorithm performs weight training according to the degree of correlation between one or more preset error states and the feature information, and gives different weights to the multiple features in the feature information.

In one embodiment of the present invention, the processor is used to access and execute at least one instruction to: execute a fault diagnosis neural network (Diagnosis-Net), so as to classify and compare the characteristic information or weight characteristic information of the radar system of the roadside unit diagnosis.

In one embodiment of the present invention, the error diagnosis method of the error diagnosis system for the status of the radar system of the roadside unit proposed by the present invention includes the following steps: (A) performing feature extraction on the radar information of the radar system of the roadside unit, extracting Extract feature information; (B) perform weight training on feature information and default errors to obtain weight feature information; (C) classify and diagnose weight feature information to determine whether there is a default error in the radar system of the roadside unit.

In one embodiment of the present invention, the step (A) includes: collecting the radar information of the radar system of the roadside unit using radar detection points and radar tracking points, and extracting them through feature extraction The feature information includes complex features, and the complex features include: the number of complex detection points, the maximum value of complex detection points at the x coordinate, the maximum value of the complex detection points at the y coordinate, and the average value of the complex detection points at the x coordinate , the average value of the complex detection points at the y-coordinate, the minimum value of the complex detection points at the x-coordinate, the minimum value of the complex detection points at the y-coordinate, the standard deviation of the complex detection points at the x-coordinate, the complex detection point at The standard deviation of the y-coordinate, the quadratic dynamic difference of the multiple detection points at the x-coordinate, the quadratic dynamic difference of the multiple detection points at the y-coordinate, the number of multiple tracking points, the average value of the multiple tracking points at the x-coordinate, The maximum value of the complex tracking points at the x coordinate, the minimum value of the complex tracking points at the x coordinate, the standard deviation of the complex tracking points at the x coordinate, the quadratic dynamic difference of the complex tracking points at the x coordinate, the The average value, the maximum value of the multiple tracking points at the y coordinate, the minimum value of the multiple tracking points at the y coordinate, the standard deviation of the multiple tracking points at the y coordinate, the quadratic dynamic difference of the multiple tracking points at the y coordinate, and the multiple tracking points at Trajectory features extracted at multiple time points.

In one embodiment of the present invention, the default error is one or more default error states of the radar system of the roadside unit, and step (B) includes: executing a weight training algorithm to combine feature information with one or more default error states Do weight training.

In an embodiment of the present invention, the weight training algorithm performs weight training according to the degree of correlation between one or more preset error states and the feature information, and gives different weights to the multiple features in the feature information.

In one embodiment of the present invention, the step (C) includes: executing the error diagnosis neural network, so as to classify and diagnose the feature information or weighted feature information of the radar system of the roadside unit.

To sum up, the technical solution of the present invention has obvious advantages and beneficial effects compared with the prior art. With the technical solution of the present invention, the error diagnosis is automated, the labor cost in operation is reduced, and the diagnosis efficiency is improved.

The above-mentioned description will be described in detail in the following implementation manners, and further explanations will be provided for the technical solution of the present invention.

In order to make the description of the present invention more detailed and complete, reference may be made to the accompanying drawings and various embodiments described below, and the same numbers in the drawings represent the same or similar elements. On the other hand, well-known elements and steps have not been described in the embodiments in order to avoid unnecessarily limiting the invention.

Please refer to FIG. 1 , the technical aspect of the present invention is a state error diagnosis system 100 of a radar system 190 of a roadside unit, which can be applied to intelligent transportation, or widely used in related technical links. The error diagnosis system 100 of the state of the radar system of the roadside unit of this technical aspect can achieve considerable technical progress, and has wide application value in the industry. The specific implementation of the error diagnosis system 100 for the status of the radar system of the roadside unit will be described below with reference to FIG. 1 .

It should be understood that various implementations of the error diagnosis system 100 for the status of the radar system 190 of the roadside unit are described in conjunction with FIG. 1 . In the following description, for ease of explanation, numerous specific details are further set forth in order to provide a comprehensive illustration of one or more embodiments. However, the technology may be practiced without these specific details. In other instances, well-known structures and devices are shown in block diagram form in order to effectively describe the embodiments. The term "for example" used here means "as an example, instance or illustration". Any embodiment described herein as "by way of example" is not necessarily to be construed as preferred or advantageous over other embodiments.

FIG. 1 is a block diagram of a state error diagnosis system 100 of a radar system 190 of a roadside unit according to an embodiment of the present invention. As shown in FIG. 1 , the error diagnosis system 100 for the state of the radar system of the roadside unit includes a storage device 110 , a processor 120 and a display 130 . For example, the storage device 110 can be a hard disk, a flash storage device or other storage media, the processor 120 can be a central processing unit, and the display 130 can be a built-in display or an external monitor.

In terms of architecture, the fault diagnosis system 100 is electrically connected to the radar system 190 of the roadside unit, the storage device 110 is electrically connected to the processor 120 , and the processor 120 is electrically connected to the display 130 . In practice, for example, the RSU radar system 190 may include a plurality of radars assigned to each RSU. It should be understood that, in the descriptions of the embodiments and the scope of the patent application, the description of "electrical connection" may generally refer to the indirect electrical coupling of one element to another element through other elements, or the direct electrical coupling of an element without passing through other elements. electrically connected to another component. For example, the storage device 110 may be a built-in storage device electrically connected to the processor 120 directly, or the storage device 110 may be an external storage device indirectly connected to the processor 120 through a wire.

When in use, the storage device 110 stores at least one instruction, and the processor 120 is used to access and execute at least one instruction to: perform feature extraction on the information of the radar system 190 of the roadside unit, extract the feature information; combine the feature information with the preset Perform weight training incorrectly to obtain weight feature information; classify and diagnose the weight feature information to determine whether the radar system of the roadside unit has a preset error. The display 130 can display relevant diagnostic results.

Specifically, the core algorithm of the error diagnosis system 100 disclosed in the present invention includes three parts: feature extraction from the information of the radar system 190 of the roadside unit, weight training of the extracted feature information and corresponding errors, state classification training and evaluation indicator design. Before implementing the algorithm, first collect relevant radar information for the state generated by the radar system 190 of the roadside unit, and obtain important radar state features through feature extraction. In order to speed up the convergence speed of the error diagnosis model, the extracted data is first normalized Preprocessing; then, classify and train the preprocessed data, and finally, judge the status of the radar system of the roadside unit based on the classification result.

In practice, for example, the radar system 190 of the roadside unit is described by taking the model IWR6843 as an example, but it does not limit the present invention. In this example, it is assumed that the radar system 190 of the RSU may have multiple configuration states, including: Normality, excessive down tilt (ED) of the radar system 190 of the RSU, and excessive down tilt of the RSU radar system 190 Excessive up tilt (EU), the horizontal angle of the radar system 190 of the roadside unit rotates clockwise (azimuth clockwise rotates, ACR), the horizontal angle of the radar system 190 of the roadside unit rotates counterclockwise (azimuth counter-clockwise rotates, ACCR) and the state of shift (shift) of the radar system 190 of the roadside unit, the scope of application of this invention includes but is not limited to the aforementioned state. In addition, in this example, the information of the radar system 190 of the roadside unit is collected by means of radar detection points and radar tracking points, and important radar system information is extracted through feature extraction technology, and then passed through the features The extracted data is fed into the error diagnosis model for error diagnosis, thereby judging the state of the radar system 190 .

In one embodiment of the present invention, the core algorithm of the error diagnosis system 100 of the state of the radar system 190 of the roadside unit is completed using machine learning technology, which is roughly divided into three steps: radar information feature extraction, weight training, and error diagnosis. part.

With regard to radar information feature extraction, in an embodiment of the present invention, the processor 120 is used to access and execute at least one instruction to: use radar detection points and radar tracking for the radar information of the radar system of the roadside unit Tracking points are collected, and the feature information extracted through feature extraction includes complex features. The complex features include: the number of complex detection points, the maximum value of the complex detection points at the x coordinate, and the y coordinate of the complex detection points The maximum value of the complex detection points at the x-coordinate, the average value of the complex detection points at the y-coordinate, the minimum value of the complex detection points at the x-coordinate, the minimum value of the complex detection points at the y-coordinate, the complex detection point Standard deviation of measuring points at x-coordinate, standard deviation of complex detection points at y-coordinate, quadratic dynamic difference of complex detection points at x-coordinate, quadratic dynamic value of complex detection points at y-coordinate, complex tracking The number of points, the average value of multiple tracking points at the x-coordinate, the maximum value of multiple tracking points at the x-coordinate, the minimum value of multiple tracking points at the x-coordinate, the standard deviation of multiple tracking points at the x-coordinate, the multiple tracking points at the x-coordinate Quadratic dynamic difference, the average value of multiple tracking points at the y-coordinate, the maximum value of multiple tracking points at the y-coordinate, the minimum value of multiple tracking points at the y-coordinate, the standard deviation of multiple tracking points at the y-coordinate, and the multiple tracking points Quadratic momentum at the y-coordinate, trajectory features extracted at multiple time points for multiple tracking points, and/or other features.

Next, with regard to weight training, in one embodiment of the present invention, the above-mentioned preset error is one or more preset error states of the radar system of the roadside unit (such as: excessive downward, excessive upward, horizontal angle clockwise rotation , horizontal angle counterclockwise rotation, offset, etc.), the processor 120 is used to access and execute at least one instruction to: execute a weight training algorithm (attention), so as to perform weight training on feature information and one or more preset error states . The weight training algorithm performs weight training according to the degree of correlation between one or more preset error states and the feature information, and gives different weights to the plural features in the feature information.

In practice, different radar information will have different degrees of correlation with the radar system status error symptoms of different roadside units, for example: the radar system 190 of the roadside unit is excessively down, which may be related to the average value of the tracking point at the y coordinate, the tracking The maximum value of the point at the y-coordinate, the minimum value of the tracking point at the y-coordinate, the standard deviation of the tracking point at the y-coordinate, and the track features extracted at multiple time points are highly correlated with the number of detection points, detection The average value of the measuring point at the x-coordinate, the standard deviation of the detection point at the x-coordinate, the number of tracking points, the average value of the tracking point at the x-coordinate, the maximum value of the tracking point at the x-coordinate, the minimum value of the tracking point at the x-coordinate, The correlation of the standard deviation of the tracking point at the x-coordinate is low. If only human professional knowledge and the radar information of the corresponding roadside unit are used for judgment and analysis, it may lead to a decrease in diagnostic accuracy due to misjudgment of the correlation. Therefore, in order to enable the error diagnosis system 100 to learn the correlation between the state of the radar system 190 of the roadside unit and the corresponding radar information, before performing the error diagnosis, a weight training algorithm (attention) is executed to make the error diagnosis system 100 according to the state The degree of correlation with the radar information of the radar system of the roadside unit is trained and given different weights to improve the diagnostic accuracy.

In practice, for example, the weight training algorithm (attention) is a deep learning mechanism. Its goal is to let the model focus on important information and fully learn and absorb it. By giving higher weights to highly relevant information, it strengthens Its degree of importance; giving low or 0 weight to information with low correlation, reducing its influence, so as to achieve its goal. For example, the weight training algorithm (attention) architecture includes three hidden fully connected layers (hidden fully connected layers), respectively composed of 50, 20 and 6 neurons (neurons), and then uses softmax as the activation function to handle nonlinearity Features (non-linear property). The overall weight training first feeds each state and its corresponding radar information into the attention framework for weight training. After the weight configuration of each state is trained through attention, the Hadamard product is performed on the input radar information and the corresponding weight. The access to the weight matrix is omitted to speed up the calculation, that is, the weight training of each state is completed.

Regarding classification and diagnosis, in practice, for example, in order to simultaneously detect multiple errors of the radar system 190 of the roadside unit, assuming that there are N types of tags, the present invention uses sigmoid as the activation function (activation function) The N-dimensional vector is used as the output of the classifier. When the predicted value is greater than 0.5, it is judged that there is such an error. The value range of the sigmoid function is [0,1], which can be regarded as the probability that the classifier believes that a certain error occurs, and judges what kind of error occurs in the radar system of the current roadside unit. The loss function for classification can be binary cross entropy (BCE).

In an embodiment of the present invention, the processor 120 is used to access and execute at least one instruction to: execute a fault diagnosis neural network (Diagnosis-Net), so that the characteristic information or weight characteristic information of the radar system of the roadside unit is calculated. classification and diagnosis.

In practice, for example, the Diagnosis-Net architecture includes four hidden fully connected layers, consisting of 30, 20, 10 and 5 neurons, each of which All neurons in a connection layer are connected to all neurons in the next layer, and the number of neurons in each layer is designed to decrease in order to extract and retain relatively important features to help machine learning. After multi-layer training and learning, it is possible to determine the error factors corresponding to the radar system status diagnostic information input to the roadside unit. However, the first three layers use a rectified linear unit (ReLU) as the activation function, and the last layer With sigmoid as the activation function, the main reason is that Sigmoid will output a value between 0-1, which represents the confidence level of the input data for the result of this diagnostic factor.

Continuing from the above, the threshold value (threshold) is selected according to the practice to classify the sigmoid output results as 0 or 1, which respectively represent no or detected errors in the radar system status of the roadside unit. Combining this error diagnosis neural network (Diagnosis-Net) architecture with the weight training algorithm (attention) mentioned in the first part and the final threshold design is the complete architecture of the 190 self-state diagnosis patent for the radar system of this roadside unit. In practice, it is possible to classify multiple errors. Machine learning can identify the error diagnosis factors corresponding to the input data through Sigmoid, and design different thresholds for classification.

In order to further elaborate the error diagnosis method of the error diagnosis system 100 for the state of the radar system of the roadside unit, please refer to Figures 1 and 2 at the same time. Figure 2 is a radar of a roadside unit according to an embodiment of the present invention A flowchart of the error diagnosis method 200 of the system 100 for error diagnosis of the state of the system 190 . As shown in Figure 2, the error diagnosis method 200 includes steps S201-S203 (it should be understood that the steps mentioned in this embodiment can be adjusted according to actual needs unless the order is specifically stated. , even simultaneously or partially simultaneously).

Error diagnosis method 200 may take the form of a computer program product on a non-transitory computer-readable recording medium having computer-readable instructions embodied on the medium. Suitable recording media may include any of the following: Non-volatile memory such as: Read Only Memory (ROM), Programmable Read Only Memory (PROM), Erasable Programmable Read Only Memory (EPROM), Electronically Erasable Programmable Read-Only Memory (EEPROM); Volatile Memory, such as: Static Access Memory (SRAM), Dynamic Access Memory (SRAM), Double Data Rate Random Access Memory (DDR -RAM); optical storage devices, such as: CD-ROM, DVD-ROM; magnetic storage devices, such as: hard disk drive, floppy disk drive.

In step S201, feature extraction is performed on the radar information of the radar system 190 of the roadside unit, and the feature information is extracted. In step S202, weight training is performed on the feature information and default errors to obtain weighted feature information. In step S203, the weighted feature information is classified and diagnosed to determine whether the radar system of the roadside unit has a preset error.

In one embodiment of the present invention, in step S201, the radar information of the radar system of the roadside unit is collected using radar detection points and radar tracking points, and features extracted through feature extraction The information includes complex features, which include: the number of complex detection points, the maximum value of complex detection points at the x coordinate, the maximum value of the complex detection points at the y coordinate, the average value of the complex detection points at the x coordinate, the complex The average value of the detection points at the y coordinate, the minimum value of the multiple detection points at the x coordinate, the minimum value of the multiple detection points at the y coordinate, the standard deviation of the multiple detection points at the x coordinate, and the y coordinate of the multiple detection points The standard deviation, the quadratic dynamic difference of multiple detection points at the x-coordinate, the quadratic dynamic difference of multiple detection points at the y-coordinate, the number of multiple tracking points, the average value of multiple tracking points at the x-coordinate, complex tracking The maximum value of the point at the x-coordinate, the minimum value of the multiple tracking points at the x-coordinate, the standard deviation of the multiple tracking points at the x-coordinate, the quadratic dynamic difference of the multiple tracking points at the x-coordinate, the average value of the multiple tracking points at the y-coordinate , the maximum value of the complex tracking points at the y coordinate, the minimum value of the complex tracking points at the y coordinate, the standard deviation of the complex tracking points at the y coordinate, the quadratic dynamic difference of the complex tracking points at the y coordinate, the multiple tracking points at multiple Trajectory features extracted at time points.

In one embodiment of the present invention, the default error is one or more default error states of the radar system 190 of the roadside unit. In step S202, a weight training algorithm is executed to combine feature information with one or more default error states Weight training.

In an embodiment of the present invention, the weight training algorithm performs weight training according to the degree of correlation between one or more preset error states and the feature information, and gives different weights to the multiple features in the feature information.

In one embodiment of the present invention, in step S203 , an error diagnosis neural network is executed to classify and diagnose the characteristic information or weighted characteristic information of the radar system of the roadside unit.

To sum up, the technical solution of the present invention has obvious advantages and beneficial effects compared with the prior art. With the error diagnosis system 100 of the state of the radar system 190 of the roadside unit and the error diagnosis method 200 of the present invention, the error diagnosis is automated, the labor cost in operation is reduced, and the diagnosis efficiency is improved.

Although the present invention has been disclosed above in terms of implementation, it is not intended to limit the present invention. Anyone skilled in this art can make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, the protection of the present invention The scope shall be defined by the appended patent application scope.

In order to make the above and other objects, features, advantages and embodiments of the present invention more obvious and understandable, the accompanying symbols are explained as follows: 100: Error diagnosis system 110: storage device 120: Processor 130: Display 190:Radar system of roadside unit 200: Error diagnosis method S201～S203: Steps

In order to make the above and other objects, features, advantages and embodiments of the present invention more clearly understood, the accompanying drawings are described as follows: FIG. 1 is a block diagram of an error diagnosis system for the status of a radar system of a roadside unit according to an embodiment of the present invention; and FIG. 2 is a flow chart of an error diagnosis method of an error diagnosis system for the status of a radar system of a roadside unit according to an embodiment of the present invention.

200: Error diagnosis method

S201~S203: steps

Claims (10)

An error diagnosis system for the status of a radar system of a roadside unit, the error diagnosis system comprising: a storage device storing at least one instruction; and A processor electrically connected to the storage device, wherein the processor is used to access and execute the at least one instruction to: Extract feature information from the radar information of the radar system of the road side unit; performing weight training on the feature information and preset errors to obtain weighted feature information; and Classifying and diagnosing the weighted feature information to determine whether the preset error occurs in the radar system of the roadside unit. The error diagnosis system as described in claim 1, wherein the processor is configured to access and execute the at least one instruction to: The radar information of the radar system of the roadside unit is collected by radar detection points and radar tracking points. The feature information extracted through the feature extraction includes multiple features, and these features include: The number of complex detection points, the maximum value of these detection points at x coordinate, the maximum value of these detection points at y coordinate, the average value of these detection points at x coordinate, the maximum value of these detection points at y coordinate The average value of the coordinates, the minimum value of the detection points at the x-coordinate, the minimum value of the detection points at the y-coordinate, the standard deviation of the detection points at the x-coordinate, the minimum value of the detection points at the y-coordinate Standard deviation, the quadratic dynamic difference of the detection points at the x-coordinate, the quadratic dynamic difference of the detection points at the y-coordinate, the number of multiple tracking points, the average value of the tracking points at the x-coordinate, The maximum value of these tracking points at x-coordinate, the minimum value of these tracking points at x-coordinate, the standard deviation of these tracking points at x-coordinate, the quadratic dynamic difference value of these tracking points at x-coordinate, the The average value of the points at the y coordinate, the maximum value of the tracking points at the y coordinate, the minimum value of the tracking points at the y coordinate, the standard deviation of the tracking points at the y coordinate, the two of the tracking points at the y coordinate Secondary motion difference and trajectory features extracted from these tracking points at multiple time points. The error diagnosis system as described in claim 2, wherein the default error is one or more default error states of the radar system of the roadside unit, and the processor is used to access and execute the at least one instruction to: Executing a weight training algorithm (attention), so as to perform weight training on the feature information and the one or more preset error states. The error diagnosis system as described in claim 3, wherein the weight training algorithm performs the weight training and gives different weights to the features in the feature information according to the degree of correlation between the one or more preset error states and the feature information. The error diagnosis system as described in claim 1, wherein the processor is configured to access and execute the at least one instruction to: Executing an error diagnosis neural network (Diagnosis-Net), so as to classify and diagnose the feature information or the weighted feature information of the radar system of the roadside unit. A method for diagnosing errors of the status of a radar system of a roadside unit, the method for diagnosing errors includes the following steps: (A) Feature extraction is performed on the radar information of the radar system of the road side unit, and the feature information is extracted; (B) performing weight training on the feature information and preset errors to obtain weighted feature information; and (C) Classifying and diagnosing the weight feature information to determine whether the preset error occurs in the radar system of the roadside unit. The error diagnosis method as described in Claim 6, wherein step (A) includes: The radar information of the radar system of the roadside unit is collected by radar detection points and radar tracking points. The feature information extracted through the feature extraction includes multiple features, and these features include: The number of complex detection points, the maximum value of the detection points at the x-coordinate, the average value of the detection points at the x-coordinate, the minimum value of the detection points at the x-coordinate, the maximum value of the detection points at the y The maximum value of the coordinates, the average value of the detection points at the y coordinate, the minimum value of the detection points at the y coordinate, the standard deviation of the detection points at the x coordinate, the distance between the detection points at the y coordinate Standard deviation, the quadratic dynamic difference of the detection points at the x-coordinate, the quadratic dynamic difference of the detection points at the y-coordinate, the number of multiple tracking points, the average value of the tracking points at the x-coordinate, The maximum value of these tracking points at x-coordinate, the minimum value of these tracking points at x-coordinate, the standard deviation of these tracking points at x-coordinate, the quadratic dynamic difference value of these tracking points at x-coordinate, the The average value of the points at the y coordinate, the maximum value of the tracking points at the y coordinate, the minimum value of the tracking points at the y coordinate, the standard deviation of the tracking points at the y coordinate, the two of the tracking points at the y coordinate Secondary motion difference and trajectory features extracted from these tracking points at multiple time points. The error diagnosis method as described in Claim 7, wherein the preset error is one or more preset error states of the radar system of the roadside unit, and the step (B) includes: Executing a weight training algorithm to perform weight training on the feature information and the one or more preset error states. The error diagnosis method as described in Claim 8, wherein the weight training algorithm performs the weight training and gives different weights to the features in the feature information according to the degree of correlation between the one or more preset error states and the feature information. The error diagnosis method as described in Claim 6, wherein step (C) includes: Executing an error diagnosis neural network to classify and diagnose the characteristic information or the weighted characteristic information of the radar system of the roadside unit.

Images