CN116343177A - Method and system for monitoring intelligent traffic abnormal driving behavior based on data processing - Google Patents
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Abstract
The invention relates to the technical field of data processing, and discloses a method and a system for monitoring intelligent traffic abnormal driving behavior based on data processing, wherein a data set is generated by collecting initial data of vehicle driving and processing; constructing a driving behavior recognition model, guiding and optimizing the driving behavior recognition model by using a loss function, and calculating a loss value between a predicted value and a true value; the method comprises the steps of training a driving behavior recognition model by using a training set, presetting the iteration times and the learning rate to be complete, stopping training until the iteration times are equal to the maximum iteration times, and generating a trained driving behavior recognition model; the method comprises the steps of collecting vehicle running data in an actual scene, inputting the collected vehicle running data as a sample to be detected into a trained driving behavior recognition model, recognizing abnormal driving behaviors, highlighting data features of driving data when the abnormal driving behaviors are sent out, and improving recognition accuracy of the abnormal driving behaviors.
Description
Technical Field
The application relates to the technical field of data processing, in particular to a method and a system for monitoring intelligent traffic abnormal driving behaviors based on data processing.
Background
With the development of economy and society, the living standard is gradually improved, and people can travel without help of vehicles. The related transportation means brings convenience to people and various problems such as traffic congestion, traffic safety and the like. While aggressive driving behavior: such as rapid acceleration and rapid deceleration, are one of the main causes of traffic accidents. Therefore, accurately identifying abnormal driving behaviors of a traveling vehicle is of great significance to traffic safety and vehicle safety.
The existing researches mainly fall into two main categories, wherein one category is identification researches aiming at individual behaviors of drivers, for example: normal driving, or smoking, making a call or taking a nap, etc. during driving. For recognition studies of personal behavior of drivers, current studies mainly use still images or video data. The other category of researches mainly aims at identifying driving behaviors occurring in the driving process of a vehicle, such as identifying, predicting and the like of some vehicle behaviors such as overtaking, lane changing, rapid deceleration, rapid acceleration and the like.
In addition to the above conventional recognition method, with the development of the neural network, more and more neural networks are proposed successively and achieve better effects, and most of the existing researches based on the neural network are combined with image video information, but the image video information is greatly influenced by scene weather and cannot capture the time characteristics of driving behaviors, and compared with the image video data, the vehicle track data such as speed, acceleration and the like have better accuracy, and the time correlation of the front and rear moment data is reserved.
At present, the application of the machine learning algorithm in the traffic field is less, the effect is not ideal, particularly, the research on the driving behavior recognition is less, and a new technical break is brought to the development of the intelligent traffic by the related-field algorithm.
Therefore, the invention provides a method and a system for monitoring intelligent traffic abnormal driving behaviors based on data processing, which aim at the data problem in the data acquisition process, preprocess the vehicle driving data, extract the data characteristics to perform abnormal driving behavior characteristic analysis, summarize the data characteristics of the abnormal driving behaviors by analyzing the driving behavior data, and then combine the constructed driving behavior recognition model on the basis to highlight the data characteristics of the driving data when the abnormal driving behaviors occur and improve the recognition accuracy of the abnormal driving behaviors.
Disclosure of Invention
The embodiment of the application provides a method for monitoring intelligent traffic abnormal driving behaviors based on data processing.
In order to achieve the above purpose, the embodiments of the present application adopt the following technical solutions:
in a first aspect, a method for monitoring abnormal driving behavior of intelligent traffic based on data processing is provided, the method comprising the following steps:
step S1, initial data of vehicle running is collected, the initial data of the vehicle running is preprocessed, the vehicle data is processed to generate a data set, and the data set is divided into a training set and a query set according to a set proportion;
s2, constructing a driving behavior recognition model, wherein the driving behavior recognition model comprises three long-term and short-term memory network modules, a multi-head self-attention module and a full-connection module;
step S3, guiding and optimizing a driving behavior recognition model by using a loss function, and calculating a loss value between a predicted value and a true value;
s4, training a driving behavior recognition model by using a training set, presetting the iteration times and the learning rate completely until the iteration times are equal to the maximum iteration times, stopping training, and generating a trained driving behavior recognition model;
and S5, collecting vehicle running data in an actual scene, and inputting the collected vehicle running data as a sample to be detected into a trained driving behavior recognition model to recognize abnormal driving behaviors.
In one possible embodiment, the initial data of the vehicle driving collected in the step S1 includes position information of the vehicle, state information of the vehicle, movement information of the vehicle, speed information of the vehicle, acceleration information of the vehicle, steering angle information of the vehicle, and instantaneous surrounding object distance information of the vehicle.
In one possible embodiment, the method for preprocessing the initial data of the vehicle driving in step S1 includes a data denoising processing method, a data reduction processing method, and a data conversion processing method.
In one possible implementation manner, the step S2 includes:
the driving behavior recognition model comprises a first long-period memory network module, a second long-period memory network module, a third long-period memory network module, a multi-head self-attention module and a full-connection module which are connected in sequence.
In one possible implementation manner, the first long-period and short-period memory network module, the second long-period and short-period memory network module and the third long-period memory network module process the vehicle data set according to the time sequence in sequence, and send the processed data set to the multi-head self-attention module;
the multi-head self-attention module identifies the training set and the query set in the processed data set, encodes the characteristic information of the training set and the query set to identify abnormal data, and sends the abnormal data and the normal data to the full-connection module;
and the full-connection module classifies and outputs the abnormal data and the normal data.
In one possible implementation, four hidden layers are provided in a multi-headed self-attention module.
In one possible implementation manner, the step S3 includes: the loss function selects a wSDR loss function.
In a second aspect, the invention also provides a system for monitoring the abnormal driving behavior of intelligent traffic based on data processing, which comprises a data acquisition unit, a model building unit, a training unit and an identification unit, wherein:
the data acquisition unit is used for acquiring initial data of vehicle running, preprocessing the initial data of vehicle running, processing the vehicle data to generate a data set, and dividing the data set into a training set and a query set according to a set proportion;
the model building unit is used for building a driving behavior recognition model, and the driving behavior recognition model comprises three long-term and short-term memory network modules, a multi-head self-attention module and a full-connection module;
the training unit is used for guiding and optimizing the driving behavior recognition model by using the loss function and calculating a loss value between the predicted value and the true value; the method comprises the steps of training a driving behavior recognition model by using a training set, presetting the iteration times and the learning rate to be complete, stopping training until the iteration times are equal to the maximum iteration times, and generating a trained driving behavior recognition model
The recognition unit is used for collecting vehicle running data in an actual scene, inputting the collected vehicle running data into a trained driving behavior recognition model as a sample to be detected, and recognizing abnormal driving behaviors.
In a third aspect, the present invention also provides an electronic device comprising a processor and a memory; the processor comprises the system for intelligent monitoring based on image recognition according to the second aspect.
In a fourth aspect, the present invention also provides a computer-readable storage medium comprising instructions; when the instructions are executed on the electronic device described in the third aspect, the electronic device is caused to perform the method described in the first aspect,
drawings
Fig. 1 is a flowchart of a method for monitoring abnormal driving behavior of intelligent traffic based on data processing according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of a driving behavior recognition model in a method and a system for monitoring abnormal driving behaviors of intelligent traffic based on data processing according to an embodiment of the present application.
According to the method, corresponding data segments are intercepted as abnormal driving according to the analysis of different types of abnormal driving behavior characteristics, an abnormal driving behavior data set is established, and a driving behavior recognition model based on a long-short-period memory network is established, so that abnormal driving behaviors can be recognized, the recognition rate is high, and the traffic driving safety is improved.
The method and the device can effectively identify abnormal driving behaviors and early warn possible traffic accidents.
The invention provides a method and a system for monitoring intelligent traffic abnormal driving behaviors based on data processing, which aim at the data problem in the data acquisition process, preprocess vehicle driving data, extract data characteristics to perform abnormal driving behavior characteristic analysis, summarize the data characteristics of abnormal driving behaviors by analyzing driving behavior data, and then combine a constructed driving behavior recognition model on the basis to highlight the data characteristics of the driving data when the abnormal driving behaviors occur and improve the recognition accuracy of the abnormal driving behaviors.
Detailed Description
It should be noted that the terms "first," "second," and the like in the embodiments of the present application are used for distinguishing between the same type of feature, and not to be construed as indicating a relative importance, quantity, order, or the like.
The terms "exemplary" or "such as" and the like, as used in connection with embodiments of the present application, are intended to be exemplary, or descriptive. Any embodiment or design described herein as "exemplary" or "for example" should not be construed as preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "such as" is intended to present related concepts in a concrete fashion.
The terms "coupled" and "connected" in connection with embodiments of the present application are to be construed broadly, and may refer, for example, to a physical direct connection, or to an indirect connection via electronic devices, such as, for example, a connection via electrical resistance, inductance, capacitance, or other electronic devices.
Example 1:
according to the method for monitoring the intelligent traffic abnormal driving behavior based on the data processing, as shown in fig. 1, corresponding data segments are intercepted as abnormal driving according to the analysis of different types of abnormal driving behavior characteristics, an abnormal driving behavior data set is established, and a driving behavior recognition model based on a long-short-period memory network is established, so that the abnormal driving behavior can be recognized, the recognition rate is high, and the traffic driving safety is improved.
As shown in FIG. 2, the invention constructs a driving behavior recognition model based on a long-short-term memory network (LSTM) and a multi-head self-attention Module (MHA) to recognize vehicle driving data of abnormal driving behaviors, and accurately recognizes driving behaviors of surrounding vehicles in the process of driving of the vehicles. The advantage of the long-term and short-term memory network on the processing time sequence is used for processing the vehicle data information, a multi-head self-attention Module (MHA) is added for highlighting the characteristic of abnormal driving data, and the recognition accuracy is improved. And uses the fully connected module (D) to take the output of the upper layer as input for identifying and classifying driving behaviors.
Behavior
Example 2:
the present embodiment is further optimized based on embodiment 1, and any driving behavior can last for a certain time during driving, and all actions can not be completed instantaneously. The process of abnormal driving behavior is represented by a time-continuous data segment containing abnormal points of driving behavior. In the invention, the data acquisition frequency f=10 Hz (0, 1 s), the time t <3s for completing the general driving behavior, and the abnormal driving behavior occurrence section driving is acquired by defining the length of a time window and sliding the window.
Other portions of this embodiment are the same as those of embodiment 1, and thus will not be described in detail.
Example 3:
the present embodiment is further optimized based on the above embodiment 1 or 2, and after initial data of the vehicle driving is obtained, because the vehicle is in a stable state in a normal driving process, the driving data of the vehicle will not change greatly in a short time, and when a driver performs some wrong operations, the vehicle will run abnormally, the driving data has abrupt change in a shorter time, and there are various quality problems in the data, such as data missing data values, noise included in the data, or multiple line repetition of the data. Therefore, the data is preprocessed before being used, the quality of the data is improved on the premise that the expression content of the data is not affected, and common data preprocessing methods include a data denoising processing method, a data reduction processing method and a data conversion processing method.
The data denoising processing method can carry out smooth denoising processing on data with noisy points, instability and obvious fluctuation. The data reduction processing method simplifies the data set size on the premise of not affecting the data quality and the integrity. After the data protocol is completed, useless data in the data can be reduced, and the proportion of useful data is obviously increased. The data conversion processing method is a common method for data processing, and the data in a unified format is acquired to make the data more beneficial.
Example 4:
this embodiment is further optimized based on any of embodiments 1-3 above, where the loss function will determine the performance of the model by comparing its predicted and expected outputs, and thus find the direction of optimization. If the deviation between the two is very large, the loss value will be large; if the deviation is small or the values are nearly the same, the loss value will be very low. Thus, there is a need to use a suitable loss function that can appropriately penalize the model as it is trained on the data set. The invention selects the wSDR loss function.
The wsr loss function is expressed as:
where x is the noise signal of mix, y is the clean signal before mix, z is the noise signal before mix;
is an estimated speech signal,/>
Is the estimated noise signal, a is the combination of clean signal energy and noise energy, and the value range is 0,1]。
Other portions of this embodiment are the same as any of embodiments 1 to 3 described above, and thus will not be described again.
Example 5:
the embodiment is further optimized based on any one of the above embodiments 1 to 4, and four hidden layers are added to the fully-connected neural network to ensure the model effect, because three long-short-period memory network modules connected in sequence are used, the excessively complex neural network structure may cause gradient disappearance or gradient explosion in the driving behavior recognition model.
In addition, the invention can also carry out alarm processing when abnormal driving behaviors are detected.
Other portions of this embodiment are the same as any of embodiments 1 to 4 described above, and thus will not be described again.
Example 6:
the invention also provides an electronic device, which comprises a processor and a memory; the processor comprises the system for intelligent monitoring based on image recognition described in the embodiment.
Example 7:
the present invention also provides a computer-readable storage medium comprising instructions; when the instructions are executed on the electronic device described in the above embodiment, the electronic device is caused to perform the method described in the above embodiment. In the alternative, the computer readable storage medium may be a memory.
The processor referred to in the embodiments of the present application may be a chip. For example, it may be a field programmable gate array (field programmable gate array, FPGA), an application specific integrated chip (application specific integrated circuit, ASIC), a system on chip (SoC), a central processing unit (central processor unit, CPU), a network processor (network processor, NP), a digital signal processing circuit (digital signal processor, DSP), a microcontroller (micro controller unit, MCU), a programmable controller (programmable logic device, PLD) or other integrated chip.
The memory to which embodiments of the present application relate may be volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory. The nonvolatile memory may be a read-only memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an electrically Erasable EPROM (EEPROM), or a flash memory. The volatile memory may be random access memory (random access memory, RAM) which acts as an external cache. By way of example, and not limitation, many forms of RAM are available, such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous DRAM (SLDRAM), and direct memory bus RAM (DR RAM). It should be noted that the memory of the systems and methods described herein is intended to comprise, without being limited to, these and any other suitable types of memory.
It should be understood that, in various embodiments of the present application, the sequence numbers of the foregoing processes do not mean the order of execution, and the order of execution of the processes should be determined by the functions and internal logic thereof, and should not constitute any limitation on the implementation process of the embodiments of the present application.
Those of ordinary skill in the art will appreciate that the various illustrative modules and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
It will be clearly understood by those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described system, apparatus and module may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.
In the several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods may be implemented in other ways. For example, the above-described device embodiments are merely illustrative, e.g., the division of the modules is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple modules or components may be combined or integrated into another device, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some interface, indirect coupling or communication connection of devices or modules, electrical, mechanical, or other form.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physically separate, i.e., may be located in one device, or may be distributed over multiple devices. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in each embodiment of the present application may be integrated in one device, or each module may exist alone physically, or two or more modules may be integrated in one device.
In the above embodiments, it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using a software program, it may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in accordance with embodiments of the present application are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by a wired (e.g., coaxial cable, fiber optic, digital subscriber line (Digital Subscriber Line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device including one or more servers, data centers, etc. that can be integrated with the medium. The usable medium may be a magnetic medium (e.g., a floppy Disk, a hard Disk, a magnetic tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a Solid State Disk (SSD)), or the like.
The foregoing is merely specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily think about changes or substitutions within the technical scope of the present application, and the changes and substitutions are intended to be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A method for monitoring intelligent traffic abnormal driving behavior based on data processing, which is characterized by comprising the following steps:
step S1, initial data of vehicle running is collected, the initial data of the vehicle running is preprocessed, the vehicle data is processed to generate a data set, and the data set is divided into a training set and a query set according to a set proportion;
s2, constructing a driving behavior recognition model, wherein the driving behavior recognition model comprises three long-term and short-term memory network modules, a multi-head self-attention module and a full-connection module;
step S3, guiding and optimizing a driving behavior recognition model by using a loss function, and calculating a loss value between a predicted value and a true value;
s4, training a driving behavior recognition model by using a training set, presetting the iteration times and the learning rate completely until the iteration times are equal to the maximum iteration times, stopping training, and generating a trained driving behavior recognition model;
and S5, collecting vehicle running data in an actual scene, and inputting the collected vehicle running data as a sample to be detected into a trained driving behavior recognition model to recognize abnormal driving behaviors.
2. The method for monitoring abnormal driving behavior of intelligent traffic based on data processing according to claim 1, wherein the initial data of the vehicle driving collected in step S1 includes position information of the vehicle, state information of the vehicle, movement information of the vehicle, speed information of the vehicle, acceleration information of the vehicle, steering angle information of the vehicle and instantaneous surrounding object distance information of the vehicle.
3. The method for monitoring abnormal driving behavior of intelligent traffic based on data processing according to claim 1, wherein the method for preprocessing the initial data of the vehicle driving in step S1 comprises a data denoising processing method, a data reduction processing method and a data conversion processing method.
4. The method for monitoring abnormal driving behavior of intelligent traffic based on data processing according to claim 1, wherein said step S2 comprises:
the driving behavior recognition model comprises a first long-period memory network module, a second long-period memory network module, a third long-period memory network module, a multi-head self-attention module and a full-connection module which are connected in sequence.
5. A method of monitoring intelligent traffic abnormal driving behavior based on data processing according to claim 4, comprising:
the first long-short-term memory network module, the second long-short-term memory network module and the third long-short-term memory network module process the vehicle data set according to the time sequence in sequence, and the processed data set is sent to the multi-head self-attention module;
the multi-head self-attention module identifies the training set and the query set in the processed data set, encodes the characteristic information of the training set and the query set to identify abnormal data, and sends the abnormal data and the normal data to the full-connection module;
and the full-connection module classifies and outputs the abnormal data and the normal data.
6. A method of monitoring intelligent traffic abnormal driving behavior based on data processing according to claim 4, comprising:
four hidden layers are arranged in the multi-head self-attention module.
7. The method for monitoring abnormal driving behavior of intelligent traffic based on data processing according to claim 1, wherein the step S3 comprises:
the loss function selects a wSDR loss function.
8. The system for monitoring the intelligent traffic abnormal driving behavior based on the data processing is characterized by comprising a data acquisition unit, a model building unit, a training unit and an identification unit, wherein:
the data acquisition unit is used for acquiring initial data of vehicle running, preprocessing the initial data of vehicle running, processing the vehicle data to generate a data set, and dividing the data set into a training set and a query set according to a set proportion;
the model building unit is used for building a driving behavior recognition model, and the driving behavior recognition model comprises three long-term and short-term memory network modules, a multi-head self-attention module and a full-connection module;
the training unit is used for guiding and optimizing the driving behavior recognition model by using the loss function and calculating a loss value between the predicted value and the true value; the method comprises the steps of training a driving behavior recognition model by using a training set, presetting the iteration times and the learning rate to be complete, stopping training until the iteration times are equal to the maximum iteration times, and generating a trained driving behavior recognition model;
the recognition unit is used for collecting vehicle running data in an actual scene, inputting the collected vehicle running data into a trained driving behavior recognition model as a sample to be detected, and recognizing abnormal driving behaviors.
9. An electronic device comprising a processor and a memory; the processor comprises the system for intelligent monitoring based on image recognition as claimed in claim 8.
10. A computer-readable storage medium, the computer-readable storage medium comprising instructions; the instructions, when executed on an electronic device as claimed in claim 8, cause the electronic device to perform the method as claimed in any one of claims 1-7.
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