CN116821805A - A vehicle service platform system for driving behavior monitoring and a driving behavior monitoring method - Google Patents

Abstract

The invention discloses a vehicle service platform system for driving behavior monitoring and a driving behavior monitoring method. The system includes: a data processing module for receiving and processing original data generated by vehicle sensors to obtain driving behavior data; and a driving behavior analysis module for It is used to analyze driving behavior data to determine the driver's driving behavior and generate corresponding driving behavior templates; the driving behavior database is used to store driving behavior data, driving behavior templates and their associated driver identity information; the driving behavior assessment module, It is used to calculate the driver's driving score based on the driving behavior analysis results; the driving identity verification module is used to identify and verify the driver's identity information. Using the embodiments of the present invention, driving behavior can be accurately identified and the driver's driving behavior can be evaluated, thereby improving the safety and effectiveness of driving behavior.

Description

A vehicle service platform system for driving behavior monitoring and a driving behavior monitoring method

Technical field

The invention belongs to the field of intelligent driving technology, and in particular is a vehicle service platform system for monitoring driving behavior and a driving behavior monitoring method.

Background technique

With the widespread use of automobiles, the safety of driving behavior has become a topic of great concern. In order to improve the safety and effectiveness of driving behavior, the development of vehicle service platform systems is particularly important. Some current vehicle monitoring systems usually use GPS-based positioning technology and sensor data collection technology, but there are still some limitations in their performance and functions. For example, GPS positioning technology may not be able to accurately determine certain driving behaviors, such as strong acceleration, sharp turns, etc., because GPS cannot provide sufficient accuracy. In addition, sensor data collection technology is difficult to process complex driving behavior information, such as steering, reversing, etc. In addition, current vehicle monitoring systems cannot reliably verify a driver’s identity and may be subject to problems such as mistaken identity or fraud.

Contents of the invention

The purpose of the present invention is to provide a driving behavior monitoring vehicle service platform system to solve the deficiencies in the existing technology, be able to accurately identify driving behavior and evaluate the driver's driving behavior, and improve the safety and effectiveness of driving behavior. .

One embodiment of the present application provides a vehicle service platform system for driving behavior monitoring. The system includes:

The data processing module is used to receive and process raw data generated by vehicle sensors to obtain driving behavior data;

The driving behavior analysis module is used to analyze driving behavior data to determine the driver's driving behavior and generate corresponding driving behavior templates;

Driving behavior database, used to store driving behavior data, driving behavior templates and their associated driver identity information;

The driving behavior assessment module is used to calculate the driver's driving score based on the driving behavior analysis results;

Driver identity verification module, used to identify and verify driver identity information.

Optionally, the driving behavior data is analyzed to determine the driver's driving behavior and generate a corresponding driving behavior template, including:

Features related to driving behavior in the driving behavior data are extracted, the extracted driving behavior features are classified, and a driving behavior template is generated based on the classification results.

Optionally, the driving score is calculated as:

Driving score = similarity score × preset weight 1 + safety behavior score × preset weight 2 + fuel economy score × preset weight 3

Wherein, the similarity score is the similarity score between the driving behavior template and the actual driving behavior, the safety behavior score is the driver's safe driving behavior score, and the fuel economy score is the driver's fuel economy score.

Optionally, the system also includes: an abnormal driving warning module, used to compare the driving score with the normal driving score range in real time, and send early warning information when the normal driving score range is exceeded.

Another embodiment of the present application provides a driving behavior monitoring method, which method includes:

Receive and process raw data generated by vehicle sensors to obtain driving behavior data;

Identify and verify driver identity information;

Store driving behavior data, driving behavior templates and their associated driver identity information;

Analyze driving behavior data to determine the driver's driving behavior and generate corresponding driving behavior templates;

Based on the driving behavior analysis results, the driver's driving score is calculated.

Optionally, the driving behavior data is analyzed to determine the driver's driving behavior and generate a corresponding driving behavior template, including:

Features related to driving behavior in the driving behavior data are extracted, the extracted driving behavior features are classified, and a driving behavior template is generated based on the classification results.

Optionally, the driving score is calculated as:

Driving score = similarity score × preset weight 1 + safety behavior score × preset weight 2 + fuel economy score × preset weight 3

Wherein, the similarity score is the similarity score between the driving behavior template and the actual driving behavior, the safety behavior score is the driver's safe driving behavior score, and the fuel economy score is the driver's fuel economy score.

Optionally, the method also includes:

The driving score is compared with the normal driving score range in real time, and when the normal driving score range is exceeded, an early warning message is sent.

Yet another embodiment of the present application provides a storage medium in which a computer program is stored, wherein the computer program is configured to execute the method described in any of the above items when running.

Yet another embodiment of the present application provides an electronic device, including a memory and a processor. A computer program is stored in the memory, and the processor is configured to run the computer program to perform any of the above. Methods.

Compared with the existing technology, the present invention provides a vehicle service platform system for driving behavior monitoring. The system includes: a data processing module for receiving and processing raw data generated by vehicle sensors to obtain driving behavior data; driving behavior The analysis module is used to analyze the driving behavior data to determine the driver's driving behavior and generate the corresponding driving behavior template; the driving behavior database is used to store the driving behavior data, driving behavior templates and their associated driver identity information; driving The behavior assessment module is used to calculate the driver's driving score based on the driving behavior analysis results; the driving identity verification module is used to identify and verify the driver's identity information, so that the driving behavior can be accurately identified and the driver's driving behavior can be evaluated. Improve the safety and effectiveness of driving behavior.

Description of the drawings

Figure 1 is a schematic architectural diagram of a driving behavior monitoring vehicle service platform system provided by an embodiment of the present invention;

Figure 2 is a schematic flow chart of a driving behavior monitoring method provided by an embodiment of the present invention;

Figure 3 is a hardware structure block diagram of a computer terminal of a driving behavior monitoring method provided by an embodiment of the present invention.

Detailed ways

The embodiments described below with reference to the drawings are exemplary and are only used to explain the present invention and cannot be construed as limiting the present invention.

With the continuous development of society, cars have become an indispensable means of transportation in people's lives. However, vehicle accidents occur from time to time due to drivers' bad behavior or fatigue driving, which poses a huge threat to people's lives and property. In order to solve this problem, the development of vehicle service platform systems has received widespread attention. The vehicle service platform system uses high-tech technology to monitor the driver's behavior. Through data analysis and prediction, it can promptly discover and correct the driver's bad behavior, improve the driver's safety and stability, and thereby reduce the risk of traffic accidents.

In view of this, embodiments of the present invention provide a vehicle service platform system for driving behavior monitoring. As shown in Figure 1, the system may include:

The data processing module 101 is used to receive and process raw data generated by vehicle sensors to obtain driving behavior data;

The data processing module can receive data generated by vehicle sensors, including acceleration, steering, braking, vehicle speed, direction, position, lane position, lane offset and other information, and process and analyze these data in real time, and convert the processed driving behavior into The data is transmitted to the driving behavior analysis module. for subsequent driving behavior analysis and evaluation. Data processing modules can be hardware or software or a combination of both.

The driving processing module can collect data from various specialized sensors installed in the vehicle, and can also read vehicle information through the CAN bus. After collecting the raw data, the data needs to be parsed. The process of parsing is to decode and convert the original data into computer-recognizable data according to the prescribed format. Typically, vehicle data is encoded and transmitted using standard formats. After data analysis, data can be processed. Data processing includes data compression, filtering and denoising, data interpolation and other processing methods. Among them, filtering and denoising is the most important step in data processing, which can be used to eliminate random errors in sensor data and improve the accuracy of driving behavior analysis.

The driving behavior analysis module 102 is used to analyze driving behavior data to determine the driver's driving behavior and generate a corresponding driving behavior template;

The driving behavior analysis module can use machine learning, image processing or other related technologies to analyze driving behavior based on the driver's driving behavior data and driving data analysis algorithms. The driving behavior analysis unit will generate a driving behavior template for subsequent driving behavior analysis and evaluation. Among them, driving behavior includes driving operations such as vehicle acceleration, braking, and turning. The driving behavior template can compare the driver's actual behavior and ideal behavior to evaluate the driver's driving level.

Specifically, in one implementation, features related to driving behavior in the driving behavior data can be extracted, the extracted driving behavior features are classified, and a driving behavior template is generated based on the classification results.

Feature extraction is the core step of driving behavior analysis, and its purpose is to extract features closely related to driving behavior. There are many methods of feature extraction, including time domain analysis, frequency domain analysis, wavelet transform, etc. Among them, time domain analysis is the most commonly used method. Time domain analysis can use various statistical parameters to describe the characteristics of acceleration, steering, braking and other behaviors, including average value, standard deviation, skewness, kurtosis, etc.

After feature extraction, driving behavior needs to be classified. Classification methods can use supervised learning or unsupervised learning. In the supervised learning method, support vector machine (SVM), neural network and other methods can be used for classification. In unsupervised learning methods, methods such as cluster analysis can be used for classification.

After the driving behavior is classified, a driving behavior template needs to be generated based on the classification results. The driving behavior template is used to describe the driver's normal driving behavior, including acceleration, steering, braking and other behaviors. Driving behavior templates can be generated by various algorithms, including mean, median, Gaussian distribution, etc.

Driving behavior database 103, used to store driving behavior data, driving behavior templates and their associated driver identity information;

The driving behavior database is used to store driving behavior data, driving behavior templates and their associated driver identity information. The processed data needs to be stored. Data storage can be done in two ways: local storage and cloud storage. Local storage can save the processed data in the on-board storage device for subsequent use. Cloud storage can upload data to the cloud server for data sharing and interaction between different devices. The database can be used for subsequent driving behavior assessment, driver identity verification, etc., and allows users to access and manage specific driver behavior data.

The driving behavior evaluation module 104 is used to calculate the driver's driving score based on the driving behavior analysis results;

The driving behavior assessment module calculates a driving score by comparing the driver's behavior template and historical data, which is used to evaluate the driver's driving behavior. Driving scores can be evaluated based on different evaluation criteria, such as safety, fuel economy, etc.

Specifically, in one implementation, the score is calculated based on three factors: similarity score, safety behavior score and fuel economy score. Driving points can be calculated as:

Driving score = similarity score × preset weight 1 + safety behavior score × preset weight 2 + fuel economy score × preset weight 3

Wherein, the similarity score is the similarity score between the driving behavior template and the actual driving behavior, the safety behavior score is the driver's safe driving behavior score, such as wearing a seat belt and obeying traffic rules; the fuel economy score Score drivers for fuel economy, such as avoiding hard acceleration and braking. Moreover, the preset weight 1, weight 2 and weight 3 are set and adjusted according to specific application conditions and driver needs.

The driving identity verification module 105 is used to identify and verify driver identity information. A variety of identity verification technologies can be used for identity verification, such as facial recognition, fingerprint recognition, iris recognition, voiceprint recognition, etc., and can be used to identify the driver's identity information in order to store driving behavior data and driving scores related to a specific driver to the corresponding driving behavior database.

Specifically, in practical applications, the system may also include: an abnormal driving warning module, used to compare the driving score with the normal driving score range in real time, and send early warning information when the normal driving score range is exceeded, Improve driving quality and safety.

It can be seen that the vehicle service platform system for driving behavior monitoring provided by the present invention includes: a data processing module for receiving and processing raw data generated by vehicle sensors to obtain driving behavior data; a driving behavior analysis module for analyzing driving behavior data to determine the driver's driving behavior and generate the corresponding driving behavior template; the driving behavior database is used to store driving behavior data, driving behavior templates and their associated driver identity information; the driving behavior evaluation module is used to determine the driving behavior based on driving behavior. Behavior analysis results calculate the driver's driving score; the driving identity verification module is used to identify and verify the driver's identity information, so that the driving behavior can be accurately identified and evaluated, improving the safety and effectiveness of driving behavior. sex.

Another embodiment of the present invention provides a driving behavior monitoring method. Referring to Figure 2, the method may include:

S201, receive and process raw data generated by vehicle sensors to obtain driving behavior data;

S202, identify and verify driver identity information;

S203, store driving behavior data, driving behavior templates and their associated driver identity information;

S204, analyze the driving behavior data to determine the driver's driving behavior and generate a corresponding driving behavior template;

S205: Calculate the driver's driving score based on the driving behavior analysis results.

It can be seen that driving behavior data is obtained by receiving and processing raw data generated by vehicle sensors; identifying and verifying driver identity information, storing driving behavior data, driving behavior templates and their associated driver identity information; analyzing driving behavior data to determine The driver's driving behavior and generate the corresponding driving behavior template; according to the driving behavior analysis results, the driver's driving score is calculated, so that the driving behavior can be accurately identified and the driver's driving behavior can be evaluated to improve the safety and safety of driving behavior. effectiveness.

The following is a detailed description taking running on a computer terminal as an example. Figure 3 is a hardware structure block diagram of a computer terminal of a driving behavior monitoring method provided by an embodiment of the present invention. As shown in Figure 3, the computer terminal may include one or more (only one is shown in Figure 3) processors 302 (the processor 302 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 304 for storing data. Optionally, the above-mentioned computer terminal may also include a transmission device 306 for communication functions and an input and output device 308. Persons of ordinary skill in the art can understand that the structure shown in FIG. 3 is only illustrative, and it does not limit the structure of the above-mentioned computer terminal. For example, the computer terminal may also include more or fewer components than shown in FIG. 3 , or have a different configuration than shown in FIG. 3 .

The memory 304 can be used to store software programs and modules of application software, such as the program instructions/modules corresponding to the driving behavior monitoring method in the embodiment of the present application. The processor 302 executes various operations by running the software programs and modules stored in the memory 304. A functional application and data processing, that is, to implement the above method. Memory 304 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 304 may further include memory located remotely relative to the processor 302, and these remote memories may be connected to the computer terminal through a network. Examples of the above-mentioned networks include but are not limited to the Internet, intranets, local area networks, mobile communication networks and combinations thereof.

The transmission device 306 is used to receive or send data via a network. Specific examples of the above-mentioned network may include a wireless network provided by a communication provider of the computer terminal. In one example, the transmission device 306 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station to communicate with the Internet. In one example, the transmission device 306 may be a radio frequency (Radio Frequency, RF) module, which is used to communicate with the Internet wirelessly.

An embodiment of the present invention also provides a storage medium in which a computer program is stored, wherein the computer program is configured to execute the steps in any of the above method embodiments when running.

Specifically, in this embodiment, the above-mentioned storage medium may be configured to store a computer program for performing the following steps:

S201, receive and process raw data generated by vehicle sensors to obtain driving behavior data;

S202, identify and verify driver identity information;

S203, store driving behavior data, driving behavior templates and their associated driver identity information;

S204, analyze the driving behavior data to determine the driver's driving behavior and generate a corresponding driving behavior template;

S205: Calculate the driver's driving score based on the driving behavior analysis results.

Specifically, in this embodiment, the above-mentioned storage medium may include but is not limited to: U disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), mobile Various media such as hard drives, magnetic disks, or optical disks that can store computer programs.

It can be seen that driving behavior data is obtained by receiving and processing raw data generated by vehicle sensors; identifying and verifying driver identity information, storing driving behavior data, driving behavior templates and their associated driver identity information; analyzing driving behavior data to determine The driver's driving behavior and generate the corresponding driving behavior template; according to the driving behavior analysis results, the driver's driving score is calculated, so that the driving behavior can be accurately identified and the driver's driving behavior can be evaluated to improve the safety and safety of driving behavior. effectiveness.

An embodiment of the present invention also provides an electronic device, including a memory and a processor. A computer program is stored in the memory, and the processor is configured to run the computer program to perform any of the above method embodiments. step.

Specifically, the above-mentioned electronic device may further include a transmission device and an input-output device, wherein the transmission device is connected to the above-mentioned processor, and the input-output device is connected to the above-mentioned processor.

Specifically, in this embodiment, the above-mentioned processor may be configured to perform the following steps through a computer program:

S201, receive and process raw data generated by vehicle sensors to obtain driving behavior data;

S202, identify and verify driver identity information;

S203, store driving behavior data, driving behavior templates and their associated driver identity information;

S204, analyze the driving behavior data to determine the driver's driving behavior and generate a corresponding driving behavior template;

S205: Calculate the driver's driving score based on the driving behavior analysis results.

Specifically, for specific examples in this embodiment, reference may be made to the examples described in the above-mentioned embodiments and optional implementations, and the details of this embodiment will not be repeated here.

It can be seen that driving behavior data is obtained by receiving and processing raw data generated by vehicle sensors; identifying and verifying driver identity information, storing driving behavior data, driving behavior templates and their associated driver identity information; analyzing driving behavior data to determine The driver's driving behavior and generate the corresponding driving behavior template; according to the driving behavior analysis results, the driver's driving score is calculated, so that the driving behavior can be accurately identified and the driver's driving behavior can be evaluated to improve the safety and safety of driving behavior. effectiveness.

The structure, features and effects of the present invention have been described in detail based on the embodiments shown in the drawings. The above descriptions are only preferred embodiments of the present invention. However, the scope of implementation of the present invention is not limited by the drawings. Any changes made to the concept of the present invention, or modifications to equivalent embodiments with equivalent changes, shall be within the protection scope of the present invention as long as they do not exceed the spirit covered by the description and drawings.

Claims (10)

1. A vehicle service platform system for driving behavior monitoring, characterized in that the system includes: The data processing module is used to receive and process raw data generated by vehicle sensors to obtain driving behavior data; The driving behavior analysis module is used to analyze driving behavior data to determine the driver's driving behavior and generate corresponding driving behavior templates; Driving behavior database, used to store driving behavior data, driving behavior templates and their associated driver identity information; The driving behavior assessment module is used to calculate the driver's driving score based on the driving behavior analysis results; Driver identity verification module, used to identify and verify driver identity information. 2. The system according to claim 1, characterized in that the analysis of driving behavior data to determine the driver's driving behavior and generate a corresponding driving behavior template includes: Features related to driving behavior in the driving behavior data are extracted, the extracted driving behavior features are classified, and a driving behavior template is generated based on the classification results. 3. The system according to claim 2, characterized in that the calculation method of the driving score is: Driving score = similarity score × preset weight 1 + safety behavior score × preset weight 2 + fuel economy score × preset weight 3 Wherein, the similarity score is the similarity score between the driving behavior template and the actual driving behavior, the safety behavior score is the driver's safe driving behavior score, and the fuel economy score is the driver's fuel economy score. 4. The system according to claim 3, characterized in that the system further includes: an abnormal driving warning module for comparing the driving score with the normal driving score range in real time. When the normal driving score range is exceeded, When, an early warning message is sent. 5. A driving behavior monitoring method according to any one of claims 1 to 4, characterized in that the method includes: Receive and process raw data generated by vehicle sensors to obtain driving behavior data; Identify and verify driver identity information; Store driving behavior data, driving behavior templates and their associated driver identity information; Analyze driving behavior data to determine the driver's driving behavior and generate corresponding driving behavior templates; Based on the driving behavior analysis results, the driver's driving score is calculated. 6. The method according to claim 5, characterized in that the analysis of driving behavior data to determine the driver's driving behavior and generate a corresponding driving behavior template includes: Features related to driving behavior in the driving behavior data are extracted, the extracted driving behavior features are classified, and a driving behavior template is generated based on the classification results. 7. The method according to claim 6, characterized in that the calculation method of the driving score is: Driving score = similarity score × preset weight 1 + safety behavior score × preset weight 2 + fuel economy score × preset weight 3 Wherein, the similarity score is the similarity score between the driving behavior template and the actual driving behavior, the safety behavior score is the driver's safe driving behavior score, and the fuel economy score is the driver's fuel economy score. 8. The method according to claim 7, characterized in that the method further comprises: The driving score is compared with the normal driving score range in real time, and when the normal driving score range is exceeded, an early warning message is sent. 9. A storage medium, characterized in that a computer program is stored in the storage medium, wherein the computer program is configured to execute the method according to any one of claims 5-8 when running. 10. An electronic device, comprising a memory and a processor, characterized in that a computer program is stored in the memory, and the processor is configured to run the computer program to execute any one of claims 5-8 the method described.