WO2023274071A1

WO2023274071A1 - Driving behavior monitoring method and apparatus, electronic device, and storage medium

Info

Publication number: WO2023274071A1
Application number: PCT/CN2022/101167
Authority: WO
Inventors: 孟鸿程
Original assignee: 中移(上海)信息通信科技有限公司; 中移智行网络科技有限公司; 中国移动通信集团有限公司
Priority date: 2021-06-30
Filing date: 2022-06-24
Publication date: 2023-01-05
Also published as: CN113247008B; CN113247008A

Abstract

A driving behavior monitoring method and apparatus, an electronic device, and a storage medium, relating to the technical field of intelligent transportation. The method comprises: receiving first driving behavior data sent by a target vehicle at a first sampling time point, and determining the target vehicle as a dangerous traveling vehicle according to the first driving behavior data (101); determining a historical compliance index of the target vehicle, the historical compliance index being related to the number of times of taking measures after the target vehicle receives first alarm prompt information (102); and performing alarm prompting according to the historical compliance index of the target vehicle (103).

Description

Driving behavior monitoring method, device, electronic device and storage medium

Cross References to Related Applications

This application is based on a Chinese patent application with application number 202110734112.X and a filing date of June 30, 2021, and claims the priority of this Chinese patent application. The entire content of this Chinese patent application is hereby incorporated by reference into this application.

technical field

The present application relates to the technical field of intelligent transportation, and in particular to a driving behavior monitoring method, device, electronic equipment and storage medium.

Background technique

With the continuous advancement of urbanization, the importance of intelligent transportation is getting higher and higher, and with the development of Internet technology, Internet technology is gradually applied to vehicle monitoring and becomes an important part of intelligent transportation. In related technologies, the vehicle's driving behavior data, such as speed and acceleration, is sent to the server through the vehicle-mounted unit, and the server stores the vehicle's driving behavior data to provide evidence when a traffic accident occurs. However, in the related art, the server only realizes the function of storing driving behavior data, and the server has poor effect on monitoring the driving behavior of the vehicle.

Contents of the invention

In order to solve related technical problems, embodiments of the present application provide a driving behavior monitoring method, device, electronic equipment, and storage medium.

The technical scheme of the embodiment of the application is realized in this way:

In a first aspect, an embodiment of the present application provides a driving behavior monitoring method, the method comprising:

receiving the first driving behavior data sent by the target vehicle at the first sampling moment, and determining that the target vehicle is a dangerous driving vehicle according to the first driving behavior data;

determining the historical compliance index of the target vehicle, where the historical compliance index is related to the number of times the target vehicle takes countermeasures after receiving the first warning message;

An alarm prompt is given according to the historical compliance indicators of the target vehicle.

In the above solution, the warning prompt based on the historical compliance indicators of the target vehicle includes:

If the historical compliance index of the target vehicle is higher than the first preset index, then send the first warning prompt information to the target vehicle;

In the above scheme, the method also includes:

receiving the second driving behavior data sent by the target vehicle at the second sampling moment, and determining whether the target vehicle has taken a response to the first warning message corresponding to the first sampling moment according to the second driving behavior data Measures, the second sampling moment is a moment after the first sampling moment;

If it is determined that the target vehicle has not taken countermeasures, sending second warning prompt information to vehicles whose distance from the target vehicle is less than a preset distance.

In the above solution, the determining that the target vehicle is a dangerous driving vehicle according to the first driving behavior data includes:

Classifying the first driving behavior data according to a clustering algorithm to obtain at least one type of driving behavior data;

Determine the driving behavior category corresponding to each type of driving behavior data according to each type of driving behavior data in the at least one type of driving behavior data;

Based on the driving behavior category, it is determined that the target vehicle is a dangerous driving vehicle.

In the above solution, the driving behavior category corresponding to each type of driving behavior data includes at least one of the following behaviors: normal driving behavior, rapid acceleration behavior, rapid deceleration behavior, and sharp turning behavior.

In the above solution, after receiving the first driving behavior data sent by the target vehicle at the first sampling moment, the method further includes:

determining whether the first driving behavior data is within a preset threshold interval, and the preset threshold interval is determined based on preset traffic rules;

If the first driving behavior data is not within the preset threshold interval, sending the first driving behavior data to a traffic management system;

The determining that the target vehicle is a dangerous driving vehicle according to the first driving behavior data includes:

If the first driving behavior data is within the preset threshold interval, it is determined according to the first driving behavior data that the target vehicle is a dangerous driving vehicle.

In the above solution, before the warning prompt is given according to the historical compliance indicators of the target vehicle, the method further includes:

Using a driving behavior classifier based on Support Vector Machines (SVM) to identify the first driving behavior data, and determine the driving safety index of the target vehicle, the driving safety index is that the target vehicle is safe Probability of driving the vehicle;

The warning prompt based on the historical compliance indicators of the target vehicle includes:

An alarm prompt is given according to the historical compliance indicators of the target vehicle and the driving safety indicators of the target vehicle.

In the above solution, the warning prompt based on the historical compliance indicators of the target vehicle and the driving safety indicators of the target vehicle includes:

If the historical compliance index of the target vehicle is lower than the first preset index, and the driving safety index of the target vehicle is lower than the second preset index, the vehicle whose distance from the target vehicle is less than the preset distance Send the second warning message;

If the historical compliance index of the target vehicle is higher than the first preset index, or the driving safety index of the target vehicle is higher than the second preset index, send the first Alarm prompt information.

In the above solution, the historical compliance index is positively correlated with the first ratio, and the first ratio is the number of times the target vehicle takes countermeasures after receiving the first warning message and the number of times the target vehicle receives the first warning message. The ratio of the total number of messages.

In a second aspect, an embodiment of the present application provides a driving behavior monitoring device, the driving behavior monitoring device comprising:

The first determination module is configured to receive first driving behavior data sent by the target vehicle at the first sampling moment, and determine that the target vehicle is a dangerous driving vehicle according to the first driving behavior data;

An acquisition module configured to determine the historical compliance indicators of the target vehicle, where the historical compliance indicators are related to the number of times the target vehicle takes countermeasures after receiving the first warning message;

The warning module is configured to give a warning prompt according to the historical compliance indicators of the target vehicle.

In the above scheme, the alarm module is also configured as:

If the historical compliance index of the target vehicle is higher than a first preset index, sending the first warning message to the target vehicle;

In the above solution, the driving behavior monitoring device also includes:

The second determination module is configured to receive the second driving behavior data sent by the target vehicle at the second sampling moment, and determine whether the target vehicle has completed the corresponding driving behavior at the first sampling moment according to the second driving behavior data. Taking countermeasures for the first alarm prompt information, and the second sampling time is a time after the first sampling time;

A sending module configured to send second warning prompt information to vehicles whose distance from the target vehicle is less than a preset distance when it is determined that the target vehicle has not taken countermeasures.

In the above solution, the first determination module is further configured to:

receiving the first driving behavior data sent by the target vehicle at the first sampling moment,

In the above solution, the first determination module is further configured to:

receiving the first driving behavior data sent by the target vehicle at the first sampling moment;

In the above solution, the driving behavior monitoring device also includes:

The third determination module is configured to identify the first driving behavior data by using a driving behavior classifier based on SVM, and determine the driving safety index of the target vehicle, and the driving safety index is that the target vehicle belongs to a safe driving vehicle The probability;

The alarm module is also configured to:

In the above scheme, the alarm module is also configured as:

In the third aspect, the embodiment of the present application provides an electronic device, including: a processor, a memory, and a program stored in the memory and operable on the processor, and the program is implemented when executed by the processor. The steps of the driving behavior monitoring method described in the first aspect.

In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the driving behavior monitoring method described in the first aspect is implemented A step of.

The solution provided by the embodiment of the present application receives the first driving behavior data sent by the target vehicle at the first sampling moment, and determines that the target vehicle is a dangerous driving vehicle according to the first driving behavior data; determines the history of the target vehicle The compliance index, the historical compliance index is related to the number of times the target vehicle takes countermeasures after receiving the first warning message; and the warning prompt is given according to the historical compliance index of the target vehicle. In this way, in the case that the target vehicle is a dangerous driving vehicle, the warning prompt based on the historical compliance indicators of the target vehicle can improve the effect of monitoring the driving behavior of the vehicle, which is conducive to the improvement of road traffic safety; thus at least it can solve the problem of related technical problems The middle server only realizes the function of storing driving behavior data, and the server has poor monitoring effect on the driving behavior of the vehicle.

Description of drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that need to be used in the description of the embodiments of the present application will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application. For those skilled in the art, other drawings can also be obtained based on these drawings without any creative effort.

FIG. 1 is a flow chart of a driving behavior monitoring method provided in an embodiment of the present application;

Fig. 2 is a schematic diagram of a method for determining the K value in a clustering algorithm provided by an embodiment of the present application;

FIG. 3 is one of the structural schematic diagrams of a driving behavior monitoring device provided in an embodiment of the present application;

Fig. 4 is the second structural schematic diagram of a driving behavior monitoring device provided by the embodiment of the present application;

Fig. 5 is the third structural schematic diagram of a driving behavior monitoring device provided by the embodiment of the present application;

FIG. 6 is a schematic structural diagram of an electronic device provided by an embodiment of the present application.

detailed description

The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of them. Based on the embodiments in this application, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of this application.

The embodiments of the present application provide a driving behavior monitoring method, device, electronic equipment, and storage medium to solve the problem in the related art that the server only realizes the function of storing driving behavior data, and the server has poor monitoring effect on the driving behavior of the vehicle.

The embodiment of the present application provides a driving behavior monitoring method, which is applied to an electronic device (such as a server). As shown in FIG. 1, the method includes the following steps:

Step 101: receiving the first driving behavior data sent by the target vehicle at the first sampling moment, and determining that the target vehicle is a dangerous driving vehicle according to the first driving behavior data;

Step 102: Determine the historical compliance index of the target vehicle, the historical compliance index is related to the number of times the target vehicle takes countermeasures after receiving the first warning message;

Step 103: giving an alarm prompt according to the historical compliance indicators of the target vehicle.

Wherein, the first driving behavior data may include at least one of the following: vehicle driving-related data, vehicle operation-related data, and driver-related data. Exemplarily, the first driving behavior data may include at least one of the following: vehicle latitude and longitude, speed, longitudinal and lateral acceleration, vehicle elevation, vehicle vertical acceleration, angle of head orientation, accelerator operation, brake operation, shift operation, Monitoring video data inside and outside the car, and driver facial data.

In actual application, the driving behavior monitoring method can be applied to electronic equipment, such as a server, and the target vehicle can collect the first driving behavior data through an on-board unit (On Board Unit, OBU), and send the collected first driving behavior data to server. The target vehicle can collect the first driving behavior data after starting, and can transmit the first driving behavior data to the server through a communication network such as the fifth generation mobile communication technology (5G).

In an embodiment, the determining that the target vehicle is a dangerous driving vehicle according to the first driving behavior data may include: determining the driving behavior category corresponding to the first driving behavior data according to a clustering algorithm; The driving behavior category corresponding to the data determines that the target vehicle is a dangerous driving vehicle.

In an embodiment, the determining that the target vehicle is a dangerous driving vehicle according to the first driving behavior data may include: using an SVM-based driving behavior classifier to identify the first driving behavior data, and determine the The driving safety index of the target vehicle, based on the driving safety index of the target vehicle, it is determined that the target vehicle is a dangerous driving vehicle.

In practical application, it can be understood that if the vehicle is not determined to be a dangerous driving vehicle based on the driving behavior category corresponding to the first driving behavior data or the driving safety index of the target vehicle, then the vehicle can be considered as a safe driving vehicle.

In an embodiment, the historical compliance index may be determined according to the number of times the target vehicle takes countermeasures after receiving the first warning message. Exemplarily, the historical compliance index may be proportional to the first ratio. Related, the first ratio is the ratio of the number of times the target vehicle takes countermeasures after receiving the first warning message to the total number of times the first warning message is received; or, the historical compliance indicators may include The number of times the target vehicle takes countermeasures after receiving the first warning prompt information is multiplied by a preset coefficient, etc., which is not limited in this embodiment of the present application.

In practical application, the preset coefficient can be preset according to requirements, such as 0.01, or 0.05, or 0.1, etc., which is not limited in this embodiment of the present application. The first warning prompt information may be used to prompt the target vehicle to be a dangerous driving vehicle, and prompt the target vehicle to take countermeasures. The first warning prompt information may carry driving behavior data of the target vehicle.

In actual application, the warning prompt based on the historical compliance index of the target vehicle may include: if the historical compliance index of the target vehicle is lower than the first preset index, then the distance to the target vehicle Sending second warning prompt information to vehicles within a preset distance; if the historical compliance index of the target vehicle is higher than the first preset index, sending the first warning prompt information to the target vehicle.

It should be noted that before obtaining the historical compliance indicators of the target vehicle, the facial feature information of the driver of the target vehicle can be collected, and the historical compliance information of the target vehicle corresponding to the driver's facial feature information can be obtained. index. The historical compliance index of the target vehicle corresponding to the facial feature information of the driver may be used to characterize the compliance degree of the driver driving the target vehicle.

In the embodiment of the present application, the first driving behavior data sent by the target vehicle at the first sampling moment is received, and the target vehicle is determined to be a dangerous driving vehicle according to the first driving behavior data; the historical compliance of the target vehicle is determined An indicator, the historical compliance indicator is related to the number of times the target vehicle takes countermeasures after receiving the first warning prompt message; and the warning prompt is performed according to the historical compliance indicator of the target vehicle. In this way, when the target vehicle is a dangerous driving vehicle, the warning prompt is given according to the historical compliance indicators of the target vehicle, which can improve the monitoring effect of the driving behavior of the vehicle and is conducive to the improvement of road traffic safety.

In an embodiment, the warning prompt based on the historical compliance indicators of the target vehicle may include:

The method may also include:

Wherein, the first preset index can be preset according to requirements, such as 20%, or 40%, or 60%, etc., which is not limited in this embodiment of the present application. The determining according to the second driving behavior data whether the target vehicle has taken countermeasures against the first warning prompt information corresponding to the first sampling moment may include: if determining according to the second driving behavior data that the If the target vehicle is a dangerous driving vehicle, it can be considered that the target vehicle has not taken countermeasures against the first warning message corresponding to the first sampling moment; if the target vehicle is determined to be safe driving according to the second driving behavior data vehicle, it may be considered that the target vehicle has taken countermeasures for the first warning prompt information corresponding to the first sampling moment. After determining whether the target vehicle has taken countermeasures against the first warning prompt information corresponding to the first sampling moment, the first ratio may be updated.

In practical applications, the second driving behavior data may include at least one of the following: vehicle driving-related data, vehicle operation-related data, and driver-related data. Exemplarily, the second driving behavior data may include at least one of the following: latitude and longitude of the vehicle, speed, longitudinal and lateral acceleration, vehicle elevation, vehicle vertical acceleration, angle of the front of the vehicle, accelerator operation, brake operation, shift operation, Monitoring video data inside and outside the car, and driver facial data. According to the second driving behavior data, it is determined whether the target vehicle has taken countermeasures against the first warning message corresponding to the first sampling moment, so as to determine whether the driver of the target vehicle has improved his driving behavior. In a case where it is determined that the target vehicle has taken countermeasures, it may be recorded that the target vehicle has taken countermeasures for the first warning prompt information corresponding to the first sampling moment.

It should be noted that the target vehicle can send the driving behavior data of the target vehicle to an electronic device (such as a server) at intervals of preset time intervals, and the preset time length can be preset according to requirements, such as 5 minutes, or 10 minutes, or 15 minutes, etc., this The embodiment of the application does not limit this. The second sampling moment can be the moment after the first sampling moment passes through the preset duration. Taking the preset duration as 5 minutes as an example, if the first sampling moment is 10:00, then the second sampling moment can be 10:05; or, The target vehicle can also send the driving behavior data of the target vehicle to the electronic device under the trigger of the preset trigger condition. For example, the target vehicle can send the target vehicle to the server when the change value of the speed is greater than or equal to the preset change value. The driving behavior data of the vehicle and the like are not limited in this embodiment of the present application.

In actual application, when it is determined that the target vehicle has not taken countermeasures, the second warning prompt information is sent to the vehicle whose distance from the target vehicle is less than the preset distance, so that the driver can not take effective corrective measures In the case of a dangerous driving situation, the vehicle information of the target vehicle in a dangerous driving state is sent to the vehicles around the target vehicle to remind other drivers to pay attention. The second warning prompt information may be used to prompt that the target vehicle is a dangerous driving vehicle, and the second warning prompt information may carry driving behavior data of the target vehicle.

In the embodiment of the present application, after sending the first warning message to the target vehicle, it is determined whether to send the second warning message to vehicles around the target vehicle according to whether the target vehicle has taken countermeasures against the first warning message, so as to avoid Frequently sending the second warning prompt information to vehicles around the target vehicle can effectively save resources, reduce communication costs and time costs of drivers on vehicles around the target vehicle.

In an embodiment, the determining that the target vehicle is a dangerous driving vehicle according to the first driving behavior data may include:

Wherein, the clustering algorithm may include a k-means clustering algorithm, or may include a mean shift clustering algorithm, or may include a density-based clustering method, etc., which are not limited in this embodiment of the present application. The driving behavior category corresponding to each type of driving behavior data may include at least one of the following behaviors: normal driving behavior, rapid acceleration behavior, rapid deceleration behavior and sharp turning behavior. The determining the driving behavior category corresponding to each type of driving behavior data according to each type of driving behavior data in the at least one type of driving behavior data may include: calculating the mean value of each type of driving behavior data, and according to each type of driving behavior data Determine the driving behavior category corresponding to each type of driving behavior data. Exemplarily, the first driving behavior data includes longitudinal acceleration, and when the average value of the longitudinal acceleration of a certain type of driving behavior data is greater than a preset acceleration, it can be considered that the driving behavior category corresponding to this type of driving behavior data is rapid acceleration behavior. The difference of various types of driving behavior data can be identified according to experience, and the driving behavior category corresponding to each type of driving behavior data can be identified.

In actual application, if the driving behavior category corresponding to each type of driving behavior data in the at least one type of driving behavior data is a normal driving behavior, it can be determined that the target vehicle is not a dangerous driving vehicle, that is, the target vehicle The vehicle is a safe driving vehicle; if there is a driving behavior category other than normal driving behavior in the driving behavior category corresponding to the at least one type of driving behavior data, it can be determined that the target vehicle is a dangerous driving vehicle. In the embodiment of the present application, when the target vehicle is determined to be a dangerous driving vehicle, an alarm prompt may be given based on the historical compliance indicators of the target vehicle; when the target vehicle is determined to be a safe driving vehicle, the The target vehicle is not processed.

In actual application, when the clustering algorithm includes a k-means clustering algorithm, the first driving behavior data may include speed, longitudinal acceleration and yaw rate, and the electronic device may analyze the first driving behavior data. Behavioral data is preprocessed to remove obviously wrong data in the first driving behavior data, for example, remove the first driving behavior data whose speed is greater than the preset value; the K value can be determined by the elbow rule, as shown in Figure 2 , the abscissa in Figure 2 is the K value, and the ordinate is the sum of squares of error (Sum of Squares due to Error, SSE). After the point, the increase of the K value has a decreasing effect on the reduction of the sum of squared errors, and the critical point is the elbow. Take the K value of the elbow point as the best K value, cluster based on the best K value to obtain at least one type of driving behavior data, calculate the mean value of each type of driving behavior data, and compare the differences between various types of driving behavior data. The characteristics of different categories are identified, and the driving behavior category corresponding to each type of driving behavior data is determined. Based on the driving behavior category, it can be determined that the target vehicle is a dangerous driving vehicle.

In the embodiment of the present application, the first driving behavior data is classified according to a clustering algorithm to obtain at least one type of driving behavior data; A driving behavior category corresponding to the driving behavior data; determining that the target vehicle is a dangerous driving vehicle based on the driving behavior category. Therefore, it is possible to use real-time data to determine whether the target vehicle is a dangerous driving vehicle without using samples, and it is not necessary to use samples to train a neural network model to identify whether the target vehicle is a dangerous driving vehicle, which can save time and reduce costs.

In an embodiment, the driving behavior category corresponding to each type of driving behavior data may include at least one of the following behaviors: normal driving behavior, rapid acceleration behavior, rapid deceleration behavior, and sharp turning behavior.

Wherein, the sharp turn behavior can be divided into a sharp left turn behavior and a sharp right turn behavior. Exemplarily, the driving behavior category corresponding to each type of driving behavior data may include one of normal driving behavior, rapid acceleration behavior, rapid deceleration behavior, and sharp turning behavior.

In an embodiment, after receiving the first driving behavior data sent by the target vehicle at the first sampling moment, the method may further include:

Wherein, by determining whether the first driving behavior data is within the preset threshold interval, it can be determined whether the driver of the target vehicle violates laws and regulations; In the case of a violation, the driver's illegal behavior can be reported to the traffic management system, so that the traffic management department can control the target vehicle. The first driving behavior data is not within the preset threshold interval, it can be considered that the first driving behavior data does not meet the preset traffic rules; the first driving behavior data is within the preset threshold interval, it can be considered that the first driving behavior data is within the preset threshold interval The driving behavior data complies with preset traffic rules. Exemplarily, when the first driving behavior data includes speed, the current position of the target vehicle can be used to determine the road speed limit of the road to which the target vehicle belongs, and when the speed of the target vehicle exceeds the road speed limit, it can be determined that the target vehicle The vehicle is speeding, so that it can be determined that the first driving behavior data does not comply with the preset traffic rules, and the preset threshold range can be (0, V), where V is the road speed limit; or, the continuous running time of the target vehicle can be counted, if the target If the continuous running time of the vehicle exceeds the time limit of traffic regulations, it can be judged that the target vehicle is driving fatigued, so that it can be determined that the first driving behavior data does not conform to the preset traffic rules. The preset threshold range can be (0, T), where T is continuous running time.

In the embodiment of the present application, if the first driving behavior data is not within the preset threshold interval, the first driving behavior data is sent to the traffic management system, so that it can be judged according to the first driving behavior data whether the driver Violation of laws and regulations, when the driver violates laws and regulations, the traffic control department will be notified in time, which can further improve the effect of monitoring the driving behavior of the vehicle.

In an embodiment, before the warning prompt according to the historical compliance indicators of the target vehicle, the method may further include:

Using an SVM-based driving behavior classifier to identify the first driving behavior data, and determine a driving safety index of the target vehicle, where the driving safety index is a probability that the target vehicle belongs to a safe driving vehicle;

The warning prompt based on the historical compliance indicators of the target vehicle may include:

Wherein, the SVM-based driving behavior classifier may output the probability that the target vehicle is a safe driving vehicle and the probability that the target vehicle is a dangerous driving vehicle. The probability that the target vehicle is a safe driving vehicle may be used as the driving safety index of the target vehicle. The value range of the driving safety index can be 0-100%, 0 can represent the driving behavior of the target vehicle as a typical safe driving behavior, and 100% can represent the driving behavior of the target vehicle as a typical dangerous driving behavior.

During practical application, the construction process of the driving behavior classifier based on the SVM may include: cleaning and standardizing the historical driving behavior data; constructing the driving behavior classifier based on the SVM, different kernel functions can be used to construct different driving behavior based on the SVM Behavior classifier, using historical driving behavior data as input, the target vehicle is a dangerous driving vehicle or a safe driving vehicle as a label to train the constructed SVM-based driving behavior classifier, and select the SVM-based driving behavior classifier with the highest accuracy Thus, the construction of the SVM-based driving behavior classifier is completed. The first driving behavior data can be input into the driving behavior classifier based on SVM, and output the probability that the target vehicle belongs to a safe driving vehicle and the probability that the target vehicle belongs to a dangerous driving vehicle. The historical driving behavior data may include multiple pieces of driving behavior data, and each piece of driving behavior data in the historical driving behavior data may be classified and processed according to a clustering algorithm to obtain at least one type of driving behavior data; For each type of driving behavior data, determine the driving behavior category corresponding to each type of driving behavior data; determine that the target vehicle is a dangerous driving vehicle based on the driving behavior category. Therefore, the SVM-based driving behavior classifier can be trained by using the historical driving behavior data that determines whether the target vehicle is a dangerous driving vehicle.

In the embodiment of the present application, when the target vehicle is a dangerous driving vehicle, the historical compliance indicators of the target vehicle and the driving safety indicators of the target vehicle are combined to give an alarm prompt, so that the danger of the target vehicle can be considered from multiple dimensions It can provide a reference for warning prompts, and can further improve the effect of monitoring the driving behavior of the vehicle.

In an embodiment, the warning prompt based on the historical compliance indicators of the target vehicle and the driving safety indicators of the target vehicle may include:

Wherein, the first preset index can be preset according to requirements, such as 20%, or 40%, or 60%, etc., which is not limited in this embodiment of the present application. The second preset index can be preset according to requirements, such as 20%, or 40%, or 60%, etc., which is not limited in this embodiment of the present application. The preset distance can be preset according to requirements, such as 5 meters, or 50 meters, or 100 meters, etc., which is not limited in this embodiment of the present application. If the historical compliance index of the target vehicle is lower than the first preset index, and the driving safety index of the target vehicle is lower than the second preset index, the first warning message may also be sent to the target vehicle . The second warning prompt information may be used to prompt the target vehicle to be a dangerous driving vehicle, so as to warn vehicles around the target vehicle. The second warning prompt information may carry the driving behavior category corresponding to the first driving behavior data, so that the vehicles around the target vehicle can take corresponding measures according to the driving behavior category of the target vehicle to ensure their own safety. The driving behavior category corresponding to the first driving behavior data may include: the driving behavior category corresponding to at least one type of driving behavior data obtained after classifying the first driving behavior data.

In the embodiment of the present application, according to the historical compliance index of the target vehicle and the driving safety index of the target vehicle, the first warning prompt information is sent to the target vehicle or the second warning prompt information is sent to the vehicles around the target vehicle, so that Giving different warning prompts based on the consideration of the danger of the target vehicle can further improve the road traffic safety.

In an embodiment, the historical compliance index may be positively correlated with a first ratio, and the first ratio is the number of times the target vehicle takes countermeasures after receiving the first warning message and the number of times the target vehicle receives the first warning message A ratio of the total number of alarm prompt messages.

Wherein, the historical compliance index may be equal to the first ratio, or the historical compliance index may be directly proportional to the first ratio, or the historical compliance index may be equal to the sum of the first ratio and the preset increment, etc., this The embodiment of the application does not limit the specific correlation manner between the historical compliance indicator and the first ratio. The preset increment can be preset according to requirements, such as 0.01, or 0.05, or 0.1, etc., which is not limited in this embodiment of the present application. The value range of the first ratio can be preset according to requirements, such as 0-100%, etc., which is not limited in this embodiment of the present application. Exemplarily, the total number of times that the target vehicle receives the first warning prompt information is 100 times, and the number of times that the target vehicle takes countermeasures after receiving the first warning prompt information is 70 times, then the first ratio is 70%.

In the embodiment of the present application, the historical compliance index is positively correlated with the first ratio, so that the warning can be given according to the proportion of the target vehicle taking countermeasures after receiving the first warning message. Based on the first ratio, it can be compared A good judgment of the historical compliance degree of the target vehicle, and an alarm prompt based on the historical compliance degree of the target vehicle can improve the driving behavior monitoring effect of the vehicle.

As a specific implementation, the driving behavior monitoring method may include the following procedures: receiving the first driving behavior data sent by the target vehicle at the first sampling moment; determining that the target vehicle is dangerous driving according to the first driving behavior data In the case of a vehicle, send the first warning prompt information to the target vehicle; receive the second driving behavior data sent by the target vehicle at the second sampling moment, and determine the target according to the second driving behavior data Whether the vehicle has taken countermeasures for the first warning message corresponding to the first sampling moment, and the second sampling moment is a moment after the first sampling moment; when it is determined that the target vehicle has not taken countermeasures Next, sending second warning prompt information to vehicles whose distance from the target vehicle is less than a preset distance. After receiving the first driving behavior data sent by the target vehicle at the first sampling moment, the method may further include: determining whether the first driving behavior data is within a preset threshold interval, and the preset threshold interval is based on determining the preset traffic rules; if the first driving behavior data is not within the preset threshold interval, sending the first driving behavior data to a traffic management system; determining the target based on the first driving behavior data The vehicle is a dangerous driving vehicle, and may include: if the first driving behavior data is within the preset threshold interval, determining that the target vehicle is a dangerous driving vehicle according to the first driving behavior data. In the case of determining that the target vehicle is a dangerous driving vehicle, sending the first warning prompt information to the target vehicle may include: determining a historical compliance index of the target vehicle, the historical compliance index Positively correlated with the first ratio, the first ratio is the ratio of the number of times the target vehicle takes countermeasures after receiving the first warning message to the total number of times the first warning message is received; if the The historical compliance index of the target vehicle is higher than the first preset index, sending the first warning message to the target vehicle; if the historical compliance index of the target vehicle is lower than the first preset index, Then, the second warning prompt information is sent to the vehicle whose distance from the target vehicle is less than the preset distance.

The embodiment of the present application also provides a driving behavior monitoring device, as shown in Figure 3, the driving behavior monitoring device 300 includes:

The first determination module 301 is configured to receive the first driving behavior data sent by the target vehicle at the first sampling moment, and determine that the target vehicle is a dangerous driving vehicle according to the first driving behavior data;

The obtaining module 302 is configured to determine the historical compliance index of the target vehicle, the historical compliance index is related to the number of times the target vehicle takes countermeasures after receiving the first warning message;

The warning module 303 is configured to give a warning prompt according to the historical compliance indicators of the target vehicle.

In an embodiment, the alarm module 303 is further configured to:

As shown in Figure 4, the driving behavior monitoring device 300 may also include:

The second determining module 401 is configured to receive the second driving behavior data sent by the target vehicle at the second sampling moment, and determine whether the target vehicle has corresponded to the first sampling moment according to the second driving behavior data. Taking countermeasures for the first alarm prompt information, the second sampling moment is a moment after the first sampling moment;

The sending module 402 is configured to send second warning prompt information to vehicles whose distance from the target vehicle is less than a preset distance when it is determined that the target vehicle has not taken countermeasures.

In an embodiment, the first determining module 301 is further configured to:

In an embodiment, the first determination module 301 is further configured to:

In an embodiment, as shown in FIG. 5 , the driving behavior monitoring device 300 may further include:

The third determination module 501 is configured to identify the first driving behavior data by using an SVM-based driving behavior classifier, and determine the driving safety index of the target vehicle, and the driving safety index is that the target vehicle belongs to safe driving the probability of the vehicle;

The alarm module 303 is also configured to:

In an embodiment, the alarm module 303 is further configured to:

In an embodiment, the historical compliance index is positively correlated with a first ratio, and the first ratio is the number of times the target vehicle takes countermeasures after receiving the first warning message and the number of times the target vehicle receives the first warning message. The ratio of the total number of alarm prompt messages.

In practical applications, the first determination module 301 , the acquisition module 302 , the alarm module 303 , the second determination module 401 , the sending module 402 and the third determination module 501 may be implemented by a processor in the driving behavior monitoring device 300 .

It should be noted that when the driving behavior monitoring device 300 provided in the above-mentioned embodiment monitors the driving behavior, it only uses the division of the above-mentioned program modules for illustration. In practical applications, the above-mentioned processing can be assigned to different program modules according to needs Completion means that the internal structure of the device is divided into different program modules to complete all or part of the processing described above. In addition, the driving behavior monitoring device 300 provided in the above embodiments can implement the various processes in the driving behavior monitoring method embodiments provided in the embodiments of the present application, and can achieve the same technical effect, so to avoid repetition, details are not repeated here.

The embodiment of the present application also provides an electronic device. As shown in FIG. 6, the electronic device 600 includes: a processor 601, a memory 602, and a program stored in the memory 602 and operable on the processor 601, When the program is executed by the processor 601, each process of the above-mentioned driving behavior monitoring method embodiment can be realized, and the same technical effect can be achieved. To avoid repetition, details are not repeated here.

The embodiment of the present application also provides a computer-readable storage medium. A computer program is stored on the computer-readable storage medium. When the computer program is executed by a processor, each process of the above-mentioned driving behavior monitoring method embodiment is realized, and the same Technical effects, in order to avoid repetition, will not be repeated here. Wherein, the computer readable storage medium is, for example, a read only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and the like.

It should be noted that in this application, the term "comprises", "comprises" or any other variation thereof is intended to cover a non-exclusive inclusion such that a process, method, article or apparatus comprising a set of elements includes not only those elements , but also includes other elements not expressly listed, or also includes elements inherent in such a process, method, article, or device. Without further limitations, an element defined by the phrase "comprising a ..." does not preclude the presence of additional identical elements in the process, method, article, or apparatus comprising that element.

Through the description of the above embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus a necessary general-purpose hardware platform, and of course also by hardware, but in many cases the former is better implementation. Based on this understanding, the essence of the technical solution of this application or the part that contributes to related technologies can be embodied in the form of software products, and the computer software products are stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk, etc.) ) includes several instructions to enable a terminal (which may include a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in various embodiments of the present application.

The embodiments of the present application have been described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementations. The above-mentioned specific implementations are only illustrative and not restrictive. Those of ordinary skill in the art will Under the inspiration of this application, without departing from the purpose of this application and the scope of protection of the claims, many forms can also be made, all of which belong to the protection of this application.

Claims

A driving behavior monitoring method, the method comprising:

receiving the first driving behavior data sent by the target vehicle at the first sampling moment, and determining that the target vehicle is a dangerous driving vehicle according to the first driving behavior data;

determining the historical compliance index of the target vehicle, where the historical compliance index is related to the number of times the target vehicle takes countermeasures after receiving the first warning message;

An alarm prompt is given according to the historical compliance indicators of the target vehicle.
The method according to claim 1, wherein the performing the warning prompt according to the historical compliance indicators of the target vehicle comprises:

If the historical compliance index of the target vehicle is higher than a first preset index, sending the first warning message to the target vehicle;

The method also includes:

receiving the second driving behavior data sent by the target vehicle at the second sampling moment, and determining whether the target vehicle has taken a response to the first warning message corresponding to the first sampling moment according to the second driving behavior data Measures, the second sampling moment is a moment after the first sampling moment;

If it is determined that the target vehicle has not taken countermeasures, sending second warning prompt information to vehicles whose distance from the target vehicle is less than a preset distance.
The method according to claim 1, wherein said determining that said target vehicle is a dangerous driving vehicle according to said first driving behavior data comprises:

Classifying the first driving behavior data according to a clustering algorithm to obtain at least one type of driving behavior data;

Determine the driving behavior category corresponding to each type of driving behavior data according to each type of driving behavior data in the at least one type of driving behavior data;

Based on the driving behavior category, it is determined that the target vehicle is a dangerous driving vehicle.
The method according to claim 3, wherein the driving behavior category corresponding to each type of driving behavior data includes at least one of the following behaviors: normal driving behavior, rapid acceleration behavior, rapid deceleration behavior, and sharp turning behavior.
The method according to claim 1, wherein, after receiving the first driving behavior data sent by the target vehicle at the first sampling moment, the method further comprises:

determining whether the first driving behavior data is within a preset threshold interval, and the preset threshold interval is determined based on preset traffic rules;

If the first driving behavior data is not within the preset threshold interval, sending the first driving behavior data to a traffic management system;

The determining that the target vehicle is a dangerous driving vehicle according to the first driving behavior data includes:

If the first driving behavior data is within the preset threshold interval, it is determined according to the first driving behavior data that the target vehicle is a dangerous driving vehicle.
The method according to claim 1, wherein, before the warning prompt according to the historical compliance indicators of the target vehicle, the method further comprises:

Using a driving behavior classifier based on a support vector machine to identify the first driving behavior data, and determine a driving safety index of the target vehicle, where the driving safety index is a probability that the target vehicle belongs to a safe driving vehicle;

The warning prompt based on the historical compliance indicators of the target vehicle includes:

An alarm prompt is given according to the historical compliance indicators of the target vehicle and the driving safety indicators of the target vehicle.
The method according to claim 6, wherein the performing the warning prompt according to the historical compliance indicators of the target vehicle and the driving safety indicators of the target vehicle comprises:

If the historical compliance index of the target vehicle is lower than the first preset index, and the driving safety index of the target vehicle is lower than the second preset index, the vehicle whose distance from the target vehicle is less than the preset distance Send the second warning message;

If the historical compliance index of the target vehicle is higher than the first preset index, or the driving safety index of the target vehicle is higher than the second preset index, send the first Alarm prompt information.
The method according to claim 1, wherein the historical compliance index is positively correlated with a first ratio, and the first ratio is the number of times the target vehicle takes countermeasures after receiving the first warning message and the number of times it receives the first warning message. The ratio of the total number of times to the first warning prompt message.
A driving behavior monitoring device, said device comprising:

The first determination module is configured to receive first driving behavior data sent by the target vehicle at the first sampling moment, and determine that the target vehicle is a dangerous driving vehicle according to the first driving behavior data;

An acquisition module configured to determine the historical compliance indicators of the target vehicle, where the historical compliance indicators are related to the number of times the target vehicle takes countermeasures after receiving the first warning message;

The warning module is configured to give a warning prompt according to the historical compliance indicators of the target vehicle.
An electronic device, comprising: a processor, a memory, and a program stored on the memory and operable on the processor, when the program is executed by the processor, any one of claims 1 to 8 is realized The steps of the driving behavior monitoring method.
A computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the driving behavior monitoring method according to any one of claims 1 to 8 are realized.