Dangerous driving behavior real-time monitoring and warning system and method based on vehicle driving track

Abstract

The invention provides a dangerous driving behavior real-time monitoring and warning system and method based on a vehicle driving track, and the method comprises the steps: collecting vehicle driving data through an electronic device, carrying out the feature extraction of the driving data, carrying out the recognition and classification of the extracted features, and obtaining a vehicle driving track feature, a vehicle speed feature and a surrounding environment feature; and determining a corresponding relation among the vehicle speed feature, the vehicle driving track feature and the surrounding environment feature, and analyzing the driving habit of the driver during driving according to the corresponding relation. By comparing the current scene and combining the driving habits of the driver, whether the driver will have the dangerous driving behavior or not is judged, the dangerous driving behavior is avoided by reminding the driver, the coverage rate of dangerous driving behavior interference reminding and danger avoidance is greatly increased, traffic accidents are reduced, casualties and losses are reduced, and the driving experience of the driver is improved. And meanwhile, a driver is assisted to change bad driving habits, and more comfortable driving experience and driving safety are provided for the driver and passengers.

Description

Technical Field

[0001] The present invention relates to the technical field of driving behavior assessment and prediction, and in particular to a real-time monitoring and warning system and method for dangerous driving behavior based on a vehicle's driving trajectory. Background Art

[0002] In today's society, dangerous driving behaviors have become a cancer of social security, threatening the lives of drivers, passengers and pedestrians at all times. According to investigations, in addition to drunk driving and force majeure, most traffic accidents are caused by bad driving habits of drivers. Bad driving habits mainly include improper steering, lane changes and U-turns, acceleration, deceleration, etc. In current related technologies, the identification of dangerous driving behaviors is mainly achieved by monitoring the driving behaviors of drivers using in-car cameras.

[0003] Camera monitoring has limited effect in assisting and correcting drivers' bad driving behaviors. Bad driving habits are not only the driver's state, but mainly the driver's corresponding operations and reactions in different driving scenarios. How to predict and warn of dangerous driving in advance based on the driver's driving habits is a technical problem that urgently needs to be solved. Summary of the invention

[0004] Technical issues solved:

[0005] In view of the deficiencies in the prior art, the present invention provides a real-time monitoring and warning system and method for dangerous driving behavior based on vehicle driving trajectory to solve the problems raised in the above background technology.

[0006] Technical solution:

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: a real-time monitoring and warning method for dangerous driving behavior based on vehicle driving trajectory, comprising the following steps:

[0008] Collecting vehicle driving data through electronic equipment, extracting features from the driving data, identifying and classifying the extracted features, and obtaining vehicle driving trajectory features, vehicle speed features, and surrounding environment features;

[0009] Determine the correspondence between the vehicle speed characteristics, the vehicle driving trajectory characteristics and the surrounding environment characteristics, and analyze the driving habits of the driver while driving according to the correspondence;

[0010] Determine whether the driver has engaged in dangerous driving behaviors while driving based on the driver's driving habits, including driving too close to the vehicle ahead, dangerous overtaking, and aggressive driving;

[0011] Based on the results of the dangerous driving behavior analysis, a warning is issued to the driver before the driver exhibits dangerous driving behavior.

[0012] As a preferred embodiment of this invention, the determining of the correspondence between the vehicle speed feature, the vehicle driving track feature and the surrounding environment feature includes:

[0013] Acquire the surrounding images and the vehicle's driving trajectory while the vehicle is driving, and identify the surrounding environment images corresponding to the current vehicle's driving trajectory;

[0014] Based on the vehicle's driving trajectory, the speed change of the vehicle while driving and the vehicle's driving speed are determined.

[0015] As a preferred embodiment of this invention, the process of analyzing the driving habits of the driver during driving according to the corresponding relationship includes:

[0016] Acquire driving data corresponding to the vehicle speed characteristics, vehicle driving trajectory characteristics and surrounding environment characteristics of the driver within a cycle, and divide the driving data within the cycle into a plurality of node sets according to the trajectory characteristics;

[0017] Analyzing the driving data of the plurality of node sets to obtain driving behavior data of users in a plurality of scenarios;

[0018] A hierarchical clustering algorithm is used to cluster the standardized feature matrix, and the number of clusters is determined by cutting the tree diagram to generate the driver's behavior pattern cluster label under the node set;

[0019] The standardization formula is:

[0020] ;

[0021] Among them, z i represents the normalized velocity value, i represents the node number code in the node set i=1, 2, 3...n, x i is the average speed of node i in the node set, µ i is the average velocity of the node set, σ i is the standard deviation of the velocity under the node set.

[0022] As a preferred embodiment of the present invention, the driving data of the plurality of node sets are analyzed to obtain the driving behavior data of the users in a plurality of scenarios, including: obtaining the behavior pattern cluster labels of the drivers in each node set by clustering the speed data of the plurality of node sets, and obtaining the driving behavior data of the users in a plurality of scenarios by further analyzing the behavior pattern cluster labels corresponding to the node sets in combination with the surrounding environment characteristics.

[0023] As a preferred embodiment of the present invention, the process of clustering the speed data of several node sets to obtain the driver's behavior pattern cluster label under each node set includes:

[0024] Extract speed statistics and dynamic features from the node set, and standardize the features to obtain the standardized matrix X∈R N*M , where N is the total number of nodes in the node set, and M is the total number of node features;

[0025] Among them, the vehicle speed characteristics include average speed and speed standard deviation, and the dynamic characteristics are vehicle speed fluctuations.

[0026] As a preferred embodiment of this invention, a real-time monitoring and warning system for dangerous driving behavior based on vehicle driving trajectory is used to implement the above-mentioned real-time monitoring and warning method for dangerous driving behavior based on vehicle driving trajectory, including:

[0027] A data acquisition module is used to collect vehicle driving data, extract features from the driving data, identify and classify the extracted features, and obtain vehicle driving trajectory features, vehicle speed features, and surrounding environment features;

[0028] A habit analysis module, used to determine the correspondence between vehicle speed characteristics, vehicle driving trajectory characteristics and surrounding environment characteristics, and analyze the driver's driving habits while driving according to the correspondence;

[0029] A behavior prediction module is used to determine whether the driver has dangerous driving behaviors while driving according to the driver's driving habits, wherein the dangerous driving behaviors include driving too close to the vehicle ahead, dangerous overtaking, and aggressive driving;

[0030] The warning module analyzes the driver's dangerous driving behavior and issues a warning to the driver before the driver engages in dangerous driving behavior.

[0031] As a preferred embodiment of this invention, the determining of the correspondence between the vehicle speed feature, the vehicle driving track feature and the surrounding environment feature includes:

[0032] Acquire the surrounding images and the vehicle's driving trajectory while the vehicle is driving, and identify the surrounding environment images corresponding to the current vehicle's driving trajectory;

[0033] Based on the vehicle's driving trajectory, the speed change of the vehicle while driving and the vehicle's driving speed are determined.

[0034] As a preferred embodiment of this invention, the process of analyzing the driving habits of the driver during driving according to the corresponding relationship includes:

[0035] Acquire driving data corresponding to the vehicle speed characteristics, vehicle driving trajectory characteristics and surrounding environment characteristics of the driver within a cycle, and divide the driving data within the cycle into a plurality of node sets according to the trajectory characteristics;

[0036] Analyzing the driving data of the plurality of node sets to obtain driving behavior data of users in a plurality of scenarios;

[0037] A hierarchical clustering algorithm is used to cluster the standardized feature matrix, and the number of clusters is determined by cutting the tree diagram to generate the driver's behavior pattern cluster label under the node set;

[0038] The standardization formula is:

[0039] ;

[0040] Among them, z i represents the normalized velocity value, i represents the node number code in the node set i=1, 2, 3...n, x i is the average speed of node i in the node set, µ i is the average velocity of the node set, σ i is the standard deviation of the velocity under the node set.

[0041] As a preferred embodiment of the present invention, the driving data of the plurality of node sets are analyzed to obtain the driving behavior data of the users in a plurality of scenarios, including: obtaining the behavior pattern cluster labels of the drivers in each node set by clustering the speed data of the plurality of node sets, and obtaining the driving behavior data of the users in a plurality of scenarios by further analyzing the behavior pattern cluster labels corresponding to the node sets in combination with the surrounding environment characteristics.

[0042] As a preferred embodiment of the present invention, the process of clustering the speed data of several node sets to obtain the driver's behavior pattern cluster label under each node set includes:

[0043] Extract speed statistics and dynamic features from the node set, and standardize the features to obtain the standardized matrix X∈R N*M , where N is the total number of nodes in the node set, and M is the total number of node features;

[0044] Among them, the vehicle speed characteristics include average speed and speed standard deviation, and the dynamic characteristics are vehicle speed fluctuations.

[0045] Beneficial effects:

[0046] The present invention provides a real-time monitoring and warning system and method for dangerous driving behaviors based on vehicle driving trajectories, which have the following beneficial effects: by analyzing the driving trajectory of the driver while driving, and the driving behavior of the driver under different trajectories, combined with the driving response in different environments, it is analyzed whether the driver will have dangerous driving behaviors in different scenarios; when encountering similar scenarios, by comparing the current scenario with the driver's driving habits, it is determined whether the driver will have dangerous driving behaviors, and the driver is reminded to avoid the occurrence of dangerous driving behaviors, which greatly improves the coverage of dangerous driving behavior interference reminders and dangerous avoidance, reduces traffic accidents, reduces casualties and losses, and improves traffic safety; at the same time, assists the driver in changing bad driving habits to provide the driver and passengers with a more comfortable driving experience and driving safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a flow chart of the method for real-time monitoring and warning of dangerous driving behavior based on vehicle driving trajectory of the present invention;

[0048] Figure 2 This is a block diagram of the real-time monitoring and warning system for dangerous driving behavior based on vehicle driving trajectory of the present invention. DETAILED DESCRIPTION

[0049] The embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.

[0050] The disclosure below provides many different embodiments or examples to realize different structures of the present invention. In order to simplify the disclosure of the present invention, the parts and settings of specific examples are described below. Of course, they are only examples, and the purpose is not to limit the present invention. In addition, the present invention can repeat reference numbers and / or reference letters in different examples, and this repetition is for the purpose of simplicity and clarity, which itself does not indicate the relationship between the various embodiments and / or settings discussed. In addition, the present invention provides various specific examples of processes and materials, but those of ordinary skill in the art can be aware of the application of other processes and / or the use of other materials.

[0051] like Figure 1 As shown, an embodiment of the present invention provides a method for real-time monitoring and warning of dangerous driving behavior based on vehicle driving trajectory, comprising the following steps:

[0052] S1: Collect vehicle driving data through electronic equipment, extract features from the driving data, identify and classify the extracted features, and obtain vehicle driving trajectory features, vehicle speed features, and surrounding environment features;

[0053] In some embodiments, the electronic device may be one or more cameras, radars, sensors or other sensing devices. The electronic device may continuously collect visual images of the surrounding environment of the vehicle as well as data of the vehicle as it travels. The data of the vehicle as it travels includes the location information, speed information, etc. of the vehicle. These electronic devices may be installed at different locations of the vehicle.

[0054] S2: determining the correspondence between the vehicle speed characteristics, the vehicle driving trajectory characteristics and the surrounding environment characteristics, and analyzing the driving habits of the driver during driving according to the correspondence;

[0055] Determining the correspondence between vehicle speed characteristics, vehicle driving trajectory characteristics and surrounding environment characteristics includes:

[0056] S211: Acquire surrounding images and a trajectory of the vehicle when the vehicle is traveling, and identify surrounding environment images corresponding to the current vehicle driving trajectory;

[0057] S212: Based on the vehicle driving trajectory, determine the speed change of the vehicle while driving and the driving speed of the vehicle.

[0058] In this embodiment, the environment around the vehicle under the trajectory can be determined based on the vehicle's driving trajectory and the vehicle's current position, and the surrounding environment includes the road environment and the distance of objects around the vehicle. By analyzing the change of the vehicle's trajectory over a period of time, the change of the vehicle's speed under the trajectory and environment can be determined.

[0059] It can be understood that when analyzing the changes in vehicle trajectories over a fixed period of time, the fixed time is divided into several time periods, the distance of the vehicle trajectory in each time period is compared, and the length of each time period is combined to determine the changes in vehicle speed.

[0060] The process of analyzing the driver's driving habits while driving according to the corresponding relationship includes:

[0061] S221: Acquire driving data corresponding to the driver's vehicle speed characteristics, vehicle driving trajectory characteristics and surrounding environment characteristics within a cycle, and divide the driving data within the cycle into a plurality of node sets according to the trajectory characteristics;

[0062] In this embodiment, the trajectory features include straight-line driving, turning, lane changing and other trajectory features. Based on these features, the periodic driving data is divided into several node sets according to the timestamps, and the trajectory of each node set is similar.

[0063] S222: analyzing the driving data of a plurality of node sets to obtain driving behavior data of users in a plurality of scenarios;

[0064] Specifically, by clustering the speed data of several node sets, the driver's behavior pattern cluster labels under each node set are obtained. By further analyzing the behavior pattern cluster labels corresponding to the node sets in combination with the surrounding environment characteristics, the user's driving behavior data under several scenarios are obtained.

[0065] The process of clustering the speed data of several node sets to obtain the driver's behavior pattern cluster label under each node set includes:

[0066] Extract speed statistics and dynamic features from the node set, and standardize the features to obtain the standardized matrix X∈R N*M , where N is the total number of nodes in the node set, and M is the total number of node features;

[0067] Among them, the vehicle speed characteristics include average speed and speed standard deviation, and the dynamic characteristics are vehicle speed fluctuations.

[0068] S223: clustering the standardized feature matrix using a hierarchical clustering algorithm, determining the number of clusters by cutting the tree diagram, and generating a cluster label of the driver's behavior pattern under the node set;

[0069] The standardization formula is:

[0070] ;

[0071] Among them, z i represents the normalized velocity value, i represents the node number code in the node set i=1, 2, 3...n, x i is the average speed of node i in the node set, µ i is the average velocity of the node set, σ i is the standard deviation of the velocity under the node set.

[0072] In this embodiment, the hierarchical clustering adopts the Ward variance minimization criterion, and the number of clusters is determined by highly adaptive cutting of the dendrogram.

[0073] In this embodiment, the behavior pattern cluster labels corresponding to the node set are further analyzed in combination with the surrounding environment characteristics to obtain the driving behavior data of the user in several scenarios. The specific process includes:

[0074] Based on the driver's behavior pattern cluster labels under each node set, the driver's behavior pattern cluster labels are further analyzed in combination with the surrounding environment characteristics to determine the driver's response habits when encountering driving conditions, thereby obtaining the user's driving behavior data under several scenarios.

[0075] The surrounding environment features include road topography and distance data of vehicles around the driving vehicle.

[0076] Specifically, the node set includes the driver's handling methods for different terrain environments (such as ground bulges, depressions, etc.) and different following distances or left and right vehicle distances. By analyzing the driver's handling methods when facing these problems and combining the driver's behavior pattern cluster labels in different node sets, the driver's driving posture behavior is analyzed, thereby obtaining the driver's behavior data in different scenarios.

[0077] S223: Analyze the user's driving habits based on the user's driving behavior data in several scenarios.

[0078] S3: Determine whether the driver has engaged in dangerous driving behaviors based on the driver's driving habits. Dangerous driving behaviors include driving too close to the vehicle ahead, dangerous overtaking, and aggressive driving.

[0079] Specifically, based on the driver's driving habits, the possible handling methods of the driver in different scenarios can be determined, and the driver's possible handling methods can be evaluated to determine whether the driver has engaged in dangerous driving behavior.

[0080] In this embodiment, aggressive driving behaviors include sudden braking, sudden acceleration, illegal lane changing, and other driving behaviors.

[0081] It is understandable that dangerous driving behavior will affect the driving experience and safety of the driver and passengers.

[0082] The steps of determining whether the driver has dangerous driving behavior of aggressive driving include:

[0083] Collect the current driver's driving speed information, surrounding vehicle information and terrain information of the vehicle's current driving road, and determine the distance between the surrounding vehicles and the driving vehicle;

[0084] Based on the information obtained and the driver's behavior habits, determine whether the driver has engaged in dangerous driving behaviors such as overtaking, sudden acceleration or sudden braking.

[0085] It can be understood that the surrounding vehicle information may also be buildings, pedestrians or public facilities in other embodiments.

[0086] Exemplarily, a step of determining whether the driver has aggressive driving dangerous driving behavior while driving includes:

[0087] Collect the current driver's driving speed information, surrounding vehicle information and terrain information of the vehicle's current driving road, and determine the distance between the surrounding vehicles and the driving vehicle;

[0088] Wherein, based on the change in the distance between the driving vehicle and surrounding vehicles between the previous moment and the current moment, the current terrain has no arch or depression;

[0089] Based on the information obtained and the driver's behavior habits, determine whether the driver has engaged in dangerous driving behaviors such as overtaking, sudden acceleration or sudden braking.

[0090] In this embodiment, it is used to judge whether the user will overtake dangerously or brake suddenly, and the possibility of the user performing such behavior in the current situation is determined by analyzing the user's driving habits.

[0091] In this embodiment, another step of determining whether the driver has dangerous driving behavior of aggressive driving while driving includes:

[0092] Collect the current driver's driving speed information, surrounding vehicle information and terrain information of the vehicle's current driving road, and determine the distance between the surrounding vehicles and the driving vehicle;

[0093] Among them, based on the fact that the change in the distance between the driving vehicle and the surrounding vehicles between the previous moment and the current moment is 0, the current terrain appears arched or sunken;

[0094] Based on the information obtained and the driver's behavior habits, determine whether the driver has engaged in dangerous driving behaviors such as overtaking, sudden acceleration or sudden braking.

[0095] In this embodiment, it is used to judge whether the user will overtake or change lanes dangerously by analyzing the user's driving habits to determine the possibility of the user performing such behavior in the current scenario.

[0096] In this embodiment, another step of determining whether the driver has dangerous driving behavior of aggressive driving while driving includes:

[0097] Collect the current driver's driving speed information, surrounding vehicle information and terrain information of the vehicle's current driving road, and determine the distance between the surrounding vehicles and the driving vehicle;

[0098] There are no vehicles around the driving vehicle, and the current terrain has no bulges or depressions;

[0099] Based on the information obtained and the driver's behavior habits, determine whether the driver has engaged in dangerous driving behaviors such as overtaking, sudden acceleration or sudden braking.

[0100] In this embodiment, it is used to determine whether the user will accelerate suddenly, and by analyzing the user's driving habits, it is determined whether the user is likely to perform such behavior in the current situation.

[0101] S4: Based on the results of the dangerous driving behavior analysis, a warning is issued to the driver before the driver exhibits dangerous driving behavior.

[0102] In this embodiment, the way of issuing a warning to the driver may be a sound warning such as a voice broadcast.

[0103] It is understandable that as long as the driver has the possibility of dangerous driving, the driver needs to be warned.

[0104] The real-time monitoring and warning method for dangerous driving behaviors based on vehicle driving trajectories provided in this embodiment analyzes the driver's driving trajectory while driving, and the driver's driving behavior under different trajectories, and combines the driving reactions in different environments to analyze whether the driver will engage in dangerous driving behaviors in different scenarios. When encountering similar scenarios, the method determines whether the driver will engage in dangerous driving behaviors by comparing the current scenario with the driver's driving habits, and avoids the occurrence of dangerous driving behaviors by extracting and reminding the driver, thereby greatly improving the coverage of dangerous driving behavior interference reminders and dangerous avoidance, reducing traffic accidents, reducing casualties and losses, and improving traffic safety. At the same time, it assists the driver in changing bad driving habits to provide the driver and passengers with a more comfortable driving experience and driving safety.

[0105] This embodiment also provides a real-time monitoring and warning system for dangerous driving behaviors based on vehicle driving trajectories, which is used to implement the above-mentioned real-time monitoring and warning method for dangerous driving behaviors based on vehicle driving trajectories, including:

[0106] The data acquisition module is used to collect vehicle driving data, extract features from the driving data, identify and classify the extracted features, and obtain vehicle driving trajectory features, vehicle speed features, and surrounding environment features;

[0107] A habit analysis module is used to determine the correspondence between vehicle speed characteristics, vehicle driving trajectory characteristics and surrounding environment characteristics, and analyze the driver's driving habits while driving based on the correspondence;

[0108] The behavior prediction module is used to determine whether the driver has dangerous driving behaviors while driving based on the driver's driving habits. Dangerous driving behaviors include driving too close to the vehicle in front, dangerous overtaking, and aggressive driving.

[0109] The warning module analyzes the driver's dangerous driving behavior and issues a warning to the driver before the driver engages in dangerous driving behavior.

[0110] Furthermore, determining the correspondence between the vehicle speed feature, the vehicle driving trajectory feature and the surrounding environment feature includes:

[0111] Acquire the surrounding images and the vehicle's driving trajectory while the vehicle is driving, and identify the surrounding environment images corresponding to the current vehicle's driving trajectory;

[0112] Based on the vehicle's driving trajectory, the speed change of the vehicle while driving and the vehicle's driving speed are determined.

[0113] Furthermore, the process of analyzing the driver's driving habits while driving according to the corresponding relationship includes:

[0114] Acquire driving data corresponding to the driver's vehicle speed characteristics, vehicle driving trajectory characteristics and surrounding environment characteristics within a cycle, and divide the driving data within the cycle into several node sets according to the trajectory characteristics;

[0115] Analyze the driving data of several node sets to obtain the driving behavior data of users in several scenarios;

[0116] A hierarchical clustering algorithm is used to cluster the standardized feature matrix, and the number of clusters is determined by cutting the tree diagram to generate the driver's behavior pattern cluster label under the node set;

[0117] The standardization formula is:

[0118] ;

[0119] Among them, z i represents the normalized velocity value, i represents the node number code in the node set i=1, 2, 3...n, x i is the average speed of node i in the node set, µ i is the average velocity of the node set, σ i is the standard deviation of the velocity under the node set.

[0120] Furthermore, the driving data of several node sets are analyzed to obtain the driving behavior data of users in several scenarios, including: clustering the speed data of several node sets to obtain the driver's behavior pattern cluster labels in each node set, and further analyzing the behavior pattern cluster labels corresponding to the node sets in combination with the surrounding environment characteristics to obtain the user's driving behavior data in several scenarios.

[0121] Furthermore, by clustering the speed data of several node sets, the process of obtaining the driver's behavior pattern cluster label under each node set includes:

[0122] Extract speed statistics and dynamic features from the node set, and standardize the features to obtain the standardized matrix X∈R N*M , where N is the total number of nodes in the node set, and M is the total number of node features;

[0123] Among them, the vehicle speed characteristics include average speed and speed standard deviation, and the dynamic characteristics are vehicle speed fluctuations.

[0124] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A real-time monitoring and warning method for dangerous driving behavior based on vehicle driving trajectory, characterized in that: The following steps are involved: Collecting vehicle driving data through electronic equipment, extracting features from the driving data, identifying and classifying the extracted features, and obtaining vehicle driving trajectory features, vehicle speed features, and surrounding environment features; Determine the correspondence between the vehicle speed characteristics, the vehicle driving trajectory characteristics and the surrounding environment characteristics, and analyze the driving habits of the driver while driving according to the correspondence; Determine whether the driver has engaged in dangerous driving behaviors while driving based on the driver's driving habits, including driving too close to the vehicle ahead, dangerous overtaking, and aggressive driving; Based on the results of the dangerous driving behavior analysis, a warning is issued to the driver before the driver exhibits dangerous driving behavior.

2. The method for real-time monitoring and warning of dangerous driving behavior based on vehicle driving trajectory according to claim 1 is characterized in that: The determining of the correspondence between the vehicle speed feature, the vehicle driving trajectory feature and the surrounding environment feature includes: Acquire the surrounding images and the vehicle's driving trajectory while the vehicle is driving, and identify the surrounding environment images corresponding to the current vehicle's driving trajectory; Based on the vehicle's driving trajectory, the speed change of the vehicle while driving and the vehicle's driving speed are determined.

3. The method for real-time monitoring and warning of dangerous driving behavior based on vehicle driving trajectory according to claim 2 is characterized in that: The process of analyzing the driver's driving habits while driving according to the corresponding relationship includes: Acquire driving data corresponding to the vehicle speed characteristics, vehicle driving trajectory characteristics and surrounding environment characteristics of the driver within a cycle, and divide the driving data within the cycle into a plurality of node sets according to the trajectory characteristics; Analyzing the driving data of the plurality of node sets to obtain driving behavior data of users in a plurality of scenarios; A hierarchical clustering algorithm is used to cluster the standardized feature matrix, and the number of clusters is determined by cutting the tree diagram to generate the driver's behavior pattern cluster label under the node set; The standardization formula is: ； Among them, z i represents the normalized velocity value, i represents the node number code in the node set i=1, 2, 3...n, x i is the average speed of node i in the node set, µ i is the average velocity of the node set, σ i is the standard deviation of the velocity under the node set.

4. The method for real-time monitoring and warning of dangerous driving behavior based on vehicle driving trajectory according to claim 3 is characterized in that: The analyzing the driving data of the plurality of node sets to obtain the driving behavior data of the user in a plurality of scenarios includes: clustering the speed data of the plurality of node sets to obtain the driver's behavior pattern cluster labels in each node set, and further analyzing the behavior pattern cluster labels corresponding to the node sets in combination with the surrounding environment characteristics to obtain the driving behavior data of the user in a plurality of scenarios.

5. The method for real-time monitoring and warning of dangerous driving behavior based on vehicle driving trajectory according to claim 4 is characterized in that: The process of clustering the speed data of several node sets to obtain the driver's behavior pattern cluster label under each node set includes: Extract speed statistics and dynamic features from the node set, and standardize the features to obtain the standardized matrix X∈R N*M , where N is the total number of nodes in the node set, and M is the total number of node features; Among them, the vehicle speed characteristics include average speed and speed standard deviation, and the dynamic characteristics are vehicle speed fluctuations.

6. A real-time monitoring and warning system for dangerous driving behavior based on vehicle driving trajectory, used to implement the real-time monitoring and warning method for dangerous driving behavior based on vehicle driving trajectory according to any one of claims 1 to 5, characterized in that: include: A data acquisition module is used to collect vehicle driving data, extract features from the driving data, identify and classify the extracted features, and obtain vehicle driving trajectory features, vehicle speed features, and surrounding environment features; A habit analysis module, used to determine the correspondence between vehicle speed characteristics, vehicle driving trajectory characteristics and surrounding environment characteristics, and analyze the driver's driving habits while driving according to the correspondence; A behavior prediction module is used to determine whether the driver has dangerous driving behaviors while driving according to the driver's driving habits, wherein the dangerous driving behaviors include driving too close to the vehicle ahead, dangerous overtaking, and aggressive driving; The warning module analyzes the driver's dangerous driving behavior and issues a warning to the driver before the driver exhibits dangerous driving behavior.

7. A real-time monitoring and warning system for dangerous driving behavior based on vehicle driving trajectory according to claim 6, characterized in that: The determining of the correspondence between the vehicle speed feature, the vehicle driving trajectory feature and the surrounding environment feature includes: Acquire the surrounding images and the vehicle's driving trajectory while the vehicle is driving, and identify the surrounding environment images corresponding to the current vehicle's driving trajectory; Based on the vehicle's driving trajectory, the speed change of the vehicle while driving and the vehicle's driving speed are determined.

8. The real-time monitoring and warning system for dangerous driving behavior based on vehicle driving trajectory according to claim 7 is characterized in that: The process of analyzing the driver's driving habits while driving according to the corresponding relationship includes: Acquire driving data corresponding to the vehicle speed characteristics, vehicle driving trajectory characteristics and surrounding environment characteristics of the driver within a cycle, and divide the driving data within the cycle into a plurality of node sets according to the trajectory characteristics; Analyzing the driving data of the plurality of node sets to obtain driving behavior data of users in a plurality of scenarios; A hierarchical clustering algorithm is used to cluster the standardized feature matrix, and the number of clusters is determined by cutting the tree diagram to generate the driver's behavior pattern cluster label under the node set; The standardization formula is: ； Among them, z i represents the normalized velocity value, i represents the node number code in the node set i=1, 2, 3...n, x i is the average speed of node i in the node set, µ i is the average velocity of the node set, σ i is the standard deviation of the velocity under the node set.

9. The real-time monitoring and warning system for dangerous driving behavior based on vehicle driving trajectory according to claim 8 is characterized in that: The analyzing the driving data of the plurality of node sets to obtain the driving behavior data of the user in a plurality of scenarios includes: clustering the speed data of the plurality of node sets to obtain the driver's behavior pattern cluster labels in each node set, and further analyzing the behavior pattern cluster labels corresponding to the node sets in combination with the surrounding environment characteristics to obtain the driving behavior data of the user in a plurality of scenarios.

10. A real-time monitoring and warning system for dangerous driving behavior based on vehicle driving trajectory according to claim 9, characterized in that: The process of clustering the speed data of several node sets to obtain the driver's behavior pattern cluster label under each node set includes: Extract speed statistics and dynamic features from the node set, and standardize the features to obtain the standardized matrix X∈R N*M , where N is the total number of nodes in the node set, and M is the total number of node features; Among them, the vehicle speed characteristics include average speed and speed standard deviation, and the dynamic characteristics are vehicle speed fluctuations.

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