Intelligent public security comprehensive management information system and reporting method

Abstract

The application provides a smart public security comprehensive management information system and a reporting method, relates to the technical field of smart public security, and is a smart public security comprehensive management information system, which comprises a monitoring and collecting module, a two-dimensional coordinate system is arranged on a monitoring road surface image, and monitoring road surface image data is collected; the application sets up monitoring on the monitoring road surface, collects image data, analyzes and calculates the right turn of a vehicle, analyzes the right turn path of the vehicle by setting the coordinate system, and compares with the pedestrian coordinates; when the pedestrian coordinates are on the upper side of the right turn path of the vehicle, the voice broadcast device is used to remind the pedestrian to avoid, which can greatly reduce the probability of traffic security accidents, specifically, the conversion speed, acceleration and turning angle of the vehicle are analyzed, the vehicle offset point is generated, the vehicle control point is generated, the actual situation is more fitted, the output is more accurate, the Bezier curve is used to analyze the right turn trajectory of the vehicle, and finally, the trajectory related formula is output.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of smart public security, in particular to a smart public security comprehensive management information system and a reporting method. BACKGROUND

[0002] Smart public security is an important field in modern city management, aiming to improve public safety and traffic safety, and protect pedestrian safety. Smart public security uses advanced technologies such as big data, artificial intelligence, and the Internet of Things to establish a comprehensive and real-time public security management system, enabling public security organs to more efficiently govern society. Through intelligent traffic systems, real-time monitoring, signal control, and traffic data analysis, real-time traffic flow, accident information, and road conditions are monitored to timely adjust traffic signals and traffic flow, reducing congestion and traffic accidents.

[0003] Generally, at the intersection of public security traffic, there is a certain blind area for right-turn vehicles, which makes it difficult for drivers to accurately determine the location of pedestrians. When pedestrians do not pay attention and enter the right-turn area of the vehicle, accidents are likely to occur.

[0004] The above information disclosed in the background section is only used to enhance the understanding of the background of the present disclosure, and therefore it can include information that does not constitute prior art known to those of ordinary skill in the art. SUMMARY

[0005] The present application aims to provide a smart public security comprehensive management information system and a reporting method to solve the problems raised in the background.

[0006] To achieve the above-mentioned purpose, the present application provides the following technical solutions:

[0007] A smart public security comprehensive management information system, comprising:

[0008] A monitoring and acquisition module sets up a two-dimensional coordinate system on a monitored road surface image, acquires monitored road surface image data, and performs grayscale processing on the image data;

[0009] An image data analysis module is used to analyze the correlation of the image data, generate vehicle speed v, acceleration a, and turning radius R;

[0010] A control point generation module is used to analyze the correlation of vehicle speed v, acceleration a, and turning radius R, generate vehicle offset points, and analyze the correlation of the vehicle offset points to generate intermediate control points;

[0011] A trajectory generation module is used to analyze the correlation of the intermediate control points using a Bezier curve to generate vehicle turning trajectories;

[0012] The early warning analysis module is used for collecting pedestrian position coordinates, performing correlation analysis on the pedestrian position coordinates and the turning track of the vehicle, outputting a pedestrian danger state prediction level, and outputting whether to enable voice early warning according to the pedestrian danger state prediction level.

[0013] Further, a two-dimensional coordinate system is set on the monitored road surface image, wherein the y-axis is parallel to the road surface after turning, and the x-axis is parallel to the road before turning. The image data is subjected to grayscale processing, and the formula is: wherein I is the grayscale value of the converted image data pixel point, R is the red component value of the image data pixel point, G is the green component value of the image data pixel point, and B is the blue component value of the image data pixel point. The right side boundary of the vehicle is detected in real time by using the YOLO algorithm, the right side boundary of the vehicle is identified in each frame of image data and the boundary coordinates thereof are returned, the boundary coordinates include the tail boundary coordinates and the head boundary coordinates, and the detected vehicle is tracked by using a Kalman filter to update the vehicle position in real time.

[0014] Further, the detected vehicle is tracked by using a Kalman filter to update the vehicle position in real time, and the vehicle boundary coordinates of two continuous frames are extracted from the target tracking result and The correlation analysis is performed on the vehicle boundary coordinates of two continuous frames and , and the vehicle speed v is generated, and the formula is:

[0015]

[0016] wherein is the time interval between two frames, and the vehicle speed v is used to reflect the speed of the vehicle;

[0017] The correlation analysis is performed on the vehicle speed to generate the vehicle acceleration a, and the formula is:

[0018]

[0019] wherein and are two continuous speed values generated by the vehicle speed v formula in three continuous frames of image data, and the vehicle acceleration a is used to reflect the acceleration of the vehicle;

[0020] The correlation analysis is performed on the vehicle speed v and the vehicle turning angle to generate the turning radius R, and the formula is:

[0021]

[0022] wherein the vehicle turning angle The angle of the front wheel of the vehicle in the image data is analyzed and generated by OpenCV software, g is the acceleration of gravity, and the turning radius R is used to reflect the size of the turning profile of the vehicle.

[0023] Further, the vehicle speed v, acceleration a, and turning radius R are analyzed for correlation, and the vehicle offset point d is generated according to the formula:

[0024]

[0025] wherein k is the offset proportionality coefficient, and the value range is [0.33, 0.25], wherein is the maximum safe driving speed of the vehicle under the current road conditions, is the maximum safe acceleration of the vehicle under the current road conditions.

[0026] Further, the intermediate control points include control point and control point , the vehicle offset point d, the vehicle steering angle , the starting point coordinates are analyzed for correlation, and the control point is generated according to the formula:

[0027]

[0028] wherein the starting point coordinates are the coordinates of the vehicle at the front end of the turning area;

[0029] The vehicle offset point d, the vehicle steering angle , and the end point coordinates are analyzed for correlation, and the control point is generated according to the formula:

[0030]

[0031] wherein the end point coordinates are the coordinates of the vehicle at the end of the turning area.

[0032] Further, the intermediate control points, the starting point coordinates , and the end point coordinates are analyzed for correlation by using the Bezier curve, and the vehicle turning trajectory is generated according to the formula:

[0033]

[0034] wherein the vehicle turning trajectory represents the coordinate point of the trajectory at parameter t, simulates the turning path of the vehicle, sets the total length as 1, and parameter t is used for indexing the point position of the turning trajectory of the vehicle at t length.

[0035] Further, the pedestrian position coordinates are collected, the correlation between the pedestrian position coordinates and the turning trajectory of the vehicle is analyzed, a pedestrian danger state prediction level is output, and whether to enable voice warning is output according to the pedestrian danger state prediction level. The pedestrian position coordinates are collected, and the correlation between the pedestrian position coordinates and the turning trajectory of the vehicle is analyzed. When the pedestrian position coordinates are on the upper side of the turning trajectory of the vehicle, the output of the pedestrian danger state prediction level is first level, at this time, the pedestrian is on the turning trajectory of the vehicle, and has a certain danger, and the voice warning is enabled to remind the pedestrian to keep away; when the pedestrian position coordinates are on the lower side of the turning trajectory of the vehicle, the output of the pedestrian danger state prediction level is second level, at this time, the pedestrian is not on the turning trajectory of the vehicle, and the voice warning is not enabled.

[0036] The application also provides a smart public security comprehensive management information reporting method, which is used for executing a smart public security comprehensive management information system, and specifically includes the following steps:

[0037] S1, a two-dimensional coordinate system is set on a monitored road surface image, monitored road surface image data is collected, and the image data is subjected to gray scale processing;

[0038] S2, the correlation of the image data is analyzed, vehicle speed v, acceleration a and turning radius R are generated;

[0039] S3, the correlation of the vehicle speed v, the acceleration a and the turning radius R is analyzed, a vehicle offset point is generated, and the correlation of the vehicle offset point is analyzed, and an intermediate control point is generated;

[0040] S4, the correlation of the intermediate control point is analyzed by using a Bezier curve, and a turning trajectory of the vehicle is generated;

[0041] S5, the pedestrian position coordinates are collected, the correlation between the pedestrian position coordinates and the turning trajectory of the vehicle is analyzed, a pedestrian danger state prediction level is output, and whether to enable voice warning is output according to the pedestrian danger state prediction level.

[0042] Compared with the prior art, the application has the beneficial effects that:

[0043] ​The present application sets up a monitor on the monitoring road surface, collects image data, analyzes and calculates the right turn of the vehicle, sets a coordinate system, analyzes the right turn path of the vehicle, and compares with the pedestrian coordinates, when the pedestrian coordinates are on the upper side of the right turn path of the vehicle, at this time, the voice broadcast device reminds the pedestrian to avoid and reminds the driver to pay attention, which can greatly reduce the probability of traffic security accidents, specifically, the conversion speed, acceleration and steering angle of the vehicle are analyzed, the vehicle offset point is generated, and the vehicle control point is generated, which is more in line with the actual output, more accurate, the right turn trajectory of the vehicle is analyzed by using the Bezier curve, and finally the trajectory related formula is output. BRIEF DESCRIPTION OF DRAWINGS

[0044] Figure 1 It is a schematic diagram of the overall system module of the present application.

[0045] Figure 2 It is a schematic diagram of the overall method flow of the present application. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical scheme and advantages of the present application clearer and more apparent, the present application will be further described in detail below in combination with specific embodiments.

[0047] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present application should be understood as the usual meaning understood by those skilled in the art to which the present application belongs. The "first", "second" and similar words used in the present application do not represent any order, quantity or importance, but are only used to distinguish different components. "Include" or "contain" and similar words mean that the elements or objects before the word cover the elements or objects listed after the word and their equivalents, and do not exclude other elements or objects. "Connected" or "connected" and similar words are not limited to physical or mechanical connection, but can include electrical connection, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to represent the relative positional relationship, when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0048] EMBODIMENT:

[0049] Please refer to Figure 1 The present application provides a technical scheme:

[0050] A smart public security comprehensive management information system is suitable for a cross intersection, analyzes the trajectory of a right turn vehicle at the cross intersection, reminds pedestrians to stay away from the trajectory route of the right turn vehicle through a voice broadcast reminding device installed beside the intersection, and includes:

[0051] The monitoring acquisition module sets a two-dimensional coordinate system on the monitoring road surface image, collects monitoring road surface image data, and performs grayscale processing on the image data;

[0052] The two-dimensional coordinate system is set on the monitoring road surface image, wherein the y-axis is parallel to the road surface after the turn, and the x-axis is parallel to the road before the turn. Perspective transformation and coordinate system drawing are realized through OpenCV. The image data is processed by grayscale, and the formula is: wherein I is the grayscale value of the pixel point of the converted image data, the converted image data is unified in color representation, which can improve the accuracy of recognition and resolution. R is the red component value of the pixel point of the image data, G is the green component value of the pixel point of the image data, and B is the blue component value of the pixel point of the image data. The right side boundary of the vehicle is detected in real time by using YOLO algorithm. Image data is read by using OpenCV. Each frame of image data is input into the YOLO model for target detection. The model output is analyzed, and the detected vehicle right side boundary box is drawn. The right side boundary of the vehicle is identified in each frame of image data, and the boundary coordinates are returned. The boundary coordinates include the tail boundary coordinates and the head boundary coordinates. The detected vehicle is tracked by using Kalman filter to update the vehicle position in real time.

[0053] The image data analysis module is used for correlation analysis of the image data, and the vehicle speed v, acceleration a and turning radius R are generated.

[0054] The detected vehicle is tracked by using Kalman filter to update the vehicle position in real time. The vehicle boundary coordinates of two consecutive frames are extracted from the target tracking result image data and wherein and are the coordinate points of the same point on the same vehicle in two consecutive frames of image data. The correlation analysis is performed on the vehicle boundary coordinates of two consecutive frames and to generate the vehicle speed v. The formula is:

[0055]

[0056] wherein, is the time interval between two frames, and the vehicle speed v is used to reflect the speed of the vehicle. The speed is not the actual vehicle speed. Strictly speaking, it is the moving speed of the vehicle boundary in the image data, which is only used for subsequent calculation and analysis;

[0057] The correlation analysis is performed on the vehicle speed to generate the vehicle acceleration a. The formula is:

[0058]

[0059] wherein, and are two consecutive speed values generated by the vehicle speed v formula in the image data of the consecutive three frames, the vehicle acceleration a is used to reflect the vehicle acceleration, and is obtained based on the speed v calculation;

[0060] The vehicle speed v, the vehicle steering angle are analyzed for correlation, and the turning radius R is generated, and the formula is:

[0061]

[0062] wherein, the vehicle steering angle is generated by analyzing the angle of the front wheel of the vehicle in the image data, g is the acceleration of gravity, and the turning radius R is used to reflect the turning profile size of the vehicle.

[0063] The vehicle motion trajectory is analyzed by using the Bezier curve, and in the analysis process, the vehicle motion trajectory is limited by the vehicle speed v, the acceleration a, and the turning radius R, so that the vehicle deviation point is accurately analyzed. Specifically, the analysis is performed by the following modules:

[0064] The control point generation module is used to analyze the vehicle speed v, the acceleration a, and the turning radius R for correlation, generate the vehicle deviation point, and analyze the vehicle deviation point for correlation to generate the intermediate control point.

[0065] The deviation point refers to a point calculated by a certain algorithm based on the Bezier curve, so as to form a new curve path, i.e. the vehicle motion trajectory, and the deviation point can be used as a constraint condition for the vehicle motion trajectory.

[0066] The vehicle speed v, the acceleration a, and the turning radius R are analyzed for correlation to generate the vehicle deviation point d, and the formula is:

[0067]

[0068] wherein, k is the deviation proportion coefficient, the value range is [0.33, 0.25], and wherein is the maximum safe driving speed of the vehicle under the current road condition, is the maximum safe acceleration of the vehicle under the current road condition, which is obtained by experimental analysis and measurement, the deviation proportion coefficient k is used to describe the parameter of the degree of deflection of the Bezier curve, and is set to , the offset ratio coefficient as the offset can ensure the smoothness of the turn, and is closer to the actual. The maximum safe driving speed and the maximum safe acceleration of the vehicle affect the lateral force and dynamic stability of the vehicle during the turning process. The dynamic characteristics of the vehicle are significantly affected by the speed and acceleration during the turning process. When turning, the vehicle not only needs to overcome the centripetal force, but also needs to manage the centrifugal force caused by the speed and acceleration. When the vehicle speed approaches the maximum safe driving speed, the centrifugal force increases, and the vehicle needs a larger offset distance to maintain its stability during the turning process, that is, the greater the degree of deflection of the vehicle speed and acceleration approaches the maximum safe driving speed and the maximum safe acceleration of the vehicle, the greater the deflection of the vehicle, resulting in a larger offset point d.

[0069] Further, the intermediate control points include control points and control points , the vehicle offset point d, the vehicle turning angle , the starting point coordinates , the control points , the formula is:

[0070]

[0071] , the starting point coordinates is the coordinates of the vehicle at the front end of the turning area;

[0072] , the vehicle offset point d, the vehicle turning angle , the end point coordinates , the control points , the formula is:

[0073]

[0074] , the end point coordinates is the coordinates of the vehicle at the end of the turning area.

[0075] The intermediate control points are key parameters in the Bezier curve generation algorithm, which determine the shape and bending degree of the vehicle trajectory curve. By analyzing the vehicle offset point and using trigonometric functions to obtain the position of the control point in the coordinate system, the offset and direction can be converted into specific coordinate values. Through the setting of the control points, the bending degree and direction of the curve can be accurately controlled.

[0076] The trajectory generation module is configured to utilize the Bezier curve to perform correlation analysis on the intermediate control points, to generate a vehicle turning trajectory.

[0077] The trajectory generation module is configured to utilize the Bezier curve to perform correlation analysis on the intermediate control points, the starting point coordinates , and the end point coordinates , to generate a vehicle turning trajectory , the formula is:

[0078]

[0079] wherein the vehicle turning trajectory represents the coordinate point of the trajectory at the parameter t, the vehicle turning path is simulated, the total length is set to 1, and the parameter t is used to index the point position of the vehicle turning trajectory at the t length, such as the starting point of the vehicle turning trajectory when t=0, and the end point of the vehicle turning trajectory when t=1.

[0080] The early warning analysis module is used for collecting the position coordinates of the pedestrians, performing correlation analysis on the position coordinates of the pedestrians and the vehicle turning trajectory, outputting a pedestrian danger state prediction level, and outputting whether to enable voice early warning according to the pedestrian danger state prediction level.

[0081] Further, the position coordinates of the pedestrians are collected, correlation analysis is performed on the position coordinates of the pedestrians and the vehicle turning trajectory, a pedestrian danger state prediction level is outputted, and whether to enable voice early warning is outputted according to the pedestrian danger state prediction level. The position coordinates of the pedestrians are collected, and the position coordinates of the pedestrians are compared with the vehicle turning trajectory When the position coordinates of the pedestrians are on the upper side of the vehicle turning trajectory , that is, the pedestrians may coincide with the vehicle turning trajectory, the output of the pedestrian danger state prediction level is level one, at this time, the pedestrians are on the vehicle turning trajectory, and have a certain danger, and voice early warning is enabled to remind the pedestrians to keep away; when the position coordinates of the pedestrians are on the lower side of the vehicle turning trajectory , the output of the pedestrian danger state prediction level is level two, at this time, the pedestrians are not on the vehicle turning trajectory, and voice early warning is not enabled.

[0082] With reference to Figure 2 , the application further provides a comprehensive management information reporting method for intelligent public security, which is used for executing a comprehensive management information system for intelligent public security, and specifically comprises the following steps:

[0083] Step 1, a two-dimensional coordinate system is set on a monitored road surface image, monitored road surface image data is collected, and the image data is subjected to gray scale processing;

[0084] Step 2, correlation analysis is performed on the image data, vehicle speed v, acceleration a, and turning radius R are generated;

[0085] Step 3, correlation analysis is performed on the vehicle speed v, acceleration a, and turning radius R, a vehicle offset point is generated, and correlation analysis is performed on the vehicle offset point to generate an intermediate control point;

[0086] Step 4, correlation analysis is performed on the intermediate control point by using a Bezier curve to generate a vehicle turning trajectory;

[0087] Step 5, collect the pedestrian position coordinates, analyze the correlation between the pedestrian position coordinates and the vehicle turning track, output the pedestrian danger state prediction level, and output whether to enable voice warning according to the pedestrian danger state prediction level.

[0088] The above formulas are all dimensionless numerical calculations, the formulas are obtained by software simulation of a large amount of data to obtain the most real situation, and the preset parameters in the formulas are set by a person skilled in the art according to the actual situation.

[0089] The above embodiments can be realized wholly or partially by software, hardware, firmware or any other combination. When realized by software, the above embodiments can be realized wholly or partially in the form of a computer program product. Those skilled in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized by hardware or software methods depends on the specific application and design constraints of the technical solutions.

[0090] The units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, which can be located in one place or distributed on multiple network units. Part or all of the units can be selected to achieve the purpose of the embodiments according to actual needs.

[0091] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical range disclosed in the present application, which should be covered within the protection scope of the present application.

Claims

1. A comprehensive management information system for intelligent public security, characterized in that, Comprise: Monitoring acquisition module, set up two-dimensional coordinate system on monitoring road surface image, collect monitoring road surface image data, carry out gray processing to image data; Image data analysis module, for correlation analysis of image data, generate vehicle speed v, acceleration a, turning radius R; Control point generation module, for correlation analysis of vehicle speed v, acceleration a, turning radius R, generate vehicle offset point, correlation analysis of vehicle offset point, generate intermediate control point; Trajectory generation module, for correlation analysis of intermediate control point by using Bezier curve, generate vehicle turning trajectory; Early warning analysis module, for collecting pedestrian position coordinates, correlation analysis of pedestrian position coordinates and vehicle turning trajectory, output pedestrian danger state prediction level, according to pedestrian danger state prediction level output whether to enable voice early warning; Correlation analysis of vehicle speed v, acceleration a, turning radius R, generate vehicle offset point d, the formula is: ； Wherein, k is the offset coefficient, the value range is [0.33, 0.25], wherein Vmax is the maximum safe driving speed of the vehicle under the current road condition, a is the maximum safe acceleration of the vehicle under the current road condition; The intermediate control points include control points and control points , vehicle offset point d, vehicle steering angle , starting point coordinates , and the control points generated by the correlation analysis, and the formula is: ； Wherein, the starting point coordinate is the coordinate of the front end of the turning area for the vehicle; The vehicle offset point d, the vehicle steering angle , the termination point coordinates Correlation analysis is performed to generate the control point , and the formula is: ； wherein the end point coordinates are the coordinates of the end of the turn area for the vehicle.

2. The intelligent public security integrated management information system according to claim 1, characterized in that: A two-dimensional coordinate system is set on the monitored road image, wherein the y-axis is parallel to the road after the turn, and the x-axis is parallel to the road before the turn, the image data is subjected to grayscale processing, and the formula is: wherein I is the grayscale value of the converted image data pixel point, R is the red component value of the image data pixel point, G is the green component value of the image data pixel point, and B is the blue component value of the image data pixel point, the right side boundary of the vehicle is detected in real time by using the YOLO algorithm, the right side boundary of the vehicle is identified in each frame of image data and the boundary coordinates thereof are returned, the boundary coordinates include the tail boundary coordinates and the head boundary coordinates, and the detected vehicle is tracked by using a Kalman filter to update the vehicle position in real time.

3. The intelligent public security integrated management information system according to claim 2, characterized in that: Kalman filter is used to track the detected vehicles and update the vehicle position in real time. The vehicle boundary coordinates of two continuous frames are extracted from the target tracking results and The correlation analysis is performed on the vehicle boundary coordinates of two continuous frames and The vehicle speed v is generated according to the formula ； wherein, is the time interval between two frames, and the vehicle speed v is used to reflect the speed of the vehicle; Correlation analysis of vehicle speed, generate vehicle acceleration a, the formula is: ； wherein, and are two consecutive speed values generated by the vehicle speed v formula in the image data of the consecutive three frames, and the vehicle acceleration a is used to reflect the vehicle acceleration. The vehicle speed v, the vehicle steering angle A correlation analysis is performed to generate the turning radius R, according to the formula: ； wherein the vehicle steering angle The angle of the front wheel of the vehicle in the image data is analyzed and generated by OpenCV software, g is the acceleration of gravity, and the turning radius R is used to reflect the size of the turning profile of the vehicle.

4. The comprehensive management information system for intelligent public security according to claim 1, characterized in that: The coordinates of the intermediate control points, the start point and the end point are determined by using the Bezier curve The correlation analysis is performed to generate the vehicle turning trajectory The formula is as follows:​ ； Wherein, the vehicle turning trajectory The coordinate point of the trajectory at the parameter t is represented, the vehicle turning path is simulated, the total length is set to 1, and the parameter t is used for indexing the point position of the vehicle turning trajectory at the t length.

5. The intelligent public security integrated management information system according to claim 1, characterized in that: Collecting pedestrian position coordinates, performing correlation analysis on the pedestrian position coordinates and the vehicle turning trajectory, outputting a pedestrian danger state prediction level, outputting whether to enable voice warning according to the pedestrian danger state prediction level, collecting pedestrian position coordinates, and the vehicle turning trajectory is compared, when the pedestrian position coordinates are on the upper side of the vehicle turning trajectory , the output of the pedestrian danger state prediction level is level one, at this time the pedestrian is on the vehicle turning trajectory and has a certain danger, voice warning is enabled to remind the pedestrian to move away; when the pedestrian position coordinates are on the lower side of the vehicle turning trajectory , the output of the pedestrian danger state prediction level is level two, at this time the pedestrian is not on the vehicle turning trajectory, and voice warning is not enabled.

6. A comprehensive management information reporting method for public security intelligence, used for executing the comprehensive management information system for public security intelligence according to claim 1, characterized in that, Specifically comprising the following steps: S1, set up two-dimensional coordinate system on monitoring road surface image, collect monitoring road surface image data, carry out gray processing to image data; S2, correlation analysis of image data, generate vehicle speed v, acceleration a, turning radius R; S3, correlation analysis of vehicle speed v, acceleration a, turning radius R, generate vehicle offset point, correlation analysis of vehicle offset point, generate intermediate control point; S4, correlation analysis of intermediate control point by using Bezier curve, generate vehicle turning trajectory; S5, collect pedestrian position coordinates, correlation analysis of pedestrian position coordinates and vehicle turning trajectory, output pedestrian danger state prediction level, according to pedestrian danger state prediction level output whether to enable voice early warning.

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