CN113192331B - An intelligent early warning system and early warning method for riding safety in a networked environment - Google Patents

Abstract

The invention discloses an intelligent early warning system and an early warning method for riding safety in an internet environment, belonging to the field of traffic safety, wherein the early warning system is arranged on a helmet worn by a rider and comprises: the system comprises a GPS positioning module, a network connection information receiving module, an information processing and judging module and a danger early warning module; the early warning method is characterized in that the GPS positioning module and the internet information receiving module send collected information to the information processing and judging module, the information processing and judging module predicts whether a driver and a motor vehicle running around the driver are in danger of collision or not by adopting a road segmentation method and an accumulative threshold algorithm, and if the driver is in danger of collision, the early warning method can effectively identify road section information and carry out early warning on road conditions, can sense the running information of the motor vehicle running around the driver, and can timely and accurately send out early warning signals so as to provide enough risk avoiding time for the driver.

Description

An intelligent early warning system and early warning method for riding safety in a networked environment

technical field

The invention relates to the field of traffic safety, in particular to an intelligent early warning system and an early warning method for riding safety in a networked environment.

Background technique

Based on my country's basic national conditions, residents are more inclined to choose cycling when traveling in a relatively close range. Due to the large population flow in my country's cities and the defects in road traffic planning in some areas, conflicts between motor vehicles and cyclists are very common, which also brings many traffic safety problems. At present, more suggestions are made from the perspective of social management, such as the implementation of the "one helmet, one belt" safety protection action, but the physical protection of cyclists' safety is still relatively lacking. There is still a large gap in the research field of cyclist safety at home and abroad. The most mainstream physical equipment for cyclists' safety protection is still helmets. Existing helmets that are widely used have the following shortcomings: first, the common helmet design is not reasonable enough, in order to protect the rider's head as best as possible, it often affects the driver's vision; The ability to perceive the environment will also be affected to a certain extent. Riders need more active safety equipment to protect. With the continuous development of vehicle-road coordination, precise positioning, continuous breakthroughs in real-time communication technology, and the expansion of the food delivery logistics industry in recent years, the presence of cyclists in the entire IoV system should not be missing. Therefore, a new technical solution is urgently needed in the art to solve this problem.

SUMMARY OF THE INVENTION

The purpose of the present invention is to solve the technical problems existing in the prior art, and propose an intelligent early warning system and early warning method for riding safety in a networked environment, which can effectively identify road section information and give early warning according to road conditions, and at the same time can perceive The driving information of the surrounding motor vehicles sends out early warning signals in a timely and accurate manner, providing enough time for cyclists to avoid danger.

To achieve the above object, the present invention adopts the following technical solutions:

The invention proposes an intelligent early warning system facing riding safety in a networked environment, characterized in that the early warning system is installed on the helmet worn by the cyclist, and the early warning system includes: a GPS positioning module, a network connection information receiving module , information processing and judgment module and danger warning module;

The GPS positioning module is connected to the information processing and judgment module, and the GPS positioning module is used to obtain the real-time geographic location information of the rider, and send the information to the information processing and judgment module;

The input end of the network connection information receiving module is connected to the Internet of Vehicles service platform, and the output end of the network connection information receiving module is connected to the information processing and judging module. The network connection information receiving module is used to obtain the cyclist and the driving machines around the cyclist from the Internet of Vehicles service platform. The driving parameter information of the motor vehicle, the geographical position information and road information of the driving motor vehicle, and the driving parameter information of the cyclist and the motor vehicles driving around the cyclist, the geographical position information and road information of the driving motor vehicle are sent to the information processing and judgment module; the driving parameter information includes speed, acceleration and heading angle; the road information includes pavement adhesion coefficient information, road segment shape information and road channelization state information, and the road segment shape information includes cross road sections and straight road sections ;

The information processing and judging module is used to predict whether there is a danger of collision between the rider and the surrounding motor vehicles according to the received information. Send corresponding early warning signals;

The danger pre-warning module is used for performing corresponding pre-warning actions according to the pre-warning signal.

Further, the danger warning level includes: a first warning level, a second warning level and a third warning level, and the severity of the danger warning level increases in order of levels;

When the danger warning level is the first warning level, the information processing and judging module is configured to send a first warning signal to the danger warning module, so that the danger warning module executes the first warning action;

When the danger warning level is the second warning level, the information processing and judging module is configured to send a second warning signal to the danger warning module, so that the danger warning module executes the second warning action;

When the danger warning level is the third warning level, the information processing and judging module is configured to send a third warning signal to the danger warning module, so that the danger warning module executes the third warning action.

Further, the danger warning module includes a vibration module and a voice module, and the vibration module is a buzzer.

The present invention also proposes an intelligent early warning method for riding safety in a networked environment. The early warning method is applied to the above early warning system, and is characterized in that it includes the following steps:

Step S1, obtain the real-time geographic location information of the rider through the GPS positioning module, and send the information to the information processing and judgment module;

Step S2, obtain the driving parameter information of the cyclist and the motor vehicle driving around the cyclist, the geographic location information and road information of the driving motor vehicle through the network connection information receiving module, and drive the cyclist and the surrounding motor vehicle. The driving parameter information of the motor vehicle, the geographic location information and road information of the driving motor vehicle are sent to the information processing and judgment module;

The driving parameter information includes speed, acceleration and heading angle; the road information includes pavement adhesion coefficient information, road segment shape information and road channelization state information, and the road segment shape information includes a cross road segment and a straight road segment;

Step S3, the information processing and judgment module is based on the received real-time geographic location information of the cyclist, the driving parameter information of the cyclist and the motor vehicles traveling around the cyclist, the geographic location information and road information of the traveling motor vehicle, Predict whether there is a danger of collision between the cyclist and the motor vehicle driving around it. If there is a danger of collision, determine the danger warning level. The danger warning level includes the first warning level, the second warning level and the third warning level. The severity increases in order of levels, and the corresponding warning signal is sent to the danger warning module according to the danger warning level;

Step S4, the danger warning module executes a corresponding warning action according to the warning signal.

Further, in step S3, the information processing and judging module is based on the received real-time geographic location information of the cyclist, the driving parameter information of the cyclist and the motor vehicles traveling around the cyclist, the geographic location information of the traveling motor vehicle, and Road information to predict whether there is a danger of a collision between the cyclist and the motor vehicles driving around it. If there is a danger of collision, it includes:

Pre-set cumulative thresholds for danger warning levels;

According to the road information, determine the prediction model of the collision between the cyclist and the surrounding motor vehicles;

1. When the prediction model is a conflict accumulation detection model based on the intersection of time and intersection, according to the real-time geographic location information of the rider, the geographic location information of the motor vehicles driving around the rider, and the information of the rider and the motor vehicles traveling around the rider. The driving parameter information and road information are used to predict vehicle conflicts, and each conflict is accumulated. When the accumulated value reaches the set accumulation threshold, it is determined that there is a danger of collision between the cyclist and the surrounding motor vehicles; the vehicle conflict refers to It is the conflict between the cyclist and the surrounding motor vehicles;

The specific process is: determine the current position information of the cyclist and the motor vehicles driving around the intersection, and the current driving parameter information of the two, the driving parameter information includes speed, acceleration and heading angle, calculate the cyclist and the surrounding motor vehicles. The conflict time of driving at the current speed, compare the time periods of the two, and determine whether to accumulate the number of conflicts by comparing the intersection of the two on the time axis, and perform conflict detection in real time. Finally, when the number of conflicts reaches the set accumulation threshold. , to determine that the cyclist is at risk of collision with the surrounding motor vehicles;

Among them, the calculation method of the conflict time is: simplify the driving motor vehicle into a rectangle of vehicle width h and vehicle length b, simplify the cyclist into a straight line segment of length H, take the geometric center of the driving motor vehicle model as the origin, and the driving direction is In the y direction, a Cartesian coordinate system is established, then the coordinates of the two intersection points of the intersection area of the two driving areas are:

Calculate the conflict time for a motor vehicle:

Calculate conflict times for cyclists:

分别为行驶机动车和骑行者到达冲突区域所用的时间；t_v2、t_b2为行驶机动车和骑行者驶出冲突区域所用的时间；冲突过程中骑行者模型与行驶机动车模型的两个交点分别为A、B；φ为骑行者的航向角；(x₁,y₁)为骑行者几何中心的位置；v₁为骑行者的速度；v₂为行驶机动车的速度；in,

are the time it takes for the driving motor vehicle and the cyclist to reach the conflict area, respectively; t _v2 and t _b2 are the time it takes for the driving motor vehicle and the cyclist to leave the conflict area; two intersection points between the cyclist model and the driving motor vehicle model during the conflict are A and B respectively; φ is the heading angle of the rider; (x ₁ , y ₁ ) is the position of the rider’s geometric center; v ₁ is the speed of the rider; v ₂ is the speed of the motor vehicle;

Ⅱ. When the prediction model is a collision prediction model based on the vehicle position in the straight section, judge whether the cyclist is driving in the specified area according to the real-time geographic location information of the cyclist. Publish dangerous voice alerts;

In the straight section, the driving behavior of the motor vehicle is divided into two states: parallel and rear-end collision. Determine a rear-end collision area with the cyclist as the geometric center, 2h in width and 50m in length, according to whether the motor vehicle is driving in this area. Early warning mode, when a motor vehicle enters the area, it is determined to be in a rear-end collision state, otherwise it is considered to be in a parallel state;

① Conflict accumulation detection model for rear-end collisions in straight sections

A model is established, and the cyclist is regarded as driving at a constant speed, and the distance difference between the driving motor vehicle that is in conflict with the cyclist and the speed of the cyclist is used as the safety distance. Within a period of time, the number of conflicts is accumulated once, and within a period of time, when the accumulated number of conflicts reaches the set accumulation threshold, it is determined whether there is a danger of collision between the cyclist and the surrounding motor vehicles;

The formula for calculating the safety distance is:

Among them, R _Warning is the minimum distance between the cyclist and the surrounding motor vehicles, that is, the warning distance; v1 is the speed of the cyclist; v ₂ is the speed of the motor vehicle; a ₂ is the maximum deceleration of the motor vehicle; D is the insurance Distance, the specified distance of the insurance distance is 2m;

② Conflict accumulation detection model for parallel rubbing collisions in straight sections

If the motor vehicle is not in the rear-end collision area, it is regarded as parallel, and it is determined whether to accumulate the number of conflicts by calculating the comparison between the evasion time TTA and the conflict time TTC. The specific steps are as follows:

The coordinate system is established with the rider as the coordinate origin and the driving direction as the positive direction. The position coordinates (x ₂ , y ₂ ) of the motor vehicle are known. The speeds of the rider and the motor vehicle are v1 and v ₂ respectively. the heading angle θ;

The distance between the center of mass of the cyclist and the moving vehicle is

The actual distance between the rider and the motor vehicle is ΔL=lR ₁ -R ₂ ;

R ₁ and R ₂ are the actual width of the rider and the actual width of the motor vehicle;

The projections of the speed of the cyclist and the speed of the motor vehicle on the line connecting the centers of mass of the two vehicles are:

α is the angle between the line connecting the vehicle and the origin of the coordinates and the abscissa;

The relative velocity v of the two vehicles in the direction of the center of mass, v=|v′ ₁ -v′ ₂ |;

conflict time TTC,

Set the risk avoidance time TTA, TTA=βt ₀ ;

Among them, t ₀ is the reaction time, t ₀ =1.5s, and β is the correction coefficient of risk avoidance.

Further, in step S3, a corresponding warning signal is sent to the danger warning module according to the danger warning level, including:

When the danger warning level is the first warning level, the information processing and judging module sends a first warning signal to the danger warning module;

When the danger warning level is the second warning level, the information processing and judging module sends a second warning signal to the danger warning module;

When the danger warning level is the third warning level, the information processing and judging module sends a third warning signal to the danger warning module.

Further, in step S4, the danger warning module executes a corresponding warning action according to the warning signal, including:

The danger warning module receives the first warning signal, and the danger warning module responds to the first warning signal and executes the first warning action to perform voice broadcast;

The danger warning module receives the second warning signal, and the danger warning module responds to the second warning signal and executes the second warning action, and performs voice broadcast and vibration reminder;

The danger warning module receives the third warning signal, the danger warning module responds to the third warning signal and executes the third warning action, performs voice broadcast and vibration reminder, and the vibration intensity of the third warning action is greater than that of the second warning action .

Further, the determining of the danger warning level is as follows: by setting different cumulative thresholds, grading the danger warning level, so as to obtain three danger warning levels.

Through the above-mentioned design scheme, the present invention can bring the following beneficial effects:

1. The present invention focuses on the current situation of insufficient active safety protection for cyclists and the development trend of the Internet of Vehicles in the future, so as to provide cyclists with safer travel options.

2. The present invention adopts the road segmentation method and the accumulation threshold algorithm to realize the intellectualization of the helmet and improve the accuracy of early warning.

3. Combined with the Internet of Vehicles technology, real-time communication is realized, and more optimization solutions are provided for decision-making.

Description of drawings

The accompanying drawings here are used to provide a further understanding of the present invention and constitute a part of the application of the present invention. The exemplary embodiments of the present invention and their descriptions are used to understand the present invention and do not constitute an improper limitation of the present invention. :

FIG. 1 is a schematic structural block diagram of an early warning system of the present invention.

FIG. 2 is a logical schematic diagram of a straight road segment model of the present invention.

FIG. 3 is a schematic diagram of the intersection model of the present invention.

FIG. 4 is a schematic diagram of the integrated logic of the control system of the present invention.

Detailed ways

In order to make the objects, features and advantages of the present invention more obvious and understandable, the technical solutions in the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the present invention is not limited by the following examples, and specific implementations can be determined according to the technical solutions and actual conditions of the present invention. Well-known methods, procedures, procedures, components and circuits have not been described in detail in order to avoid obscuring the essence of the present invention.

As shown in Figure 1, Figure 2, Figure 3 and Figure 4, the present invention provides an intelligent early warning system for riding safety in a networked environment. The early warning system is installed on the helmet worn by the rider, and the early warning system Including: GPS positioning module, network information receiving module, information processing and judgment module and danger warning module;

The GPS positioning module is connected to the information processing and judgment module, and the GPS positioning module is used to obtain the real-time geographic location information of the rider, and send the information to the information processing and judgment module;

The input end of the network connection information receiving module is connected to the Internet of Vehicles service platform, and the output end of the network connection information receiving module is connected to the information processing and judging module. The network connection information receiving module is used to obtain the cyclist and the driving machines around the cyclist from the Internet of Vehicles service platform. The driving parameter information of the motor vehicle, the geographical position information and road information of the driving motor vehicle, and the driving parameter information of the cyclist and the motor vehicles driving around the cyclist, the geographical position information and road information of the driving motor vehicle are sent to the information processing and judgment module; the driving parameter information includes speed, acceleration and heading angle; the road information includes pavement adhesion coefficient information, road segment shape information and road channelization state information, and the road segment shape information includes cross road sections and straight road sections , specifically, according to the real-time cyclist's geographic location information and the electronic map stored in the Internet of Vehicles service platform, to determine the shape of the road section where the cyclist is currently located;

The information processing and judging module is used as the main controller. The information processing and judging module is used to predict whether there is a danger of collision between the cyclist and the surrounding motor vehicles according to the received information, and if there is a danger of collision, determine the danger warning level , and send the corresponding warning signal to the danger warning module according to the danger warning level;

The danger warning module is used as a signal output device. The danger warning module includes a buzzer and a voice module. The buzzer is used as a vibration module. The danger warning module responds to the warning signal and performs corresponding warning actions according to the warning signal.

The danger warning level includes: a first warning level, a second warning level, and a third warning level, and the severity of the danger warning level increases in order of levels;

When the danger warning level is the first warning level, the information processing and judging module is configured to send a first warning signal to the danger warning module, so that the danger warning module executes the first warning action;

When the danger warning level is the second warning level, the information processing and judging module is configured to send a second warning signal to the danger warning module, so that the danger warning module executes the second warning action;

When the danger warning level is the third warning level, the information processing and judging module is configured to send a third warning signal to the danger warning module, so that the danger warning module executes the third warning action.

As a hardware device for an intelligent early warning system for riding safety in a networked environment:

GPS positioning module: Using MC20 module, it has the advantages of small size, low power consumption, dual-card single-waiting, etc., and can provide wireless mobile communication and accurate navigation and positioning functions;

Network information receiving module: the CC1101 module is a micro-power UHF wireless transceiver;

Information processing and judgment module (MCU): STM32F103C8T6 MCU is used;

Danger warning module: The danger warning module includes a buzzer and a voice module, and the voice module selects the JQ8900-16P voice module.

Specifically: the danger warning module receives the first warning signal, the first warning signal represents the primary warning, the voice module responds to the first warning signal and executes the warning action, and performs a voice broadcast, and the voice reminds "please pay attention to the car coming from the side";

The danger warning module receives the second warning signal, the second warning signal represents a danger warning, the voice module and the buzzer respond to the second warning signal at the same time and perform the warning action at the same time, perform voice broadcast and vibration reminder, and the voice module voice reminds "Pay attention to avoid cars coming from the side", and the buzzer vibrates to remind;

The danger warning module receives the third warning signal, the third warning signal represents the danger warning, the voice module and the buzzer simultaneously respond to the third warning signal and execute the warning action at the same time, carry out the voice broadcast and vibrate the reminder, and the voice sends out the alarm at the same time. The buzzer vibrates strongly.

Based on the above-mentioned intelligent early warning system for riding safety in the networked environment, the realization of the intelligent early warning method for riding safety in the networked environment includes the following steps:

Step S1, obtain the real-time geographic location information of the rider through the GPS positioning module, and send the information to the information processing and judgment module;

Step S2, obtain the driving parameter information of the cyclist and the motor vehicle driving around the cyclist, the geographic location information and road information of the driving motor vehicle through the network connection information receiving module, and drive the cyclist and the surrounding motor vehicle. The driving parameter information of the motor vehicle, the geographic location information and road information of the driving motor vehicle are sent to the information processing and judgment module;

The driving parameter information includes speed, acceleration and heading angle; the road information includes pavement adhesion coefficient information, road segment shape information and road channelization state information, and the road segment shape information includes a cross road segment and a straight road segment;

Step S3, the information processing and judgment module is based on the received real-time geographic location information of the cyclist, the driving parameter information of the cyclist and the motor vehicles traveling around the cyclist, the geographic location information and road information of the traveling motor vehicle, Predict whether there is a danger of collision between the cyclist and the motor vehicle driving around it. If there is a danger of collision, determine the danger warning level. The danger warning level includes the first warning level, the second warning level and the third warning level. The severity increases in order of levels, and the corresponding warning signal is sent to the danger warning module according to the danger warning level;

Step S4, the danger warning module executes a corresponding warning action according to the warning signal.

The concrete logic control of the method proposed by the present invention is as follows:

1. Data collection

In order to achieve the accuracy and precision of data collection and early warning judgment, the characteristics of the local road network of complex traffic roads are identified according to the existing electronic map, and the entire road network system is divided into intersection sections with intersections and straight sections. The map is loaded in the Internet of Vehicles service platform;

After the information processing and judgment module obtains the vehicle GPS positioning position data and electronic map data in real time, it analyzes the data, extracts the key data required for subsequent calculation, and performs road matching. After data interaction, it can be known whether there are oncoming vehicles and their number in the road section, as well as the speed, acceleration, heading angle and other driving parameters of other vehicles. When the cyclist is in different road sections, the early warning system will use the corresponding algorithm.

2. Data processing

(1) Pre-set the cumulative threshold of the danger warning level. The method for determining the level cumulative thresholds a, b, and c is: because the division of the danger warning level is not only related to the distance between the cyclist and the surrounding motor vehicles, but also to the speed. , acceleration, heading angle driving parameters and the influence of the road adhesion coefficient at that time. At that time, the acceleration information of the motor vehicle has a linear relationship with the vehicle condition information (the vehicle condition information is the driving parameter information) and the road condition (the road adhesion coefficient). , which lays the foundation for the development of the early warning algorithm in the future, and the cumulative thresholds a, b, and c correspond to the first warning level, the second warning level, and the third warning level, respectively.

(2) Crossroads:

Determine the motor vehicle driving around the cyclist at the intersection, detect the driving trajectory of the motor vehicle and the cyclist's driving trajectory, establish a conflict accumulation detection model based on time intersection in the intersection, analyze the conflict time, and propose a method. A collision accumulation detection algorithm based on time intersection. The main driving behaviors of cyclists when passing through intersections are going straight, turning left and turning right. Oncoming vehicles from different directions and different driving behaviors will produce cross-conflicts, merge conflicts, and no conflicts. First, determine the location information of the cyclist and other motor vehicles in the intersection, and package it with the vehicle condition information (vehicle condition information that is driving parameter information) collected by the Internet of Vehicles service platform and transmit it to the information processing and judgment module. Matches the cumulative thresholds a, b, c that determine the hazard warning level. Secondly, calculate the conflict time between the cyclist and other motor vehicles at the current speed at this moment, compare the time periods of the two, and determine whether to accumulate the number of conflicts by comparing whether the two intersect on the time axis; Finally, when the number of conflicts reaches the set accumulation threshold, an alarm is issued to the cyclist, and whether an early warning is determined by means of danger accumulation, which avoids misjudgment caused by a single conflict, and effectively improves the accuracy of the early warning.

The specific process is: determine the current position information of the cyclist and the motor vehicles driving around the intersection, and the current driving parameter information of the two, the driving parameter information includes speed, acceleration and heading angle, calculate the cyclist and the surrounding motor vehicles. The conflict time of driving at the current speed, compare the time periods of the two, and determine whether to accumulate the number of conflicts by comparing the intersection of the two on the time axis, and perform conflict detection in real time. Finally, when the number of conflicts reaches the set accumulation threshold. , to determine that the cyclist is at risk of collision with the surrounding motor vehicles;

Among them, the calculation method of the conflict time is: simplify the driving motor vehicle into a rectangle of vehicle width h and vehicle length b, simplify the cyclist into a straight line segment of length H, take the geometric center of the driving motor vehicle model as the origin, and the driving direction is In the y direction, a Cartesian coordinate system is established, then the coordinates of the two intersection points of the intersection area of the two driving areas are:

Calculate the conflict time for a motor vehicle:

Calculate conflict times for cyclists:

是否成立，成立则冲突数增加一次，否则判断

是否成立，成立则冲突数增加一次；Among them, t _v1 and t _b1 are the time it takes for the motor vehicle and the cyclist to reach the conflict area respectively; t _v2 and t _b2 are the time it takes for the motor vehicle and the cyclist to leave the conflict area; The two intersection points of the motor vehicle model are A and B respectively; φ is the heading angle of the rider; (x ₁ , y ₁ ) is the position of the rider’s geometric center; v ₁ is the speed of the rider; v ₂ is the driving motor vehicle speed; judgment

Whether it is established or not, the number of conflicts will increase once if established, otherwise judge

Whether it is established or not, the number of conflicts will increase once if established;

(3) Straight section

According to the real-time geographic location information of the cyclist, it is judged whether the cyclist is driving in the specified area. If the cyclist drives out of the non-motorized vehicle lane, a dangerous voice reminder will be issued to the cyclist, and the voice reminds "please drive on the non-motorized vehicle lane";

In the straight section, the driving behavior of the motor vehicle is divided into two states: parallel and rear-end collision. Determine a rear-end collision area with the cyclist as the geometric center, 2h in width and 50m in length, according to whether the motor vehicle is driving in this area. Early warning mode, when a motor vehicle enters the area, it is determined to be in a rear-end collision state, otherwise it is considered to be in a parallel state;

The collision form judgment algorithm based on the vehicle position in the straight section: the information processing and judgment module determines a rear-end collision area with the cyclist as the geometric center in the front and rear directions of the cyclist. When other vehicles enter the area, it is judged that the rear-end collision is possible, otherwise Parallel scratches and collisions are possible.

A conflict accumulation detection model for rear-end collisions in a straight road section is established, and the cyclist is regarded as driving at a constant speed. The information processing and judgment module takes the distance difference between the driving motor vehicle that is in conflict with the cyclist before decelerating to the cyclist's driving speed as the distance difference. safe distance. When the distance between the cyclist and the motor vehicle is less than the safe distance, it is regarded as a conflict. When the cumulative number of conflicts within the set time reaches the set early warning threshold, an early warning will be issued.

The collision accumulation detection model of the parallel rubbing collision of the straight road section, the information processing and judging module determines whether a collision occurs and accumulates by comparing the evasion time TTA and the collision time TTC. When the cumulative number of conflicts within the set time reaches the set early warning threshold, an early warning will be issued.

The detailed instructions are as follows:

① Conflict accumulation detection model for rear-end collisions in straight sections

A model is established, and the cyclist is regarded as driving at a constant speed, and the distance difference between the driving motor vehicle that is in conflict with the cyclist and the speed of the cyclist is used as the safety distance. Within a period of time, the number of collisions is accumulated once, and within a period of time, when the accumulated number of collisions reaches the set accumulation threshold, it indicates that there is a high probability of collision, and an early warning signal should be issued to the cyclist.

The formula for calculating the safety distance is:

Among them, R _Warning is the minimum distance between the cyclist and the surrounding motor vehicles, that is, the warning distance; v1 is the speed of the cyclist; v ₂ is the speed of the motor vehicle; a ₂ is the maximum deceleration of the motor vehicle; D is the insurance Distance, the specified distance of the insurance distance is 2m;

② Conflict accumulation detection model for parallel rubbing collisions in straight sections

If the motor vehicle is not in the rear-end collision area, it is regarded as parallel, and it is determined whether to accumulate the number of conflicts by calculating the comparison between the evasion time TTA and the conflict time TTC. The specific steps are as follows:

The coordinate system is established with the rider as the coordinate origin and the driving direction as the positive direction. The position coordinates (x ₂ , y ₂ ) of the motor vehicle are known. The speeds of the rider and the motor vehicle are v1 and v ₂ respectively. the heading angle θ;

The distance between the center of mass of the cyclist and the moving vehicle is

The actual distance between the rider and the motor vehicle is ΔL=lR ₁ -R ₂ ;

R ₁ and R ₂ are the actual width of the rider and the actual width of the motor vehicle;

The projections of the speed of the cyclist and the speed of the motor vehicle on the line connecting the centers of mass of the two vehicles are:

α is the angle between the line connecting the vehicle and the origin of the coordinates and the abscissa;

The relative velocity v of the two vehicles in the direction of the center of mass, v=|v′ ₁ -v′ ₂ |;

conflict time TTC,

Set the risk avoidance time TTA, TTA=βt ₀ ;

Among them, t ₀ is the reaction time, t ₀ =1.5s, and β is the correction coefficient of risk avoidance.

The integrated logic diagram of the control system of the present invention, as shown in FIG. 4 , transmits the vehicle road information obtained through the Internet of Vehicles service platform to the information processing and judgment module, that is, the central processing unit; Dangers are classified and processed, and then the execution command is transmitted to the alarm device, that is, the danger early warning module. The system can automatically identify and eliminate some short-term occasional interference, ensuring that the collision probability can be accurately identified. The system integration diagram is shown in Figure 4. Through system integration, the integrated operation of the Internet of Vehicles service platform, information collection device, data processing device, early warning and emergency device is realized, wherein the vehicle and road information includes the cyclist and the driving parameter information and road information of the motor vehicles around the cyclist; The driving parameter information includes speed, acceleration and heading angle; the road information includes road adhesion coefficient information, road segment shape information and road channelization state information, and the road segment shape information includes cross road segments and straight road segments.

Claims (4)

1. An intelligent early warning method for riding safety in a networked environment, the early warning method is applied to an intelligent early warning system for riding safety in a networked environment, the early warning system is installed on the helmet worn by the cyclist, and the warning The system includes: a GPS positioning module, a network connection information receiving module, an information processing and judgment module, and a danger warning module; the GPS positioning module is connected to the information processing and judgment module, and the GPS positioning module is used to obtain the real-time geographic location information of the cyclist, and to store all the information. The above information is sent to the information processing and judgment module; the input end of the network connection information receiving module is connected to the Internet of Vehicles service platform, the output end of the network connection information receiving module is connected to the information processing and judgment module, and the network connection information receiving module is used to provide services from the Internet of Vehicles The platform obtains the driving parameter information of the cyclist and the motor vehicles driving around the cyclist, the geographic location information and road information of the driving motor vehicle, and transmits the driving parameter information of the cyclist and the motor vehicles driving around the cyclist, the driving The geographic location information and road information of the motor vehicle are sent to the information processing and judgment module; the driving parameter information includes speed, acceleration and heading angle; the road information includes road adhesion coefficient information, road segment shape information and road channelization state information, And the road section shape information includes cross sections and straight sections; the information processing and judgment module is used to predict whether there is a danger of collision between the cyclist and the surrounding motor vehicles according to the received information, and if there is a danger of collision, determine the danger warning and send a corresponding warning signal to the danger warning module according to the danger warning level; the danger warning module is used to execute the corresponding warning action according to the warning signal; it is characterized in that the method includes the following steps: Step S1, obtain the real-time geographic location information of the rider through the GPS positioning module, and send the information to the information processing and judgment module; Step S2, obtain the driving parameter information of the cyclist and the motor vehicle driving around the cyclist, the geographic location information and road information of the driving motor vehicle through the network connection information receiving module, and drive the cyclist and the surrounding motor vehicle. The driving parameter information of the motor vehicle, the geographic location information and road information of the driving motor vehicle are sent to the information processing and judgment module; The driving parameter information includes speed, acceleration and heading angle; the road information includes pavement adhesion coefficient information, road segment shape information and road channelization state information, and the road segment shape information includes a cross road segment and a straight road segment; Step S3, the information processing and judgment module is based on the received real-time geographic location information of the cyclist, the driving parameter information of the cyclist and the motor vehicles traveling around the cyclist, the geographic location information and road information of the traveling motor vehicle, Predict whether there is a danger of collision between the cyclist and the motor vehicle driving around it. If there is a danger of collision, determine the danger warning level. The danger warning level includes the first warning level, the second warning level and the third warning level. The severity increases in order of levels, and the corresponding warning signal is sent to the danger warning module according to the danger warning level; Step S4, the danger early warning module executes a corresponding early warning action according to the early warning signal; Wherein, in step S3, the information processing and judging module is based on the received real-time geographic location information of the cyclist, the driving parameter information of the cyclist and the motor vehicles traveling around the cyclist, the geographic location information of the traveling motor vehicle, and Road information to predict whether there is a danger of a collision between the cyclist and the motor vehicles driving around it. If there is a danger of collision, it includes: Pre-set cumulative thresholds for danger warning levels; According to the road information, determine the prediction model of the collision between the cyclist and the surrounding motor vehicles; 1. When the prediction model is a conflict accumulation detection model based on the intersection of time and intersection, according to the real-time geographic location information of the rider, the geographic location information of the motor vehicles driving around the rider, and the information of the rider and the motor vehicles traveling around the rider. The driving parameter information and road information are used to predict vehicle conflicts, and each conflict is accumulated. When the accumulated value reaches the set accumulation threshold, it is determined that there is a danger of collision between the cyclist and the surrounding motor vehicles; the vehicle conflict refers to It is the conflict between the cyclist and the surrounding motor vehicles; The specific process is: determine the current position information of the cyclist and the motor vehicles driving around the intersection, and the current driving parameter information of the two, the driving parameter information includes speed, acceleration and heading angle, calculate the cyclist and the surrounding motor vehicles. The conflict time of driving at the current speed, compare the time periods of the two, and determine whether to accumulate the number of conflicts by comparing the intersection of the two on the time axis, and perform conflict detection in real time. Finally, when the number of conflicts reaches the set accumulation threshold. , to determine that the cyclist is at risk of collision with the surrounding motor vehicles; Among them, the calculation method of the conflict time is: simplify the driving motor vehicle into a rectangle of vehicle width h and vehicle length b, simplify the cyclist into a straight line segment of length H, take the geometric center of the driving motor vehicle model as the origin, and the driving direction is In the y direction, a Cartesian coordinate system is established, then the coordinates of the two intersection points of the intersection area of the two driving areas are:

Calculate the conflict time for a motor vehicle:

Calculate conflict times for cyclists:

in,

are the time it takes for the driving motor vehicle and the cyclist to reach the conflict area, respectively; t _v2 and t _b2 are the time it takes for the driving motor vehicle and the cyclist to leave the conflict area; two intersection points between the cyclist model and the driving motor vehicle model during the conflict are A and B respectively; φ is the heading angle of the rider; (x ₁ , y ₁ ) is the position of the rider’s geometric center; v ₁ is the speed of the rider; v ₂ is the speed of the motor vehicle; Ⅱ. When the prediction model is a collision prediction model based on the vehicle position in the straight section, judge whether the cyclist is driving in the specified area according to the real-time geographic location information of the cyclist. Publish dangerous voice alerts; In the straight section, the driving behavior of the motor vehicle is divided into two states: parallel and rear-end collision. Determine a rear-end collision area with the cyclist as the geometric center, 2h in width and 50m in length, according to whether the motor vehicle is driving in this area. Early warning mode, when a motor vehicle enters the area, it is determined to be in a rear-end collision state, otherwise it is considered to be in a parallel state; ① Conflict accumulation detection model for rear-end collisions in straight sections A model is established, and the cyclist is regarded as driving at a constant speed, and the distance difference between the driving motor vehicle that pre-conflicts with the cyclist decelerates to the cyclist's driving speed is used as the safety distance. Within a period of time, the number of conflicts is accumulated once, and within a period of time, when the accumulated number of conflicts reaches the set accumulation threshold, it is determined whether there is a danger of collision between the cyclist and the surrounding motor vehicles; The formula for calculating the safety distance is:

Among them, R _Warning is the minimum distance between the cyclist and the surrounding motor vehicles, that is, the warning distance; v ₁ is the speed of the cyclist; v ₂ is the speed of the motor vehicle; a ₂ is the maximum deceleration of the motor vehicle; D is the speed of the motor vehicle. Insurance distance, the specified distance of insurance distance is 2m; ② Conflict accumulation detection model for parallel rubbing collisions in straight sections If the motor vehicle is not in the rear-end collision area, it is regarded as parallel, and it is determined whether to accumulate the number of conflicts by calculating the comparison between the evasion time TTA and the conflict time TTC. The specific steps are as follows: Taking the rider as the coordinate origin and the driving direction as the positive direction, a coordinate system is established. The position coordinates (x ₂ , y ₂ ) of the motor vehicle are known. The speeds of the rider and the motor vehicle are v ₁ and v ₂ respectively. The heading angle θ of the moving car; The distance between the center of mass of the cyclist and the moving vehicle is

The actual distance between the rider and the motor vehicle is ΔL=lR ₁ -R ₂ ; R ₁ and R ₂ are the actual width of the rider and the actual width of the motor vehicle; The projections of the speed of the cyclist and the speed of the traveling motor vehicle on the line connecting the centers of mass of the two vehicles are:

φ=α+θ;

α is the angle between the line connecting the vehicle and the origin of the coordinates and the abscissa; The relative velocity v of the two vehicles in the direction of the center of mass, v=|v′ ₁ -v′ ₂ |; conflict time TTC,

Set the risk avoidance time TTA, TTA=βt ₀ ; Among them, t ₀ is the reaction time, t ₀ =1.5s, and β is the correction coefficient of risk avoidance. 2. The intelligent early-warning method for riding safety in a networked environment according to claim 1, wherein in step S3, a corresponding early-warning signal is sent to the danger early-warning module according to the danger early-warning level, comprising: When the danger warning level is the first warning level, the information processing and judging module sends a first warning signal to the danger warning module; When the danger warning level is the second warning level, the information processing and judging module sends a second warning signal to the danger warning module; When the danger warning level is the third warning level, the information processing and judging module sends a third warning signal to the danger warning module. 3. The intelligent pre-warning method for riding safety in a networked environment according to claim 1, wherein in step S4, the danger pre-warning module executes a corresponding pre-warning action according to the pre-warning signal, comprising: The danger warning module receives the first warning signal, and the danger warning module responds to the first warning signal and executes the first warning action to perform voice broadcast; The danger early warning module receives the second early warning signal, and the danger early warning module responds to the second early warning signal and executes the second early warning action, and performs voice broadcast and vibration reminder; The danger warning module receives the third warning signal, the danger warning module responds to the third warning signal and executes the third warning action, and performs voice broadcast and vibration reminder, and the vibration intensity of the third warning action is greater than that of the second warning action . 4. The intelligent early-warning method for riding safety in a networked environment according to claim 1, characterized in that: said determining the danger warning level is: by setting different cumulative thresholds, grading the danger warning level, thereby obtaining Three hazard warning levels.

Images