CN113192331B - Intelligent early warning system and early warning method for riding safety in internet environment - Google Patents
Intelligent early warning system and early warning method for riding safety in internet environment Download PDFInfo
- Publication number
- CN113192331B CN113192331B CN202110463835.0A CN202110463835A CN113192331B CN 113192331 B CN113192331 B CN 113192331B CN 202110463835 A CN202110463835 A CN 202110463835A CN 113192331 B CN113192331 B CN 113192331B
- Authority
- CN
- China
- Prior art keywords
- rider
- early warning
- information
- running
- danger
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Expired - Fee Related
Links
- 238000000034 method Methods 0.000 title claims abstract description 33
- 230000010365 information processing Effects 0.000 claims abstract description 65
- 230000009471 action Effects 0.000 claims description 28
- 230000006855 networking Effects 0.000 claims description 25
- 238000009825 accumulation Methods 0.000 claims description 20
- 238000001514 detection method Methods 0.000 claims description 14
- 230000001133 acceleration Effects 0.000 claims description 13
- 230000008569 process Effects 0.000 claims description 6
- 238000004364 calculation method Methods 0.000 claims description 4
- 238000012937 correction Methods 0.000 claims description 3
- 230000035484 reaction time Effects 0.000 claims description 3
- 238000007790 scraping Methods 0.000 claims description 3
- 230000011218 segmentation Effects 0.000 abstract description 2
- 238000010586 diagram Methods 0.000 description 6
- 230000006399 behavior Effects 0.000 description 4
- 238000011161 development Methods 0.000 description 3
- 230000018109 developmental process Effects 0.000 description 3
- 230000010354 integration Effects 0.000 description 3
- 238000012545 processing Methods 0.000 description 3
- 238000004891 communication Methods 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 206010063385 Intellectualisation Diseases 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000009977 dual effect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 238000010295 mobile communication Methods 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 238000004806 packaging method and process Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
- G08G1/012—Measuring and analyzing of parameters relative to traffic conditions based on the source of data from other sources than vehicle or roadside beacons, e.g. mobile networks
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/048—Detecting movement of traffic to be counted or controlled with provision for compensation of environmental or other condition, e.g. snow, vehicle stopped at detector
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/16—Anti-collision systems
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/16—Anti-collision systems
- G08G1/161—Decentralised systems, e.g. inter-vehicle communication
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Chemical & Material Sciences (AREA)
- Engineering & Computer Science (AREA)
- Analytical Chemistry (AREA)
- Computing Systems (AREA)
- General Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Health & Medical Sciences (AREA)
- Traffic Control Systems (AREA)
- Emergency Alarm Devices (AREA)
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
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 an internet environment.
Background
Based on the basic national conditions of China, residents are more inclined to choose to ride when traveling in a closer range. Due to the large population flow of cities in China and the defects in road traffic planning in some areas, conflicts between motor vehicles and riders are very common, and a plurality of traffic safety problems are brought. At present, more suggestions are made from the perspective of social management, such as the implementation of a safety guard action of 'one helmet with one' and the lack of physical protection for the safety of the rider. The field of safety research of riders at home and abroad still has a large blank. The most prevalent of physical devices for safety protection of riders remains the helmet. The existing helmet which is used more has the following defects: firstly, the design of a common helmet is not reasonable enough, and in order to achieve the best possible protection effect on the head of a rider, the visual field of a driver is often influenced; secondly, the driver's ability to perceive the external environment is also affected to some extent. More active safety devices are needed to protect the rider. With the continuous development of vehicle-road coordination, the continuous breakthrough of accurate positioning and real-time communication technology and the expansion of the takeaway logistics industry in recent years, the existence of riders in the whole vehicle networking system should not be lost. Therefore, there is a need in the art for a new solution to this problem.
Disclosure of Invention
The invention aims to provide an intelligent early warning system and an early warning method for riding safety in an internet environment aiming at the technical problems in the prior art, which can effectively identify road section information and carry out early warning for road conditions, can sense the running information of surrounding running motor vehicles and accurately send out early warning signals in time, and provide enough risk avoiding time for riders.
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention provides an intelligent early warning system for riding safety in an internet environment, which is characterized in that 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 GPS positioning module is connected with the information processing and judging module and is used for acquiring real-time geographic position information of a rider and sending the information to the information processing and judging module;
the system comprises a vehicle networking service platform, an internet information receiving module, an information processing and judging module, a vehicle networking service platform, an information processing and judging module, a vehicle networking information processing and judging module and an information processing and judging module, wherein the input end of the internet information receiving module is connected with the vehicle networking service platform, the output end of the internet information receiving module is connected with the information processing and judging module, and the internet information receiving module is used for acquiring the driving parameter information of a rider and a driving motor vehicle around the rider, the geographical position information of the driving motor vehicle and the road information from the vehicle networking service platform, and sending the driving parameter information of the rider and the driving motor vehicle around the rider, the geographical position information of the driving motor vehicle and the road information to the information processing and judging module; the driving parameter information comprises speed, acceleration and course angle; the road information comprises road surface adhesion coefficient information, road section form information and road canalization state information, and the road section form information comprises a cross road section and a straight road section;
the information processing and judging module is used for predicting whether the rider and the motor vehicles running around the rider are in collision danger or not according to the received information, determining a danger early warning level if the rider and the motor vehicles running around the rider are in collision danger, and sending a corresponding early warning signal to the danger early warning module according to the danger early warning level;
and the danger early warning module is used for executing corresponding early warning actions according to the early warning signals.
Further, the hazard warning levels include: the first early warning level, the second early warning level and the third early warning level, wherein the severity of the danger early warning level is increased in sequence according to the level sequence;
when the danger early warning level is a first early warning level, the information processing and judging module is configured to send a first early warning signal to the danger early warning module so that the danger early warning module executes a first early warning action;
when the danger early warning level is a second early warning level, the information processing and judging module is configured to send a second early warning signal to the danger early warning module so that the danger early warning module executes a second early warning action;
and when the danger early warning level is a third early warning level, the information processing and judging module is configured to send a third early warning signal to the danger early warning module so that the danger early warning module executes a third early warning action.
Further, the danger early warning module comprises a vibration module and a voice module, and the vibration module is a buzzer.
The invention also provides an intelligent early warning method for riding safety in an internet environment, which is applied to the early warning system and is characterized by comprising the following steps:
step S1, acquiring real-time geographic position information of the rider through the GPS positioning module, and sending the information to the information processing and judging module;
step S2, acquiring the driving parameter information of the rider and the driving motor vehicles around the rider, the geographical position information of the driving motor vehicles and the road information through the internet information receiving module, and sending the driving parameter information of the rider and the driving motor vehicles around the rider, the geographical position information of the driving motor vehicles and the road information to an information processing and judging module;
the driving parameter information comprises speed, acceleration and course angle; the road information comprises road surface adhesion coefficient information, road section form information and road canalization state information, and the road section form information comprises a cross road section and a straight road section;
step S3, the information processing and judging module predicts whether the rider and the motor vehicle running around the rider have collision danger or not according to the received real-time geographic position information of the rider, the driving parameter information of the rider and the motor vehicle running around the rider, the geographic position information of the motor vehicle running and the road information, determines a danger early warning level if the rider and the motor vehicle running around the rider have collision danger, wherein the danger early warning level comprises a first early warning level, a second early warning level and a third early warning level, the severity of the danger early warning level is sequentially increased according to the level sequence, and sends corresponding early warning signals to the danger early warning module according to the danger early warning level;
and step S4, the danger early warning module executes corresponding early warning action according to the early warning signal.
Further, in step S3, the information processing and determining module predicts whether the rider is in collision with the vehicle running around the rider according to the received real-time geographic position information of the rider, the vehicle running parameter information of the rider and the vehicle running around the rider, the geographic position information of the vehicle running and the road information, and if so, includes:
presetting an accumulation threshold value of a danger early warning level;
determining a prediction model of collision between a rider and a motor vehicle running around the rider according to road information;
when the prediction model is a conflict accumulation detection model of the cross road section based on time intersection, predicting vehicle conflicts according to real-time geographic position information of a rider, geographic position information of vehicles running around the rider, driving parameter information of the rider and the vehicles running around the rider and road information, accumulating conflicts every time, and determining that the rider is in collision with the vehicles running around the rider when an accumulated value reaches a set accumulation threshold value; the vehicle conflict refers to a conflict between a rider and surrounding motor vehicles;
the specific process is as follows: determining the current position information of a rider and the motor vehicles running around the rider on the cross road section and the current driving parameter information of the rider and the motor vehicles running around the cross road section, wherein the driving parameter information comprises speed, acceleration and course angle, calculating the collision time of the rider and the motor vehicles running around the rider according to the current speed, comparing the time periods of the rider and the motor vehicles running around the rider, determining whether to accumulate collision numbers or not by comparing whether the collision numbers are intersected on a time axis, performing collision detection in real time, and finally determining that the rider and the motor vehicles running around the rider are in danger of collision when the collision numbers reach a set accumulation threshold value;
the method for calculating the conflict time comprises the following steps: simplifying the running motor vehicle into a rectangle with a vehicle width H and a vehicle length b, simplifying a rider into a straight line segment with a length H, establishing a rectangular coordinate system by taking the geometric center of a running motor vehicle model as an origin and the running direction as a y direction, and setting the coordinates of two intersection points of intersection areas of the two running areas as follows:
calculating the conflict time of the running motor vehicle:
calculating the conflict time of the rider:
wherein,
respectively the time taken by the running motor vehicle and the rider to reach the conflict area; t is tv2、tb2The time taken for the running vehicle and the rider to exit the conflict area; the two intersection points of the rider model and the driving motor vehicle model in the collision process are A, B respectively; phi is the course angle of the rider; (x)1,y1) Is the position of the geometric center of the rider; v. of1Is the speed of the rider; v. of2Is the speed of the running vehicle;
II, when the prediction model is a collision prediction model of a straight road section based on the vehicle position, judging whether the rider drives in a specified area or not according to the real-time geographic position information of the rider, and if the rider drives out of a non-motor vehicle lane, issuing dangerous voice prompt to the rider;
on a straight road section, the driving behavior of the running motor vehicle is divided into a parallel state and a rear-end collision state, a rear-end collision area which takes a rider as a geometric center, has the width of 2h and the length of 50m is determined, an early warning mode is determined according to whether the running motor vehicle runs in the area, the rear-end collision state is determined when the running motor vehicle runs into the area, and the running motor vehicle is considered to be in the parallel state if the running motor vehicle does not run in the area;
collision accumulation detection model for rear-end collision of straight road section
Establishing a model, regarding a rider as constant-speed running, taking the distance difference between running motor vehicles which are expected to conflict with the rider and the running speed of the rider as a safe distance, accumulating the number of conflicts once when the running motor vehicles which are expected to conflict with the rider are within the safe distance, and determining whether the rider is in danger of collision with the running motor vehicles around the rider when the accumulated number of conflicts reaches a set accumulated threshold value within a period of time;
the safe distance calculation formula is as follows:
wherein R isWarningThe minimum distance between the rider and the motor vehicle running around the rider is the early warning distance; v1 is the speed of the rider; v. of2Is the speed of the running vehicle; a is2Maximum deceleration for a running vehicle; d is a safety distance, and the specified distance of the safety distance is 2 m;
collision accumulated detection model for parallel scraping, rubbing and collision in straight road section
The running motor vehicle is not in the rear-end area, is regarded as parallel, and determines whether to accumulate the collision number by calculating the danger avoiding time TTA and comparing the collision time TTC, and the specific steps are as follows:
establishing a coordinate system by taking the rider as the origin of coordinates and the driving direction as the positive direction, and knowing the position coordinates (x) of the driving motor vehicle2，y2) The speeds of the rider and the running vehicle are v1 and v respectively2The course angle theta of the running motor vehicle;
the distance of the center of mass between the rider and the running motor vehicle is
The actual distance between the rider and the running motor vehicle is delta L-R1-R2；
R1、R2Actual vehicle width of a rider and actual vehicle width of a running motor vehicle are respectively;
the projection of the speed of a rider on the connecting line of the mass centers of the two vehicles and the speed of a running motor vehicle is respectively as follows:
alpha is an included angle between a connecting line of the running motor vehicle and the origin of coordinates and the abscissa;
relative speed v, v ═ v 'of two vehicles in the direction of centroid connecting line'1-v′2|；
The time of collision TTC is set to,
setting the risk avoiding time TTA, wherein the TTA is beta t0；
Wherein t is0For reaction time, t0β is a risk avoidance correction coefficient, 1.5 s.
Further, in step S3, the method for sending a corresponding warning signal to the danger warning module according to the danger warning level includes:
when the danger early warning level is a first early warning level, the information processing and judging module sends a first early warning signal to the danger early warning module;
when the danger early warning level is a second early warning level, the information processing and judging module sends a second early warning signal to the danger early warning module;
and when the danger early warning level is a third early warning level, the information processing and judging module sends a third early warning signal to the danger early warning module.
Further, in step S4, the danger early warning module executes a corresponding early warning action according to the early warning signal, including:
the danger early warning module receives a first early warning signal, responds to the first early warning signal, executes a first early warning action and broadcasts in voice;
the danger early warning module receives a second early warning signal, responds to the second early warning signal and executes a second early warning action, and voice broadcast and vibration reminding are carried out;
and the danger early warning module receives a third early warning signal, responds to the third early warning signal and executes a third early warning action, carries out voice broadcast and vibration reminding, and the vibration intensity of the third early warning action is greater than that of the second early warning action.
Further, the determining the danger early warning level is: and classifying the danger early warning grades by setting different accumulation threshold values so as to obtain three danger early warning grades.
Through the design scheme, the invention can bring the following beneficial effects:
1. the invention provides safer travel selection for the rider under the current actual situation that the active safety protection of the rider is insufficient and the development trend of the internet of vehicles does not come.
2. The invention adopts a road segmentation method and an accumulative threshold algorithm, realizes the intellectualization of the helmet and improves the accuracy of early warning.
3. And by combining the car networking technology, real-time communication is realized, and more optimization schemes are provided for decision making.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limitation and are not intended to limit the invention in any way, and in which:
fig. 1 is a block diagram of an early warning system according to the present invention.
FIG. 2 is a logic diagram of a straight road section model according to the present invention.
FIG. 3 is a schematic diagram of a cross-road segment model logic of the present invention.
FIG. 4 is a schematic diagram of the control system integration logic of the present invention.
Detailed Description
In order to make the objects, features and advantages of the present invention more obvious and understandable, the technical solutions of the present invention are 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 embodiments can be determined according to the technical solutions and practical situations of the present invention. Well-known methods, procedures, components and circuits have not been described in detail so as not to obscure the present invention.
As shown in fig. 1, fig. 2, fig. 3 and fig. 4, the present invention provides an intelligent early warning system for riding safety in an internet environment, wherein the early warning system is installed 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 GPS positioning module is connected with the information processing and judging module and is used for acquiring real-time geographic position information of a rider and sending the information to the information processing and judging module;
the system comprises a vehicle networking service platform, an internet information receiving module, an information processing and judging module, a vehicle networking service platform, an information processing and judging module, a vehicle networking information processing and judging module and an information processing and judging module, wherein the input end of the internet information receiving module is connected with the vehicle networking service platform, the output end of the internet information receiving module is connected with the information processing and judging module, and the internet information receiving module is used for acquiring the driving parameter information of a rider and a driving motor vehicle around the rider, the geographical position information of the driving motor vehicle and the road information from the vehicle networking service platform, and sending the driving parameter information of the rider and the driving motor vehicle around the rider, the geographical position information of the driving motor vehicle and the road information to the information processing and judging module; the driving parameter information comprises speed, acceleration and course angle; the road information comprises road surface adhesion coefficient information, road section form information and road canalization state information, the road section form information comprises a cross road section and a straight road section, and specifically, the form of the road section where a rider is located at present is judged according to the comparison of real-time rider geographical position information and an electronic map stored in an internet-of-vehicles service platform;
the information processing and judging module is used as a main controller and used for predicting whether the rider and the motor vehicles running around the rider are in collision danger or not according to the received information, determining a danger early warning level if the rider and the motor vehicles running around the rider are in collision danger, and sending a corresponding early warning signal to the danger early warning module according to the danger early warning level;
the danger early warning module is used as a signal output device and comprises a buzzer and a voice module, the buzzer is used as a vibration module, the danger early warning module responds to an early warning signal and executes a corresponding early warning action according to the early warning signal.
The hazard early warning levels include: the first early warning level, the second early warning level and the third early warning level, wherein the severity of the danger early warning level is increased in sequence according to the level sequence;
when the danger early warning level is a first early warning level, the information processing and judging module is configured to send a first early warning signal to the danger early warning module so that the danger early warning module executes a first early warning action;
when the danger early warning level is a second early warning level, the information processing and judging module is configured to send a second early warning signal to the danger early warning module so that the danger early warning module executes a second early warning action;
and when the danger early warning level is a third early warning level, the information processing and judging module is configured to send a third early warning signal to the danger early warning module so that the danger early warning module executes a third early warning action.
Hardware equipment as intelligent early warning system towards riding safety under networking environment:
a GPS positioning module: the MC20 module has the advantages of small size, low power consumption, dual cards, single standby and the like, and can provide wireless mobile communication and accurate navigation and positioning functions;
the networking information receiving module: the CC1101 module is a micropower UHF wireless transceiver;
information processing and judging module (singlechip): an STM32F103C8T6 singlechip is adopted;
danger early warning module: the danger early warning module comprises a buzzer and a voice module, and the voice module is a JQ8900-16P voice module.
Specifically, the method comprises the following steps: the danger early warning module receives a first early warning signal, the first early warning signal represents primary early warning, the voice module responds to the first early warning signal and executes early warning action to perform voice broadcast, and voice prompt 'please notice a car coming from the side';
the danger early warning module receives a second early warning signal, the second early warning signal represents danger warning, the voice module and the buzzer simultaneously respond to the second early warning signal and simultaneously execute early warning action to perform voice broadcasting and vibration reminding, the voice module performs voice reminding to pay attention to avoiding a car coming from the side, and the buzzer performs vibration reminding;
the danger early warning module receives a third early warning signal, the third early warning signal represents danger alarm, the voice module and the buzzer simultaneously respond to the third early warning signal and simultaneously execute early warning action, voice broadcast and vibration reminding are carried out, and the buzzer strongly vibrates when the voice gives an alarm.
The intelligent early warning method for the riding safety in the internet environment is realized based on the intelligent early warning system for the riding safety in the internet environment, and comprises the following steps:
step S1, acquiring real-time geographic position information of the rider through the GPS positioning module, and sending the information to the information processing and judging module;
step S2, acquiring the driving parameter information of the rider and the driving motor vehicles around the rider, the geographical position information of the driving motor vehicles and the road information through the internet information receiving module, and sending the driving parameter information of the rider and the driving motor vehicles around the rider, the geographical position information of the driving motor vehicles and the road information to an information processing and judging module;
the driving parameter information comprises speed, acceleration and course angle; the road information comprises road surface adhesion coefficient information, road section form information and road canalization state information, and the road section form information comprises a cross road section and a straight road section;
step S3, the information processing and judging module predicts whether the rider and the motor vehicle running around the rider have collision danger or not according to the received real-time geographic position information of the rider, the driving parameter information of the rider and the motor vehicle running around the rider, the geographic position information of the motor vehicle running and the road information, determines a danger early warning level if the rider and the motor vehicle running around the rider have collision danger, wherein the danger early warning level comprises a first early warning level, a second early warning level and a third early warning level, the severity of the danger early warning level is sequentially increased according to the level sequence, and sends corresponding early warning signals to the danger early warning module according to the danger early warning level;
and step S4, the danger early warning module executes corresponding early warning action according to the early warning signal.
The specific logic control of the method provided by the invention is as follows:
1. data acquisition
In order to realize the accuracy and precision of data acquisition and early warning judgment, local road network feature recognition is carried out on complex traffic roads according to the existing electronic map, the whole road network system is divided into a cross road section and a straight road section with an intersection, and the electronic map is loaded in an internet of vehicles service platform;
and the information processing and judging module is used for analyzing the data after acquiring the GPS positioning position data and the electronic map data of the vehicle in real time, extracting key data required by subsequent calculation and carrying out road matching. After data interaction, whether vehicles come and the number of the vehicles come in the road section can be known, and the speed, the acceleration, the course angle and other driving parameters of the vehicles can be known. When the rider is in different road sections, the early warning system adopts a corresponding algorithm.
2. Data processing
(1) Presetting an accumulated threshold of a danger early warning level, wherein the determination method of the level accumulated thresholds a, b and c comprises the following steps: the danger early warning level division is not only related to the distance between a rider and a motor vehicle running around the rider, but also influenced by the speed, the acceleration, the course angle driving parameters and the current road adhesion coefficient. The acceleration information of the motor vehicle and the vehicle condition information (vehicle condition information, namely driving parameter information) and the road surface condition (namely road surface adhesion coefficient) present a linear relation at that time, appropriate accumulated thresholds are given out under different driving states according to historical data and input into an early warning system to lay a foundation for the development of a later early warning algorithm, and the accumulated thresholds a, b and c respectively correspond to a first early warning level, a second early warning level and a third early warning level.
(2) A cross road section:
determining motor vehicles running around a rider at an intersection, detecting the running tracks of the motor vehicles and the running tracks of the rider, establishing a time intersection-based conflict accumulation detection model for the intersection section, analyzing conflict time, and providing a time intersection-based conflict accumulation detection algorithm. When a rider passes through an intersection, main driving behaviors include straight running, left turning and right turning. The coming vehicles in different directions and different driving behaviors generate cross conflict, confluence conflict, non-conflict and the like. The method comprises the steps of firstly determining the position information of a rider and other running motor vehicles in a cross road section, packaging the position information with vehicle condition information (vehicle condition information, namely driving parameter information) collected by a vehicle networking service platform, and transmitting the information and the vehicle condition information to an information processing and judging module, wherein the information processing and judging module determines the accumulation threshold values a, b and c of the danger early warning level through data matching. Secondly, calculating the conflict time of the rider and other motor vehicles running at the current speed at the moment, comparing the time periods of the rider and other motor vehicles, and determining whether to accumulate the conflict number by comparing whether the two are intersected on a time axis; and collision detection is carried out in real time, finally, when the number of collisions reaches a set accumulation threshold value, an alarm is issued to the rider, whether early warning is carried out or not is determined in a dangerous accumulation mode, misjudgment caused by single collision is avoided, and the accuracy of early warning is effectively improved.
The specific process is as follows: determining the current position information of a rider and the motor vehicles running around the rider on the cross road section and the current driving parameter information of the rider and the motor vehicles running around the cross road section, wherein the driving parameter information comprises speed, acceleration and course angle, calculating the collision time of the rider and the motor vehicles running around the rider according to the current speed, comparing the time periods of the rider and the motor vehicles running around the rider, determining whether to accumulate collision numbers or not by comparing whether the collision numbers are intersected on a time axis, performing collision detection in real time, and finally determining that the rider and the motor vehicles running around the rider are in danger of collision when the collision numbers reach a set accumulation threshold value;
the method for calculating the conflict time comprises the following steps: simplifying the running motor vehicle into a rectangle with a vehicle width H and a vehicle length b, simplifying a rider into a straight line segment with a length H, establishing a rectangular coordinate system by taking the geometric center of a running motor vehicle model as an origin and the running direction as a y direction, and setting the coordinates of two intersection points of intersection areas of the two running areas as follows:
calculating the conflict time of the running motor vehicle:
calculating the conflict time of the rider:
wherein, tv1、tb1Respectively the time taken by the running motor vehicle and the rider to reach the conflict area; t is tv2、tb2The time taken for the running vehicle and the rider to exit the conflict area; the two intersection points of the rider model and the driving motor vehicle model in the collision process are A, B respectively; phi is the course angle of the rider; (x)1,y1) Is the position of the geometric center of the rider; v. of1Is the speed of the rider; v. of2Is the speed of the running vehicle; judgment of
If yes, the number of conflicts is increased once, otherwise, the judgment is made
If yes, the number of conflicts is increased once;
(3) straight road section
Judging whether the rider drives in a specified area or not according to the real-time geographic position information of the rider, and if the rider drives out of the non-motor lane, issuing dangerous voice prompt to the rider, wherein the voice prompt is 'please drive in the non-motor lane';
on a straight road section, the driving behavior of the running motor vehicle is divided into a parallel state and a rear-end collision state, a rear-end collision area which takes a rider as a geometric center, has the width of 2h and the length of 50m is determined, an early warning mode is determined according to whether the running motor vehicle runs in the area, the rear-end collision state is determined when the running motor vehicle runs into the area, and the running motor vehicle is considered to be in the parallel state if the running motor vehicle does not run in the area;
the collision form judgment algorithm of the straight road section based on the vehicle position comprises the following steps: and the information processing and judging module determines a rear-end collision region taking the rider as a geometric center in the front-rear direction of the rider, and judges that the rear-end collision is possible when other vehicles drive into the region, otherwise, the rear-end collision is possible due to parallel scratch and graze.
And the information processing and judging module takes the distance difference of the running motor vehicle which is expected to collide with the rider when the running motor vehicle decelerates to the running speed of the rider as a safe distance. When the distance between the rider and the running motor vehicle is less than the safe distance, the rider is regarded as a conflict. And when the accumulated conflict number reaches a set early warning threshold value within set time, early warning is carried out.
And the information processing and judging module determines whether conflicts occur and accumulates the conflicts by calculating the danger avoiding time TTA and the conflict time TTC. And when the accumulated conflict number reaches a set early warning threshold value within set time, early warning is carried out.
The detailed description is as follows:
collision accumulation detection model for rear-end collision of straight road section
Establishing a model, regarding a rider as constant-speed running, taking the distance difference between running motor vehicles which are expected to conflict with the rider and the running speed of the rider as a safe distance, accumulating the number of conflicts once when the running motor vehicles which are expected to conflict with the rider are within the safe distance, and indicating that a great opportunity rate of collision occurs when the accumulated number of conflicts reaches a set accumulation threshold value within a period of time so as to deal with the issue of an early warning signal for the rider.
The safe distance calculation formula is as follows:
wherein R isWarningThe minimum distance between the rider and the motor vehicle running around the rider is the early warning distance; v1 is the speed of the rider; v. of2Is the speed of the running vehicle; a is2Maximum deceleration for a running vehicle; d is a safety distance, and the specified distance of the safety distance is 2 m;
collision accumulated detection model for parallel scraping, rubbing and collision in straight road section
The running motor vehicle is not in the rear-end area, is regarded as parallel, and determines whether to accumulate the collision number by calculating the danger avoiding time TTA and comparing the collision time TTC, and the specific steps are as follows:
establishing a coordinate system by taking the rider as the origin of coordinates and the driving direction as the positive direction, and knowing the position coordinates (x) of the driving motor vehicle2，y2) The speeds of the rider and the running vehicle are v1 and v respectively2The course angle theta of the running motor vehicle;
the distance of the center of mass between the rider and the running motor vehicle is
The actual distance between the rider and the running motor vehicle is delta L-R1-R2；
R1、R2Actual vehicle width of a rider and actual vehicle width of a running motor vehicle are respectively;
the projection of the speed of a rider on the connecting line of the mass centers of the two vehicles and the speed of a running motor vehicle is respectively as follows:
alpha is an included angle between a connecting line of the running motor vehicle and the origin of coordinates and the abscissa;
relative speed v, v ═ v 'of two vehicles in the direction of centroid connecting line'1-v′2|；
The time of collision TTC is set to,
setting the risk avoiding time TTA, wherein the TTA is beta t0；
Wherein t is0For reaction time, t0β is a risk avoidance correction coefficient, 1.5 s.
The control system of the invention integrates the logic diagram, as shown in fig. 4, and transmits the vehicle path information obtained by the vehicle networking service platform to the information processing and judging module, i.e. the central processing unit; safety evaluation is carried out on the current driving state of the rider, danger is graded, and then an execution command is transmitted to an alarm device, namely a danger early warning module. The system can automatically identify and eliminate some short-time accidental interference, and can ensure accurate identification of the collision probability. A system integration diagram is shown in fig. 4. The integrated operation of a vehicle networking service platform, an information collection device, a data processing device and an early warning and emergency device is realized through system integration, wherein the vehicle path information comprises a rider and vehicle parameter information and road information of running motor vehicles around the rider; the driving parameter information comprises speed, acceleration and course angle; the road information comprises road surface adhesion coefficient information, road section form information and road canalization state information, and the road section form information comprises a cross road section and a straight road section.
Claims (4)
1. The intelligent early warning method for the riding safety in the internet environment is applied to an intelligent early warning system for the riding safety in the internet environment, the early warning system is installed on a helmet worn by a rider, and the early warning system 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 GPS positioning module is connected with the information processing and judging module and is used for acquiring real-time geographic position information of a rider and sending the information to the information processing and judging module; the system comprises a vehicle networking service platform, an internet information receiving module, an information processing and judging module, a vehicle networking service platform, an information processing and judging module, a vehicle networking information processing and judging module and an information processing and judging module, wherein the input end of the internet information receiving module is connected with the vehicle networking service platform, the output end of the internet information receiving module is connected with the information processing and judging module, and the internet information receiving module is used for acquiring the driving parameter information of a rider and a driving motor vehicle around the rider, the geographical position information of the driving motor vehicle and the road information from the vehicle networking service platform, and sending the driving parameter information of the rider and the driving motor vehicle around the rider, the geographical position information of the driving motor vehicle and the road information to the information processing and judging module; the driving parameter information comprises speed, acceleration and course angle; the road information comprises road surface adhesion coefficient information, road section form information and road canalization state information, and the road section form information comprises a cross road section and a straight road section; the information processing and judging module is used for predicting whether the rider and the motor vehicles running around the rider are in collision danger or not according to the received information, determining a danger early warning level if the rider and the motor vehicles running around the rider are in collision danger, and sending a corresponding early warning signal to the danger early warning module according to the danger early warning level; the danger early warning module is used for executing corresponding early warning actions according to the early warning signals; characterized in that the method comprises the following steps:
step S1, acquiring real-time geographic position information of the rider through the GPS positioning module, and sending the information to the information processing and judging module;
step S2, acquiring the driving parameter information of the rider and the driving motor vehicles around the rider, the geographical position information of the driving motor vehicles and the road information through the internet information receiving module, and sending the driving parameter information of the rider and the driving motor vehicles around the rider, the geographical position information of the driving motor vehicles and the road information to an information processing and judging module;
the driving parameter information comprises speed, acceleration and course angle; the road information comprises road surface adhesion coefficient information, road section form information and road canalization state information, and the road section form information comprises a cross road section and a straight road section;
step S3, the information processing and judging module predicts whether the rider and the motor vehicle running around the rider have collision danger or not according to the received real-time geographic position information of the rider, the driving parameter information of the rider and the motor vehicle running around the rider, the geographic position information of the motor vehicle running and the road information, determines a danger early warning level if the rider and the motor vehicle running around the rider have collision danger, wherein the danger early warning level comprises a first early warning level, a second early warning level and a third early warning level, the severity of the danger early warning level is sequentially increased according to the level sequence, and sends corresponding early warning signals to the danger early warning module according to the danger early warning level;
step S4, the danger early warning module executes corresponding early warning action according to the early warning signal;
in step S3, the information processing and determining module predicts whether the rider is in collision with the vehicle running around the rider according to the received real-time geographic position information of the rider, the vehicle running parameter information of the rider and the vehicle running around the rider, the geographic position information of the vehicle running and the road information, and if so, the information processing and determining module includes:
presetting an accumulation threshold value of a danger early warning level;
determining a prediction model of collision between a rider and a motor vehicle running around the rider according to road information;
when the prediction model is a conflict accumulation detection model of the cross road section based on time intersection, predicting vehicle conflicts according to real-time geographic position information of a rider, geographic position information of vehicles running around the rider, driving parameter information of the rider and the vehicles running around the rider and road information, accumulating conflicts every time, and determining that the rider is in collision with the vehicles running around the rider when an accumulated value reaches a set accumulation threshold value; the vehicle conflict refers to a conflict between a rider and surrounding motor vehicles;
the specific process is as follows: determining the current position information of a rider and the motor vehicles running around the rider on the cross road section and the current driving parameter information of the rider and the motor vehicles running around the cross road section, wherein the driving parameter information comprises speed, acceleration and course angle, calculating the collision time of the rider and the motor vehicles running around the rider according to the current speed, comparing the time periods of the rider and the motor vehicles running around the rider, determining whether to accumulate collision numbers or not by comparing whether the collision numbers are intersected on a time axis, performing collision detection in real time, and finally determining that the rider and the motor vehicles running around the rider are in danger of collision when the collision numbers reach a set accumulation threshold value;
the method for calculating the conflict time comprises the following steps: simplifying the running motor vehicle into a rectangle with a vehicle width H and a vehicle length b, simplifying a rider into a straight line segment with a length H, establishing a rectangular coordinate system by taking the geometric center of a running motor vehicle model as an origin and the running direction as a y direction, and setting the coordinates of two intersection points of intersection areas of the two running areas as follows:
calculating the conflict time of the running motor vehicle:
calculating the conflict time of the rider:
wherein,
respectively the time taken by the running motor vehicle and the rider to reach the conflict area; t is tv2、tb2The time taken for the running vehicle and the rider to exit the conflict area; the two intersection points of the rider model and the driving motor vehicle model in the collision process are A, B respectively; phi is the course angle of the rider; (x)1,y1) Is the position of the geometric center of the rider; v. of1Is the speed of the rider; v. of2Is the speed of the running vehicle;
II, when the prediction model is a collision prediction model of a straight road section based on the vehicle position, judging whether the rider drives in a specified area or not according to the real-time geographic position information of the rider, and if the rider drives out of a non-motor vehicle lane, issuing dangerous voice prompt to the rider;
on a straight road section, the driving behavior of the running motor vehicle is divided into a parallel state and a rear-end collision state, a rear-end collision area which takes a rider as a geometric center, has the width of 2h and the length of 50m is determined, an early warning mode is determined according to whether the running motor vehicle runs in the area, the rear-end collision state is determined when the running motor vehicle runs into the area, and the running motor vehicle is considered to be in the parallel state if the running motor vehicle does not run in the area;
collision accumulation detection model for rear-end collision of straight road section
Establishing a model, regarding a rider as constant-speed running, taking the distance difference between running motor vehicles which are expected to conflict with the rider and the running speed of the rider as a safe distance, accumulating the number of conflicts once when the running motor vehicles which are expected to conflict with the rider are within the safe distance, and determining whether the rider is in danger of collision with the running motor vehicles around the rider when the accumulated number of conflicts reaches a set accumulated threshold value within a period of time;
the safe distance calculation formula is as follows:
wherein R isWarningThe minimum distance between the rider and the motor vehicle running around the rider is the early warning distance; v. of1Is the speed of the rider; v. of2Is the speed of the running vehicle; a is2Maximum deceleration for a running vehicle; d is a safety distance, and the specified distance of the safety distance is 2 m;
collision accumulated detection model for parallel scraping, rubbing and collision in straight road section
The running motor vehicle is not in the rear-end area, is regarded as parallel, and determines whether to accumulate the collision number by calculating the danger avoiding time TTA and comparing the collision time TTC, and the specific steps are as follows:
establishing a coordinate system by taking the rider as the origin of coordinates and the driving direction as the positive direction, and knowing the position coordinates (x) of the driving motor vehicle2，y2) The speeds of the rider and the running vehicle are v1、v2Course angle of running motor vehicleθ；
The distance of the center of mass between the rider and the running motor vehicle is
The actual distance between the rider and the running motor vehicle is delta L-R1-R2；
R1、R2Actual vehicle width of a rider and actual vehicle width of a running motor vehicle are respectively;
the projection of the speed of a rider on the connecting line of the mass centers of the two vehicles and the speed of a running motor vehicle is respectively as follows:
alpha is an included angle between a connecting line of the running motor vehicle and the origin of coordinates and the abscissa;
relative speed v, v ═ v 'of two vehicles in the direction of centroid connecting line'1-v′2|；
The time of collision TTC is set to,
setting the risk avoiding time TTA, wherein the TTA is beta t0；
Wherein t is0For reaction time, t0β is a risk avoidance correction coefficient, 1.5 s.
2. The intelligent early warning method for riding safety in the networking environment according to claim 1, wherein: in step S3, the method for sending a corresponding warning signal to the danger warning module according to the danger warning level includes:
when the danger early warning level is a first early warning level, the information processing and judging module sends a first early warning signal to the danger early warning module;
when the danger early warning level is a second early warning level, the information processing and judging module sends a second early warning signal to the danger early warning module;
and when the danger early warning level is a third early warning level, the information processing and judging module sends a third early warning signal to the danger early warning module.
3. The intelligent early warning method for riding safety in the networking environment according to claim 1, wherein: in step S4, the danger early warning module executes a corresponding early warning action according to the early warning signal, including:
the danger early warning module receives a first early warning signal, responds to the first early warning signal, executes a first early warning action and broadcasts in voice;
the danger early warning module receives a second early warning signal, responds to the second early warning signal and executes a second early warning action, and voice broadcast and vibration reminding are carried out;
and the danger early warning module receives a third early warning signal, responds to the third early warning signal and executes a third early warning action, carries out voice broadcast and vibration reminding, and the vibration intensity of the third early warning action is greater than that of the second early warning action.
4. The intelligent early warning method for riding safety in the networking environment according to claim 1, wherein: the determined danger early warning level is as follows: and classifying the danger early warning grades by setting different accumulation threshold values so as to obtain three danger early warning grades.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110463835.0A CN113192331B (en) | 2021-04-26 | 2021-04-26 | Intelligent early warning system and early warning method for riding safety in internet environment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110463835.0A CN113192331B (en) | 2021-04-26 | 2021-04-26 | Intelligent early warning system and early warning method for riding safety in internet environment |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113192331A CN113192331A (en) | 2021-07-30 |
| CN113192331B true CN113192331B (en) | 2022-04-15 |
Family
ID=76979737
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110463835.0A Expired - Fee Related CN113192331B (en) | 2021-04-26 | 2021-04-26 | Intelligent early warning system and early warning method for riding safety in internet environment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113192331B (en) |
Families Citing this family (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113888901B (en) * | 2021-09-29 | 2023-01-31 | 英华达（南京）科技有限公司 | Intelligent safe driving reminding device and method and helmet |
| CN113726464A (en) * | 2021-10-21 | 2021-11-30 | 浙江吉利控股集团有限公司 | Bidirectional early warning method, system, equipment and storage medium for vehicle and rider |
| CN113888903A (en) * | 2021-11-05 | 2022-01-04 | 四川启睿克科技有限公司 | Head-mounted vehicle approach warning system |
| CN114038239B (en) * | 2021-11-08 | 2022-11-25 | 青岛海信网络科技股份有限公司 | Vehicle collision early warning method and device |
| CN114572329B (en) * | 2022-01-27 | 2023-05-26 | 深圳市发掘科技有限公司 | Motorcycle monitoring method, device and system based on Internet of vehicles and storage medium |
| CN116211008A (en) * | 2022-12-05 | 2023-06-06 | 北京声智科技有限公司 | Intelligent safety helmet, driving safety early warning method and storage medium |
| CN117911961B (en) * | 2024-01-18 | 2024-08-16 | 中傲智能科技（苏州）有限公司 | Digital twinning-based intelligent city comprehensive law enforcement management method and system |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110858973A (en) * | 2018-08-23 | 2020-03-03 | 中国移动通信集团山东有限公司 | Method and device for predicting network traffic of cell |
| CN112026700A (en) * | 2020-08-12 | 2020-12-04 | 深圳市森国科科技股份有限公司 | Automobile anti-collision early warning method and system and storage medium |
| CN112508392A (en) * | 2020-12-02 | 2021-03-16 | 云南省交通规划设计研究院有限公司 | Dynamic evaluation method for traffic conflict risk of hidden danger road section of mountain area double-lane highway |
Family Cites Families (32)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9026594B2 (en) * | 2012-01-05 | 2015-05-05 | Apifia, Inc. | Method and system for determining user impact on their content pools within an online social network |
| US9162642B2 (en) * | 2012-10-05 | 2015-10-20 | Ford Global Technologies | Method and system for determining a primary direction of force resulting from a vehicle collision |
| US10039174B2 (en) * | 2014-08-11 | 2018-07-31 | RAB Lighting Inc. | Systems and methods for acknowledging broadcast messages in a wireless lighting control network |
| CN104882025B (en) * | 2015-05-13 | 2017-02-22 | 东华大学 | Crashing detecting and warning method based on vehicle network technology |
| DE102015217498A1 (en) * | 2015-09-14 | 2017-03-16 | Volkswagen Aktiengesellschaft | Method and device in a motor vehicle for automated driving |
| CN105774800B (en) * | 2016-03-28 | 2018-06-26 | 清华大学 | A kind of impact-moderation method and device in hybrid vehicle queue between vehicle |
| CN106056972A (en) * | 2016-06-29 | 2016-10-26 | 江苏科技大学 | Security anti-collision early-warning method based on vehicle driving speed and position information fusion |
| CN106255153B (en) * | 2016-07-25 | 2019-03-19 | 北京航空航天大学 | Vehicle under car networking with caching capabilities divides group's cooperation transmission method |
| CN108282502B (en) * | 2017-01-05 | 2020-07-10 | 上海竺程信息科技有限公司 | Vehicle network message optimization broadcasting method based on dynamic priority |
| CN106713049B (en) * | 2017-02-04 | 2020-08-04 | 杭州迪普科技股份有限公司 | Monitoring alarm method and device |
| US10522040B2 (en) * | 2017-03-03 | 2019-12-31 | Kennesaw State University Research And Service Foundation, Inc. | Real-time video analytics for traffic conflict detection and quantification |
| CN107103787A (en) * | 2017-06-30 | 2017-08-29 | 北京理工大学 | A kind of avoiding collision and system based on bicycle and vehicle communication |
| KR102452774B1 (en) * | 2017-08-24 | 2022-10-12 | 현대자동차주식회사 | Simulation system for vehicle, and simulation method for vehicle |
| KR102335632B1 (en) * | 2017-09-07 | 2021-12-07 | 현대자동차주식회사 | Vehicle and method for controlling the same |
| DE102018001055A1 (en) * | 2017-12-08 | 2019-06-13 | Knorr-Bremse Systeme für Nutzfahrzeuge GmbH | Method for triggering an automatic emergency braking operation in a vehicle convoy |
| CN108399794B (en) * | 2018-02-27 | 2021-03-30 | 吉林大学 | Tunnel driving safety early warning system and method based on vehicle driving state detection |
| CN109035807B (en) * | 2018-07-02 | 2021-03-02 | 东南大学 | Method for calculating full red time of two-phase signal control at road plane intersection |
| CN109102696B (en) * | 2018-07-06 | 2020-11-06 | 北京工业大学 | Cross frequency-dense road section conflict early warning method based on active safety |
| CN109334563B (en) * | 2018-08-31 | 2021-06-22 | 江苏大学 | Anti-collision early warning method based on pedestrians and riders in front of road |
| CN109544993A (en) * | 2019-01-08 | 2019-03-29 | 吉林大学 | A kind of intelligent vehicle right-hand bend anticollision three-level early warning system and method for early warning |
| CN109987046A (en) * | 2019-04-16 | 2019-07-09 | 南京理工大学 | Automobile door opening anti-collision early warning method and device with active safety |
| US11474610B2 (en) * | 2019-05-20 | 2022-10-18 | Meta Platforms Technologies, Llc | Systems and methods for generating dynamic obstacle collision warnings for head-mounted displays |
| CN110213720A (en) * | 2019-06-04 | 2019-09-06 | 哈尔滨工业大学 | Unexpected prevention method in mobile phone use process based on user behavior analysis |
| CN110276988A (en) * | 2019-06-26 | 2019-09-24 | 重庆邮电大学 | A kind of DAS (Driver Assistant System) based on collision warning algorithm |
| CN111354156A (en) * | 2020-03-09 | 2020-06-30 | 长安大学 | Pedestrian/rider risk monitoring and early warning device and early warning method |
| CN111383465B (en) * | 2020-03-23 | 2022-03-22 | 中交第一公路勘察设计研究院有限公司 | Highway danger early warning and speed control system based on car networking |
| CN111489588B (en) * | 2020-03-30 | 2024-01-09 | 腾讯科技（深圳）有限公司 | Vehicle driving risk early warning method and device, equipment and storage medium |
| CN111267734A (en) * | 2020-04-01 | 2020-06-12 | 上海神添实业有限公司 | Safety protection system for large transport vehicle and early warning method thereof |
| CN111445699B (en) * | 2020-04-13 | 2021-10-26 | 黑龙江工程学院 | Intersection traffic conflict discrimination method based on real-time vehicle track |
| CN112002144B (en) * | 2020-09-02 | 2022-03-01 | 中国科学技术大学 | Method and system for evaluating driving risk situation of signalless intersection |
| CN112601194B (en) * | 2020-12-08 | 2022-04-29 | 兰州理工大学 | Internet of vehicles position privacy protection method and system under road network environment |
| CN112509341A (en) * | 2020-12-11 | 2021-03-16 | 东南大学 | Signal control device and method for analyzing traffic conflicts based on high-level video |
-
2021
- 2021-04-26 CN CN202110463835.0A patent/CN113192331B/en not_active Expired - Fee Related
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110858973A (en) * | 2018-08-23 | 2020-03-03 | 中国移动通信集团山东有限公司 | Method and device for predicting network traffic of cell |
| CN112026700A (en) * | 2020-08-12 | 2020-12-04 | 深圳市森国科科技股份有限公司 | Automobile anti-collision early warning method and system and storage medium |
| CN112508392A (en) * | 2020-12-02 | 2021-03-16 | 云南省交通规划设计研究院有限公司 | Dynamic evaluation method for traffic conflict risk of hidden danger road section of mountain area double-lane highway |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113192331A (en) | 2021-07-30 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113192331B (en) | Intelligent early warning system and early warning method for riding safety in internet environment | |
| CN109559532B (en) | Expressway exit diversion area vehicle road cooperative safety early warning control method | |
| CN110264783B (en) | Vehicle anti-collision early warning system and method based on vehicle-road cooperation | |
| CN102390320B (en) | Vehicle anti-collision early warning system based on vehicle-mounted sensing network | |
| CN108475472B (en) | Driving assistance method and device | |
| CN111210662A (en) | Intersection safety early warning system and method based on machine vision and DSRC | |
| CN103465907B (en) | A kind of automotive correlation prevention device and method | |
| CN110164183A (en) | A kind of safety assistant driving method for early warning considering his vehicle driving intention under the conditions of truck traffic | |
| CN110155046A (en) | Automatic emergency brake hierarchical control method and system | |
| CN106448190B (en) | Real-time monitoring and early warning device and method for traffic flow around self-vehicle on highway | |
| EP3403219A1 (en) | Driver behavior monitoring | |
| CN105151043A (en) | Emergency avoidance system and method for unmanned automobile | |
| CN103065502B (en) | Vehicular access cooperative system with function of emergency collision avoidance early warning for red light running and method | |
| CN110562222B (en) | Emergency braking control method for curve scene, vehicle-mounted device and storage medium | |
| CN113724531B (en) | Intersection human-vehicle road cooperation early warning system and method under Internet of vehicles environment | |
| CN104192063B (en) | Vehicle safe driving caution system and corresponding alarming method for power | |
| WO2022213542A1 (en) | Method and system for clearing information-controlled intersection on basis of lidar and trajectory prediction | |
| CN112258832B (en) | Method for operating vehicle information-based cluster cloud meteorological information perception and release system | |
| CN112026761A (en) | Automobile auxiliary driving method based on data sharing | |
| CN113570747B (en) | Driving safety monitoring system and method based on big data analysis | |
| CN111645682A (en) | Cruise control method and system and vehicle | |
| CN113470433A (en) | Vehicle inner wheel difference region danger early warning method and system based on V2I, vehicle and roadside equipment | |
| CN109501798B (en) | Travel control device and travel control method | |
| CN112489383A (en) | Early warning system and method for preventing red light running accident based on machine vision | |
| CN114512002B (en) | Road surface abnormity detection method and device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20220415 |