CN109272775B - Highway curve safety monitoring and early warning method, system and medium - Google Patents
Highway curve safety monitoring and early warning method, system and medium Download PDFInfo
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- CN109272775B CN109272775B CN201811228322.6A CN201811228322A CN109272775B CN 109272775 B CN109272775 B CN 109272775B CN 201811228322 A CN201811228322 A CN 201811228322A CN 109272775 B CN109272775 B CN 109272775B
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Abstract
The invention discloses a method, a system and a medium for monitoring and early warning the curve safety of an expressway, wherein the method comprises the steps of obtaining an image of a current vehicle driving road section, and judging whether a front road section is a curve or not through characteristic identification; if the front road section is a curve, acquiring a curve signal and acquiring the traffic flow state of the road section where the vehicle is located; acquiring personal information of a vehicle driver before entering a curve and vehicle speed information of a current vehicle, and judging to obtain the level of the perception risk of the driver at the moment; according to the physiological and psychological information, curve information and traffic flow states of a driver, the driving safety level of the current curve is obtained by the expressway safety level monitoring model based on the SVM neural network, and corresponding operation is carried out according to different levels. The invention fully ensures the driving safety of the vehicle.
Description
Technical Field
The invention relates to the field of road safety monitoring, in particular to a method, a system and a medium for monitoring and early warning the safety of a curve of an expressway.
Background
The rapid development of the expressway drives the linkage development of regional economy, improves the living standard of people and promotes the development of society. However, the highway brings convenience and rapidness to people, and meanwhile, the traffic safety problem of part of the highway is very prominent due to environmental factors such as complex terrain, more sharp curves and steep slopes, bad weather, variable road line shapes and the like, as well as factors such as unfamiliarity of drivers with road sections and long driving danger sensing time of drivers. And accidents occurring at the curve have a large proportion, so that the driver needs to be warned of danger in advance before entering the curve.
At present, the common curve early warning modes mainly comprise side-turning and side-slipping early warning, vehicle speed monitoring early warning, early warning vehicle-meeting early warning and curve anti-collision early warning, corresponding curve early warning control systems, equipment for detecting road information or facilities such as a navigation system are developed aiming at different types of vehicles, and due to the fact that the device system or the facilities are difficult to install or the cost is low, the device system or the facilities cannot be effectively popularized.
Disclosure of Invention
In order to overcome the defects and shortcomings in curve running of vehicles on the highway in the prior art, the invention aims to provide a curve safety monitoring and early warning method for the highway, which can scientifically and reasonably warn vehicles at the risk of insufficient perception on the curve of the highway in advance so as to prevent the vehicles from generating traffic accidents.
The second purpose is to provide a highway bend safety monitoring and early warning system;
a third object is to provide a storage medium.
The first purpose of the invention is realized by adopting the following technical scheme:
a highway bend safety monitoring and early warning method comprises the following steps:
acquiring an image of a current vehicle driving road section, and judging whether a front road section is a curve or not through feature identification;
if the front road section is a curve, acquiring the curve information;
acquiring the traffic flow state of a road section where a vehicle is located;
acquiring personal information of a vehicle driver before entering a curve and vehicle speed information of a current vehicle, and judging to obtain the level of the perception risk of the driver at the moment;
according to the physiological and psychological information, curve information and traffic flow states of a driver, the driving safety level of the current curve is obtained by the expressway safety level monitoring model based on the SVM neural network, and corresponding operation is carried out according to different levels.
The corresponding operation is carried out according to different grades, and specifically comprises the following steps:
if the driving safety level of the current curve is low risk, the system does not send out early warning;
if the driving safety level of the current curve is medium risk and the perception risk of the driver is medium level or high level, the driving safety level is matched with the risk perception level at the moment, and early warning is not performed;
if the driving safety level of the current curve is high risk, and the perception risk of the driver is high level at the moment, the driving safety level is matched with the risk perception level at the moment, early warning is not carried out, if the perception risk of the driver is low level or medium level at the moment, early warning is carried out, and the driving safety level is sent to surrounding vehicles through wireless equipment to be reminded.
The personal information includes heart rate, heart rate variability, blood pressure information, gender, age, driving mileage, and familiarity with the road segment.
The traffic flow states include heavy congestion, moderate congestion, light congestion, unobstructed and free flow.
The curve information comprises curve radius, longitudinal section gradient, curve length and speed limit information.
The monitoring model of the safety level of the expressway based on the SVM neural network is obtained by utilizing the SVM neural network unit for training in advance according to personal information, traffic flow state information and curve information of different drivers.
The second purpose of the invention is realized by adopting the following technical scheme:
a highway bend safety monitoring early warning system includes:
the curve identification module comprises an image acquisition unit, an image processing unit and an image identification unit and is used for acquiring an image of a current vehicle driving road section and judging whether the front road section is a curve or not through characteristic identification;
the curve information acquisition module comprises a GPS signal receiver, a GIS map and a curve information storage unit and is used for acquiring the position of a vehicle according to the GPS signal receiver and matching the position of the vehicle with the GIS map to acquire the curve information of the vehicle;
the vehicle networking control module is used for acquiring the traffic flow state of the road section where the vehicle is located and the direction, distance and speed information of other vehicles on the road section;
the intelligent bracelet comprises an information collector and a wireless signal transmitter and is used for collecting personal information of a driver and sending the personal information to the central processor;
the central processing unit is used for monitoring the safety level of the curve according to the personal information of the driver, the curve information and the traffic flow state information, matching the perception risk level of the driver with the safety level of the curve and judging whether to give an early warning or not;
and the mobile terminal is used for receiving the early warning information of the central processing unit.
The central processing unit comprises
The system comprises an information input module, a traffic flow state identification module, an information collection module, an information storage module, an information processing module and an information transmitting module;
the information input module is used for inputting personal information of a driver;
the traffic flow state identification module is used for identifying the current traffic flow state by using the information collected by the Internet of vehicles control module; the information collection module is used for collecting the physiological and psychological information of the driver transmitted by the intelligent bracelet, the curve information transmitted by the curve acquisition module, the vehicle speed information transmitted by the internet of vehicles control module and the traffic flow state information transmitted by the traffic flow state identification module;
the information processing module comprises an SVM neural network unit and is used for constructing an expressway curve safety level monitoring model based on an SVM neural network, obtaining the driving safety level of a driver at the current curve by using the model, and judging whether the driver is in a safe driving state or not by combining with the risk perception level information of the driver, so that early warning is carried out on the driver in advance;
the information storage module is used for storing the collected information and the vehicle track information of the past driver;
the information transmitting module is used for transmitting the processing information to the mobile terminal.
The image acquisition unit adopts a CMOS camera sensor.
The third purpose of the invention is realized by adopting the following technical scheme:
a storage medium stores a program, and when the program is executed by a processor, the method realizes a highway curve safety monitoring and early warning method.
The invention has the beneficial effects that:
(1) according to the method, the safety level of the curve is monitored according to the collected personal information of the driver, the curve information and the traffic flow state information, the driving safety level of the curve is divided into low risk, middle risk and high risk, and the driving safety level of the curve is quantitatively known.
(2) The method defines the current perception risk level of the driver at the curve according to the collected physiological and psychological information and the vehicle speed information of the driver, quantifies the risk perception level of the driver according to objective indexes, and determines whether to perform early warning or not according to whether the safety level of the curve is matched with the perception risk level of the driver or not on the basis, thereby having great reference significance on the early warning method.
(3) The system is relatively simple in structure, utilizes the commonly used smart bracelet and smart phone, uses fewer devices, can save the installation cost to a greater extent, is high in operability and practicability, and can be popularized to the highway curve early warning quickly.
Drawings
FIG. 1 is a schematic structural view of the present invention;
fig. 2 is a flow chart of the method of the present invention.
Detailed Description
The present invention will be described in further detail with reference to examples and drawings, but the present invention is not limited to these examples.
Examples
As shown in fig. 1 and 2, a method for monitoring the safety of a curve of an expressway includes the following steps:
s1, acquiring an image of a current vehicle driving road section, and judging whether the front road section is a curve or not through feature recognition;
s2, if the road section ahead is a curve, the position of the current vehicle is obtained through GPS positioning, and then the current vehicle is matched with a GIS map to obtain the curve information, wherein the curve information comprises curve radius, longitudinal section gradient, curve length, speed limit information and the like.
S3, acquiring the traffic flow state of the road section where the vehicle is located;
the traffic flow states comprise five state levels of heavy congestion, medium congestion, light congestion, unobstructed flow and free flow.
S4, collecting personal information of a vehicle driver before entering a curve and vehicle speed information of a current vehicle, and judging to obtain the level of the driver perception risk at the moment;
the personal information of the driver comprises information such as character, age, driving age, familiarity of each road section, driving mileage and the like, wherein the familiarity of each road section is divided into five grades of very unfamiliar, common, familiar and very familiar, and is quantified through the driving track record of the vehicle; the physiological and psychological information of the driver comprises heart rate, heart rate variability and blood pressure information; the vehicle speed information comprises the speed of the vehicle, the acceleration and the absolute value information of the speed difference of the road section. The level of the driver perception risk comprises a low level, a medium level and a high level, and is mainly characterized and determined by information such as heart rate change, vehicle speed change and the like.
According to the physiological and psychological information, curve information and traffic flow states of a driver, the driving safety level of the current curve is obtained by the expressway safety level monitoring model based on the SVM neural network, and corresponding operation is carried out according to different levels.
The model for monitoring the safety level of the curve of the expressway based on the SVM neural network is obtained by training in advance by utilizing the SVM neural network unit according to personal information, traffic flow state information and curve information of different drivers in a large number of collected test sections.
If the driving safety level of the current curve is low risk, the system does not send out early warning;
if the driving safety level of the current curve is medium risk and the perception risk of the driver is medium level or high level, the driving safety level is matched with the risk perception level at the moment, and early warning is not performed;
if the driving safety level of the current curve is high risk, and the perception risk of the driver is high level at the moment, the driving safety level is matched with the risk perception level at the moment, early warning is not carried out, if the perception risk of the driver is low level or medium level at the moment, early warning is carried out, and the driving safety level is sent to surrounding vehicles through wireless equipment to be reminded.
A highway curve safety monitoring system comprises a curve identification module, a curve information acquisition module, an Internet of vehicles control module, an intelligent bracelet, wireless communication equipment, a central processing unit and an intelligent mobile phone;
bend identification module, bend information acquisition module, wireless communication equipment, car networking information control module all are connected through the cable with central processing unit, bend identification module is connected through the cable with bend information acquisition module, intelligence bracelet passes through radio signal transmitter and central processing unit wireless connection, the smart mobile phone passes through bluetooth and central processing unit wireless connection.
The curve identification module comprises an image acquisition unit, an image processing unit and an image identification unit;
the image acquisition unit is used for acquiring an image of a current vehicle driving road section, the image processing unit is used for processing the image of the driving road section into a digital signal and inputting the digital signal into the image recognition unit, and the image recognition unit is used for recognizing whether the road section is a curve or not. The image acquisition unit adopts a CMOS camera sensor, and the CMOS camera is provided with a vehicle head.
The curve information acquisition module comprises a GPS signal receiver, a GIS map and a curve information storage unit, wherein the curve information storage unit acquires an output signal of the image recognition unit, and when the curve is judged, the current vehicle position is acquired through the GPS signal receiver and the curve information where the vehicle is located is acquired through accurate matching with the GIS map.
The vehicle networking control module is used for connecting various sensors of vehicles running on a road section, and communicating devices for information interaction with various vehicles can acquire the direction, distance and speed information of external vehicles.
The intelligent bracelet is worn on the body of a driver and comprises an information collector and a wireless signal transmitter, wherein the information collector is used for collecting physiological and psychological information of the driver and transmitting the physiological and psychological information to the central processing unit through the wireless signal generator, and other devices with the same function can also be selected for use by the intelligent bracelet.
The wireless communication equipment comprises an information receiving terminal and an information transmitting terminal, and is used for receiving information transmitted by the intelligent watch and transmitting the processing information of the central processing unit to the wireless communication equipment of the surrounding vehicle;
the smart phone is used for receiving the processing information of the central processing unit and giving an early warning to the driver through the forms of video display, mobile phone vibration, audio broadcasting and the like. This embodiment uses the smart mobile phone to realize this function, and the user also can select for use other mobile terminal.
And the central processing unit is used for monitoring the safety level of the curve according to the collected personal information of the driver, the curve information and the traffic flow state information, judging the current perception risk level of the driver at the curve according to the collected physiological and psychological information of the driver and the collected vehicle speed information, and finally determining whether to give an early warning according to whether the safety level of the curve is matched with the perception risk level of the driver.
The central processing unit comprises an information input module, a traffic flow state identification module, an information collection module, an information storage module, an information processing module and an information transmitting module. The information input module is used for inputting personal information of a driver; the traffic flow state identification module is used for identifying the current traffic flow state by using the information collected by the Internet of vehicles control module; the information collection module is used for collecting the physiological and psychological information of the driver transmitted by the intelligent bracelet, the curve information transmitted by the curve acquisition module, the vehicle speed information transmitted by the vehicle networking control module and the traffic flow state information transmitted by the traffic flow state identification module. The information processing module mainly comprises an SVM neural network unit and is used for constructing an expressway curve safety level monitoring model based on an SVM neural network, obtaining the driving safety level of a driver at the current curve by using the model, and judging whether the driver is in a safe driving state or not by combining with the risk perception level information of the driver, so that early warning is carried out on the driver in advance; the information storage module is used for storing the vehicle track information of the past driver besides the information collected by the information collection module; the information transmitting module is used for transmitting the processing information to the wireless communication equipment and the smart phone.
In the embodiment, the TMS320C6748 is adopted as the central processor, and the zigbee wireless communication technology is adopted as the wireless communication equipment.
Bend identification module, bend information acquisition module, car networking control module, wireless communication equipment, central processing unit all install at the car locomotive, the intelligence bracelet is worn in driver's left wrist, smart mobile phone erects at the locomotive.
A storage medium storing a program which, when executed by a processor, is a method for highway curve safety monitoring based on risk awareness.
According to the method, the safety grade of the curve is monitored according to the collected personal information of the driver, the curve information and the traffic flow state information, the current perception risk level of the driver at the curve is judged according to the collected physiological and psychological information of the driver and the vehicle speed information, and whether early warning is needed or not is determined according to whether the safety grade of the curve is matched with the perception risk level of the driver, so that the safety monitoring and early warning at the curve of the expressway are completed, and the driving safety of vehicles is guaranteed.
The above embodiments are preferred embodiments of the present invention, but the present invention is not limited to the above embodiments, and any other changes, modifications, substitutions, combinations, and simplifications which do not depart from the spirit and principle of the present invention should be construed as equivalents thereof, and all such changes, modifications, substitutions, combinations, and simplifications are intended to be included in the scope of the present invention.
Claims (9)
1. A safety monitoring and early warning method for a curve of an expressway is characterized by comprising the following steps:
acquiring an image of a current vehicle driving road section, and judging whether a front road section is a curve or not through feature identification;
if the front road section is a curve, acquiring the curve information;
acquiring the traffic flow state of a road section where a vehicle is located;
acquiring personal information of a vehicle driver before entering a curve and vehicle speed information of a current vehicle, and judging to obtain the level of the perception risk of the driver at the moment;
the levels of the driver perception risk comprise three levels of low, medium and high;
according to the physiological and psychological information, curve information and traffic flow states of a driver, obtaining the driving safety level of the current curve by an expressway safety level monitoring model based on an SVM neural network, and performing corresponding operation according to different levels;
the corresponding operation is carried out according to different grades, and specifically comprises the following steps:
if the driving safety level of the current curve is low risk, the system does not send out early warning;
if the driving safety level of the current curve is medium risk and the perception risk of the driver is medium level or high level, the driving safety level is matched with the risk perception level at the moment, and early warning is not performed;
if the driving safety level of the current curve is high risk, and the perception risk of the driver is high level at the moment, the driving safety level is matched with the risk perception level at the moment, early warning is not carried out, if the perception risk of the driver is low level or medium level at the moment, early warning is carried out, and the driving safety level is sent to surrounding vehicles through wireless equipment to be reminded.
2. The highway curve safety monitoring and early warning method according to claim 1, wherein the personal information comprises heart rate, heart rate variability, blood pressure information, gender, age, driving age, annual mileage and familiarity with the road section.
3. A highway curve safety monitoring and early warning method as recited in claim 1, wherein the traffic flow states include heavy congestion, medium congestion, light congestion, clear and free flow.
4. The highway curve safety monitoring and early warning method according to claim 1, wherein the curve information comprises curve radius, longitudinal section gradient, curve length and speed limit information.
5. The highway curve safety monitoring and early warning method according to claim 1, wherein the highway safety level monitoring model based on the SVM neural network is obtained by training with an SVM neural network unit in advance according to personal information, traffic flow state information and curve information of different drivers.
6. A system for realizing the highway curve safety monitoring and early warning method as claimed in any one of claims 1 to 5, which is characterized by comprising
The curve identification module comprises an image acquisition unit, an image processing unit and an image identification unit and is used for acquiring an image of a current vehicle driving road section and judging whether the front road section is a curve or not through characteristic identification;
the curve information acquisition module comprises a GPS signal receiver, a GIS map and a curve information storage unit and is used for acquiring the position of a vehicle according to the GPS signal receiver and matching the position of the vehicle with the GIS map to acquire the curve information of the vehicle;
the vehicle networking control module is used for acquiring the traffic flow state of the road section where the vehicle is located and the direction, distance and speed information of other vehicles on the road section;
the intelligent bracelet comprises an information collector and a wireless signal transmitter and is used for collecting personal information of a driver and sending the personal information to the central processor;
the central processing unit is used for monitoring the safety level of the curve according to the personal information of the driver, the curve information and the traffic flow state information, matching the perception risk level of the driver with the safety level of the curve and judging whether to give an early warning or not;
and the mobile terminal is used for receiving the early warning information of the central processing unit.
7. The system of claim 6, wherein said central processor comprises
The system comprises an information input module, a traffic flow state identification module, an information collection module, an information storage module, an information processing module and an information transmitting module;
the information input module is used for inputting personal information of a driver;
the traffic flow state identification module is used for identifying the current traffic flow state by using the information collected by the Internet of vehicles control module; the information collection module is used for collecting the physiological and psychological information of the driver transmitted by the intelligent bracelet, the curve information transmitted by the curve acquisition module, the vehicle speed information transmitted by the internet of vehicles control module and the traffic flow state information transmitted by the traffic flow state identification module;
the information processing module comprises an SVM neural network unit and is used for constructing an expressway curve safety level monitoring model based on an SVM neural network, obtaining the driving safety level of a driver at the current curve by using the model, and judging whether the driver is in a safe driving state or not by combining risk perception level information of the driver, so that early warning is carried out on the driver in advance;
the information storage module is used for storing the collected information and the vehicle track information of the past driver;
the information transmitting module is used for transmitting the information stored by the information storage module to the mobile terminal.
8. The system of claim 6, wherein the image acquisition unit employs a CMOS camera sensor.
9. A storage medium storing a program, characterized in that: when being executed by a processor, the program realizes the highway curve safety monitoring and early warning method in any one of claims 1-5.
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| CN111415533B (en) * | 2020-04-22 | 2021-09-21 | 湖北民族大学 | Bend safety early warning monitoring method, device and system |
| CN112365716B (en) * | 2021-01-13 | 2021-03-23 | 西南交通大学 | Urban elevated expressway dynamic security evaluation method based on GPS data |
| CN113160593A (en) * | 2021-01-18 | 2021-07-23 | 重庆交通大学 | Mountain road driving safety early warning method based on edge cloud cooperation |
| CN113449790A (en) * | 2021-06-25 | 2021-09-28 | 贵州省都匀公路管理局 | Mountain trunk highway high-risk road section identification method based on SVM |
| CN113352989B (en) * | 2021-06-30 | 2023-12-22 | 深圳市路卓科技有限公司 | Intelligent driving safety auxiliary method, product, equipment and medium |
| CN114435429A (en) * | 2022-03-10 | 2022-05-06 | 湖南铁路科技职业技术学院 | Safety management and control system for electric locomotive driving |
| CN115331423A (en) * | 2022-06-23 | 2022-11-11 | 华南理工大学 | Roadside equipment for reducing safety risk of entrance and exit tunnel portal and guidance passing system |
| CN115320626B (en) * | 2022-10-11 | 2022-12-30 | 四川省公路规划勘察设计研究院有限公司 | Danger perception capability prediction method and device based on human-vehicle state and electronic equipment |
| CN117962747A (en) * | 2024-03-29 | 2024-05-03 | 长城汽车股份有限公司 | Early warning method and device, vehicle-mounted terminal and vehicle |
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| CN102298693B (en) * | 2011-05-18 | 2013-04-24 | 浙江大学 | Expressway bend detection method based on computer vision |
| CN105225509A (en) * | 2015-10-28 | 2016-01-06 | 努比亚技术有限公司 | A kind of road vehicle intelligent early-warning method, device and mobile terminal |
| CN106491144B (en) * | 2016-09-22 | 2019-07-05 | 昆明理工大学 | A kind of test and evaluation method of the latent risk perceptions ability of driver |
| JP2018077612A (en) * | 2016-11-08 | 2018-05-17 | 三菱自動車工業株式会社 | Driving assist apparatus |
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