CN106781570B - A kind of identification of highway danger road conditions and alarm method suitable for vehicle-mounted short distance communication network - Google Patents
A kind of identification of highway danger road conditions and alarm method suitable for vehicle-mounted short distance communication network Download PDFInfo
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Abstract
The invention discloses a kind of highway danger road conditions identification suitable for vehicle-mounted short distance communication network and alarm methods, object is shed in selection, the recognition element of fault car and crowded three factors of road as Dangerous Area, recognition methods is the bayesian network structure and algorithm of section risk identification established first, then the bayesian network structure and algorithm for determining above three recognition element are established, the heartbeat message comprising vehicle location of vehicle-mounted node periodic broadcasting in coverage area is received by the trackside node of vehicle-mounted short distance communication network to obtain the position of each vehicle, and then obtain the variation of the speed and driving direction of vehicle, then the posterior probability of above three recognition element is obtained by Bayesian network, pass through Bayesian network again, obtain the posterior probability of section risk, when probability is higher than the threshold value of default, Vehicle broadcast alarms message into coverage area.It realizes effective identification highway danger road conditions and issues alarm.
Description
Technical field
The present invention relates to a kind of highway danger road conditions identification suitable for vehicle-mounted short distance communication network and alarm sides
Method belongs to command, control, communications, and information field, especially vehicle-carrying communication technical field.
Background technique
For highway since speed is very fast, all kinds of burst phenomenons easily cause major traffic accidents.Highway
Dangerous road conditions can be identified in time and be issued in advance to vehicle and alert, and can substantially reduce the generation of major traffic accidents, but high
The identification of fast highway needs a kind of effective mode to realize due to complicated technology realization.
Vehicle-mounted short haul connection (Vehicle to X:V2X) network be by wireless communication, the short distances such as GPS/GIS, sensing
Car (CAN-Controller Area Network), bus or train route (Vehicle-2-RSU), workshop from communication technology realization
(Vehicle-2-Vehicle), between vehicle outer (vehicle-2-Infrastructure), people's vehicle (Vehicle-2-Person)
Communication.
For vehicle-mounted node in V2X equipped with equipment such as GPS or Beidous, vehicle-mounted node is regular at set time intervals
The information (referred to as heartbeat message) such as the geographical location to surrounding broadcast this node, while also receiving the heartbeat letter that surroundings nodes are sent
Breath, so that the distance of around vehicle-mounted node with this vehicle is obtained, to calculate the relative distance information with this vehicle.
Trackside node is one of V2X network important equipment, and functions of the equipments are identical with vehicle-mounted node, but due to its day
Line deployed position is high, powers unaffected, therefore its coverage area is big, can receive the heartbeat of larger range of vehicle-mounted node broadcasts
Information.
Summary of the invention
The invention discloses a kind of identifications of highway danger road conditions and alarm suitable for vehicle-mounted short distance communication network
Method, main realize is to select to shed the recognition element of object, fault car and crowded three factors of road as Dangerous Area.Know
Other method is the bayesian network structure and algorithm of section risk identification established first, then establishes and determines to shed object, failure
The bayesian network structure and algorithm of vehicle and crowded three recognition elements of road, recognition methods is by vehicle-mounted short haul connection
It is each to obtain that the trackside node of network receives the heartbeat message comprising vehicle location of vehicle-mounted node periodic broadcasting in coverage area
The position of vehicle, so obtain vehicle speed and driving direction variation, then obtained by Bayesian network shed object,
Then the posterior probability of fault car and crowded three recognition elements of road obtains section risk by Bayesian network again
Posterior probability, when probability is higher than the threshold value of default, vehicle broadcast alarms message into coverage area.
Specifically, the invention discloses a kind of highway danger road conditions identifications suitable for vehicle-mounted short distance communication network
And alarm method, which comprises
It selects to shed the recognition element of object, fault car and crowded three factors of road as Dangerous Area, by vehicle-mounted
The trackside node of short distance communication network receives the heartbeat letter comprising vehicle location of vehicle-mounted node periodic broadcasting in coverage area
Cease to obtain the position of each vehicle, and then obtain the variation of the speed and driving direction of vehicle, then by Bayesian network come
The posterior probability for shedding object, fault car and crowded three recognition elements of road is obtained, is then obtained again by Bayesian network
The posterior probability of section risk, when there is dangerous phenomenon, the vehicle into coverage area is alerted.
Preferably, after trackside node calculates the threshold value that the probability of section risk is more than default, by vehicle-mounted short
Range communication network, the vehicle broadcast alarms information into coverage area.
Preferably, it is identified using Bayesian network, method is the Bayesian network knot of established section risk identification
Structure and algorithm, then establish determine shed object, fault car and crowded three recognition elements of road bayesian network structure and
Algorithm.
Preferably, trackside node is the vehicle region detour obtained by V2X network and vehicle to object recognition element is shed
Two information walk or drive slowly to calculate the probability of generation.
Preferably, trackside node is that the vehicle obtained by V2X network stops and vehicle is slow to fault car recognition element
Two information of row calculate the probability of generation.
Preferably, trackside node is that the vehicle obtained by V2X network stops and vehicle is slow to the crowded recognition element of road
Two information of row calculate the probability of generation.
The embodiment of the present invention is deployed in the received vehicle-mounted node broadcasts of the trackside node along highway using V2X network
Heartbeat message, obtain trackside coverage within the scope of vehicle position, and then obtain vehicle speed and vehicle line variation,
By the change in location of vehicle, cooperate electronic map, the risk of highway is identified, when risk is more than threshold value
When, to vehicle broadcast alarms information, to improve the travel safety of highway.
Detailed description of the invention
Fig. 1 is the main processing steps figure of embodiment according to the present invention.
Fig. 2 is the Bayesian Network Topology Structures figure of embodiment according to the present invention.
Specific embodiment
The present embodiment realizes a kind of highway danger road conditions identification suitable for vehicle-mounted short distance communication network and accuses
Alarm method, it is therefore an objective to it is big by the trackside coverage range of vehicle-mounted short haul connection, there is the advantage of link connection between each other,
Receive the message comprising geographical position coordinates that the V2X mobile unit periodic broadcasting within the scope of trackside coverage in vehicle is sent
(hereinafter referred to as heartbeat message), using Bayesian network model, the comprehensive road conditions satisfied the need in side gusset coverage area carry out dangerous
Situation differentiates, if road conditions danger probability is more than the threshold value of default, warning information is passed through this trackside node and neighbour
Close trackside node is broadcasted, and driver is reminded to take appropriate measures early, thus the traffic of effectively Improving Expressway
Level of security.
For highway since speed is very fast, several factors make certain section become Dangerous Area, and three kinds of the present embodiment selection is right
Highway influences big factor, is to shed object or temporary obstructions, fault car or traffic accident vehicle, road are gathered around respectively
It squeezes, comprehensive judgement is carried out by Bayesian network.
Highway sheds object due to its Unpredictability, can bring great danger to the vehicle run at high speed.In addition
Provisional barrier or paroxysmal road abnormal conditions, also belong to this kind of risk factor.Vehicle encounters this kind of situation, and one
As be regular detour.The heartbeat message that trackside node is broadcasted by V2X mobile unit in the vehicle in reception coverage area,
This regular detour can be identified, to judge that road has the probability for shedding object or road exception.
Fault car or traffic accident vehicle are that another to the vehicle run at high speed can bring great risk factor,
This factor trackside node can accurately obtain the location information of vehicle by receiving the heartbeat message of fault car broadcast, still
The case where needing the position according to fault car and occupying road is come hazard recognition severity.
Road congested conditions trackside node can be by receiving speed and the vehicle density progress by the vehicle in the section
Identification.
The degree of danger in section is related to several factors, and each factor has biggish uncertainty, Bayesian network mould again
Type can be used for expressing and analyzing uncertain and probabilistic event, can be from incomplete, inaccurate or uncertain knowledge
Or reasoning is made in information, it is effective theoretical model in uncertain knowledge and reasoning field.The danger in section in highway
Property is an extremely complex system problem.Therefore, there is preferable feasibility using the method for probability theory.
In Bayesian network model, unavailable stochastic variable is inferred by obtainable stochastic variable, is carried out general
Rate reasoning.Therefore, solving the differentiation of section risk using Bayesian network can state are as follows: in existing road risk bring
In the case where the empirical data that vehicle-state changes, trackside node is under conditions of obtaining real-time vehicle state, road hazard shape
Condition is in the probability of various degree (dangerous, safety).
Bayesian network binary group BN=<G, Θ>statement probability uncertain inference network, G be node with it is discrete
Stochastic variable { X1,X2,...,XnOne-to-one directed acyclic graph, directed edge then indicates to be determined between variable by conditional probability
Dependence；Θ is the set for describing BN local condition probability distribution parametersIndicate nodes XiIn his father
Node collection YiA certain valued combinations state yiUnder conditional probability distribution.BN can be quantified between chance event by conditional probability
Causality is accordingly used in the identification of fatigue driving.
The implementation of the present embodiment includes three parts, and process is as shown in Figure 1.
First part, the bayesian network structure and algorithm for establishing the identification of section risk.
1.1, Bayesian network is constructed with directed acyclic graph, element of the node therein for section dangerous discernment is corresponding
Variable, directed edge represent the condition dependence of variable.
1.2, the Bayesian Network Topology Structures of the present embodiment are as shown in Figure 2.Road hazard identification in the present embodiment
Using three acceleration and deceleration characteristic, deviation characteristic, direction of traffic characteristic characteristics, hereinafter referred to as three characteristics of fatigue identification.Father's section
Point (Rd) indicates section risk, and object factor is shed in child node (Ao) expression, and child node (Fv) indicates fault car factor, sub- section
Point (Rc) indicates the crowded factor of road.Father node (Rd) and child node (Ao), child node (Fv), the line between child node (Rc)
Indicate section risk with shed the conditional probability p (Ao | Rd) of object factor, fault car factor and the crowded factor of road, p (Fv |
) and p (Rc | Rd) Rd.
1.3, the Bayes net algorithm that section risk differentiates.The size of section risk using Probability p (Rd | Ao,
Fv, Rc) it indicates, according to Bayesian formula,
In the present embodiment, the item of line between father node and child node in Bayesian network is determined according to expertise first
After part probability, then obtain 3 nodes prior probability, so that it may obtain section risk posterior probability p (Rd | Ao, Fv,
Rc)。
For simplify operation, each state value be with and without, respectively correspond 0 and 1, then:
Second part establishes the bayesian network structure and algorithm for determining each recognition element.
2.1, bayesian network structure and algorithm that object (Ao) is shed in identification are established.In this fact Example, the identification of object is shed
It is to be identified by vehicle region detour (Aa) and vehicle jogging (Va) two vehicle running characteristics.Shed probability existing for object
It is indicated with p (Ao | Aa, Va), according to Bayesian formula,
Wherein, p (Aa, Va)=p (Aa | Va) p (Va), p (Aa, Va | Ao)=p (Aa | Va, Ao) p (Va | Ao), it is above-mentioned general
Rate can be obtained from the historical data that trackside node is collected.
2.2, the bayesian network structure and algorithm of identification fault car (Fv) are established.In this fact Example, the knowledge of disabled vehicle
It is not to be identified by vehicle stopping (Vs) and vehicle jogging (Va) two vehicle running characteristics.Probability existing for fault car
P (Fv | Vs, Va) indicates, according to Bayesian formula,
Wherein, p (Vs, Va)=p (Vs | Va) p (Va), p (Vs, Va | Fv)=p (Vs | Va, Fv) p (Va | Fv), it is above-mentioned general
Rate can be obtained from the historical data that trackside node is collected.
2.3, the bayesian network structure and algorithm of identification road crowded (Rc) are established.In this fact Example, road is crowded
It is identified by vehicle stopping (Vs) and vehicle jogging (Va) two vehicle running characteristics is identified.It is general existing for fault car
Rate p (Rc | Vs, Va) indicates, according to Bayesian formula,
Wherein, p (Vs, Va)=p (Vs | Va) p (Va), p (Vs, Va | Rc)=p (Vs | Va, Rc) p (Va | Rc), it is above-mentioned general
Rate can be obtained from the historical data that trackside node is collected.
Part III, express highway section degree of danger sail knowledge and alarm.
3.1, the trackside node of highway on the way is deployed in by receiving vehicle on-board V2X device broadcasts in coverage area
The message comprising vehicle geographical location information sent；
3.2, trackside node is according to the location information of the vehicle received, and the form change in location obtained, so as to
Judged with detour, jogging and the probability of stopping to vehicle.Judgement is carried out by the way of counting in set period of time:
Wherein, CAaIt is the vehicle fleet size for occurring to deflect to the same direction in one place, CVsIt is to occur to stop phenomenon
Vehicle fleet size, CVaIt is that the slow vehicle fleet size of speed occurs, C is total vehicle fleet size of identification section.
3.3, when V2X trackside node device initializes, the experience number of setting can be passed through according to the electronic map in this section
According to then by formula (1) to formula (6), the danger classes in this available section；
3.4, after the risk probability in section is more than threshold value, warning information is passed through into vehicle of the V2X network into coverage area
It is broadcasted, it, can be using sound and display at that time to the danger of Dangerous Area after vehicle-mounted V2X equipment receives warning information
Factor and generation position are reminded；
3.5, by prior probability p (Rd) of the posterior probability p (Rd | Ao, Fv, Rc) as next calculating cycle at this time,
Make prediction that can more meet current character of road.
The above is presently preferred embodiments of the present invention and its technical principle used, for those skilled in the art
For, without departing from the spirit and scope of the present invention, any equivalent change based on the basis of technical solution of the present invention
Change, simple replacement etc. is obvious changes, all fall within the protection scope of the present invention.
Claims (6)
1. a kind of identification of highway danger road conditions and alarm method suitable for vehicle-mounted short distance communication network, the method packet
It includes:
It selects to shed the recognition element of object, fault car and crowded three factors of road as Dangerous Area, passes through vehicle-mounted short distance
The heartbeat message comprising vehicle location that trackside node from communication network receives vehicle-mounted node periodic broadcasting in coverage area comes
The position of each vehicle is obtained, and then obtains the variation of the speed and driving direction of vehicle, is then obtained by Bayesian network
The posterior probability of object, fault car and crowded three recognition elements of road is shed, section is then obtained by Bayesian network again
The posterior probability of risk, when there is dangerous phenomenon, the vehicle into coverage area is alerted, and is specifically included:
First part, the bayesian network structure and algorithm for establishing the identification of section risk
1.1, Bayesian network is constructed with directed acyclic graph, node therein is used for the corresponding change of element of section dangerous discernment
Amount, directed edge represent the condition dependence of variable；
1.2, road hazard identification uses acceleration and deceleration characteristic, three deviation characteristic, direction of traffic characteristic characteristics, father node
(Rd) section risk is indicated, object factor is shed in child node (Ao) expression, and child node (Fv) indicates fault car factor, child node
(Rc) the crowded factor of road is indicated；Father node (Rd) and child node (Ao), child node (Fv), the line table between child node (Rc)
Show section risk with shed the conditional probability p (Ao | Rd) of object factor, fault car factor and the crowded factor of road, p (Fv |
) and p (Rc | Rd) Rd；
1.3, the Bayes net algorithm that section risk differentiates；
The size of section risk indicated using Probability p (Rd | Ao, Fv, Rc), according to Bayesian formula,
It is determined in Bayesian network between father node and child node after the conditional probability of line according to expertise, is then obtained first
3 nodes prior probability, obtain the posterior probability p (Rd | Ao, Fv, Rc) of section risk；
For simplify operation, each state value be with and without, respectively correspond 0 and 1, then:
Second part establishes the bayesian network structure and algorithm for determining each recognition element
2.1, bayesian network structure and algorithm that object (Ao) is shed in identification are established；
Vehicle region (Aa) and vehicle (Va) two vehicle running characteristics of walking or drive slowly that detour that are identified by for shedding object are identified；
Shedding probability p existing for object (Ao | Aa, Va) indicates, according to Bayesian formula,
Wherein, p (Aa, Va)=p (Aa | Va) p (Va), p (Aa, Va | Ao)=p (Aa | Va, Ao) p (Va | Ao), above-mentioned probability from
It is obtained in the historical data that trackside node is collected；
2.2, the bayesian network structure and algorithm of identification fault car (Fv) are established；
The vehicle that is identified by of disabled vehicle stops (Vs) and vehicle (Va) two vehicle running characteristics of walking or drive slowly and is identified；Failure
Probability p existing for vehicle (Fv | Vs, Va) indicates, according to Bayesian formula,
Wherein, p (Vs, Va)=p (Vs | Va) p (Va), p (Vs, Va | Fv)=p (Vs | Va, Fv) p (Va | Fv), above-mentioned probability from
It is obtained in the historical data that trackside node is collected；
2.3, the bayesian network structure and algorithm of identification road crowded (Rc) are established；
Vehicle stops (Vs) and vehicle jogging (Va) two vehicle running characteristics are identified for road crowded being identified by；Therefore
Hindering Probability p (Rc | Vs, Va) existing for vehicle indicates, according to Bayesian formula,
Wherein, p (Vs, Va)=p (Vs | Va) p (Va), p (Vs, Va | Rc)=p (Vs | Va, Rc) p (Va | Rc), above-mentioned probability from
It is obtained in the historical data that trackside node is collected；
Part III, express highway section degree of danger sail knowledge and alarm
3.1, it is deployed in the trackside node of highway on the way and is sent by receiving vehicle on-board V2X device broadcasts in coverage area
The message comprising vehicle geographical location information；
3.2, trackside node is according to the location information of the vehicle received, and the form change in location obtained, thus to vehicle
Detour, jogging and the probability of stopping judged；Judgement is carried out by the way of counting in set period of time:
Wherein, CAaIt is the vehicle fleet size for occurring to deflect to the same direction in one place, CVsIt is the vehicle for occurring to stop phenomenon
Quantity, CVaIt is that the slow vehicle fleet size of speed occurs, C is total vehicle fleet size of identification section；
3.3, when V2X trackside node device initializes, according to the electronic map in this section, by the empirical data of setting, then
By formula (1) to formula (6), the danger classes in this section is obtained；
3.4, the risk probability in section be more than threshold value after, by warning information by vehicle of the V2X network into coverage area into
Row broadcast after vehicle-mounted V2X equipment receives warning information, can use sound and display at that time to the risk factor of Dangerous Area
And position occurs and is reminded；
3.5, by prior probability p (Rd) of the posterior probability p (Rd | Ao, Fv, Rc) as next calculating cycle at this time, make pre-
Survey can more meet current character of road.
2. a kind of identification of highway danger road conditions and announcement suitable for vehicle-mounted short distance communication network as described in claim 1
Alarm method, which is characterized in that after the probability of trackside node calculating section risk is more than the threshold value of default, by vehicle-mounted
Short distance communication network, the vehicle broadcast alarms information into coverage area.
3. a kind of identification of highway danger road conditions and announcement suitable for vehicle-mounted short distance communication network as described in claim 1
Alarm method, which is characterized in that identified using Bayesian network, method is the Bayesian network of established section risk identification
Then structure and algorithm establish the bayesian network structure for determining to shed object, fault car and crowded three recognition elements of road
And algorithm.
4. a kind of identification of highway danger road conditions and announcement suitable for vehicle-mounted short distance communication network as described in claim 1
Alarm method, which is characterized in that trackside node is the vehicle region detour obtained by V2X network and vehicle to object recognition element is shed
Two information of jogging calculate the probability of generation.
5. a kind of identification of highway danger road conditions and announcement suitable for vehicle-mounted short distance communication network as described in claim 1
Alarm method, which is characterized in that trackside node is the vehicle stopping obtained by V2X network and vehicle to fault car recognition element
Two information walk or drive slowly to calculate the probability of generation.
6. a kind of identification of highway danger road conditions and announcement suitable for vehicle-mounted short distance communication network as described in claim 1
Alarm method, which is characterized in that trackside node is the vehicle stopping obtained by V2X network and vehicle to the crowded recognition element of road
Two information walk or drive slowly to calculate the probability of generation.
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| CN108388947B (en) * | 2018-02-06 | 2021-11-02 | 大唐高鸿信息通信(义乌)有限公司 | Improved machine learning method suitable for vehicle-mounted short-distance communication network safety early warning |
| CN108847016A (en) * | 2018-07-05 | 2018-11-20 | 晁保锁 | Hot-short information interaction system based on information sharing |
| CN109886199B (en) * | 2019-02-21 | 2022-04-12 | 阿波罗智联(北京)科技有限公司 | Information processing method and device, vehicle and mobile terminal |
| CN109785633A (en) * | 2019-03-14 | 2019-05-21 | 百度在线网络技术（北京）有限公司 | Dangerous road conditions based reminding method, device, car-mounted terminal, server and medium |
| US20210067917A1 (en) | 2019-09-03 | 2021-03-04 | Pixart Imaging Inc. | Method, node for processing messages within an iot network and network packet thereof |
| CN110544380B (en) * | 2019-09-17 | 2021-04-27 | 东南大学 | Real-time lane-level safety situation assessment method for road confluence area |
| TWI737051B (en) * | 2019-11-28 | 2021-08-21 | 啓碁科技股份有限公司 | Vehicle warning method, telematics control unit and device based on vehicle (v2x) communications |
| CN113808409B (en) * | 2020-06-17 | 2023-02-10 | 华为技术有限公司 | Road safety monitoring method, system and computer equipment |
| CN111814081B (en) * | 2020-07-08 | 2021-03-09 | 北京嘀嘀无限科技发展有限公司 | High-risk intersection detection method, detection model establishing method, device, electronic equipment and readable storage medium |
| CN111833608B (en) * | 2020-07-14 | 2023-12-19 | 腾讯科技（深圳）有限公司 | Road condition information processing method and device |
| CN111970661A (en) * | 2020-07-16 | 2020-11-20 | 山东行远智能网联科技有限公司 | Method for improving communication reliability of V2X |
| CN111951573A (en) * | 2020-07-21 | 2020-11-17 | 华设设计集团股份有限公司 | Intelligent public transportation system and method based on vehicle-road cooperation technology |
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Effective date of registration: 20211223 Address after: 400040 No. 35, Jinghe Road, Huxi street, high tech Zone, Shapingba District, Chongqing Patentee after: Datang Gaohong Zhilian Technology (Chongqing) Co.,Ltd. Address before: 322002 no.968, Xuefeng West Road, Beiyuan street, Yiwu City, Zhejiang Province Patentee before: Datang Gaohong information communication (Yiwu) Co.,Ltd. |
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