CN105448108A - Overspeed discrimination method based on hidden Markov road network matching - Google Patents

Overspeed discrimination method based on hidden Markov road network matching Download PDF

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CN105448108A
CN105448108A CN201511018790.7A CN201511018790A CN105448108A CN 105448108 A CN105448108 A CN 105448108A CN 201511018790 A CN201511018790 A CN 201511018790A CN 105448108 A CN105448108 A CN 105448108A
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speed
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CN105448108B (en
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罗赞文
文巨峰
穆祥强
陈晓洁
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Shanghai Jingda Information Technology Co Ltd
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    • 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
    • G08G1/052—Detecting movement of traffic to be counted or controlled with provision for determining speed or overspeed

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Abstract

The invention discloses an overspeed discrimination method based on hidden Markov road network matching. On the basis of a conventional satellite positioning device, the method achieves the real-time monitoring of the running speed of a vehicle, can judge whether the vehicle at a road segment is overspeed or not, and reduces the cost of vehicle monitoring on the basis of maintaining high accuracy. The method can effectively reduce the positioning error, and can find the optimal matching path of the running of the vehicle more accurately. The method enables the running speed of the vehicle to be compared with the maximum running speed at the road segment according to a built national road speed limit database, obtains a result whether the running vehicle is overspeed or not, and reminds a driver that the vehicle is overspeed through a navigation device if the vehicle is overspeed.

Description

A kind of hypervelocity method of discrimination based on Hidden Markov road network
Technical field
The present invention relates to physical field, particularly relate to measuring technique, particularly a kind of hypervelocity method of discrimination based on Hidden Markov road network.
Background technology
Along with improving constantly of living standard, automobile quantity also continues to increase.The problem brought is that car accident quantity also constantly rises.It is the most also be the maximum traffic violation of harmfulness that overspeed of vehicle travels, the particularly serious traffic hazard of more than 90% with exceed the speed limit relevant.Because drive over the speed limit, braking distance is extended greatly, the process crisis time shortens greatly, is often caught unprepared when driver faces a danger.Meanwhile, drive over the speed limit and also easily make people tired, because chance of overtaking other vehicles increases, vehicular gap shortens, and the energy ezpenditure of driver's psychology and health increases greatly.To drive over the speed limit the working strength and operating load that also increase vehicle, exacerbate component wear, tyre temperature particularly can be made to raise and cause accident etc. of blowing out, so be badly in need of vehicle to travel hypervelocity decision technology.For this reason, China, according to different sections of highway, different time sections, sets different road driving speed limits.
Current vehicle major part has installed the Big Dipper/gps satellite locating device, for realizing software approach, this judges whether vehicle exceeds the speed limit and bring opportunity.The method is on the basis of existing satellite positioning device, and design monitoring software realizes Vehicle Speed and monitors in real time and can judge whether vehicle exceeds the speed limit at this running section, and this greatly reduces vehicle monitoring cost.In prior art, extensively utilize the locating information collected according to the Big Dipper/gps satellite positioning system to judge whether vehicle exceeds the speed limit.But the drift of the Big Dipper/gps satellite positioning signal and low sampling rate affect the accuracy of locator data, thus be difficult to judge whether vehicle exceeds the speed limit.
Summary of the invention
The object of the present invention is to provide a kind of hypervelocity method of discrimination based on Hidden Markov road network, the described this hypervelocity method of discrimination based on Hidden Markov road network will solve the drift of prior art Satellite positioning signal and low sampling rate affects the technical matters that overspeed of vehicle judges.
A kind of hypervelocity method of discrimination based on Hidden Markov road network of the present invention, comprises one obtains locator data process from satellite, obtains the process of locator data, comprise the following steps described from satellite:
Step one: utilize Hidden Markov road network algorithm to find out the best running section of vehicle;
Step 2: utilize electronic chart to find out the details of vehicle running section, comprise speed limit size, therefrom finds out path that vehicle travels and obtains the maximum travelling speed in these paths, obtaining corresponding road section speed limit size from road speed limit database;
Whether step 3: obtain locator data from satellite and obtain Vehicle Speed, and the speed limit in described Vehicle Speed and this section compared, exceed the speed limit to vehicle traveling and differentiate.
Further, comprise the steps: in described step one
The step of a calculating observation probability, first by anchor point z tat the road r that each closes on ion possibility be defined as observation probability p (z t| r i), postulated point z tsubpoint to each road is x t,i, the great-circle distance of coordinate points and candidate's subpoint is || z t,i-x t,i|| greatcircle, anchor point z t+1subpoint to each road is x t+1, j, subpoint x t,ito subpoint x t+1, jvehicle operating range is called and is designated as || x t+1, j-x t.i|| route,
Suppose that positioning error is the Gaussian distribution of obeying zero-mean, then observation probability is:
p ( z t | r i ) = 1 2 π σ z e - 0.5 ( | | z t , i - x t , i | g r e a t c i r c l e σ z ) 2 - - - ( 1 )
Wherein σ zfor anchor point z tstandard error;
The step of a calculating initial observation probability, utilizes initial observation probability π irepresent before road network near section find out the probability of vehicle in this section, have π by first fix data points as the starting point of initial observation probability i=p (z 1| r i),
Calculate the step of road transition probability, utilize road transition probability to represent the transition probability of vehicle between coupling road between moment t to moment t+1, be expressed as p (q t+1=r j| q t=r i), the transition probability p drawn based on anchor point distance is defined respectively from Distance geometry speed two aspect l(q t+1=r j| q t=r i) and based on the transition probability p of anchor point speed v(q t+1=r j| q t=r i):
p l ( q t + 1 = r j | q t = r i ) = αe - βd t - - - ( 2 )
p v(q t+1＝r j|q t＝r i)＝V(r i→r j).F(r i→r j)(3)
Wherein,
i in formula *, j *represent the real running section of vehicle, β is for regulating d tsize is affected on result, setting β=median t(|| r i-r j||)/ln (2),
F ( r i → r j ) = | | z t - z t + 1 | | E u c l i d e a n D i s tan c e | | r i - r j | | r o u t e
V ( r i → r j ) = Σ u = 1 k ( e u ′ . v × v ‾ i j ) Σ u = 1 k ( e u ′ . v ) 2 × Σ u = 1 k ( v ‾ i j ) 2
U represents the section number around t anchor point, || z t-z t+1|| euclidenceDistancerepresent earth surface camber line distance, e' u.v the maximal rate in section is represented, v ijrepresent average velocity, F (r i→ r j) represent the information of observation station between former and later two time points and real candidate point, when the nearlyer then transition probability of both distances is larger, V (r i→ r j) represent the rate information of former and later two time points, when mean speed then probability more similar with the intensity of variation of maximum rate is larger;
Calculate the step of the best running section of vehicle, above-mentioned formula (1), formula (2) and viterbi algorithm is utilized to calculate the optimal path of vehicle traveling, the corresponding status switch in this path, thus infer the best section that vehicle travels, in described dimension bit algorithm, calculate t state be probability in all single path of i and therefrom find out maximal value max (δ t(i)), use formula (2) as path transition probability, and establish likelihood use formula (3) as the path transition probability in this moment when the likelihood of each path probability of t is less than the threshold value of setting, then obtain the path probability in other one group of moment and therefrom select maximum probability, above-mentioned steps is repeated in the t+1 moment, after all observation probabilities of calculating and transition probability, determine the optimum matching road in last moment, and oppositely release the best matching path of the first moment vehicle to last moment vehicle traveling with this, finally export the best running section of vehicle that vehicle travels.
Further, in described step 2, set up each sections of road speed limit database.
Further, in described step 3, when the speed that vehicle travels is greater than this section speed limit, prompting driver is exceeded the speed limit.
Hypervelocity differentiates it is the important technology guarantee reducing vehicle safety accident rate, is based on computer software technology, judges by particular model and algorithm the Data Analysis Services technology whether vehicle exceeds the speed limit.According to the locating information that the Big Dipper/gps satellite positioning system collects, utilize the impact that algorithm minimizing drift and low sampling rate bring, vehicle driving trace is matched on corresponding section, then the speed limit size that Vehicle Speed and retrieval obtain corresponding road section is contrasted, finally judge that vehicle travels and whether exceed the speed limit.
The Hidden Markov Model (HMM) that the present invention proposes can effectively reduce positioning error and find out the best matching path of vehicle traveling comparatively accurately.Then according to the database of the national road speed limit size set up, the speed of vehicle travel and the maximum travelling speed of this road are compared and obtains vehicle and travel the conclusion whether exceeded the speed limit, if hypervelocity so reminds driver vehicle to exceed the speed limit by guider.Whether whether the present invention adopt software engineering to exceed the speed limit in different time sections to the vehicle travelled on different sections of highway to differentiate, substantially increase hypervelocity differentiation rate, and can real-time reminding driver exceed the speed limit, thus reduce the incidence of accident.
The present invention and prior art compare, and its effect is actively with obvious.The present invention, on the basis of existing satellite positioning device, realizes Vehicle Speed and monitors in real time and can judge whether vehicle exceeds the speed limit at this running section, and the basis keeping high-accuracy reduces the cost of vehicle monitoring.
Accompanying drawing explanation
Fig. 1 is the schematic diagram of the hypervelocity method of discrimination based on Hidden Markov road network of the present invention.
Embodiment
Embodiment 1:
As shown in Figure 1, a kind of hypervelocity method of discrimination based on Hidden Markov road network of the present invention, comprises one obtains locator data process from satellite, obtains the process of locator data, comprise the following steps described from satellite:
Step one: utilize Hidden Markov road network algorithm to find out the best running section of vehicle;
Step 2: utilize electronic chart to find out the details of vehicle running section, and therefrom find the maximal rate of sections of road.
Whether step 3: obtain locator data from satellite and obtain Vehicle Speed, and the speed limit in described Vehicle Speed and this section compared, exceed the speed limit to vehicle traveling and differentiate.
Further, comprise the steps: in described step one
1. calculating observation probability:
Anchor point z treally the road r closed at each ion possibility, be defined as observation probability p (z t| r i); Postulated point z tsubpoint to each road is x t,i, the great-circle distance of coordinate points and candidate's subpoint is || z t,i-x t,i|| greatcircle.Anchor point z t+1subpoint to each road is x t+1, j, subpoint x t,ito subpoint x t+1, jvehicle operating range is called that " path distance " is designated as || x t+1, j-x t.i|| route;
Suppose that positioning error is the Gaussian distribution of obeying zero-mean, so observation probability is:
p ( z t | r i ) = 1 2 π σ z e - 0.5 ( | | z t , i - x t , i | | g r e a t c i r c l e σ z ) 2 - - - ( 1 )
Wherein σ zfor anchor point z tstandard error.
2. calculate initial observation probability:
Initial observation probability π irepresented before road network, from neighbouring section, find out the probability of vehicle in this section.Generally there is π with first anchor point as the starting point of initial observation probability i=p (z 1| r i).
3. calculate road transition probability:
Transition probability refers to the transition probability of vehicle between coupling road between moment t to moment t+1, is expressed as p (r j| r i).The present invention does not consider the information between observation station, show that road transfering probability distribution is by path distance distance:
p l ( q t + 1 = r j | q t = r i ) = βe - βd t - - - ( 2 )
p v(q t+1＝r j|q t＝r i)＝V(r i→r j).F(r i→r j)(3)
Wherein i in formula *, j *represent the real running section of vehicle.β is for regulating d tsize is affected on result.General setting β=median t(|| r i-r j||)/ln (2).
F ( r i → r j ) = | | z t - z t + 1 | | E u c l i d e a n D i s tan c e | | r i - r j | | r o u t e
V ( r i → r j ) = Σ u = 1 k ( e u ′ . v × v ‾ i j ) Σ u = 1 k ( e u ′ . v ) 2 × Σ u = 1 k ( v ‾ i j ) 2
U represents the section number around t anchor point, || z t-z t+1|| euclidenceDistancerepresent earth surface camber line distance.E' u.v the maximal rate in section is represented, v ijrepresent average velocity, F (r i→ r j) represent the information of anchor point between former and later two time points and real candidate point, when the nearlyer then transition probability of both distances is larger.V (r i→ r j) represent the rate information of former and later two time points, when mean speed then probability more similar with the intensity of variation of maximum rate is larger.
4. export the best running section of vehicle
Formula (1), formula (2) and viterbi algorithm is utilized to calculate the optimal path of vehicle traveling.Dimension bit utilizes the thought of dynamic programming to obtain maximum probability path and optimal path, and this paths correspond to a status switch, thus infers the best section that vehicle travels.
In dimension bit algorithm, need to calculate t state be probability in all single path of i and therefrom find out maximal value max (δ t(i)), consider the problem of calculated amount, use formula (2) as road transition probability, but the optimal path drawn according to this calculating transition probability when path probability differs less and actual path matching precision not high.Be multiplied continuously owing to calculating transition probability and cause the single path probability that obtains more and more less, so need the concept of an introducing likelihood.If likelihood then think therefrom can not judge optimal path when the likelihood of each path probability of t is less than the threshold value of setting, need use formula (3) as the road transition probability in this moment, because formula (3) introduces velocity standard can obtain road transition probability more accurately.Then obtain the path probability of other one group of t and therefrom select maximum probability.The t+1 moment repeats above-mentioned steps.
After all observation probabilities and transition probability calculate, determine the optimum matching road in last moment, and oppositely release the best matching path of the first moment vehicle to last moment vehicle traveling with this, finally export the best running section of vehicle.
Step 2, set up the database of each section maximum travelling speed.
Whether the speed in step 3, the speed utilizing the GPS/ Big Dipper to obtain and this section contrasts, travel to exceed the speed limit to differentiate vehicle.

Claims (4)

1. based on a hypervelocity method of discrimination for Hidden Markov road network, comprise the process obtaining locator data from satellite, it is characterized in that: obtain the process of locator data from satellite described, comprise the following steps:
Step one: utilize Hidden Markov road network algorithm to find out the best running section of vehicle;
Step 2: utilize electronic chart to find out the details of vehicle running section, and therefrom find the maximal rate of sections of road;
Whether step 3: obtain locator data from satellite and obtain Vehicle Speed, and the speed limit in described Vehicle Speed and this section compared, exceed the speed limit to vehicle traveling and differentiate.
2. the hypervelocity method of discrimination based on Hidden Markov road network according to claim 1, is characterized in that: comprise the steps: in described step one
The step of a calculating observation probability, first by anchor point z tat the road r that each closes on ion possibility be defined as observation probability p (z t| r i), postulated point z tsubpoint to each road is x t,i, the great-circle distance of coordinate points and candidate's subpoint is || z t,i-x t,i|| greatcircle, anchor point z t+1subpoint to each road is x t+1, j, subpoint x t,ito subpoint x t+1, jvehicle operating range is called and is designated as || x t+1, j-x t.i|| route
Suppose that positioning error is the Gaussian distribution of obeying zero-mean, then observation probability is:
p ( z t | r i ) = 1 2 π σ z - 0.5 ( | | z t , i - x t , i | | g r e a t c i r c l e σ z ) 2 - - - ( 1 )
Wherein σ zfor anchor point z tstandard error,
The step of a calculating initial observation probability, utilizes initial observation probability π irepresent before road network near section find out the probability of vehicle in this section, have π by first fix data points as the starting point of initial observation probability i=p (z 1| r i);
The step of a calculating road transition probability, utilizes road transition probability to represent the transition probability of vehicle between coupling road between moment t to moment t+1, is expressed as p (q t+1=r j| q t=r i), wherein q tfor the running section of t automobile, define the transition probability p drawn based on anchor point distance respectively from Distance geometry speed two aspect l(q t+1=r j| q t=r i) and based on the transition probability p of anchor point speed v(q t+1=r j| q t=r i):
p l ( q t + 1 = r j | q t = r i ) = αe - β d t - - - ( 2 )
p v(q t+1＝r j|q t＝r i)＝V(r i→r j).F(r i→r j)(3)
Wherein,
wherein d trepresent t subpoint to subpoint operating range, i in formula *, j *represent i-th real section and a jth real section respectively, β is for regulating d tsize is affected on result, setting β=median t(|| r i-r j||)/ln (2),
F ( r i → r j ) = | | z t - z t + 1 | | E u c l i d e a n d i s tan c e | | r i - r j | | r o u t e
V ( r i → r j ) = Σ u = 1 k ( e u ′ . v × v ‾ i j ) Σ u = 1 k ( e u ′ . v ) 2 × Σ u = 1 k ( v ‾ i j ) 2
U represents the section number around t anchor point, || z t-z t+1|| euclidenceDistancerepresent earth surface camber line distance, e' u.v the maximal rate in section is represented, v ijrepresent average velocity, F (r i→ r j) represent the information of observation station between former and later two time points and real candidate point, when the nearlyer then transition probability of both distances is larger, V (r i→ r j) represent the rate information of former and later two time points, when mean speed then probability more similar with the intensity of variation of maximum rate is larger;
The step of a best running section of calculating vehicle, above-mentioned formula (1), formula (2) and viterbi algorithm is utilized to calculate the optimal path of vehicle traveling, the corresponding status switch in this path, thus infer the best section that vehicle travels, in described dimension bit algorithm, calculate t state be probability in all single path of i and therefrom find out maximal value max (δ t(i)), use formula (2) as path transition probability, and establish likelihood wherein a, b represents that t vehicle match is to the probability on two paths respectively, use formula (3) as the path transition probability in this moment when the likelihood of each path probability of t is less than the threshold value of setting, then obtain the path probability in other one group of moment and therefrom select maximum probability, above-mentioned steps is repeated in the t+1 moment, after all observation probabilities of calculating and transition probability, determine the optimum matching road in last moment, and oppositely release the best matching path of the first moment vehicle to last moment vehicle traveling with this, finally export the best running section of vehicle that vehicle travels.
3. the hypervelocity method of discrimination based on Hidden Markov road network according to claim 1, is characterized in that: in described step 2, sets up each sections of road speed limit database.
4. the hypervelocity method of discrimination based on Hidden Markov road network according to claim 1, is characterized in that: in described step 3, and when the speed that vehicle travels is greater than this section speed limit, prompting driver is exceeded the speed limit.
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CN107742126A (en) * 2017-09-26 2018-02-27 南京航空航天大学 A kind of Floating Car map-matching method based on hidden Markov model
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CN106683418A (en) * 2016-12-15 2017-05-17 清华大学苏州汽车研究院（吴江） Vehicle overspeed real-time analyzing system and vehicle overspeed real-time analyzing method
CN107742126A (en) * 2017-09-26 2018-02-27 南京航空航天大学 A kind of Floating Car map-matching method based on hidden Markov model
CN108492563A (en) * 2018-04-12 2018-09-04 西南交通大学 A kind of excess speed event detection method based on average speed
CN108492564A (en) * 2018-04-18 2018-09-04 山东省交通规划设计院 The method and system of express-road vehicle running speed are measured based on road network
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CN109270563A (en) * 2018-10-12 2019-01-25 南通大学 A kind of map-matching method based on enhanced hidden Markov model
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CN111785012A (en) * 2020-04-27 2020-10-16 南斗六星系统集成有限公司 Energy consumption road spectrum analysis method for vehicle cloud cooperative computing
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CN111783034B (en) * 2020-04-27 2024-04-02 南斗六星系统集成有限公司 Emission road spectrum analysis method for vehicle-cloud cooperative computing
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CN113129603B (en) * 2021-03-26 2022-06-17 深圳市跨越新科技有限公司 Parallel road overspeed determination method, device, terminal and storage medium
CN113870580A (en) * 2021-09-01 2021-12-31 北京中交兴路信息科技有限公司 Overspeed detection method and device for truck, truck vehicle and truck system
CN113870580B (en) * 2021-09-01 2022-09-20 北京中交兴路信息科技有限公司 Overspeed detection method and device for truck, truck vehicle and truck system

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