CN108364491A - Driver information based reminding method based on vehicle flowrate prediction - Google Patents
Driver information based reminding method based on vehicle flowrate prediction Download PDFInfo
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- CN108364491A CN108364491A CN201810045083.4A CN201810045083A CN108364491A CN 108364491 A CN108364491 A CN 108364491A CN 201810045083 A CN201810045083 A CN 201810045083A CN 108364491 A CN108364491 A CN 108364491A
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- 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
- G08G1/0112—Measuring and analyzing of parameters relative to traffic conditions based on the source of data from the vehicle, e.g. floating car data [FCD]
-
- 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
- G08G1/0116—Measuring and analyzing of parameters relative to traffic conditions based on the source of data from roadside infrastructure, e.g. beacons
-
- 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
- G08G1/0125—Traffic data processing
-
- 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
- G08G1/0125—Traffic data processing
- G08G1/0129—Traffic data processing for creating historical data or processing based on historical data
-
- 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
- G08G1/0141—Measuring and analyzing of parameters relative to traffic conditions for specific applications for traffic information dissemination
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
- G08G1/0967—Systems involving transmission of highway information, e.g. weather, speed limits
- G08G1/096708—Systems involving transmission of highway information, e.g. weather, speed limits where the received information might be used to generate an automatic action on the vehicle control
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
- G08G1/0967—Systems involving transmission of highway information, e.g. weather, speed limits
- G08G1/096766—Systems involving transmission of highway information, e.g. weather, speed limits where the system is characterised by the origin of the information transmission
- G08G1/096775—Systems involving transmission of highway information, e.g. weather, speed limits where the system is characterised by the origin of the information transmission where the origin of the information is a central station
Abstract
The invention discloses a kind of driver information based reminding methods based on vehicle flowrate prediction, including step：S1. preset configuration word template or sound template；S2. traffic data is acquired, and is uploaded to Cloud Server；S3. cloud server traffic data, and handled based on neural network algorithm, obtain vehicle flowrate prediction data；S4. the equipment that vehicle flowrate prediction data is transmitted to end side by Cloud Server；S5. the equipment of end side receives the data of Cloud Server passback, is stored in local storage and is handled；S6. word template is presented in the equipment of end side, the history road condition data of prediction data and/or end side based on high in the clouds neural network algorithm reminds driver.The present invention carries out congestion in road prediction to the driving behavior of driver using artificial intelligence technology and reminds, and improves driving behavior and reminds real-time and efficiency.
Description
Technical field
The present invention relates to technical field of transportation, more specifically, are related to a kind of driver information predicted based on vehicle flowrate
Based reminding method.
Background technology
As the development of auto industry has also manufactured a series of problems while automobile is that people bring various convenient,
Such as traffic congestion, environmental pollution, traffic accident.The traffic national conditions in China are that motor vehicle mixes row, bicycle with non-motor vehicle
Quantity is big, and vehicles number is more, and road network carrying capacity is limited etc., implements traffic power strategy, needs solve problem
It is exactly traffic congestion.And in road traffic control field, traffic is monitored, the information system of data acquisition and processing (DAP) etc. is deposited
In many insurmountable problems, especially in the control of intersection, there are the period is long, vehicle delay and the traffic capacity are limited etc.
Problem.
Conventional traffic control mode, using historical traffic flow data as the foundation of regulation and control, by analyzing in different time
Traffic flow changing rule, carry out timing using artificial mode, be then entered into timing scheme by computer technology
In traffic controller, in the application by calling different timing schemes to carry out traffic control, since a large amount of vehicle pours in city
City's traffic route, causes serious traffic blocking problem, and the regulation and control traffic flow of timing scheme is increasingly difficult to adapt to traffic congestion
Improvement demand.
For example, the Chinese patent application of Publication No. CN106991815A discloses a kind of traffic congestion control method, packet
Include following steps：S1. obtain target road set parameter, including road passage capability Q, free stream velocity vf and obstruction it is close
Spend kj；S2. the real-time traffic parameter of target road, including real-time traffic amount q, real-time speed v and real-time traffic density are obtained
k；S3. build traffic state judging model, according to the model judge present road traffic speed and the magnitude of traffic flow to traffic flow
Influence degree, and control measure made to traffic according to influence degree can be in conjunction with the virtual condition of the traffic flow of road
And the volume of traffic and traffic speed are analyzed in traffic flow, make accurate traffic control measure, target can be effectively relieved
The traffic congestion of road, and can effectively avoid the waste of path resource.But, however it remains defect, such as algorithm
The problems such as design is complicated, can not achieve self study, and real-time is poor.The Chinese patent of Publication No. CN102741780B discloses
A method of the driver for reminding vehicle, vehicle include tactile driver's interface, and method may include determining vehicle
Speed；Determine the distance between vehicle and another vehicle；Based on speed and apart from startup tactile driver's interface.But still
Right existing defects, such as the large-scale road grid traffic system in city, prompting efficiency poor, to driving behavior that there are real-times
The problems such as relatively low.
Invention content
It is an object of the invention to overcome the deficiencies of the prior art and provide a kind of driver informations based on vehicle flowrate prediction
Based reminding method carries out prediction prompting using artificial intelligence technology to the driving behavior of driver, is ensureing the same of driving safety
When, traffic congestion can be alleviated, improve the real-time and efficiency reminded driving behavior.
The purpose of the present invention is achieved through the following technical solutions：A kind of driver information based on vehicle flowrate prediction
Based reminding method includes the following steps：
S1, in the equipment of end side, a plurality of word template of preset configuration or sound template, the word template or voice
Template is for reminding the driving behavior of driver；
S2 utilizes the sensor of end side in the equipment for being built-in with the end side of the word template or sound template
System acquires the first traffic data, is uploaded to Cloud Server, meanwhile, it is acquired using the traffic surveillance and control system at intersection
Second traffic data, is uploaded to Cloud Server；
S3, the first traffic data and second traffic data described in cloud server, and it is based on improved nerve net
Network algorithm is handled, and the first vehicle flowrate prediction data is obtained；
S4, the equipment that the first vehicle flowrate prediction data is transmitted to end side by Cloud Server；
The equipment of S5, the end side receive the data of Cloud Server passback, and save it in local storage,
Then following steps are executed：
The history road conditions number that present road whether is preserved in local storage detected in the equipment of end side by S51
According to, if so, the history road condition data of present road is then extracted, and according to the first label of temporal characteristics progress；If it is not,
It is directly entered step S54；
S52, first calculates, and calculates the speed mean value of the history road condition data on present road；Second calculates, and calculates
The running time mean value of history road condition data on present road, and be stored in data are calculated in local storage；
S53, according to the first label information in step S51, extraction present road is in history road conditions number in different time periods
According to speed mean value and/or running time mean value；
S54 carries out the second label to the first vehicle flowrate prediction data being stored in local storage, and sets multiple
Cut off value, according to different cut off value, corresponding different word template or sound template；
Word template corresponding with the cut off value in step S54 is presented on the display dress of the equipment of end side by S6
It sets, sound template corresponding with the cut off value in step S54 is presented in the display device of the equipment of end side,
The history road condition data of prediction data and/or end side based on high in the clouds neural network algorithm is as a result, according to road traffic congestion
Situation carries out Word-predictor prompting to driver or voice prediction is reminded；
S7, Cloud Server obtain the traffic data of the sensing system acquisition of end side, obtain intersection in real time in real time
Second traffic data of the traffic surveillance and control system acquisition at mouthful, using the first traffic data and the second traffic data as new input
Variable is based on the improved neural network algorithm continuous learning, continues to optimize the first vehicle flowrate prediction data；
S8, the first vehicle flowrate prediction data based on optimization, continuous updating Word-predictor prompting message or voice are pre-
Survey prompting message.
Further, include the steps that being counted to the vehicle flowrate of intersection, in step sl in intersection
Vehicle flowrate device, and the vehicle flowrate data transmission for the intersection that the vehicle flowrate device is acquired are installed at mouthful
To Cloud Server, in Cloud Server using the vehicle flowrate data of intersection as the input variable of neural network algorithm.
Further, the word template or the content of sound template include the title of road.
Further, in step s3, include the following steps：
S31：Multiple first traffic datas and multiple second traffic datas are chosen, while the first traffic data and second being handed over
Logical sample data of the data as neural network sets weights error vector as e (w), weights error is calculated using following formula
With function E (w)：
Wherein, eT(w) be e (w) transposed vector；
S32：Calculate the Jacobian matrix Js (w) of neural network weight；
Wherein, i is i-th of neuron, and j is j-th of neuron, the weights between i-th of neuron and j-th of neuron
For wij；
S33：New weight w (k+1) is calculated using following formula：
W (k+1)=w (k)-[JT(wk)J(wk)+μI]-1JT(wk)e(wk)
Wherein, μ is additional factor, and μ ＞ 0, I are unit matrix；
S34：It brings new weight w (k+1) into weights error and function calculation formula in step S31, is calculated new
Weights error and function E (w)2, compare E (w)2With original E (w)1Size, if E (w)2Numerical value reduce, then utilize
Numerical value μ divided by factor θ more than 11, then, the value of w (k+1) is updated, step S31 is then gone to；If E (w)2Number
Value does not reduce, then is multiplied by a factor θ more than 1 using numerical value μ2, w (k+1) and the S33 that gos to step are not updated, work as god
When error through network reaches preset value or convergence, stop the iterative calculation of function.
Further, the first traffic data includes in real time running speed, residence time, running time, mileage
It is any.
Further, the second traffic data is included in travel speed at signal lamp, running time, residence time, signal
Lamp period, green time, red time any one or more of.
The beneficial effects of the invention are as follows：
(1) present invention carries out prediction prompting using artificial intelligence technology to the driving behavior of driver, is driven ensureing
While safe, traffic congestion can be alleviated, improve the real-time and efficiency reminded driving behavior.
(2) the present invention is based on artificial intelligence technologys to alleviate traffic jam issue, has self-learning function, improves traffic letter
Breath system experiences the efficiency of the variation of traffic flow in road network, can effectively adjust the magnitude of traffic flow, reduces traffic loading, reduces
Traffic delay and parking rate, improve the traffic capacity of road network, improve entire urban traffic conditions.
(3) present invention uses improved neural network algorithm, fast convergence rate to have calculation amount small, and precision is high, consumption money
The few feature in source is especially easy to restrain so that the real-time of information exchange is significantly improved.
(4) unmanned platform is very high to the requirement of real-time of information, therefore, applies the present invention to unmanned vapour
Vehicle, since improved neural network algorithm improves operation convergence rate, real-time, predetermined speed is fast, has very big
Meaning.
(5) present invention is acted when the history road condition data of the equipment storage to end side is handled by label
Processing step so that when calling data, program runnability is improved, and then improves data-handling efficiency, enhancing
The runnability of equipment.
(6) it is acquired present invention incorporates the real time traffic data of the equipment of end side acquisition and road traffic monitoring system
Intelligent algorithm module is deployed in cloud service by input variable of the traffic data as the neural network algorithm of high in the clouds side
Device, beyond the clouds server carry out that prediction data is issued to terminal side equipment after study processing, a large amount of distributions can be focused on
The data that formula equipment or system upload reduce the cost that analyzing processing is carried out in the equipment or system of end side, improve pair
The forecasting efficiency of traffic road congestion, the traffic trip of Effective Regulation city commuter.
Description of the drawings
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technology description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this
Some embodiments of invention without having to pay creative labor, may be used also for those of ordinary skill in the art
With obtain other attached drawings according to these attached drawings.
Fig. 1 is the method and step flow chart of the present invention.
Specific implementation mode
Technical scheme of the present invention is described in further detail below in conjunction with the accompanying drawings, but protection scope of the present invention is not limited to
It is as described below.All features disclosed in this specification, or implicit disclosed all methods or in the process the step of, in addition to mutual
Other than the feature and/or step of repulsion, it can combine in any way.
Any feature disclosed in this specification (including any accessory claim, abstract and attached drawing), except non-specifically chatting
It states, can be replaced by other alternative features that are equivalent or have similar purpose.That is, unless specifically stated, each feature is only
It is an example in a series of equivalent or similar characteristics.
Specific embodiments of the present invention are described more fully below, it should be noted that the embodiments described herein is served only for illustrating
Illustrate, is not intended to restrict the invention.In the following description, in order to provide a thorough understanding of the present invention, a large amount of spies are elaborated
Determine details.It will be apparent, however, to one skilled in the art that：This hair need not be carried out using these specific details
It is bright.In other instances, in order to avoid obscuring the present invention, well known circuit, software or method are not specifically described.
As shown in Figure 1, a kind of driver information based reminding method based on vehicle flowrate prediction, includes the following steps：
S1, in the equipment of end side, a plurality of word template of preset configuration or sound template, the word template or voice
Template is for reminding the driving behavior of driver；
S2 utilizes the sensor of end side in the equipment for being built-in with the end side of the word template or sound template
System acquires the first traffic data, is uploaded to Cloud Server, meanwhile, it is acquired using the traffic surveillance and control system at intersection
Second traffic data, is uploaded to Cloud Server；
S3, the first traffic data and second traffic data described in cloud server, and it is based on improved nerve net
Network algorithm is handled, and the first vehicle flowrate prediction data is obtained；
S4, the equipment that the first vehicle flowrate prediction data is transmitted to end side by Cloud Server；
The equipment of S5, the end side receive the data of Cloud Server passback, and save it in local storage,
Then following steps are executed：
The history road conditions number that present road whether is preserved in local storage detected in the equipment of end side by S51
According to, if so, the history road condition data of present road is then extracted, and according to the first label of temporal characteristics progress；If it is not,
It is directly entered step S54；
S52, first calculates, and calculates the speed mean value of the history road condition data on present road；Second calculates, and calculates
The running time mean value of history road condition data on present road, and be stored in data are calculated in local storage；
S53, according to the first label information in step S51, extraction present road is in history road conditions number in different time periods
According to speed mean value and/or running time mean value；
S54 carries out the second label to the first vehicle flowrate prediction data being stored in local storage, and sets multiple
Cut off value, according to different cut off value, corresponding different word template or sound template；
Word template corresponding with the cut off value in step S54 is presented on the display dress of the equipment of end side by S6
It sets, sound template corresponding with the cut off value in step S54 is presented in the display device of the equipment of end side,
The history road condition data of prediction data and/or end side based on high in the clouds neural network algorithm is as a result, according to road traffic congestion
Situation carries out Word-predictor prompting to driver or voice prediction is reminded；
S7, Cloud Server obtain the traffic data of the sensing system acquisition of end side, obtain intersection in real time in real time
Second traffic data of the traffic surveillance and control system acquisition at mouthful, using the first traffic data and the second traffic data as new input
Variable is based on the improved neural network algorithm continuous learning, continues to optimize the first vehicle flowrate prediction data；
S8, the first vehicle flowrate prediction data based on optimization, continuous updating Word-predictor prompting message or voice are pre-
Survey prompting message.
Further, include the steps that being counted to the vehicle flowrate of intersection, in step sl in intersection
Vehicle flowrate device, and the vehicle flowrate data transmission for the intersection that the vehicle flowrate device is acquired are installed at mouthful
To Cloud Server, in Cloud Server using the vehicle flowrate data of intersection as the input variable of neural network algorithm.
Further, the word template or the content of sound template include the title of road.
Further, in step s3, include the following steps：
S31：Multiple first traffic datas and multiple second traffic datas are chosen, while the first traffic data and second being handed over
Logical sample data of the data as neural network sets weights error vector as e (w), weights error is calculated using following formula
With function E (w)：
Wherein, eT(w) be e (w) transposed vector；
S32：Calculate the Jacobian matrix Js (w) of neural network weight；
Wherein, i is i-th of neuron, and j is j-th of neuron, the weights between i-th of neuron and j-th of neuron
For wij；
S33：New weight w (k+1) is calculated using following formula：
W (k+1)=w (k)-[JT(wk)J(wk)+μI]-1JT(wk)e(wk)
Wherein, μ is additional factor, and μ ＞ 0, I are unit matrix；
S34：It brings new weight w (k+1) into weights error and function calculation formula in step S31, is calculated new
Weights error and function E (w)2, compare E (w)2With original E (w)1Size, if E (w)2Numerical value reduce, then utilize
Numerical value μ divided by factor θ more than 11, then, the value of w (k+1) is updated, step S31 is then gone to；If E (w)2Number
Value does not reduce, then is multiplied by a factor θ more than 1 using numerical value μ2, w (k+1) and the S33 that gos to step are not updated, work as god
When error through network reaches preset value or convergence, stop the iterative calculation of function.
Further, the first traffic data includes in real time running speed, residence time, running time, mileage
It is any.
Further, the second traffic data is included in travel speed at signal lamp, running time, residence time, signal
Lamp period, green time, red time any one or more of.
The basic principle of neural network algorithm, including input layer, hidden layer (learning layer) and output layer, signal is in neuron
Between positive transmission, obtain after network output with desired output compared with, obtaining error amount, error amount backpropagation again, use
Change initial weights and threshold value, continuous iteration is until the error and minimum that are exported.In this process, neural network
Weights and threshold value are constantly adjusted by the backpropagation of error, and by after adjustment weights and threshold value preserve, for pair
The variable newly inputted is calculated, and achievees the purpose that prediction.Include the following steps：
SS1：For one group of sequential value (x, y), correspond to each input variable x, all there are one desired output y is right therewith
It answers, it needs to be determined that inputting the number of nodes of neuron in the actual operation of neural network, for example, it is set to n, output neuron
Number of nodes be set as m, the number of nodes of intermediate hidden layer is set as p；
SS2：Hidden layer output calculates, according to the connection weight and threshold between the preset input of neural network and neuron
Value b can obtain the output H of intrerneuron j,
Wherein, w is weights, and b is threshold value, and f is excitation function, and calculation formula is as follows：
SS3：Output layer output calculates, and according to the result of calculation and weights of hidden layer neuron, is calculated using following formula
The output result O of entire neural networkk：
SS4：Error calculation obtains neural network output, it is compared with reality output, calculates neural network output
With the error amount e of reality output：
E=Yk-OK
SS5：Right value update, error e carry out backpropagation, while the adjustment of weights is carried out using the variable quantity of weights,
Expression formula is as follows：
wjk=wjk+ηHjek
Wherein, η is learning rate, can be adjusted according to actual conditions；
SS6：The iteration stopping if the prediction error for reaching setting is transferred to SS2 if not obtaining and continues to change
In generation, calculates.
Wherein, to initial value and threshold value, the random number between 0~1 is generally chosen, for learning rate, in the same god
It might have different learning rates through the different phase in network, can be configured according to actual conditions, generally be may be configured as
Between 0.01~0.8.
Further, the principle of improved neural network algorithm,
If the weights between neuron i and neuron j are wij, for an input variable xk, the output of network is yk, section
The output of point i is oik, to s layers of j-th of neuron, when inputting k-th of sample, the output of node j is：
Wherein,When inputting k-th of sample for s-1 layers, the output of j-th of neuron node,It is j-th of nerve
The threshold value of member；
Define error and function Ek,
The training of neural network be ask E be minimum value when weights；
It defines s layers of neural network and shares n node, the error vector of weights is e (W), is column vector, then weights miss
Difference and function E are represented by,
Wherein, the gradient of square error and function E (w) is calculated,
Wherein,
New weight w (k+1) is calculated,
W (k+1)=w (k)-[JT(wk)J(wk)+μI]-1JT(wk)e(wk)
Due to JT(wk)J(wk) not necessarily reversible, fractional increments μ I are added, for ensureing its invertibity.In the present embodiment
Remaining technical characteristic, those skilled in the art can carry out flexibly selecting according to actual conditions and different specific to meet
Actual demand.It will be apparent, however, to one skilled in the art that：This need not be carried out using these specific details
Invention.In other instances, in order to avoid obscuring the present invention, well known algorithm, method or system etc., at this are not specifically described
The claimed technical solution of claims of invention limits within technical protection scope.
For embodiment of the method above-mentioned, for simple description, therefore it is all expressed as a series of combination of actions, still
Those skilled in the art should understand that the application is not limited by the described action sequence, because according to the application, it is a certain
A little steps can be performed in other orders or simultaneously.Secondly, it those skilled in the art should also know that, is retouched in specification
The embodiment stated belongs to preferred embodiment, necessary to involved action and unit not necessarily the application.
It will be appreciated by those of skill in the art that unit described in conjunction with the examples disclosed in the embodiments of the present disclosure and
Algorithm steps can be realized with the combination of electronic hardware or computer software and electronic hardware.These functions are actually with hard
Part or software mode execute, and depend on the specific application and design constraint of technical solution.Professional technician can be with
Distinct methods are used to realize described function each specific application, but this realization should not exceed the model of the present invention
It encloses.
Disclosed system, module and method, may be implemented in other ways.For example, device described above
Embodiment, only schematically, for example, the division of the unit, can be only a kind of division of logic function, it is practical to realize
When there may be another division manner, such as multiple units or component can be combined or can be integrated into another system, or
Some features can be ignored or not executed.Another point, shown or discussed mutual coupling or direct-coupling or communication
Connection is it may be said that by some interfaces, the INDIRECT COUPLING or communication connection of device or unit can be electrical, machinery or other
Form.
The unit that the discrete parts illustrates can be or can not also receive and be physically separated, and be shown as unit
Component can be or can not receive physical unit, you can be located at a place, or may be distributed over multiple network lists
In member.Some or all of unit therein can be selected according to the actual needs to realize the purpose of the scheme of the present embodiment.
It, can be with if the function is realized in the form of SFU software functional unit and when sold or used as an independent product
It is stored in a computer readable storage medium.Based on this understanding, technical scheme of the present invention is substantially right in other words
The part of part or the technical solution that the prior art contributes can be expressed in the form of software products, the calculating
Machine software product is stored in a storage medium, including some instructions are used so that a computer equipment (can be personal
Computer, server or network equipment etc.) it performs all or part of the steps of the method described in the various embodiments of the present invention.And
Storage medium above-mentioned includes：USB flash disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory
The various media that can store program code such as device (RandomAccess Memory, RAM), magnetic disc or CD.
One of ordinary skill in the art will appreciate that all or part of flow in the method for realization above-described embodiment, being can
It is completed with instructing relevant hardware by computer program, the program can be stored in computer read/write memory medium
In, the program is when being executed, it may include such as the flow of the embodiment of above-mentioned each method.Wherein, the storage medium can be magnetic
Dish, CD, ROM, RAM etc..
The above is only a preferred embodiment of the present invention, it should be understood that the present invention is not limited to described herein
Form is not to be taken as excluding other embodiments, and can be used for other combinations, modifications, and environments, and can be at this
In the text contemplated scope, modifications can be made through the above teachings or related fields of technology or knowledge.And those skilled in the art institute into
Capable modifications and changes do not depart from the spirit and scope of the present invention, then all should be in the protection domain of appended claims of the present invention
It is interior.
Claims (6)
1. a kind of driver information based reminding method based on vehicle flowrate prediction, which is characterized in that include the following steps：
S1, in the equipment of end side, a plurality of word template of preset configuration or sound template, the word template or sound template
For reminding the driving behavior of driver；
S2, in the equipment for being built-in with the end side of the word template or sound template, using the sensing system of end side,
The first traffic data is acquired, Cloud Server is uploaded to, meanwhile, it is handed over using the traffic surveillance and control system acquisition second at intersection
Logical data, are uploaded to Cloud Server；
S3, the first traffic data and second traffic data described in cloud server, and calculated based on improved neural network
Method is handled, and the first vehicle flowrate prediction data is obtained；
S4, the equipment that the first vehicle flowrate prediction data is transmitted to end side by Cloud Server；
The equipment of S5, the end side receive the data of Cloud Server passback, and save it in local storage, then
Execute following steps：
The history road condition data that present road whether is preserved in local storage detected in the equipment of end side by S51,
If so, then extracting the history road condition data of present road, and the first label is carried out according to temporal characteristics；If it is not, straight
It connects and enters step S54；
S52, first calculates, and calculates the speed mean value of the history road condition data on present road；Second calculates, and calculates current
The running time mean value of history road condition data on road, and be stored in data are calculated in local storage；
S53, according to the first label information in step S51, extraction present road is in history road condition data in different time periods
Speed mean value and/or running time mean value；
S54 carries out the second label to the first vehicle flowrate prediction data being stored in local storage, and sets multiple boundary
Value, according to different cut off value, corresponding different word template or sound template；
Word template corresponding with the cut off value in step S54 is presented on the display device of the equipment of end side by S6
On, sound template corresponding with the cut off value in step S54 is presented in the display device of the equipment of end side, base
In the prediction data of high in the clouds neural network algorithm and/or the history road condition data of end side as a result, according to road traffic congestion feelings
Condition carries out Word-predictor prompting to driver or voice prediction is reminded；
S7, Cloud Server obtain the traffic data of the sensing system acquisition of end side, obtain at intersection in real time in real time
Traffic surveillance and control system acquisition the second traffic data, become the first traffic data and the second traffic data as new input
Amount is based on the improved neural network algorithm continuous learning, continues to optimize the first vehicle flowrate prediction data；
S8, the first vehicle flowrate prediction data based on optimization, continuous updating Word-predictor prompting message or voice prediction carry
Awake information.
2. a kind of driver information based reminding method based on vehicle flowrate prediction according to claim 1, which is characterized in that packet
The step of being counted to the vehicle flowrate of intersection is included, vehicle flowrate dress is installed at intersection in step sl
It sets, and by the vehicle flowrate data transmission of the intersection of vehicle flowrate device acquisition to Cloud Server, in cloud service
Device is using the vehicle flowrate data of intersection as the input variable of neural network algorithm.
3. a kind of driver information based reminding method based on vehicle flowrate prediction according to claim 1, which is characterized in that institute
State the title that the content of word template or sound template includes road.
4. a kind of driver information based reminding method based on vehicle flowrate prediction according to claim 1, which is characterized in that
In step S3, include the following steps：
S31：Choose multiple first traffic datas and multiple second traffic datas, while by the first traffic data and the second traffic number
According to the sample data as neural network, weights error vector is set as e (w), weights error and letter are calculated using following formula
Number E (w)：
Wherein, eT(w) be e (w) transposed vector；
S32：Calculate the Jacobian matrix Js (w) of neural network weight；
Wherein, i is i-th of neuron, and j is j-th of neuron, and the weights between i-th of neuron and j-th of neuron are
wij；
S33：New weight w (k+1) is calculated using following formula：
W (k+1)=w (k)-[JT(wk)J(wk)+μI]-1JT(wk)e(wk)
Wherein, μ is additional factor, and μ ＞ 0, I are unit matrix；
S34：It brings new weight w (k+1) into weights error and function calculation formula in step S31, new power is calculated
It is worth error and function E (w)2, compare E (w)2With original E (w)1Size, if E (w)2Numerical value reduce, then utilize numerical value μ
Divided by the factor θ more than 11, then, the value of w (k+1) is updated, step S31 is then gone to；If E (w)2Numerical value do not have
There is reduction, is then multiplied by a factor θ more than 1 using numerical value μ2, w (k+1) and the S33 that gos to step are not updated, work as nerve net
When the error of network reaches preset value or convergence, stop the iterative calculation of function.
5. special according to a kind of driver information based reminding method based on vehicle flowrate prediction of claim 1-4 any one of them
Sign is that the first traffic data includes any one of real time running speed, residence time, running time, mileage.
6. special according to a kind of driver information based reminding method based on vehicle flowrate prediction of claim 1-4 any one of them
Sign is, the second traffic data is included in travel speed at signal lamp, running time, residence time, signal lamp cycle, green light
Time, red time any one or more of.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113689721A (en) * | 2021-07-30 | 2021-11-23 | 深圳先进技术研究院 | Automatic driving vehicle speed control method, system, terminal and storage medium |
| CN114333311A (en) * | 2021-12-28 | 2022-04-12 | 南京民基悠步信息技术有限公司 | Comprehensive operation and maintenance management system based on Internet of things |
| WO2023004775A1 (en) * | 2021-07-30 | 2023-02-02 | 深圳先进技术研究院 | Autonomous vehicle speed control method and system, terminal, and storage medium |
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2018
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113689721A (en) * | 2021-07-30 | 2021-11-23 | 深圳先进技术研究院 | Automatic driving vehicle speed control method, system, terminal and storage medium |
| WO2023004775A1 (en) * | 2021-07-30 | 2023-02-02 | 深圳先进技术研究院 | Autonomous vehicle speed control method and system, terminal, and storage medium |
| CN114333311A (en) * | 2021-12-28 | 2022-04-12 | 南京民基悠步信息技术有限公司 | Comprehensive operation and maintenance management system based on Internet of things |
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