CN109785628A - Road conditions alarm system and alarm method based on car networking communication - Google Patents
Road conditions alarm system and alarm method based on car networking communication Download PDFInfo
- Publication number
- CN109785628A CN109785628A CN201910144507.7A CN201910144507A CN109785628A CN 109785628 A CN109785628 A CN 109785628A CN 201910144507 A CN201910144507 A CN 201910144507A CN 109785628 A CN109785628 A CN 109785628A
- Authority
- CN
- China
- Prior art keywords
- vehicle
- alarm
- section
- monitored
- road
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Abstract
The invention discloses the road conditions alarm systems communicated based on car networking, comprising: GPS positioning system is set on vehicle, for monitoring vehicle location；Vehicle speed sensor is arranged at vehicle hub, for monitoring speed；Camera, setting is in monitoring section, for acquiring road surface picture.The road conditions alarm system based on car networking communication is provided, road information can be acquired in real time by vehicle and road equipment, understands the traffic information in monitoring section in time.The present invention also provides the road conditions alarm methods communicated based on car networking, can assess the road conditions in monitoring section in real time, make alarm and corresponding measure in time.
Description
Technical field
The present invention relates to vehicle networking technical fields, more particularly to road conditions alarm system and alarm side based on car networking communication
Method.
Background technique
With the continuous development of society, it is economically constantly progressive, vehicle population is more and more.At the same time, it drives out
Capable convenience causes more and more people to select self-driving trip, this causes biggish pressure to traffic.
And in the prior art, when traffic pressure is more serious, when especially there is traffic accident, if traffic police cannot
One time reached floor manager traffic, then traffic pressure can be caused even more serious, if therefore traffic police can be reminded to have friendship in time.
Car networking (Internet of Vehicles) is the huge friendship being made of information such as vehicle location, speed and routes
Mutual network.By devices such as GPS, RFID, sensor, camera image processing, vehicle can complete itself environment and state letter
The acquisition of breath；By Internet technology, all vehicles can be by the various information Transmission Convergences of itself to central processing unit；It is logical
Cross computer technology, the information of these a large amount of vehicles can be analyzed and processed, thus calculate the best route of different vehicle,
It reports without delay road conditions and arranges signal lamp cycle.
Summary of the invention
The present invention is to solve current technology shortcoming, provides the road conditions alarm system based on car networking communication,
Road information can be acquired in real time by vehicle and road equipment, understand the traffic information in monitoring section in time.
The present invention also provides the road conditions alarm methods communicated based on car networking, can assess the road in monitoring section in real time
Condition makes alarm and corresponding measure in time.
Technical solution provided by the invention are as follows: the road conditions alarm system based on car networking communication, comprising:
GPS positioning system is set on vehicle, for monitoring vehicle location；
Vehicle speed sensor is arranged at vehicle hub, for monitoring speed；
Camera, setting is in monitoring section, for acquiring road surface picture.
Preferably, further includes:
Data acquisition and storage module is arranged at vehicle, with the GPS positioning system, the vehicle speed sensor, uses
In acquisition data and stores, sends data
Sensing module acquires the location information and the camera of the vehicle that the Data acquisition and storage module is sent
The ambient image of the vehicle present position of acquisition；
Communication module receives the information of the sensing module acquisition, breath of concurrently delivering letters；
Server receives the information that the communication module is sent, and handles analysis information, generates alarm signal；
Terminal display screen receives and shows the alarm signal of the server；
Alarm system receives the alarm signal that the server is sent.
Preferably, further includes:
Receiver is arranged at vehicle, the information sent for receiving the server；
Display is arranged at meter panel of motor vehicle, for showing traffic information.
Preferably,
The ambient image and vehicle operation data are carried out integrated treatment by the server, and with preset congestion warning
Picture and data are matched, and alarm command is generated.
Road conditions alarm method based on car networking communication, comprising the following steps:
Step 1, according to the sampling period, acquire vehicle fleet N, the average speed in section to be monitoredAverage spacing S, vehicle
Flow Q, and determine the lane load factor ξ in monitoring section；
Step 2 successively standardizes above-mentioned parameter, determines the input layer vector x of three layers of BP neural network
={ x1,x2,x3,x4,x5, wherein x1For the vehicle fleet coefficient in section to be monitored, x2For the average speed system in section to be monitored
Number, x3For the average spacing coefficient in section to be monitored, x4For the wagon flow coefficient of discharge in section to be monitored, x5For the vehicle in section to be monitored
Road load factor coefficient；
Step 3, the input layer DUAL PROBLEMS OF VECTOR MAPPING to hidden layer, the hidden layer vector y={ y1,y2,…,ym, m is hidden layer section
Point number；
Step 4 obtains output layer neuron vector o={ o1,o2,o3,o4}；Wherein o1For level-one alarm level, o2It is two
Grade alarm level, o3For three-level alarm level, o4For level Four alarm level；The output layer neuron value isK is output layer neuron sequence number, and k={ 1,2,3,4 }, i are i-th of alarm of setting
Grade, i={ 1,2,3,4 }, works as okWhen being 1, at this point, road to be monitored is in okCorresponding alarm level；
Step 5, server judge that terminal display screen shows alarm condition according to the alarm level of output；Wherein, described one
Grade alarm level is safe condition, is not necessarily to make regulation measure to road to be monitored, and the secondary alarm grade is alert status,
Treat prison road and make close prosecution, the three-level alarm level is congestion status, to road to be monitored make urgent early warning and
Dredging measure, the level Four alarm level are heavy congestion state, make to road to be monitored and shunt dredging measure.
Preferably,
The calculation method of the lane load factor ξ in section:
In formula, b is the road width in section to be monitored, and L ' is the link length in section to be monitored, and τ is number of track-lines, and a is vehicle
Mean breadth, V be monitor section Maximum speed limit.
Preferably,
The hidden node number m meets:Wherein n is input layer number, and p is output
Node layer number.
Preferably,
By vehicle fleet N, the average speed in section to be monitoredAverage spacing S, vehicle flowrate Q, lane load factor ξ into
The normalized formula of row are as follows:
Wherein, xjFor the parameter in input layer vector, XjRespectively measurement parameter N,S,Q,ξ；
, j=1,2,3,4,5；XjmaxAnd XjminMaximum value and minimum value in respectively corresponding measurement parameter.
Preferably,
The excitation function of the hidden layer and the output layer is all made of S type function fj(x)=1/ (1+e-x)。
Preferably,
In the step 5, when output layer neuron vector is o3Or o4When, server sends to the receiver of automobile and alarms
Information reminds vehicle front road conditions.
It is of the present invention the utility model has the advantages that can lead to the present invention provides the road conditions alarm system communicated based on car networking
It crosses vehicle and road equipment acquires road information in real time, understand the traffic information in monitoring section in time.The present invention also provides bases
In the road conditions alarm method of car networking communication, the road conditions in monitoring section can be assessed in real time, make alarm in time and dredged accordingly
Logical and measure of control, and vehicle can be prompted in time, it enables a driver to understand front road conditions, makes lane change preparation or road in time
The planning again of line, saves the time.
Specific embodiment
The present invention is described in further detail below, to enable those skilled in the art's refer to the instruction text being capable of evidence
To implement.
The present invention provides the road conditions alarm systems communicated based on car networking, including the GPS positioning system installed on vehicle
And vehicle speed sensor, GPS positioning system are set on vehicle, for monitoring vehicle location；Vehicle speed sensor is arranged in vehicle wheel
At hub, for monitoring speed.In order to facilitate the acquisition, storage and transmission of vehicle data, be provided on vehicle data acquisition with
Memory module.Data acquisition and storage module is connect with the GPS positioning system, the vehicle speed sensor, for acquiring data
(information such as vehicle location and speed) simultaneously store, send data.Monitoring section is provided with camera, for acquiring road surface figure
Piece.Sensing module is used to acquire locating for information and the camera acquisition vehicle for the vehicle that the Data acquisition and storage module is sent
The ambient image of position.Communication module receives the information of the sensing module acquisition, and sends out information.
In terminal setting server, terminal display screen and the alarm system of traffic control center.Server receives described logical
Believe the information that module is sent, and handle analysis information, generates alarm signal；Terminal display screen receives and shows the server
Alarm signal；Alarm system receives the alarm signal that the server is sent.
The server is according to preset neural network model to information of vehicles (vehicle location, the speed etc. in monitoring section
Information) and corresponding ambient image processing with obtain the corresponding traffic conditions in each position (monitoring section vehicle fleet N, put down
Equal speedAverage spacing S, vehicle flowrate Q information), classification results then are generated according to the traffic conditions, by the environment map
Picture and vehicle operation data carry out integrated treatment, and are matched with preset congestion warning picture and data, generate alarm and refer to
It enables.
The present invention also provides the road conditions alarm methods communicated based on car networking, comprising the following steps:
Totally interconnected connection is formed on BP model between the neuron of each level, is not connected between the neuron in each level
It connects, the output of input layer is identical as input, i.e. oi=xi.The operating characteristic of the neuron of intermediate hidden layer and output layer
For
opj=fj(netpj)
Wherein p indicates current input sample, ωjiFor from neuron i to the connection weight of neuron j, opiFor neuron
The current input of j, opjIt is exported for it；fjFor it is non-linear can micro- non-decreasing function, be generally taken as S type function, i.e. fj(x)=1/ (1
+e-x)。
For the BP network architecture that the present invention uses by up of three-layer, first layer is input layer, total n node, corresponding
Indicate that n detection signal of equipment working state, these signal parameters are provided by data preprocessing module；The second layer is hidden layer,
Total m node is determined in an adaptive way by the training process of network；Third layer is output layer, total p node, by system
Actual needs output in response to determining that.
The mathematical model of the network are as follows:
Input vector: x=(x1,x2,...,xn)T
Middle layer vector: y=(y1,y2,...,ym)T
Output vector: o=(o1,o2,...,op)T
In the present invention, input layer number is n=5, and output layer number of nodes is p=4, and hidden layer number of nodes m is estimated by following formula
It obtains:
5 parameters of input layer respectively indicate are as follows: x1For the vehicle fleet coefficient in section to be monitored, x2For section to be monitored
Average speed coefficient, x3For the average spacing coefficient in section to be monitored, x4For the wagon flow coefficient of discharge in section to be monitored, x5For monitoring
The lane load factor coefficient in section.
Since the data of acquisition belong to different physical quantitys, dimension is different.Therefore, artificial neuron is inputted in data
Before network, need to turn to data requirement into the number between 0-1.
Normalized formula isWherein, xjFor the parameter in input layer vector, XjRespectively
Measurement parameter N,S, Q, ξ, j=1,2,3,4,5；XjmaxAnd XjminMaximum value and minimum in respectively corresponding measurement parameter
Value.
Specifically, after being standardized, the vehicle for obtaining monitoring section is total for the vehicle fleet N in section to be monitored
Number system number x1:
Wherein, NmaxAnd NminRespectively monitor the vehicle fleet maximum value and minimum value in section.
Likewise, for the average speed in section to be monitoredAfter being standardized, the average speed in monitoring section is obtained
Coefficient x2:
Wherein,WithRespectively monitor the maximum average speed and minimum average B configuration speed in section.
For the vehicle flowrate Q in section to be monitored, after being standardized, the vehicle flowrate Q coefficient x in monitoring section is obtained3:
Wherein, QmaxAnd QminRespectively monitor the maximum vehicle flowrate and minimum vehicle flowrate in section.
For the average spacing S in section to be monitored, after being standardized, the average spacing coefficient x in monitoring section is obtained3:
Wherein, SmaxAnd SminThe maximum for respectively monitoring section is averaged spacing and minimum average B configuration spacing.
For monitoring the vehicle flowrate Q in section, after being standardized, the wagon flow coefficient of discharge x in monitoring section is obtained4:
Wherein, QmaxAnd QminRespectively monitor the maximum vehicle flowrate and minimum vehicle flowrate in section.
For the lane load factor ξ in section to be monitored, after being standardized, monitoring section lane load factor system is obtained
Number x5:
Wherein, ξmaxAnd ξminRespectively monitor the maximum lane load factor and minimum lane load factor in section.
The calculation method of the lane load factor ξ in section:
In formula, b is the road width in monitored section, and L ' is the link length for monitoring section, and τ is number of track-lines, and a is vehicle
Mean breadth, V be monitor section Maximum speed limit.
The mean breadth of vehicle is calculated after being sent to server by the Data acquisition and storage module of vehicle, or
Person has server measurement when handling image to acquire.
It exports 4 parameters and is respectively as follows: o1For level-one alarm level, o2For secondary alarm grade, o3For three-level alarm level,
o4For level Four alarm level；The output layer neuron value isK is output layer neuron sequence
Row number, k={ 1,2,3,4 }, i are i-th of alarm level of setting, and i={ 1,2,3,4 } works as okWhen being 1, at this point, road to be monitored
Road is in okCorresponding alarm level；
Step 2, the training for carrying out BP neural network.
After establishing BP neural network nodal analysis method, the training of BP neural network can be carried out.It is passed through according to the history of product
Test the sample of data acquisition training, and the connection weight between given input node i and hidden layer node j, hidden node j and defeated
Connection weight between node layer k out.
(1) training method
Each subnet is using individually trained method；When training, first have to provide one group of training sample, each of these sample
This, to forming, when all reality outputs of network and its consistent ideal output, is shown to train by input sample and ideal output
Terminate；Otherwise, by correcting weight, keep the ideal output of network consistent with reality output；Output sample when the training of each subnet
As shown in table 1.
The output sample of 1 network training of table
(2) training algorithm
BP network is trained using error back propagation (Backward Propagation) algorithm, and step can be concluded
It is as follows:
Step 1: a selected structurally reasonable network, is arranged the initial value of all Node B thresholds and connection weight.
Step 2: making following calculate to each input sample:
(a) forward calculation: to l layers of j unit
In formula,L layers of j unit information weighted sum when being calculated for n-th,For l layers of j units with
Connection weight between the unit i of preceding layer (i.e. l-1 layers),For preceding layer (i.e. l-1 layers, number of nodes nl-1) unit
The working signal that i is sent；When i=0, enable For l layers of j units
Threshold value.
If the activation primitive of unit j is sigmoid function,
And
If neuron j belongs to the first hidden layer (l=1), have
If neuron j belongs to output layer (l=L), have
And ej(n)=xj(n)-oj(n)；
(b) retrospectively calculate error:
For output unit
To hidden unit
(c) weight is corrected:
η is learning rate.
Step 3: new sample or a new periodic samples are inputted, and until network convergence, the sample in each period in training
Input sequence is again randomly ordered.
BP algorithm seeks nonlinear function extreme value using gradient descent method, exists and falls into local minimum and convergence rate is slow etc.
Problem.A kind of more efficiently algorithm is Levenberg-Marquardt optimization algorithm, it makes the e-learning time shorter,
Network can be effectively inhibited and sink into local minimum.Its weighed value adjusting rate is selected as
Δ ω=(JTJ+μI)-1JTe
Wherein J is error to Jacobi (Jacobian) matrix of weight differential, and I is input vector, and e is error vector,
Variable μ is the scalar adaptively adjusted, for determining that study is completed according to Newton method or gradient method.
In system design, system model is one merely through the network being initialized, and weight needs basis using
The data sample obtained in journey carries out study adjustment, devises the self-learning function of system thus.Specify learning sample and
In the case where quantity, system can carry out self study, to constantly improve network performance.
Step 3, server judge that terminal display screen shows alarm condition according to the alarm level of output；Wherein, described one
Grade alarm level is safe condition, is not necessarily to make regulation measure to road to be monitored, and the secondary alarm grade is alert status,
Treat prison road and make close prosecution, the three-level alarm level is congestion status, to road to be monitored make urgent early warning and
Dredging measure, the level Four alarm level are heavy congestion state, make shunting dredging or traffic lights control to road to be monitored
Equal measures.
When output layer neuron vector is o3Or o4When, server sends warning message to the receiver of automobile, reminds vehicle
Front road conditions.
Although the embodiments of the present invention have been disclosed as above, but its is not only in the description and the implementation listed
With it can be fully applied to various fields suitable for the present invention, for those skilled in the art, can be easily
Realize other modification, therefore without departing from the general concept defined in the claims and the equivalent scope, the present invention is simultaneously unlimited
In specific details and embodiment shown and described herein.
Claims (10)
1. the road conditions alarm system based on car networking communication characterized by comprising
GPS positioning system is set on vehicle, for monitoring vehicle location；
Vehicle speed sensor is arranged at vehicle hub, for monitoring speed；
Camera, setting is in monitoring section, for acquiring road surface picture.
2. the road conditions alarm system according to claim 1 based on car networking communication, which is characterized in that further include:
Data acquisition and storage module is arranged at vehicle, and the GPS positioning system, the vehicle speed sensor, for adopting
Collection data simultaneously store, send data
Sensing module acquires location information and the camera acquisition of the vehicle that the Data acquisition and storage module is sent
Vehicle present position ambient image；
Communication module receives the information of the sensing module acquisition, breath of concurrently delivering letters；
Server receives the information that the communication module is sent, and handles analysis information, generates alarm signal；
Terminal display screen receives and shows the alarm signal of the server；
Alarm system receives the alarm signal that the server is sent.
3. the road conditions alarm system according to claim 2 based on car networking communication, which is characterized in that further include:
Receiver is arranged at vehicle, the information sent for receiving the server；
Display is arranged at meter panel of motor vehicle, for showing traffic information.
4. the road conditions alarm system according to claim 3 based on car networking communication, which is characterized in that
The ambient image and vehicle operation data are carried out integrated treatment by the server, and with preset congestion warning picture
And data are matched, and alarm command is generated.
5. the road conditions alarm method based on car networking communication, which comprises the following steps:
Step 1, according to the sampling period, acquire the vehicle fleet N, average speed V, average spacing S, vehicle flowrate Q in section to be monitored,
And determine the lane load factor ξ in monitoring section；
Step 2 successively standardizes above-mentioned parameter, determines the input layer vector x={ x of three layers of BP neural network1,
x2,x3,x4,x5, wherein x1For the vehicle fleet coefficient in section to be monitored, x2For the average speed coefficient in section to be monitored, x3
For the average spacing coefficient in section to be monitored, x4For the wagon flow coefficient of discharge in section to be monitored, x5For the lane load in section to be monitored
Index coefficient；
Step 3, the input layer DUAL PROBLEMS OF VECTOR MAPPING to hidden layer, the hidden layer vector y={ y1,y2,…,ym, m is hidden node
Number；
Step 4 obtains output layer neuron vector o={ o1,o2,o3,o4}；Wherein o1For level-one alarm level, o2For second level report
Alert grade, o3For three-level alarm level, o4For level Four alarm level；The output layer neuron value isK is output layer neuron sequence number, and k={ 1,2,3,4 }, i are i-th of alarm of setting
Grade, i={ 1,2,3,4 }, works as okWhen being 1, at this point, road to be monitored is in okCorresponding alarm level；
Step 5, server judge that terminal display screen shows alarm condition according to the alarm level of output；Wherein, the level-one report
Alert grade is safe condition, is not necessarily to make regulation measure to road to be monitored, and the secondary alarm grade is alert status, is treated
Prison road makes close prosecution, and the three-level alarm level is congestion status, makes urgent early warning and dredging to road to be monitored
Measure, the level Four alarm level are heavy congestion state, make to road to be monitored and shunt dredging measure.
6. the road conditions alarm method according to claim 5 based on car networking communication, which is characterized in that the lane in section is negative
The calculation method of lotus index ξ:
In formula, b is the road width in section to be monitored, and L ' is the link length in section to be monitored, and τ is number of track-lines, and a is vehicle
Mean breadth, V are the Maximum speed limit for monitoring section.
7. the road conditions alarm method according to claim 6 based on car networking communication, which is characterized in that
The hidden node number m meets:Wherein n is input layer number, and p is output layer section
Point number.
8. the road conditions alarm method according to claim 6 based on car networking communication, which is characterized in that by section to be monitored
Vehicle fleet N, average speedAverage spacing S, vehicle flowrate Q, lane load factor ξ carry out normalized formula are as follows:
Wherein, xjFor the parameter in input layer vector, XjRespectively measurement parameter N,S,Q,ξ；
J=1,2,3,4,5；XjmaxAnd XjminMaximum value and minimum value in respectively corresponding measurement parameter.
9. the road conditions alarm method according to claim 8 based on car networking communication, which is characterized in that the hidden layer and institute
The excitation function for stating output layer is all made of S type function fj(x)=1/ (1+e-x)。
10. the road conditions alarm method according to claim 5 based on car networking communication, which is characterized in that
In the step 5, when output layer neuron vector is o3Or o4When, server sends warning message to the receiver of automobile,
Remind vehicle front road conditions.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910144507.7A CN109785628A (en) | 2019-02-27 | 2019-02-27 | Road conditions alarm system and alarm method based on car networking communication |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910144507.7A CN109785628A (en) | 2019-02-27 | 2019-02-27 | Road conditions alarm system and alarm method based on car networking communication |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN109785628A true CN109785628A (en) | 2019-05-21 |
Family
ID=66487248
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910144507.7A Pending CN109785628A (en) | 2019-02-27 | 2019-02-27 | Road conditions alarm system and alarm method based on car networking communication |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109785628A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110154961A (en) * | 2019-05-29 | 2019-08-23 | 辽宁工业大学 | A kind of anti-collision device for automobile and method based on Internet of Things control |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH1079096A (en) * | 1996-09-03 | 1998-03-24 | Toshiba Corp | Device for providing path selection support information |
| CN102737508A (en) * | 2012-06-19 | 2012-10-17 | 银江股份有限公司 | Urban road traffic state detection method combined with support vector machine (SVM) and back propagation (BP) neural network |
| CN102750824A (en) * | 2012-06-19 | 2012-10-24 | 银江股份有限公司 | Urban road traffic condition detection method based on voting of network sorter |
| CN103903468A (en) * | 2012-12-25 | 2014-07-02 | 上海博泰悦臻电子设备制造有限公司 | Time early-warning method for traffic jam and device thereof |
| WO2014197911A1 (en) * | 2013-06-07 | 2014-12-11 | Yandex Europe Ag | Methods and systems for representing a degree of traffic congestion using a limited number of symbols |
| CN108545082A (en) * | 2018-04-28 | 2018-09-18 | 辽宁工业大学 | A kind of automobile lane change method for early warning |
| CN108766007A (en) * | 2018-08-02 | 2018-11-06 | 成都秦川物联网科技股份有限公司 | Road conditions alarm method based on car networking and car networking system |
-
2019
- 2019-02-27 CN CN201910144507.7A patent/CN109785628A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH1079096A (en) * | 1996-09-03 | 1998-03-24 | Toshiba Corp | Device for providing path selection support information |
| CN102737508A (en) * | 2012-06-19 | 2012-10-17 | 银江股份有限公司 | Urban road traffic state detection method combined with support vector machine (SVM) and back propagation (BP) neural network |
| CN102750824A (en) * | 2012-06-19 | 2012-10-24 | 银江股份有限公司 | Urban road traffic condition detection method based on voting of network sorter |
| CN103903468A (en) * | 2012-12-25 | 2014-07-02 | 上海博泰悦臻电子设备制造有限公司 | Time early-warning method for traffic jam and device thereof |
| WO2014197911A1 (en) * | 2013-06-07 | 2014-12-11 | Yandex Europe Ag | Methods and systems for representing a degree of traffic congestion using a limited number of symbols |
| CN108545082A (en) * | 2018-04-28 | 2018-09-18 | 辽宁工业大学 | A kind of automobile lane change method for early warning |
| CN108766007A (en) * | 2018-08-02 | 2018-11-06 | 成都秦川物联网科技股份有限公司 | Road conditions alarm method based on car networking and car networking system |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110154961A (en) * | 2019-05-29 | 2019-08-23 | 辽宁工业大学 | A kind of anti-collision device for automobile and method based on Internet of Things control |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108545082B (en) | A kind of automobile lane change method for early warning | |
| CN109859469A (en) | A kind of vehicle flowrate prediction technique based on integrated LSTM neural network | |
| CN109697852A (en) | Urban road congestion degree prediction technique based on timing traffic events | |
| CN104376716B (en) | Method for dynamically generating bus timetables on basis of Bayesian network models | |
| Akgüngör et al. | An artificial intelligent approach to traffic accident estimation: Model development and application | |
| CN107437339A (en) | Variable information advices plate control method for coordinating and system under a kind of information guidance | |
| CN109455178A (en) | A kind of road vehicles traveling active control system and method based on binocular vision | |
| CN106373390B (en) | Traffic state evaluation method based on Adaptive Neuro-fuzzy Inference | |
| CN108944946A (en) | A kind of bend risk based on truck traffic estimates calculation method | |
| CN110111573A (en) | A kind of tailback integrated dispatch method based on Internet of Things | |
| CN114170789B (en) | Intelligent network link lane change decision modeling method based on space-time diagram neural network | |
| CN106503868A (en) | A kind of fleet's layout of roads method and relevant device | |
| CN109118787A (en) | A kind of car speed prediction technique based on deep neural network | |
| CN109799700A (en) | A kind of harmful influence transport early warning system based on fuzzy neural network | |
| CN110781873A (en) | Driver fatigue grade identification method based on bimodal feature fusion | |
| CN107976934B (en) | A kind of oil truck oil and gas leakage speed intelligent early-warning system based on wireless sensor network | |
| Furtlehner et al. | A belief propagation approach to traffic prediction using probe vehicles | |
| CN113393665A (en) | Planning method for dangerous goods transportation path under uncertain time-varying road network | |
| CN109886439A (en) | Electronic-control vehicle remote diagnosis system and its diagnostic method | |
| CN106683222A (en) | Detection device and detection method for technical performances of engineering vehicle | |
| CN104331746B (en) | A kind of dynamic path optimization system and method for separate type | |
| CN110682903B (en) | Safety overtaking early warning system and method based on visual perception | |
| CN116901975A (en) | Vehicle-mounted AI security monitoring system and method thereof | |
| CN109785628A (en) | Road conditions alarm system and alarm method based on car networking communication | |
| CN109532831B (en) | Vehicle speed control method and related device thereof |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20190521 |
|
| RJ01 | Rejection of invention patent application after publication |