CN108801261A - A kind of proving ground test routine planing method based on ant group algorithm - Google Patents
A kind of proving ground test routine planing method based on ant group algorithm Download PDFInfo
- Publication number
- CN108801261A CN108801261A CN201810517257.2A CN201810517257A CN108801261A CN 108801261 A CN108801261 A CN 108801261A CN 201810517257 A CN201810517257 A CN 201810517257A CN 108801261 A CN108801261 A CN 108801261A
- Authority
- CN
- China
- Prior art keywords
- node
- thread
- test
- pheromones
- proving ground
- 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.)
- Granted
Links
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/20—Instruments for performing navigational calculations
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M17/00—Testing of vehicles
- G01M17/007—Wheeled or endless-tracked vehicles
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/004—Artificial life, i.e. computing arrangements simulating life
- G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Biophysics (AREA)
- Evolutionary Computation (AREA)
- Artificial Intelligence (AREA)
- Biomedical Technology (AREA)
- Health & Medical Sciences (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- Life Sciences & Earth Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Automation & Control Theory (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a kind of proving ground test routine planning algorithm based on ant group algorithm, the characteristics of being tested for proving ground, ant colony intelligence algorithm is introduced into test routine planning, it helps test site to effectively reduce to carry out testing caused time cost, the waste of oil consumption cost using inefficient redundant path, the efficiency of lifting feature section reliability test, the whole effective rate of utilization for improving test site, can quickly and efficiently cook up optimal path.
Description
Fields
The present invention relates to proving ground test specifications to formulate field, especially a kind of to be directed to the multiple experiments of proving ground
Section, the proving ground test routine planing method based on ant group algorithm.
Background technology
Automobile Reliability Test is a kind of important means of examination and evaluation vehicle durability, in order to ensure in worst operating mode
Lower vehicle component is not broken, it is desirable that vehicle completes the reliability examination of no less than setting mileage number on various reinforcing roads
It tests.The reinforcing road type and mileage number that testing crew is tested by formulating the clear vehicle of reliability test specification,
Since the reinforcing road section length that test site is built up is fixed, referred to characterize mileage by number by each reinforcing section in test specification
Mark.
All kinds of reinforcing pavement of road features are different in proving ground, for each test section of flexible configuration, experiment
Build when take many forms, section be to be laid with parallel, section be connected with each other.Adjacent different tests road
Prevailing roadway connection is all had between section.The road layout of one test specification is mainly to determine which test section connection of needs
Get up for a cycle and corresponding cycle-index, test specification includes more than ten of partial circulating.As can be seen that for reliable
There are multiple test courses met the requirements for each test cycle in property test specification.Currently, test site is usually to allow examination
It tests driver oneself and plans drive route, in this way to the same test cycle, different test drive persons can cause different total
Body test miles, some test drive persons take considerable time that traveling on useless contact road, increases oil consumption and to vehicle
Loss, be unfavorable for experiment normalization.
Thus, the practical development in field is formulated in conjunction with current proving ground test specification, based on China's vehicle tests
Present situation, studying a kind of path specification method of test section has highly important application value.
Invention content
The present invention is exactly the blank to proving ground test routine planning field for the prior art, and proposes one kind
Proving ground test routine planing method based on ant group algorithm is arranged according to the location information of actual place and laboratory technician
Specification, i.e., each feature road is respectively necessary for by several times, giving a kind of optimization path, test vehicle being made to complete to require
Ensure that the total mileage of traveling is minimum, reduces invalid mileage travelled while feature section is tested, improves feature section reliability
The efficiency of experiment, the oil consumption and vehicle body for reducing vehicle are lost, and improve the effective rate of utilization in test site place.
To achieve the goals above, the technical solution adopted by the present invention is：A kind of proving ground based on ant group algorithm
Test routine planing method, which is characterized in that include the following steps：
S1, position and contact road length are determined：The relative position in terminal garage and each feature section in test site is obtained,
Measure garage and the contact road length between each feature section, feature section；
S2, Weighted Directed Graph is drawn：Make node with terminal garage and feature section, contact road makees side, draws test site
The corresponding Weighted Directed Graph G=(N, E) in ground, wherein N={ 0,1,2,3 ..., n }, E=(i, j) | and i, j ∈ N }, get in touch with the road on road
The long weights as corresponding sides；
S3, adjacency matrix is generated：Adjacency matrix C is generated according to the Weighted Directed Graph that step S2 is obtained, adjacency matrix is as follows
Formula defines；
Wherein di,jRefer to side (i, j) apart from weights, when there is no side (i, j), i.e. i-node can not directly reach j nodes
When, weights are set as -1.
S4, constraint array is generated：The requirement of each feature section corresponding node is set after number, generates constraint array R, about
Beam array length is equal to number of nodes, and the i-th element corresponds to the requirement of i-node after number；
S5, setting initialization global parameter：Including parallel research number of threads m, iterations n, pheromones attenuation coefficient r
And Pheromone Matrix T, Pheromone Matrix are defined as；
When there are side (i, j), pheromones are initialized as 1.0, are otherwise initialized as 0.0.
S6, each parallel research thread is completed according to behavior probability function：
S61：Initialized location is set as start node；
S62：Next node to be reached, structure is selected to explore each node passed through in thread according to behavior probability function
Number；
S63：The node that each exploration thread passes through successively is stored in path memory vector path, line will be each explored
Journey is deposited in by each degree of node after number array Rant；
S64：It completes each parallel research thread and makes a record；
S7, each node is completed after number requirement when each parallel research thread, i.e. each element is not less than right in R in Rant
Element is answered, and back to the node that sets out, then completes this exploration；Otherwise, step S6 is repeated.
S8, when all m threads complete explore, find and record the short line S in m thread and its it is corresponding can
Walking along the street diameter.
S9, Pheromone Matrix T updates are carried out according to Pheromone update function；
S10, judge whether to complete n times iteration, if completing, stop iteration, obtain optimal path, export optimal path, it is no
Then, S6 is gone to, continues to explore iteration.
As an improvement of the present invention, behavior probability function is in the step S62：
Wherein α is information heuristic factor, β is visibility factor, Rj(t) indicate t moment j-th of node need to also after time
Number, Ti,j(t) the pheromones value of t moment (i, j) is indicated.
Another as the present invention improves, and Pheromone update function is in the step S9：
Wherein r is pheromones attenuation coefficient, Q is pheromones constant, PathkIndicate set of paths, the Sk tables of k-th of thread
Show the total kilometrage that k-th of thread current iteration is explored.
Compared with prior art, the present invention proposes a kind of proving ground test routine planning side based on ant group algorithm
Method compensates for the blank at present in terms of proving ground test routine planning, and general paths planning method can not solve
Certainly carry Dominator and the problem of after count constraint, can guarantee test vehicle complete to require with shorter total mileage it is special
Section experiment is levied, effectively reduces and carries out testing caused time cost, the waste of oil consumption cost using inefficient redundant path, promoted
The efficiency of feature section reliability test improves the whole effective rate of utilization in test site place.This method is set according to ant colony principle
Meter, the node for exploring initial stage high frequency time is more attractive to parallel research thread, increases the spy for exploring thread to destination node
Rope probability improves the early stage search efficiency of algorithm, thereafter as each exploration thread completes the visiting demand of high frequency minor node, respectively
Exploration probability between node reaches unanimity, and is conducive to, in global search target solution, effectively prevent algorithm and be absorbed in local solution, improves
The stability of algorithm.
Description of the drawings
Fig. 1 is the operational flow diagram of ant group algorithm of the present invention；
Fig. 2 is test site exemplary plot provided by the invention.
Specific implementation mode
Below with reference to drawings and examples, the present invention is described in detail.
A kind of proving ground test routine planing method based on ant group algorithm, figure as indicated with 1, include the following steps：
S1, position and contact road length are determined：The relative position in terminal garage and each feature section in test site is obtained,
Measure garage and the contact road length between each feature section, feature section；
By taking Fig. 2 as an example, No. 0 node in figure indicates that the terminal garage of test vehicle, 1-7 nodes indicate respectively for we
7 feature sections determine the position of node 0-7, and respectively in the contact road length and node 1-7 of measuring node 0 and 1-7
Contact road length each other gets in touch with road length table specific as follows：
S2, Weighted Directed Graph is drawn：Make node with terminal garage and feature section, contact road makees side, draws test site
The corresponding Weighted Directed Graph G=(N, E) in ground, wherein N={ 0,1,2,3 ..., n }, E=(i, j) | and i, j ∈ N }, get in touch with the road on road
The long weights as corresponding sides；As shown in Fig. 2, the one-way road that the directed edge (i, j) in figure represents corresponding travel direction gets in touch with road,
The weights on side indicate that the road on the contact road is long, i.e., such as upper table；
S3, adjacency matrix is generated：Adjacency matrix C is generated according to the Weighted Directed Graph that step S2 is obtained, adjacency matrix is as follows
Formula defines；
Wherein di,jRefer to side (i, j) apart from weights, when there is no side (i, j), i.e. i-node can not directly reach j nodes
When, weights are set as -1.
The adjacency matrix such as formula (1) as obtained by Fig. 2 examples, -1 represents between corresponding node that there is no contact road, positive values in the matrix
Represent the length that road is got in touch between corresponding node, such as (2,1) position -1 represents in test site that there is no No. 2 feature sections extremely
The contact road in No. 1 feature section, the 29 of (1,0) position represent in test site that there are No. 1 feature sections to No. 0 feature in matrix
The contact road that the length in section is 29；
S4, constraint array is generated：The requirement of each feature section corresponding node is set after number, generates constraint array R, about
Beam array length is equal to number of nodes, and the i-th element corresponds to the requirement of i-node after number；
Assuming that the present embodiment constrains array such as formula (2), 6 represent No. 1 section and need at least access 6 times in array, and No. 2 sections are extremely
It accesses 8 times less, No. 3 sections at least access 2 times, and No. 4 sections at least access 4 times, and No. 5 sections at least access 9 times, and No. 6 sections are extremely
It accesses 5 times less, No. 7 sections at least access 3 times；
[0 6 8 2 4 9 5 3] (2)
S5, setting initialization global parameter：Including parallel research number of threads m, iterations n, pheromones attenuation coefficient r
And Pheromone Matrix T, Pheromone Matrix are defined as；
Assuming that m=50, n=1000, r=0.5 in the present embodiment, in ant colony algorithm theory, the exploration line of the present embodiment
Number of passes amount is ant number, and 50 parallel research threads are that 50 ants are carried out at the same time way exploration, explore 1000 in total
Wheel.Pheromone Matrix initialization is carried out according to starting adjacency matrix, when there are side (i, j), that is, there is the correspondence position on contact road
It installs and is set to 1, be otherwise provided as 0, obtain shown in pheromones initial matrix such as formula (3)；
S6, each parallel research thread is completed according to behavior probability function：
S61：Initialized location is set as start node；
S62：Next node to be reached is selected according to a behavior probability function, is passed through in structure exploration thread each
Node number, the behavior probability function are：
Wherein α is information heuristic factor, is used for the significance level of set information element；β is visibility factor, for setting section
The significance level of distance between point；Rj(t) indicate t moment j-th of node need to also after number, it is ensured that the node more maximum probability quilt
It accesses；Ti,j(t) the pheromones value of t moment (i, j) is indicated.This example settings α=1.0, β=2.0.
S63：The node that each exploration thread passes through successively is stored in path memory vector path, line will be each explored
Journey is deposited in by each degree of node after number array Rant；
S64：It completes each parallel research thread and makes a record；
S7, each node is completed after number requirement when each parallel research thread, i.e. each element is not less than right in R in Rant
Element is answered, and back to the node that sets out, then completes this exploration；Otherwise, step S6 is repeated.
S8, when all m threads complete explore, find and record the short line S in m thread and its it is corresponding can
Walking along the street diameter.
S9, Pheromone Matrix update：Pheromone Matrix T updates are carried out by following Pheromone update function,
Wherein r is pheromones attenuation coefficient, is used for set information element rate of decay；Q is pheromones constant, for setting letter
The plain concentration level of breath；PathkIndicate the set of paths of kth ant, the i.e. ordered set on its side passed through；SkIndicate kth ant
The total kilometrage that ant current iteration is explored.The update that pheromones are put to the proof can be preferably step S6 services, and probability function is more accurate
Really；
S10, judge whether to complete the iteration of the present embodiment n=1000 time, if completing, stopping iteration obtaining optimal path,
Otherwise output optimal path goes to S6, continue to explore iteration.
This example final result distance is 2062.0, and planning path is as follows：
0-1-2-3-4-5-1-2-3-4-5-1-6-4-5-1-7-4-5-1-7-4-5-1-2-3-4-5-1-6-4-5-1-6-
4-5-1-6-4-5-1-2-3-4-5-1-2-3-4-5-1-2-3-4-5-1-2-3-4-5-1-7-4-5-1-2-3-4-5-1-6-4-0
The basic principles, main features and advantages of the present invention have been shown and described above.The technology of the industry
For personnel it should be appreciated that the present invention is not limited by examples detailed above, described in examples detailed above and specification is to illustrate the present invention
Principle, various changes and improvements may be made to the invention without departing from the spirit and scope of the present invention, these variation and
Improvement all fall within the protetion scope of the claimed invention.The claimed scope of the invention is by appended claims and its is equal
Object defines.
Claims (3)
1. a kind of proving ground test routine planing method based on ant group algorithm, which is characterized in that include the following steps：
S1, position and contact road length are determined：The relative position in terminal garage and each feature section in test site is obtained, is measured
Contact road length between garage and each feature section, feature section；
S2, Weighted Directed Graph is drawn：Make node with terminal garage and feature section, contact road makees side, draws test site pair
The Weighted Directed Graph G=(N, E) answered, wherein N={ 0,1,2,3 ..., n }, E=(i, j) | and i, j ∈ N }, get in touch with the Lu Changzuo on road
For the weights of corresponding sides；
S3, adjacency matrix is generated：Adjacency matrix C is generated according to the Weighted Directed Graph that step S2 is obtained, adjacency matrix such as following formula is fixed
Justice；
Wherein di,jRefer to side (i, j) apart from weights, when there is no side (i, j), i.e., when i-node can not directly reach j nodes, power
Value is set as -1.
S4, constraint array is generated：The requirement of each feature section corresponding node is set after number, constraint array R is generated, constrains number
Group length is equal to number of nodes, and the i-th element corresponds to the requirement of i-node after number；
S5, setting initialization global parameter：Including parallel research number of threads m, iterations n, pheromones attenuation coefficient r and
Pheromone Matrix T, Pheromone Matrix are defined as；
When there are side (i, j), pheromones are initialized as 1.0, are otherwise initialized as 0.0.
S6, each parallel research thread is completed according to behavior probability function：
S61：Initialized location is set as start node；
S62：Next node to be reached, structure is selected to explore each node passed through in thread according to behavior probability function
Number；
S63：The node that each exploration thread passes through successively is stored in path memory vector path, thread warp will be each explored
Each degree of node is crossed to deposit in after number array Rant；
S64：It completes each parallel research thread and makes a record；
S7, each node is completed after number requirement when each parallel research thread, i.e. each element is not less than corresponding element in R in Rant
Element, and back to the node that sets out, then complete this exploration；Otherwise, step S6 is repeated.
S8, when all m threads complete explore, find and record the short line S in m thread and its it is corresponding can walking along the street
Diameter.
S9, Pheromone Matrix T updates are carried out according to Pheromone update function；
S10, judge whether to complete n times iteration, if completing, stop iteration, obtain optimal path, export optimal path, otherwise, turn
To S6, continue to explore iteration.
2. a kind of proving ground test routine planing method based on ant group algorithm as described in claim 1, feature exist
In behavior probability function is in the step S62：
Wherein α is information heuristic factor, β is visibility factor, Rj(t) indicate t moment j-th of node need to also after number,
Ti,j(t) the pheromones value of t moment (i, j) is indicated.
3. a kind of proving ground test routine planing method based on ant group algorithm as claimed in claim 2, feature exist
In Pheromone update function is in the step S9：
Wherein r is pheromones attenuation coefficient, Q is pheromones constant, PathkIndicate that the set of paths of k-th of thread, Sk indicate kth
The total kilometrage that a thread current iteration is explored.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810517257.2A CN108801261B (en) | 2018-05-25 | 2018-05-25 | Automobile test field test path planning method based on ant colony algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810517257.2A CN108801261B (en) | 2018-05-25 | 2018-05-25 | Automobile test field test path planning method based on ant colony algorithm |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108801261A true CN108801261A (en) | 2018-11-13 |
| CN108801261B CN108801261B (en) | 2021-05-11 |
Family
ID=64089108
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810517257.2A Active CN108801261B (en) | 2018-05-25 | 2018-05-25 | Automobile test field test path planning method based on ant colony algorithm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108801261B (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109489667A (en) * | 2018-11-16 | 2019-03-19 | 楚天智能机器人（长沙）有限公司 | A kind of improvement ant colony paths planning method based on weight matrix |
| CN110245776A (en) * | 2019-04-26 | 2019-09-17 | 惠州学院 | A kind of intelligent transportation paths planning method based on more attribute ant group algorithms |
| CN113704964A (en) * | 2021-07-21 | 2021-11-26 | 一汽解放汽车有限公司 | Route design method and device for reliability test of whole vehicle and computer equipment |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040143560A1 (en) * | 2003-01-20 | 2004-07-22 | Chun Bao Zhu | Path searching system using multiple groups of cooperating agents and method thereof |
| CN102289712A (en) * | 2011-08-10 | 2011-12-21 | 天津商业大学 | Method for optimizing minimum emergency logistic path based on fish-ant colony algorithm |
| CN105760954A (en) * | 2016-02-15 | 2016-07-13 | 南通大学 | Parking system path planning method based on improved ant colony algorithm |
| CN106225788A (en) * | 2016-08-16 | 2016-12-14 | 上海理工大学 | The robot path planning method of ant group algorithm is expanded based on path |
| CN107883973A (en) * | 2016-09-29 | 2018-04-06 | 奥迪股份公司 | Method for selecting route for emission test |
| CN107992051A (en) * | 2017-12-26 | 2018-05-04 | 江南大学 | Unmanned vehicle paths planning method based on improved multi-objective particle swarm algorithm |
-
2018
- 2018-05-25 CN CN201810517257.2A patent/CN108801261B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040143560A1 (en) * | 2003-01-20 | 2004-07-22 | Chun Bao Zhu | Path searching system using multiple groups of cooperating agents and method thereof |
| CN102289712A (en) * | 2011-08-10 | 2011-12-21 | 天津商业大学 | Method for optimizing minimum emergency logistic path based on fish-ant colony algorithm |
| CN105760954A (en) * | 2016-02-15 | 2016-07-13 | 南通大学 | Parking system path planning method based on improved ant colony algorithm |
| CN106225788A (en) * | 2016-08-16 | 2016-12-14 | 上海理工大学 | The robot path planning method of ant group algorithm is expanded based on path |
| CN107883973A (en) * | 2016-09-29 | 2018-04-06 | 奥迪股份公司 | Method for selecting route for emission test |
| CN107992051A (en) * | 2017-12-26 | 2018-05-04 | 江南大学 | Unmanned vehicle paths planning method based on improved multi-objective particle swarm algorithm |
Non-Patent Citations (1)
| Title |
|---|
| 崔辰: ""蚁群算法在整车耐久试验中的应用研究"", 《上海汽车》 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109489667A (en) * | 2018-11-16 | 2019-03-19 | 楚天智能机器人（长沙）有限公司 | A kind of improvement ant colony paths planning method based on weight matrix |
| CN110245776A (en) * | 2019-04-26 | 2019-09-17 | 惠州学院 | A kind of intelligent transportation paths planning method based on more attribute ant group algorithms |
| CN110245776B (en) * | 2019-04-26 | 2023-08-01 | 惠州学院 | Intelligent traffic path planning method based on multi-attribute ant colony algorithm |
| CN113704964A (en) * | 2021-07-21 | 2021-11-26 | 一汽解放汽车有限公司 | Route design method and device for reliability test of whole vehicle and computer equipment |
| CN113704964B (en) * | 2021-07-21 | 2024-02-23 | 一汽解放汽车有限公司 | Route design method and device for whole vehicle reliability test and computer equipment |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108801261B (en) | 2021-05-11 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108801261A (en) | A kind of proving ground test routine planing method based on ant group algorithm | |
| Kong et al. | Efficient traffic state estimation for large-scale urban road networks | |
| CN102176283B (en) | Traffic network simplifying model and navigating method based on same | |
| Chawathe | Organizing hot-spot police patrol routes | |
| CN108571978A (en) | Discharge beyond standards vehicle shortest path tracking and matching method based on topology and weight | |
| CN106529815A (en) | Estimation method of passenger trip spatial-temporal trajectory of urban rail transit network and application thereof | |
| CN104217579B (en) | Transportation network key road segment searching method based on section sub-network redundancy | |
| CN106327865A (en) | Urban traffic reliability index based on seepage analysis, and implementation method for urban traffic reliability index | |
| CN110503848A (en) | A kind of best truck space guiding system in parking lot based on Internet of Things | |
| Kriegel et al. | Statistical density prediction in traffic networks | |
| CN105069522A (en) | Touring line evaluation and improvement method in scenic area | |
| CN108537376A (en) | Buried oil-gas pipeline soil corrosion prediction technique based on RS-PSO-GRNN | |
| CN106021902A (en) | Grading method used for urban rail transit passenger flow congestion index | |
| CN116050723A (en) | TOD (time of day) station domain collaborative development evaluation method and device based on crowd source geographic data | |
| CN108876075A (en) | Urban Rail Transit Stations area preferably walkability evaluation method | |
| CN110400045A (en) | Urban railway station radiation area walking accessibility measure method based on microcell | |
| CN100446015C (en) | Method and system in use for menstruating traffic movement on ground road network | |
| Sharif et al. | CaFIRST: A context-aware hybrid fuzzy inference system for the similarity measure of multivariate trajectories | |
| CN106556402A (en) | A kind of paths planning method and device | |
| Chen et al. | Research on parking lot management system based on parking space navigation technology | |
| CN111724625A (en) | Parking lot parking path optimization system based on 5G and Internet of things | |
| CN110413713A (en) | One kind being based on complexity network analysis and its three-dimensional effect evaluation method | |
| Zhang et al. | Cross-modal travel route recommendation algorithm based on internet of things awareness | |
| CN115293427A (en) | Planning method and system for optimal travel route of city early peak | |
| Yasarer et al. | Performance Evaluation of Jointed Concrete Pavements on Mississippi Highways via Artificial Neural Network |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |