CN112017324A - Real-time driving information interaction system and method - Google Patents
Real-time driving information interaction system and method Download PDFInfo
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- CN112017324A CN112017324A CN201910468398.4A CN201910468398A CN112017324A CN 112017324 A CN112017324 A CN 112017324A CN 201910468398 A CN201910468398 A CN 201910468398A CN 112017324 A CN112017324 A CN 112017324A
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- 230000005540 biological transmission Effects 0.000 claims abstract description 19
- 230000008569 process Effects 0.000 claims abstract description 10
- 238000012545 processing Methods 0.000 claims description 28
- 239000011159 matrix material Substances 0.000 claims description 10
- 238000004458 analytical method Methods 0.000 claims description 9
- 238000006243 chemical reaction Methods 0.000 claims description 9
- 238000007405 data analysis Methods 0.000 claims description 9
- 238000013500 data storage Methods 0.000 claims description 9
- 230000001133 acceleration Effects 0.000 claims description 7
- 238000004364 calculation method Methods 0.000 claims description 7
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- 238000001514 detection method Methods 0.000 claims description 5
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- 230000002159 abnormal effect Effects 0.000 claims description 3
- 238000013499 data model Methods 0.000 claims description 3
- 238000007781 pre-processing Methods 0.000 claims description 3
- 238000012549 training Methods 0.000 claims description 3
- 238000012544 monitoring process Methods 0.000 abstract description 3
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- G—PHYSICS
- G07—CHECKING-DEVICES
- G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
- G07C5/00—Registering or indicating the working of vehicles
- G07C5/008—Registering or indicating the working of vehicles communicating information to a remotely located station
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/30—Services specially adapted for particular environments, situations or purposes
- H04W4/38—Services specially adapted for particular environments, situations or purposes for collecting sensor information
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/30—Services specially adapted for particular environments, situations or purposes
- H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
- H04W4/44—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P] for communication between vehicles and infrastructures, e.g. vehicle-to-cloud [V2C] or vehicle-to-home [V2H]
Abstract
The invention discloses a driving information real-time interaction system and method, and relates to the technical field of automobiles. The system comprises: the vehicle-mounted information acquisition device is used for acquiring the running information of the automobile in the running process; a vehicle electronic ID chip for uniquely identifying a vehicle; the system comprises a vehicle-mounted data transmission device and a roadbed base station; and the vehicle-mounted information acquisition device transmits the acquired operation information to the roadbed base station through the data transmission device. The intelligent monitoring system has the advantages of high intelligent degree, high safety and low cost.
Description
Technical Field
The invention relates to the technical field of automobiles, in particular to a short video-based commodity display system and method.
Background
In the prior art, an electronic police for monitoring traffic violation of automobiles usually depends on various detection sensors (such as external detection circuits, radars, microwaves, coils and the like) to realize the function of vehicle illegal judgment and evidence collection, and has the defects of complex installation, single function, narrow application range and the like.
Disclosure of Invention
In view of this, the present invention provides a system and a method for real-time interaction of driving information, which have the advantages of high intelligence degree, high security and low cost.
In order to achieve the purpose, the invention adopts the following technical scheme:
a driving information real-time interaction system, the system comprising: the vehicle-mounted information acquisition device is used for acquiring the running information of the automobile in the running process; a vehicle electronic ID chip for uniquely identifying a vehicle; the system comprises a vehicle-mounted data transmission device and a roadbed base station; and the vehicle-mounted information acquisition device transmits the acquired operation information to the roadbed base station through the data transmission device.
Further, the vehicle-mounted information acquisition device comprises: the device comprises a speed sensor, an acceleration sensor, an angular velocity sensor, a brake sensor, an information recorder, a data conversion device and a central processing chip; the central processing chip is connected with the data conversion device through signals; the data conversion device is respectively in signal connection with the speed sensor, the acceleration sensor, the angular velocity sensor, the brake sensor and the information recorder.
Further, the roadbed base station comprises: the chip identification device, the data storage device and the data analysis device; the chip identification device is respectively connected with the data storage device and the data analysis device through signals; the data storage device is in signal connection with the data analysis device.
Further, the system further comprises: a cloud end; the roadbed base station further comprises: an information transmission device; and the roadbed base station is connected with the cloud signal through the information transmission device.
Further, the operation information at least includes: the system comprises automobile speed per hour information, lane changing operation information, brake operation information, steering lamp use information, headlamp use information and turning information.
A real-time driving information interaction method comprises the following steps:
step 1: the vehicle-mounted information acquisition device acquires the running information of the automobile in real time, and transmits the acquired real-time information of the automobile and the electronic ID of the automobile to the roadbed base station through the vehicle-mounted data transmission device;
step 2: the subgrade base station judges whether the vehicle is a newly recorded vehicle or not according to the received vehicle electronic ID; if the vehicle is newly recorded, creating a storage space belonging to the vehicle; if the vehicle is not newly recorded, the vehicle is linked to the existing storage space;
and step 3: the roadbed base station analyzes the received real-time information and stores the received real-time information;
and 4, step 4: and the roadbed base station sends the analysis result to a cloud terminal.
Further, in step 3, the method for analyzing the received real-time information by the roadbed base station performs the following steps:
the data processing is carried out according to the received data information, data modeling is carried out according to the data processing result, and a data model of the automatic automobile analysis system is generated, and the method specifically comprises the following steps:
step S3.1: performing data preprocessing, including: removing the unique attribute, processing missing values and abnormal value detection and processing;
step S3.2: and carrying out data specification processing, including: mean value removing, covariance matrix calculation, eigenvalue and eigenvector calculation of the covariance matrix, sorting the eigenvalues from large to small, reserving the largest k eigenvectors, and converting data into a new space constructed by the k eigenvectors; finally, new processed data are obtained, and the data are irrelevant pairwise, but original information can be kept as far as possible.
Step S3.3: carrying out data standardization processing, and scaling the data in proportion to make the data fall into a small specific interval; the data is linearly transformed by using the following transformation function, so that the result falls in the [0,1] interval, wherein the transformation function is as follows:
step S3.4: carrying out data modeling;
step S3.5: performing an effect analysis comprising: after the model training is finished, calculating the accuracy of the customer satisfaction data generated by the model and the original customer satisfaction data by adopting the following formula, namely obtaining R2Scoring, wherein the higher the score is, the better the model accuracy is represented;
where y represents model-generated customer satisfaction data (predicted values);
nsamplesrepresenting the size of the sample size entering the model.
Compared with the prior art, the invention has the following beneficial effects: the intelligent monitoring system has the advantages of high intelligent degree, high safety and low cost.
Drawings
The invention is described in further detail below with reference to the following figures and detailed description:
fig. 1 is a schematic system structure diagram of a driving information real-time interaction system disclosed in an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention is provided for illustrative purposes, and other advantages and effects of the present invention will become apparent to those skilled in the art from the present disclosure.
Please refer to fig. 1. It should be understood that the structures, ratios, sizes, and the like shown in the drawings and described in the specification are only used for matching with the disclosure of the specification, so as to be understood and read by those skilled in the art, and are not used to limit the conditions under which the present invention can be implemented, so that the present invention has no technical significance, and any structural modification, ratio relationship change, or size adjustment should still fall within the scope of the present invention without affecting the efficacy and the achievable purpose of the present invention. In addition, the terms "upper", "lower", "left", "right", "middle" and "one" used in the present specification are for clarity of description, and are not intended to limit the scope of the present invention, and the relative relationship between the terms and the terms is not to be construed as a scope of the present invention.
Example 1
A driving information real-time interaction system, the system comprising: the vehicle-mounted information acquisition device is used for acquiring the running information of the automobile in the running process; a vehicle electronic ID chip for uniquely identifying a vehicle; the system comprises a vehicle-mounted data transmission device and a roadbed base station; and the vehicle-mounted information acquisition device transmits the acquired operation information to the roadbed base station through the data transmission device.
Example 2
On the basis of the above embodiment, the on-vehicle information acquisition apparatus includes: the device comprises a speed sensor, an acceleration sensor, an angular velocity sensor, a brake sensor, an information recorder, a data conversion device and a central processing chip; the central processing chip is connected with the data conversion device through signals; the data conversion device is respectively in signal connection with the speed sensor, the acceleration sensor, the angular velocity sensor, the brake sensor and the information recorder.
Example 3
On the basis of the above embodiment, the roadbed base station includes: the chip identification device, the data storage device and the data analysis device; the chip identification device is respectively connected with the data storage device and the data analysis device through signals; the data storage device is in signal connection with the data analysis device.
Specifically, the vehicle-mounted data transmission device further includes: and the data encryption device is used for encrypting the transmitted information when the vehicle ID and the operation information are transmitted.
The method for encrypting may include: receiving original data; calculating the dimension of an encryption matrix; calculating an encryption length to obtain a plurality of data fragments conforming to the encryption length from the original data in sequence according to the encryption length; and encrypting each of the retrieved data segments by means of the encryption matrix to obtain a plurality of encrypted segments; wherein the step of calculating the dimension of the encryption matrix comprises: determining a power of a finite field based on the basis of the original data; and determining the dimension of the encryption matrix according to the power of the finite field; wherein the base of the original data and the power of the finite field satisfy a first constraint, the first constraint comprising: 2k ^ d; where k represents the power of the finite field and d represents the base of the original data.
Example 4
On the basis of the above embodiment, the system further includes: a cloud end; the roadbed base station further comprises: an information transmission device; and the roadbed base station is connected with the cloud signal through the information transmission device.
Further, the information transmission apparatus also includes: a data encryption unit; the data encryption unit encrypts the data by the following method; generating and storing random seeds with the size of H according to a preset method; collecting data from the random seeds for multiple times according to the times u, cascading the data collected each time into a 0 and 1 value random string which is not less than the length of a plaintext, and generating a plaintext encryption bit identification data string by using the random string; selecting more than one half of plaintext data to encrypt by utilizing the plaintext encryption bit identification data string: and arranging the encrypted data and the unencrypted data according to the positions of the plaintext to form a ciphertext. The invention provides a cloud storage data encryption device, which comprises: and the random seed size and acquisition frequency calculation module is used for calculating the size H of a random seed to be generated according to the data volume X expected to be stored in the cloud storage data center in a preset time period, the local storage space occupation ratio R and the data security level Z, and calculating the frequency u of random data acquisition on the random seed according to the data volume Y of a plaintext to be encrypted each time.
Example 5
On the basis of the above embodiment, the operation information at least includes: the system comprises automobile speed per hour information, lane changing operation information, brake operation information, steering lamp use information, headlamp use information and turning information.
Example 6
A real-time driving information interaction method comprises the following steps:
step 1: the vehicle-mounted information acquisition device acquires the running information of the automobile in real time, and transmits the acquired real-time information of the automobile and the electronic ID of the automobile to the roadbed base station through the vehicle-mounted data transmission device;
step 2: the subgrade base station judges whether the vehicle is a newly recorded vehicle or not according to the received vehicle electronic ID; if the vehicle is newly recorded, creating a storage space belonging to the vehicle; if the vehicle is not newly recorded, the vehicle is linked to the existing storage space;
and step 3: the roadbed base station analyzes the received real-time information and stores the received real-time information;
and 4, step 4: and the roadbed base station sends the analysis result to a cloud terminal.
Further, the operation information at least includes: the system comprises automobile speed per hour information, lane changing operation information, brake operation information, steering lamp use information, headlamp use information and turning information. The data are collected by a speed sensor, an acceleration sensor, an angular velocity sensor, a brake sensor and an information recorder.
Example 7
On the basis of the previous embodiment, in step 3, the method for analyzing the received real-time information by the roadbed base station performs the following steps:
the data processing is carried out according to the received data information, data modeling is carried out according to the data processing result, and a data model of the automatic automobile analysis system is generated, and the method specifically comprises the following steps:
step S3.1: performing data preprocessing, including: removing the unique attribute, processing missing values and abnormal value detection and processing;
step S3.2: and carrying out data specification processing, including: mean value removing, covariance matrix calculation, eigenvalue and eigenvector calculation of the covariance matrix, sorting the eigenvalues from large to small, reserving the largest k eigenvectors, and converting data into a new space constructed by the k eigenvectors; finally, new processed data are obtained, and the data are irrelevant pairwise, but original information can be kept as far as possible.
Step S3.3: carrying out data standardization processing, and scaling the data in proportion to make the data fall into a small specific interval; the data is linearly transformed by using the following transformation function, so that the result falls in the [0,1] interval, wherein the transformation function is as follows:
step S3.4: carrying out data modeling;
step S3.5: performing an effect analysis comprising: after the model training is finished, calculating the accuracy of the customer satisfaction data generated by the model and the original customer satisfaction data by adopting the following formula, namely obtaining R2Scoring, wherein the higher the score is, the better the model accuracy is represented;
where y represents model-generated customer satisfaction data (predicted values);
nsamplesrepresenting the size of the sample size entering the model.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working process and related description of the system described above may refer to the corresponding process in the foregoing method embodiments, and will not be described herein again.
It should be noted that, the system provided in the foregoing embodiment is only illustrated by dividing the functional modules, and in practical applications, the functions may be distributed by different functional modules according to needs, that is, the modules or steps in the embodiment of the present invention are further decomposed or combined, for example, the modules in the foregoing embodiment may be combined into one module, or may be further split into multiple sub-modules, so as to complete all or part of the functions described above. The names of the modules and steps involved in the embodiments of the present invention are only for distinguishing the modules or steps, and are not to be construed as unduly limiting the present invention.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes and related descriptions of the storage device and the processing device described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
Those of skill in the art would appreciate that the various illustrative modules, method steps, and modules described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that programs corresponding to the software modules, method steps may be located in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. To clearly illustrate this interchangeability of electronic hardware and software, various illustrative components and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as electronic hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The terms "first," "second," and the like are used for distinguishing between similar elements and not necessarily for describing or implying a particular order or sequence.
The terms "comprises," "comprising," or any other similar term are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
So far, the technical solutions of the present invention have been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of the present invention is obviously not limited to these specific embodiments. Equivalent changes or substitutions of related technical features can be made by those skilled in the art without departing from the principle of the invention, and the technical scheme after the changes or substitutions can fall into the protection scope of the invention.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention.
The foregoing embodiments are merely illustrative of the principles and utilities of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or change the above-mentioned embodiments without departing from the spirit and scope of the present invention. Accordingly, it is intended that all equivalent modifications or changes which can be made by those skilled in the art without departing from the spirit and technical spirit of the present invention be covered by the claims of the present invention.
Claims (7)
1. A driving information real-time interaction system, characterized in that the system comprises: the vehicle-mounted information acquisition device is used for acquiring the running information of the automobile in the running process; a vehicle electronic ID chip for uniquely identifying a vehicle; the system comprises a vehicle-mounted data transmission device and a roadbed base station; and the vehicle-mounted information acquisition device transmits the acquired operation information to the roadbed base station through the data transmission device.
2. The system of claim 1, wherein the on-board information collection device comprises: the device comprises a speed sensor, an acceleration sensor, an angular velocity sensor, a brake sensor, an information recorder, a data conversion device and a central processing chip; the central processing chip is connected with the data conversion device through signals; the data conversion device is respectively in signal connection with the speed sensor, the acceleration sensor, the angular velocity sensor, the brake sensor and the information recorder.
3. The system of claim 1, wherein the roadbed base station comprises: the chip identification device, the data storage device and the data analysis device; the chip identification device is respectively connected with the data storage device and the data analysis device through signals; the data storage device is in signal connection with the data analysis device.
4. The system of claim 3, wherein the system further comprises: a cloud end; the roadbed base station further comprises: an information transmission device; and the roadbed base station is connected with the cloud signal through the information transmission device.
5. The system of claim 1, wherein the operational information includes at least: the system comprises automobile speed per hour information, lane changing operation information, brake operation information, steering lamp use information, headlamp use information and turning information.
6. A real-time driving information interaction method based on the system of any one of claims 1 to 4, characterized in that the method performs the following steps:
step 1: the vehicle-mounted information acquisition device acquires the running information of the automobile in real time, and transmits the acquired real-time information of the automobile and the electronic ID of the automobile to the roadbed base station through the vehicle-mounted data transmission device;
step 2: the subgrade base station judges whether the vehicle is a newly recorded vehicle or not according to the received vehicle electronic ID; if the vehicle is newly recorded, creating a storage space belonging to the vehicle; if the vehicle is not newly recorded, the vehicle is linked to the existing storage space;
and step 3: the roadbed base station analyzes the received real-time information and stores the received real-time information;
and 4, step 4: and the roadbed base station sends the analysis result to a cloud terminal.
7. The method of claim 6, wherein in the step 3, the method for analyzing the received real-time information by the roadbed base station performs the following steps:
the data processing is carried out according to the received data information, data modeling is carried out according to the data processing result, and a data model of the automatic automobile analysis system is generated, and the method specifically comprises the following steps:
step S3.1: performing data preprocessing, including: removing the unique attribute, processing missing values and abnormal value detection and processing;
step S3.2: and carrying out data specification processing, including: mean value removing, covariance matrix calculation, eigenvalue and eigenvector calculation of the covariance matrix, sorting the eigenvalues from large to small, reserving the largest k eigenvectors, and converting data into a new space constructed by the k eigenvectors; finally, new processed data are obtained, and the data are irrelevant pairwise, but original information can be kept as far as possible.
Step S3.3: carrying out data standardization processing, and scaling the data in proportion to make the data fall into a small specific interval; the data is linearly transformed by using the following transformation function, so that the result falls in the [0,1] interval, wherein the transformation function is as follows:
step S3.4: carrying out data modeling;
step S3.5: performing an effect analysis comprising: after the model training is finished, calculating the accuracy of the customer satisfaction data generated by the model and the original customer satisfaction data by adopting the following formula, namely obtaining R2Scoring, wherein the higher the score is, the better the model accuracy is represented;
where y represents model-generated customer satisfaction data (predicted values);
nsamplesrepresenting the size of the sample size entering the model.
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Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20010032029A1 (en) * | 1999-07-01 | 2001-10-18 | Stuart Kauffman | System and method for infrastructure design |
| CN103092189A (en) * | 2012-12-28 | 2013-05-08 | 深圳市元征科技股份有限公司 | Method and system of identifying vehicle cheating insurance action and vehicle-mounted terminal |
| CN104064048A (en) * | 2014-06-30 | 2014-09-24 | 奇瑞汽车股份有限公司 | Automobile curve operation speed processing system and automobile curve operation speed processing method based on vehicle-road cooperation |
| CN104679771A (en) * | 2013-11-29 | 2015-06-03 | 阿里巴巴集团控股有限公司 | Individual data searching method and device |
| CN105139677A (en) * | 2015-07-28 | 2015-12-09 | 苏州大学张家港工业技术研究院 | Uncontrolled intersection vehicle passing guiding system based on cooperative vehicle infrastructure and guiding method thereof |
| CN105516294A (en) * | 2013-03-15 | 2016-04-20 | 安徽皖通科技股份有限公司 | Internet of Vehicles road side information acquisition and issuing road network structure |
| CN106803008A (en) * | 2017-02-15 | 2017-06-06 | 武汉极目智能技术有限公司 | A kind of real-time driving behavior methods of marking and system based on ADAS systems |
| WO2018028069A1 (en) * | 2016-08-11 | 2018-02-15 | 深圳市元征科技股份有限公司 | Safe driving evaluation method and system based on vehicle-mounted intelligent unit |
| CN108282504A (en) * | 2017-01-05 | 2018-07-13 | 北京四维图新科技股份有限公司 | Automatic Pilot map data collecting vehicle, base station and collaboration acquisition system and method |
| CN108665711A (en) * | 2018-05-18 | 2018-10-16 | 张晔 | A kind of intelligent vehicle interactive system and method |
| CN108805747A (en) * | 2018-06-13 | 2018-11-13 | 山东科技大学 | A kind of abnormal electricity consumption user detection method based on semi-supervised learning |
| CN108883725A (en) * | 2016-03-06 | 2018-11-23 | 预见汽车有限责任公司 | A kind of driving vehicle warning system and method |
| CN108898244A (en) * | 2018-06-11 | 2018-11-27 | 北京工商大学 | A kind of digital signage position recommended method coupling multi-source element |
| CN109118787A (en) * | 2018-08-20 | 2019-01-01 | 浙江工业大学 | A kind of car speed prediction technique based on deep neural network |
| CN109272745A (en) * | 2018-08-20 | 2019-01-25 | 浙江工业大学 | A kind of track of vehicle prediction technique based on deep neural network |
-
2019
- 2019-05-31 CN CN201910468398.4A patent/CN112017324A/en active Pending
Patent Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20010032029A1 (en) * | 1999-07-01 | 2001-10-18 | Stuart Kauffman | System and method for infrastructure design |
| CN103092189A (en) * | 2012-12-28 | 2013-05-08 | 深圳市元征科技股份有限公司 | Method and system of identifying vehicle cheating insurance action and vehicle-mounted terminal |
| CN105516294A (en) * | 2013-03-15 | 2016-04-20 | 安徽皖通科技股份有限公司 | Internet of Vehicles road side information acquisition and issuing road network structure |
| CN104679771A (en) * | 2013-11-29 | 2015-06-03 | 阿里巴巴集团控股有限公司 | Individual data searching method and device |
| CN104064048A (en) * | 2014-06-30 | 2014-09-24 | 奇瑞汽车股份有限公司 | Automobile curve operation speed processing system and automobile curve operation speed processing method based on vehicle-road cooperation |
| CN105139677A (en) * | 2015-07-28 | 2015-12-09 | 苏州大学张家港工业技术研究院 | Uncontrolled intersection vehicle passing guiding system based on cooperative vehicle infrastructure and guiding method thereof |
| CN108883725A (en) * | 2016-03-06 | 2018-11-23 | 预见汽车有限责任公司 | A kind of driving vehicle warning system and method |
| WO2018028069A1 (en) * | 2016-08-11 | 2018-02-15 | 深圳市元征科技股份有限公司 | Safe driving evaluation method and system based on vehicle-mounted intelligent unit |
| CN108282504A (en) * | 2017-01-05 | 2018-07-13 | 北京四维图新科技股份有限公司 | Automatic Pilot map data collecting vehicle, base station and collaboration acquisition system and method |
| CN106803008A (en) * | 2017-02-15 | 2017-06-06 | 武汉极目智能技术有限公司 | A kind of real-time driving behavior methods of marking and system based on ADAS systems |
| CN108665711A (en) * | 2018-05-18 | 2018-10-16 | 张晔 | A kind of intelligent vehicle interactive system and method |
| CN108898244A (en) * | 2018-06-11 | 2018-11-27 | 北京工商大学 | A kind of digital signage position recommended method coupling multi-source element |
| CN108805747A (en) * | 2018-06-13 | 2018-11-13 | 山东科技大学 | A kind of abnormal electricity consumption user detection method based on semi-supervised learning |
| CN109118787A (en) * | 2018-08-20 | 2019-01-01 | 浙江工业大学 | A kind of car speed prediction technique based on deep neural network |
| CN109272745A (en) * | 2018-08-20 | 2019-01-25 | 浙江工业大学 | A kind of track of vehicle prediction technique based on deep neural network |
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