CN105389985B - A kind of intelligent driving behavior analysis method based on mobile phone sensor - Google Patents
A kind of intelligent driving behavior analysis method based on mobile phone sensor Download PDFInfo
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- CN105389985B CN105389985B CN201510789286.0A CN201510789286A CN105389985B CN 105389985 B CN105389985 B CN 105389985B CN 201510789286 A CN201510789286 A CN 201510789286A CN 105389985 B CN105389985 B CN 105389985B
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Abstract
The invention discloses a kind of intelligent driving behavior analysis methods based on mobile phone sensor, are as follows：After recording is opened in APP front ends, APP is responsible for front end collecting mobile phone sensor and GPS data；APP front ends pre-process the data of acquisition, and by compressed package sent after pretreated data correlation to APP from the background；APP backstages parse data packet, sensing data are reprocessed after the data that APP front ends are passed to are received；Data after reprocessing are subjected to centralized calculation, analyze driving behavior as a result, and result is back to APP front ends for showing and storing.The present invention uses driving behavior Model Matching algorithm, while three anxious data are analyzed, also using hypervelocity as the scoring factor, and all strokes for passing through history are classified the driving habit of driver, increase the accuracy of driving behavior judgement, insurance company can objectively judge the driving behavior of driver by judging result of the present invention, and premium is classified so as to more scientific.
Description
Technical field
The present invention relates to driving behavior Intellectual Analysis Technology field, specifically a kind of intelligent driving based on mobile phone sensor
Behavior analysis method.
Background technology
With the continuous development of vehicle-mounted OBD equipment and internet, the technology realization rate of car networking is more and more, is opened from one
The OBD of beginning collects driving data and is analyzed, and mobile phone sensor collection driving data finally is analyzed, the mould of car networking
Formula is more and more diversified.Vehicle insurance UBI is the most representative, and vehicle insurance UBI can carry out vehicle insurance user by the analysis to driving behavior
Category division provides the foundation to following premium classification.Traditional OBD mobile units can accurately obtain driving behavior number
According to, but there are clutters for the equipment filled afterwards, can influence the accuracy of data, and user is for installing this form of OBD equipment simultaneously
Do not approve.Therefore, mobile phone sensor becomes driving behavior collecting device popular now, onboard same by the way that mobile phone is fixed
Sample can reach the purpose of data acquisition, while will not also generate any influence to vehicle.
Traditional OBD equipment can only obtain three anxious data (emergency brake, anxious oil, racing) and some vehicle-state numbers of driving
According to.And a large amount of mobile phone algorithm is similarly the three anxious data for going out driving by obtaining mobile phone sensor data analysis, by these
Data score as the factor is calculated to driving behavior, but driving behavior is still not complete enough as the scoring factor only according to three anxious data
It is kind.
Invention content
Reasonable, the high intelligent driving row based on mobile phone sensor of accuracy is analyzed the purpose of the present invention is to provide a kind of
For analysis method, to solve the problems mentioned in the above background technology.
To achieve the above object, the present invention provides following technical solution：
A kind of intelligent driving behavior analysis method based on mobile phone sensor, is as follows：
(1) data acquire：APP front ends are opened record after, APP is responsible for front end collecting adopting for mobile phone sensor and GPS data
Collection, wherein APP front ends read a GPS data every one second, every 200 milliseconds of reading primary transducer data；
(2) data prediction：APP front ends pre-process the data of acquisition, and useless GPS data is screened out, biography
Sensor data were once filtered by one second；
(3) data transmission：APP front ends will be transmitted after pretreated data correlation to APP backstages；
(4) data are reprocessed：APP backstages parse data packet, to sensor number after the data that APP front ends are passed to are received
According to the filtering that tries again, the data more than thresholding are rejected；
(5) result is exported：Data after reprocessing are subjected to centralized calculation, analyze driving behavior as a result, and by result
APP front ends are back to for showing and storing；
(6) using driving behavior Model Matching algorithm, while three anxious data are analyzed, will also hypervelocity as score because
Son, and all strokes for passing through history classify to the driving habit of driver, increase the accuracy that driving behavior judges, insurance
Company judges the driving behavior of driver by the judging result.
As further scheme of the invention：The centralized calculation be by acceleration, gyroscope, magnetometer and
The data of GPS, binding model library analyze driving behavior result.
Compared with prior art, the beneficial effects of the invention are as follows：
The present invention is using driving behavior Model Matching algorithm, while three anxious data are analyzed, will also hypervelocity as commenting
Molecular group, and all strokes for passing through history are classified the driving habit of driver, increase the standard of driving behavior judgement
Exactness, insurance company can objectively judge the driving behavior of driver by judging result of the present invention, so as to more scientific
Premium is classified.
Description of the drawings
Fig. 1 is the flow diagram of the present invention.
Specific embodiment
The technical solution of this patent is described in more detail With reference to embodiment.
Referring to Fig. 1, a kind of intelligent driving behavior analysis method based on mobile phone sensor, is as follows：
(1) data acquire：APP front ends are opened record after, APP is responsible for front end collecting adopting for mobile phone sensor and GPS data
Collection, wherein APP front ends read a GPS data every one second, every 200 milliseconds of reading primary transducer data；
(2) data prediction：APP front ends pre-process the data of acquisition, and useless GPS data is screened out, biography
Sensor data were once filtered by one second；
(3) data transmission：APP front ends will be transmitted after pretreated data correlation to APP backstages；
(4) data are reprocessed：APP backstages parse data packet, to sensor number after the data that APP front ends are passed to are received
According to the filtering that tries again, the data more than thresholding are rejected；
(5) result is exported：By after reprocessing data carry out centralized calculation, by acceleration, gyroscope, magnetometer and
The data of GPS, binding model library analyze driving behavior as a result, and result is back to APP front ends for showing and storing；
(6) using driving behavior Model Matching algorithm, while three anxious data are analyzed, will also hypervelocity as score because
Son, and all strokes for passing through history classify to the driving habit of driver, increase the accuracy that driving behavior judges, insurance
Company judges the driving behavior of driver by the judging result.
The present invention is analyzed by reading mobile phone sensor data in real time, and to data, show that the three of driving are anxious and super
Fast data provide driver driving behavior classification information by the processing to data for insurance company, and APP front ends are main in the present invention
It is responsible for collecting the acquisition of mobile phone sensor and GPS data, and will be defeated to data progress screening and pretreatment, pretreated result
Enter to APP backstages algorithm, calculated by APP backstages algorithm and APP front ends are returned to after result for showing.The present invention includes
Screening, pretreatment and the APP backstages driving behavior analysis algorithm of APP front end data acquisitions, the screening master of APP front end data acquisitions
If the validity of screening GPS, since GPS is in search or during poor signal, the deviation of GPS data ranging from can reach hundreds of
Rice, has seriously affected the reliability of data, and therefore, when reading GPS data, APP front ends combine the precision model in GPS states
It encloses and falls invalid GPS data with time difference screening, and exported after GPS data is matched with sensing data to after APP
The data that APP is passed to mainly are input to the driving behavior model in algorithm by platform service, APP backstages driving behavior analysis algorithm
Library by Model Matching, analyzes various driving behaviors, and hypervelocity data are obtained by the matching with road data, and by result
APP front ends are returned to show.
The present invention is using driving behavior Model Matching algorithm, while three anxious data are analyzed, will also hypervelocity as commenting
Molecular group, and all strokes for passing through history are classified the driving habit of driver, increase the standard of driving behavior judgement
Exactness, insurance company can objectively judge the driving behavior of driver by judging result of the present invention, so as to more scientific
Premium is classified.
The better embodiment of this patent is explained in detail above, but this patent is not limited to above-mentioned embodiment party
Formula, can also be under the premise of this patent objective not be departed from the knowledge that one skilled in the relevant art has
Various changes can be made.
Claims (2)
1. a kind of intelligent driving behavior analysis method based on mobile phone sensor, which is characterized in that be as follows：
(1) data acquire：After recording is opened in APP front ends, APP is responsible for front end collecting the acquisition of mobile phone sensor and GPS data,
A GPS data was read in middle APP front ends every one second, every 200 milliseconds of reading primary transducer data；
(2) data prediction：APP front ends pre-process the data of acquisition, useless GPS data are screened out, sensor
Data were once filtered by one second；
(3) data transmission：APP front ends will be transmitted after pretreated data correlation to APP backstages；
(4) data are reprocessed：APP backstages parse data packet, to sensing data again after the data that APP front ends are passed to are received
Primary filtering is done, rejects the data more than thresholding；
(5) result is exported：Data after reprocessing are subjected to centralized calculation, analyze driving behavior as a result, and returning to result
It is used to show and store to APP front ends；
(6) it using driving behavior Model Matching algorithm, while three anxious data are analyzed, will also exceed the speed limit as the scoring factor, and
Classified by all strokes of history to the driving habit of driver, increase the accuracy that driving behavior judges, insurance company
The driving behavior of driver is judged by the judging result.
2. the intelligent driving behavior analysis method according to claim 1 based on mobile phone sensor, which is characterized in that described
Centralized calculation be by the data of acceleration, gyroscope, magnetometer and GPS, binding model library analyzes driving behavior knot
Fruit.
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| CN105812571A (en) * | 2016-04-26 | 2016-07-27 | 上海保橙网络科技有限公司 | Method for automatically judging user driving behavior by using mobile phone |
| CN107662613B (en) * | 2017-09-27 | 2019-07-05 | 西北工业大学 | A method and system for extreme driving behavior recognition based on mobile crowd-sensing |
| CN108062808A (en) * | 2017-12-29 | 2018-05-22 | 深圳市四维码科技有限公司 | A kind of vehicle driving state monitors system |
| CN111861077A (en) * | 2019-08-22 | 2020-10-30 | 北京嘀嘀无限科技发展有限公司 | A method and system for determining user driving habits and pushing service information |
| CN112351419A (en) * | 2020-06-02 | 2021-02-09 | 北京车与车科技有限公司 | Vehicle insurance method based on non-hardware equipment paying according to actual application |
| CN112379400B (en) * | 2020-11-13 | 2024-05-28 | 深圳市兴之佳科技有限公司 | Method, device, computer equipment and storage medium for detecting driving stroke starting point |
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| EP3690596A3 (en) * | 2005-06-01 | 2020-12-09 | Allstate Insurance Company | Motor vehicle operating data collection and analysis |
| US20140095212A1 (en) * | 2012-10-03 | 2014-04-03 | Terje Gloerstad | Systems and methods for providing quality of service for data supporting a driving performance product |
| CN103175987B (en) * | 2013-03-07 | 2014-10-29 | 江苏大学 | Method for detecting vehicle speed on parking condition with assistance of acceleration sensor |
| CN103546577A (en) * | 2013-10-31 | 2014-01-29 | 深圳先进技术研究院 | A method and system for realizing safe driving |
| CN103871263B (en) * | 2014-01-02 | 2017-01-25 | 深圳市成为智能交通系统有限公司 | Method for realizing driving risk rating by utilizing vehicle diagnose interface |
| CN103793787B (en) * | 2014-01-23 | 2017-06-20 | 南京邮电大学 | The processing system and method for car networking |
| CN103870927A (en) * | 2014-03-06 | 2014-06-18 | 重庆思建科技有限公司 | Vehicle accident insurance reporting system and method based on smartphone and OBD (on-board diagnostics) equipment |
| CN104077819B (en) * | 2014-06-17 | 2015-03-18 | 深圳前向启创数码技术有限公司 | Remote monitoring method and system based on driving safety |
| CN104092736A (en) * | 2014-06-25 | 2014-10-08 | 国信彩石（北京）科技股份有限公司 | Vehicle networking device, server and system, scoring method and data collection method |
| CN104063914A (en) * | 2014-07-01 | 2014-09-24 | 深圳市成为智能交通系统有限公司 | Terminal achieving driving risk management and use method thereof |
| CN104243713B (en) * | 2014-09-24 | 2017-01-11 | 深圳先进技术研究院 | Curve detecting method on basis of smart cellphone |
| CN104463244A (en) * | 2014-12-04 | 2015-03-25 | 上海交通大学 | Aberrant driving behavior monitoring and recognizing method and system based on smart mobile terminal |
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