CN108537198A - A kind of analysis method of the driving habit based on artificial intelligence - Google Patents
A kind of analysis method of the driving habit based on artificial intelligence Download PDFInfo
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- CN108537198A CN108537198A CN201810348399.0A CN201810348399A CN108537198A CN 108537198 A CN108537198 A CN 108537198A CN 201810348399 A CN201810348399 A CN 201810348399A CN 108537198 A CN108537198 A CN 108537198A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/59—Context or environment of the image inside of a vehicle, e.g. relating to seat occupancy, driver state or inner lighting conditions
- G06V20/597—Recognising the driver's state or behaviour, e.g. attention or drowsiness
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- 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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- 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
- G07C5/08—Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle or waiting time
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Abstract
The invention discloses a kind of analysis methods of the driving habit based on artificial intelligence, the specific method is as follows, CAN bus data and driving habit data are acquired in server end, training decision-tree model and CNN models, and the decision-tree model of generation and CNN models are moved into vehicle-mounted end, to realize the evaluation of evaluation driver's driving habit.A kind of analysis method of driving habit based on artificial intelligence of the present invention is compared to the prior art, analyzed in real time by driving habit to driver and current state, effectively avoid because fatigue driving, bad steering custom, operate it is lack of standardization etc. caused by damaged vehicle and traffic accident.
Description
Technical field
The present invention relates to the technical field that artificial intelligence drives, specifically a kind of driving habits based on artificial intelligence
Analysis method.
Background technology
As industrial society continues to develop, this walking-replacing tool of automobile progresses into people’s lives, and as extremely heavy
The trip tool wanted, the whole world have welcome " automotive society " therewith.People use, control automobile, while also relying on automobile.So
And while automobile brings convenience for us, also bring no small disaster.The data announced according to the World Health Organization are aobvious
Show, the annual traffic death toll of China is close to 300,000 people, it means that daily because traffic death is up to people more than 800, it is average less than
Just there are one people to become the road soul of the newly deceased within two minutes.What is drawn it mild describes, be equivalent to all can once Tangshan Earthquake occur every year.
So severe traffic safety situation, under the ongoing effort of relevant department, the number of casualties is declined, but is produced little effect,
Traffic accident is not still contained effectively.In addition to Chinese road conditions complexity, vehicle is numerous, and the security configuration of motor vehicle itself is no
Outside high odjective cause, fundamentally, caused by traffic accident 90% is all people, and it is artificial at traffic accident the main reason for
It is undesirable driving habit.
Safe driving of vehicle auxiliary system pays attention to detecting vehicle at a distance from the barrier of periphery by radar at present, ventilation
It crosses camera and watches ambient enviroment, to help driver to prompt warning message to driver when moving backward, passing through slype, allow
Driver takes care.But what this active safety was reminded is limited in scope, for example fatigue driving occurs in driver, driving habit is dashed forward
Phenomena such as so changing, existing safe driving assistant system can not ensure the driving safety of driver to greatest extent.
Invention content
Purpose of the present invention is to driver driving habit and current state analyzed in real time, it is possible to prevente effectively from because tired
Please sail, bad steering custom, operate it is lack of standardization etc. caused by damaged vehicle and traffic accident.
The technical solution adopted by the present invention to solve the technical problems is：A kind of point of the driving habit based on artificial intelligence
Analysis method, the specific method is as follows：Acquire CAN bus data and driving habit data in server end, training decision-tree model and
CNN models, and the decision-tree model of generation and CNN models are moved into vehicle-mounted end, to realize evaluation driver's driving habit
Evaluation.
Further, detailed step is：
The data of S1, collection vehicle CAN bus；
S2, the data of acquisition are arranged and are extracted feature vector, training decision-tree model；
S3, the facial expression and action video data for acquiring driver；
S4, the data of acquisition are arranged and are extracted feature vector, training CNN models；
S5, the decision-tree model of generation and CNN models are moved into vehicle-mounted end；
The facial expression and action video data of S6, the CAN bus data of vehicle-mounted end collection vehicle and driver；
S7, the data of acquisition are based on decision-tree model and CNN models, carry out analysis in real time and sort out, obtains evaluation result.
A kind of analytical equipment of the driving habit based on artificial intelligence, decision-tree model generation unit, CNN models generate single
Member and assay unit；
The decision-tree model generation unit is used for the data of server end collection vehicle CAN bus, and by the data of acquisition
It is trained to decision-tree model；
The CNN model generation units, for the facial expression and action video data of server end acquisition driver, and will
The data of acquisition are trained to CNN models；
The assay unit, after being loaded into decision-tree model and CNN models for vehicle-mounted end, information of vehicles to acquisition and
Driver information is analyzed and evaluated.
Further, preferred structure is:The decision-tree model generation unit includes data acquisition module and model life
At module；Data acquisition module is used for the data of server end collection vehicle CAN bus；Model generation module is used for server
The data of acquisition are arranged and are extracted feature vector, training decision-tree model by end；
The CNN model generation units include data acquisition module and model generation module, data acquisition module, for servicing
Device end acquires the facial expression and action video data of driver；Model generation module is used for server end by the data of acquisition
Arranged and extracted feature vector, training CNN models；
The assay unit, including data acquisition module and analysis evaluation module, the data acquisition module are used for
The CAN bus data of vehicle-mounted end collection vehicle and the facial expression of driver and action video data；The assay mould
The data of acquisition are carried out analysis classification using decision-tree model and CNN models for vehicle-mounted end and evaluated by block.
Compared to the prior art a kind of analysis method of driving habit based on artificial intelligence of the present invention, has the beneficial effect that：
1, driver can be identified whether in the state for influencing driving safety such as tired or drunk using CNN algorithms.
2, CAN data are analyzed and is classified using decision Tree algorithms, analyze the undesirable driving habit of driver.
3, the driving habit of driver and current state are analyzed in real time, it is possible to prevente effectively from because fatigue is driven
Sail, bad steering custom, operate it is lack of standardization etc. caused by damaged vehicle and traffic accident.
Description of the drawings
The following further describes the present invention with reference to the drawings.
Attached drawing 1 is a kind of logic diagram of the analysis method of the driving habit based on artificial intelligence.
Specific implementation mode
The invention will be further described in the following with reference to the drawings and specific embodiments.
CAN bus principle is connected by serial data line by CAN bus, sensor, controller and actuator.It
Not only cable is connected by tree structure, communication protocol is equivalent to the data link in ISO/OSI reference models
Layer, network can detect and correct the error in data generated by electromagnetic interference in data transmission procedure according to agreement.CAN network
Preparation is easier, and allows directly to be communicated between any station, after all data are all aggregated into master computer
Row processing again.When a node in CAN bus (stands) transmission data, it is broadcast to all sections in network in the form of message
Point.For each node, no matter whether data are intended for oneself, are all received to it.11 of every group of message beginning
Character is identifier, defines the priority of message, and this message format is known as the addressing scheme of content oriented.
Decision tree (Decision Tree) be it is known it is various happen probability on the basis of, pass through constitute decision tree
Desired value to seek net present value (NPV) is more than or equal to zero probability, and assessment item risk judges the method for decision analysis of its feasibility,
It is a kind of intuitive graphical method for using probability analysis.Since this decision branch is drawn as limb of the figure like one tree, therefore claim
Decision tree.In machine learning, decision tree is a prediction model, and what he represented is one kind between object properties and object value
Mapping relations.The clutter of Entropy=system, using algorithm ID3, C4.5 and C5.0 spanning tree algorithms use entropy.This
One measurement is the concept based on entropy in information theory.
Deep learning is a new field in machine learning research, and motivation is that foundation, simulation human brain are divided
The neural network of study is analysed, it imitates the mechanism of human brain to explain data, such as image, sound and text.
The same with machine learning method, also supervised learning and unsupervised learning divide different to depth machine learning method
Learning framework under the learning model very difference that establishes for example, convolutional neural networks (Convolutional neural
Networks, abbreviation CNNs) it is exactly a kind of machine learning model under the supervised learning of depth, and depth confidence net (Deep
Belief Nets, abbreviation DBNs) it is exactly a kind of machine learning model under unsupervised learning.
The present invention is a kind of analysis method of the driving habit based on artificial intelligence, it is possible to prevente effectively from because fatigue is driven
Sail, bad steering custom, operate it is lack of standardization etc. caused by damaged vehicle and traffic accident.
Embodiment 1：
A kind of analysis method of the driving habit based on artificial intelligence, the specific method is as follows：CAN bus number is acquired in server end
According to and driving habit data, training decision-tree model and CNN models, and the decision-tree model of generation and CNN models are moved into vehicle
End is carried, to realize the evaluation of evaluation driver's driving habit.
Further, detailed step is：
The data of S1, collection vehicle CAN bus, e.g., the current speed of vehicle, the speed changer of vehicle, brake, throttle and
Clutch information.
S2, the data of acquisition are arranged and are extracted feature vector, training decision-tree model；
S3, the facial expression and action video data for acquiring driver；
S4, the data of acquisition are arranged and is extracted feature vector, and video data is carried out using deep learning CNN algorithms
Training, training CNN models；
S5, the decision-tree model of generation and CNN models are moved into vehicle-mounted end；
The facial expression and action video data of S6, the CAN bus data of vehicle-mounted end collection vehicle and driver；
S7, the data of acquisition are based on decision-tree model and CNN models, carry out analysis in real time and sort out, obtains evaluation result.That is root
The driving habit of driver is evaluated in real time according to the positive expression and behavioural information of classification results and driver.
A kind of analytical equipment of the driving habit based on artificial intelligence, decision-tree model generation unit, CNN models generate single
Member and assay unit；
The decision-tree model generation unit is used for the data of server end collection vehicle CAN bus, and by the data of acquisition
It is trained to decision-tree model；
The CNN model generation units, for the facial expression and action video data of server end acquisition driver, and will
The data of acquisition are trained to CNN models；
The assay unit, after being loaded into decision-tree model and CNN models for vehicle-mounted end, information of vehicles to acquisition and
Driver information is analyzed and evaluated.
Further, preferred structure is:The decision-tree model generation unit includes data acquisition module and model life
At module；Data acquisition module is used for the data of server end collection vehicle CAN bus；Model generation module is used for server
The data of acquisition are arranged and are extracted feature vector, training decision-tree model by end；
The CNN model generation units include data acquisition module and model generation module, data acquisition module, for servicing
Device end acquires the facial expression and action video data of driver；Model generation module is used for server end by the data of acquisition
Arranged and extracted feature vector, training CNN models；
The assay unit, including data acquisition module and analysis evaluation module, the data acquisition module are used for
The CAN bus data of vehicle-mounted end collection vehicle and the facial expression of driver and action video data；The assay mould
The data of acquisition are carried out analysis classification using decision-tree model and CNN models for vehicle-mounted end and evaluated by block.
The present invention acquires the data of vehicle CAN bus under different scenes, and the positive video information of driver uses deep learning
Method is in real time identified the facial expression image of driver, extracts feature vector to the data of vehicle CAN line, and obtain and determine
Plan tree-model；The decision-tree model of generation is moved into vehicle-mounted end, carrying out analysis in real time in the running data obtained to CAN bus returns
Class.The driving habit of driver is evaluated in real time according to the positive expression and behavioural information of classification results and driver.
Driving habit and current state to driver are analyzed in real time, so as to effectively avoid because fatigue driving, bad
Damaged vehicle and traffic accident caused by driving habit, operation are lack of standardization etc..
The technical personnel in the technical field can readily realize the present invention with the above specific embodiments,.But it should manage
Solution, the present invention is not limited to above-mentioned several specific implementation modes.On the basis of the disclosed embodiments, the technical field
Technical staff can arbitrarily combine different technical features, to realize different technical solutions.
Claims (4)
1. a kind of analysis method of the driving habit based on artificial intelligence, which is characterized in that the specific method is as follows：In server end
Acquire CAN bus data and driving habit data, training decision-tree model and CNN models, and by the decision-tree model of generation and
CNN models move to vehicle-mounted end, to realize the evaluation of evaluation driver's driving habit.
2. a kind of analysis method of driving habit based on artificial intelligence according to claim 1, which is characterized in that specific
Method is as follows：
The data of S1, collection vehicle CAN bus；
S2, the data of acquisition are arranged and are extracted feature vector, training decision-tree model；
S3, the facial expression and action video data for acquiring driver；
S4, the data of acquisition are arranged and are extracted feature vector, training CNN models；
S5, the decision-tree model of generation and CNN models are moved into vehicle-mounted end；
The facial expression and action video data of S6, the CAN bus data of vehicle-mounted end collection vehicle and driver；
S7, the data of acquisition are based on decision-tree model and CNN models, carry out analysis in real time and sort out, obtains evaluation result.
3. a kind of analytical equipment of the driving habit based on artificial intelligence, which is characterized in that decision-tree model generation unit, CNN
Model generation unit and assay unit；
The decision-tree model generation unit is used for the data of server end collection vehicle CAN bus, and by the data of acquisition
It is trained to decision-tree model；
The CNN model generation units, for the facial expression and action video data of server end acquisition driver, and will
The data of acquisition are trained to CNN models；
The assay unit, after being loaded into decision-tree model and CNN models for vehicle-mounted end, information of vehicles to acquisition and
Driver information is analyzed and evaluated.
4. a kind of analytical equipment of driving habit based on artificial intelligence according to claim 3, which is characterized in that described
Decision-tree model generation unit include data acquisition module and model generation module；Data acquisition module is used for server end
The data of collection vehicle CAN bus；The data of acquisition are arranged for server end and extract feature by model generation module
Vector, training decision-tree model；
The CNN model generation units include data acquisition module and model generation module, data acquisition module, for servicing
Device end acquires the facial expression and action video data of driver；Model generation module is used for server end by the data of acquisition
Arranged and extracted feature vector, training CNN models；
The assay unit, including data acquisition module and analysis evaluation module, the data acquisition module are used for
The CAN bus data of vehicle-mounted end collection vehicle and the facial expression of driver and action video data；The assay mould
The data of acquisition are carried out analysis classification using decision-tree model and CNN models for vehicle-mounted end and evaluated by block.
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Cited By (8)
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| CN110069988A (en) * | 2019-01-31 | 2019-07-30 | 中国平安财产保险股份有限公司 | AI based on multidimensional data drives risk analysis method, server and storage medium |
| CN110399793A (en) * | 2019-06-19 | 2019-11-01 | 深圳壹账通智能科技有限公司 | Driving behavior method for early warning, device and computer equipment based on image recognition |
| CN110458214A (en) * | 2019-07-31 | 2019-11-15 | 上海远眸软件有限公司 | Driver replaces recognition methods and device |
| CN110615001A (en) * | 2019-09-27 | 2019-12-27 | 汉纳森（厦门）数据股份有限公司 | Driving safety reminding method, device and medium based on CAN data |
| CN112319488A (en) * | 2020-10-20 | 2021-02-05 | 易显智能科技有限责任公司 | Method and system for identifying driving style of motor vehicle driver |
| CN112336349A (en) * | 2020-10-12 | 2021-02-09 | 易显智能科技有限责任公司 | Method and related device for recognizing psychological state of driver |
| CN116494991A (en) * | 2023-06-20 | 2023-07-28 | 深圳市美力高集团有限公司 | Driving habit analysis system and method based on AI identification |
| CN117644837A (en) * | 2024-01-30 | 2024-03-05 | 沈阳桢林文化科技有限公司 | Man-machine interaction method and system based on active learning |
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| CN110069988A (en) * | 2019-01-31 | 2019-07-30 | 中国平安财产保险股份有限公司 | AI based on multidimensional data drives risk analysis method, server and storage medium |
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| CN116494991A (en) * | 2023-06-20 | 2023-07-28 | 深圳市美力高集团有限公司 | Driving habit analysis system and method based on AI identification |
| CN117644837A (en) * | 2024-01-30 | 2024-03-05 | 沈阳桢林文化科技有限公司 | Man-machine interaction method and system based on active learning |
| CN117644837B (en) * | 2024-01-30 | 2024-05-03 | 沈阳桢林文化科技有限公司 | Man-machine interaction method and system based on active learning |
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