CN109910902B - Intelligent automobile driving system according to driving habits of driver - Google Patents
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Abstract
The invention discloses an intelligent automobile driving system according to the driving habits of a driver, which comprises a driver selection module, an urban road selection module, a driving behavior acquisition module, a driving evaluation module, a sample type determination module, a driving behavior establishment module and a driving behavior selection output module, wherein the driver selection module and the urban road selection module are respectively connected with the driving behavior acquisition module, the driving behavior acquisition module and the driving evaluation module are respectively connected with the sample type determination module, and the sample type determination module, the driving behavior establishment module and the driving behavior selection output module are sequentially connected. Through the mode, the invention is designed according to the habits of the whole driver group, can effectively improve the safety of the driving process, relieve the driving pressure of the driver, ensure the comfort of the riding process, reduce the occurrence rate of traffic accidents and meet the requirements of the driver and passengers.
Description
Technical Field
The invention relates to the technical field of intelligent automobiles, in particular to an intelligent automobile driving system according to the driving habit of a driver.
Background
The intelligent automobile is a comprehensive system integrating functions of environmental perception, planning decision, multi-level auxiliary driving and the like, intensively applies technologies such as computer, modern sensing, information fusion, communication, artificial intelligence, automatic control and the like, and is a typical high and new technology complex. The current research on intelligent vehicles mainly aims to improve the safety and the comfort of automobiles and provide excellent human-vehicle interaction interfaces. In recent years, intelligent vehicles have become hot spots for the research in the field of vehicle engineering in the world and new power for the growth of the automobile industry, and many developed countries incorporate the intelligent vehicles into intelligent transportation systems which are intensively developed. In the operation process of the intelligent automobile, a driver is the weakest and uncertain loop and has strong complexity, so that in the driving system of the intelligent automobile, the consideration of the driving habit of the driver is very important.
Disclosure of Invention
The invention mainly solves the technical problem of providing the intelligent automobile driving system according to the driving habits of the driver, and the application effect is good.
In order to solve the technical problems, the invention adopts a technical scheme that: the intelligent automobile driving system comprises a driver selection module, an urban road selection module, a driving behavior acquisition module, a driving evaluation module, a sample type determination module, a driving behavior establishment module and a driving behavior selection output module, wherein the driver selection module and the urban road selection module are respectively connected with the driving behavior acquisition module, the driving behavior acquisition module and the driving evaluation module are respectively connected with the sample type determination module, and the sample type determination module, the driving behavior establishment module and the driving behavior selection output module are sequentially connected;
the driver selection module is used for selecting a driver sample from a driver database and sending the driver sample to the driving behavior acquisition module;
the urban road selection module is used for selecting an urban road sample from an urban road database and sending the urban road sample to the driving behavior acquisition module;
the driving behavior acquisition module is used for matching a driver in the driver sample with an urban road in the urban road sample, carrying out simulation, acquiring driving behavior data and sending the driving behavior data to the sample type determination module;
the driving evaluation module is used for acquiring the evaluation of a driver and passengers on the same car, establishing a driver habit evaluation index and sending the driver habit evaluation index to the sample type determination module;
the sample type determining module is used for combining the driving behavior data with the corresponding driver habit evaluation index, determining a sample type and sending the sample type to the driving habit establishing module;
the driving habit establishing module is used for establishing the driving habit corresponding to the sample type according to the sample type;
the driving habit selection output module is used for outputting corresponding driving habits according to the requirements of the driver.
In a preferred embodiment of the invention, the selection of the driver sample is extracted according to the age, the sex, the violation record and the working field of the driver.
In a preferred embodiment of the present invention, the number of the driver samples is 300-400.
In a preferred embodiment of the present invention, the urban road samples are extracted according to the longitude position, the latitude position, the urban modernization degree, the road condition, and the number of road intersections of the urban road.
In a preferred embodiment of the present invention, the number of the urban road samples is 50-70.
In a preferred embodiment of the invention, the simulation is realized by adopting a dSPACE real-time simulation system and constructing a vehicle driving simulator.
In a preferred embodiment of the invention, the evaluation is in the form of a questionnaire.
In a preferred embodiment of the invention, the combination of the driving behavior data and the corresponding driver behavior assessment indicators is based on k-means cluster analysis.
In a preferred embodiment of the present invention, the driving habit is established by solving the optimal parameters based on the EM machine learning algorithm to establish the corresponding driving habit.
In a preferred embodiment of the present invention, the driver's request is input through a liquid crystal screen.
The invention has the beneficial effects that: the intelligent automobile driving system based on the driving habits of the drivers is designed according to the habits of the whole driver group, can effectively improve the safety of the driving process, relieve the driving pressure of the drivers, ensure the comfort of the riding process, reduce the occurrence rate of traffic accidents and meet the requirements of the drivers and passengers.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without inventive efforts, wherein:
FIG. 1 is a schematic structural diagram of a preferred embodiment of the intelligent driving system of a vehicle according to the driving habit of a driver.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, an intelligent driving system for a vehicle according to driving habits of a driver is provided, which includes a driver selection module 1, an urban road selection module 2, a driving behavior acquisition module 3, a driving evaluation module 4, a sample type determination module 5, a driving habit establishment module 6, and a driving habit selection output module 7. The driving behavior selection module 1 and the urban road selection module 2 are respectively connected with the driving behavior acquisition module 3, the driving behavior acquisition module 3 and the driving evaluation module 4 are respectively connected with the sample type determination module 5, and the sample type determination module 5, the driving behavior establishment module 6 and the driving behavior selection output module 7 are sequentially connected.
The driver selection module 1 is used for selecting a driver sample from a driver database and sending the driver sample to the driving behavior acquisition module 3. The driver samples are selected and extracted according to the age, the vehicle age, the sex, the violation records and the working field of the driver, so that the representative samples can be selected, and the driving habits of the driver can be more widely determined. The number of the driver samples is 300-400, and in this embodiment, the number of the driver samples is 350.
The urban road selecting module 2 is used for selecting an urban road sample from an urban road database and sending the urban road sample to the driving behavior collecting module 3. The urban road samples are selected and extracted according to the longitude positions, the latitude positions, the urban modernization degree, the road conditions and the road intersection number of the urban roads, so that representative samples can be selected, drivers can drive on various urban roads, and more extensive driving habits are reflected. The number of the urban road samples is 50-70, and in this embodiment, the number of the urban road samples is 60.
The driving behavior acquisition module 3 is configured to match a driver in the driver sample with an urban road in the urban road sample, perform simulation, acquire driving behavior data, and send the driving behavior data to the sample type determination module 5. The simulation is realized by adopting a dSPACE real-time simulation system and building a vehicle driving simulator. The driving evaluation module 4 is used for collecting the evaluation of a driver and passengers in the same vehicle, establishing a driver habit evaluation index and sending the driver habit evaluation index to the sample type determination module 5. The evaluation is realized in the form of questionnaires, and the driver and the passengers on the same vehicle can selectively answer the inquiry files, so that the answers can be easily taken, and the time and the labor are saved.
The sample type determining module 5 is configured to combine the driving behavior data with the corresponding driver behavior evaluation index based on k-means cluster analysis, determine a sample type, and send the sample type to the driving behavior establishing module 6. The driving habit establishing module 6 is used for establishing the driving habit corresponding to the sample type according to the sample type. The driving habit is established by solving the optimal parameters based on an EM machine learning algorithm and establishing the corresponding driving habit. The driving habit selection output module 7 is used for outputting corresponding driving habits according to the requirements of the driver. The driver's request is input through a liquid crystal screen.
The invention has the beneficial effects that:
the intelligent automobile driving system is designed according to the driving habits of the whole driver group, so that the safety of the driving process can be effectively improved;
secondly, according to the car intelligence driving system of driver's driving habit can alleviate driver's driving pressure, ensures the travelling comfort of taking the process, reduces the incidence of traffic accident, satisfies driver and passenger's requirement.
The above description is only an embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by the present specification, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (9)
1. An intelligent automobile driving system according to the driving habits of a driver is characterized by comprising a driver selecting module, an urban road selecting module, a driving behavior collecting module, a driving evaluation module, a sample type determining module, a driving behavior establishing module and a driving behavior selection output module, wherein the driver selecting module and the urban road selecting module are respectively connected with the driving behavior collecting module, the driving behavior collecting module and the driving evaluation module are respectively connected with the sample type determining module, and the sample type determining module, the driving behavior establishing module and the driving behavior selection output module are sequentially connected;
the driver selection module is used for selecting a driver sample from a driver database and sending the driver sample to the driving behavior acquisition module;
the urban road selection module is used for selecting an urban road sample from an urban road database and sending the urban road sample to the driving behavior acquisition module;
the driving behavior acquisition module is used for matching a driver in the driver sample with an urban road in the urban road sample, carrying out simulation, acquiring driving behavior data and sending the driving behavior data to the sample type determination module;
the driving evaluation module is used for collecting the evaluation of a driver and passengers on the same car, establishing a driver habit evaluation index and sending the driver habit evaluation index to the sample type determination module, wherein the evaluation is realized in the form of questionnaire;
the sample type determining module is used for combining the driving behavior data with the corresponding driver habit evaluation index, determining a sample type and sending the sample type to the driving habit establishing module;
the driving habit establishing module is used for establishing the driving habit corresponding to the sample type according to the sample type;
the driving habit selection output module is used for outputting corresponding driving habits according to the requirements of the driver.
2. The intelligent driving system for the automobile according to the driving habits of the driver, as recited in claim 1, wherein the selection of the driver sample is extracted according to the age, the sex, the violation record and the working field of the driver.
3. The intelligent driving system for automobiles according to the driving habits of drivers of claim 1, wherein the number of the driver samples is 300-400.
4. The intelligent driving system according to the driving habit of the driver of claim 1, wherein the urban road samples are extracted according to the longitude and latitude positions of the urban road, the urban modernization degree, the road conditions and the number of road intersections.
5. The intelligent driving system according to the driving habit of driver of claim 1, wherein the number of city road samples is 50-70.
6. The intelligent automobile driving system according to the driving habits of the driver, as claimed in claim 1, wherein the simulation is implemented by adopting a dSPACE real-time simulation system and constructing a vehicle driving simulator.
7. The intelligent driving system according to the driving habit of driver as recited in claim 1, wherein the combination of the driving behavior data and the corresponding evaluation index of the driving habit is implemented based on k-means cluster analysis.
8. The intelligent driving system according to the driving habit of the driver as recited in claim 1, wherein the driving habit is established by solving the optimal parameters based on the EM machine learning algorithm to establish the corresponding driving habit.
9. The intelligent driving system according to the driving habit of driver as claimed in claim 1, wherein the driver's request is inputted through a liquid crystal screen.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN102774382A (en) * | 2011-05-04 | 2012-11-14 | 通用汽车环球科技运作有限责任公司 | System and method for vehicle driving style determination |
| CN105705395A (en) * | 2013-12-11 | 2016-06-22 | 英特尔公司 | Individual driving preference adapted computerized assist or autonomous driving of vehicles |
| JP2018136934A (en) * | 2017-02-23 | 2018-08-30 | タタ・コンサルタンシー・サーヴィシズ・リミテッド | System and method for driver's profiling according to vehicle driving |
| CN108694367A (en) * | 2017-04-07 | 2018-10-23 | 北京图森未来科技有限公司 | A kind of method for building up of driving behavior model, device and system |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| US6879969B2 (en) * | 2001-01-21 | 2005-04-12 | Volvo Technological Development Corporation | System and method for real-time recognition of driving patterns |
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102774382A (en) * | 2011-05-04 | 2012-11-14 | 通用汽车环球科技运作有限责任公司 | System and method for vehicle driving style determination |
| CN105705395A (en) * | 2013-12-11 | 2016-06-22 | 英特尔公司 | Individual driving preference adapted computerized assist or autonomous driving of vehicles |
| JP2018136934A (en) * | 2017-02-23 | 2018-08-30 | タタ・コンサルタンシー・サーヴィシズ・リミテッド | System and method for driver's profiling according to vehicle driving |
| CN108694367A (en) * | 2017-04-07 | 2018-10-23 | 北京图森未来科技有限公司 | A kind of method for building up of driving behavior model, device and system |
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