CN110733509A - Driving behavior analysis method, device, equipment and storage medium - Google Patents
Driving behavior analysis method, device, equipment and storage medium Download PDFInfo
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Abstract
The method comprises the steps of obtaining vehicle information and surrounding environment information of a vehicle in the driving process, and analyzing the vehicle information and the surrounding environment information in a data modeling and/or artificial intelligence mode to obtain a driving behavior analysis result aiming at a driver of the vehicle.
Description
Technical Field
The present disclosure relates to the field of transportation, and in particular, to driving behavior analysis methods, apparatuses, devices, and storage media.
Background
With the popularization of automobiles, the service industry related to automobiles is gradually rising. For example, automobile insurance, taxis, cyber-appointment cars, etc. are increasingly appearing in people's daily lives. As more and more automobiles are on the road, the problem of safe driving becomes more and more prominent.
In europe, in order to encourage safe driving, the insurer introduced insurance services that charge according to driving performance.2014, the sales volume of the policy based on the automobile information service increased by 42%. in north america, the total sales volume of the policy based on the automobile information service is expected to increase from 420 ten thousand in 2014 to 3250 ten thousand in 2019, with an average annual composite growth rate as high as 50%.
Such business models, which provide corresponding services based on the driving behavior exhibited by the driver, will become increasingly popular. Therefore, how to accurately analyze the driving behavior of the driver is a major problem facing the present.
Disclosure of Invention
of the present disclosure are directed to providing driving behavior analysis schemes capable of accurately analyzing the driving behavior of a driver.
According to an th aspect of the disclosure, driving behavior analysis methods are provided, and the methods comprise the steps of obtaining vehicle information and surrounding environment information of a vehicle during driving, and analyzing the vehicle information and the surrounding environment information in a data modeling and/or artificial intelligence mode to obtain a th driving behavior analysis result aiming at a driver of the vehicle.
Optionally, the vehicle information includes one or more of body information, travel history, positioning information, and information collected by vehicle sensors.
Optionally, the surrounding environment information includes one or more of surrounding vehicle information, surrounding pedestrian information, roadside information, and environment variation information with the vehicle running process.
Optionally, the roadside information includes or more items of information including intersection information, road information, captured images, traffic light information, and road identification information collected based on the road test unit.
Optionally, the step of analyzing the vehicle information and the surrounding environment information based on data modeling and/or artificial intelligence includes modeling according to the vehicle information and the surrounding environment information, and analyzing the established model to obtain th driving behavior analysis results, or analyzing the vehicle information and the surrounding environment information based on artificial intelligence technology to obtain th driving behavior analysis results, or modeling according to the vehicle information and the surrounding environment information, analyzing the established model, analyzing the vehicle information and the surrounding environment information based on artificial intelligence technology, and synthesizing th driving behavior analysis results according to the two analysis results.
Optionally, the step of analyzing the vehicle information and the surrounding environment information based on an artificial intelligence technique includes: and analyzing the driving behavior of the driver by using a driving behavior analysis model based on the vehicle information and the ambient environment information, wherein the driving behavior analysis model is obtained based on deep learning algorithm training.
Optionally, the driving behavior analysis method further comprises analyzing the driving state of the driver to obtain a second driving behavior analysis result for the driving state and/or analyzing whether the driving behavior violates the traffic regulation based on the vehicle information and the surrounding environment information to obtain a third driving behavior analysis result, and obtaining the current driving behavior analysis result based on the th driving behavior analysis result, the second driving behavior analysis result and/or the third driving behavior analysis result.
Optionally, the driving behavior analysis method further includes: identifying the identity of the driver; and acquiring a historical driving behavior analysis result corresponding to the driver based on the recognition result.
Optionally, the driving behavior analysis method further includes: determining a total behavior analysis result of the driver based on the current driving behavior analysis result and the historical driving behavior analysis result.
Optionally, the driving behavior analysis method further includes: and re-analyzing the driving behaviors in the previous preset time according to the vehicle information and the surrounding environment information to obtain a historical driving behavior analysis calibration result.
Optionally, the driving behavior analysis method further includes: determining a total behavior analysis result of the driver based on the current driving behavior analysis result, the historical driving behavior analysis calibration result, and the historical driving behavior analysis result.
Optionally, the driving behavior analysis method further includes: and grading the driver according to the total behavior analysis result so as to provide corresponding service for the driver according to the grade.
According to the second aspect of the disclosure, driving behavior analysis devices are further provided, which comprise a acquisition module for acquiring vehicle information and surrounding environment information of a vehicle during driving, and a analysis module for analyzing the vehicle information and the surrounding environment information based on data modeling and/or artificial intelligence to obtain a driving behavior analysis result aiming at a driver of the vehicle.
Optionally, the vehicle information includes one or more of body information, travel history, positioning information, and information collected by vehicle sensors.
Optionally, the surrounding environment information includes one or more of surrounding vehicle information, surrounding pedestrian information, roadside information, and environment variation information with the vehicle running process.
Optionally, the roadside information includes or more items of information including intersection information, road information, captured images, traffic light information, and road identification information collected based on the road test unit.
Optionally, the th analysis module models according to the vehicle information and the ambient environment information to obtain a th driving behavior analysis result by analyzing the established model, or the th analysis module analyzes the vehicle information and the ambient environment information based on an artificial intelligence technique to obtain a th driving behavior analysis result, or the th analysis module models according to the vehicle information and the ambient environment information to analyze the established model and analyzes the vehicle information and the ambient environment information based on the artificial intelligence technique to obtain a th driving behavior analysis result by integrating the two analysis results.
Optionally, based on the vehicle information and the ambient environment information, an th analysis module analyzes the driving behavior of the driver using a driving behavior analysis model, wherein the driving behavior analysis model is trained based on a deep learning algorithm.
Optionally, the driving behavior analysis device further comprises a second analysis module for analyzing the driving state of the driver to obtain a second driving behavior analysis result for the driving state, and/or a third analysis module for analyzing whether the driving behavior violates the traffic regulation based on the vehicle information and the surrounding environment information to obtain a third driving behavior analysis result, and an determination module for determining the current driving behavior analysis result based on the , the second driving behavior analysis result and/or the third driving behavior analysis result.
Optionally, the driving behavior analysis device further includes: the identification module is used for identifying the identity of the driver; and a second obtaining module for obtaining a historical driving behavior analysis result corresponding to the driver based on the recognition result.
Optionally, the driving behavior analysis device further includes: and the second determination module is used for determining the total behavior analysis result of the driver based on the current driving behavior analysis result and the historical driving behavior analysis result.
Optionally, the driving behavior analysis device further includes: and the calibration module is used for reanalyzing the driving behavior in the previous preset time according to the vehicle information and the surrounding environment information to obtain a historical driving behavior analysis calibration result.
Optionally, the driving behavior analysis device further includes: and the third determination module is used for determining the total behavior analysis result of the driver based on the current driving behavior analysis result, the historical driving behavior analysis calibration result and the historical driving behavior analysis result.
Optionally, the driving behavior analysis device further includes: and the scoring module is used for scoring the driver according to the total behavior analysis result so as to provide corresponding service for the driver according to the score.
According to a third aspect of the present disclosure there is also provided computing devices comprising a processor and a memory having stored thereon executable code which, when executed by the processor, causes the processor to perform a method as set forth in aspect of the present disclosure.
According to a fourth aspect of the present disclosure, there is also provided non-transitory machine-readable storage media having executable code stored thereon, which when executed by a processor of an electronic device, causes the processor to perform a method as set forth in aspect of the present disclosure.
According to the driving behavior analysis method and device, comprehensive and careful analysis on the driving behaviors can be achieved by combining richer data, and therefore the accuracy of the driving behavior analysis result can be improved.
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The above and other objects, features and advantages of the present disclosure will become more apparent by describing in greater detail exemplary embodiments thereof with reference to the attached drawings, in which like reference numerals generally represent like parts throughout.
Fig. 1 is a schematic flow chart diagram illustrating a driving behavior analysis method according to an embodiment of the present disclosure .
FIG. 2 is a flow chart illustrating a driving behavior analysis according to an embodiment of the present disclosure .
Fig. 3 is a schematic block diagram showing the structure of a driving behavior analysis apparatus according to an embodiment of the present disclosure .
Fig. 4 shows a schematic structural diagram of a computing device for data processing that can be used to implement the driving behavior analysis method according to an embodiment of the invention.
Detailed Description
Preferred embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While the preferred embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
[ term interpretation ]
V2X, namely Vehicle to event, is a generic name of series Vehicle-mounted communication technologies, , and V2X mainly includes six major categories, i.e., Vehicle to Vehicle (V2V), Vehicle to roadside equipment (V2R), Vehicle to infrastructure (V2I), Vehicle to pedestrian (V2P), Vehicle to locomotive (V2M), and Vehicle to bus (V2T).
OBU is installed On vehicle and can be regarded as microwave devices which adopt DSRC (dedicated Short Range communication) technology to communicate with RSU.
RSU: the abbreviation of Road Side Unit is interpreted to mean the roadside Unit, installed in the roadside, and communicates with the On Board Unit (OBU) by using dsrc (dedicated Short Range communication) technology.
[ scheme overview ]
However, when the driving behavior of the driver is analyzed to obtain the driver representation, generally analyzes the driving data of a single vehicle, the accuracy of the analysis result is poor, the obtained driver representation is rough, the reliability is low, and the driving behavior of the driver cannot be truly evaluated.
In view of the above, the present disclosure provides that, when analyzing the driving behavior of the driver, not only data of the vehicle driven by the driver (i.e., vehicle data mentioned below) but also information of surrounding environment during the driving process of the vehicle, such as surrounding vehicle information, surrounding pedestrian information, roadside information, and information of environment variation along with the driving process of the vehicle, may be referred to, so as to combine with more abundant data to perform a comprehensive and detailed analysis on the driving behavior of the driver, thereby improving the accuracy of the driving behavior analysis result.
Reference is now made to for various aspects of the disclosure.
[ Driving behavior analysis method ]
Fig. 1 is a schematic flow chart illustrating a driving behavior analysis method according to an embodiment of the present disclosure , where the driving behavior analysis method of the present disclosure may be implemented as driving behavior analysis programs, the driving behavior analysis programs may be deployed at a vehicle terminal, a cloud terminal, or other terminals, such as a roadside terminal.
Referring to fig. 1, in step S110, vehicle information and surrounding environment information of a vehicle during traveling are acquired.
The vehicle information may include, but is not limited to, body size, body state (e.g., data related to the state of vehicle lights, turn lights, vehicle faults, seat belt states, airbag lights, etc.), etc. further , the body information may include information such as the size of the body, the state of the body (e.g., data related to the state of vehicle lights, turn lights, vehicle faults, airbag states, airbag lights, etc.).
The roadside information may be information collected based on a roadside unit (RSU), such as or more of information that may include, but is not limited to, intersection information (e.g., information of intersection position, intersection lane direction, etc.), road information (e.g., information of road type, position, direction, curvature, road surface condition, road weather, road fault, road construction, etc.), photographed images, traffic signal information (e.g., information of traffic light state and duration, etc.), and road identification information (e.g., information of road sign, transit time, lane speed limit, etc.), etc.
The surrounding vehicle information may be items or more items of information such as vehicle body information, driving records, positioning information, information collected by vehicle sensors, etc. the surrounding pedestrian information may be pedestrian information in the vicinity of the vehicle during the vehicle driving process, such as location information of pedestrians (which may include people riding bicycles and non-motor vehicles).
The environment-based vehicle driving process change information can be obtained by calculating and analyzing originally collected data, for example, the vehicle camera in the vehicle driving process can analyze the video shot by the vehicle in the vehicle driving process, and the vehicle-based vehicle driving process change information can also be obtained by analyzing the vehicle camera in the vehicle driving process, and the vehicle-based vehicle driving process location information can be obtained without limitation.
Taking the case that the driving behavior analysis program is deployed at a vehicle terminal as an example, the surrounding environment information may be collected based on the V2X technology. For example, the vehicle may acquire vehicle information of other surrounding vehicles based on the V2X technology, and may also acquire information of the roadside unit RSU based on the V2X technology, and the acquired information may be transferred to the driving behavior analysis program together with own vehicle information. In addition, the driving behavior analysis program can also be deployed at the cloud or the roadside terminal, and when the driving behavior analysis program is deployed at other terminals, the specific data transmission flows are different, and are not repeated. In addition, the present disclosure does not limit the specific communication method for acquiring the vehicle data and the surrounding environment information, and may acquire data necessary for analysis based on a communication method such as 3G or 4G.
In step S120, the vehicle information and the surrounding environment information are analyzed to obtain th driving behavior analysis results for the driver of the vehicle.
After obtaining the vehicle information and the ambient information, analysis may be performed based on various ways to obtain a driving behavior analysis result for the driver (which may be referred to as " th driving behavior analysis result" for convenience of distinction), for example, analysis may be performed by data modeling, analysis may be performed by artificial intelligence (i.e., AI technology, such as machine learning technology in AI technology), and comprehensive analysis may be performed by combining the two ways.
As examples of the present disclosure, modeling can be performed according to vehicle information and ambient environment information, and the built model can be analyzed to obtain driving behavior analysis results, that is, based on the modeling analysis manner, the analysis of the ambient environment information (such as ambient vehicle information) can be added on the basis of the analysis of the driving behavior data of the vehicle, so that the analysis results are more accurate.
The vehicle information and the surrounding environment information can be analyzed by using a driving behavior analysis model established based on a machine learning technology (such as a deep learning technology) to obtain a driving behavior analysis model, and particularly, the vehicle information and the surrounding environment information can be used as sample characteristics, a determined driving behavior analysis result (such as an analysis result determined based on a modeling mode) is used as a sample mark, and a machine learning algorithm (such as a deep learning algorithm) is used for model training to obtain the driving behavior analysis model.
For example, modeling can be carried out according to the vehicle information and the ambient environment information, the established model can be analyzed, and the vehicle information and the ambient environment information can be analyzed based on machine learning technology.
So far, a basic implementation flow of the driving behavior analysis method of the present disclosure is described with reference to fig. 1. Therefore, when the driving behavior of the driver is analyzed, the surrounding environment information is fully combined, and therefore the analysis result can be more accurate.
, combining the vehicle information and the surrounding environment information, it is also possible to use a variety of other analysis methods to further analyze the driving behavior, and the analysis results can be used to summarize the th driving behavior analysis results to obtain the current driving behavior analysis results.
As examples of the present disclosure, the driving state of the driver may be analyzed to obtain a driving behavior analysis result (which may be referred to as a "second driving behavior analysis result" for convenience of distinction) for the driving state, specifically, an abnormal driving state such as a fatigue state, distraction detection, drunk driving, etc. of the driver may be recognized through image detection from in-vehicle imaging, and then a corresponding second driving behavior analysis result may be obtained based on the recognized driving state.
As another examples of the present disclosure, it is also possible to analyze whether the driving behavior violates the traffic regulation based on the vehicle information and the surrounding environment information to obtain a driving behavior analysis result (for convenience of distinguishing, it may be referred to as a "third driving behavior analysis result") of the violation of the traffic regulation.
After the second driving behavior analysis result and/or the third driving behavior analysis result are obtained, the obtained driving behavior analysis results (the second driving behavior analysis result and/or the third driving behavior analysis result) and the driving behavior analysis result can be summarized to obtain a current driving behavior analysis result, wherein the current driving behavior analysis result can be regarded as the driving behavior analysis result obtained by current analysis, for example, the driving behavior of the driver can be analyzed according to a preset time interval by using the driving behavior analysis method disclosed by the present disclosure to obtain the current driving behavior analysis result, or the driving behavior can be analyzed by using the driving behavior analysis method disclosed by the present disclosure to obtain the current driving behavior analysis result after the driver completes driving behaviors.
Further , before analyzing the driving behavior of the driver of the vehicle, the driver's identity may be first identified and historical driving behavior analysis results corresponding to the driver may be obtained based on the identification results to facilitate data aggregation.
For example, for driving behaviors of mutually different vehicles, the danger degree of the driving behaviors should exceed the overtaking behaviors, but at the time of times of overtaking or the second overtaking, based on only the currently acquired data, dangerous overtaking behaviors may be determined, and the danger degree is far lower than that of the mutually different vehicles.
For example, merchants such as insurance companies, automobile manufacturers, software service providers and the like can obtain driving behavior analysis results under the authorization of users (namely drivers) to develop and apply , for example, insurance companies can be used for determining vehicle insurance rates, and automobile manufacturers can be used for improving vehicles according to the driving behavior analysis results to improve the driving experience of the users.
[ application example ]
Fig. 2 is a flow chart illustrating a driving behavior analysis according to an embodiment of the present disclosure , wherein the driving behavior analysis program may be regarded as a program capable of executing the driving behavior analysis method of the present disclosure.
The driving behavior analysis program is deployed on vehicles, for example, the vehicles can communicate with each other through V2X, and the roadside RSU can also communicate with the vehicles through V2X, wherein proprietary protocols can be used to replace the protocol of V2X, or other communication modes can be used to replace the communication of V2X.
The vehicle can transmit the vehicle information of the vehicle, the acquired vehicle information of surrounding vehicles and the RSU information of the road side to a driving behavior analysis program through , and the driving behavior analysis program executes the driving behavior analysis method disclosed by the invention to analyze the driving behavior of the driver, wherein partial data provided by V2X can be replaced by other sensors, for example, millimeter wave radars can be used for judging the relative position, the vehicle speed, the direction angle and the like of the surrounding vehicles.
As an example, the host vehicle can collect the information of surrounding vehicles and roadside RSUs through V2X and output the information to a driving behavior analysis program, wherein the information of the surrounding vehicles comprises data such as vehicle body size, position time (current time, GPS position), vehicle body state (data related to states of vehicle lamps, steering lamps, vehicle faults, safety belt states, safety air bag lamps and the like), speed direction (data related to speed of vehicle head direction angles, speed, triangular acceleration, cross-axis angular speed, gears, rotating speed, oil and the like), and the like;
as shown in fig. 2, the driving behavior analysis program may integrate a plurality of functions such as data modeling, deep learning, driving record analysis, image detection, state detection, classification labels, comprehensive adjustment, and the like with to comprehensively and accurately analyze the driving behavior.
The driving behavior analysis program may be analyzed as follows.
1. The driving is output to a driving behavior analysis program for face recognition, the identity of the driver is determined, and a historical driving behavior analysis result A is obtained, so that subsequent summary analysis of data is facilitated, and the problem of data mixing caused by multiple drivers in vehicles is avoided.
2. According to the shooting in the vehicle, the situations of fatigue state, distraction detection and the like of the driver are identified through image detection, and the behavior analysis result of the driving state of the user is obtained and recorded as a result B.
3. Calculating a current belonged lane according to the position, the speed and the direction angle of the head of the vehicle, combining map information and road information of the RSU at the road side, and checking whether illegal driving exists or not according to the speed, the direction angle of the head of the vehicle, lane information and the state of the vehicle body, wherein the speed, the direction angle of the head of the vehicle, the lane information and the state of the vehicle body are matched with identification information (such as a road sign, passing time and lane speed limit) defined by the RSU at the road side, other information (including temporary control and the like; recording as a result C; the digitized traffic laws and regulations can be from the cloud, can also be at the local end, and are not limited to the vehicle-mounted system.
4. According to the collected data of the surrounding vehicles and the information of the road-side RSU, the data such as the size of the vehicle body, the position time (the current time and the gps position), the state of the vehicle body (data related to the states of a vehicle lamp, a steering lamp, a vehicle fault, the state of a safety belt, an airbag lamp and the like), the speed direction (data related to the speed of a vehicle head direction angle, the speed, a triangular acceleration, a transverse shaft angular speed, a gear position, a rotating speed, oil and the like) and the like are combined, data analysis is carried out, a driving behavior analysis result D is generated, two analysis means are available, and when conditions allow, deployment can be tried, and finally the result is synthesized.
4.1 through traditional modeling analysis, on the traditional analysis of driving behavior data, the analysis result is more accurate by adding peripheral vehicle factors for analysis, for example, the behavior of slamming on the brake in the traditional single-vehicle analysis is determined as behaviors of dangerous driving, but through combining the peripheral vehicle factors, the reason can be confirmed in a step , the sudden brake in the case of a fault of a front vehicle cannot be determined as dangerous driving, and in addition, through combining the peripheral vehicle factors, the result which cannot be analyzed by the single vehicle can be analyzed, for example, whether the vehicle keeps enough safety distance when overtaking the vehicle.
4.2, analysis is carried out by means of AI, and under the condition of having a large amount of training data, a deep learning method is suitable for data analysis; the factors of all aspects of the data can be more fully mined, and more accurate analysis results can be obtained.
5. The data of the surrounding vehicles and the information of the RSU on the road side are analyzed in combination with the driving records, driving behaviors within time threshold are analyzed again, time calibration is carried out in combination with the current situation, historical record behavior analysis calibration results E are generated, for example, dangerous behaviors of mutual overtaking are considered to be dangerous overtaking at time, and the danger degree is far lower than that of the mutual overtaking.
6. The driving behavior analysis program comprehensively summarizes the complaint results B, C, D to obtain an analysis result F of the current driving, and performs weighted average on the historical analysis result A, the calibration result E and the current analysis result F to generate a total behavior analysis result, namely a user portrait of the driver; in the process, the driving score can also be obtained by adopting a digital mode of safe driving and dangerous driving, and the score can be directly applied to commercialization.
The generated analysis result and the driving record (if allowed) can be stored under the cloud end corresponding to the identity of the user, so that authorized application calling and further analysis can be carried out, insurance companies, automobile manufacturers and software service providers can obtain the driving behavior analysis result under the condition of user authorization to further carry out development and application, for example, the insurance companies can be used for determining vehicle insurance rates, and the automobile manufacturers can be used for improving driving experience according to driving behaviors.
[ Driving behavior analysis device ]
The present disclosure may also be implemented as driving behavior analysis devices fig. 3 is a schematic block diagram illustrating the structure of a driving behavior analysis device according to an embodiment of the present disclosure , wherein the functional modules of the driving behavior analysis device may be implemented by hardware, software, or a combination of hardware and software implementing the principles of the present invention.
In the following, functional modules that the driving behavior analysis device may have and operations that each functional module may perform are briefly described, and for the details related thereto, reference may be made to the above description of the driving planning method, which is not repeated herein.
Referring to fig. 3, the driving behavior analysis apparatus 300 includes an th acquisition module 310 and an th analysis module 320, a th acquisition module 310 is used for acquiring vehicle information and surrounding environment information of a vehicle during driving, wherein the vehicle information may include or more of vehicle body information, driving records, positioning information, and information collected by a vehicle sensor, roadside information may include or more of intersection information, road information, photographed images, traffic light information, and road identification information collected based on a road test unit, and the surrounding environment information may include or more of surrounding vehicle information, surrounding pedestrian information, roadside information, and environment variation information with the vehicle driving.
The analysis module 320 may analyze the vehicle information and the ambient information based on data modeling and/or artificial intelligence to obtain driving behavior analysis results for a driver of the vehicle.
As an example, the th analysis module 320 may model based on the vehicle information and the ambient environment information and analyze the built model to obtain a th driving behavior analysis result, or the th analysis module 320 may analyze the vehicle information and the ambient environment information based on an artificial intelligence technique (e.g., a machine learning technique) to obtain a th driving behavior analysis result, or the th analysis module 320 may model based on the vehicle information and the ambient environment information, analyze the built model, analyze the vehicle information and the ambient environment information based on an artificial intelligence technique (e.g., a machine learning technique), and combine the th driving behavior analysis result according to the two analysis results.
As shown in fig. 3, the driving behavior analysis apparatus 300 may further optionally include a second analysis module 330 and/or a third analysis module 340 and an -th determination module 350, which are indicated by dashed boxes in the figure.
The second analysis module 330 is used for analyzing the driving state of the driver to obtain a second driving behavior analysis result aiming at the driving state, the third analysis module 340 is used for analyzing whether the driving behavior violates the traffic regulation or not to obtain a third driving behavior analysis result based on the vehicle information and the surrounding environment information, and the determination module 350 is used for determining the current driving behavior analysis result based on the driving behavior analysis result, the second driving behavior analysis result and/or the third driving behavior analysis result.
As shown in fig. 3, the driving behavior analysis apparatus 300 may further optionally include a recognition module 360 and a second acquisition module 365, which are shown by dashed boxes in the figure. The identification module 360 is used for identifying the identity of the driver. The second obtaining module 365 is used for obtaining the historical driving behavior analysis result corresponding to the driver based on the recognition result.
As shown in fig. 3, the driving behavior analysis device 300 may further optionally include a second determination module 370 shown by a dashed box in the figure. The second determination module 370 is used to determine the overall behavior analysis result of the driver based on the current driving behavior analysis result and the historical driving behavior analysis result.
As shown in fig. 3, the driving behavior analysis device 300 may also optionally include a calibration module 380 shown in a dashed box. The calibration module 380 is configured to re-analyze the driving behavior in the previous predetermined time according to the vehicle information and the surrounding environment information to obtain a historical driving behavior analysis calibration result.
As shown in fig. 3, the driving behavior analysis device 300 may also optionally include a third determination module 390 shown in a dashed box. The third determination module 390 is configured to determine a total behavior analysis result of the driver based on the current driving behavior analysis result, the historical driving behavior analysis calibration result, and the historical driving behavior analysis result.
As shown in fig. 3, the driving behavior analysis apparatus 300 may further optionally include a scoring module 395 shown by a dotted line box in the figure. The scoring module 395 is configured to score the driver according to the total behavior analysis result, so as to provide the driver with corresponding services according to the score.
[ calculating device ]
Fig. 4 shows a schematic structural diagram of a computing device for data processing that can be used to implement the driving behavior analysis method according to an embodiment of the invention.
Referring to fig. 4, computing device 400 includes memory 410 and processor 420.
Processor 420 may be a multicore processor or may include multiple processors, in embodiments processor 420 may include general purpose host processors and one or more special purpose coprocessors such as Graphics Processing Units (GPUs), digital signal processing units (DSPs), etc. in embodiments processor 420 may be implemented using custom circuits such as Application Specific Integrated Circuits (ASICs) or Field Programmable logic Arrays (FPGAs).
The memory 410 may include various types of storage units such as system memory, Read Only Memory (ROM), and permanent storage devices, where the ROM may store static data or instructions required by the processor 420 or other modules of the computer, the permanent storage devices may be read-write storage devices, the permanent storage devices may be non-volatile storage devices that do not lose stored instructions and data even after the computer is powered down, in some embodiments the permanent storage devices employ mass storage devices (e.g., magnetic or optical disks, flash memory) as the permanent storage devices, in some embodiments the permanent storage devices may be removable storage devices (e.g., floppy disks, optical drives), the system memory may be writable or readable storage devices, such as dynamic random access memory, the system memory may store or all instructions and data required by the processor when operating, furthermore, the memory 410 may include any combination of computer-readable storage media including various types of semiconductor memory chips (DRAM, compact disks, SRAM, flash, read only memory, and/or flash memory cards), and in some embodiments the SD or DVD-read only memory devices may include CD-ROM, DVD.
The memory 410 has stored thereon executable code that, when executed by the processor 420, may cause the processor 420 to perform the driving behavior analysis methods described above.
The driving behavior analysis method, apparatus, device, and storage medium according to the present invention have been described in detail above with reference to the accompanying drawings.
Furthermore, the method according to the invention may also be implemented as computer programs or computer program products comprising computer program code instructions for carrying out the above-mentioned steps defined in the above-mentioned method of the invention.
Alternatively, the present invention may also be embodied as non-transitory machine-readable storage media (or computer-readable storage media or machine-readable storage media) having stored thereon executable code (or a computer program, or computer instruction code) which, when executed by a processor of an electronic device (or computing device, server, etc.), causes the processor to perform the steps of the above-described method according to the present invention.
Those of skill would further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the disclosure herein may be implemented as electronic hardware, computer software, or combinations of both.
It should also be noted that, in some alternative implementations, the functions noted in the block diagrams and/or flowchart block or blocks, and combinations of blocks in the block diagrams and/or flowchart block or blocks, may occur out of the order noted in the figures, for example, two sequential blocks may in fact be executed substantially concurrently, or in reverse order, depending on the functionality involved.
Having described embodiments of the present invention, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.
Claims (15)
1, A driving behavior analysis method, comprising:
acquiring vehicle information and surrounding environment information of a vehicle in a driving process; and
analyzing the vehicle information and the ambient environment information based on data modeling and/or artificial intelligence to obtain th driving behavior analysis results for a driver of the vehicle.
2. The driving behavior analysis method of claim 1, wherein the vehicle information includes one or more of:
vehicle body information;
recording the driving;
positioning information; and
information collected by vehicle sensors.
3. The driving behavior analysis method of claim 1, wherein the ambient environment information comprises one or more of:
surrounding vehicle information;
surrounding pedestrian information;
road side information; and
and (3) information of environment along with the running process of the vehicle.
4. The driving behavior analysis method according to claim 3, wherein the roadside information includes or more items of information collected based on a drive test unit:
intersection information;
road information;
shooting an image;
traffic light information; and
road identification information.
5. The driving behavior analysis method according to claim 1, wherein the step of analyzing the vehicle information and the ambient environment information in a data modeling and/or artificial intelligence based manner includes:
modeling according to the vehicle information and the surrounding environment information, and analyzing the established model to obtain the th driving behavior analysis result, or
Analyzing the vehicle information and the surrounding environment information based on artificial intelligence technology to obtain the th driving behavior analysis result, or
And modeling according to the vehicle information and the ambient environment information, analyzing the established model, analyzing the vehicle information and the ambient environment information based on an artificial intelligence technology, and comprehensively obtaining th driving behavior analysis results according to two analysis results.
6. The driving behavior analysis method according to claim 5, wherein the step of analyzing the vehicle information and the surrounding environment information based on an artificial intelligence technique includes:
and analyzing the driving behavior of the driver by using a driving behavior analysis model based on the vehicle information and the ambient environment information, wherein the driving behavior analysis model is obtained based on deep learning algorithm training.
7. The driving behavior analysis method according to claim 1, characterized by further comprising:
the driving state of the driver is analyzed to obtain a second driving behavior analysis result for the driving state, and/or,
analyzing whether the driving behavior violates a traffic regulation or not based on the vehicle information and the ambient environment information to obtain a third driving behavior analysis result; and
and obtaining a current driving behavior analysis result based on the th driving behavior analysis result, the second driving behavior analysis result and/or the third driving behavior analysis result.
8. The driving behavior analysis method according to claim 7, characterized by further comprising:
identifying the identity of the driver; and
and obtaining a historical driving behavior analysis result corresponding to the driver based on the recognition result.
9. The driving behavior analysis method according to claim 8, characterized by further comprising:
determining a total behavior analysis result of the driver based on the current driving behavior analysis result and the historical driving behavior analysis result.
10. The driving behavior analysis method according to claim 8, characterized by further comprising:
and re-analyzing the driving behaviors in the preset time to obtain a historical driving behavior analysis calibration result according to the vehicle information and the ambient environment information.
11. The driving behavior analysis method according to claim 10, characterized by further comprising:
determining a total behavior analysis result of the driver based on the current driving behavior analysis result, the historical driving behavior analysis calibration result, and the historical driving behavior analysis result.
12. The driving behavior analysis method according to claim 8 or 10, characterized by further comprising:
and according to the total behavior analysis result, scoring the driver so as to provide corresponding service for the driver according to the score.
The driving behavior analysis device of kinds, characterized by comprising:
an th acquisition module for acquiring vehicle information and surrounding environment information of the vehicle during running, and
an analysis module for analyzing the vehicle information and the ambient environment information based on data modeling and/or artificial intelligence to obtain driving behavior analysis results for a driver of the vehicle.
14, a computing device, comprising:
a processor; and
a memory having executable code stored thereon, which when executed by the processor, causes the processor to perform the method of any of claims 1-12.
15, non-transitory machine-readable storage medium having stored thereon executable code that, when executed by a processor of an electronic device, causes the processor to perform the method of any of claims 1-12, wherein is defined as a set of instructions for performing the method.
Priority Applications (2)
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| PCT/CN2019/095533 WO2020015574A1 (en) | 2018-07-18 | 2019-07-11 | Driving behavior analysis method, device and apparatus, and storage medium |
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| CN201810792927.1A CN110733509A (en) | 2018-07-18 | 2018-07-18 | Driving behavior analysis method, device, equipment and storage medium |
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Cited By (14)
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|---|---|---|---|---|
| CN111353471A (en) * | 2020-03-17 | 2020-06-30 | 北京百度网讯科技有限公司 | Safe driving monitoring method, device, equipment and readable storage medium |
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Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090234552A1 (en) * | 2005-12-28 | 2009-09-17 | National University Corporation Nagoya University | Driving Action Estimating Device, Driving Support Device, Vehicle Evaluating System, Driver Model Creating Device, and Driving Action Determining Device |
| CN102874188A (en) * | 2012-09-01 | 2013-01-16 | 北京车网互联科技股份有限公司 | Driving behavior warning method based on vehicle bus data |
| CN104590274A (en) * | 2014-11-26 | 2015-05-06 | 浙江吉利汽车研究院有限公司 | Driving behavior self-adaptation system and method |
| WO2016028228A1 (en) * | 2014-08-21 | 2016-02-25 | Avennetz Technologies Pte Ltd | System, method and apparatus for determining driving risk |
| CN105575115A (en) * | 2015-12-17 | 2016-05-11 | 福建星海通信科技有限公司 | Driving behavior analysis method based on vehicle-mounted monitoring and management platform |
| WO2017080822A1 (en) * | 2015-11-10 | 2017-05-18 | Robert Bosch Gmbh | Method and device for analysing a driving manner of a driver of a vehicle |
| US20170144671A1 (en) * | 2015-11-19 | 2017-05-25 | Shalini MEMANI | System for monitoring and classifying vehicle operator behavior |
| CN107331179A (en) * | 2017-05-27 | 2017-11-07 | 东风商用车有限公司 | Economic driving auxiliary system based on big data cloud platform and implementation method |
| US20180113458A1 (en) * | 2016-10-24 | 2018-04-26 | International Business Machines Corporation | Method of effective driving behavior extraction using deep learning |
| CN108189763A (en) * | 2018-01-17 | 2018-06-22 | 北京万得嘉瑞汽车技术有限公司 | A kind of analysis method of driver's driving behavior and special intelligent vehicular rear mirror |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6502035B2 (en) * | 2000-08-02 | 2002-12-31 | Alfred B. Levine | Automotive safety enhansing system |
| CN105679020B (en) * | 2016-01-29 | 2017-12-05 | 深圳市美好幸福生活安全系统有限公司 | A kind of driving behavior analysis apparatus and method |
-
2018
- 2018-07-18 CN CN201810792927.1A patent/CN110733509A/en active Pending
-
2019
- 2019-07-11 WO PCT/CN2019/095533 patent/WO2020015574A1/en not_active Ceased
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090234552A1 (en) * | 2005-12-28 | 2009-09-17 | National University Corporation Nagoya University | Driving Action Estimating Device, Driving Support Device, Vehicle Evaluating System, Driver Model Creating Device, and Driving Action Determining Device |
| CN102874188A (en) * | 2012-09-01 | 2013-01-16 | 北京车网互联科技股份有限公司 | Driving behavior warning method based on vehicle bus data |
| WO2016028228A1 (en) * | 2014-08-21 | 2016-02-25 | Avennetz Technologies Pte Ltd | System, method and apparatus for determining driving risk |
| CN104590274A (en) * | 2014-11-26 | 2015-05-06 | 浙江吉利汽车研究院有限公司 | Driving behavior self-adaptation system and method |
| WO2017080822A1 (en) * | 2015-11-10 | 2017-05-18 | Robert Bosch Gmbh | Method and device for analysing a driving manner of a driver of a vehicle |
| US20170144671A1 (en) * | 2015-11-19 | 2017-05-25 | Shalini MEMANI | System for monitoring and classifying vehicle operator behavior |
| CN105575115A (en) * | 2015-12-17 | 2016-05-11 | 福建星海通信科技有限公司 | Driving behavior analysis method based on vehicle-mounted monitoring and management platform |
| US20180113458A1 (en) * | 2016-10-24 | 2018-04-26 | International Business Machines Corporation | Method of effective driving behavior extraction using deep learning |
| CN107331179A (en) * | 2017-05-27 | 2017-11-07 | 东风商用车有限公司 | Economic driving auxiliary system based on big data cloud platform and implementation method |
| CN108189763A (en) * | 2018-01-17 | 2018-06-22 | 北京万得嘉瑞汽车技术有限公司 | A kind of analysis method of driver's driving behavior and special intelligent vehicular rear mirror |
Cited By (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111353636A (en) * | 2020-02-24 | 2020-06-30 | 交通运输部水运科学研究所 | A method and system for predicting ship driving behavior based on multimodal data |
| CN111353471A (en) * | 2020-03-17 | 2020-06-30 | 北京百度网讯科技有限公司 | Safe driving monitoring method, device, equipment and readable storage medium |
| CN112512890A (en) * | 2020-04-02 | 2021-03-16 | 华为技术有限公司 | Abnormal driving behavior recognition method |
| CN111739288A (en) * | 2020-05-22 | 2020-10-02 | 腾讯科技（深圳）有限公司 | Artificial intelligence-based vehicle driving risk analysis method and related device |
| CN114694368A (en) * | 2020-12-28 | 2022-07-01 | 比亚迪股份有限公司 | vehicle control system |
| CN114969559A (en) * | 2021-02-20 | 2022-08-30 | 阿里巴巴集团控股有限公司 | Information processing method for driver, computing device and storage medium |
| CN113085873A (en) * | 2021-04-30 | 2021-07-09 | 东风小康汽车有限公司重庆分公司 | Method and device for acquiring driving strategy, computer equipment and storage medium |
| CN113506447A (en) * | 2021-08-16 | 2021-10-15 | 深圳市沅欣智能科技有限公司 | Park intelligent traffic control method based on Internet of things and related device |
| CN116161047A (en) * | 2021-11-24 | 2023-05-26 | 北京罗克维尔斯科技有限公司 | Vehicle safe driving scoring method and device, electronic equipment and storage medium |
| WO2023221848A1 (en) * | 2022-05-17 | 2023-11-23 | 华为技术有限公司 | Vehicle starting behavior prediction method and apparatus, storage medium, and program product |
| WO2024001848A1 (en) * | 2022-06-28 | 2024-01-04 | 深圳市中兴微电子技术有限公司 | Automatic driving method and system |
| CN116279499A (en) * | 2023-02-14 | 2023-06-23 | 上汽通用五菱汽车股份有限公司 | Intelligent driving control method and device |
| WO2024243804A1 (en) * | 2023-05-30 | 2024-12-05 | 深圳市锐明技术股份有限公司 | Driving risk analysis method and system for driver, and electronic device and storage medium |
| WO2025175916A1 (en) * | 2024-02-20 | 2025-08-28 | 武汉路特斯汽车有限公司 | Driving behavior data processing method and apparatus, and device |
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