CN114140270A - Method, system and storage medium for judging vehicle rescue based on vehicle driving data - Google Patents
Method, system and storage medium for judging vehicle rescue based on vehicle driving data Download PDFInfo
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Abstract
The application provides a method for judging vehicle rescue based on vehicle driving data, which is used for acquiring vehicle report information reported by a user; extracting vehicle track data in a period of time before and after the vehicle is out of danger according to the vehicle report information; obtaining vehicle parking data and accident related factor data according to the vehicle track data; and evaluating the size of the vehicle accident, the vehicle rescue authenticity and/or calculating the vehicle rescue distance according to the vehicle parking data and the accident correlation factor data. According to the vehicle rescue evaluation method and device, user communication data are not needed, the actual rescue situation of the vehicle can be analyzed according to the reported information of the user, and accidents and vehicle rescue of the vehicle can be evaluated according to the track data of the vehicle.
Description
Technical Field
The application belongs to the technical field of vehicle detection, and particularly relates to a method, a system and a storage medium for judging vehicle rescue based on vehicle driving data.
Background
In the existing vehicle rescue or vehicle insurance, most of the communication numbers of the reporting users are based on, and operators acquire communication data corresponding to the communication numbers to determine the occurrence time of traffic accidents and the positioning positions of the reporting users so as to confirm the accuracy of identifying the reporting.
In the event of a truck accident, the following conditions will typically exist: the personnel reporting the case are not at the place where the car accident happens; or the accident degree is smaller, the driving of the truck is not influenced, the driver can drive to the target first, and the reporting personnel can report the case. In the two situations, the traditional method relies on communication data to position the place where the car accident happens, the method cannot accurately judge the place of the car accident, because the base station positioning of an operator determines the position of a mobile phone by utilizing the measured distance of the base station to the mobile phone, the positioning precision depends on the distribution of the base station and the size of the coverage range to a large extent, the positioning precision is relatively low, and if a person reporting the case falsely or wrongly reports the place where the car accident happens, the truth of the case report and the place where the car accident actually happens cannot be identified.
Disclosure of Invention
The invention provides a method, a system and a storage medium for judging vehicle rescue based on vehicle driving data, and aims to solve the problem that vehicle data is inaccurate when vehicle rescue is judged based on user communication data in the prior art.
According to a first aspect of embodiments of the present application, there is provided a method of determining vehicle rescue based on vehicle driving data, comprising the steps of:
acquiring vehicle report information reported by a user;
extracting vehicle track data in a period of time before and after the vehicle is out of danger according to the vehicle report information;
obtaining vehicle parking data and accident related factor data according to the vehicle track data;
and evaluating the size of the vehicle accident, the vehicle rescue authenticity and/or calculating the vehicle rescue distance according to the vehicle parking data and the accident correlation factor data.
In some embodiments of the present application, the vehicle application information comprises: vehicle license plate number, accident occurrence place, accident occurrence time and reporting time.
In some embodiments of the present application, the accident-related factor data includes vehicle braking data before and after the vehicle is at risk.
In some embodiments of the present application, the vehicle braking data includes vehicle rapid deceleration data, vehicle gps speed, and/or trajectory warning data.
In some embodiments of the present application, obtaining vehicle parking data and accident-related factor data according to vehicle trajectory data specifically includes:
calculating vehicle parking time, vehicle parking duration and a vehicle parking coordinate point according to the vehicle track data;
sequencing and grading the vehicle parking time according to the time difference between the parking time and the accident occurrence time;
and converting the vehicle parking coordinate point and the accident occurrence place into standard geographic information interest points.
In some embodiments of the present application, evaluating vehicle accident size, vehicle rescue authenticity and/or calculating vehicle rescue distance based on vehicle parking data and accident correlation factor data specifically comprises:
and matching the vehicle stop coordinate points and the standard geographic information interest points corresponding to the accident occurrence places, and selecting the vehicle stop coordinate point with the highest matching degree as a vehicle danger-leaving stop point.
In some embodiments of the present application, evaluating vehicle accident size, vehicle rescue authenticity and/or calculating vehicle rescue distance based on vehicle parking data and accident correlation factor data specifically comprises:
acquiring vehicle braking data of a vehicle emergency stop point;
comparing the vehicle braking data with the braking threshold data, and judging whether the vehicle braking data is a real danger rescue and/or evaluating the vehicle accident grade;
and obtaining the vehicle rescue distance according to the distance of the vehicle stopping point within a period of time after the vehicle is in danger.
According to a second aspect of the embodiments of the present application, there is provided a system for determining vehicle rescue based on vehicle driving data, specifically including:
vehicle report information module: the system is used for acquiring vehicle report information reported by a user;
a vehicle trajectory data module: the vehicle track data acquisition system is used for extracting vehicle track data in a period of time before and after the vehicle is out of danger according to vehicle report information;
vehicle stop data module: the system comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring vehicle parking data and accident related factor data according to vehicle track data;
vehicle rescue evaluation module: the vehicle accident assessment system is used for assessing the size of the vehicle accident, the vehicle rescue authenticity and/or calculating the vehicle rescue distance according to the vehicle parking data and the accident correlation factor data.
According to a third aspect of the embodiments of the present application, there is provided an apparatus for determining vehicle rescue based on vehicle driving data, including: a memory: for storing executable instructions; and a processor: for interfacing with a memory to execute executable instructions to perform a method for determining vehicle assistance based on vehicle travel data.
According to a fourth aspect of embodiments of the present application, there is provided a computer-readable storage medium having a computer program stored thereon; the computer program is executed by a processor to implement a method of determining vehicle rescue based on vehicle travel data.
By adopting the method, the system and the storage medium for judging vehicle rescue based on vehicle driving data in the embodiment of the application, vehicle report information reported by a user is obtained; extracting vehicle track data in a period of time before and after the vehicle is out of danger according to the vehicle report information; obtaining vehicle parking data and accident related factor data according to the vehicle track data; and evaluating the size of the vehicle accident, the vehicle rescue authenticity and/or calculating the vehicle rescue distance according to the vehicle parking data and the accident correlation factor data. According to the vehicle rescue evaluation method and device, user communication data are not needed, the actual rescue situation of the vehicle can be analyzed according to the reported information of the user, and accidents and vehicle rescue of the vehicle can be evaluated according to the track data of the vehicle.
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The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
FIG. 1 is a flow chart illustrating steps of a method for determining vehicle assistance based on vehicle travel data according to an embodiment of the present application;
fig. 2 is a schematic diagram illustrating a method for determining vehicle assistance based on vehicle driving data according to an embodiment of the present application;
fig. 3 is a schematic structural diagram illustrating a system for determining vehicle assistance based on vehicle driving data according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a device for judging vehicle rescue according to an embodiment of the present application.
Detailed Description
In the process of implementing the present application, the inventor finds that in the event of a vehicle accident, the following situations generally occur: the personnel reporting the case are not at the place where the car accident happens; the accident degree is smaller, and the driving of the truck is not influenced, so that a driver can drive to the target first, and the reporting personnel can report the case. In the two situations, the traditional method for positioning the traffic accident occurrence place by relying on the communication data cannot accurately judge the traffic accident occurrence place.
Based on the method, the system and the storage medium for judging vehicle rescue based on vehicle driving data, the position of a reporting person and user communication data are not needed, the actual rescue situation of the vehicle can be analyzed according to the reported information of the user, and the accident and the vehicle rescue of the vehicle can be evaluated according to the track data of the vehicle.
For example, not only the actual rescue situation of the vehicle can be analyzed, but also the accident size of the vehicle can be evaluated according to the track data of the vehicle to evaluate the possibility of the vehicle needing rescue; and calculating the rescue distance of the vehicle to evaluate whether the claim settlement cost is reasonable.
And calculating two sections of rescue fee evaluation according to the actual vehicle rescue flow, including rescue calculation from the emergency place to the traffic police team and from the traffic police team to the repair shop.
Specifically, vehicle report information reported by a user is acquired; extracting vehicle track data in a period of time before and after the vehicle is out of danger according to the vehicle report information; obtaining vehicle parking data and accident related factor data according to the vehicle track data; and evaluating the size of the vehicle accident, the vehicle rescue authenticity and/or calculating the vehicle rescue distance according to the vehicle parking data and the accident correlation factor data.
In order to make the technical solutions and advantages of the embodiments of the present application more apparent, the following further detailed description of the exemplary embodiments of the present application with reference to the accompanying drawings makes it clear that the described embodiments are only a part of the embodiments of the present application, and are not exhaustive of all embodiments. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
Example 1
A flow chart of method steps for determining vehicle rescue based on vehicle travel data according to an embodiment of the present application is shown in fig. 1.
As shown in fig. 1, the method for determining vehicle rescue based on vehicle driving data of the embodiment specifically includes the following steps:
s101: and acquiring vehicle report information reported by a user.
Specifically, the vehicle registration information includes: vehicle license plate number, accident occurrence place, accident occurrence time and reporting time.
S102: and extracting vehicle track data in a period of time before and after the vehicle is out of danger according to the vehicle report information.
S103: and obtaining vehicle parking data and accident related factor data according to the vehicle track data.
Specifically, the accident-related factor data includes vehicle braking data before and after the vehicle is in danger.
Further, the vehicle braking data includes vehicle rapid deceleration data, vehicle gps speed, and/or trajectory warning data.
When the method is specifically implemented, vehicle parking data and accident related factor data are obtained according to vehicle track data, and the method specifically comprises the following steps: calculating vehicle parking time, vehicle parking duration and a vehicle parking coordinate point according to the vehicle track data; sequencing and grading the vehicle parking time according to the time difference between the parking time and the accident occurrence time; and converting the vehicle parking coordinate point and the accident occurrence place into standard geographic information interest points.
S104: and evaluating the size of the vehicle accident, the vehicle rescue authenticity and/or calculating the vehicle rescue distance according to the vehicle parking data and the accident correlation factor data.
Wherein, include: and matching the vehicle stop coordinate points and the standard geographic information interest points corresponding to the accident occurrence places, and selecting the vehicle stop coordinate point with the highest matching degree as a vehicle danger-leaving stop point.
Further, in the above-mentioned case,
firstly, vehicle braking data of a vehicle emergency stop point is obtained;
then, comparing the vehicle braking data with the braking threshold value data, and judging whether the vehicle braking data is a real danger rescue and/or evaluating the vehicle accident grade;
or obtaining the vehicle rescue distance according to the distance of the vehicle stopping point within a period of time after the vehicle is in danger.
A schematic diagram of a method for determining vehicle assistance based on vehicle driving data according to an embodiment of the present application is shown in fig. 2.
As shown in fig. 2, first, according to the information of the number plate, the accident occurrence location, the accident occurrence time, and the reporting time reported by the reporting personnel, the number plate, the accident occurrence time, and the reporting time are used by the feature calculation engine to query a large number of vehicle driving tracks on line. And finally, judging rescue related data, including: identifying and judging actual danger occurrence time and danger occurrence place of the vehicle; analyzing braking signals before and after the vehicle goes out of danger, vehicle rapid deceleration data, vehicle gps speed and track alarm data; analyzing and evaluating the size of the accident of the vehicle, and calculating vehicle parking data after the vehicle is in danger; and judging whether the vehicle is subjected to real rescue or not, and calculating the real rescue distance of the vehicle.
Specifically, as shown in fig. 2, the method mainly includes the following steps:
the method comprises the following steps: obtaining the registration plate number, the frame number, the reporting time, the reporting place and other reporting information provided by the user.
Step two: and judging whether the license plate number provided by the user is in the national freight platform or not, and continuing the subsequent operation if the license plate number is on the network.
Step three: and extracting vehicle track data before and after the vehicle emergency time.
And inquiring the track before and after the traffic accident occurrence time according to the license plate number and the traffic accident occurrence time by the track inquiry service.
Step four: and calculating stopping points and related characteristic factor data before and after the vehicle emergency time. The relevant characteristic factor data is vehicle braking data.
And calculating the parking time, the parking duration and the parking coordinate points (longitude and latitude) based on the vehicle running track points in the previous step.
Specifically, the driving track drift point is removed, and the stopping point is calculated according to the track time axis.
If the speed is less than or equal to 5 km/h, the distance between each subsequent point and the base point is calculated, if the continuous time is less than or equal to 200 m and the duration is more than 10 minutes, the base point is marked as a docking starting point, the docking starting time of the base point is taken as the docking starting time of the current time, and the docking longitude and latitude of the base point are taken as the longitude and latitude of the current docking point;
continuously judging all subsequent running track points, finding a point, wherein the distance between the point and the base point is more than 200 m, the duration exceeds 2 minutes, marking the time difference between the end point and the base point when the current stop is finished, and the total stop duration of the current stop is the time difference between the end point and the base point; when the time difference between two successive reporting points is more than 10 minutes or after the last reporting point exists 3 hours before or after the risk is brought out, the two reporting points are counted as the stop.
Filtering out the stopping points with stopping time larger than the reporting time, because the traffic accident occurrence time always occurs before the reporting; and screening the docking data with the difference between the docking time and the time for leaving the risk within 3 hours.
And finally, sequencing and grading the parking time according to the time difference between the parking time and the risk time. For example, three grades are classified within 1 hour, 2 hours and 3 hours.
Then, evaluating the rapid deceleration of the vehicle before parking, the early warning condition of the vehicle machine of the vehicle, the condition of 24 hours before and after the brake signal of the vehicle machine of the vehicle stops, and the maximum speed of the vehicle before parking; vehicle gps speed condition of vehicle 24 hours after parking.
Step five: and performing word segmentation service and input prompt service aiming at the reported insurance place, analyzing to obtain a standard vehicle insurance address, such as point of interest (POI) information of a village and town in province, city and county, and finishing judgment if the standard province information cannot be obtained.
The word segmentation service converts the reported vehicle accident occurrence place into a standard street interest point poi of a road name of a city, a county, a town.
Step six: and analyzing by geographic information service according to the longitude and latitude of the stop point to obtain standard road information of villages and towns in province and city.
Step seven: matching stop points and reporting insurance addresses, wherein the consistency of the names of the roads, the names of the cities, the counties, the towns and the places in provinces, cities, counties and counties is divided into different matching degrees of the names of the roads, the names of the cities, the counties and the towns.
Step eight: and selecting the vehicle stop point with the highest matching degree and the vehicle stop point with the closest reported insurance time as the judged real vehicle insurance stop point.
Step nine: and judging whether the vehicle is really rescued or not according to the judged real brake signal data before and after the vehicle danger-leaving stopping point, such as the gps speed of the stopped vehicle.
For example, the calculation of the speed characteristics before and after parking judges whether the vehicle is subjected to false rescue, and the false rescue is calculated for the condition that a brake signal exists after the vehicle is in danger or the condition that the gps speed of the vehicle is greater than or equal to 60km/h in 24 hours after the vehicle is in danger.
Step ten: and calculating the distance between two stops aiming at the stop points after the danger stop point is judged to obtain the suspected rescue distance.
Step eleven: and evaluating the probability of major accidents of the vehicle according to the emergency deceleration and speed before and after the dangerous stop point and collision rollover alarm data.
By adopting the method for judging vehicle rescue based on vehicle driving data in the embodiment of the application, vehicle report information reported by a user is obtained; extracting vehicle track data in a period of time before and after the vehicle is out of danger according to the vehicle report information; obtaining vehicle parking data and accident related factor data according to the vehicle track data; and evaluating the size of the vehicle accident, the vehicle rescue authenticity and/or calculating the vehicle rescue distance according to the vehicle parking data and the accident correlation factor data. According to the vehicle rescue evaluation method and device, user communication data are not needed, the actual rescue situation of the vehicle can be analyzed according to the reported information of the user, and accidents and vehicle rescue of the vehicle can be evaluated according to the track data of the vehicle.
Example 2
For details not disclosed in the system for determining vehicle rescue based on vehicle driving data of the present embodiment, please refer to specific implementation contents of the method for determining vehicle rescue based on vehicle driving data in other embodiments.
Fig. 3 is a schematic structural diagram illustrating a system for determining vehicle assistance based on vehicle driving data according to an embodiment of the present application.
As shown in fig. 3, the system for determining vehicle rescue based on vehicle driving data provided in this embodiment specifically includes a vehicle report information module 10, a vehicle trajectory data module 20, a vehicle stop data module 30, and a vehicle rescue evaluation module 40.
In particular, the method comprises the following steps of,
vehicle report information module 10: the method is used for acquiring vehicle report information reported by a user.
Specifically, the vehicle registration information includes: vehicle license plate number, accident occurrence place, accident occurrence time and reporting time.
Vehicle trajectory data module 20: the method is used for extracting vehicle track data in a period of time before and after the vehicle is out of danger according to vehicle report information.
Vehicle stop data module 30: and the data acquisition module is used for acquiring vehicle parking data and accident related factor data according to the vehicle track data.
Specifically, the accident-related factor data includes vehicle braking data before and after the vehicle is in danger.
Further, the vehicle braking data includes vehicle rapid deceleration data, vehicle gps speed, and/or trajectory warning data.
When the method is specifically implemented, vehicle parking data and accident related factor data are obtained according to vehicle track data, and the method specifically comprises the following steps: calculating vehicle parking time, vehicle parking duration and a vehicle parking coordinate point according to the vehicle track data; sequencing and grading the vehicle parking time according to the time difference between the parking time and the accident occurrence time; and converting the vehicle parking coordinate point and the accident occurrence place into standard geographic information interest points.
Vehicle rescue evaluation module 40: the vehicle accident assessment system is used for assessing the size of the vehicle accident, the vehicle rescue authenticity and/or calculating the vehicle rescue distance according to the vehicle parking data and the accident correlation factor data.
Wherein, include: and matching the vehicle stop coordinate points and the standard geographic information interest points corresponding to the accident occurrence places, and selecting the vehicle stop coordinate point with the highest matching degree as a vehicle danger-leaving stop point.
Further, in the above-mentioned case,
firstly, vehicle braking data of a vehicle emergency stop point is obtained;
then, comparing the vehicle braking data with the braking threshold value data, and judging whether the vehicle braking data is a real danger rescue and/or evaluating the vehicle accident grade;
or obtaining the vehicle rescue distance according to the distance of the vehicle stopping point within a period of time after the vehicle is in danger.
By adopting the system for judging vehicle rescue based on vehicle driving data in the embodiment of the application, the vehicle report information module 10 acquires vehicle report information reported by a user; the vehicle track data module 20 extracts vehicle track data in a period of time before and after the vehicle is in danger according to the vehicle report information; the vehicle parking data module 30 obtains vehicle parking data and accident-related factor data according to the vehicle trajectory data; vehicle rescue evaluation module 40 evaluates vehicle accident size, vehicle rescue authenticity, and/or calculates vehicle rescue distance based on vehicle stopping data and accident correlation factor data.
According to the vehicle rescue evaluation method and device, user communication data are not needed, the actual rescue situation of the vehicle can be analyzed according to the reported information of the user, and accidents and vehicle rescue of the vehicle can be evaluated according to the track data of the vehicle.
Example 3
For details not disclosed in the present embodiment of the apparatus for determining vehicle rescue based on vehicle driving data, please refer to specific implementation contents of the method or system for determining vehicle rescue based on vehicle driving data in other embodiments.
A schematic structural diagram of an apparatus 400 for determining vehicle rescue based on vehicle driving data according to an embodiment of the present application is shown in fig. 4.
As shown in fig. 4, the apparatus 400 for judging vehicle rescue based on vehicle driving data includes:
the memory 402: for storing executable instructions; and
a processor 401 is coupled to the memory 402 to execute executable instructions to perform the motion vector prediction method.
Those skilled in the art will appreciate that the schematic diagram 4 is merely an example of the apparatus 400 for determining vehicle assistance based on vehicle driving data, and does not constitute a limitation of the apparatus 400 for determining vehicle assistance based on vehicle driving data, and may include more or less components than those shown, or combine some components, or different components, for example, the apparatus 400 for determining vehicle assistance based on vehicle driving data may further include an input-output device, a network access device, a bus, etc.
The Processor 401 (CPU) may be other general-purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, a discrete hardware component, or the like. The general processor may be a microprocessor or the processor 401 may be any conventional processor or the like, and the processor 401 is a control center of the apparatus for judging vehicle rescue 400 based on vehicle driving data, and various interfaces and lines are used to connect the respective parts of the entire apparatus for judging vehicle rescue 400 based on vehicle driving data.
The memory 402 may be used to store computer readable instructions and the processor 401 may implement various functions of the apparatus 400 for determining vehicle rescue based on vehicle driving data by executing or executing computer readable instructions or modules stored in the memory 402 and invoking data stored in the memory 402. The memory 402 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data created from use of the device 400 for judging vehicle rescue based on vehicle travel data, and the like. In addition, the Memory 402 may include a hard disk, a Memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash Memory Card (Flash Card), at least one disk storage device, a Flash Memory device, a Read-Only Memory (ROM), a Random Access Memory (RAM), or other non-volatile/volatile storage devices.
The module integrated with the apparatus for judging vehicle rescue 400 based on the vehicle driving data may be stored in a computer-readable storage medium if it is implemented in the form of a software function module and sold or used as a separate product. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by hardware related to computer readable instructions, which may be stored in a computer readable storage medium, and when the computer readable instructions are executed by a processor, the steps of the method embodiments may be implemented.
Example 4
The present embodiment provides a computer-readable storage medium having stored thereon a computer program; the computer program is executed by a processor to implement the method of determining vehicle rescue based on vehicle travel data in other embodiments.
The device for judging vehicle rescue based on vehicle driving data and the computer storage medium in the embodiment of the application acquire vehicle report information reported by a user; extracting vehicle track data in a period of time before and after the vehicle is out of danger according to the vehicle report information; obtaining vehicle parking data and accident related factor data according to the vehicle track data; and evaluating the size of the vehicle accident, the vehicle rescue authenticity and/or calculating the vehicle rescue distance according to the vehicle parking data and the accident correlation factor data. According to the vehicle rescue evaluation method and device, user communication data are not needed, the actual rescue situation of the vehicle can be analyzed according to the reported information of the user, and accidents and vehicle rescue of the vehicle can be evaluated according to the track data of the vehicle.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in this specification and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.
It is to be understood that although the terms first, second, third, etc. may be used herein to describe various information, these information should not be limited to these terms. These terms are only used to distinguish one type of information from another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope of the present invention. The word "if" as used herein may be interpreted as "at … …" or "when … …" or "in response to a determination", depending on the context.
While the preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all alterations and modifications as fall within the scope of the application.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the spirit and scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such modifications and variations as well.
Claims (10)
1. A method for judging vehicle rescue based on vehicle driving data is characterized by comprising the following steps:
acquiring vehicle report information reported by a user;
extracting vehicle track data in a period of time before and after the vehicle is out of danger according to the vehicle report information;
obtaining vehicle parking data and accident related factor data according to the vehicle track data;
and evaluating the size of the vehicle accident, the vehicle rescue authenticity and/or calculating the vehicle rescue distance according to the vehicle parking data and the accident correlation factor data.
2. The method of determining vehicle rescue based on vehicle driving data as claimed in claim 1, wherein the vehicle declaration information includes: vehicle license plate number, accident occurrence place, accident occurrence time and reporting time.
3. The method for judging vehicle rescue based on vehicle driving data as claimed in claim 2, wherein the accident-related factor data includes vehicle braking data before and after the vehicle is out of danger.
4. The method for determining vehicle rescue based on vehicle driving data as claimed in claim 3, wherein the vehicle braking data comprises vehicle rapid deceleration data, vehicle gps speed and/or trajectory warning data.
5. The method for judging vehicle rescue based on vehicle driving data according to claim 4, wherein the obtaining of vehicle parking data and accident-related factor data according to the vehicle trajectory data specifically comprises:
calculating vehicle parking time, vehicle parking duration and a vehicle parking coordinate point according to the vehicle track data;
sequencing and grading the vehicle parking time according to the time difference between the parking time and the accident occurrence time;
and converting the vehicle parking coordinate point and the accident occurrence place into standard geographic information interest points.
6. The method for determining vehicle rescue based on vehicle driving data according to claim 5, wherein the evaluating the vehicle accident size, the vehicle rescue authenticity and/or calculating the vehicle rescue distance according to the vehicle stopping data and the accident related factor data comprises:
and matching the vehicle stopping coordinate points and the standard geographic information interest points corresponding to the accident occurrence places, and selecting the vehicle stopping coordinate point with the highest matching degree as a vehicle danger-leaving stopping point.
7. The method for determining vehicle rescue based on vehicle driving data according to claim 6, wherein the evaluating the vehicle accident size, the vehicle rescue authenticity and/or calculating the vehicle rescue distance according to the vehicle stopping data and the accident related factor data comprises:
obtaining vehicle braking data of the vehicle emergency stop point;
comparing the vehicle braking data with braking threshold data to judge whether the vehicle braking data is a real danger rescue and/or evaluate the vehicle accident grade;
and obtaining the vehicle rescue distance according to the distance of the vehicle stopping point within a period of time after the vehicle danger-leaving stopping point.
8. The utility model provides a system for judge vehicle rescue based on vehicle data of traveling which characterized in that specifically includes:
vehicle report information module: the system is used for acquiring vehicle report information reported by a user;
a vehicle trajectory data module: the vehicle track data acquisition module is used for extracting vehicle track data in a period of time before and after the vehicle is out of danger according to the vehicle report information;
vehicle stop data module: the system is used for obtaining vehicle parking data and accident related factor data according to the vehicle track data;
vehicle rescue evaluation module: and the vehicle rescue distance estimation module is used for estimating the size of the vehicle accident, the vehicle rescue authenticity and/or calculating the vehicle rescue distance according to the vehicle parking data and the accident correlation factor data.
9. An apparatus for determining vehicle rescue based on vehicle driving data, comprising:
a memory: for storing executable instructions; and
a processor: method for interfacing with a memory to execute executable instructions to perform a method of determining vehicle assistance based on vehicle driving data according to any one of claims 1-7.
10. A computer-readable storage medium, having stored thereon a computer program; the computer program is executed by a processor to implement a method of determining vehicle rescue based on vehicle driving data as claimed in any one of claims 1 to 7.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115190427A (en) * | 2022-09-13 | 2022-10-14 | 深圳位置网科技有限公司 | Emergency rescue calling system for motor vehicle |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20150119763A (en) * | 2014-04-16 | 2015-10-26 | 동아전장주식회사 | Method for determining car accident using On-Board Diagnostics |
| KR20160011389A (en) * | 2014-07-22 | 2016-02-01 | 현대모비스 주식회사 | System and method for estimating location of accident vehicle |
| CN105480177A (en) * | 2016-01-15 | 2016-04-13 | 深圳市险萝卜科技有限公司 | Method for preventing cheating used when vehicle accident appears by utilizing vehicle-mounted intelligent terminal |
| JP2018120293A (en) * | 2017-01-23 | 2018-08-02 | 株式会社デンソー | Vehicle operation management system |
| CN109656245A (en) * | 2018-10-31 | 2019-04-19 | 百度在线网络技术（北京）有限公司 | The determination method and apparatus of normal danger |
| CN109785951A (en) * | 2019-02-20 | 2019-05-21 | 江苏大学 | The prediction of occupant's condition of the injury and preferential rescue mode in a kind of more vehicles collision |
| CN113222331A (en) * | 2021-03-29 | 2021-08-06 | 北京中交兴路信息科技有限公司 | Method, device, equipment and storage medium for identifying authenticity of vehicle accident |
-
2021
- 2021-10-26 CN CN202111250716.3A patent/CN114140270A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20150119763A (en) * | 2014-04-16 | 2015-10-26 | 동아전장주식회사 | Method for determining car accident using On-Board Diagnostics |
| KR20160011389A (en) * | 2014-07-22 | 2016-02-01 | 현대모비스 주식회사 | System and method for estimating location of accident vehicle |
| CN105480177A (en) * | 2016-01-15 | 2016-04-13 | 深圳市险萝卜科技有限公司 | Method for preventing cheating used when vehicle accident appears by utilizing vehicle-mounted intelligent terminal |
| JP2018120293A (en) * | 2017-01-23 | 2018-08-02 | 株式会社デンソー | Vehicle operation management system |
| CN109656245A (en) * | 2018-10-31 | 2019-04-19 | 百度在线网络技术（北京）有限公司 | The determination method and apparatus of normal danger |
| CN109785951A (en) * | 2019-02-20 | 2019-05-21 | 江苏大学 | The prediction of occupant's condition of the injury and preferential rescue mode in a kind of more vehicles collision |
| CN113222331A (en) * | 2021-03-29 | 2021-08-06 | 北京中交兴路信息科技有限公司 | Method, device, equipment and storage medium for identifying authenticity of vehicle accident |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115190427A (en) * | 2022-09-13 | 2022-10-14 | 深圳位置网科技有限公司 | Emergency rescue calling system for motor vehicle |
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