CN111047861A - Traffic accident processing method and device and electronic equipment - Google Patents
Traffic accident processing method and device and electronic equipment Download PDFInfo
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Abstract
One or more embodiments of the present specification provide a traffic accident handling method and apparatus, and an electronic device, where the method includes: acquiring vehicle driving data of a target vehicle; extracting feature data based on the vehicle travel data; wherein the characteristic data is data relating to traffic accident detection performed on the target vehicle; inputting the characteristic data into a detection model to detect whether the target vehicle has a traffic accident or not based on the characteristic data by the detection model; the detection model is a machine learning model trained on the basis of a plurality of characteristic data samples marked with traffic accident detection results; and outputting a traffic accident detection result corresponding to the target vehicle.
Description
Technical Field
One or more embodiments of the present disclosure relate to the field of computer application technologies, and in particular, to a method and an apparatus for processing a traffic accident, and an electronic device.
Background
Now, after a traffic accident occurs, it is usually necessary for a person (e.g. a driver of an accident vehicle in the traffic accident) involved in the traffic accident to report to a traffic management department or an insurance company so as to enable the traffic management department to carry out a filing investigation on the traffic accident or enable the insurance company to carry out a claim on the traffic accident. That is, the traffic administration department or the insurance company cannot find the traffic accident in time, thereby causing inefficiency in performing corresponding business processes (e.g., filing investigation or claim settlement) on the traffic accident.
Disclosure of Invention
The present specification proposes a traffic accident handling method, the method comprising:
acquiring vehicle driving data of a target vehicle;
extracting feature data based on the vehicle travel data; wherein the characteristic data is data relating to traffic accident detection performed on the target vehicle;
inputting the characteristic data into a detection model to detect whether the target vehicle has a traffic accident or not based on the characteristic data by the detection model; the detection model is a machine learning model trained on the basis of a plurality of characteristic data samples marked with traffic accident detection results;
and outputting a traffic accident detection result corresponding to the target vehicle.
Optionally, the vehicle travel data includes: vehicle acceleration data; and positioning location data within a preset time period before performing the traffic accident detection.
Optionally, the acquiring vehicle driving data of the target vehicle includes:
acquiring vehicle acceleration data acquired by an acceleration sensor carried by a target vehicle corresponding to the target traffic accident;
and acquiring positioning position data collected by the mobile terminal equipment carried by the target vehicle in a preset time period before the traffic accident detection is executed.
Optionally, the extracting feature data based on the vehicle travel data includes:
determining a travel acceleration of the target vehicle based on the vehicle acceleration data;
determining a driving direction of the target vehicle based on the positioning position data;
the travel acceleration and the travel direction are determined as characteristic data.
Optionally, the machine learning model is a binary model.
Optionally, the method further comprises:
and if the traffic accident detection result corresponding to the target vehicle indicates that the target vehicle has a traffic accident, sending a notification message indicating that the target vehicle sends the traffic accident to a traffic management department and/or an insurance company.
The present specification also provides a traffic accident handling apparatus, the apparatus comprising:
the acquisition module acquires vehicle driving data of a target vehicle;
an extraction module that extracts feature data based on the vehicle travel data; wherein the characteristic data is data relating to traffic accident detection performed on the target vehicle;
the detection module inputs the characteristic data into a detection model to detect whether the target vehicle has a traffic accident or not based on the characteristic data by the detection model; the detection model is a machine learning model trained on the basis of a plurality of characteristic data samples marked with traffic accident detection results;
and the output module outputs a traffic accident detection result corresponding to the target vehicle.
Optionally, the vehicle travel data includes: vehicle acceleration data; and positioning location data within a preset time period before performing the traffic accident detection.
Optionally, the obtaining module:
acquiring vehicle acceleration data acquired by an acceleration sensor carried by a target vehicle corresponding to the target traffic accident;
and acquiring positioning position data collected by the mobile terminal equipment carried by the target vehicle in a preset time period before the traffic accident detection is executed.
Optionally, the extraction module:
determining a travel acceleration of the target vehicle based on the vehicle acceleration data;
determining a driving direction of the target vehicle based on the positioning position data;
the travel acceleration and the travel direction are determined as characteristic data.
Optionally, the machine learning model is a binary model.
Optionally, the apparatus further comprises:
and the sending module is used for sending a notification message indicating that the target vehicle sends the traffic accident to a traffic management department and/or an insurance company if the traffic accident detection result corresponding to the target vehicle indicates that the traffic accident happens to the target vehicle.
This specification also proposes an electronic device including:
a processor;
a memory for storing processor-executable instructions;
wherein the processor implements the steps of the above method by executing the executable instructions.
The present specification also contemplates a computer-readable storage medium having stored thereon computer instructions which, when executed by a processor, implement the steps of the above-described method.
In the technical scheme, whether the vehicle has a traffic accident or not can be detected based on the vehicle running data of the vehicle to be detected, and the traffic accident detection result corresponding to the vehicle is output, so that the corresponding business processing can be executed based on the traffic accident detection result, that is, the traffic accident which is possibly generated can be automatically detected without waiting for the report of related personnel of the traffic accident, so that the traffic accident can be timely found, and the efficiency of executing the corresponding business processing according to the traffic accident detection result in the follow-up process is improved.
Drawings
FIG. 1 is a schematic view of a traffic accident handling system shown in an exemplary embodiment of the present description;
FIG. 2 is a flow chart of a traffic accident handling method shown in an exemplary embodiment of the present description;
fig. 3 is a hardware configuration diagram of an electronic device in which a traffic accident handling apparatus according to an exemplary embodiment of the present disclosure is located;
fig. 4 is a block diagram of a traffic accident handling apparatus according to an exemplary embodiment of the present specification.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with one or more embodiments of the present specification. Rather, they are merely examples of apparatus and methods consistent with certain aspects of one or more embodiments of the specification, as detailed in the claims which follow.
The terminology used in the description herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the description. 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 should 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, the first information may also be referred to as second information, and similarly, the second information may also be referred to as first information, without departing from the scope of the present specification. The word "if" as used herein may be interpreted as "at … …" or "when … …" or "in response to a determination", depending on the context.
The present specification aims to provide a technical solution for detecting whether a traffic accident occurs in a vehicle to be detected based on vehicle driving data of the vehicle, and outputting a traffic accident detection result corresponding to the vehicle.
In specific implementation, a machine learning model can be trained in advance based on a plurality of characteristic data samples marked with traffic accident detection results, and the trained machine learning model is used as a detection model for detecting traffic accidents; the feature data may be data related to traffic accident detection, which is extracted based on vehicle travel data of accident vehicles in a traffic accident.
For a target vehicle to be detected, vehicle driving data of the target vehicle can be acquired first, data related to traffic accident detection performed on the target vehicle is extracted based on the acquired vehicle driving data to serve as feature data, the extracted feature data is input to the detection model, and the detection model detects whether the target vehicle has a traffic accident or not based on the feature data.
Subsequently, a traffic accident detection result corresponding to the target vehicle may be output to perform corresponding business processes based on the traffic accident detection result.
In the technical scheme, whether the vehicle has a traffic accident or not can be detected based on the vehicle running data of the vehicle to be detected, and the traffic accident detection result corresponding to the vehicle is output, so that the corresponding business processing can be executed based on the traffic accident detection result, that is, the traffic accident which is possibly generated can be automatically detected without waiting for the report of related personnel of the traffic accident, so that the traffic accident can be timely found, and the efficiency of executing the corresponding business processing according to the traffic accident detection result in the follow-up process is improved.
Referring to fig. 1, fig. 1 is a schematic diagram of a traffic accident handling system according to an exemplary embodiment of the present disclosure.
In practical applications, for a vehicle to be detected, a mobile terminal device mounted on the vehicle may detect whether the vehicle has a traffic accident based on vehicle driving data of the vehicle, and output a traffic accident detection result corresponding to the vehicle, so as to report a case to a traffic management department and/or an insurance company for the vehicle, so as to perform a case investigation on the vehicle by the traffic management department, and/or pay a claim to the vehicle by the insurance company when the traffic accident detection result indicates that the vehicle has a traffic accident.
Alternatively, the traffic control department or the insurance company may detect whether a traffic accident occurs in the vehicle based on the vehicle driving data of the vehicle, and perform a filing investigation or a claim settlement for the vehicle when the traffic accident detection result indicates that the traffic accident occurs in the vehicle.
That is, in the traffic accident handling system shown in fig. 1, the electronic device on the detection side may be a mobile terminal device mounted in a vehicle, or may be an electronic device used by a service execution side that needs to perform traffic accident detection on a vehicle, such as a traffic administration department or an insurance company; the electronic device of the detecting party may be a server, a computer, a mobile phone, a tablet device, a notebook computer, or a handheld computer (PDAs), which is not limited in this specification.
In practical applications, the vehicle driving data of the vehicle may be periodically uploaded to the electronic device on the detection side by electronic devices such as a mobile terminal device for positioning and an electronic chip for recording data collected by a sensor, which are installed in the vehicle, for example: the mobile terminal device may upload the positioning position data within a certain period of time before the start of the execution of the traffic accident detection this time to the detecting-side electronic device as the vehicle travel data, or the electronic chip may upload the data collected by the sensor mounted on the vehicle to the detecting-side electronic device as the vehicle travel data.
The detecting-side electronic device may further detect whether a traffic accident occurs to the vehicle based on the vehicle travel data, and output a traffic accident detection result corresponding to the vehicle.
Referring to fig. 2, fig. 2 is a flowchart illustrating a traffic accident handling method according to an exemplary embodiment of the present disclosure.
The traffic accident handling method can be applied to the detecting-side electronic device shown in fig. 1, and comprises the following steps:
and 208, outputting a traffic accident detection result corresponding to the target vehicle.
In the present embodiment, for a vehicle to be detected (referred to as a target vehicle), the electronic device may first acquire vehicle driving data of the target vehicle.
In practical applications, on one hand, an electronic chip or an electronic device such as a mobile terminal device for recording data collected by a sensor mounted on a vehicle can be mounted on the vehicle; on the other hand, a mobile terminal device may be mounted on a vehicle to perform Positioning by the mobile terminal device based on a GPS (Global Positioning System), and the obtained Positioning position data of the vehicle may be recorded.
That is, for the target vehicle, the electronic device mounted on the target vehicle and recording data acquired by the sensor may periodically upload data acquired by the sensor mounted on the target vehicle to the electronic device on the detection side at a predetermined time period; the time period may be preset by a user of the detecting party, or may be a default value, which is not limited in this specification.
In one embodiment shown, data collected by an acceleration sensor mounted on the target vehicle may be uploaded to the detection-side electronic device by an electronic device mounted on the target vehicle as vehicle acceleration data of the target vehicle.
The mobile terminal device for positioning carried by the target vehicle can upload the positioning position data of the target vehicle to the electronic device of the detection party periodically according to the time period; wherein the position location data may include a longitude and latitude of the position location.
In practical applications, when recording certain positioning position data of the vehicle obtained by positioning, the mobile terminal device mounted on the vehicle usually records the time when the positioning is performed, that is, records the corresponding relationship between the positioning position data and the time when the positioning position data is obtained.
In order to improve the data processing efficiency, the detecting-side electronic device may acquire only the positioning position data of the target vehicle within a preset time period before the traffic accident detection is performed.
Specifically, a suitable time duration may be preset by the user of the detecting party, and the electronic device of the detecting party may acquire the positioning location data of the target vehicle before the start of performing the traffic accident detection at this time within a time period of the time duration preset by the user of the detecting party, for example: assuming that the preset time period of the user of the detecting party is 5 minutes, and the time when the traffic accident detection is started this time is 18 hours and 20 minutes, the electronic device of the service executing party may only acquire the positioning position data of the target vehicle in the period from 18 hours 15 to 18 hours and 20 minutes.
Alternatively, a suitable time period may be set by the user of the detecting party according to actual conditions, and the electronic device of the detecting party may acquire the positioning position data of the target vehicle in the time period, for example: assuming that the time when the traffic accident detection is started this time is 18 hours and 20 minutes, the user of the detecting party can set the time period from 18 hours 15 to 18 hours and 20 minutes as the time period in which the positioning position data needs to be acquired, so that the electronic device of the detecting party can acquire only the positioning position data of the target vehicle in the time period from 18 hours 15 to 18 hours and 20 minutes.
In the above case, the detection-side electronic device may use the acquired data collected by the sensor mounted on the target vehicle (specifically, vehicle acceleration data of the target vehicle), and the positioning position data of the target vehicle as the vehicle travel data of the target vehicle.
In this embodiment, after acquiring the vehicle travel data of the target vehicle, the detecting-side electronic device may extract data relating to traffic accident detection performed on the target vehicle as feature data based on the vehicle travel data.
In one embodiment shown, on the one hand, the detecting-side electronic device may determine, based on vehicle acceleration data in the vehicle travel data, a travel acceleration of the target accident vehicle before the target traffic accident occurs, to take the travel acceleration as characteristic data; on the other hand, the detecting-side electronic device may determine the traveling direction of the target accident vehicle and the relative positional relationship of the target accident vehicle with other accident vehicles in the target traffic accident based on the positioning position data in the vehicle traveling data to also take the traveling direction and the relative positional relationship as characteristic data.
In practical applications, the detection-side electronic device may determine the driving direction of the target vehicle according to the change of the positioning position data of the target vehicle over time, for example: assuming that the longitude of the target vehicle is always kept constant and the latitude gradually increases with time, the traveling direction of the target vehicle can be determined as the north traveling.
In one illustrated embodiment, since the detection-side electronic device can generally acquire the positioning position data of all vehicles related to the target vehicle, the detection-side electronic device can also determine the relative positional relationship between the target vehicle and related other vehicles based on the positioning position data in the vehicle travel data and the positioning position data of the related other vehicles, so as to use the relative positional relationship as the feature data.
For example, the direction of the relevant other vehicle relative to the target vehicle may be determined according to the specific values of the longitude and latitude of the target vehicle and the relevant other vehicle. Assuming that the longitude of the target vehicle is the same as the longitude of the associated other vehicle, the latitude of the target vehicle is greater than the latitude of the other vehicle, it may be determined that the target vehicle is in the right-south direction of the other vehicle.
In another example, assume that the longitude of the target vehicle remains constant throughout, while the latitude gradually increases over time; assume again that the longitude and latitude of the associated other vehicle both gradually increase over time. In this case, in conjunction with the road extending direction (assumed to be the north-south direction) of the place where the target vehicle is located, it can be determined that the target vehicle is in a straight-ahead state with respect to the other vehicle, and the other vehicle is in a turning state with respect to the target vehicle.
In this embodiment, after the feature data is extracted, the detecting electronic device may input the feature data to a detection model trained in advance, so that the detection model may detect whether or not a traffic accident occurs in the target vehicle based on the feature data.
It should be noted that the detection model may be a machine learning model trained based on a plurality of characteristic data samples labeled with the traffic accident detection result.
In practical applications, the machine learning model may be a binary model, and the traffic accident detection result may be that the target vehicle has a traffic accident or that the target vehicle has no traffic accident.
In this embodiment, after the detection model detects the traffic accident detection result corresponding to the target vehicle, the detecting-side electronic device may output the traffic accident detection result to perform corresponding business processing based on the traffic accident detection result.
In practical applications, on one hand, the electronic device of the detecting party may output the detected traffic accident detection result corresponding to the target vehicle to a display screen, that is, the detected traffic accident detection result is displayed on the display screen for the user of the detecting party to view, so that the user of the detecting party may perform a filing survey or an amendment for the vehicle when the detected traffic accident result is that the target vehicle has a traffic accident, or report the target vehicle to a traffic administration department and/or an insurance company, so that the traffic administration department performs a filing survey for the target vehicle, and/or the insurance company performs an amendment for the target vehicle.
On the other hand, when it is determined that the traffic accident detection result corresponding to the target vehicle is that the traffic accident occurs in the target vehicle, if the detector electronic device is a mobile terminal device carried by the target vehicle, the detector electronic device may send a notification message indicating that the traffic accident occurs in the target vehicle to a traffic management department and/or an insurance company for performing a filing investigation by the traffic management department and/or a claim by the insurance company; when the traffic accident detection result corresponding to the target vehicle is determined to be that the target vehicle has a traffic accident, if the electronic device of the detection party is an electronic device used by a business executive party such as a traffic administration department or an insurance company, the target vehicle can be directly subjected to filing investigation or claim settlement.
The following describes a process of training a machine learning model to obtain the above detection model.
The training step of the machine learning model may be executed by the detecting-side electronic device, or may be executed by another electronic device, and the user of the detecting side uploads the trained detecting model to the detecting-side electronic device, so that the detecting-side electronic device can perform traffic accident detection through the detecting model.
In practical application, a proper number of characteristic data samples (which can be specifically set by the user of the detection party) can be obtained from historical traffic accidents recorded on the scheme and relevant data of vehicles without traffic accidents; one characteristic data sample may specifically include characteristic data of one vehicle.
The type of data in the feature data samples used for training the machine learning model is the same as the type of data in the feature data used for detecting a traffic accident by the detection model.
For example, assuming that the feature data samples used in training the machine learning model include three types of data, i.e., the traveling speed and the traveling direction of the vehicle and the relative positional relationship between the vehicle and the other relevant vehicles, the feature data used in detecting the traffic accident through the detection model should include three types of data, i.e., the traveling speed and the traveling direction of the target vehicle and the relative positional relationship between the target vehicle and the other relevant vehicles.
In another example, assuming that the feature data samples used in training the machine learning model only include two types of data, namely, the driving speed and the driving direction of the vehicle, the feature data used in detecting the traffic accident through the detection model should only include two types of data, namely, the driving speed and the driving direction of the target vehicle.
After the characteristic data samples are obtained, corresponding traffic accident detection results may be respectively labeled for the characteristic data samples, for example: if a certain characteristic data sample comprises the characteristic data of an accident vehicle A in the historical traffic accidents A, the traffic accident detection result labeled for the characteristic data sample is the traffic accident of the vehicle; and if a certain characteristic data sample comprises the characteristic data of the vehicle B without the traffic accident, the traffic accident detection result labeled for the characteristic data sample is that the vehicle does not have the traffic accident.
Subsequently, the feature data samples labeled with the traffic accident detection result can be input into a machine learning model preset by the user of the detection party for calculation, and the model parameters of the machine learning model are adjusted according to the calculation result so as to reduce the loss function of the machine learning model. When the loss function of the machine learning model is reduced to an expected threshold (the expected threshold may be specifically set by the user of the detecting party), the machine learning model may be considered to be trained, and then the trained machine learning model may be used as the detecting model, so as to detect the traffic accident through the detecting model.
In the above technical solution, since the responsibility confirmation result of the accident vehicle in the traffic accident can be predicted based on the vehicle driving data of the accident vehicle corresponding to the traffic accident that has occurred, and the predicted responsibility confirmation result is output to perform the corresponding business process based on the responsibility confirmation result, that is, the traffic accident responsibility confirmation can be automatically performed on the accident vehicle in the traffic accident, and the traffic accident responsibility confirmation on the accident vehicle in the traffic accident does not need to be performed manually, the efficiency of the traffic accident responsibility confirmation can be improved, and convenience is provided for performing the corresponding business process according to the responsibility confirmation result in the following process.
In the technical scheme, whether the vehicle has a traffic accident or not can be detected based on the vehicle running data of the vehicle to be detected, and the traffic accident detection result corresponding to the vehicle is output, so that the corresponding business processing can be executed based on the traffic accident detection result, that is, the traffic accident which is possibly generated can be automatically detected without waiting for the report of related personnel of the traffic accident, so that the traffic accident can be timely found, and the efficiency of executing the corresponding business processing according to the traffic accident detection result in the follow-up process is improved.
Corresponding to the embodiment of the traffic accident handling method, the specification also provides an embodiment of a traffic accident handling device.
The embodiment of the traffic accident processing device can be applied to electronic equipment. The device embodiments may be implemented by software, or by hardware, or by a combination of hardware and software. Taking a software implementation as an example, as a logical device, the device is formed by reading, by a processor of the electronic device where the device is located, a corresponding computer program instruction in the nonvolatile memory into the memory for operation. From a hardware aspect, as shown in fig. 3, the electronic device in the traffic accident handling apparatus in this specification is a hardware structure diagram of the electronic device, except for the processor, the memory, the network interface, and the nonvolatile memory shown in fig. 3, the electronic device in the embodiment may further include other hardware according to the actual function of the traffic accident handling, which is not described again.
Referring to fig. 4, fig. 4 is a block diagram of a traffic accident handling apparatus according to an exemplary embodiment of the present disclosure. The traffic accident handling apparatus 40 may be applied to the electronic device shown in fig. 3, and includes:
an acquisition module 401 that acquires vehicle travel data of a target vehicle;
an extraction module 402 that extracts feature data based on the vehicle travel data; wherein the characteristic data is data relating to traffic accident detection performed on the target vehicle;
a detection module 403, which inputs the characteristic data into a detection model to detect whether the target vehicle has a traffic accident based on the characteristic data by the detection model; the detection model is a machine learning model trained on the basis of a plurality of characteristic data samples marked with traffic accident detection results;
and an output module 404 for outputting a traffic accident detection result corresponding to the target vehicle.
In the present embodiment, the vehicle travel data includes: vehicle acceleration data; and positioning location data within a preset time period before performing the traffic accident detection.
In this embodiment, the obtaining module 401:
acquiring vehicle acceleration data acquired by an acceleration sensor carried by a target vehicle corresponding to the target traffic accident;
and acquiring positioning position data collected by the mobile terminal equipment carried by the target vehicle in a preset time period before the traffic accident detection is executed.
In this embodiment, the extracting module 402:
determining a travel acceleration of the target vehicle based on the vehicle acceleration data;
determining a driving direction of the target vehicle based on the positioning position data;
the travel acceleration and the travel direction are determined as characteristic data.
In this embodiment, the machine learning model is a binary model.
In this embodiment, the apparatus 40 may further include:
the sending module 405, if the traffic accident detection result corresponding to the target vehicle is that the traffic accident occurs to the target vehicle, sends a notification message indicating that the target vehicle sends the traffic accident to a traffic management department and/or an insurance company.
The implementation process of the functions and actions of each module in the above device is specifically described in the implementation process of the corresponding step in the above method, and is not described herein again.
For the device embodiments, since they substantially correspond to the method embodiments, reference may be made to the partial description of the method embodiments for relevant points. The above-described embodiments of the apparatus are merely illustrative, wherein the modules described as separate parts may or may not be physically separate, and the parts displayed as modules may or may not be physical modules, may be located in one place, or may be distributed on a plurality of network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution in the specification. One of ordinary skill in the art can understand and implement it without inventive effort.
The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. A typical implementation device is a computer, which may take the form of a personal computer, laptop computer, cellular telephone, camera phone, smart phone, personal digital assistant, media player, navigation device, email messaging device, game console, tablet computer, wearable device, or a combination of any of these devices.
In a typical configuration, a computer includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic disk storage, quantum memory, graphene-based storage media or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
The terminology used in the description of the one or more embodiments is for the purpose of describing the particular embodiments only and is not intended to be limiting of the description of the one or more embodiments. As used in one or more embodiments of the present 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 should be understood that although the terms first, second, third, etc. may be used in one or more embodiments of the present description to describe various information, such 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 one or more embodiments herein. The word "if" as used herein may be interpreted as "at … …" or "when … …" or "in response to a determination", depending on the context.
The above description is only for the purpose of illustrating the preferred embodiments of the one or more embodiments of the present disclosure, and is not intended to limit the scope of the one or more embodiments of the present disclosure, and any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the one or more embodiments of the present disclosure should be included in the scope of the one or more embodiments of the present disclosure.
Claims (14)
1. A traffic accident handling method, the method comprising:
acquiring vehicle driving data of a target vehicle;
extracting feature data based on the vehicle travel data; wherein the characteristic data is data relating to traffic accident detection performed on the target vehicle;
inputting the characteristic data into a detection model to detect whether the target vehicle has a traffic accident or not based on the characteristic data by the detection model; the detection model is a machine learning model trained on the basis of a plurality of characteristic data samples marked with traffic accident detection results;
and outputting a traffic accident detection result corresponding to the target vehicle.
2. The method of claim 1, the vehicle travel data comprising: vehicle acceleration data; and positioning location data within a preset time period before performing the traffic accident detection.
3. The method of claim 2, the obtaining vehicle travel data for a target vehicle, comprising:
acquiring vehicle acceleration data acquired by an acceleration sensor carried by a target vehicle corresponding to the target traffic accident;
and acquiring positioning position data collected by the mobile terminal equipment carried by the target vehicle in a preset time period before the traffic accident detection is executed.
4. The method of claim 2, the extracting feature data based on the vehicle travel data, comprising:
determining a travel acceleration of the target vehicle based on the vehicle acceleration data;
determining a driving direction of the target vehicle based on the positioning position data;
the travel acceleration and the travel direction are determined as characteristic data.
5. The method of claim 1, the machine learning model being a binary model.
6. The method of claim 1, further comprising:
and if the traffic accident detection result corresponding to the target vehicle indicates that the target vehicle has a traffic accident, sending a notification message indicating that the target vehicle sends the traffic accident to a traffic management department and/or an insurance company.
7. A traffic accident management apparatus, the apparatus comprising:
the acquisition module acquires vehicle driving data of a target vehicle;
an extraction module that extracts feature data based on the vehicle travel data; wherein the characteristic data is data relating to traffic accident detection performed on the target vehicle;
the detection module inputs the characteristic data into a detection model to detect whether the target vehicle has a traffic accident or not based on the characteristic data by the detection model; the detection model is a machine learning model trained on the basis of a plurality of characteristic data samples marked with traffic accident detection results;
and the output module outputs a traffic accident detection result corresponding to the target vehicle.
8. The apparatus of claim 7, the vehicle travel data comprising: vehicle acceleration data; and positioning location data within a preset time period before performing the traffic accident detection.
9. The apparatus of claim 8, the acquisition module to:
acquiring vehicle acceleration data acquired by an acceleration sensor carried by a target vehicle corresponding to the target traffic accident;
and acquiring positioning position data collected by the mobile terminal equipment carried by the target vehicle in a preset time period before the traffic accident detection is executed.
10. The apparatus of claim 8, the extraction module to:
determining a travel acceleration of the target vehicle based on the vehicle acceleration data;
determining a driving direction of the target vehicle based on the positioning position data;
the travel acceleration and the travel direction are determined as characteristic data.
11. The apparatus of claim 7, the machine learning model being a binary model.
12. The apparatus of claim 7, further comprising:
and the sending module is used for sending a notification message indicating that the target vehicle sends the traffic accident to a traffic management department and/or an insurance company if the traffic accident detection result corresponding to the target vehicle indicates that the traffic accident happens to the target vehicle.
13. An electronic device, comprising:
a processor;
a memory for storing processor-executable instructions;
wherein the processor implements the method of any one of claims 1 to 6 by executing the executable instructions.
14. A computer readable storage medium having stored thereon computer instructions which, when executed by a processor, carry out the steps of the method according to any one of claims 1 to 6.
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| CN201911228509.0A CN111047861A (en) | 2019-12-04 | 2019-12-04 | Traffic accident processing method and device and electronic equipment |
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Application publication date: 20200421 |