CN109523652B - Insurance processing method, device and equipment based on driving behaviors and storage medium - Google Patents
Insurance processing method, device and equipment based on driving behaviors and storage medium Download PDFInfo
- Publication number
- CN109523652B CN109523652B CN201811149959.6A CN201811149959A CN109523652B CN 109523652 B CN109523652 B CN 109523652B CN 201811149959 A CN201811149959 A CN 201811149959A CN 109523652 B CN109523652 B CN 109523652B
- Authority
- CN
- China
- Prior art keywords
- driver
- driving
- violation
- behavior
- insurance
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G07—CHECKING-DEVICES
- G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
- G07C5/00—Registering or indicating the working of vehicles
- G07C5/08—Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle or waiting time
- G07C5/0808—Diagnosing performance data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/08—Insurance
Abstract
The embodiment of the application provides a method, a device, equipment and a storage medium for processing insurance based on driving behaviors. The invention relates to a processing method of insurance based on driving behavior, which comprises the following steps: the method comprises the steps of obtaining a driving image of a driver and vehicle state information, analyzing the driving image of the driver and the vehicle state information, determining whether the driving behavior of the driver belongs to a violation type, and if so, sending violation data corresponding to the driving behavior of the driver to a cloud server so that the cloud server can construct a vehicle insurance actuarial model based on the violation data, wherein the vehicle insurance actuarial model is used for determining the cost of insurance based on the driving behavior. The accuracy of the vehicle insurance actuarial model can be improved.
Description
Technical Field
The embodiment of the application relates to the technical field of computers, in particular to a method, a device, equipment and a storage medium for processing insurance based on driving behaviors.
Background
The internet of vehicles is a large system network which is based on an in-vehicle network, an inter-vehicle network and a vehicle-mounted mobile internet and performs wireless communication and information exchange between vehicles-X (X: vehicles, roads, pedestrians, the internet and the like) according to an agreed communication protocol and a data interaction standard, is an integrated network capable of realizing intelligent traffic management, intelligent dynamic information service and intelligent vehicle control, and is a typical application of the internet of things technology in the field of traffic systems.
One important application of the car networking is Insurance Based On driving behavior (UBI), which uses On-Board Diagnostic (OBD) equipment and Global Positioning System (GPS) equipment to complete the collection of the self-state and environmental information data of the vehicle, such as the quarter, the driving distance, the maximum instantaneous speed, the time of trip, and the like, and performs data processing to construct a car Insurance actuarial model, wherein the car Insurance actuarial module is used for determining the Insurance cost of the vehicle.
However, the data collected by the OBD device and the GPS device cannot reflect the actual driving habits of the driver, and the accuracy of the car insurance actuarial model constructed based on the data is not high.
Disclosure of Invention
The embodiment of the application provides a processing method, a device, equipment and a storage medium of insurance based on driving behaviors, so as to accurately calculate a model by vehicle insurance.
In a first aspect, an embodiment of the present application provides a processing method for driving behavior-based insurance, including: acquiring a driving image of a driver and vehicle state information; analyzing the driver driving image and the vehicle state information; determining whether the driving behavior of the driver belongs to an illegal type; and if the driver's driving behavior belongs to the preset driving behavior, sending violation data corresponding to the driving behavior of the driver to a cloud server so that the cloud server constructs a vehicle insurance actuarial model based on the violation data, wherein the vehicle insurance actuarial model is used for determining insurance cost based on the driving behavior.
With reference to the first aspect, in a possible implementation manner of the first aspect, the analyzing the driver driving image and the vehicle state information includes: analyzing the driver driving image and the vehicle state information using a deep learning network.
With reference to the first aspect or one possible implementation manner of the first aspect, in another possible implementation manner of the first aspect, the analyzing the driver driving image and the vehicle state information using a deep learning network includes: detecting a target object in the driving image of the driver by using a target detection algorithm based on deep learning, and determining the position of the target object; and performing multi-label classification on the driving image of the driver by using a neural network model according to the position of the target object and the vehicle state information, and determining the behavior state of the driver.
With reference to the first aspect or any one of the possible implementations of the first aspect, in another possible implementation of the first aspect, the determining whether the driving behavior of the driver belongs to an illegal behavior includes: determining whether the driving behavior of the driver belongs to an illegal type according to at least one behavior state of the driver in a preset time period; the illegal type of driving behaviors comprises any behavior of unsafe driving.
With reference to the first aspect or any one of the possible implementations of the first aspect, in another possible implementation of the first aspect, the method further includes: generating an alarm instruction according to the violation behavior of the driver; and sending the alarm instruction to interactive equipment, wherein the alarm instruction is used for triggering the interactive equipment to feed back prompt information to a user.
With reference to the first aspect or any one of the possible implementations of the first aspect, in another possible implementation of the first aspect, the violation data includes identification information of the driver, video data of a violation, and violation information.
In a second aspect, an embodiment of the present application provides a processing method for driving behavior-based insurance, including: receiving violation data corresponding to the driving behavior of the driver; updating the driving ability description information of the driver according to the violation data; and constructing a vehicle insurance actuarial model according to the driving ability description information of the driver, wherein the vehicle insurance actuarial model is used for determining insurance cost based on driving behaviors.
With reference to the second aspect, in a possible implementation manner of the second aspect, the violation data includes identification information of the driver, video data of a violation, and violation information, and the method further includes: and storing the video data of the illegal behavior and the illegal behavior information into a database by taking the identification information of the driver as a main key.
With reference to the second aspect or one possible implementation manner of the second aspect, in another possible implementation manner of the second aspect, the identification information includes face identification information.
In a third aspect, an embodiment of the present application provides a processing apparatus for driving behavior-based insurance, including: the acquisition module is used for acquiring a driving image of a driver and vehicle state information; the analysis module is used for analyzing the driving image of the driver and the vehicle state information; the judging module is used for determining whether the driving behavior of the driver belongs to the violation type; the sending module is used for sending violation data corresponding to the driving behavior of the driver to a cloud server if the driving behavior of the driver belongs to a violation type, so that the cloud server can construct a vehicle insurance actuarial model based on the violation data, and the vehicle insurance actuarial model is used for determining insurance cost based on the driving behavior.
With reference to the third aspect, in a possible implementation manner of the third aspect, the analysis module is configured to: analyzing the driver driving image and the vehicle state information using a deep learning network.
With reference to the third aspect or one possible implementation manner of the third aspect, in another possible implementation manner of the third aspect, the analysis module is configured to: detecting a target object in the driving image of the driver by using a target detection algorithm based on deep learning, and determining the position of the target object; and performing multi-label classification on the driving image of the driver by using a neural network model according to the position of the target object and the vehicle state information, and determining the behavior state of the driver.
With reference to the third aspect or any possible implementation manner of the third aspect, in another possible implementation manner of the third aspect, the determining module is configured to: determining whether the driving behavior of the driver belongs to an illegal type according to at least one behavior state of the driver in a preset time period; the illegal type of driving behaviors comprises any behavior of unsafe driving.
With reference to the third aspect or any possible implementation manner of the third aspect, in another possible implementation manner of the third aspect, the apparatus further includes: a generation module; the generating module is used for generating an alarm instruction according to the violation behavior of the driver; the sending module is further configured to send the warning instruction to an interactive device, where the warning instruction is used to trigger the interactive device to feed back prompt information to a user.
With reference to the third aspect or any one of the possible implementations of the third aspect, in another possible implementation of the third aspect, the violation data includes identification information of the driver, video data of a violation, and violation information.
In a fourth aspect, an embodiment of the present application provides a processing apparatus for driving behavior-based insurance, including: the receiving module is used for receiving violation data corresponding to the driving behavior of the driver; the processing module is used for updating the driving ability description information of the driver according to the violation data; the processing module is further used for constructing a vehicle insurance actuarial model according to the driving ability description information of the driver, and the vehicle insurance actuarial model is used for determining insurance cost based on driving behaviors.
With reference to the fourth aspect, in a possible implementation manner of the fourth aspect, the violation data includes identification information of the driver, video data of a violation, and violation information, and the processing module is further configured to: and storing the video data of the illegal behavior and the illegal behavior information into a database by taking the identification information of the driver as a main key.
With reference to the fourth aspect or one possible implementation manner of the fourth aspect, in another possible implementation manner of the fourth aspect, the identification information includes face identification information.
In a fifth aspect, an embodiment of the present application provides an intelligent processing device, including: a memory and a processor; the memory is for instructions to cause the processor to execute the instructions to implement a method of processing a driving behaviour based insurance as described in the first aspect or any one of the possible implementations of the first aspect.
In a sixth aspect, an embodiment of the present application provides a cloud server, including: a memory and a processor; the memory is for instructions to cause the processor to execute the instructions to implement a method of processing a driving behaviour based insurance as set forth in the second aspect or any one of the possible implementations of the second aspect.
In a seventh aspect, an embodiment of the present application provides a storage medium, including: the storage medium includes: instructions for implementing a method of processing a driving behaviour-based insurance as described in the first aspect or any one of its possible implementations, or instructions for implementing a method of processing a driving behaviour-based insurance as described in the second aspect or any one of its possible implementations.
According to the processing method, the device, the equipment and the storage medium of the insurance based on the driving behaviors, the driving image and the vehicle state information of the driver are obtained through the intelligent processing equipment, the intelligent processing equipment analyzes the driving image and the vehicle state information of the driver, whether the driving behavior of the driver belongs to the violation type is determined, if the driving behavior of the driver belongs to the violation type, the intelligent processing equipment sends violation data corresponding to the driving behavior of the driver to the cloud server, so that the cloud server constructs the automobile insurance actuarial model based on the violation data, the automobile insurance actuarial model is used for determining the insurance cost based on the driving behavior, the automobile insurance actuarial model is constructed based on the violation data, the accuracy of the automobile insurance actuarial model is higher, for example, the insurance cost of a user with good driving habits can be reduced by using the automobile insurance cost actuarial model to determine the insurance cost, the cost of insurance for users with poor driving ability and/or driving habits is increased.
Drawings
Reference will now be made in brief to the accompanying drawings, which are needed for purposes of illustration and description of the prior art.
Fig. 1 is a schematic view of an application scenario of the technical solution of the present application according to an embodiment of the present application;
FIG. 2 is a flow chart of a method for processing driving behavior based insurance provided in an embodiment of the present application;
fig. 3 is a schematic view of another application scenario of the technical solution of the present application according to an embodiment of the present application;
FIG. 4 is a flow chart of another driving performance based insurance processing method according to an embodiment of the present application;
fig. 5 is a schematic diagram of a processing device 500 for driving behavior based insurance according to an embodiment of the present application;
FIG. 6 is a schematic diagram of an intelligent processing device 600 according to an embodiment of the present application;
fig. 7 is a schematic diagram of a processing device 700 for driving behavior based insurance according to an embodiment of the present application;
fig. 8 is a schematic diagram of a cloud server 800 according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application.
Fig. 1 is an application scene diagram illustration intention of the technical solution of the present application, as shown in fig. 1, an image capturing device and a vehicle state capturing device are respectively connected to an intelligent processing device, and the intelligent processing device is in communication connection with a cloud server. The image acquisition equipment acquires a driving image of a driver and provides the driving image for the intelligent processing equipment of the embodiment of the application, and the vehicle state acquisition equipment acquires vehicle state information which can comprise driving mileage, average speed, maximum instantaneous speed, travel time, emergency, real-time position information and the like. The image acquisition device may be any electronic device provided with a camera, for example, a monitoring camera, and the vehicle state acquisition device may specifically include an OBD device, a GPS device, and the like. The intelligent processing device can analyze the driving image and the vehicle state information of the driver, determine whether the driving behavior of the driver belongs to the violation type, and if the driving behavior of the driver belongs to the violation type, send violation data corresponding to the driving behavior of the driver to the cloud server, so that a vehicle insurance actuarial model is constructed based on the violation data, the accuracy of the vehicle insurance actuarial model is higher, for example, the vehicle insurance actuarial model is used for determining the insurance cost, the insurance cost of a user with good driving habits can be reduced, and the insurance cost of a user with poor driving ability and/or driving habits is improved.
The communication connection may include any of 3G, 4G, 5G, and the like.
The "driver driving image" referred to herein is used to reflect the real-time driving behavior of the driver.
The "vehicle state information" referred to herein is used to reflect the state of the vehicle itself and the environment in which the vehicle is located. The method can be acquired by OBD equipment, GPS equipment and the like.
The term "intelligent processing device" referred to herein, which may also be referred to as an artificial intelligence Box (AI-Box), may be an in-vehicle electronic device, and the name of the intelligent processing device is not particularly limited in the embodiments of the present application. The intelligent processing device can monitor the driving behavior of the driver in real time.
Fig. 2 is a flowchart of a processing method of insurance based on driving behavior according to an embodiment of the present application, and as shown in fig. 2, the method of the present embodiment may include:
step 101, the intelligent processing device acquires a driving image of a driver and vehicle state information.
The image acquisition equipment acquires a driving image of a driver and provides the driving image to the intelligent processing equipment of the embodiment of the application, and the vehicle state acquisition equipment acquires vehicle state information and provides the vehicle state information to the intelligent processing equipment of the embodiment of the application.
And 102, analyzing the driving image of the driver and the vehicle state information by the intelligent processing equipment, and determining whether the driving behavior of the driver belongs to the violation type.
The intelligent processing device uses the vehicle state information as assistance, analyzes the driving image of the driver, and monitors whether the driving behavior of the driver belongs to the violation type. Namely, the driving image of the driver is analyzed to identify the driving behavior of the driver, and then whether the driving behavior is illegal or not is determined.
The illegal driving behavior includes any behavior of unsafe driving, such as fatigue driving, smoking while driving, making a call while driving, inattention while driving for a long time, fastening a safety belt while driving, and leaving the steering wheel with both hands while driving, which are not illustrated by the embodiments.
The intelligent processing device may analyze each frame of driver driving image in conjunction with the vehicle status information. In one implementation, the intelligent processing device may analyze the driver driving image and the vehicle status information using a deep learning network.
And 103, if the driver behavior belongs to the preset rule, the intelligent processing equipment sends violation data corresponding to the driving behavior of the driver to a cloud server.
When the driving behavior of the driver belongs to the violation type, the intelligent processing device sends violation data corresponding to the driving behavior of the driver to the cloud server.
The violation data may include identification information of the driver, video data of the violation, and violation information. The identification information of the driver may be any information that can uniquely identify the driver, for example, a face identification of the driver, a mobile phone number of the driver, and the like. The video data of the violation may be video data of a preset time period including the occurrence time of the violation, and the time length of the preset time period may be flexibly set according to a requirement, for example, 30 seconds. The violation information specifically refers to information for describing the driving behavior belonging to the violation type, for example, fatigue driving.
And step 104, the cloud server updates the driving ability description information of the driver according to the violation data.
Specifically, the cloud server receives the violation data sent by the intelligent processing device, and updates the driving ability description information of the driver according to the violation data. The driving ability description information may be a structural body, and the driving ability description information may objectively describe the driving ability and driving habits of the driver, for example, a driving time period, a preferred driving speed, probabilities of various violations, and the like.
And 105, the cloud server constructs a vehicle insurance actuarial model according to the driving ability description information of the driver.
Wherein the vehicle insurance actuarial model is used for insurance cost determination based on driving behavior. The driving ability description information can reflect the driving ability and driving habits of the driver, so that the accuracy of the driving insurance actuarial model can be improved.
In the embodiment, the driving image of the driver and the vehicle state information are acquired by the intelligent processing device, the intelligent processing device analyzes the driving image of the driver and the vehicle state information to determine whether the driving behavior of the driver belongs to the violation type, if so, the intelligent processing device sends violation data corresponding to the driving behavior of the driver to the cloud server, to cause the cloud server to construct a driving insurance actuarial model based on the violation data, the driving insurance actuarial model being used for cost determination of driving behavior-based insurance, therefore, the vehicle insurance actuarial model is constructed based on the violation data, so that the vehicle insurance actuarial model has higher accuracy, for example, the vehicle insurance actuarial model is used for determining insurance cost, the insurance cost of the user with good driving habits can be reduced, and the insurance cost of the user with poor driving capability and/or driving habits can be improved.
Fig. 3 is another application scenario illustration of the technical solution of the present application provided in an embodiment of the present application, and as shown in fig. 3, on the basis of the application scenario shown in fig. 1, the application scenario of the present embodiment may further include an interactive device, a database, and a terminal. The interactive device is connected with the intelligent processing device, and the interactive device can be an output device such as a display screen and a loudspeaker on the vehicle. The terminal can be a computer, a mobile phone, a tablet computer and other electronic equipment.
On the basis of the above embodiment, the cloud server may further store the video data of the violation and the violation information into the database shown in fig. 3 by using the identification information of the driver as a primary key. The identification information may be a face ID.
When an accident happens to a vehicle under insurance, a user can send a data calling instruction to the cloud server through the terminal, the data calling instruction carries identification information of a driver, such as face ID of the driver, the cloud server obtains corresponding video data from the database according to the identification information of the driver and sends the video data to the terminal, and the user can identify the cause of the accident by watching the video, namely, determine whether the accident happens due to illegal driving behaviors of the driver, so that the insurance claim payment amount is judged to restrict the illegal behaviors of the driver. The user may be an insurance company worker.
Optionally, the cloud server may further generate an alarm instruction according to the violation of the driver, and send the alarm instruction to the interactive device, where the alarm instruction is used to trigger the interactive device to feed back prompt information to the user. For example, when the cloud server determines that the driver has a fatigue driving behavior according to the embodiment of the embodiment, the cloud server may generate an alarm instruction, and the alarm instruction may trigger a speaker to emit a warning tone, so as to alarm an illegal behavior, thereby avoiding an accident.
The above embodiment will be specifically explained below using one embodiment.
Fig. 4 is a flowchart of another processing method for insurance based on driving behavior according to an embodiment of the present application, where an execution subject of the embodiment is an intelligent processing device, and as shown in fig. 4, the method of the embodiment may include:
step 201, obtaining a driving image of a driver and vehicle state information.
For explanation of step 201, refer to step 101 in the embodiment shown in fig. 2, which is not described herein again.
Step 202, detecting a target object in the driving image of the driver by using a target detection algorithm based on deep learning, and determining the position of the target object.
The target detection algorithm based on deep learning can be a fast-rcnn, ssd and other target detection algorithms. The target object may include various parts of the driver's face, hands, body, and the like.
And 203, performing multi-label classification on the driving image of the driver by using a neural network model according to the position of the target object and the vehicle state information, and determining the behavior state of the driver.
Specifically, the neural network model may be any neural network model, and the neural network model may identify a behavior state of the driver, that is, determine the behavior state of the driver, where the behavior state of the driver includes eye closing, mouth opening, phone call during driving, and the like.
And 204, determining whether the driving behavior of the driver belongs to the violation type according to at least one behavior state of the driver in a preset time period. If the violation type is present, step 205 is executed, and if the violation type is not present, step 201 is executed.
Specifically, multiple frames of driving images of the driver are collected within a preset time period, the driving images of the driver are classified respectively, the behavior state of the driver corresponding to each frame of image is determined, whether the driving behavior of the driver belongs to the violation type or not can be determined according to the behavior state of the driver corresponding to one frame of image, and whether the driving behavior of the driver belongs to the violation type or not can also be determined according to the behavior state of the driver corresponding to multiple frames of images, so that the recognition accuracy is improved.
For example, if the behavior state of the driver corresponding to one frame of image is open mouth, and the behavior state of the driver corresponding to the next frame of image is closed eye, it may be determined that the driver has yawned and closed eye, that is, the driving behavior of the driver is fatigue driving behavior, and belongs to the violation type.
And step 205, sending violation data corresponding to the driving behavior of the driver to a cloud server.
For explanation of step 205, refer to step 103 in the embodiment shown in fig. 1, which is not described herein again.
In the embodiment, the driving image of the driver and the vehicle state information are acquired by the intelligent processing device, the intelligent processing device analyzes the driving image of the driver and the vehicle state information to determine whether the driving behavior of the driver belongs to the violation type, if so, the intelligent processing device sends violation data corresponding to the driving behavior of the driver to the cloud server, to cause the cloud server to construct a driving insurance actuarial model based on the violation data, the driving insurance actuarial model being used for cost determination of driving behavior-based insurance, therefore, the vehicle insurance actuarial model is constructed based on the violation data, so that the vehicle insurance actuarial model has higher accuracy, for example, the vehicle insurance actuarial model is used for determining insurance cost, the insurance cost of the user with good driving habits can be reduced, and the insurance cost of the user with poor driving capability and/or driving habits can be improved.
And determining whether the driving behavior of the driver belongs to the violation type according to the behavior state of the driver corresponding to the multi-frame image so as to improve the recognition accuracy.
Fig. 5 is a schematic diagram of a processing apparatus 500 for driving behavior based insurance according to an embodiment of the present application, as shown in fig. 5, the apparatus includes:
an obtaining module 501, configured to obtain a driving image of a driver and vehicle state information;
an analysis module 502 for analyzing the driver driving image and the vehicle state information;
a judging module 503, configured to determine whether a driving behavior of the driver belongs to an violation type;
a sending module 504, configured to send violation data corresponding to the driving behavior of the driver to a cloud server if the driving behavior of the driver belongs to a violation type, so that the cloud server constructs a vehicle insurance actuarial model based on the violation data, where the vehicle insurance actuarial model is used to determine the cost of insurance based on the driving behavior.
Optionally, the analysis module 502 is configured to: analyzing the driver driving image and the vehicle state information using a deep learning network.
Optionally, the analysis module 502 is configured to: detecting a target object in the driving image of the driver by using a target detection algorithm based on deep learning, and determining the position of the target object; and performing multi-label classification on the driving image of the driver by using a neural network model according to the position of the target object and the vehicle state information, and determining the behavior state of the driver.
Optionally, the determining module 503 is configured to: determining whether the driving behavior of the driver belongs to an illegal type according to at least one behavior state of the driver in a preset time period; the illegal type of driving behaviors comprises any behavior of unsafe driving.
Optionally, the apparatus further comprises: a generation module 505; the generating module 505 is configured to generate a warning instruction according to the violation of the driver; the sending module 504 is further configured to send the warning instruction to an interactive device, where the warning instruction is used to trigger the interactive device to feed back prompt information to a user.
Optionally, the violation data includes identification information of the driver, video data of a violation, and violation information.
The processing apparatus for driving behavior based insurance provided in this application may be an intelligent processing device or an internal chip of the intelligent processing device, and may be configured to perform the method steps related to the intelligent processing device in the foregoing embodiments, and the content and effect thereof are not described herein again.
Fig. 6 is a schematic diagram of an intelligent processing device 600 according to an embodiment of the present application, and as shown in fig. 6, the device includes: memory 601, processor 602, and transceiver 603.
The memory 601 is used for instructions to cause the processor 602 to execute the instructions to implement the processing method of driving behavior based insurance described above.
The transceiver 603 is used for communication with other devices.
The Processor 602 may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components.
The Memory 601 may be implemented by any type of volatile or non-volatile Memory device or combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic Memory, flash Memory, magnetic disk or optical disk.
The intelligent processing device provided in the present application may be configured to execute the method steps related to the intelligent processing device in the foregoing embodiments, and the content and effect of the method steps are not described herein again.
Fig. 7 is a schematic diagram of a processing apparatus 700 for driving behavior based insurance according to an embodiment of the present application, as shown in fig. 7, the apparatus including:
the receiving module 701 is used for receiving violation data corresponding to the driving behavior of the driver;
a processing module 702, configured to update driving ability description information of the driver according to the violation data;
the processing module 702 is further configured to construct a vehicle insurance actuarial model according to the driving ability description information of the driver, where the vehicle insurance actuarial model is used for determining the insurance cost based on the driving behavior.
Optionally, the violation data includes identification information of the driver, video data of a violation, and violation information, and the processing module 702 is further configured to: and storing the video data of the illegal behavior and the illegal behavior information into a database by taking the identification information of the driver as a main key.
Optionally, the identification information includes face identification information.
The processing device of the insurance based on the driving behavior provided by the application can be a cloud server or an internal chip of the cloud server, and can be used for executing the method steps related to the cloud server in the embodiment, and the content and the effect of the processing device are not repeated herein.
Fig. 8 is a schematic diagram of a cloud server 800 according to an embodiment of the present application, and as shown in fig. 8, the apparatus includes: a memory 801, a processor 802, and a transceiver 803.
The memory 801 is used for instructions to cause the processor 802 to execute the instructions to implement the processing method of driving-behavior-based insurance described above.
The transceiver 803 is used for communication with other devices.
The Processor 802 may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components.
The Memory 801 may be implemented by any type of volatile or non-volatile Memory device or combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic Memory, flash Memory, magnetic disk or optical disk.
The cloud server provided by the present application may be configured to execute the method steps related to the cloud server in the foregoing embodiments, and the content and the effect of the method steps are not described herein again.
The present application also provides a storage medium comprising: the storage medium includes: instructions for implementing the method of processing the driving-behavior-based insurance described above. The contents and effects thereof will not be described in detail herein.
The present application provides a computer program product comprising instructions for implementing the method of processing driving behavior-based insurance described above. The contents and effects thereof will not be described in detail herein.
Those of ordinary skill in the art will understand that: all or a portion of the steps of implementing the above-described method embodiments may be performed by hardware associated with program instructions. The program may be stored in a computer-readable storage medium. When executed, the program performs steps comprising the method embodiments described above; and the aforementioned storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (15)
1. A method for processing insurance based on driving behavior, comprising:
acquiring a driving image of a driver and vehicle state information;
detecting a target object in the driving image of the driver by using a target detection algorithm based on deep learning, and determining the position of the target object;
according to the position of the target object and the vehicle state information, performing multi-label classification on the driving image of the driver by using a neural network model, and determining the behavior state of the driver;
determining whether the driving behavior of the driver belongs to an illegal type according to at least one behavior state of the driver in a preset time period; the violation type driving behaviors comprise any one of behaviors of unsafe driving;
and if the driver's driving behavior belongs to the preset driving behavior, sending violation data corresponding to the driving behavior of the driver to a cloud server so that the cloud server constructs a vehicle insurance actuarial model based on the violation data, wherein the vehicle insurance actuarial model is used for determining insurance cost based on the driving behavior.
2. The method of claim 1, further comprising:
generating an alarm instruction according to the violation behavior of the driver;
and sending the alarm instruction to interactive equipment, wherein the alarm instruction is used for triggering the interactive equipment to feed back prompt information to a user.
3. The method according to claim 1 or 2, wherein the violation data comprises identification information of the driver, video data of a violation, and violation information.
4. A method for processing insurance based on driving behavior, comprising:
receiving violation data corresponding to the driving behavior of the driver; the violation data is the violation data corresponding to the driving behavior of the driver, which is sent by the intelligent processing equipment after the intelligent processing equipment determines that the driving behavior of the driver belongs to the violation type according to at least one behavior state of the driver in a preset time period; the violation type driving behaviors comprise any one of behaviors of unsafe driving; the behavior state of the driver is determined by detecting a target object in the driving image of the driver by the intelligent processing equipment by using a target detection algorithm based on deep learning and performing multi-label classification on the driving image of the driver by using a neural network model according to the determined position and vehicle state information of the target object;
updating the driving ability description information of the driver according to the violation data;
and constructing a vehicle insurance actuarial model according to the driving ability description information of the driver, wherein the vehicle insurance actuarial model is used for determining insurance cost based on driving behaviors.
5. The method of claim 4, wherein the violation data includes identification information of the driver, video data of a violation, and violation information, the method further comprising:
and storing the video data of the illegal behavior and the illegal behavior information into a database by taking the identification information of the driver as a main key.
6. The method of claim 5, wherein the identification information comprises face identification information.
7. A processing apparatus for driving behavior based insurance, comprising:
the acquisition module is used for acquiring a driving image of a driver and vehicle state information;
the analysis module is used for detecting a target object in the driving image of the driver by using a target detection algorithm based on deep learning and determining the position of the target object;
according to the position of the target object and the vehicle state information, performing multi-label classification on the driving image of the driver by using a neural network model, and determining the behavior state of the driver;
the judging module is used for determining whether the driving behavior of the driver belongs to the violation type according to at least one behavior state of the driver in a preset time period; the violation type driving behaviors comprise any one of behaviors of unsafe driving;
the sending module is used for sending violation data corresponding to the driving behavior of the driver to a cloud server if the driving behavior of the driver belongs to a violation type, so that the cloud server can construct a vehicle insurance actuarial model based on the violation data, and the vehicle insurance actuarial model is used for determining insurance cost based on the driving behavior.
8. The apparatus of claim 7, further comprising: a generation module;
the generating module is used for generating an alarm instruction according to the violation behavior of the driver;
the sending module is further configured to send the warning instruction to an interactive device, where the warning instruction is used to trigger the interactive device to feed back prompt information to a user.
9. The apparatus according to claim 7 or 8, wherein the violation data comprises identification information of the driver, video data of a violation, and violation information.
10. A processing apparatus for driving behavior based insurance, comprising:
the receiving module is used for receiving violation data corresponding to the driving behavior of the driver; the violation data is the violation data corresponding to the driving behavior of the driver, which is sent by the intelligent processing equipment after the intelligent processing equipment determines that the driving behavior of the driver belongs to the violation type according to at least one behavior state of the driver in a preset time period; the violation type driving behaviors comprise any one of behaviors of unsafe driving; the behavior state of the driver is determined by detecting a target object in the driving image of the driver by the intelligent processing equipment by using a target detection algorithm based on deep learning and performing multi-label classification on the driving image of the driver by using a neural network model according to the determined position and vehicle state information of the target object;
the processing module is used for updating the driving ability description information of the driver according to the violation data;
the processing module is further used for constructing a vehicle insurance actuarial model according to the driving ability description information of the driver, and the vehicle insurance actuarial model is used for determining insurance cost based on driving behaviors.
11. The apparatus of claim 10, wherein the violation data comprises identification information of the driver, video data of a violation, and violation information, and wherein the processing module is further configured to:
and storing the video data of the illegal behavior and the illegal behavior information into a database by taking the identification information of the driver as a main key.
12. The apparatus of claim 11, wherein the identification information comprises face identification information.
13. An intelligent processing device, comprising:
a memory and a processor;
the memory is for instructions to cause the processor to execute the instructions to implement a method of processing a driving behaviour based insurance according to any one of claims 1 to 3.
14. A cloud server, comprising:
a memory and a processor;
the memory is for instructions to cause the processor to execute the instructions to implement a method of processing a driving behaviour based insurance according to any one of claims 4 to 6.
15. A storage medium, comprising: the storage medium includes: instructions for implementing a method of processing a driving behaviour based insurance according to any one of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811149959.6A CN109523652B (en) | 2018-09-29 | 2018-09-29 | Insurance processing method, device and equipment based on driving behaviors and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811149959.6A CN109523652B (en) | 2018-09-29 | 2018-09-29 | Insurance processing method, device and equipment based on driving behaviors and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109523652A CN109523652A (en) | 2019-03-26 |
| CN109523652B true CN109523652B (en) | 2022-03-22 |
Family
ID=65772143
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811149959.6A Active CN109523652B (en) | 2018-09-29 | 2018-09-29 | Insurance processing method, device and equipment based on driving behaviors and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109523652B (en) |
Families Citing this family (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111866032A (en) * | 2019-04-11 | 2020-10-30 | 阿里巴巴集团控股有限公司 | Data processing method and device and computing equipment |
| CN110126835A (en) * | 2019-04-12 | 2019-08-16 | 中国平安财产保险股份有限公司 | Interference method, device, computer equipment and the storage medium of driving behavior |
| CN110737688B (en) * | 2019-09-30 | 2023-04-07 | 上海商汤临港智能科技有限公司 | Driving data analysis method and device, electronic equipment and computer storage medium |
| CN111062701A (en) * | 2019-12-18 | 2020-04-24 | 上海博泰悦臻电子设备制造有限公司 | Insurance management method and related device |
| CN113139403A (en) * | 2020-01-17 | 2021-07-20 | 顺丰科技有限公司 | Violation behavior identification method and device, computer equipment and storage medium |
| CN111243236A (en) * | 2020-01-17 | 2020-06-05 | 南京邮电大学 | Fatigue driving early warning method and system based on deep learning |
| CN112149511A (en) * | 2020-08-27 | 2020-12-29 | 深圳市点创科技有限公司 | Method, terminal and device for detecting violation of driver based on neural network |
| CN114627613A (en) * | 2020-12-11 | 2022-06-14 | 深圳酷派技术有限公司 | Irregular driving behavior recognition method and device, storage medium and electronic equipment |
| CN113792691B (en) * | 2021-09-22 | 2024-03-22 | 平安国际智慧城市科技股份有限公司 | Video identification method, system, equipment and medium |
| CN114550147B (en) * | 2022-04-26 | 2022-08-09 | 天津大学 | Automobile data acquisition, analysis and processing method and system based on Internet of things |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104794856A (en) * | 2014-01-22 | 2015-07-22 | 径卫视觉科技（上海）有限公司 | Driver's attention comprehensive evaluation system |
| CN106600427A (en) * | 2016-12-22 | 2017-04-26 | 安徽保腾网络科技有限公司 | Novel vehicle insurance system |
Family Cites Families (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8090598B2 (en) * | 1996-01-29 | 2012-01-03 | Progressive Casualty Insurance Company | Monitoring system for determining and communicating a cost of insurance |
| US6931309B2 (en) * | 2003-05-06 | 2005-08-16 | Innosurance, Inc. | Motor vehicle operating data collection and analysis |
| KR101617349B1 (en) * | 2012-08-28 | 2016-05-03 | (주)나노포인트 | Diagnostic system and method for the analysis of driving behavior |
| CN104732208B (en) * | 2015-03-16 | 2018-05-18 | 电子科技大学 | Video human Activity recognition method based on sparse subspace clustering |
| CN107784251A (en) * | 2016-08-25 | 2018-03-09 | 大连楼兰科技股份有限公司 | The method evaluated based on image recognition technology driving behavior |
| CN106778538A (en) * | 2016-11-28 | 2017-05-31 | 上海工程技术大学 | Intelligent driving behavior evaluation method based on analytic hierarchy process (AHP) |
| CN108416258B (en) * | 2018-01-23 | 2020-05-08 | 华侨大学 | Multi-human body tracking method based on human body part model |
-
2018
- 2018-09-29 CN CN201811149959.6A patent/CN109523652B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104794856A (en) * | 2014-01-22 | 2015-07-22 | 径卫视觉科技（上海）有限公司 | Driver's attention comprehensive evaluation system |
| CN106600427A (en) * | 2016-12-22 | 2017-04-26 | 安徽保腾网络科技有限公司 | Novel vehicle insurance system |
Non-Patent Citations (2)
| Title |
|---|
| 基于UBI的车辆个性化保险费率厘定模式研究与应用;王罡;《中国优秀硕士学位论文全文数据库 经济与管理学辑》;20180415;第21-36页，第50-52页 * |
| 王罡.基于UBI的车辆个性化保险费率厘定模式研究与应用.《中国优秀硕士学位论文全文数据库 经济与管理学辑》.2018, * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109523652A (en) | 2019-03-26 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109523652B (en) | Insurance processing method, device and equipment based on driving behaviors and storage medium | |
| US20200151479A1 (en) | Method and apparatus for providing driver information via audio and video metadata extraction | |
| WO2020042984A1 (en) | Vehicle behavior detection method and apparatus | |
| US11475770B2 (en) | Electronic device, warning message providing method therefor, and non-transitory computer-readable recording medium | |
| US9583000B2 (en) | Vehicle-based abnormal travel event detecting and reporting | |
| CN107895410B (en) | Vehicle accident reporting processing method, storage device and vehicle-mounted terminal | |
| TWI627084B (en) | A computing platform for development and deployment of sensor-driven vehicle telemetry applications and services | |
| WO2020024457A1 (en) | Liability cognizance method and device of traffic accident and computer readable storage medium | |
| US10101244B2 (en) | Self-learning simulation environments | |
| US20100305806A1 (en) | Portable Multi-Modal Emergency Situation Anomaly Detection and Response System | |
| US10783360B1 (en) | Apparatuses, systems and methods for vehicle operator gesture recognition and transmission of related gesture data | |
| CN111210620B (en) | Method, device and equipment for generating driver portrait and storage medium | |
| Beauchemin et al. | Portable and scalable vision-based vehicular instrumentation for the analysis of driver intentionality | |
| EP3895950B1 (en) | Methods and systems for automated driving system monitoring and management | |
| WO2022078077A1 (en) | Driving risk early warning method and apparatus, and computing device and storage medium | |
| CN107867295B (en) | Accident early warning method based on vehicle risk probability, storage device and vehicle-mounted terminal | |
| CN111209797A (en) | Method, device, equipment and storage medium for monitoring driving behavior | |
| US20230351894A1 (en) | Accident reporter | |
| US10198773B2 (en) | Cooperative evidence gathering | |
| CN112766746A (en) | Traffic accident recognition method and device, electronic equipment and storage medium | |
| US11853741B2 (en) | Federated learning for connected camera | |
| US20220148319A1 (en) | Apparatuses, systems and methods for integrating vehicle operator gesture detection within geographic maps | |
| WO2024065093A1 (en) | Intrusion detection method, apparatus and system | |
| CN114220285A (en) | Positioning and warning method and device for overspeed vehicle, electronic equipment and readable medium | |
| CN114475623A (en) | Vehicle control method and device, electronic equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20211021 Address after: 100176 101, floor 1, building 1, yard 7, Ruihe West 2nd Road, Beijing Economic and Technological Development Zone, Daxing District, Beijing Applicant after: Apollo Zhilian (Beijing) Technology Co.,Ltd. Address before: 100085 Baidu Building, 10 Shangdi Tenth Street, Haidian District, Beijing Applicant before: BAIDU ONLINE NETWORK TECHNOLOGY (BEIJING) Co.,Ltd. |
|
| TA01 | Transfer of patent application right | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |