CN105374211A - System and method for calculating driving risks and assisting automobile insurance pricing based on multi-source data - Google Patents
System and method for calculating driving risks and assisting automobile insurance pricing based on multi-source data Download PDFInfo
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Abstract
The invention discloses a system and method for calculating driving risks and assisting automobile insurance pricing based on multi-source data. The system includes an intelligent mobile internet terminal and a remote server connected with each other; a plurality of data sensing acquisition units and applications are installed in the intelligent mobile terminal; and the remote server is provided with a risk model algorithm system and includes a scene driving risk analysis sub module, a distracted driving model recognition sub module, a user travel behavior analysis sub module and a driving behavior evaluation and grading system sub module. According to the system and method of the invention, data acquired by the intelligent mobile internet terminal and road traffic information acquired by the remote server are analyzed, and the scores of the sub modules are calculated, and automobile insurance pricing is carried out according to the scores. With the system and method of the invention adopted, based on multi-source data acquisition and fusion, the driving behaviors of users are analyzed, and the travel habits, driving habits and driving risks of the drivers can be effectively calibrated, and therefore, theoretical basis and technical support can be provided for driving behavior-based automobile insurance pricing models.
Description
Technical field
The invention belongs to traffic safety and data statistics technical field, be specifically related to a kind of system and method for fixing a price based on multi-source data calculating driving risk and auxiliary vehicle insurance.
Background technology
Common vehicle insurance pricing model usually based on static informations such as such as driver's age, sex, driving age, Living city, accident record, Claims Resolution history, and ignores the driving custom of driver, the individual factors such as driving of diverting one's attention.And in fact, these factors may need the compensation risk born may be even more important for the traffic safety of driver and insurance company.All identical in age, driving age etc., if driver drive every year 20000 kilometers, often drive over the speed limit, braking accelerator is all stepped on more violent, often make a phone call during driving or send short messages, and another driver only opens 5,000 kilometers every year, drive at ordinary times all to observe traffic rules and regulations, generally speaking the danger coefficient of first driver can far above second with the probability that traffic hazard occurs.If insurance company imposes same premium to two people, a series of problem may be brought, also be unfavorable for insurance company's accurate Calculation customer risk, segmentation customer group.
Be directed to this, the automobile insurance computation schema (UsageBasedInsurance, UBI) based on driving behavior comes into vogue in American-European countries in recent years.The method that they adopt, the overwhelming majority is all from OBD (OBD-II) port, by a hardware, vehicle traveling process is comprised the information such as real-time speed, acceleration and deceleration and record, and pass remote server back and use for data analysis.This technology hardware costs, data acquisition duration, data acquisition comprehensive etc. in all there is certain defect.Due to hardware facility expense costly, user generally only allows from insurance company's short-term lease.During this period, driver is cautious trip often, and waits observation period one mistake, the driving behavior of custom before user just can be returned to.In addition, this method also cannot detect driver and whether carried out playing phone, transmitting-receiving note etc. when driving and driven the closely-related driving behavior of diverting one's attention of risk.
Summary of the invention
The object of the invention is according to above-mentioned the deficiencies in the prior art part, a kind of system and method for fixing a price based on multi-source data calculating driving risk and auxiliary vehicle insurance is provided, this system and method by the driver's driving trace to intelligent mobile terminal collection, sensing data and on the way the multi-source data such as Traffic Net information, dynamic traffic situation carry out fusion calculation, analyze with the driving behavior to user, driving custom, thus the driving risk of user is quantized, for the vehicle insurance pricing model based on driving behavior is provided fundamental basis.
The object of the invention realizes being completed by following technical scheme:
Calculate the system of driving risk and auxiliary vehicle insurance and fixing a price based on multi-source data, it is characterized in that described system comprises can the intelligent mobile terminal of interactive communication and remote server; There is in described intelligent mobile terminal some data sensor collectors, and the application for user is installed; Described remote server is provided with risk model algorithmic system and the data acquisition system (DAS) of interactive communication, described risk model algorithmic system comprises sight and drives venture analysis submodule, driving model recognin module of diverting one's attention, user's travel behaviour analysis submodule and driving behavior Assessment for classification system submodule; Wherein, described intelligent mobile terminal is with described data acquisition system (DAS) interactive communication, and described driving behavior Assessment for classification system submodule is driven venture analysis submodule, driving model recognin module of diverting one's attention and user's travel behaviour with described sight and analyzed submodule interactive communication.
Data acquisition system (DAS) on described remote server is for obtaining Traffic Information and described intelligent mobile terminal applies the data gathered, and described data acquisition system (DAS) is driven venture analysis submodule, driving model recognin module of diverting one's attention and user's travel behaviour with described sight and analyzed submodule interactive communication.
Some data sensor collectors in described intelligent mobile terminal are global location chip, accelerometer and gyroscope.
Relate to an above-mentioned arbitrary method of fixing a price based on multi-source data calculating driving risk and auxiliary vehicle insurance, it is characterized in that described method comprises the steps:
User on the run, real-time latitude and longitude coordinates, direction of traffic, instantaneous velocity, acceleration and angular velocity carry out gathering and are sent on described remote server by described application by global location chip, accelerometer and gyroscope in described intelligent mobile terminal;
Described data acquisition system (DAS) automatic acquisition Traffic Net information, dynamic traffic data and come from described intelligent mobile terminal application input data, and being sent in described risk model algorithmic system, input data are carried out fusion treatment by described risk model algorithmic system;
Described sight drives venture analysis submodule: (1) utilizes real-time latitude and longitude coordinates, direction of traffic, instantaneous velocity to obtain user's travel speed through section in transit, utilize the vehicle average velocity on dynamic traffic data acquisition track, user place, vehicle average velocity in the travel speed of user and track, place is compared, and judge the driving scene residing for user by Traffic Information acquisition system, synthetic user defines dependent variable, the risk a under this sight caused by this difference relative to the difference V-V of vehicle average velocity on track, place; And to the driving risk A that each risk accumulated weights by way of section is gone on a journey based on relative velocity as this time after trip terminates; (2) the speed limit L on track, Traffic Net acquisition of information user place is utilized, if the difference of the speed limit L on the travel speed V of user and track, place exceedes certain threshold value, then judge hypervelocity behavior and hypervelocity behavior harmful grade, then residing sight is judged according to Traffic Net information, calculate the risk b that corresponding hypervelocity behavior harmful grade causes, and to the driving risk B that the risk accumulated weights in all moment is gone on a journey based on hypervelocity behavior as this after trip terminates; (3) real-time latitude and longitude coordinates and acceleration information is utilized to count brake number of times and the acceleration and deceleration number of times of user, the scene residing for user is determined in comprehensive vehicle position, working direction, dynamic traffic data, calculate the risk c that user exists vehicle operating aspect, and to the driving risk C that the risk accumulated weights in all moment is gone on a journey based on acceleration and deceleration as this after trip terminates;
Described driving model recognin module of diverting one's attention, utilizes instantaneous velocity, acceleration and angular velocity to judge the behavior of diverting one's attention of the operating handset of user in state of motion of vehicle, and according to the kind of behavior of diverting one's attention and the risk assessment D of time span accumulation correspondence thereof;
Described user's travel behaviour analyzes the latitude and longitude coordinates and instantaneous velocity that submodule gathered by described intelligent mobile terminal, according to the distance travelled of user's this trip, duration, path, suddenly to accelerate, suddenly to slow down and zig zag information, this trip of user and driving behavior are analyzed and given corresponding risk assessment E;
Described driving behavior Assessment for classification system submodule calculate respectively described sight drive venture analysis submodule, described in divert one's attention driving model recognin module and described user's travel behaviour analyze the final driving risk score value of submodule.
After user's accumulation reaches the driving risk score value of stipulated number and duration, calculate the corresponding vehicle insurance price of this user according to described driving risk score value.
Advantage of the present invention is, by collection and the fusion of multi-source data, the driving behavior of user is analyzed, effectively can demarcate the travel behaviour of driver, custom of driving, driving risk, thus be provide fundamental basis and technical support based on the vehicle insurance pricing model of driving behavior; The result of driving behavior analysis also can present to user on mobile terminal client terminal, makes the problem in user cognition oneself driving behavior, and is improved; On the whole, this technology can improve the traffic safety of road network, and reduces the various losses that traffic hazard causes; Popularize based on the extensive of current smart mobile phone, this method has advantage with low cost, to promote convenient, applicable continual analysis driving behavior, the more important thing is, the user trajectory data of mobile terminal collection can combine with Traffic Net information, dynamic traffic situation etc., thus the contextual model residing for user, estimate driving behavior and the risk thereof of driver more accurately; In addition, by the data in the sensor such as accelerometer, gyroscope on gather and analysis intelligent mobile terminal, can judge whether driver has carried out playing the behavior of diverting one's attention that phone, transmitting-receiving note etc. use mobile phone in startup procedure.
Accompanying drawing explanation
Fig. 1 is based on the method flow diagram that multi-source data calculating driving risk and auxiliary vehicle insurance are fixed a price in the present invention;
Fig. 2 is that in the present invention, sight drives venture analysis schematic flow sheet;
Fig. 3 diverts one's attention in the present invention to drive venture analysis schematic flow sheet;
Fig. 4 analyzes in the present invention to drive venture analysis case schematic diagram;
Fig. 5 is the schematic diagram of driving behavior staging hierarchy in the present invention.
Embodiment
Feature of the present invention and other correlated characteristic are described in further detail by embodiment below in conjunction with accompanying drawing, so that the understanding of technician of the same trade:
Embodiment: the present embodiment is specifically related to a kind of system and method for fixing a price based on multi-source data calculating driving risk and auxiliary vehicle insurance, the method is the data based on intelligent mobile terminal data acquisition, in conjunction with multi-source datas such as Traffic Net information, dynamic traffic situations, by features such as the travel behaviour of data mining analysis driver, customs of driving, and carry out effective quantitatively calibrating to driving risk, thus for providing support based on the vehicle insurance pricing model of driving behavior.
As shown in Figure 1, calculating based on multi-source data in the present embodiment is driven the system that risk and auxiliary vehicle insurance fix a price and is comprised interconnected intelligent mobile terminal and remote server, wherein:
Intelligent mobile terminal is provided with can for the customized application of user, this customized application is by some data sensor collectors, be specially global location chip (GPS), accelerometer and gyroscope, for data such as real-time latitude and longitude coordinates, sea level elevation, direction of traffic, positional precision, instantaneous velocity, acceleration and the angular velocity in collection vehicle driving process, these data will be uploaded in remote server by this customized application.
Remote server is provided with data acquisition system (DAS) and risk model algorithmic system, wherein, (1) data acquisition system (DAS) is for obtaining the input data comprising Traffic Net information, dynamic traffic data and come from intelligent mobile terminal application, and inputs in risk model algorithmic system by these data; (2) risk model algorithmic system comprises sight driving venture analysis submodule, driving model recognin module of diverting one's attention, user's travel behaviour analysis submodule and driving behavior Assessment for classification system submodule, each submodule all can obtain multi-source input data to carry out venture analysis from data acquisition system (DAS), and provides theoretical foundation for actuarial contribution.
As Figure 1-5, calculating based on multi-source data in the present embodiment is driven the method that risk and auxiliary vehicle insurance fix a price and is specifically comprised the steps:
(1) as shown in Figure 1, first user opens the customized application be installed on intelligent mobile terminal, input destination, and confirms to drive beginning, and the navigation information that user can be provided by customized application arrives destination;
In user's driving procedure, the data that intelligent mobile terminal can gather global location chip (GPS), accelerometer, gyroscope at a certain time interval, transmit back remote server carry out storing and analyze, the data of collection comprise real-time latitude and longitude coordinates, sea level elevation, direction of traffic, positional precision, instantaneous velocity, acceleration and angular velocity etc.;
(2) the data acquisition system (DAS) automatic acquisition in remote server also stores Traffic Net information and dynamic traffic data, wherein, Traffic Net information spinner will comprise the attribute that road section, position, crossing, section speed limit, category of roads etc. characterize transportation network essential characteristic, dynamic traffic data then comprises in each time period (usually with 5 minutes or 15 minutes for interval), the traffic flow speed on each bar road section and traffic flow information; In addition, this data acquisition system (DAS) also obtains and comprises the data such as real-time latitude and longitude coordinates, sea level elevation, direction of traffic, positional precision, instantaneous velocity, acceleration and angular velocity in intelligent mobile terminal application;
(3), after the risk model algorithmic system in remote server obtains the multi-source input data in above-mentioned steps (1), (2), the information that different types of data can be associated according to geographic position, timestamp etc., carries out data link operation; According to positional information, the user trajectory data gathered from intelligent mobile terminal can with Traffic Net informational linkage to together with, namely can judge that user is in certain special time, is just travelling on for which section; According to Time and place attribute, user and dynamic traffic situation can be linked together, namely can judge on the section of user's traveling, the average velocity of the traffic flow around it and the degree of crowding; After data fusion completes, link together from the various user data of intelligent mobile terminal collection and Traffic Net information, dynamic traffic situation, think the driving behavior analysis module of system, comprise that sight drives venture analysis submodule, driving model recognin module of diverting one's attention, user's travel behaviour analyzes submodule provides the input data of driving required for Risk Calculation;
(4) as shown in Figure 2, what the sight driving venture analysis module in risk model algorithmic system referred to is exactly after multisource data fusion, in conjunction with Traffic Net information, dynamic traffic condition information that user is now in, judge the contextual model residing for user, and to the calculating that the driving behavior of user under various sight and risk are carried out, specific as follows:
A. the difference based on relative velocity calculates driving risk, namely, utilize real-time latitude and longitude coordinates, direction of traffic, instantaneous velocity obtains this user travel speed through section in transit, utilize the vehicle average velocity on dynamic traffic data acquisition track, user place, vehicle average velocity in the travel speed of user and track, place is compared, and judge the driving scene residing for this user by road network information and traffic, the expressway such as blocked up, there is signal lamp in front, downstream road section is blocked up, then synthetic user defines dependent variable relative to the difference v-V of traffic flow speed, the risk a under this sight caused by this difference, and to the driving risk A that the risk accumulated weights in each section is gone on a journey based on relative velocity as this after trip terminates,
Generally speaking, the existence being owing to following garage in traffic flow, the difference of both can not be very large; If the average velocity apparently higher than around wagon flow that the travel speed of vehicle continues, model algorithm system can identify that the driving behavior of driver is more radical compared with ordinary people, passing behavior happens occasionally, and means the existence of driving risk; And on the other hand, if the travel speed of vehicle continue be starkly lower than average velocity, then show that the driving behavior of this driver is comparatively conservative, there is larger difference with the transport condition of the vehicle of surrounding, also can produce certain driving risk;
B. the drive speed based on user drives risk with the comparison calculating of the speed limit in its track, place, namely, utilize the speed limit on track, Traffic Net acquisition of information user place, if the difference of the travel speed v of user and speed limit L exceedes certain threshold value, then judge hypervelocity behavior, and hypervelocity behavior harmful grade.Then judge residing sight according to traffic and road network information, and calculate under this sight, the risk that the hypervelocity behavior of corresponding harmful grade causes b; And to the driving risk B that the risk accumulated weights in all moment is gone on a journey based on hypervelocity behavior as this after trip terminates;
Generally speaking, even if when traffic is unimpeded, the speed of user also should significantly not exceed road speed limit; If systems axiol-ogy exceedes section speed limit for a long time to the drive speed of user, then can judge that user exists hypervelocity behavior on this section, thus the driving risk of presumption user is higher; In addition, in speed limit be 50km/h section on (as urban road) exceed the speed limit risk effect of 5km/h and the 5km/h that exceeds the speed limit in the section (as highway) of speed limit 110km/h be different; Equally, the risk exceeded the speed limit in same section the rush hours (peak period) also has difference relative to the effect exceeded the speed limit during free flow speed; By analyzing road speed limit, traffic flow conditions, the contextual model residing for user can be judged, and for the drive speed of user, calculate the furious driving risk existed;
C. the acceleration and deceleration information based on user calculates driving risk, namely, by characterizing consumer to the acceleration and deceleration information of vehicle operating behavior and number of times of bringing to a halt, with the mean velocity information characterizing traffic congestion degree, the positional information of next crossing in road network, vehicle needs the operation behavior taked at next crossing, the information such as real-time traffic condition combine, the driving environment residing for user is judged with this, need the operation carried out, thus to the risk danger c that vehicle operating aspect exists in the driving behavior of demarcation user that can be quantitative, and to the driving risk C that the risk accumulated weights in all moment is gone on a journey based on acceleration and deceleration as this after trip terminates,
In this submodule, more common several sights are: more at the cornering operation at crossing, user to the judgement done required for vehicle running state, correct also more frequent, driving risk also increases accordingly; When traffic flow is blocked up more serious, the brake that user needs to take, start-up operation are more frequent, may occur that the probability of traffic hazard is also higher; When traffic conditions is comparatively unimpeded, the good user of driving experience is when the sections of road that distance crossing is far away, should not there is larger fluctuation in the traveling shape of vehicle, namely should not occur touching on the brake frequently too much, the behavior such as acceleration, otherwise the driving behavior of user is more unstable, more unskilled to the manipulation of vehicle.For above-mentioned sight, if these behaviors that user occurs are more, show in its driving behavior, also higher in the risk existed the operation link of vehicle;
(5) as shown in Figure 3,4, driving model recognin module of diverting one's attention in model algorithm system utilizes instantaneous velocity, acceleration and angular velocity to judge the behavior of diverting one's attention of the operating handset of user in state of motion of vehicle, and according to the kind of behavior of diverting one's attention and time span thereof, and the risk assessment D corresponding on the impact accumulation of driving behavior;
Carry out in mutual process user and mobile phone, for different type of action, hardware data sensing acquisition device can capture dissimilar data; Such as, mobile phone is taken out and is put into the process that ear limit receives calls by user from pocket, hardware data sensing acquisition device can be seen comparatively significantly data fluctuations, and it is complete to receive calls, when mobile phone is put back to pocket, an obvious wave process can be seen again; And receive and dispatch note, browse hardware data sensing acquisition device data fluctuations type that the actions such as webpage trigger with play compared with phone comparatively different; By the data to acceleration and mobile phone angle, application model recognizer, beginning, end time, action duration, the type of action of the amount of action can carried out in startup procedure user, each action, the influence degree etc. of vehicle drive to be analyzed, and in this, as evaluating the foundation of driving risk;
Three-axis sensor data on smart mobile phone, namely the x-axis of accelerometer, y-axis, z-axis data and gyrostatic x-axis, y-axis, z-axis data can respectively from different directions, different angles are described the action that user carries out; After Data acquisition and storage, system can carry out pre-service to these raw data, comprise application data sampling algorithm, namely the sample extracting the action of part possibility characterizing consumer from gathered data complete or collected works carries out subsequent analysis, and application denoise algorithm carries out data de-noising, to reduce the undesired signal that exists in data acquisition to the impact of the movement recognition algorithm that will carry out below; Subsequently, model algorithm system adopts an application characteristic extraction algorithm to extract the feature occurred in data, comprises the moment of each unique point, geographic position, and the shape of each characteristic curve; Then, based on extracted data characteristics, system usage operation decision algorithm identifies start time and the terminal node of the action of driver's each use mobile phone, i.e. the time span of each action;
After the time span of each behavior is identified, system can according to the data fluctuations situation that gyroscope detects, and user application action verification algorithm is verified this result further; First algorithm can carry out sampling operation to the data gathered in gyroscope, and in the signal trifle be partitioned at the action node to identify for every a pair, calculates the characteristic parameters such as data capacity based on gyrostatic data fluctuations; Above parameter finally can be used to verify the user action previously inferred out, namely examines the action authenticity in each signal trifle; The result examined finally can the divert one's attention driving behavior of characterizing consumer in driving procedure, number of times, the duration of the mobile phone application etc. that such as plays phone, receives and dispatches note, browses webpage, uses, and the impact etc. on traveling state of vehicle; Finally, in conjunction with the driving speed information in GPS movement locus, namely user at any one time, the instantaneous driving speed information in arbitrary geographic position, system can identify the operation conditions of the vehicle when these driving behaviors of diverting one's attention occur, thus extract vehicle in motion process, behavior that what user carried out divert one's attention, and calculate based on this analysis result diverting one's attention to drive risk;
Be illustrated in figure 4 the case analysis to the raw data involved by Fig. 3 and result of calculation, wherein, " accelerometer data " chart and " gyro data " chart are respectively the raw data that intelligent mobile terminal accelerometer x-axis and gyroscope x-axis gather, and the horizontal axis representing time in figure, vertical pivot represent the data value that sensor collects; As we can see from the figure, about the 250th second, 450 seconds, 1000 seconds equal times, when user brings into use mobile phone, in " accelerometer data " chart and " gyro data " chart, all can there is corresponding signal fluctuation;
The process of venture analysis submodule is driven through diverting one's attention, the i.e. step such as Signal Pretreatment, denoising, feature extraction, action recognition, action checking, model algorithm system can extrapolate the time section that the driver as shown in chart uses mobile phone as " divert one's attention to drive and calculate result " in Fig. 4, in figure, transverse axis is the time, when vertical pivot is 0, represent that event occurs, when vertical pivot equals 1, represent that user uses mobile phone;
In Fig. 4, " Vehicle Speed " chart describes the Vehicle Speed collected from the global location chip intelligent mobile terminal, therefrom can see that vehicle is in high-speed travel state sometimes, is then in stationary condition sometimes; According to travel speed, the travel conditions of vehicle and stationary condition are demarcated by " travel condition of vehicle " chart further; In conjunction with the vehicle running state described in " travel condition of vehicle " chart, the situation of smart mobile phone is used with the user described in " divert one's attention to drive and calculate result " chart, the user action not affecting traveling state of vehicle under stationary state finally can weed out by system, only identify divert one's attention driver behavior number of times, section and duration that vehicle user under transport condition is engaged in, and calculate the risk in driving procedure with this;
(6) the user's travel behaviour in model algorithm system is analyzed submodule and is led to the information such as gathered position, speed, according to the mileage of user's this trip, time, path, suddenly to accelerate, the behavior such as anxious decelerations, zig zag, this trip of driver and driving behavior are carried out to holistic approach and are given corresponding risk assessment E;
Generally speaking, distance travelled is higher, and the time that driver is exposed in traffic flow also can be longer, and the driving risk caused due to factors such as fatigue drivings also increases accordingly.Drive every day two hours drivers on and off duty, and it is driven risk and compares one and only use the people of vehicle once in a while at weekend and explain aobvious higher; And with regard to the travel time, if driver goes on a journey after midnight of being everlasting, consider based on factors such as the low visibility of road and the physiological situations of driver, the highest when the danger coefficient of now driving is usual; And peak period trip, because the vehicle on road is more, space headway is shorter, wagon flow loiters, driving risk is also relatively large; If select the trip of non-peak hours on daytime, because the vehicle fleet size on road is less, visibility is higher simultaneously, drives a vehicle normally safest; In addition, generally speaking brake is stepped on more violent, or during automobile starting, throttle is stepped on more ruthless, and the driving behavior of this driver of ordinary representation is comparatively radical relative to other people, thus cause when braking with after wait and knock into the back, and the probability of the front truck that knocks into the back when starting is higher;
(7) as shown in Figure 5, the driving behavior Assessment for classification system submodule of system, the result of calculation of venture analysis submodule, driving model recognin module of diverting one's attention, user's travel behaviour analysis submodule then can be driven according to system sight, indices is weighted consideration, the overall risk of Comprehensive Assessment user driving behavior; To be displayed on three coordinates after the evaluation score unified metric of three submodules, coordinate figure, the closer to summit, is expressed in the mark that in this coordinate axis, user obtains lower, and its driving risk is higher; The area that three coordinate lines mark off signifies the composite score of user's driving behavior, and the risk that each module exports is higher, corresponding scores is lower, and the area finally marked off on figure is less, and the risk of its driving behavior is higher;
(8) model algorithm system can store each user trip driving behavior mark each time, and result is returned to user and be presented in the customized application of intelligent mobile terminal; For some behaviors had much room for improvement that user may exist, system can provide some friendly promptings, with for reference to user;
(9) user also can authoring system, the long-term result of driving behavior Comprehensive Assessment is shared with the insurance institution of cooperation, thus the aided solving user vehicle insurance number that should pay on this basis; When user repeatedly uses intelligent mobile terminal to complete trip, the driving behavior of system of users have accumulated abundant data as foundation afterwards, system together with the insurance institution of cooperation, according to user's request, can calculate the vehicle insurance price of driving risk based on user; Generally speaking, the driving behavior of user is safer, and its required premium paid will be lower; If the dangerous driving behavior that systems axiol-ogy arrives is higher, the required premium paid of user also can go up accordingly.
Claims (5)
1. calculate the system of driving risk and auxiliary vehicle insurance and fixing a price based on multi-source data, it is characterized in that described system comprises can the intelligent mobile terminal of interactive communication and remote server; There is in described intelligent mobile terminal some data sensor collectors, and the application for user is installed; Described remote server is provided with risk model algorithmic system and the data acquisition system (DAS) of interactive communication, described risk model algorithmic system comprises sight and drives venture analysis submodule, driving model recognin module of diverting one's attention, user's travel behaviour analysis submodule and driving behavior Assessment for classification system submodule; Wherein, described intelligent mobile terminal is with described data acquisition system (DAS) interactive communication, and described driving behavior Assessment for classification system submodule is driven venture analysis submodule, driving model recognin module of diverting one's attention and user's travel behaviour with described sight and analyzed submodule interactive communication.
2. a kind of system of fixing a price based on multi-source data calculating driving risk and auxiliary vehicle insurance according to claim 1, it is characterized in that data acquisition system (DAS) on described remote server is for obtaining Traffic Information and described intelligent mobile terminal applies the data gathered, described data acquisition system (DAS) is driven venture analysis submodule, driving model recognin module of diverting one's attention and user's travel behaviour with described sight and is analyzed submodule interactive communication.
3. a kind of system of fixing a price based on multi-source data calculating driving risk and auxiliary vehicle insurance according to claim 1, is characterized in that the some data sensor collectors in described intelligent mobile terminal are global location chip, accelerometer and gyroscope.
4. relate to a method of fixing a price based on multi-source data calculating driving risk and auxiliary vehicle insurance of arbitrary claim 1-3, it is characterized in that described method comprises the steps:
User on the run, real-time latitude and longitude coordinates, direction of traffic, instantaneous velocity, acceleration and angular velocity carry out gathering and are sent on described remote server by described application by global location chip, accelerometer and gyroscope in described intelligent mobile terminal;
Described data acquisition system (DAS) automatic acquisition Traffic Net information, dynamic traffic data and come from described intelligent mobile terminal application input data, and being sent in described risk model algorithmic system, input data are carried out fusion treatment by described risk model algorithmic system;
Described sight drives venture analysis submodule: (1) utilizes real-time latitude and longitude coordinates, direction of traffic, instantaneous velocity to obtain user's travel speed through section in transit, utilize the vehicle average velocity on dynamic traffic data acquisition track, user place, vehicle average velocity in the travel speed of user and track, place is compared, and judge the driving scene residing for user by Traffic Information acquisition system, synthetic user defines dependent variable, the risk a under this sight caused by this difference relative to the difference V-V of vehicle average velocity on track, place; And to the driving risk A that each risk accumulated weights by way of section is gone on a journey based on relative velocity as this time after trip terminates; (2) the speed limit L on track, Traffic Net acquisition of information user place is utilized, if the difference of the speed limit L on the travel speed V of user and track, place exceedes certain threshold value, then judge hypervelocity behavior and hypervelocity behavior harmful grade, then residing sight is judged according to Traffic Net information, calculate the risk b that corresponding hypervelocity behavior harmful grade causes, and to the driving risk B that the risk accumulated weights in all moment is gone on a journey based on hypervelocity behavior as this after trip terminates; (3) real-time latitude and longitude coordinates and acceleration information is utilized to count brake number of times and the acceleration and deceleration number of times of user, the scene residing for user is determined in comprehensive vehicle position, working direction, dynamic traffic data, calculate the risk c that user exists vehicle operating aspect, and to the driving risk C that the risk accumulated weights in all moment is gone on a journey based on acceleration and deceleration as this after trip terminates;
Described driving model recognin module of diverting one's attention, utilizes instantaneous velocity, acceleration and angular velocity to judge the behavior of diverting one's attention of the operating handset of user in state of motion of vehicle, and according to the kind of behavior of diverting one's attention and the risk assessment D of time span accumulation correspondence thereof;
Described user's travel behaviour analyzes the latitude and longitude coordinates and instantaneous velocity that submodule gathered by described intelligent mobile terminal, according to the distance travelled of user's this trip, duration, path, suddenly to accelerate, suddenly to slow down and zig zag information, this trip of user and driving behavior are analyzed and given corresponding risk assessment E;
Described driving behavior Assessment for classification system submodule calculate respectively described sight drive venture analysis submodule, described in divert one's attention driving model recognin module and described user's travel behaviour analyze the final driving risk score value of submodule.
5. a kind of method of fixing a price based on multi-source data calculating driving risk and auxiliary vehicle insurance according to claim 4, it is characterized in that, after user's accumulation reaches the driving risk score value of stipulated number and duration, calculating the corresponding vehicle insurance price of this user according to described driving risk score value.
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| CN105930943A (en) * | 2016-07-11 | 2016-09-07 | 上海安吉星信息服务有限公司 | Method and device for predicting driving risk |
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