Intelligent networked automobile driving behavior analysis method and system and related equipment

Abstract

The invention provides an intelligent network connection automobile driving behavior analysis method and system and related equipment, and the method comprises the steps: obtaining a plurality of driving behavior characteristic indexes based on the driving behavior data of an intelligent network connection automobile; based on the driving behavior characteristic indexes, obtaining a driving behavior comprehensive score; and based on the driving behavior comprehensive score, respectively determining a risk level and an automobile insurance rate adjustment coefficient of the intelligent network connection automobile, and the intelligent network connection automobile with the best driving behavior quality. According to the method, multiple driving behavior characteristic indexes are comprehensively considered, the driving mileage, the driving duration and the abnormal behavior of the intelligent networked automobile are subjected to multi-dimensional scoring, and then the driving behavior comprehensive score is obtained. And then the risk level and the car insurance rate adjustment coefficient are calculated through the driving behavior comprehensive score, and the intelligent networked car with the best driving behavior quality is identified, so that comprehensive and accurate evaluation of the driving behavior of the intelligent networked car is realized, and a scientific basis is provided for risk management and control, insurance pricing and intelligent driving technology optimization.

Description

Technical Field

[0001] This application relates to the field of vehicle networking technology, and in particular to a method, system and related equipment for analyzing the driving behavior of intelligent connected vehicles. Background Technology

[0002] Intelligent connected vehicles refer to vehicles equipped with advanced onboard sensors, controllers, actuators, and other devices, and integrated with modern communication and network technologies to achieve intelligent information exchange and sharing between vehicles, people, roads, and back-end systems, thereby achieving safe, comfortable, energy-saving, and efficient driving.

[0003] With the popularization and application of intelligent connected vehicles, the driving behavior data they generate is becoming increasingly abundant. This data provides valuable resources for in-depth analysis of driving behavior characteristics. By comprehensively analyzing the driving behavior data of intelligent connected vehicles, we can accurately grasp their driving behavior characteristics, and thus provide a scientific basis for regulation, insurance pricing, and other aspects related to driving behavior.

[0004] However, how to evaluate driving behavior characteristics and how to accurately calculate the risk level and insurance rate adjustment coefficient of intelligent connected vehicles are currently pressing and challenging issues in the field of intelligent connected vehicle research. These issues not only relate to the safety supervision of intelligent connected vehicles but also directly affect the pricing strategies and risk management of the insurance industry. Therefore, designing a scientific and effective method for analyzing the driving behavior of intelligent connected vehicles is of great significance for promoting the healthy development of intelligent connected vehicles. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this application provides a method, system, and related equipment for analyzing the driving behavior of intelligent connected vehicles. The aim is to comprehensively and accurately assess the driving behavior of intelligent connected vehicles, providing a scientific basis for risk management, insurance pricing, and optimization of intelligent driving technology for intelligent connected vehicles.

[0006] To achieve the above objectives and other advantages, this application adopts the following technical solution:

[0007] Firstly, this application provides a method for analyzing the driving behavior of intelligent connected vehicles, including:

[0008] Based on the driving behavior data of intelligent connected vehicles, several driving behavior characteristic indicators are obtained.

[0009] Based on the aforementioned driving behavior characteristic indicators, a comprehensive driving behavior score is obtained;

[0010] Based on the comprehensive driving behavior score, the risk level and car insurance rate adjustment coefficient of the intelligent connected vehicle are determined, as well as the intelligent connected vehicle with the best driving behavior quality.

[0011] According to the intelligent connected vehicle driving behavior analysis method provided in this application, the driving behavior characteristic indicators include: driving mileage characteristic indicators, driving duration characteristic indicators and abnormal behavior characteristic indicators;

[0012] The driving mileage characteristic indicators include: cumulative driving mileage within a set time period, peak-hour driving mileage, and nighttime driving mileage;

[0013] The driving time characteristic indicators include: cumulative driving time, peak period driving time, and nighttime driving time;

[0014] The abnormal behavior characteristic indicators include: number of first-level following warnings, number of second-level following warnings, number of first-level lane change warnings, number of second-level lane change warnings, number of times the automatic driving system disengages, number of times of rapid acceleration, number of times of rapid deceleration, number of times of sudden stop while following another vehicle, number of times of excessive acceleration from a standstill, number of times the vehicle takes over at intersections, number of times the vehicle crosses a solid line to change lanes, number of times the vehicle runs a red light, number of times the vehicle crosses a stop line, and number of times the vehicle changes lanes continuously.

[0015] According to the intelligent connected vehicle driving behavior analysis method provided in this application, the step of obtaining a comprehensive driving behavior score based on the driving behavior characteristic indicators includes:

[0016] The cumulative mileage within a set time period is obtained from the driving behavior characteristic indicators used to characterize the driving mileage characteristic indicators. Based on the preset mileage coefficient and the upper limit of the mileage score, the mileage score is calculated.

[0017] The peak driving time and night driving time used to characterize the driving time feature index are obtained from the driving behavior feature index. Based on the preset night and peak driving time coefficients and the upper limit of driving time score, the night and peak driving time scores are calculated.

[0018] Obtain each abnormal behavior feature index from the driving behavior feature indexes, and calculate the abnormal behavior score based on the preset abnormal behavior coefficient and the upper limit of the abnormal behavior score corresponding to each abnormal behavior feature index.

[0019] The comprehensive driving behavior score is calculated based on the mileage score, the nighttime and peak-hour driving duration score, and the abnormal behavior score.

[0020] According to the intelligent connected vehicle driving behavior analysis method provided in this application, the step of determining the risk level of the intelligent connected vehicle based on the comprehensive driving behavior score includes:

[0021] The risk level set and classification threshold of the intelligent connected vehicle are set. The risk level set consists of multiple risk levels. The classification threshold is used to divide the upper and lower limits of the comprehensive driving behavior score into multiple threshold intervals. Each threshold interval corresponds to a risk level.

[0022] The threshold range into which the comprehensive score of the driving behavior falls is determined, thereby identifying the corresponding risk level.

[0023] According to the intelligent connected vehicle driving behavior analysis method provided in this application, the step of determining the vehicle insurance rate adjustment coefficient of the intelligent connected vehicle based on the comprehensive driving behavior score includes:

[0024] Set a set of vehicle insurance rate adjustment coefficients for the intelligent connected vehicle, wherein the set of vehicle insurance rate adjustment coefficients consists of multiple vehicle insurance rate adjustment coefficients, and each threshold interval corresponds to one vehicle insurance rate adjustment coefficient.

[0025] The threshold range into which the comprehensive driving behavior score falls is determined, and the corresponding vehicle insurance rate adjustment coefficient is then determined.

[0026] According to the intelligent connected vehicle driving behavior analysis method provided in this application, the step of determining the intelligent connected vehicle with the best driving behavior quality based on the comprehensive driving behavior score includes:

[0027] Set the evaluation duration and determine the set of intelligent connected vehicles participating in the driving behavior evaluation;

[0028] The evaluation period is divided into multiple time periods. For each intelligent connected vehicle in the intelligent connected vehicle set, the comprehensive driving behavior score for each time period is calculated, thereby obtaining the average value of the comprehensive driving behavior score for each intelligent connected vehicle within the evaluation period.

[0029] The intelligent connected vehicle with the highest average value of the comprehensive driving behavior score is identified as the intelligent connected vehicle with the best driving behavior quality.

[0030] Secondly, this application provides an intelligent connected vehicle driving behavior analysis system, which implements the intelligent connected vehicle driving behavior analysis method as described above, the system comprising:

[0031] An input data layer is used to store driving behavior data and threshold configuration tables of intelligent connected vehicles;

[0032] The calculation module layer is used to calculate driving behavior characteristic indicators, comprehensive driving behavior score, risk level, vehicle insurance rate adjustment coefficient, and intelligent connected vehicle with the best driving behavior quality.

[0033] The output data layer is used to store driving behavior characteristic indicators, comprehensive driving behavior score, risk level, vehicle insurance rate adjustment coefficient, and intelligent connected vehicle with the best driving behavior quality.

[0034] The visualization layer is used to visually display the driving behavior characteristic indicators, comprehensive driving behavior scores, risk levels, car insurance rate adjustment coefficients, and the intelligent connected vehicles with the best driving behavior quality for different intelligent connected vehicles.

[0035] Thirdly, this application provides an electronic device, the electronic device comprising:

[0036] One or more processors; and a memory storing computer program instructions that, when executed, cause the processors to perform the intelligent connected vehicle driving behavior analysis method as described above.

[0037] Fourthly, this application provides a computer-readable storage medium storing a computer program / instructions thereon, which, when executed by a processor, implements the intelligent connected vehicle driving behavior analysis method as described above.

[0038] Fifthly, this application provides a computer program product, including a computer program / instructions, which, when executed by a processor, implements the intelligent connected vehicle driving behavior analysis method as described above.

[0039] This application provides a method, system, and related equipment for analyzing the driving behavior of intelligent connected vehicles. It obtains several driving behavior characteristic indicators based on driving behavior data from intelligent connected vehicles; a comprehensive driving behavior score is obtained based on these indicators; and the risk level, insurance rate adjustment coefficient, and the intelligent connected vehicle with the best driving behavior quality are determined based on the comprehensive driving behavior score. This application comprehensively considers multiple driving behavior characteristic indicators to score the mileage, nighttime and peak-hour driving time, and abnormal behavior of intelligent connected vehicles from multiple dimensions, thereby obtaining a comprehensive driving behavior score. The risk level is calculated based on the comprehensive driving behavior score, providing a decision-making basis for vehicle risk management such as dangerous driving behavior control and key vehicle supervision; the insurance rate adjustment coefficient is calculated based on the comprehensive driving behavior score, providing a reference for insurance pricing of intelligent connected vehicles; and the intelligent connected vehicle with the best driving behavior quality is identified based on the comprehensive driving behavior score, providing an important reference for further research and optimization of intelligent driving technology. Therefore, a comprehensive and accurate assessment of the driving behavior of intelligent connected vehicles provides a scientific basis for vehicle risk management, insurance pricing, and the optimization of intelligent driving technology. Attached Figure Description

[0040] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other implementation methods can be obtained based on these drawings without creative effort.

[0041] Figure 1 This is one of the flowcharts illustrating the intelligent connected vehicle driving behavior analysis method provided in this application embodiment;

[0042] Figure 2 This is the second flowchart illustrating the intelligent connected vehicle driving behavior analysis method provided in this application embodiment;

[0043] Figure 3 This is a schematic diagram of the structure of the intelligent connected vehicle driving behavior analysis system provided in the embodiments of this application;

[0044] Figure 4 This is a schematic diagram of the structure of the electronic device provided in this application. Detailed Implementation

[0045] The above description is merely an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings.

[0046] It should be noted that those skilled in the art will understand, explicitly and implicitly, that the embodiments described in this application can be combined with other embodiments without conflict. Unless otherwise defined, the technical or scientific terms used in this application should have the ordinary meaning understood by those skilled in the art. The terms "a," "an," "an," "the," and similar words used in this application do not indicate quantity limitation and can represent singular or plural. The terms "comprising," "including," "having," and any variations thereof used in this application are intended to cover non-exclusive inclusion; the terms "first," "second," "third," etc., used in this application are merely to distinguish similar objects and do not represent a specific ordering of objects.

[0047] Example 1

[0048] Reference Figure 1 , Figure 2 As shown, Embodiment 1 of this application provides a method for analyzing the driving behavior of intelligent connected vehicles, including:

[0049] Step S1: Based on the driving behavior data of intelligent connected vehicles, obtain several driving behavior characteristic indicators.

[0050] For example, driving behavior data of intelligent connected vehicles refers to data collected by sensors, cameras, and other devices during autonomous or assisted driving modes, including vehicle operating status, surrounding environment information, and driver operation information. Examples include vehicle speed, location information, driving trajectory, vehicle following, lane changing, start-up response, and abnormal behavior data. This data covers various details of the vehicle's driving process and is typically used to analyze driving behavior, assess driving safety, optimize vehicle performance, and provide personalized services.

[0051] The collected raw data is cleaned and processed to remove noise and outliers, ensuring the accuracy and completeness of the data. Based on the definition of driving behavior characteristic indicators, corresponding feature values ​​are extracted from the driving behavior data.

[0052] Driving behavior characteristic indicators include: driving mileage characteristic indicators, driving time characteristic indicators, and abnormal behavior characteristic indicators. Among them, driving mileage characteristic indicators include: cumulative mileage within a set period, peak-hour driving mileage, and nighttime driving mileage; driving time characteristic indicators include: cumulative driving time, peak-hour driving time, and nighttime driving time; abnormal behavior characteristic indicators include: number of first-level following warnings, number of second-level following warnings, number of first-level lane change warnings, number of second-level lane change warnings, number of times autopilot disengagement, number of times rapid acceleration, number of times rapid deceleration, number of times sudden stop while following another vehicle, number of times excessive acceleration from a standstill, number of times traffic takeover at intersections, number of times lane change across solid lines, number of times running red lights, number of times crossing stop lines, and number of times continuous lane changes.

[0053] For example, cumulative mileage can be obtained by calculating the total distance traveled by the vehicle within a set time period; cumulative driving time can be obtained by calculating the total time traveled by the vehicle within a set time period; peak-hour driving mileage and time can be obtained by filtering driving data from peak hours (such as morning and evening rush hours) and calculating them; nighttime driving mileage and time can be obtained by filtering driving data from nighttime hours (such as 9 pm to 6 am the next day) and calculating them; the number of following warnings, lane change warnings, and autonomous driving disengagements can be obtained by analyzing the relative position and speed changes of the vehicle with surrounding vehicles and the state changes of the autonomous driving system; the number of rapid accelerations, rapid decelerations, and sudden stops can be obtained by analyzing the vehicle's acceleration and deceleration data; the number of intersection takeovers, lane changes over solid lines, red light violations, stop line crossings, and continuous lane changes can be obtained by analyzing the vehicle's position, speed, steering angle, and traffic rule data.

[0054] Step S2: Obtain a comprehensive driving behavior score based on driving behavior characteristic indicators.

[0055] In this embodiment, step S2 specifically includes:

[0056] Step S201: Obtain the cumulative mileage within a set time period used to characterize the driving mileage feature index in the driving behavior feature index, and calculate the driving mileage score based on the preset driving mileage coefficient and the upper limit of the driving mileage score.

[0057] Specifically, the mileage score is calculated using the cumulative mileage from the driving behavior characteristic indicators. Before calculating the mileage score, a mileage coefficient and a mileage score cap are preset. The mileage score is equal to the vehicle's cumulative mileage multiplied by the mileage coefficient.

[0058] When the mileage score is less than or equal to the upper limit of the mileage score, the mileage score equals the vehicle's cumulative mileage multiplied by the mileage coefficient; when the mileage score is greater than the upper limit of the mileage score, the mileage score equals the upper limit of the mileage score. The formula for calculating the mileage score is as follows:

[0059]

[0060] In the formula, Indicates vehicle In the Mileage score for the time period This indicates the upper limit of the mileage score. Indicates the mileage coefficient. Indicates vehicle In the The cumulative mileage during the time period.

[0061] Step S202: Obtain the peak driving time and night driving time used to characterize the driving time feature index from the driving behavior feature index. Calculate the night driving time score based on the preset night and peak driving time coefficients and the upper limit of the driving time score.

[0062] Specifically, the calculation of nighttime and peak-hour driving time scores uses peak-hour driving time and nighttime driving time from the driving behavior characteristic indicators. Before calculating the nighttime and peak-hour driving time scores, a driving time coefficient and an upper limit for the nighttime and peak-hour driving time scores are preset. The nighttime and peak-hour driving time score is equal to the total driving time of the vehicle during nighttime and peak hours multiplied by the driving time coefficient. The total driving time of the vehicle during nighttime and peak hours is the sum of the peak-hour driving time and the nighttime driving time.

[0063] When the nighttime and peak-hour driving time score is less than or equal to the upper limit of the nighttime and peak-hour driving time score, the nighttime and peak-hour driving time score is equal to the total driving time of the vehicle during nighttime and peak hours multiplied by a driving time coefficient; when the nighttime and peak-hour driving time score is greater than the upper limit of the nighttime and peak-hour driving time score, the nighttime and peak-hour driving time score is equal to the upper limit of the nighttime and peak-hour driving time score. The calculation formula for the nighttime and peak-hour driving time score is as follows:

[0064]

[0065] In the formula, Indicates vehicle In the Nighttime and peak-hour driving time ratings, When indicating the upper limit of the score for nighttime and peak-hour driving time, Indicates vehicle In the Peak driving time during the period Indicates vehicle In the Nighttime driving duration during the period This indicates the coefficient for nighttime and peak-hour driving time.

[0066] Step S203: Obtain each abnormal behavior feature index in the driving behavior feature index, and calculate the abnormal behavior score based on the preset abnormal behavior coefficient and abnormal behavior score upper limit value corresponding to each abnormal behavior feature index.

[0067] Specifically, the abnormal behavior scoring uses abnormal behavior characteristic indicators from driving behavior feature indicators, such as the number of times the car followed at level one, the number of times the car followed at level two, the number of times the lane changed at level one, the number of times the lane changed at level two, the number of times the automatic driving disengages, the number of times the car accelerated suddenly, the number of times the car decelerated suddenly, the number of times the car stopped suddenly, the number of times the car accelerated too much from a standstill, the number of times the car took over at intersections, the number of times the car crossed solid lines to change lanes, the number of times the car ran a red light, the number of times the car crossed the stop line, and the number of times the car changed lanes continuously.

[0068] Before calculating the abnormal behavior score, the abnormal behavior coefficient corresponding to each abnormal behavior feature index and the upper limit of the abnormal behavior score are pre-defined. The abnormal behavior score is equal to the sum of the products of each abnormal behavior feature index and its corresponding abnormal behavior coefficient.

[0069] When the abnormal behavior score is less than or equal to the upper limit of the abnormal behavior score, the abnormal behavior score is equal to the sum of the products of each abnormal behavior feature index and its corresponding abnormal behavior coefficient; when the abnormal behavior score is greater than the upper limit of the abnormal behavior score, the abnormal behavior score is equal to the upper limit of the abnormal behavior score. The formula for calculating the abnormal behavior score is as follows:

[0070]

[0071] In the formula, Indicates vehicle In the Abnormal behavior scoring for a given time period This indicates the upper limit of the abnormal behavior score. Indicates vehicle In the The first period A type of abnormal behavior characteristic indicator, Indicates the first Anomalous behavior coefficients This indicates the number of indicators representing abnormal behavior.

[0072] Step S204: Calculate the comprehensive driving behavior score based on the mileage score, nighttime and peak-hour driving duration score, and abnormal behavior score.

[0073] Specifically, the comprehensive driving behavior score is calculated using mileage scores, nighttime and peak-hour driving duration scores, and abnormal behavior scores. An upper limit for the comprehensive driving behavior score is preset before calculation.

[0074] vehicle In the Mileage rating for the time period Nighttime and peak-hour driving time ratings are as follows: Abnormal behavior score ,use The overall score for driving behavior is calculated as follows:

[0075]

[0076] In the formula, This indicates the upper limit of the overall driving behavior score, i.e. ,and .

[0077] It should be noted that the upper limit of the comprehensive driving behavior score is a preset maximum score, representing the highest driving behavior evaluation a driver can achieve under ideal conditions. The inclusion of mileage, nighttime and peak-hour driving time, and abnormal behavior scores as deduction items is significant. For example, including mileage as a deduction item helps remind drivers to schedule rest time appropriately and avoid fatigue driving. Nighttime and peak-hour periods are high-risk times for traffic accidents; therefore, including driving time scores during these periods as deduction items aims to encourage drivers to avoid driving during these times as much as possible to reduce the risk of traffic accidents. Abnormal behavior scores are an important deduction item in the comprehensive driving behavior score, directly reflecting the driver's driving safety awareness and degree of compliance with traffic rules. Deducting points can encourage greater attention to safe driving and reduce violations. This design aims to comprehensively and objectively assess vehicle driving behavior to emphasize the importance of safe driving.

[0078] Step S3: Based on the comprehensive driving behavior score, determine the risk level and car insurance rate adjustment coefficient of the intelligent connected vehicle, as well as the intelligent connected vehicle with the best driving behavior quality.

[0079] In this embodiment, step S3, which involves determining the risk level of an intelligent connected vehicle based on a comprehensive driving behavior score, specifically includes:

[0080] Step SA301: Set the risk level set and classification threshold for intelligent connected vehicles. The risk level set consists of multiple risk levels. The classification threshold is used to divide the upper and lower limits of the comprehensive driving behavior score into multiple threshold intervals. Each threshold interval corresponds to a risk level.

[0081] Step SA302: Determine the threshold range into which the comprehensive driving behavior score falls, and then determine the corresponding risk level.

[0082] Specifically, risk levels for intelligent connected vehicles are defined. The risk level set can be exemplified as {5, 4, 3, 2, 1}, sorted from highest to lowest risk, where 5 represents the highest risk level and 1 represents the lowest. Multiple threshold values ​​are pre-determined. These threshold values ​​divide the range formed by the upper and lower limits of the comprehensive driving behavior score into multiple threshold intervals, each corresponding to a risk level. Then, it is determined which threshold interval the comprehensive driving behavior score falls into, thereby determining the corresponding risk level for that threshold interval.

[0083] If the overall driving behavior score falls within the threshold range of the lowest risk level, the vehicle's risk level is determined to be 1; if it falls within the threshold range of the second lowest risk level, the vehicle's risk level is determined to be 2; if it falls within the threshold range of the medium risk level, the vehicle's risk level is determined to be 3; if it falls within the threshold range of the second highest risk level, the vehicle's risk level is determined to be 4; and if it falls within the threshold range of the highest risk level, the vehicle's risk level is determined to be 5.

[0084] use Indicates vehicle In the The risk level for a given time period, based on the aforementioned determination mechanism, is calculated as follows:

[0085]

[0086] In the formula, , , , These represent the thresholds for classifying risk levels. and These represent the lower limit (minimum) and upper limit (maximum) of the overall driving behavior score, respectively. .

[0087] Therefore, the above-mentioned judgment method can intuitively and accurately assess the risk level of intelligent connected vehicles, objectively evaluate the overall driving behavior quality of intelligent connected vehicles, and provide a basis for decision-making in vehicle risk management such as dangerous driving behavior control and key vehicle supervision.

[0088] In this embodiment, step S3, which involves determining the vehicle insurance rate adjustment coefficient for intelligent connected vehicles based on a comprehensive driving behavior score, specifically includes:

[0089] Step SB301: Set the set of vehicle insurance rate adjustment coefficients for intelligent connected vehicles. The set of vehicle insurance rate adjustment coefficients consists of multiple vehicle insurance rate adjustment coefficients, with each threshold range corresponding to one vehicle insurance rate adjustment coefficient.

[0090] Step SB302: Determine the threshold range into which the comprehensive driving behavior score falls, and then determine the corresponding car insurance rate adjustment coefficient.

[0091] Specifically, the set of adjustment coefficients for vehicle insurance rates for intelligent connected vehicles can be exemplified as { , , , , },in, .

[0092] Driving behavior composite scores serve as an important basis for insurance companies to assess vehicle risk and determine insurance premiums, contributing to fairer and more reasonable insurance pricing. If the driving behavior composite score falls within the threshold range of the lowest risk level or the vehicle's risk level is 1, then the vehicle's auto insurance rate adjustment factor is determined to be [missing value]. If the overall driving behavior score falls within the threshold range of the second lowest risk level, or if the vehicle's risk level is 2, then the vehicle's insurance rate adjustment factor is determined to be... If the overall driving behavior score falls within the threshold range of the medium risk level, or if the vehicle's risk level is 3, then the vehicle's insurance rate adjustment factor is determined to be... If the overall driving behavior score falls within the threshold range of the second-highest risk level, or if the vehicle's risk level is 4, then the vehicle's insurance rate adjustment factor is determined to be... If the overall driving behavior score falls within the threshold range of the highest risk level, or if the vehicle's risk level is 5, then the vehicle's insurance rate adjustment factor is determined to be... .

[0093] use Indicates vehicle In the The formula for calculating the time-based auto insurance rate adjustment factor is as follows:

[0094]

[0095] In the formula, , , , , This represents the car insurance rate adjustment factor. .

[0096] Therefore, based on the positive correlation between risk and premiums, by setting different levels of auto insurance rate adjustment coefficients, vehicles with high overall driving behavior scores can obtain lower auto insurance rates, while vehicles with low overall driving behavior scores will need to pay higher rates. This setting of auto insurance rate adjustment coefficients provides insurance companies with a scientific basis, thereby achieving fairer and more reasonable insurance pricing.

[0097] In this embodiment, step S3, which involves determining the intelligent connected vehicle with the best driving behavior quality based on a comprehensive driving behavior score, includes:

[0098] Step SC301: Set the evaluation duration and determine the set of intelligent connected vehicles participating in the driving behavior evaluation;

[0099] Step SC302: Divide the evaluation period into multiple time periods. For each intelligent connected vehicle in the intelligent connected vehicle set, calculate the comprehensive driving behavior score for each time period, and then obtain the average value of the comprehensive driving behavior score for each intelligent connected vehicle within the evaluation period.

[0100] Step SC303: Identify the intelligent connected vehicle with the highest average driving behavior comprehensive score as the intelligent connected vehicle with the best driving behavior quality.

[0101] Specifically, set the evaluation duration. Determine the set of intelligent connected vehicles participating in driving behavior evaluation. Evaluation duration The system is divided into several time periods, and the driving behavior of each vehicle in each time period is scored to capture the dynamic changes of driving behavior over time. Then, the average score of the comprehensive driving behavior of each vehicle in all time periods is calculated.

[0102] Based on the average value of the comprehensive driving behavior scores of each intelligent connected vehicle calculated in step SC302, the intelligent connected vehicle with the highest average value is identified, which is the intelligent connected vehicle with the best driving behavior quality.

[0103] For example, the `argmax` function is used to identify the intelligent connected vehicle with the best driving behavior quality. The average of the comprehensive driving behavior scores for each vehicle is used as input to the `argmax` function. The function iterates through the scores of all intelligent connected vehicles and identifies the intelligent connected vehicle with the highest average score as having the best driving behavior quality. The calculation formula is as follows:

[0104]

[0105] In the formula, Indicate duration The best-performing intelligent connected car in terms of in-car driving experience. For vehicles In the A comprehensive score of driving behavior over a given time period. This represents the set of intelligent connected vehicles participating in the evaluation. , Indicate duration Number of time periods within.

[0106] Therefore, the aim is to use quantitative comparisons to clearly identify which vehicle has the best overall driving performance within a given evaluation period. By objectively evaluating the driving behavior quality of the best intelligent connected vehicle, it can be used as a benchmark. Through analysis of its driving behavior data, targeted technological optimizations and improvements can then be made.

[0107] In summary, the driving behavior analysis method for intelligent connected vehicles provided in this application obtains several driving behavior characteristic indicators based on the driving behavior data of intelligent connected vehicles; a comprehensive driving behavior score is obtained based on the driving behavior characteristic indicators; and the risk level, vehicle insurance rate adjustment coefficient, and the intelligent connected vehicle with the best driving behavior quality are determined based on the comprehensive driving behavior score. This application comprehensively considers multiple driving behavior characteristic indicators to score the driving mileage, nighttime and peak driving time, and abnormal behavior of intelligent connected vehicles from multiple dimensions, thereby obtaining a comprehensive driving behavior score. The risk level is calculated based on the comprehensive driving behavior score, providing a decision-making basis for vehicle risk management such as dangerous driving behavior control and key vehicle supervision; the vehicle insurance rate adjustment coefficient is calculated based on the comprehensive driving behavior score, providing a reference for insurance pricing of intelligent connected vehicles; and the intelligent connected vehicle with the best driving behavior quality is identified based on the comprehensive driving behavior score, providing an important reference for further research and optimization of intelligent driving technology. Therefore, a comprehensive and accurate assessment of the driving behavior of intelligent connected vehicles can provide a scientific basis for vehicle risk management, insurance pricing, and intelligent driving technology optimization.

[0108] Those skilled in the art will understand that, in the above-described method of the specific implementation, the order in which each step is written does not imply a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of each step should be determined by its function and possible internal logic.

[0109] Example 2

[0110] Reference Figure 3 As shown, Embodiment 2 of this application provides an intelligent connected vehicle driving behavior analysis system, which implements the intelligent connected vehicle driving behavior analysis method described in Embodiment 1 above. The system includes:

[0111] An input data layer is used to store driving behavior data and threshold configuration tables of intelligent connected vehicles;

[0112] The calculation module layer is used to calculate driving behavior characteristic indicators, comprehensive driving behavior score, risk level, vehicle insurance rate adjustment coefficient, and intelligent connected vehicle with the best driving behavior quality.

[0113] The output data layer is used to store driving behavior characteristic indicators, comprehensive driving behavior score, risk level, vehicle insurance rate adjustment coefficient, and intelligent connected vehicle with the best driving behavior quality.

[0114] The visualization layer is used to visually display the driving behavior characteristic indicators, comprehensive driving behavior scores, risk levels, car insurance rate adjustment coefficients, and the intelligent connected vehicles with the best driving behavior quality for different intelligent connected vehicles.

[0115] Specifically, the intelligent connected vehicle driving behavior analysis system includes an input data layer, a computing module layer, an output data layer, and a visualization layer.

[0116] Input Data Layer: Stores driving behavior data and threshold configuration tables for intelligent connected vehicles. Driving behavior data includes various driving behavior data such as vehicle following, lane changing, and start-up response, to further generate driving behavior characteristic indicators. The threshold configuration table allows users to pre-set various parameter values, including the upper limit of mileage score, mileage coefficient, upper limit of nighttime and peak-hour driving time score, nighttime and peak-hour driving time coefficient, upper limit of abnormal behavior score, abnormal behavior coefficient, upper limit of comprehensive driving behavior score, risk level classification threshold, and vehicle insurance rate adjustment coefficient.

[0117] The calculation module layer includes a driving behavior characteristic index calculation module, a driving behavior score calculation module, a risk level calculation module, a vehicle insurance rate adjustment coefficient calculation module, and a smart connected vehicle identification module for vehicles with the best driving behavior quality. The driving behavior characteristic index calculation module calculates driving behavior characteristic indicators. The driving behavior score calculation module calculates mileage scores, nighttime and peak-hour driving duration scores, abnormal behavior scores, and a comprehensive driving behavior score. The risk level calculation module calculates risk levels. The vehicle insurance rate adjustment coefficient calculation module calculates vehicle insurance rate adjustment coefficients. The smart connected vehicle identification module for vehicles with the best driving behavior quality identifies smart connected vehicles with the best driving behavior quality.

[0118] The calculation results layer stores the driving behavior characteristic indicators calculated by the driving behavior characteristic indicator calculation module; it stores the mileage score, night and peak driving time score, abnormal behavior score, and comprehensive driving behavior score calculated by the driving behavior scoring calculation module; it stores the risk level calculated by the risk level calculation module; it stores the car insurance rate adjustment coefficient calculated by the car insurance rate adjustment coefficient calculation module; and it stores the intelligent connected vehicle with the best driving behavior quality identified by the intelligent connected vehicle recognition module.

[0119] Visualization layer: Visualizes the calculation results of vehicle information, driving behavior characteristics, driving behavior scores, risk levels, car insurance rate adjustment coefficients, and intelligent connected vehicles with the best driving behavior quality.

[0120] It should be noted that each application layer and each module within each application layer involved in this embodiment are logical modules. In practical applications, a logical unit can be a physical unit, a part of a physical unit, or a combination of multiple physical units. Furthermore, to highlight the innovative aspects of this application, this embodiment does not introduce units that are not closely related to solving the technical problem proposed in this application; however, this does not mean that other units are absent in this embodiment. The functional implementation of each logical module can be referred to the corresponding description in Embodiment 1, and will not be repeated here.

[0121] Furthermore, some embodiments of this application also provide an electronic device. The electronic device can be various forms of digital computer, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, etc. The electronic device can also be various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices.

[0122] The electronic device includes: one or more processors; and a memory storing computer program instructions, which, when executed, cause the processor to perform the intelligent connected vehicle driving behavior analysis method provided in any one or more of the above embodiments. Figure 4 An exemplary structural diagram of the electronic device is disclosed. For example... Figure 4 As shown, the electronic device includes one or more processors 1101, a memory 1102, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components are interconnected via different buses and can be mounted on a common motherboard or otherwise as required. The processors can process instructions executed within the electronic device, including instructions stored in or on memory to display graphical information of a GUI on an external input / output device (such as a display device coupled to the interface). In some other embodiments, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple electronic devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). The components, their connections and relationships, and their functions shown herein are merely examples and are not intended to limit the implementation of the present application described and / or claimed herein.

[0123] The electronic device may further include an input device 1103 and an output device 1104. The processor 1101, memory 1102, input device 1103, and output device 1104 may be connected via a bus or other means. Figure 4Taking the example of a connection between China and Israel via a bus.

[0124] Input device 1103 can receive input numerical or character information, and generate key signal inputs related to user settings and function control of the electronic device, such as a touch screen, keypad, mouse, trackpad, touchpad, joystick, one or more mouse buttons, trackball, joystick, etc. Output device 1104 may include a display device, auxiliary lighting device (e.g., LED), and haptic feedback device (e.g., vibration motor). The display device may include, but is not limited to, a liquid crystal display (LCD), a light-emitting diode (LED) display, and a plasma display. In some embodiments, the display device may be a touch screen.

[0125] To provide interaction with the user, the electronic device can be a computer. The computer has: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (e.g., voice input, or tactile input).

[0126] In this embodiment, a computer-readable medium stores a computer program / instruction, which, when executed by a processor, implements the intelligent connected vehicle driving behavior analysis method provided in any one or more of the above embodiments. This computer-readable medium may be included in the electronic device described in the above embodiments; or it may exist independently and not assembled into that device. The aforementioned computer-readable medium carries one or more computer-readable instructions.

[0127] The memory 1102 can serve as a non-transitory computer-readable storage medium, used to store non-transitory software programs, non-transitory computer-executable programs, and modules. The processor 1101 executes various functional applications and data processing of the server by running the non-transitory software programs, instructions, and modules stored in the memory 1102, thereby implementing the program instructions / modules corresponding to the methods provided in any one or more of the embodiments described above in this application.

[0128] The memory 1102 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the electronic device. Furthermore, the memory 1102 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory 1102 may optionally include memory remotely located relative to the processor 1101, and these remote memories can be connected to the electronic device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0129] It should be noted that more specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0130] Computer-readable storage media include permanent and non-permanent, removable and non-removable media, which can store information by any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, read-only optical disc (CD-ROM), digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device.

[0131] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0132] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. For example, it can be implemented using an application-specific integrated circuit (ASIC), a general-purpose computer, or any other similar hardware device. In some embodiments, the software program of this application can be executed by a processor to implement the steps or functions described above. Similarly, the software program of this application (including related data structures) can be stored in a computer-readable recording medium, such as RAM memory, magnetic or optical drives, floppy disks, or similar devices. Furthermore, some steps or functions of this application can be implemented in hardware, for example, as circuitry that works with a processor to perform the various steps or functions.

[0133] The computer program product provided in this application includes one or more computer programs / instructions. When executed by a processor, these computer programs / instructions generate, in whole or in part, the processes or functions described in this application. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0134] The flowcharts or block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of devices, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-specific system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0135] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily made by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims, and the above embodiments should be regarded as exemplary and non-limiting.

Claims

1. A method for analyzing driving behavior of intelligent connected vehicles, characterized in that, include: Based on the driving behavior data of intelligent connected vehicles, several driving behavior characteristic indicators are obtained. Based on the aforementioned driving behavior characteristic indicators, a comprehensive driving behavior score is obtained; Based on the comprehensive driving behavior score, the risk level and car insurance rate adjustment coefficient of the intelligent connected vehicle are determined, as well as the intelligent connected vehicle with the best driving behavior quality.

2. The intelligent connected vehicle driving behavior analysis method according to claim 1, characterized in that, The aforementioned driving behavior characteristic indicators include: driving mileage characteristic indicators, driving duration characteristic indicators, and abnormal behavior characteristic indicators; The driving mileage characteristic indicators include: cumulative driving mileage within a set time period, peak-hour driving mileage, and nighttime driving mileage; The driving time characteristic indicators include: cumulative driving time, peak period driving time, and nighttime driving time; The abnormal behavior characteristic indicators include: number of first-level following warnings, number of second-level following warnings, number of first-level lane change warnings, number of second-level lane change warnings, number of times the automatic driving system disengages, number of times of rapid acceleration, number of times of rapid deceleration, number of times of sudden stop while following another vehicle, number of times of excessive acceleration from a standstill, number of times the vehicle takes over at intersections, number of times the vehicle crosses a solid line to change lanes, number of times the vehicle runs a red light, number of times the vehicle crosses a stop line, and number of times the vehicle changes lanes continuously.

3. The intelligent connected vehicle driving behavior analysis method according to claim 1 or 2, wherein the step of obtaining a comprehensive driving behavior score based on the driving behavior characteristic indicators includes: The cumulative mileage within a set time period is obtained from the driving behavior characteristic indicators used to characterize the driving mileage characteristic indicators. Based on the preset mileage coefficient and the upper limit of the mileage score, the mileage score is calculated. The peak driving time and night driving time used to characterize the driving time feature index are obtained from the driving behavior feature index. Based on the preset night and peak driving time coefficients and the upper limit of driving time score, the night and peak driving time scores are calculated. Obtain each abnormal behavior feature index from the driving behavior feature indexes, and calculate the abnormal behavior score based on the preset abnormal behavior coefficient and the upper limit of the abnormal behavior score corresponding to each abnormal behavior feature index. The comprehensive driving behavior score is calculated based on the mileage score, the nighttime and peak-hour driving duration score, and the abnormal behavior score.

4. The intelligent connected vehicle driving behavior analysis method according to claim 1, characterized in that, The step of determining the risk level of the intelligent connected vehicle based on the comprehensive driving behavior score includes: The risk level set and classification threshold of the intelligent connected vehicle are set. The risk level set consists of multiple risk levels. The classification threshold is used to divide the upper and lower limits of the comprehensive driving behavior score into multiple threshold intervals. Each threshold interval corresponds to a risk level. The threshold range into which the comprehensive score of the driving behavior falls is determined, thereby identifying the corresponding risk level.

5. The intelligent connected vehicle driving behavior analysis method according to claim 4, characterized in that, The step of determining the vehicle insurance rate adjustment coefficient for the intelligent connected vehicle based on the comprehensive driving behavior score includes: Set a set of vehicle insurance rate adjustment coefficients for the intelligent connected vehicle, wherein the set of vehicle insurance rate adjustment coefficients consists of multiple vehicle insurance rate adjustment coefficients, and each threshold interval corresponds to one vehicle insurance rate adjustment coefficient. The threshold range into which the comprehensive driving behavior score falls is determined, and the corresponding vehicle insurance rate adjustment coefficient is then determined.

6. The intelligent connected vehicle driving behavior analysis method according to claim 1, characterized in that, The step of determining the intelligent connected vehicle with the best driving behavior quality based on the comprehensive driving behavior score includes: Set the evaluation duration and determine the set of intelligent connected vehicles participating in the driving behavior evaluation; The evaluation period is divided into multiple time periods. For each intelligent connected vehicle in the intelligent connected vehicle set, the comprehensive driving behavior score for each time period is calculated, thereby obtaining the average value of the comprehensive driving behavior score for each intelligent connected vehicle within the evaluation period. The intelligent connected vehicle with the highest average value of the comprehensive driving behavior score is identified as the intelligent connected vehicle with the best driving behavior quality.

7. A driving behavior analysis system for intelligent connected vehicles, implementing the driving behavior analysis method for intelligent connected vehicles as described in any one of claims 1-6, characterized in that, The system includes: An input data layer is used to store driving behavior data and threshold configuration tables of intelligent connected vehicles; The calculation module layer is used to calculate driving behavior characteristic indicators, comprehensive driving behavior score, risk level, vehicle insurance rate adjustment coefficient, and intelligent connected vehicle with the best driving behavior quality. The output data layer is used to store driving behavior characteristic indicators, comprehensive driving behavior score, risk level, vehicle insurance rate adjustment coefficient, and intelligent connected vehicle with the best driving behavior quality. The visualization layer is used to visually display the driving behavior characteristic indicators, comprehensive driving behavior scores, risk levels, car insurance rate adjustment coefficients, and the intelligent connected vehicles with the best driving behavior quality for different intelligent connected vehicles.

8. An electronic device, characterized in that, The electronic device includes: One or more processors; and a memory storing computer program instructions, which, when executed, cause the processors to perform the intelligent connected vehicle driving behavior analysis method as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program / instructions stored thereon, characterized in that, When the computer program / instructions are executed by the processor, they implement the intelligent connected vehicle driving behavior analysis method as described in any one of claims 1-6.

10. A computer program product, comprising a computer program / instructions, characterized in that, When the computer program / instruction is executed by the processor, it implements the intelligent connected vehicle driving behavior analysis method as described in any one of claims 1-6.

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