Vehicle intelligent driving permission regulation and control method, device and equipment and storage medium

Abstract

The invention discloses a vehicle intelligent driving permission regulation and control method, device and equipment and a storage medium, and the method comprises the steps: obtaining the identity information of a driver, querying the current intelligent driving evaluation grade and historical driving data (including at least one of intelligent driving record data, self-driving record data, emergency processing data and vehicle driving parameters) of the driver; and the intelligent driving authority is dynamically adjusted after comprehensive evaluation. If the intelligent driving evaluation grade is improved, a higher-order intelligent driving function is opened; and if the intelligent driving evaluation grade is reduced, limiting the use duration of part of functions. According to the invention, authority level-to-level management based on the actual driving ability and behavior habits of the driver is realized, the user is encouraged to correctly use the intelligent driving function, the safety risk caused by dependence or operation lesseness can be reduced through dynamic authority adjustment, and the safety and adaptability of the intelligent driving system can be effectively improved. The method can be widely applied to the technical field of vehicles.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicles, and in particular to a vehicle intelligent driving permission regulation method and device, equipment and a storage medium. BACKGROUND

[0002] At present, many vehicles are equipped with intelligent auxiliary driving functions. This technology can learn the driving habits of users and provide personalized auxiliary functions, such as adaptive cruise control, lane keeping, and automatic parking. The popularity of intelligent auxiliary driving not only improves driving convenience, but also reduces fatigue during long-distance driving or congested road sections, and helps to reduce the risk of human operation errors through algorithm optimization, thereby improving overall road safety.

[0003] In related technologies, excessive reliance on intelligent driving may lead to a decline in user driving proficiency, especially when human intervention is required in emergency situations. Some drivers may be confused due to a lack of real-time road condition response experience, increasing the risk of accidents. Therefore, vehicle manufacturers generally require drivers to bind personal accounts on associated software and learn relevant knowledge before releasing intelligent driving permissions. However, in actual applications, some users may not have seriously studied the relevant knowledge, or in practice, they may not have complied with the requirements for using intelligent driving, resulting in a high level of safety hazards.

[0004] In summary, the problems in related technologies need to be solved urgently. SUMMARY

[0005] The present application aims to at least partially solve one of the technical problems in related technologies.

[0006] To this end, an object of the present application is to provide a vehicle intelligent driving permission regulation method, device, equipment and storage medium.

[0007] In order to achieve the above technical purpose, the technical solutions adopted by the embodiments of the present application include:

[0008] On the one hand, the present application provides a vehicle intelligent driving permission regulation method, which comprises:

[0009] Obtaining the identity information of the driver of the target vehicle, querying the first intelligent driving evaluation level of the driver and the historical driving data in the predetermined historical period before the current time point according to the identity information; wherein the historical driving data includes at least one of intelligent driving record data, self-driving record data, emergency handling data and vehicle driving parameters, the first intelligent driving evaluation level corresponds to a first permission range, and the first permission range is used to indicate the use permission of a plurality of intelligent driving functions;

[0010] According to the historical driving data, a second intelligent driving evaluation level corresponding to the driver at a current time point is determined; the second intelligent driving evaluation level corresponds to a second permission range, and the second permission range is used to indicate the use permission of a plurality of intelligent driving functions;

[0011] If the second intelligent driving evaluation level is higher than the first intelligent driving evaluation level, the use permission of a first intelligent driving function is opened to the driver; the first intelligent driving function is an intelligent driving function included in the second permission range and not included in the first permission range;

[0012] If the second intelligent driving evaluation level is lower than the first intelligent driving evaluation level, the use time length of a second intelligent driving function is limited for the driver; the second intelligent driving function is an intelligent driving function included in the first permission range and not included in the use permission of the second permission range.

[0013] In addition, according to the vehicle intelligent driving permission regulation method of the above-mentioned embodiments of the present application, the following additional technical features can be further included:

[0014] Further, in an embodiment of the present application, the obtaining of the identity information of the driver of the target vehicle and the querying of the first intelligent driving evaluation level of the driver according to the identity information include:

[0015] Obtaining the identity information of the driver of the target vehicle and querying whether the driver has learned intelligent driving functions according to the identity information;

[0016] If it is determined that the driver has learned intelligent driving functions, the first intelligent driving evaluation level is obtained as the intelligent driving evaluation level of the driver determined at the nearest time point from the current time point.

[0017] Further, in an embodiment of the present application, the method further includes:

[0018] If it is determined that the driver has not learned intelligent driving functions, a channel entry for learning intelligent driving functions is pushed to the driver;

[0019] In response to the completion of the task of learning intelligent driving functions performed by the driver, the intelligent driving evaluation level of the driver is determined as an initial intelligent driving evaluation level.

[0020] Further, in an embodiment of the present application, the obtaining of the identity information of the driver of the target vehicle includes:

[0021] The identity information of the driver is obtained by at least one of biological recognition, a digital account number, or a physical certificate.

[0022] Further, in an embodiment of the present application, the evaluating and determining the second intelligent driving evaluation level corresponding to the driver at the current time point according to the historical driving data comprises:

[0023] obtaining a driving ability evaluation model built in advance;

[0024] inputting the historical driving data into the driving ability evaluation model, evaluating the intelligent driving ability of the driver through the historical driving data, and obtaining the second intelligent driving evaluation level corresponding to the driver at the current time point.

[0025] Further, in an embodiment of the present application, the driving ability evaluation model is built through the following steps:

[0026] obtaining a training data set, wherein the training data set comprises sample driving data corresponding to a plurality of sample persons and a level label, the sample driving data comprises at least one of intelligent driving record data, self-driving record data, emergency handling data and vehicle driving parameters of the sample persons, and the level label is used to represent a standard expected result of the intelligent driving evaluation level corresponding to the sample persons;

[0027] inputting the sample driving data into an initialized driving ability evaluation model, evaluating the intelligent driving ability of the sample persons through the sample driving data, and obtaining a predicted intelligent driving evaluation level corresponding to the sample persons;

[0028] determining a training loss value according to the predicted intelligent driving evaluation level and the level label;

[0029] updating parameters of the driving ability evaluation model according to the loss value, and obtaining a trained driving ability evaluation model.

[0030] Further, in an embodiment of the present application, the limiting the use time length of the second intelligent driving function by the driver comprises:

[0031] limiting the single use time length of the second intelligent driving function by the driver, or limiting the total use time length of the second intelligent driving function by the driver.

[0032] On the other hand, an embodiment of the present application provides a vehicle intelligent driving permission control device, the device comprises:

[0033] An obtaining unit is configured to obtain identity information of a driver of a target vehicle, query a first intelligent driving evaluation level of the driver and historical driving data of a predetermined historical period before a current time point according to the identity information, wherein the historical driving data comprises at least one of intelligent driving record data, self-driving record data, emergency handling data and vehicle driving parameters, the first intelligent driving evaluation level corresponds to a first permission range, and the first permission range is used to indicate the use permission of a plurality of intelligent driving functions.

[0034] An evaluation unit is configured to evaluate and determine a second intelligent driving evaluation level of the driver at the current time point according to the historical driving data, wherein the second intelligent driving evaluation level corresponds to a second permission range, and the second permission range is used to indicate the use permission of a plurality of intelligent driving functions.

[0035] An opening unit is configured to open the use permission of a first intelligent driving function to the driver if the second intelligent driving evaluation level is higher than the first intelligent driving evaluation level, wherein the first intelligent driving function is an intelligent driving function included in the second permission range and not included in the first permission range.

[0036] A limiting unit is configured to limit the use time length of a second intelligent driving function by the driver if the second intelligent driving evaluation level is lower than the first intelligent driving evaluation level, wherein the second intelligent driving function is an intelligent driving function included in the first permission range and not included in the use permission of the second permission range.

[0037] In another aspect, an electronic device is provided, comprising:

[0038] at least one processor;

[0039] at least one memory configured to store at least one program;

[0040] When the at least one program is executed by the at least one processor, the at least one processor implements the above-mentioned vehicle intelligent driving permission regulation method.

[0041] In another aspect, the embodiments of the present application further provide a computer readable storage medium, wherein a processor executable program is stored, and the processor executable program is used to implement the above-mentioned vehicle intelligent driving permission regulation method when executed by a processor.

[0042] In another aspect, the embodiments of the present application further provide a computer program product, which comprises a computer program stored in a computer readable storage medium, and a processor of a computer device reads the computer program from the computer readable storage medium, and the processor executes the computer program, so that the computer device executes the above-mentioned vehicle intelligent driving permission regulation method.

[0043] The advantages and benefits of this application will be partially given in the following description, and partially become apparent from the following description, or learned through practice of this application:

[0044] The embodiments of the present application disclose a method, device, equipment and storage medium for regulating vehicle intelligent driving authority. The present application obtains the driver's identity information, queries his current intelligent driving assessment level and historical driving data (including at least one of intelligent driving record data, self-driving record data, emergency handling data and vehicle driving parameters), and dynamically adjusts his intelligent driving authority after comprehensive evaluation. If the intelligent driving assessment level is improved, higher-level intelligent driving functions will be opened; if the intelligent driving assessment level is reduced, the usage time of some functions will be restricted. The present application implements hierarchical authority management based on the driver's actual driving ability and behavioral habits, which not only encourages users to use intelligent driving functions correctly, but also reduces safety risks caused by dependence or unfamiliar operation through dynamic adjustment of authority, and can effectively improve the safety and adaptability of the intelligent driving system. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following introduction is made to the drawings of the embodiments of the present application or the related technical solutions in the prior art. It should be understood that the drawings introduced below are only for the convenience of clearly describing some embodiments of the technical solutions of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without any creative work.

[0046] Figure 1 A schematic diagram of an implementation environment for a method for regulating vehicle intelligent driving authority provided in an embodiment of the present application;

[0047] Figure 2 A flowchart of a method for controlling vehicle intelligent driving authority provided in an embodiment of the present application;

[0048] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0049] The present application is further described below in conjunction with the accompanying drawings and specific embodiments. The described embodiments should not be considered as limiting the present application. All other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0050] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is to be understood that "some embodiments" can be the same subset or different subsets as each other and as other subsets of all possible embodiments, and can be combined with each other and with other subsets of all possible embodiments without contradiction.

[0051] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to be limiting of this application.

[0052] At present, many vehicles are configured with the function of intelligent auxiliary driving. This technology can provide personalized auxiliary functions such as adaptive cruise, lane keeping and automatic parking by learning the driving habits of users. The popularity of intelligent auxiliary driving not only improves driving convenience, but also reduces fatigue in long-distance driving or congested road sections, and helps to reduce the risk of human operation errors through algorithm optimization, thereby improving overall road safety.

[0053] In the related art, excessive reliance on intelligent driving can lead to a decline in user driving proficiency, especially when manual intervention is required in emergency situations. Some drivers may be confused due to lack of real-time road condition response experience, increasing the risk of accidents. Therefore, vehicle manufacturers generally require drivers to bind personal accounts on associated software and learn relevant knowledge before releasing intelligent driving permissions. However, in actual application, some users may not have seriously studied the relevant knowledge, or in practice, they may not have complied with the requirements for using intelligent driving, resulting in a high security risk.

[0054] Therefore, in the embodiments of the present application, a vehicle intelligent driving permission control method, device, equipment and storage medium are provided to improve the security risk problem caused by user dependence or illegal use of related intelligent driving technology. The present application dynamically adjusts the intelligent driving permission of the driver by obtaining the identity information of the driver, querying the current intelligent driving evaluation level and historical driving data (including at least one of intelligent driving record data, self-driving record data, emergency handling data and vehicle driving parameters), and comprehensively evaluating the intelligent driving permission. If the intelligent driving evaluation level is improved, higher order intelligent driving functions are opened; if the intelligent driving evaluation level is decreased, the use time of part of the functions is limited. The present application realizes the permission hierarchical management based on the actual driving ability and behavior habit of the driver, encourages users to correctly use intelligent driving functions, and reduces the security risk caused by dependence or operation inexperience through dynamic adjustment of permissions, which can effectively improve the safety and adaptability of the intelligent driving system.

[0055] Please refer to Figure 1 , Figure 1An implementation environment of a vehicle intelligent driving permission regulation method provided in an embodiment of the present application is shown. In the implementation environment, the main software and hardware subjects involved include a terminal device 110 and a background server 120. The terminal device 110 and the background server 120 are communicatively connected.

[0056] Specifically, the vehicle intelligent driving permission regulation method provided in the embodiment of the present application can be executed on the terminal device 110 alone or based on data interaction between the terminal device 110 and the background server 120. The terminal device 110 can be a vehicle-mounted terminal, for example, a central control unit of a vehicle. The background server 120 can be a standalone physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud service, cloud database, cloud computing, cloud function, cloud storage, network service, cloud communication, middleware service, domain name service, security service, CDN (Content Delivery Network), and big data and artificial intelligence platform.

[0057] The terminal device 110 and the background server 120 can be communicatively connected through a wireless network or a wired network. The wireless network or the wired network uses standard communication technology and / or protocol. The network can be set as the Internet or any other network, for example, any combination of a local area network (LAN), a metropolitan area network (MAN), a wide area network (WAN), a mobile, wired or wireless network, a private network or a virtual private network, but is not limited to these.

[0058] Of course, it can be understood that the implementation environment in Figure 1 is only some optional application scenarios of the vehicle intelligent driving permission regulation method provided in the embodiment of the present application, and the actual application is not fixed to the software and hardware environment shown in Figure 1 .

[0059] Next, the vehicle intelligent driving permission regulation method provided in the embodiment of the present application is introduced and described in combination with the foregoing introduction of the implementation environment.

[0060] Please refer to Figure 2 , Figure 2 is a schematic diagram of a vehicle intelligent driving permission regulation method provided in an embodiment of the present application. The vehicle intelligent driving permission regulation method includes but is not limited to the following steps.

[0061] Step 210, obtaining identity information of a driver of a target vehicle, querying a first intelligent driving evaluation level and historical driving data of the driver in a predetermined historical period before a current time point according to the identity information; wherein the historical driving data comprises at least one of intelligent driving record data, self-driving record data, emergency handling data and vehicle driving parameters, the first intelligent driving evaluation level corresponds to a first permission range, and the first permission range is used to indicate the use permission of a plurality of intelligent driving functions;

[0062] Step 220, evaluating and determining a second intelligent driving evaluation level of the driver corresponding to the current time point according to the historical driving data; wherein the second intelligent driving evaluation level corresponds to a second permission range, and the second permission range is used to indicate the use permission of a plurality of intelligent driving functions;

[0063] Step 230, if the second intelligent driving evaluation level is higher than the first intelligent driving evaluation level, opening the use permission of a first intelligent driving function to the driver; wherein the first intelligent driving function is an intelligent driving function included in the second permission range and not included in the first permission range;

[0064] Step 240, if the second intelligent driving evaluation level is lower than the first intelligent driving evaluation level, limiting the use time length of a second intelligent driving function by the driver; wherein the second intelligent driving function is an intelligent driving function included in the first permission range and not included in the use permission of the second permission range.

[0065] In the embodiments of the present application, a vehicle intelligent driving permission control method is provided, which realizes permission hierarchical management based on the actual driving ability and behavior habits of the driver, encourages users to correctly use intelligent driving functions, reduces safety risks caused by dependence or unfamiliar operation through dynamic adjustment of permissions, and effectively improves the safety and adaptability of the intelligent driving system.

[0066] Specifically, in the embodiments of the present application, for a vehicle that needs to be regulated in terms of vehicle intelligent driving permission, it can be recorded as a target vehicle, and the target vehicle needs to be configured with an intelligent driving function. The intelligent driving function in the embodiments of the present application refers to the intelligent driving assistance or automatic driving capability equipped in the vehicle. Generally, the intelligent driving function can realize the perception, decision and control of the vehicle driving environment through sensors (such as cameras, radars, lidar, etc.), high-precision maps, vehicle networking technology and artificial intelligence algorithms, so as to replace or assist the driver to complete the driving task to a certain extent. The intelligent driving function usually covers multiple levels, from basic assisted driving to high-level automatic driving, and specifically includes but is not limited to adaptive cruise control (ACC), lane centering keeping (LCC), automatic emergency braking (AEB), traffic sign recognition (TSR), automatic parking (APA), navigation assisted driving (NOA) and the like. These functions can automatically adjust the vehicle speed, maintain the vehicle distance, change the lane and even complete the full automatic driving under complex road conditions according to the road conditions and driving scene, and at the same time, the system will monitor the driver's state in real time to ensure that the control right can be safely transferred when necessary. The implementation of the intelligent driving function depends on continuous data learning and algorithm optimization to ensure adaptability to different driving environments and user habits, and the goal is to improve driving comfort and efficiency while significantly reducing the risk of accidents caused by human operation errors.

[0067] In the embodiments of the present application, in the process of regulating the vehicle intelligent driving permission, the identity information of the driver of the target vehicle is first obtained, which can be obtained through the vehicle terminal or the mobile application, for example, through biological recognition (such as face recognition, fingerprint verification), digital account (such as the binding account of the vehicle enterprise cloud service) or physical certificate (such as smart key or NFC card) and the like to ensure the uniqueness and accuracy of the identity recognition.

[0068] After identifying the identity information of the driver, the system will associate the personal driving archives of the driver stored in the vehicle enterprise cloud or the local database, and call the intelligent driving evaluation level, which is recorded as the first intelligent driving evaluation level in the embodiments of the present application. Here, the intelligent driving evaluation level is a permission level based on the comprehensive evaluation of the driver's past driving behavior, training record, test result and the like, which can have different levels, such as primary, intermediate and advanced. Each intelligent driving evaluation level corresponds to a permission range, which is used to indicate the use permission of a number of intelligent driving functions, that is, records the situation of the intelligent driving functions that the driver can use under the corresponding intelligent driving evaluation level.

[0069] Exemplarily, in some embodiments, the intelligent driving evaluation level can be divided into four levels of L1 to L4: L1 level (novice) driver is only open basic auxiliary functions such as cruise control and lane departure warning, the system will be forced to keep a larger following distance and disable automatic lane changing; L2 level (skilled) driver can enable adaptive cruise control and lane centering, and in the highway scene, detection of hands off the steering wheel for 10 seconds will remind attention, but the city road limits the use of related functions; L3 level (senior) driver unlocks automatic lane changing and traffic light recognition function, and the system can perform navigation assisted driving (NOA) on qualified highway sections; L4 level (expert) driver opens full-scene automatic driving permission, including complex urban unprotected left turn and automatic parking (AVP), and the system only needs to request to take over in extreme cases. Of course, it can be understood that the above intelligent driving evaluation level and the corresponding permission range are only used for exemplary introduction, and do not mean to limit the actual application of the present application.

[0070] In the embodiments of the present application, for the first intelligent driving evaluation level of the driver, the corresponding permission range is recorded as the first permission range. In addition to querying the first intelligent driving evaluation level of the driver, the historical driving data of the driver in a predetermined historical period before the current time point is also queried. The end time point of the predetermined historical period can be the current time point, and the specific time length thereof is not limited by the present application, for example, it can be one week, 30 days or other time length, etc. The historical driving data can be some driving data in the predetermined historical period, including but not limited to intelligent driving record data (such as cumulative time length of using intelligent driving, number of times of triggering takeover, scene distribution of function use), self-driving record data (such as steering wheel holding detection when manual driving, accelerator brake operation smoothness), emergency handling data (such as response delay of the driver after the system alarm, effectiveness of manual intervention) and vehicle driving parameters (such as average vehicle speed, acceleration standard deviation, lane deviation frequency). These data are collected and stored in real time through vehicle-mounted sensors, vehicle networking modules or third-party data interfaces, forming a multi-dimensional driving behavior portrait to provide data support for subsequent dynamic evaluation.

[0071] In the embodiments of the present application, after obtaining the historical driving data, the new intelligent driving evaluation level corresponding to the driver at the current time point can be determined according to the historical driving data, which is recorded as the second intelligent driving evaluation level. For example, in some embodiments, a multi-dimensional weighting algorithm can be used to deeply analyze the historical driving data, thereby generating an accurate second intelligent driving evaluation level. The evaluation process can standardize different data types: for example, the intelligent driving record data focuses on analyzing the compliance of function use, including the frequency of system intervention, the number of manual overrides, and the richness of function use scenarios; the autonomous driving record data calculates the driving stability index through signals such as steering wheel torque and pedal travel; the emergency handling data focuses on investigating the reaction time and operation accuracy when taking over in an emergency, and establishes a crisis response capability model; and the vehicle driving parameters are analyzed by machine learning to analyze acceleration curves, lane deviation rates, and other characteristics to generate a driving style portrait. In this way, the second intelligent driving evaluation level corresponding to the driver at the current time point can be determined according to the historical driving data.

[0072] In some embodiments, the present application can also assign dynamic weights to each type of historical driving data, for example, for novice drivers, the corresponding weight under autonomous driving record data analysis is higher, and for experienced drivers, the corresponding weight under intelligent driving record data analysis is higher. In some embodiments, the evaluation process can also introduce a time decay factor to make the recent data have a larger impact. Through this comprehensive calculation, not only can it be determined whether the driver meets the current level standard, but also the upgrade potential can be predicted.

[0073] In the embodiments of the present application, the second intelligent driving evaluation level also corresponds to a permission range, which is recorded as the second permission range.

[0074] After obtaining the second intelligent driving evaluation level, it can be compared with the first intelligent driving evaluation level of the driver. When it is determined that the second intelligent driving evaluation level of the driver is higher than the original first intelligent driving evaluation level, the permission upgrade mechanism is triggered, and higher-order intelligent driving functions are opened to the driver. In this process, first, the permission difference comparison can be performed, and the function items not included in the first permission range are selected from the intelligent driving function list included in the second permission range. These incremental functions are the first intelligent driving functions to be opened. For example, the original first level (L2 level) permission of a driver includes adaptive cruise control and lane keeping, and the newly evaluated second level (L3 level) permission adds automatic lane changing and traffic light recognition functions. The system will accurately identify these two functions as to-be-unlocked items, thereby opening the use permission of the first intelligent driving function to the driver.

[0075] In contrast, when the system detects that the driver’s second intelligent driving assessment level is lower than the original first level, the intelligent driving authority downgrade protection mechanism will be activated. The process first performs an authority difference analysis to accurately identify the second intelligent driving function that was previously available but is no longer included in the new assessment level. For example, when a driver is downgraded from L3 to L2, his previously available automatic lane change and high-speed navigation functions will be listed as restricted functions. However, it should be noted that in the embodiment of the present application, the second intelligent driving function is not directly listed as an unusable function, but a progressive restriction strategy will be adopted, that is, the driver’s use time of the second intelligent driving function is limited. For example, for these second intelligent driving functions, 2 hours / day is allowed on weekdays and 1 hour / day on weekends, and the remaining available time is displayed in real time on the HUD interface. When the remaining available time is less than a predetermined threshold (such as 10 minutes), the steering wheel will trigger vibration feedback to prompt the driver.

[0076] The technical solution of this application, by establishing a scientific intelligent driving evaluation level system, achieves accurate quantification of driver capabilities, so that the authority allocation is no longer fixed, but can be dynamically adjusted according to the driver's actual performance, avoiding the rigidity of one-size-fits-all authority management, and ensuring that users of different driving levels can obtain an intelligent driving experience suitable for their own abilities. Specifically, this application adopts a differentiated authority upgrade and demotion strategy, which helps drivers smoothly adapt to high-level functions through gradual opening and accompanying guidance when upgrading authority; when downgrading authority, it adopts time limits instead of direct disabling, which not only ensures driving safety, but also gives drivers the opportunity to improve. This flexible management can significantly improve user experience and acceptance.

[0077] In particular, the technical solution of this application, through real-time data monitoring and multi-dimensional assessment, can promptly identify changing trends in a driver's abilities and take preventative measures before potential risks arise. This embodies the forward-looking security concept of intelligent systems and provides a scalable and verifiable solution for rights management in the era of intelligent driving. This application can effectively reduce the risks of using intelligent driving systems while promoting the continuous improvement of driver abilities, achieving an optimal balance between safety and practicality.

[0078] Specifically, in some embodiments, obtaining the identity information of the driver of the target vehicle and querying the first intelligent driving assessment level of the driver based on the identity information includes:

[0079] Obtain the identity information of the driver of the target vehicle, and query whether the driver has learned the intelligent driving function based on the identity information;

[0080] If it is determined that the driver has performed intelligent driving function learning, the most recent intelligent driving evaluation level of the driver determined from the current time point is obtained as the first intelligent driving evaluation level.

[0081] In the embodiments of the present application, after obtaining the identity information of the driver, it can be queried whether the driver has learned the intelligent driving function according to the identity information of the driver. For example, in some embodiments, an intelligent driving training system can be set up in the background of the target vehicle manufacturer, and it can be queried whether the driver has learned the intelligent driving function. The relevant learning records can be queried in the intelligent driving training system, for example, three key elements can be checked: whether the basic theory course learning (including mandatory content such as intelligent driving function principle and emergency takeover process) is completed, whether the simulator practical operation examination (at least including multiple tests of typical dangerous scene response) is passed, and whether the real car following training (actual road experience supervised by professional trainers) is completed. Of course, it can be understood that the relevant content of the intelligent driving function learning can be flexibly set according to specific needs, and the present application does not limit this.

[0082] In the embodiments of the present application, only when the driver has learned the intelligent driving function, the permission of the intelligent driving function will be opened. Therefore, only when it is determined that the driver has learned the intelligent driving function, the driver will have the corresponding intelligent driving evaluation level. Therefore, when it is determined that the driver has learned the intelligent driving function, the intelligent driving evaluation level of the driver determined at the nearest time point from the current time point can be obtained as the first intelligent driving evaluation level.

[0083] In some cases, when the system confirms that the driver has not completed the intelligent driving function learning according to the identity information, the novice guidance process will be started immediately, and the channel entrance of the intelligent driving function learning will be pushed to the driver. The driver can complete the task of learning the intelligent driving function through the channel entrance. When it is determined that the driver completes the task of learning the intelligent driving function, the intelligent driving evaluation level of the driver will be automatically set as the initial intelligent driving evaluation level. In the embodiments of the present application, the initial intelligent driving evaluation level can be the lowest intelligent driving evaluation level (for example, L1 level by default), and the present application does not limit this.

[0084] Specifically, in some embodiments, the second intelligent driving evaluation level corresponding to the driver at the current time point is determined by evaluating the historical driving data, comprising:

[0085] obtaining a driving ability evaluation model built in advance;

[0086] inputting the historical driving data into the driving ability evaluation model, evaluating the intelligent driving ability of the driver through the historical driving data, and obtaining the second intelligent driving evaluation level corresponding to the driver at the current time point.

[0087] In the embodiments of the present application, when determining the second intelligent driving evaluation level corresponding to the driver at the current time point, the driving ability evaluation model can be implemented based on the pre-built driving ability evaluation model. The driving ability evaluation model can be developed based on a deep neural network architecture, and the training data comes from a large number of sample personnel long-period driving behavior data sets. For example, the anonymous driving data uploaded by each vehicle terminal can be continuously optimized online under the premise of ensuring data privacy.

[0088] Specifically, when evaluating the second intelligent driving evaluation level of the driver, the preprocessed historical driving data can be divided into multiple analysis windows (such as the last 7 days, 30 days, and 90 days) according to the time dimension, and the key indicators in each period are extracted respectively: intelligent driving record data focuses on analyzing the compliance rate of function use (such as whether to respond in time after the system prompts to take over), function dependence (intelligent driving mileage ratio); self-driving record data calculates the operation stability index (based on steering wheel angle variance and pedal opening rate of change); emergency handling data quantifies crisis response efficiency (including takeover delay time and operation accuracy); and vehicle driving parameters are used to construct driving style portraits (such as aggressive or conservative). After standardization processing of these feature data, they are input into the sub-network of the driving ability evaluation model according to different weights. For example, the driving ability evaluation model can include: a basic ability evaluation network (analyzing regular driving performance), a risk prediction network (identifying potential dangerous behavior patterns), and a development potential evaluation network (predicting high-level function adaptation ability). Finally, the output layer of the driving ability evaluation model can use a softmax function to generate a probability distribution of the level, and the level with a probability of more than 60% is taken as the second intelligent driving evaluation level. If all level probabilities do not meet the standard, the original level is kept unchanged. Of course, it can be understood that the structure of the actual driving ability evaluation model can be flexibly adjusted according to the needs, and the present application does not limit this.

[0089] Specifically, in some embodiments, the driving ability evaluation model is built by the following steps:

[0090] Obtaining a training data set; wherein the training data set includes sample driving data corresponding to a plurality of sample personnel and a level label, the sample driving data includes at least one of intelligent driving record data, self-driving record data, emergency handling data and vehicle driving parameters of the sample personnel, and the level label is used to represent the standard expected result of the intelligent driving evaluation level corresponding to the sample personnel;

[0091] Inputting the sample driving data into the initialized driving ability evaluation model, evaluating the intelligent driving ability of the sample personnel through the sample driving data, and obtaining the predicted intelligent driving evaluation level corresponding to the sample personnel;

[0092] According to the predicted intelligent driving evaluation level and the level label, a training loss value is determined;

[0093] update the parameters of the driving ability evaluation model according to the loss value, to obtain a trained driving ability evaluation model.

[0094] It should be noted that, in the embodiments of the present application, the driving ability evaluation model needs to be trained before it is actually put into use, in order to improve its prediction performance. Specifically, when training the driving ability evaluation model, a batch of training data can be obtained, which includes sample driving data corresponding to a plurality of sample personnel and a grade label, wherein the sample driving data includes at least one of the intelligent driving record data, the self-driving record data, the emergency handling data and the vehicle driving parameter of the sample personnel, and the grade label is used to represent the standard expected result of the intelligent driving evaluation grade corresponding to the sample personnel, which can be obtained by labeling by the technical personnel related to the vehicle enterprise.

[0095] For each sample personnel, the driving ability evaluation model can be used to evaluate the intelligent driving ability of the sample personnel based on the sample driving data, to obtain the predicted intelligent driving evaluation grade corresponding to the sample personnel. It can be understood that, in the embodiments of the present application, if the prediction effect of the driving ability evaluation model is good, the predicted intelligent driving evaluation grade corresponding to the sample personnel and the grade label should be relatively close. Therefore, in the embodiments of the present application, the accuracy of the prediction of the driving ability evaluation model can be determined based on the predicted intelligent driving evaluation grade and the grade label. Specifically, the deviation between the predicted intelligent driving evaluation grade and the grade label can be determined to obtain the loss value of the prediction of the driving ability evaluation model. After obtaining the loss value, the prediction accuracy of the driving ability evaluation model can be evaluated according to the size of the loss value, so as to perform back propagation training on the driving ability evaluation model and update the related parameters inside the driving ability evaluation model.

[0096] For models in the field of artificial intelligence, the accuracy of its prediction can be measured by a loss function. The loss function is defined on a single training data, and is used to measure the prediction error of a training data. Specifically, the loss value of a training data is determined by the label of the training data and the prediction result of the model on the training data. In actual training, a training data set has many training data (such as the plurality of first training images in the embodiments of the present application), therefore, a cost function is generally used to measure the overall error of the training data set. The cost function is defined on the entire training data set, and is used to calculate the average value of the prediction errors of all training data, which can better measure the prediction effect of the model. For general models, based on the aforementioned cost function, plus a regular term that measures the complexity of the model, the target function for training can be obtained, and based on the target function, the loss value of the entire training data set can be obtained.

[0097] In the embodiments of the present application, the parameter updating of the driving ability evaluation model can be performed in a cyclic iteration manner. That is, after updating the parameters of a round of model, the model with updated parameters is used to continue prediction to determine a new loss value, and then the parameters of the model are updated again. This cycle is repeated until the pre-set training end condition is met, and it is considered that the training is completed and a trained model is obtained.

[0098] The training end condition can be flexibly set according to requirements. For example, in some embodiments, the target round of training cycle iteration can be set as the training end condition, and when the update round of the parameters of the model reaches the target round, it is considered that the training is completed; in some embodiments, the difference threshold of the loss values obtained in adjacent two training processes can be set as the training end condition, and when the parameters of a round of model are updated, the difference absolute value between the loss value obtained in the current training process and the loss value obtained in the last round of training is calculated, if the difference absolute value is greater than the set difference threshold, the iteration training is continued; if the difference absolute value is less than or equal to the set difference threshold, it is considered that the training is completed. Of course, the above is only an exemplary introduction of some optional training end condition setting modes in the embodiments of the present application, and does not mean to limit the actual implementation.

[0099] Specifically, in some embodiments, the restriction strategy for the second intelligent driving function usage time length can be divided into two implementation modes:

[0100] The first is to limit the single use time length, the system will start the countdown when the driver activates the restricted second intelligent driving function each time, for example, it is set that the single continuous use of the automatic lane changing function cannot exceed 30 minutes, and the time is expired, the warning is triggered (visual prompt first, then voice reminder, and finally tactile feedback), and the function is forced to exit, and the function can be re-enabled after at least 10 minutes, this way is suitable for preventing the problem of attention distraction caused by long-term dependence on intelligent driving;

[0101] The second is to limit the total use time length, the system will count the cumulative time of the driver using the restricted function every day / week, for example, it is set that the highway navigation function is limited to 2 hours per day, when the usage time reaches 80% (or other threshold) of the pre-set threshold, the car machine status bar will display an orange warning, and when it reaches 100%, it is automatically switched to the basic cruise mode and cannot be restored on the same day, this restriction mode is mainly used to control the over-dependence of the driver on specific high-level functions.

[0102] Of course, in the embodiments of the present application, the determination of the intelligent driving evaluation level of the driver can be performed periodically, that is, the use permission of different intelligent driving functions can be adjusted based on the actual driving performance of the driver every period of time. In some cases, the driver can also actively apply for evaluation according to his own needs to obtain more use permissions of rich intelligent driving functions. In this way, different driving needs and levels can be flexibly adapted.

[0103] In the embodiments of the present application, a vehicle intelligent driving permission control device is also provided, and the device comprises:

[0104] An acquisition unit is configured to acquire identity information of a driver of a target vehicle, query a first intelligent driving evaluation level of the driver and historical driving data of a predetermined historical period before a current time point according to the identity information, wherein the historical driving data comprises at least one of intelligent driving record data, self-driving record data, emergency handling data and vehicle driving parameters, the first intelligent driving evaluation level corresponds to a first permission range, and the first permission range is used to indicate use permissions of a plurality of intelligent driving functions.

[0105] An evaluation unit is configured to evaluate and determine a second intelligent driving evaluation level of the driver at the current time point according to the historical driving data, wherein the second intelligent driving evaluation level corresponds to a second permission range, and the second permission range is used to indicate use permissions of a plurality of intelligent driving functions.

[0106] An opening unit is configured to open use permissions of first intelligent driving functions to the driver if the second intelligent driving evaluation level is higher than the first intelligent driving evaluation level, wherein the first intelligent driving functions are intelligent driving functions included in the second permission range and not included in the first permission range.

[0107] A limiting unit is configured to limit use time lengths of second intelligent driving functions by the driver if the second intelligent driving evaluation level is lower than the first intelligent driving evaluation level, wherein the second intelligent driving functions are intelligent driving functions included in the first permission range and not included in the second permission range in terms of use permissions.

[0108] Reference Figure 3 The embodiments of the present application provide an electronic device, which comprises:

[0109] at least one processor 310;

[0110] at least one memory 320 for storing at least one program;

[0111] When the at least one program is executed by the at least one processor 310, the at least one processor 310 implements the above-mentioned vehicle intelligent driving permission control method.

[0112] Similarly, the contents in the method embodiments are applicable to the electronic device embodiments, the electronic device embodiments specifically implement the same functions as the method embodiments, and achieve the same beneficial effects as the method embodiments.

[0113] The embodiment of the present application further provides a computer readable storage medium, wherein the computer readable storage medium stores a program executable by the processor 310, and the program executable by the processor 310 is used for executing the vehicle intelligent driving permission regulation method.

[0114] Similarly, the contents in the method embodiments are applicable to the computer readable storage medium embodiments, the computer readable storage medium embodiments specifically implement the same functions as the method embodiments, and achieve the same beneficial effects as the method embodiments.

[0115] The embodiment of the present application further provides a computer program product, the computer program product comprises a computer program, the computer program is stored in a computer readable storage medium, a processor of a computer device reads the computer program from the computer readable storage medium, and the processor executes the computer program, so that the computer device executes the vehicle intelligent driving permission regulation method.

[0116] In some alternative embodiments, the functions / operations mentioned in the block diagram can not occur in the order mentioned in the operation diagram. For example, depending on the functions / operations involved, two blocks shown in succession can actually be executed substantially simultaneously or the blocks can sometimes be executed in reverse order. In addition, the embodiments presented and described in the flowcharts of the present application are provided by way of example, and the purpose is to provide a more comprehensive understanding of the technology. The disclosed method is not limited to the operations and logical flows presented herein. Alternative embodiments are contemplated in which the order of various operations is changed and in which sub-operations described as part of larger operations are independently executed.

[0117] Furthermore, although the present application is described in the context of functional modules, it is understood that one or more of the functions and / or features can be integrated in a single physical device and / or software module, or one or more functions and / or features can be implemented in separate physical devices or software modules. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary to an understanding of the present application. Rather, the actual implementation is within the routine skill of engineers familiar with the property, function and internal relationships of the various functional modules disclosed herein. Accordingly, the present application is not limited to the specific details of the functional modules described herein. Rather, it is understood that one of ordinary skill in the art is able to practice the application as claimed without undue experimentation having regard to the property, function and internal relationships of the various functional modules disclosed herein. It is also understood that the specific concepts disclosed are merely illustrative and that the scope of the present application is determined by the appended claims and their equivalents.

[0118] If the functions are implemented in software, the functions can be stored in or implemented as one or more computer program products. The computer program product can be stored in a computer readable medium, which can include, but is not limited to, RAM, ROM, electrically programmable ROM (EPROM or EEPROM), flash memory, or a magnetic or optical card, or any suitable device used for storing a computer program. Furthermore, the computer program product can be implemented as at least one program that runs on a computer, which can be a personal computer, a server, a network device, or any suitable device capable of executing a program.

[0119] The logic and / or steps represented in the flowcharts and / or otherwise described herein, for example, can be embodied in non-transitory computer-readable media, which can be executed by an instruction execution system, apparatus, or device such as a computer-based system, processor, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. In the context of this specification, a "computer-readable medium" can be any means that can contain, store, communicate, propagate or transport the program for use by or in connection with the instruction execution system, apparatus, or device.

[0120] More specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electrical connection (electronic) having one or more wires, a portable computer diskette (magnetic), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can also be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, for example, via optical scanning of the paper or other medium, then compiled, interpreted, or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.

[0121] It should be understood that aspects of the application can be implemented in hardware, software, firmware or combinations thereof. In the above described embodiments, various steps or methods can be implemented in software or firmware that is stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any of the following technologies, known in the art, or combinations thereof, can be used: a discrete logic circuit having logic gates for implementing logic functions upon data signals, an application specific integrated circuit having appropriate combinational logic gates, a programmable gate array (PGA), a field programmable gate array (FPGA), and / or the like.

[0122] In the above description of the present specification, reference to the description of the terms "one embodiment / one example", "another embodiment / another example" or "certain embodiments / certain examples" and the like means that a particular feature, structure, material or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. The illustrative appearances of the above described terms in the present specification are not necessarily referred to the same embodiment or example. Also, the particular features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0123] Although embodiments of the present application have been shown and described, it would be recognized by those of ordinary skill in the art that various changes, modifications, alternatives, and variations can be made thereto without departing from the spirit and scope of the application, which should be limited only by the scope of the claims and the equivalents thereof.

[0124] The above is a specific description of the preferred embodiments of the present application, but the present application is not limited to the embodiments, and those skilled in the art can make various equivalent modifications or replacements without departing from the spirit of the present application, and these equivalent modifications or replacements are all included in the scope defined by the claims of the present application.

Claims

1. A method for regulating vehicle intelligent driving authority, characterized in that: The method comprises: Obtaining identity information of a driver of a target vehicle, and querying, based on the identity information, a first intelligent driving assessment level of the driver and historical driving data for a predetermined historical period before a current time point; wherein the historical driving data includes at least one of intelligent driving record data, self-driving record data, emergency handling data, and vehicle driving parameters; the first intelligent driving assessment level corresponds to a first permission range, and the first permission range is used to indicate permission to use a plurality of intelligent driving functions; Determining, based on the historical driving data, a second intelligent driving assessment level corresponding to the driver at a current time point; wherein the second intelligent driving assessment level corresponds to a second permission range, and the second permission range is used to indicate permission to use a plurality of intelligent driving functions; If the second intelligent driving assessment level is higher than the first intelligent driving assessment level, granting the driver access to a first intelligent driving function; wherein the first intelligent driving function is an intelligent driving function included in the second permission scope but not included in the first permission scope; If the second intelligent driving assessment level is lower than the first intelligent driving assessment level, the driver's usage time of the second intelligent driving function is limited; wherein, the second intelligent driving function is an intelligent driving function whose usage authority is included in the first authority scope and not included in the second authority scope.

2. The method for controlling vehicle intelligent driving authority according to claim 1, characterized in that: The obtaining of identity information of the driver of the target vehicle and querying the first intelligent driving assessment level of the driver according to the identity information includes: Obtain the identity information of the driver of the target vehicle, and query whether the driver has learned the intelligent driving function based on the identity information; If it is determined that the driver has performed intelligent driving function learning, the most recent intelligent driving evaluation level of the driver determined from the current time point is obtained as the first intelligent driving evaluation level.

3. The method for controlling vehicle intelligent driving authority according to claim 2, characterized in that: The method further comprises: If it is determined that the driver has not learned the intelligent driving function, push the channel entrance for learning the intelligent driving function to the driver; In response to the driver completing the task of performing the intelligent driving function learning, the driver's intelligent driving evaluation level is determined to be an initial intelligent driving evaluation level.

4. The method for controlling vehicle intelligent driving authority according to claim 2, characterized in that: The step of obtaining the identity information of the driver of the target vehicle includes: The driver's identity information is obtained through at least one of biometric identification, a digital account, or a physical credential.

5. The method for controlling vehicle intelligent driving authority according to claim 1, characterized in that: The evaluating and determining, based on the historical driving data, a second intelligent driving assessment level corresponding to the driver at a current time point, includes: Get pre-built driving ability assessment models; The historical driving data is input into the driving ability evaluation model, and the driver's intelligent driving ability is evaluated based on the historical driving data to obtain a second intelligent driving evaluation level corresponding to the driver at the current time point.

6. A method for controlling vehicle intelligent driving authority according to claim 5, characterized in that: The driving ability assessment model is built by the following steps: Obtaining a training data set; wherein the training data set includes sample driving data corresponding to a number of sample persons and grade labels, the sample driving data including at least one of intelligent driving record data, self-driving record data, emergency handling data, and vehicle driving parameters of the sample persons, and the grade labels are used to represent the standard expected results of the intelligent driving assessment grade corresponding to the sample persons; Inputting the sample driving data into an initialized driving ability evaluation model, evaluating the intelligent driving ability of the sample person using the sample driving data, and obtaining a predicted intelligent driving evaluation level corresponding to the sample person; Determining a training loss value according to the predicted intelligent driving evaluation level and the level label; The parameters of the driving ability evaluation model are updated according to the loss value to obtain a trained driving ability evaluation model.

7. A method for controlling vehicle intelligent driving authority according to any one of claims 1 to 6, characterized in that: The limiting of the time period for the driver to use the second intelligent driving function includes: Limit the single usage duration of the second intelligent driving function by the driver; or limit the total usage duration of the second intelligent driving function by the driver.

8. A vehicle intelligent driving authority control device, characterized in that: The device comprises: an acquisition unit, configured to acquire identity information of a driver of a target vehicle, and query, based on the identity information, a first intelligent driving assessment level of the driver and historical driving data for a predetermined historical period prior to a current time point; wherein the historical driving data includes at least one of intelligent driving record data, self-driving record data, emergency handling data, and vehicle driving parameters; the first intelligent driving assessment level corresponds to a first permission range, and the first permission range is used to indicate permission to use a plurality of intelligent driving functions; an evaluation unit, configured to evaluate and determine a second intelligent driving evaluation level corresponding to the driver at a current point in time based on the historical driving data; wherein the second intelligent driving evaluation level corresponds to a second permission range, and the second permission range is used to indicate permission to use a plurality of intelligent driving functions; an opening unit, configured to open permission for the driver to use a first intelligent driving function if the second intelligent driving assessment level is higher than the first intelligent driving assessment level; wherein the first intelligent driving function is an intelligent driving function included in the second permission scope but not included in the first permission scope; A restriction unit is used to limit the driver's usage time of a second intelligent driving function if the second intelligent driving assessment level is lower than the first intelligent driving assessment level; wherein the second intelligent driving function is an intelligent driving function included in the first authority scope and not included in the second authority scope.

9. An electronic device, characterized in that: include: at least one processor; at least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements a method for regulating vehicle intelligent driving authority as described in any one of claims 1-7.

10. A computer-readable storage medium storing a program executable by a processor, characterized in that: The program executable by the processor is used to implement a method for controlling vehicle intelligent driving authority as described in any one of claims 1 to 7 when executed by the processor.

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