CN116080524A - Vehicle reminding method, system, electronic equipment, vehicle and computer program product - Google Patents

Abstract

The embodiment of the application provides a vehicle reminding method, a system, electronic equipment, a vehicle and a computer program product. Wherein, the method is suitable for vehicles with intelligent driving function, and the method comprises the following steps: determining the function execution capacity of the intelligent driving function under the current use scene; determining a reminding strategy according to the function execution capacity; acquiring driving behavior data related to a user from the acquired driving data information of the vehicle; and prompting the user according to the prompting strategy based on the driving behavior data. According to the technical scheme provided by the embodiment of the application, the intelligent driving function is combined to remind the user according to the function execution capacity of the corresponding use scene, and the moderate and flexible adjustment reminding mode is realized on the premise of ensuring safe driving, so that the user can truly feel the convenience brought by intelligent driving and the riding experience of the whole user is improved.

Description

Vehicle reminder method, system, electronic device, vehicle and computer program product

technical field

The present application relates to the technical field of intelligent vehicles, in particular to a vehicle reminder method, system, electronic equipment, vehicle and computer program product.

Background technique

With the development of intelligent vehicles, vehicles with intelligent driving functions (ie, intelligent driving functions) are more and more popular. The smart driving function can reduce the user's driving stress and assist the user to prevent or reduce the risks encountered during driving.

In order to ensure the safety of smart driving, corresponding reminder devices are often set on the vehicle for the smart driving function. At present, most of the reminder devices are only based on the driver's driving behavior, fatigue and other information monitored by the vehicle, and adopt an excessive and fixed mode of reminder to remind and warn the user, making the user's overall smart driving function experience poor.

Contents of the invention

In view of the problems described in the background art above, the present application provides a vehicle reminder method, system, electronic device, vehicle and computer program product that solve the above problems or at least partially solve the above problems.

In one embodiment of the present application, a vehicle reminder method is provided. The method is suitable for vehicles with smart driving functions, and the method includes:

Determine the function execution capability of the smart driving function in the current usage scenario;

Determine the reminder strategy according to the function execution capability;

Obtaining driving behavior data related to the user from the collected driving data information of the vehicle;

Based on the driving behavior data, the user is reminded according to the reminder policy.

In another embodiment of the present application, a vehicle reminder system is also provided. The system includes:

The collection device is used to collect the driving data of the vehicle; from the collected driving data information of the vehicle, obtain the driving behavior data related to the user; send the driving behavior data to the interaction device

A capability determination device, configured to determine the function execution capability of the intelligent driving function of the vehicle in the current usage scenario, and send the function execution capability to the interaction device;

The interaction device is configured to determine a reminder strategy according to the function execution capability; and to remind the user according to the reminder strategy based on the driving behavior data.

In yet another embodiment of the present application, an electronic device is also provided. The electronic device includes: a memory and a processor, wherein the memory is used to store one or more computer programs; the processor is coupled to the memory and is used to execute the one or more computer programs stored in the memory. A plurality of computer programs are used to implement the steps in the vehicle reminder method provided by the embodiment of the present application.

Another embodiment of the present application further provides a vehicle, the vehicle includes a vehicle body and the electronic device provided in the embodiment of the present application, and the electronic device is arranged on the vehicle body.

In yet another embodiment of the present application, a computer program product is also provided. The computer program product includes a computer program/instruction, and when the computer program/instruction processor is executed, the steps in the vehicle reminding method provided by the embodiment of the present application can be realized.

The technical solutions provided by the various embodiments of the present application, on the basis of determining the function execution capability of the intelligent driving function on the vehicle in the current usage scenario, can determine the reminder strategy according to the determined function execution capability; further, according to the The driving behavior data related to the user is obtained from the collected driving data information of the vehicle, and the user is reminded according to the reminder strategy. This solution combines the function execution ability of the smart driving function in the corresponding usage scenario to remind the user. On the premise of ensuring safe driving, the reminder mode can be adjusted appropriately and flexibly, which will help users truly feel the convenience brought by smart driving and Improve the user's vehicle ride experience.

Description of drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the accompanying drawings that need to be used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the accompanying drawings in the following description These are some embodiments of the present application. Those skilled in the art can also obtain other drawings based on these drawings without creative work.

FIG. 1 is a schematic flowchart of a vehicle reminder method provided by an embodiment of the present application;

FIG. 2 is a relationship diagram between the index test value of the performance index provided by an embodiment of the present application and the scene test parameters of the target preset use scene;

Fig. 3a is a schematic structural diagram of a vehicle reminder system provided by an embodiment of the present application;

Fig. 3b is a schematic structural diagram of a vehicle reminder system provided by another embodiment of the present application;

Fig. 4 is a schematic structural diagram of a vehicle reminder device provided by an embodiment of the present application;

FIG. 5 is a schematic structural diagram of an electronic device provided by an embodiment of the present application;

Fig. 6 is a schematic structural diagram of a vehicle provided by an embodiment of the present application;

FIG. 7 is a schematic structural diagram of a computer program product provided by an embodiment of the present application.

Detailed ways

With the continuous development of intelligent driving technology, more and more vehicles are equipped with some intelligent driving systems, such as assisted driving systems (such as lane keeping assist system, automatic parking assist system, brake assist system, reversing assist system, etc.), Partially automated driving system, highly automated driving system and fully automated driving system, etc., the vehicle can realize the corresponding intelligent driving function through the intelligent system mounted on it, so that the vehicle can drive automatically. The so-called smart driving system (smart driving function) is mainly based on the preset automatic control programs such as artificial intelligence and visual computing on the vehicle. Environmental information such as obstacles, weather conditions, etc., allows the vehicle to control the steering and speed of the vehicle without human operation, so that the vehicle can drive safely and reliably on the road.

The execution capability of the smart driving function of the smart driving system is not static, and it will change with the changes in the driving environment such as the geographical location of the vehicle, traffic environment, weather, and network conditions. For example, in the driving environment in rainy and snowy weather, due to the influence of factors such as rain and snow, the environmental images collected by the sensors on the vehicle, such as obstacles and driving roads around the vehicle, often appear blurred, resulting in a decrease in recognition accuracy, which also It will lead to a reduction in the execution ability of intelligent driving functions such as intelligent obstacle avoidance and lane keeping on the vehicle, such as obstacles that cannot be successfully avoided. From the above, in order to maintain the execution ability of the intelligent driving function, the user's participation needs to be improved at any time when the execution ability of the intelligent driving function decreases, so as to ensure that the human-vehicle system can respond in a timely manner in the face of emergencies. Ensure driving safety.

At present, in order to ensure the driving safety in the process of smart driving, the vehicle will also be equipped with a monitoring system to monitor the driving behavior, fatigue and other information of the user (driver), and based on the information related to the user detected by the monitoring system Information, using excessive and fixed-mode reminders to remind users, the reminder logic is not linked to the actual execution ability of the smart driving function. In practice, with the continuous improvement of intelligent driving technology, in the case that the execution ability of the intelligent driving function of some intelligent driving systems can fully handle the corresponding driving tasks, the moderate reduction of user participation will not affect the driving safety of the vehicle. However, the above-mentioned over-frequent and fixed-mode reminder method is still used to remind and warn the user, which will reduce the entire driving experience of the user. In addition, the current broadcast strategy for intelligent driving information (such as PNC (Planning and Control, planning and control) decision information, traffic status information, etc.) on vehicles is also relatively simple.

In order to solve the above problems, various embodiments of the present application provide a new vehicle reminder technical solution. The vehicle reminder technical solution provided by this application can bring users an overall better driving experience by linking the vehicle reminder logic with the execution capability of the intelligent driving function.

In order to enable those skilled in the art to better understand the solutions of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

Some processes described in the specification, claims, and above-mentioned drawings of the present application contain multiple operations appearing in a specific order, and these operations may not be executed in the order in which they appear herein or executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. Additionally, these processes can include more or fewer operations, and these operations can be performed sequentially or in parallel. It should be noted that the descriptions of "first" and "second" in this article are used to distinguish different messages, devices, modules, etc. are different types. However, the term "or/and" in this application is only an association relationship describing associated objects, which means that there may be three relationships, such as: A or/and B, which means that A can exist alone, and A and B can exist simultaneously. There are three cases of B; the character "/" in this application generally indicates that the contextual objects are an "or" relationship. It should also be noted that the term "comprises", "comprises" or any other variation thereof is intended to cover a non-exclusive inclusion such that a good or system comprising a set of elements includes not only those elements but also includes items not expressly listed. other elements of the product, or elements inherent in the commodity or system. Without further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the article or system comprising said element. In addition, the following embodiments are only some of the embodiments of the present application, not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts belong to the scope of protection of this application.

Before introducing the technical solutions provided by the embodiments of the present application, some nouns or terms involved in the following will be described in detail.

Intelligent driving function refers to the driving function that can be realized by the intelligent driving system on the vehicle. The intelligent driving system on the vehicle can be but not limited to: assisted driving system (such as lane keeping assist system, automatic parking assist system, brake assist system, reversing assist system, obstacle avoidance system, etc.), partially automated driving system, highly automated Driving systems and fully automated driving systems, etc. Correspondingly, the intelligent driving function of the vehicle may be, but not limited to: assisted driving function (such as lane keeping function, automatic parking function, obstacle avoidance function, etc.), partially automated driving function, fully automated driving function, etc.

The functional execution ability of the intelligent driving function refers to the ability to complete the corresponding goal. For example, taking the automatic parking function as an example, the function execution capability of the automatic parking function may refer to the ability of the vehicle to complete automatic parking.

The usage scenario of the smart driving function refers to the environment where the smart driving function is used (or it can also be understood as the driving scene of the vehicle). The possible use scenarios of the smart driving function can be divided by combining different types of factors that affect the smart driving function. For example, according to the weather, the usage scenarios can include rainy weather, snowy weather, light, windy weather, etc.; according to the driving road surface, the usage scenarios can include road slope scenarios, road attachment scenarios, etc.; Including network dependence scenarios, network signal coverage scenarios (specifically, scenarios where mobile network signals (such as 4G, 5G) cover road sections (more specifically, scenarios where HD (High Definition, high-definition voice calls) covers road sections), mobile network signal coverage scenarios Point scenarios (more specifically, such as HD coverage road section breakpoint scenarios); divided by positioning, usage scenarios can include positioning scenarios (specifically, such as positioning accuracy and robustness scenarios), map coverage area scenarios (specifically, such as high-precision map coverage regional scene); etc. Each of the above use scenarios has corresponding scene parameters. Focus (represents the adhesion between the tire and the road surface, the adhesion is the product of the vehicle weight and the road adhesion coefficient, the adhesion coefficient is related to the speed of the vehicle and the degree of slippage (including slip and slip) of the wheel on the road surface), the scene parameters of the network signal coverage scene It can be signal strength, signal coverage, number of breakpoints, etc., and the scene parameter of the network-dependent scene can be the degree to which the smart driving function depends on the network.

The functional execution limit of the smart driving function in a usage scenario may refer to the scenario parameter limit for the smart driving function to complete the corresponding goal in a usage scenario, and the completion status can meet the requirements of the corresponding performance indicators. For example, if the intelligent driving function is an obstacle avoidance function, the functional execution limit of the obstacle avoidance function in the lighting scene can refer to the intelligent driving function completing the obstacle avoidance in the lighting scene, and the completion status of the obstacle avoidance can satisfy the obstacle avoidance function. The limit of light intensity required by performance indicators such as reliability and obstacle avoidance success rate (for example, the lowest light intensity).

Fig. 1 shows a schematic flowchart of a vehicle reminder method provided by an embodiment of the present application, which is applicable to vehicles with intelligent functions, and the vehicles may be, but not limited to: pure electric vehicles, fuel vehicles, and hybrid vehicles. During specific implementation, the execution subject of the vehicle reminder method provided in this embodiment can be a component/device with logic calculation and analysis functions on the vehicle, for example, it can be a vehicle control unit (Vehicle control unit, VCU), an electronic control unit of the vehicle, etc. Unit (Electronic Control Unit, ECU), etc. As shown in Figure 1, the vehicle reminder method includes the following steps:

101. Determine the function execution capability of the smart driving function in the current usage scenario;

102. Determine a reminder strategy according to the function execution capability;

103. Obtain driving behavior data related to the user from the collected driving data information of the vehicle;

104. Based on the driving behavior data, remind the user according to the reminding policy.

During the driving process of the vehicle, the vehicle's control device (such as VCU) can obtain the driving data information of the vehicle in real time through the sensors and interactive devices on the vehicle. The driving data information may include but not limited to: vehicle-related data, such as vehicle data (such as gear position, vehicle door status, vehicle window status, vehicle network status, vehicle navigation data, accelerator pedal, driving speed, current driving mode of the vehicle (manual driving) , assisted driving, automatic driving, etc.), etc.), external environmental data (such as road information, weather conditions, lighting information, etc.); user-related driving behavior data, for example, the user in the vehicle (such as the driver) through the interactive device interactive information (such as setting reminder style (broadcasting style), controlling air-conditioning temperature, seat temperature, calling multimedia applications, etc.), seat belt status, seat position status, hands-free driving, manual driving, and user images collected by the on-board camera Analyzed user driving attention, fatigue status, etc. It should be noted that the above-mentioned driving behavior data related to the user is collected and obtained when the user authorizes it, and is not collected and obtained when the user is not authorized. According to the driving data information obtained above, more specifically, such as the vehicle-related data contained in the driving data information, the current use scene of the vehicle can be analyzed and the scene parameters of the current use scene of the vehicle can be determined, so as to determine the intelligence The functional execution ability corresponding to the driving function. Based on

In an achievable technical solution, the above 101 "determining the function execution capability of the smart driving function in the current usage scenario" may specifically include:

1011. Determine the scene parameters of the current usage scene according to the vehicle-related data included in the driving data information;

1012. Obtain the function execution limit of the smart driving function in the current usage scenario;

1013. Compare the scene parameter with the function execution limit to determine the function execution capability of the smart driving function in the current use scene.

For the relevant introduction of vehicle-related data in the above 1011, please refer to the relevant content above. By analyzing vehicle-related data, it is possible to analyze the current use scene of the vehicle and the corresponding scene parameters.

For example, if the vehicle-related data includes the capacitance value of the capacitive rain sensor on the vehicle (the capacitance value changes with the amount of rain), the image of the environment outside the vehicle (including raindrops), the weather information sent by the cloud (such as heavy rain), etc., through the above data When the rain is analyzed, it can be determined that the current usage scene is a rainy scene; further, the capacitance value of the capacitive rain sensor, or the shape and size of the raindrops (or the density of raindrops) included in the recognized image of the external environment of the vehicle can be determined. ), etc., as the scene parameters of the rainy scene.

For another example, if the vehicle-related data includes the capacitance value of the capacitive rain sensor on the vehicle, the voltage of the photosensitive probe in the photosensitive sensor (used to detect the ambient light intensity outside the vehicle) (because the resistance value of the photosensitive probe changes with the light intensity), , correspondingly, its voltage also changes with the change of light intensity), the image of the environment outside the car, etc., through the analysis of the above data, it can be determined that the current use scene is a light scene; further, the photosensitive probe can be used The voltage, or the brightness and darkness of the recognized image of the environment outside the vehicle, etc., are used as the scene parameters of the lighting scene (that is, the light intensity corresponding to the lighting scene).

It should be noted that by analyzing vehicle-related data, it can be determined that the current usage scenario of the smart driving function is one usage scenario or multiple (two or more) usage scenarios, which are not limited here.

In the above 1012, the function execution limit of the smart driving function in the current usage scenario can be queried from the corresponding relationship information of multiple preset usage scenarios and the function execution limit of the smart driving function in multiple preset usage scenarios Obtain. That is, in a specific achievable solution, the above-mentioned 1012 "obtaining the function execution limit of the smart driving function in the current usage scenario" can be implemented by specifically adopting the following steps:

10121. Establish corresponding first correspondence information in advance for multiple preset usage scenarios; wherein, the first correspondence information includes multiple first correspondences, and one first correspondence includes a scene identifier of a preset usage scenario, The corresponding function execution limit of the smart driving function in the preset usage scenario;

10122. Based on the scene identifier of the current use scene, query the function execution limit corresponding to the current use scene from the first correspondence information.

In the above-mentioned 10121, the preset usage scene is preset in this embodiment in combination with some factors related to the smart driving function, such as the complexity of traffic conditions, weather, illumination, road surface, network, high-precision map coverage area, etc. . For specific usage scenarios that can be included in multiple preset usage scenarios, please refer to the specific introduction of the above-mentioned "Usage Scenarios" and related content, and details will not be repeated here. At the same time, for the preset usage scenarios, this embodiment also presets a number of performance indicators in advance, so as to evaluate the performance of the smart driving function when performing experimental test analysis on the smart driving function in multiple preset usage scenarios . During the specific implementation, considering that the performance of the smart driving function in practice is mainly evaluated from the two aspects of function possibility and function realization accuracy, the multiple performance indicators preset in this embodiment are mainly divided into two categories , that is, the functional reliability index and the functional accuracy index; among them, the functional reliability index may include but not limited to unexpected acceleration and deceleration frequency, non-immediate acceleration and deceleration frequency, planned lane change success rate, hands-off detection miss rate , parking success rate, obstacle avoidance success rate, etc. Functional accuracy indicators may include but are not limited to functional response closed-loop time, vehicle speed range and accuracy, lane change execution time, obstacle avoidance opening accuracy, and detour execution time, etc. Table 1 below exemplarily shows some preset usage scenarios and performance indicators.

Table 1

The above-mentioned first correspondence information can be obtained by conducting a large number of experimental tests and analyzing test data of the smart driving function in multiple preset usage scenarios. Based on this, in the case that the target preset usage scenario is one of the plurality of preset usage scenarios, correspondingly, in the above-mentioned 10121, "establish a corresponding first corresponding relationship in advance for the target preset usage scenario A possible implementation of " may include the following steps:

S11. Obtain multiple sets of test data of the smart driving function in the target preset usage scenario, wherein a set of test data includes scene test parameters of the target preset usage scenario, and index tests of multiple performance indicators preset for the smart driving function value;

S12. According to the multiple sets of test data, determine at least one target performance indicator affected by the target preset usage scenario from the multiple performance indicators;

S13. Based on the at least one target performance index, determine a corresponding function execution limit for the smart driving function in the target preset usage scenario;

S14. Establish a first corresponding relationship between the scene identifier of the target preset usage scenario and the function execution limit of the smart function in the target preset usage scenario.

In the above S11-S12, by analyzing the index test values of multiple performance indicators, the range of change produced by the change of the scene test parameters of the target preset usage scenario can be determined to determine the performance indicators affected by the target preset usage scenario. target performance metrics.

For example, as shown in Figure 2, it is assumed that multiple performance indicators include: performance indicator ind1, performance indicator ind2, performance indicator ind3, performance indicator ind4, and performance indicator ind5; increasing, the test values of the performance indicators ind1 and ind2 show a gradual upward trend first and then a gradual downward trend after reaching the peak, while the test values of the performance indicator ind3 first show a gradual downward trend and then show a trend Gradually rising trend, the performance index ind4 and performance index ind5 fluctuate slightly above and below the test value of a certain index. It can be seen from the above that with the gradual increase of the scene test parameters of the target preset usage scenarios, the respective index test values of performance indicators ind1, The respective index test values of the performance index ind5 and the performance index ind5 only fluctuate within a small range, so it can be determined that the performance index ind1, the performance index ind2 and the performance index ind3 are mainly affected by the target preset usage scenarios and are the target performance indicators.

In the example given above, if the target preset use scene is a lighting scene, the performance index ind1, performance index ind2, and performance index ind3 can be, but not limited to: the success rate of obstacle avoidance, the accuracy of whether the obstacle avoidance is enabled, Execution time around obstacles.

In the above S13-S14, by scoring at least one determined target performance index, the score of each target performance index can be obtained according to the scoring to determine the corresponding function execution limit of the smart driving function in the target preset usage scenario, so that A first corresponding relationship between the scene identifier of the target preset usage scenario and the function execution limit of the smart function in the target preset usage scenario is established. In addition, because different performance indicators are of different importance to the intelligent driving function, for example, following the above example, at least one target performance indicator determined includes: success rate of obstacle avoidance, accuracy of whether obstacle avoidance is enabled, obstacle avoidance execution time , the intelligent driving function is the obstacle avoidance function as an example. For the obstacle avoidance function, the success rate of the obstacle avoidance and the accuracy of whether the obstacle avoidance is turned on are often more concerned, while the execution time of the obstacle avoidance is less concerned. Therefore, when determining the execution limit of the corresponding function of the smart driving function in the target preset usage scenario, it can further be determined in combination with the weight coefficient assigned to the target performance indicator according to the importance of the target performance indicator relative to the target preset usage scenario .

Based on the above content, in a specific achievable technical solution, the above S13 "determine the corresponding function execution limit for the smart driving function in the target preset usage scenario based on the at least one target performance index", which can be adopted as follows Steps to achieve:

S131. Determine a score corresponding to each target performance indicator according to a plurality of index test values corresponding to each target performance indicator in the at least one target performance indicator;

S132. Based on the importance of each target performance index relative to the smart driving function, determine the weight coefficient of each target performance index;

S133. Based on the weight coefficients and corresponding scores of each target performance index, determine a corresponding function execution limit for the smart driving function in the target preset usage scenario.

During specific implementation, various scoring methods such as expert scoring method, fuzzy scoring method, and analytic hierarchy process scoring method can be used to score each target performance index to obtain the corresponding score of each target performance index.

For example, assuming that a target performance index is the obstacle avoidance success rate, multiple index test values corresponding to the obstacle avoidance success rate can be compared with the preset obstacle avoidance success rate threshold (for example, to meet the requirements of the intelligent driving function (obstacle avoidance function) The lowest success rate of obstacle avoidance) is compared to determine the index test value greater than the threshold of the success rate of obstacle avoidance among the multiple index test values; The ratio of the total number of index test values, and the product of the calculated ratio and the full score (such as 10 points) is used as the score of the obstacle avoidance success rate. That is, assuming that the number of the above-mentioned index test values greater than the obstacle avoidance success rate threshold is M, the total number of multiple index test values is N, and the full score is 10, then the score Score of the above-mentioned obstacle avoidance success rate is: (M/ N)*10.

After determining the score corresponding to each target performance index, a target score can be determined in combination with the weight coefficient assigned to each target performance index, so as to determine the corresponding value of the smart driving function in the target preset usage scenario according to the target score. Function execution bounds. For example, based on the correspondence information between the preset score and the scene parameter of the target preset use scene, the scene parameter of the target preset use scene corresponding to the target score can be determined as the corresponding function execution limit. Wherein, the above-mentioned weight coefficient assigned to each target performance index is determined based on the importance of each target performance index relative to the smart driving function, specifically, various weight coefficient determination methods such as the entropy method can be used for determination. For example, the scores of each target performance index can be used as the importance of each target performance index relative to the smart driving function, and then corresponding weights can be assigned to it. The more important the performance index is relative to the intelligent driving function, the greater the weight coefficient assigned to the target performance index will be. Based on the above content, in a possible embodiment, the above S133 "determine the corresponding function execution limit for the smart driving function in the target preset usage scenario based on the weight coefficient and the corresponding score of each target performance index", The following steps can be used to achieve this:

S1331. Based on the weight coefficient of each target performance index and the corresponding score, determine the target score that meets the requirements;

S1332. Acquire second correspondence information, wherein the second correspondence information includes a plurality of second correspondences, and one second correspondence includes a score and a scene parameter of the target preset use scene;

S1333. According to the second correspondence information, determine the scene parameter of the target preset use scene corresponding to the target score, as the corresponding value of the smart driving function in the target preset use scene Function execution bounds.

During specific implementation, each target performance index can be sorted according to the weight coefficient of each target performance index, specifically, each target performance index can be sorted according to the weight coefficient from large to small; then, the first ranked target can be The score of the performance (with the largest weight coefficient) index is used as the target score, or the average value of the scores of the first preset number (such as 2, 3) target performance indicators can be used as the target score . Alternatively, it is also possible to first calculate the product value of the weight coefficient of each target performance index and the corresponding score. If the sum of the weight coefficients of each target performance index is 1, the product corresponding to each target performance index can be directly calculated. The first sum of values is used as the target score; if the sum of the weight coefficients of each target performance index is not 1, in order to standardize, the first sum of the product values corresponding to each target performance index can be combined with each The quotient of the second sum of the weight coefficients of the target performance index is used as the target score, etc., which are not limited here.

Furthermore, in the second correspondence information, the scene parameters of the target preset use scene corresponding to the target score can be queried as the corresponding function execution limit of the smart driving function in the target preset use scene .

It should be added here that the corresponding score included in any one of the second correspondence information in the above-mentioned second correspondence information may be a specific score or a score range, which is not limited here. If it is a score range, on the basis of determining the score range to which the target score belongs, query the scene parameters of the target preset use scene corresponding to the target score in the second correspondence information. In addition, you can refer to the relevant content of establishing the corresponding first relationship information for the target preset usage scenario described above to establish Corresponding first correspondence information.

With the above content, based on the above-mentioned first correspondence information established in advance for multiple preset usage scenarios, the execution of the above step 10122 can be triggered to realize the scenario identification based on the current usage scenario, and query and There is a function execution limit corresponding to the current usage scenario, and the scene parameters of the current usage scenario are compared with the queried function execution limit to determine the degree of dependence of the smart driving function on the user based on the comparison result, so that the smart driving function is currently used. Ability to perform functions in context.

That is, in a specific achievable technical solution, the above-mentioned 1013 "comparing the scene parameters with the function execution limit to determine the function execution capability of the smart driving function in the current use scene" can specifically adopt the following steps to fulfill:

10131. Compare the scene parameters with the function execution limit, and determine the degree of dependence of the smart driving function on the user in the current use scene;

10132. Based on the dependency, determine the function execution capability of the smart driving function in the current usage scenario.

During specific implementation, the difference between the scene parameters of the current usage scenario and the corresponding function execution limit of the smart driving function in the current usage scenario can be used to determine the degree of dependence of the smart driving function on the user in the current usage scenario, thereby determining the smart driving function. Functional execution capability of a function in the current usage scenario. Among them, the larger the difference, the lower the dependence of the smart driving function on the user in the current usage scenario. Correspondingly, the stronger the function execution ability of the smart driving function in the current usage scenario, and the less need for user participation , and can also independently complete corresponding target tasks (such as circumventing obstacles).

For example, if the difference is much greater than zero, it can indicate that the smart driving function has a strong function execution ability in the current usage scenario, and can complete the corresponding target tasks independently without the user's participation in driving; if the difference is close to zero, it can indicate that the smart driving function is in the The function execution ability in the current usage scenario is relatively weak, and there may be situations where the corresponding target task cannot be completed independently, and the user needs to participate in driving in an appropriate amount (for example, the user needs to concentrate and pay attention to the front of the vehicle); if the difference is negative, It can be shown that the function execution ability of the smart driving function in the current usage scenario is extremely weak, and the corresponding target tasks cannot be completed independently, and the user needs to drive manually.

With the above content, in practice, the difference between the scene parameters of the above-mentioned current usage scenario and the corresponding function execution limit of the smart driving function in the current usage scenario can directly reflect the function execution ability of the smart driving function in the current usage scenario. For this reason, the difference can be directly used as the function execution ability of the smart driving function in the current usage scenario, and the corresponding reminder strategy can be determined according to the relationship between the function execution ability of the smart driving function in the current usage scenario and the preset threshold. Based on this, in an achievable technical solution, the above 102 "determine a reminder strategy based on the function execution capability" may specifically include any of the following:

Item 1. If the function execution capability is greater than or equal to the first threshold, the reminder policy is the first policy; the user is allowed to drive the vehicle out of the vehicle under the first policy;

Item 2. If the function execution ability is less than the first threshold and greater than or equal to the second threshold, then the reminder strategy is the second strategy; the second strategy requires the user to drive the vehicle with eyes-focused attention ;

Item 3. If the degree of dependence is less than the second threshold, the reminder policy is a third policy; under the third policy, the user is required to drive the vehicle manually.

During specific implementation, in order to cater to the reminder style set by the user and further improve the user's overall vehicle experience, the corresponding reminder strategy can also be determined in combination with the reminder style set by the user obtained from the collected vehicle driving data information. For example, if the reminder style set by the user is simple, the user may be reminded in a low-frequency manner.

Based on this, before performing the above 102, this embodiment may further include the following steps:

S21. Obtain the reminder style selected by the user from the driving data information;

S22. Trigger the operation of step 102 based on the reminder grid.

In the above 103-104, based on the driving behavior data related to the user obtained from the collected driving data information of the vehicle, the user may be reminded according to the determined reminder strategy. For a specific introduction to the driving behavior data related to the user, please refer to the relevant content above, and will not be repeated here.

For the relevant content of the above steps 102 to 104, take the current usage scene only including the lighting scene, the scene parameter of the lighting scene is the light value (light intensity), and the reminder style set by the user is detailed and simple as an example. Let’s give an example to explain The vehicle reminder scheme provided in this embodiment. specifically,

If the current illumination value is much greater than the function execution limit of the smart driving function in the lighting scene, for example, the difference between the current lighting value and the function execution limit of the smart driving function in the lighting scene is greater than the first threshold (as mentioned above. If the value is much greater than zero), it can indicate that the intelligent driving function has a strong function execution ability in the current lighting scene, and can independently complete the corresponding target task (such as avoiding obstacles). In this case, according to laws and regulations, users are allowed to If the user's current driving behavior data is hands-off driving, not wearing a seat belt, and not paying attention to the front of the car, you can follow the first reminder strategy with a low frequency (such as interval 1 minute, or 2 Minutes, etc.) only reminds the user of the driving behavior of "not wearing a seat belt", and does not remind the user of "driving without hands and not paying attention to the front of the car". For example, the following voice broadcast content can be output, "For the safety of the little master, please fasten your seat belt", or you can also output regular warning sounds such as "Didi".

If the current illumination value is close to the function execution limit of the smart driving function in the lighting scene, for example, the difference between the current lighting value and the function execution limit of the smart driving function in the lighting scene is less than the first threshold and greater than or equal to the second threshold (As mentioned above, when the difference is close to zero), it can indicate that the function execution ability of the intelligent driving function in the current lighting scene is weak, and it may not be possible to complete the corresponding target task independently. In order to ensure driving safety, the user needs to Participate in driving in an appropriate amount (for example, the user needs to concentrate and keep his eyes on the front of the vehicle), just in case he is ready to take over the intelligent driving for manual driving at any time. In this case, if the user's current driving behavior is hands-off driving Pay attention to the front of the car. According to the second reminder strategy, the user can be reminded only for the driving behavior of "not paying attention to the front of the car" with a relatively high frequency (such as intervals of 30 seconds, 20 seconds, etc.). For example, the following voice broadcast content can be output: "Smart driving function is not good, please pay attention to the front at all times" or corresponding regular warning sounds can be output. In addition, the user's seat belt can also be pre-tightened to warn the user.

If the current illumination value is lower than the function execution limit of the smart driving function in the lighting scene, for example, the difference between the current lighting value and the function execution limit of the smart driving function in the lighting scene is less than the second threshold (as mentioned above. If the value is not negative), it can indicate that the function execution ability of the intelligent driving function in the current lighting scene is very weak, and the corresponding target task cannot be completed independently, and the user is required to participate in manual driving. In this case, if the user's current driving behavior is hands-off driving, etc., the user can be reminded at a relatively high frequency according to the third reminder strategy to remind the user to manually drive the vehicle. For example, the following voice broadcast content can be output "smart driving The function cannot be executed, please drive the vehicle manually." In addition, it can also control the vibration of the steering wheel of the vehicle to warn the user.

In the above example, the reminder to the user, in addition to delivering smart driving information (function execution ability information of the smart driving function) to the user, can also play a role in improving the user's concentration, and the function execution ability of the smart driving function is sufficient, When the user chooses a simple reminder style (such as broadcast style), moderately reducing the reminder frequency of smart driving information can improve the user's overall vehicle experience.

What needs to be added here is that in the lighting scene, the larger the current lighting value, the better. If the light intensity is too strong, it may cause reflections and other phenomena in the collected environmental images outside the car, resulting in blurred images such as roads, pedestrians, objects, etc. Therefore, in the above example, the current Only when the illumination value is much greater than the function execution limit of the smart driving function in the lighting scene, but less than a certain lighting threshold, can it be determined that the smart driving function can independently respond to the target task in the current lighting scene.

In the technical solution provided by this embodiment, on the basis of determining the function execution capability of the smart driving function in the current usage scenario, the reminder strategy can be determined according to the determined function execution capability; further, according to the collected The driving behavior data related to the user is obtained from the driving data information of the vehicle, and the user is reminded according to the reminder strategy. This solution combines the function execution ability of the smart driving function in the corresponding usage scenario to remind the user. On the premise of ensuring safe driving, the reminder mode can be adjusted appropriately and flexibly, which will help users truly feel the convenience brought by smart driving and Improve the user's vehicle ride experience.

Based on the content of the method provided in the above application, an embodiment of the present application also provides a vehicle reminder system. Fig. 3a and Fig. 3b show a schematic structural diagram of a vehicle reminder system provided by the present application. As shown in Figure 3a and Figure 3b, the vehicle reminder system provided by this embodiment includes: a collection device 10, an analysis device 20 and an interaction device 30; wherein,

The collection device 10 is configured to collect driving data of the vehicle; obtain driving behavior data related to the user from the collected driving data information of the vehicle; send the driving behavior data to the interaction device;

A capability determination device 20, configured to determine the function execution capability of the intelligent driving function of the vehicle in the current usage scenario, and send the function execution capability to the interaction device;

The interaction device 30 is configured to determine a reminder strategy according to the function execution capability; and to remind the user according to the reminder strategy based on the driving behavior data.

During specific implementation, the above-mentioned collection device 10 may include a user (such as a driver) monitoring system set on the vehicle, a vehicle environment collection system, a vehicle data collection system, etc., wherein the monitoring system is mainly used to collect driving behaviors related to the user The main users of the data and vehicle-related data collection system collect vehicle-related data, and the main users of the vehicle-related environment collection system collect vehicle-related environmental data. For the specific data content that can be included in each of the above data, please refer to the relevant content above, which will not be repeated here. The capability determining device 20 described above is a function execution capability evaluation system (may be referred to as a smart driving capability evaluation system) of the intelligent driving function added to the vehicle in this embodiment. The interaction device 30 is an interaction system for intelligent driving functions (may be referred to as an intelligent driving information interaction system) provided on the vehicle.

What needs to be explained here is: the processing system provided by the above-mentioned embodiment can realize the technical solution described in the embodiment of the reminding method shown in FIG. content, which will not be repeated here. In addition, the vehicle reminder system provided by this embodiment may include other functional components in addition to the above-mentioned devices. etc. Based on the driving behavior data related to the user, the interaction device 30 can generate a corresponding reminder instruction according to the determined reminder strategy to send to the reminder execution component, so that the reminder execution component outputs reminder information according to the reminder instruction to remind the user. For example, the instruction type of the generated reminder instruction can be an acousto-optic feedback instruction, and the acousto-optic feedback instruction can be sent to the cockpit controller, and the cockpit controller outputs relevant voice broadcast content and regular warning sounds according to the acousto-optic feedback instruction. etc., or control the corresponding warning light to light up, output the light of the corresponding color, flash the light at a certain frequency, and so on. For another example, the instruction type of the generated reminder instruction can be an instruction to control the functional components on the vehicle body, and the instruction can be sent to the vehicle body controller, and the vehicle body controller controls the functional components on the vehicle body according to the received instruction, such as It can control the vibration of the steering wheel, control the tightening of the seat belt, control the closing of the car window, adjust the position of the seat, etc., which are not limited here.

FIG. 4 shows a schematic structural diagram of a vehicle reminder device provided by an embodiment of the present application, and the device is arranged on a vehicle suitable for intelligent driving functions. As shown in Figure 4, the vehicle reminder device provided in this embodiment includes: a determination module 21, an acquisition module 22 and a reminder module 23; wherein,

The determining module 21 is used to determine the function execution capability of the smart driving function in the current usage scenario; and is also used to determine a reminder strategy according to the function execution capability;

An acquisition module 22, configured to acquire driving behavior data related to the user from the collected driving data information of the vehicle;

The reminder module 23 is configured to remind the user according to the reminder strategy based on the driving behavior data.

Further, when the above-mentioned determination module 21 is used to determine the function execution capability of the smart driving function in the current usage scenario, it is specifically used to: determine the current usage The scene parameters of the scene; obtain the function execution limit of the smart driving function in the current use scene; compare the scene parameters with the function execution limit to determine the function execution of the smart driving function in the current use scene Capability; wherein, the vehicle-related data includes at least one item of vehicle data and environment data of the environment in which the vehicle is located.

Further, when the acquisition module 22 is used to acquire the function execution limit of the smart driving function in the current usage scenario, it is specifically configured to: establish first correspondence information for multiple preset usage scenarios in advance; wherein, The first correspondence information includes a plurality of first correspondences, and one first correspondence includes a scene identification of a preset use scene, and a function execution limit corresponding to the intelligent driving function in the preset use scene; based on the The scene identifier of the current use scene, querying the function execution limit corresponding to the current use scene from the first correspondence information.

Further, the aforementioned target preset usage scenario is one of the plurality of preset usage scenarios; In the case of a corresponding relationship, it is specifically used to: obtain multiple sets of test data of the smart driving function in the target preset usage scenario, wherein a set of test data includes the scene test parameters of the target preset usage scenario, and is used for the smart driving function. Index test values of multiple performance indicators; according to the multiple sets of test data, determine at least one target performance indicator affected by the target preset usage scenario from the multiple performance indicators; based on the at least one target Performance indicators, determining the corresponding function execution limit in the target preset usage scenario for the smart driving function; establishing the scene identification of the target preset usage scenario and the smart function in the target preset usage scenario The first correspondence of the functional execution boundaries of .

Further, when the above acquisition module 22 is used to determine the corresponding function execution limit for the smart driving function in the target preset usage scenario based on the at least one target performance index, it is specifically used to: according to the A plurality of index test values corresponding to each target performance index in at least one target performance index, determine the score corresponding to each target performance index; Coefficient; based on the weight coefficient and corresponding score of each target performance index, determine the corresponding function execution limit in the target preset usage scenario for the smart driving function

Further, when the above acquisition module 22 is used to determine the corresponding function execution limit for the smart driving function in the target preset usage scenario based on the weight coefficient and the corresponding score of each target performance index, it specifically uses In: based on the weight coefficients and corresponding scores of each target performance index, determine the target score that meets the requirements; obtain the second correspondence information; wherein, the second correspondence information includes a plurality of second correspondences, one first The two correspondences include a score and a scene parameter of the target preset use scene; according to the second correspondence information, determine the scene parameter of the target preset use scene corresponding to the target score , as the corresponding function execution limit of the smart driving function in the target preset usage scenario.

Further, when the above-mentioned determination module 21 is used to compare the scene parameters with the function execution limit to determine the function execution capability of the smart driving function in the current use scene, it is specifically used to: The scenario parameters are compared with the function execution limit to determine the degree of dependence of the smart driving function on the user in the current usage scenario; based on the dependency, the function execution capability of the smart driving function in the current usage scenario is determined.

Further, when the above-mentioned determining module 21 is used to determine the reminder strategy according to the function execution capability, it is specifically used to: if the function execution capability is greater than or equal to a first threshold, then the reminder strategy is the first Strategy; under the first strategy, the user is allowed to disengage the vehicle; if the function execution capability is less than the first threshold and greater than or equal to the second threshold, the reminder strategy is the second strategy; Under the second strategy, the user is required to focus on driving the vehicle; if the degree of dependence is less than the second threshold, the reminder strategy is the third strategy; under the third strategy, the user is required to pay attention to the vehicle. Do manual driving.

Further, the acquisition module 22 is also configured to acquire the reminder style selected by the user from the driving data information; based on the reminder style, trigger the operation of determining the reminder strategy according to the function execution capability.

What needs to be explained here is: the vehicle reminding device provided by the above-mentioned embodiment can realize the technical solution described in the embodiment of the vehicle reminding method shown in FIG. The corresponding content in the embodiment of the vehicle reminding method will not be repeated here.

To sum up, the technical solutions provided by the various embodiments of the present application have the following benefits: in the process of evolving to the level of fully automatic driving, the constraints on user behavior and participation and the logic of human-computer interaction should also be adjusted appropriately, which can avoid one size fits all, and can Let users truly feel the convenience and riding experience brought by intelligent driving, and enhance the highlights and reputation of the product.

FIG. 5 shows a schematic structural diagram of an electronic device provided by an embodiment of the present application. As shown in FIG. 4 , the electronic device includes: a memory 31 and a processor 32 . Wherein, the memory 31 is used to store one or more computer instructions; the processor 32, coupled with the memory 31, is used for one or more computer instructions (such as computer instructions for implementing data storage logic) for use in In order to realize the vehicle reminder method provided by the above-mentioned embodiment of the present application.

Memory 31 can be realized by any type of volatile or non-volatile storage device or their combination, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable Programmable Read Only Memory (EPROM), Programmable Read Only Memory (PROM), Read Only Memory (ROM), Magnetic Memory, Flash Memory, Magnetic or Optical Disk.

Further, as shown in FIG. 5 , the electronic device may further include: a communication component 33 , a power supply component 34 , an audio component 35 , a display 36 and other components. FIG. 5 only schematically shows some components, which does not mean that the electronic device only includes the components shown in FIG. 5 .

During specific implementation, the above-mentioned electronic devices may simply refer to devices on the vehicle, or may also refer to combined devices of the vehicle and the computer platform, which are not specifically limited here. Correspondingly, the processor 32 mentioned above may refer to the processor in the vehicle, or may also refer to the combination of the calculator in the vehicle and the processor corresponding to the computer platform, wherein the computer platform may include one or more processors. A processor is a circuit with signal processing capabilities. In one implementation, the processor may be a circuit with instruction reading and execution capabilities, such as a central processing unit (central processing unit, CPU), microprocessor, graphics processing Graphics processing unit (GPU) (which can be understood as a kind of microprocessor), or digital signal processor (digital signal processor, DSP), etc.; in another implementation, the processor can be realized through the logical relationship of the hardware circuit Certain functions, the logical relationship of the hardware circuit is fixed or reconfigurable, such as a hardware circuit implemented by a processor as an application-specific integrated circuit (ASIC) or a programmable logic device (PLD), such as Field programmable logic gate array (fieldprogrammablegatearray, FPGA). In a reconfigurable hardware circuit, the process of the processor loading the configuration file to realize the configuration of the hardware circuit can be understood as the process of the processor loading instructions to realize the functions of some or all of the above units. In addition, it can also be a hardware circuit designed for artificial intelligence, which can be understood as an ASIC, such as a neural network processing unit (neural network processing unit, NPU), tensor processing unit (tensor processing unit, TPU), deep learning processing Unit (deep learningprocessing unit, DPU), etc. In addition, the computing platform may further include a memory for storing instructions, and part or all of the processors may call the instructions in the memory and execute the instructions to implement corresponding functions.

It should be understood that the relevant operations of the vehicle reminder method provided in this embodiment may be executed by the same processor, or may also be executed by one or more processors, which is not specifically limited in this embodiment of the present application.

Correspondingly, the embodiment of the present application also provides a vehicle. Fig. 6 shows a schematic structural diagram of a vehicle provided by the present application. Referring to FIG. 5 , the vehicle 500 includes a vehicle body 41 and the electronic equipment (not shown in the figure) shown in FIG. 4 ; the electronic equipment is arranged on the vehicle body 41 .

The embodiment of the present application also provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a computer, the steps or functions in the vehicle reminder method provided by the above-mentioned embodiments can be realized.

The methods in this application may be fully or partially implemented by software, hardware, firmware or any combination thereof. When implemented using software, it may be implemented in whole or in part in the form of a computer program product. Fig. 7 schematically shows a block diagram of a computer program product provided by the present application. Said computer program product includes computer program/instruction 51, when said computer program/instruction 51 is executed by a processor such as processor 32 shown in FIG. Alert the flow or function in the method. The computer may be a general computer, a special computer, a computer network, a network device, a user device, a core network device, an OAM or other programmable devices.

The computer program or instructions may be stored in or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer program or instructions may be downloaded from a website, computer, A server or data center transmits to another website site, computer, server or data center by wired or wireless means. The computer-readable storage medium may be any available medium that can be accessed by a computer, or a data storage device such as a server or a data center integrating one or more available media. The available medium may be a magnetic medium, such as a floppy disk, a hard disk, or a magnetic tape; it may also be an optical medium, such as a digital video disk; or it may be a semiconductor medium, such as a solid state disk. The computer readable storage medium may be a volatile or a nonvolatile storage medium, or may include both volatile and nonvolatile types of storage media.

Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be realized by means of software plus a necessary general-purpose hardware platform, and of course also by hardware. Based on this understanding, the essence of the above technical solution or the part that contributes to the prior art can be embodied in the form of software products, and the computer software products can be stored in computer-readable storage media, such as ROM/RAM, magnetic discs, optical discs, etc., including several instructions to make a computer device (which may be a personal computer, server, or network device, etc.) execute the methods described in various embodiments or some parts of the embodiments.

Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, rather than limiting them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: it can still Modifications are made to the technical solutions described in the foregoing embodiments, or equivalent replacements are made to some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the various embodiments of the present application.

Claims (13)

1. A vehicle reminder method, characterized in that it is suitable for vehicles with intelligent driving functions, the method comprising: Determine the function execution capability of the smart driving function in the current usage scenario; Determine the reminder strategy according to the function execution capability; Obtaining driving behavior data related to the user from the collected driving data information of the vehicle; Based on the driving behavior data, the user is reminded according to the reminder policy. 2. The method according to claim 1, wherein determining the function execution capability of the smart driving function in the current usage scenario comprises: Determine the scene parameters of the current usage scene according to the vehicle-related data contained in the driving data information; Obtain the function execution limit of the smart driving function in the current usage scenario; Comparing the scene parameters with the function execution limit to determine the function execution capability of the smart driving function in the current usage scene; Wherein, the vehicle-related data includes at least one item of vehicle data and environment data of the environment in which the vehicle is located. 3. The method according to claim 2, wherein obtaining the function execution limit of the smart driving function in the current usage scenario comprises: Establishing first correspondence information in advance for a plurality of preset usage scenarios; wherein, the first correspondence information includes a plurality of first correspondences, and one first correspondence includes a scene identifier of a preset usage scenario, the smart driving The function execution limit corresponding to the function in the preset usage scenario; Based on the scene identifier of the current use scene, query the function execution limit corresponding to the current use scene from the first correspondence information. 4. The method according to claim 3, wherein the target preset usage scenario is one of the plurality of preset usage scenarios; Preset usage scenarios for the target, and establish a corresponding first corresponding relationship, including: Obtain multiple sets of test data of the smart driving function in the target preset usage scenario, wherein a set of test data includes scene test parameters of the target preset usage scenario, and index tests of multiple performance indicators preset for the smart driving function value; determining at least one target performance indicator affected by the target preset usage scenario from the plurality of performance indicators according to the plurality of sets of test data; Based on the at least one target performance index, determine a corresponding function execution limit for the smart driving function in the target preset usage scenario; Establishing a first corresponding relationship between the scene identifier of the target preset use scene and the function execution limit of the intelligent driving function in the target preset use scene. 5. The method according to claim 3, wherein, based on the at least one target performance index, determining a corresponding function execution limit for the smart driving function in the target preset usage scenario includes: Determining a score corresponding to each target performance index according to multiple index test values corresponding to each target performance index in the at least one target performance index; Based on the importance of each target performance index relative to the smart driving function, determine the weight coefficient of each target performance index; Based on the weight coefficient and corresponding score of each target performance index, determine the corresponding function execution limit in the target preset usage scenario for the intelligent driving function. 6. The method according to claim 5, characterized in that, based on the weight coefficients and corresponding scores of each target performance index, determine the corresponding function execution limit for the smart driving function in the target preset usage scenario ,include: Determine the target score that meets the requirements based on the weight coefficient of each target performance index and the corresponding score; Acquiring second correspondence information; wherein, the second correspondence information includes a plurality of second correspondences, and one second correspondence includes a score and a scene parameter of the target preset usage scene; According to the second correspondence information, determine the scene parameters of the target preset use scene corresponding to the target score, as the corresponding function execution of the smart driving function in the target preset use scene limit. 7. The method according to any one of claims 2 to 6, wherein the scene parameter is compared with the function execution limit to determine the function execution of the smart driving function in the current usage scene capabilities, including: Comparing the scene parameters with the function execution limit, determining the degree of dependence of the smart driving function on the user in the current use scene; Based on the degree of dependence, the function execution capability of the smart driving function in the current usage scenario is determined. 8. The method according to any one of claims 2 to 6, wherein determining the reminder strategy according to the function execution capability includes: If the function execution ability is greater than or equal to the first threshold, the reminder strategy is the first strategy; the user is allowed to drive the vehicle out of the vehicle under the first strategy; If the function execution ability is less than the first threshold and greater than or equal to the second threshold, then the reminder strategy is the second strategy; under the second strategy, the user is required to drive the vehicle with a focus on sight; If the function execution capability is less than the second threshold, the reminder strategy is a third strategy; under the third strategy, the user is required to manually drive the vehicle. 9. The method according to claim 8, further comprising: Obtain the reminder style selected by the user from the driving data information; Based on the reminder style, the operation of determining a reminder strategy according to the function execution capability is triggered. 10. A vehicle reminder system, characterized in that it comprises: The collection device is used to collect the driving data of the vehicle; from the collected driving data information of the vehicle, obtain the driving behavior data related to the user; send the driving behavior data to the interaction device A capability determination device, configured to determine the function execution capability of the intelligent driving function of the vehicle in the current usage scenario, and send the function execution capability to the interaction device; The interaction device is configured to determine a reminder strategy according to the function execution capability; and to remind the user according to the reminder strategy based on the driving behavior data. 11. An electronic device, comprising: a memory and a processor, wherein, The memory is used to store programs; The processor, coupled with the memory, is used to execute the program stored in the memory, so as to realize the vehicle reminding method according to any one of claims 1-9. 12. A vehicle, characterized in that the vehicle comprises a vehicle body and the electronic device according to claim 11; the electronic device is arranged on the vehicle body. 13. A computer program product, comprising computer programs/instructions, characterized in that, when the computer program/instruction processor is executed, the vehicle reminder method according to any one of claims 1 to 9 is realized.

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