Vehicle driving behavior recommendation method and device, server and storage medium

Abstract

The application discloses a vehicle driving behavior recommendation method and device, a server and a storage medium. The method comprises the following steps: collecting driving behavior data of a current vehicle; extracting at least one behavior feature from the driving data information, and determining the driving preference of the current vehicle according to the at least one behavior feature; and when the driving behavior data does not satisfy a preset specification condition, pushing behavior correction information generated by the driving preference to the current vehicle, wherein the correction behavior information is obtained from driving behavior data satisfying the preset specification condition. Thus, the technical problem that the driving behavior data of the vehicle is not fully utilized and it is difficult to provide targeted help guidance for the current abnormal driving behavior of the user in the related art is solved.

Description

Technical Field

[0001] This application relates to the field of vehicle interaction technology, and in particular to a method, apparatus, server, and storage medium for recommending vehicle driving behavior. Background Technology

[0002] With the advancement of technology, the ownership rate of smart vehicles has surged. Some novice users or those with limited knowledge of smart vehicle functions are prone to improper driving behavior when driving smart vehicles. Among related technologies, models can be built to identify abnormal driving behaviors and provide assistance to users based on the vehicle's built-in manuals, user guides, etc.

[0003] However, in related technologies, vehicle driving behavior data is not fully utilized, and it is difficult to provide targeted assistance and guidance for users' current abnormal driving behavior, which needs to be improved. Summary of the Invention

[0004] This application provides a method, apparatus, server, and storage medium for recommending vehicle driving behavior, in order to solve the technical problem in the related art that vehicle driving behavior data is not fully utilized and it is difficult to provide targeted assistance and guidance for users' current abnormal driving behavior.

[0005] The first aspect of this application provides a method for recommending vehicle driving behavior, applied to a server, wherein the method includes the following steps: collecting driving behavior data of a current vehicle; extracting at least one behavioral feature from the driving data information, and determining the driving preference of the current vehicle based on the at least one behavioral feature; and pushing behavior correction information generated by the driving preference to the current vehicle when the driving behavior data does not meet preset standard conditions, wherein the correction behavior information is obtained from driving behavior data that meets the preset standard conditions.

[0006] Optionally, in one embodiment of this application, the method further includes: when the driving behavior data meets the preset standard conditions, generating a reference behavior standard based on the at least one behavior feature to obtain the corrective behavior information.

[0007] Optionally, in one embodiment of this application, determining the driving preference of the current vehicle based on the at least one behavioral feature includes: querying a preset database using the at least one behavioral feature as an index to obtain the driving preference.

[0008] Optionally, in one embodiment of this application, querying a preset database using the at least one behavioral feature as an index includes: querying the preset database based on the at least one behavioral feature to generate driving behavior habit tags, driving preference tags, and / or non-standard driving behavior tags for the current vehicle; and determining the driving preference based on the driving behavior habit tags, driving preference tags, and / or non-standard driving behavior tags for the current vehicle.

[0009] Optionally, in one embodiment of this application, the driving behavior data includes at least one of vehicle speed, vehicle destination, waypoints, energy consumption, and road conditions.

[0010] A second aspect of this application provides a vehicle driving behavior recommendation device applied to a server, wherein the device includes: a collection module for collecting driving behavior data of a current vehicle; an extraction module for extracting at least one behavioral feature from the driving data information and determining the driving preference of the current vehicle based on the at least one behavioral feature; and a recommendation module for pushing behavior correction information generated by the driving preference to the current vehicle when the driving behavior data does not meet preset standard conditions, wherein the correction behavior information is obtained from driving behavior data that meets the preset standard conditions.

[0011] Optionally, in one embodiment of this application, it further includes: a generation module, configured to generate a reference behavior standard based on the at least one behavior feature when the driving behavior data meets the preset specification conditions, so as to obtain the corrective behavior information.

[0012] Optionally, in one embodiment of this application, the extraction module includes: a query unit, used to query a preset database using the at least one behavioral feature as an index to obtain the driving preference.

[0013] Optionally, in one embodiment of this application, the query unit includes: a generation subunit, configured to query the preset database based on the at least one behavioral feature to generate driving behavior habit tags, driving preference tags, and / or non-standard driving behavior tags for the current vehicle; and a determination subunit, configured to determine the driving preference based on the driving behavior habit tags, driving preference tags, and / or non-standard driving behavior tags for the current vehicle.

[0014] Optionally, in one embodiment of this application, the driving behavior data includes at least one of vehicle speed, vehicle destination, waypoints, energy consumption, and road conditions.

[0015] A third aspect of this application provides a server, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement a recommended method for vehicle driving behavior as described in the above embodiments.

[0016] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the recommended method for the above-described vehicle driving behavior.

[0017] This application embodiment can collect current vehicle driving behavior data, extract at least one behavioral feature to determine the current vehicle's driving preferences, and push behavior correction information generated by the driving preferences to the current vehicle when the driving behavior data does not meet preset standard conditions. This fully utilizes the vehicle's driving behavior data to provide realistic user guidance and suggestions for abnormal driving behavior, thereby establishing an effective learning and assistance channel for users. Thus, it solves the technical problem in related technologies where vehicle driving behavior data is not fully utilized and it is difficult to provide targeted assistance and guidance for users' current abnormal driving behavior.

[0018] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0019] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0020] Figure 1 A flowchart illustrating a method for recommending vehicle driving behavior according to an embodiment of this application;

[0021] Figure 2 A flowchart of a method for recommending vehicle driving behavior according to an embodiment of this application;

[0022] Figure 3 This is a schematic diagram illustrating the principle of a method for recommending vehicle driving behavior according to an embodiment of this application;

[0023] Figure 4 This is an illustration of the application of a method for recommending vehicle driving behavior according to an embodiment of this application;

[0024] Figure 5 This is a schematic diagram of the structure of a vehicle driving behavior recommendation device provided according to an embodiment of this application;

[0025] Figure 6 This is a schematic diagram of the structure of a server provided according to an embodiment of this application. Detailed Implementation

[0026] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0027] The following description, with reference to the accompanying drawings, outlines a method, apparatus, server, and storage medium for recommending vehicle driving behavior according to embodiments of this application. Addressing the technical problem mentioned in the background section of the related technologies where vehicle driving behavior data is not fully utilized and it is difficult to provide targeted guidance and assistance for users' current abnormal driving behavior, this application provides a method for recommending vehicle driving behavior. In this method, current vehicle driving behavior data is collected, at least one behavioral feature is extracted to determine the current vehicle's driving preferences, and when the driving behavior data does not meet preset specifications, behavior correction information generated from the driving preferences is pushed to the current vehicle. This fully utilizes the vehicle's driving behavior data to provide realistic user guidance and suggestions for abnormal driving behavior, thereby establishing an effective learning and assistance channel for users. This solves the technical problem in the related technologies where vehicle driving behavior data is not fully utilized and it is difficult to provide targeted guidance and assistance for users' current abnormal driving behavior.

[0028] Specifically, Figure 1 This is a flowchart illustrating a method for recommending vehicle driving behavior provided in an embodiment of this application.

[0029] like Figure 1 As shown, the recommended method for vehicle driving behavior is applied to the server, and the method includes the following steps:

[0030] In step S101, driving behavior data of the current vehicle is collected.

[0031] In actual implementation, the embodiments of this application can obtain the user's authorization information and, after obtaining authorization, collect the current vehicle's driving behavior data. The source of the driving behavior data can be the recorded data on the vehicle terminal or the recorded data that the vehicle terminal periodically uploads to the cloud.

[0032] Optionally, in one embodiment of this application, driving behavior data includes at least one of vehicle speed, vehicle destination, waypoints, energy consumption, and road conditions.

[0033] Specifically, in this embodiment of the application, driving behavior data may include vehicle speed, destination, waypoints, energy consumption, road conditions, travel time, and whether there are any traffic violations along the way.

[0034] In step S102, at least one behavioral feature is extracted from the driving data information, and the driving preference of the current vehicle is determined based on the at least one behavioral feature.

[0035] As one possible implementation method, embodiments of this application can extract at least one behavioral feature from the collected driving data information, thereby facilitating the determination of the current vehicle's driving preferences based on at least one behavioral feature.

[0036] Optionally, in one embodiment of this application, determining the driving preference of the current vehicle based on at least one behavioral feature includes: querying a preset database using at least one behavioral feature as an index to obtain the driving preference.

[0037] The preset database may consist of driving preference data for multiple vehicles obtained from the server, and each driving preference corresponds to at least one characteristic behavior. This embodiment of the application can query the preset database based on at least one characteristic behavior to obtain the driving preference corresponding to that at least one characteristic behavior.

[0038] Optionally, in one embodiment of this application, querying a preset database using at least one behavioral feature as an index includes: querying the preset database based on at least one behavioral feature to generate a driving behavior habit label, driving preference label, and / or non-standard driving behavior label for the current vehicle; and determining driving preferences based on the driving behavior habit label, driving preference label, and / or non-standard driving behavior label for the current vehicle.

[0039] In actual implementation, embodiments of this application can query a preset database based on at least one behavioral feature and generate corresponding tags for the behavioral feature, such as driving behavior habit tags, driving preference tags, and non-standard driving behavior tags. This allows data to be classified based on the tags, and then the preset database can be traversed to find the behavioral feature with the most corresponding tags, thereby determining driving preferences.

[0040] In step S103, when the driving behavior data does not meet the preset standard conditions, behavior correction information generated by driving preferences is pushed to the current vehicle, wherein the correction behavior information is obtained from the driving behavior data that meets the preset standard conditions.

[0041] As one possible implementation, this application embodiment can push corrective information with the same driving preferences to the current vehicle when it is determined that the obtained driving behavior data does not meet the preset standard conditions, such as speeding, not using turn signals when turning, or crossing solid lines while driving. The corrective information can be driving behavior data that meets the preset standard conditions in a preset database, thereby facilitating users to obtain real and practical driving guidance or suggestions.

[0042] The preset standard conditions can be set by those skilled in the art based on regional traffic regulations, driving safety standards, etc., and no specific restrictions are imposed here.

[0043] Optionally, in one embodiment of this application, the method further includes: when the driving behavior data meets preset normative conditions, generating a reference behavior standard based on at least one behavior feature to obtain corrective behavior information.

[0044] In some embodiments, when driving behavior data meets preset norm conditions, reference behavior standards can be generated based on at least one behavioral feature of the current driving preference to obtain corrective behavior information, thereby providing guidance for driving preferences that do not meet preset norm conditions.

[0045] Combination Figures 2 to 4 The working principle of the vehicle driving behavior recommendation method of this application embodiment will be described in detail using one example. Figure 2 As shown, embodiments of this application may include the following steps:

[0046] Step S201: Driving behavior data collection. This embodiment of the application can collect user driving behavior information with user authorization to obtain driving behavior data. The collected information may include, but is not limited to, vehicle speed, destination, waypoints, energy consumption, road conditions, etc. The sources of information collection may include the cloud and the vehicle's infotainment system.

[0047] Step S202: Data Analysis. This embodiment of the application can analyze, summarize, and tag the collected data. The results of the data analysis can be stored in a preset database and sent to the vehicle according to the vehicle's request, enabling recommendations based on driving behavior data through the vehicle's interactive platform.

[0048] Step S203: Driving behavior recommendation. This embodiment of the application can recommend driving behavior information of other users based on the user's driving habits and preferences.

[0049] Step S204: Driving Behavior Guidance. This embodiment of the application can push the compliant driving behaviors of other users to the user when the user's driving behavior does not meet preset norms, thereby providing driving behavior guidance. Users receive realistic and practical driving guidance or suggestions, effectively improving their current driving behavior and enhancing their driving experience.

[0050] For example, such as Figure 3 As shown, the embodiments of this application can be implemented through the vehicle terminal and the cloud.

[0051] The vehicle-side system can collect users' driving behavior information with their authorization and upload it to the cloud. Through the cloud, such as a driving behavior guidance platform, users can obtain real and practical driving guidance or suggestions from other users, effectively improving their current driving behavior, enhancing their car-using experience, and allowing them to enjoy the driving process.

[0052] In the cloud, i.e., on servers, driving behavior big data analysis can be used to recommend information based on algorithms.

[0053] like Figure 4 As shown, in actual implementation, the application process of this application embodiment can be as follows:

[0054] User A: With authorization and consent, you can anonymously upload driving behavior data and view data related to your personal driving behavior.

[0055] In the cloud: driving behavior data is uploaded to a pre-set database, which analyzes, summarizes, and tags the data, and gradually enriches the information dimensions and content.

[0056] User B: With authorization and consent, based on the user's driving behavior, user B can select driving preferences or areas for improvement, obtain driving behavior-related recommendations, and provide feedback on the accuracy of the recommendations. This will help optimize the recommendation algorithm, thereby improving the user's driving experience and enriching the driving behavior database.

[0057] The vehicle driving behavior recommendation method proposed in this application can collect current vehicle driving behavior data, extract at least one behavioral feature to determine the current vehicle's driving preferences, and push behavior correction information generated by the driving preferences to the current vehicle when the driving behavior data does not meet preset standard conditions. This fully utilizes the vehicle's driving behavior data to provide realistic user guidance and suggestions for abnormal driving behavior, thereby establishing an effective learning and assistance channel for users. This solves the technical problem in related technologies where vehicle driving behavior data is not fully utilized and it is difficult to provide targeted assistance and guidance for users' current abnormal driving behavior.

[0058] Next, referring to the accompanying drawings, a recommended device for vehicle driving behavior according to an embodiment of this application is described.

[0059] Figure 5 This is a block diagram of a vehicle driving behavior recommendation device according to an embodiment of this application.

[0060] like Figure 5 As shown, the vehicle driving behavior recommendation device 10 is applied to a server, wherein the device 10 includes: a collection module 100, an extraction module 200, and a recommendation module 300.

[0061] Specifically, the data acquisition module 100 is used to collect driving behavior data of the current vehicle.

[0062] The extraction module 200 is used to extract at least one behavioral feature from driving data information and determine the driving preference of the current vehicle based on the at least one behavioral feature.

[0063] The recommendation module 300 is used to push behavior correction information generated by driving preferences to the current vehicle when the driving behavior data does not meet the preset standard conditions. The correction behavior information is obtained from the driving behavior data that meets the preset standard conditions.

[0064] Optionally, in one embodiment of this application, the vehicle driving behavior recommendation device 10 further includes a generation module.

[0065] The generation module is used to generate reference behavior standards based on at least one behavior feature when the driving behavior data meets the preset standard conditions, so as to obtain corrective behavior information.

[0066] Optionally, in one embodiment of this application, the extraction module 200 includes a query unit.

[0067] The query unit is used to query a preset database using at least one behavioral feature as an index to obtain driving preferences.

[0068] Optionally, in one embodiment of this application, the query unit includes: a generation subunit and a determination subunit.

[0069] The generation subunit is used to query a preset database based on at least one behavioral feature to generate driving behavior habit tags, driving preference tags, and / or non-standard driving behavior tags for the current vehicle.

[0070] The sub-unit is determined based on the current vehicle's driving behavior habit tags, driving preference tags, and / or non-standard driving behavior tags to determine driving preferences.

[0071] Optionally, in one embodiment of this application, driving behavior data includes at least one of vehicle speed, vehicle destination, waypoints, energy consumption, and road conditions.

[0072] It should be noted that the explanation of the aforementioned method embodiment for recommending vehicle driving behavior also applies to the vehicle driving behavior recommendation device of this embodiment, and will not be repeated here.

[0073] The vehicle driving behavior recommendation device proposed in this application can collect current vehicle driving behavior data, extract at least one behavioral feature to determine the current vehicle's driving preferences, and push behavior correction information generated by the driving preferences to the current vehicle when the driving behavior data does not meet preset standard conditions. This fully utilizes the vehicle's driving behavior data to provide realistic user guidance and suggestions for abnormal driving behavior, thereby establishing an effective learning and assistance channel for users. This solves the technical problem in related technologies where vehicle driving behavior data is not fully utilized and it is difficult to provide targeted assistance and guidance for users' current abnormal driving behavior.

[0074] Figure 6 A schematic diagram of the structure of a server provided in an embodiment of this application. The server may include:

[0075] The memory 601, the processor 602, and the computer program stored on the memory 601 and capable of running on the processor 602.

[0076] When the processor 602 executes the program, it implements the recommended method for vehicle driving behavior provided in the above embodiments.

[0077] Furthermore, the server also includes:

[0078] Communication interface 603 is used for communication between memory 601 and processor 602.

[0079] The memory 601 is used to store computer programs that can run on the processor 602.

[0080] The memory 601 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0081] If the memory 601, processor 602, and communication interface 603 are implemented independently, then the communication interface 603, memory 601, and processor 602 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, Figure 6 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0082] Optionally, in a specific implementation, if the memory 601, processor 602, and communication interface 603 are integrated on a single chip, then the memory 601, processor 602, and communication interface 603 can communicate with each other through an internal interface.

[0083] The processor 602 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0084] This embodiment also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the recommended method for the above-mentioned vehicle driving behavior.

[0085] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0086] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0087] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0088] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0089] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0090] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0091] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0092] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for recommending vehicle driving behavior, characterized in that, Applied to a server, the method includes the following steps: Collect current vehicle driving behavior data; At least one behavioral feature is extracted from the driving behavior data, and the driving preferences of the current vehicle are matched against a preset database based on the at least one behavioral feature. The preset database is constructed from driving behavior data uploaded anonymously by multiple authorized vehicles. When the driving behavior data does not meet the preset standard conditions, behavior correction information generated by the driving preference is pushed to the current vehicle. The behavior correction information is obtained from driving behavior data that meets the preset standard conditions. The behavior correction information is driving behavior data that meets the preset standard conditions in the preset database. The step of matching the driving preferences of the current vehicle from a preset database based on the at least one behavioral feature includes: querying the preset database based on the at least one behavioral feature to generate driving behavior habit tags, driving preference tags, and / or non-standard driving behavior tags for the current vehicle; and determining the driving preference based on the driving behavior habit tags, driving preference tags, and / or non-standard driving behavior tags for the current vehicle.

2. The method according to claim 1, characterized in that, Also includes: When the driving behavior data meets the preset standard conditions, a reference behavior standard is generated based on the at least one behavior feature to obtain the behavior correction information.

3. The method according to claim 1, characterized in that, The driving behavior data includes at least one of the following: vehicle speed, destination, waypoints, energy consumption, and road conditions.

4. A device for recommending vehicle driving behavior, characterized in that, Applied to a server, wherein the device includes: The data acquisition module is used to collect driving behavior data of the current vehicle. An extraction module is configured to extract at least one behavioral feature from the driving behavior data, and match the driving preferences of the current vehicle from a preset database based on the at least one behavioral feature, wherein the preset database is constructed from driving behavior data uploaded anonymously by multiple authorized vehicles; and The recommendation module is used to push behavior correction information generated by the driving preference to the current vehicle when the driving behavior data does not meet the preset standard conditions. The behavior correction information is obtained from driving behavior data that meets the preset standard conditions. The behavior correction information is driving behavior data that meets the preset standard conditions in the preset database. The extraction module includes: a generation subunit, used to query the preset database based on the at least one behavioral feature to generate driving behavior habit tags, driving preference tags, and / or non-standard driving behavior tags for the current vehicle; and a determination subunit, used to determine the driving preference based on the driving behavior habit tags, driving preference tags, and / or non-standard driving behavior tags for the current vehicle.

5. The apparatus according to claim 4, characterized in that, Also includes: The generation module is used to generate reference behavior standards based on at least one behavior feature when the driving behavior data meets the preset standard conditions, so as to obtain the behavior correction information.

6. A server, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the recommended method of vehicle driving behavior as described in any one of claims 1-3.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the recommended method of vehicle driving behavior as described in any one of claims 1-3.

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