Methods, systems, devices, and media for improving the activation rate of intelligent driving functions
By building user profiles and generating personalized activation strategies through a cloud system, the personalization and iteration issues in improving the activation rate of intelligent driving functions were resolved, achieving efficient improvement in user activation rate and strategy optimization.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- ANHUI DEEPWAY TECHNOLOGY CO LTD
- Filing Date
- 2026-01-13
- Publication Date
- 2026-06-02
AI Technical Summary
Existing technologies lack personalized strategies for improving user activation rates of intelligent driving functions, have insufficient data analysis capabilities, long iteration cycles, and lack closed-loop verification, resulting in low conversion efficiency and slow response.
By receiving vehicle-side data through a cloud system, user profiles are built and personalized activation guidance strategies are generated. Machine learning models are used to identify key negative scenarios, and strategies based on scenario triggering, capability adaptation, and incentive drive are combined for rapid iterative optimization.
It has achieved a precise and personalized activation strategy for intelligent driving functions, which has improved the user activation rate, increased the accuracy of data-driven decision-making and the efficiency of strategy iteration, and formed a highly efficient closed-loop improvement system.
Smart Images
Figure CN122126300A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of vehicle technology, and in particular to a method, system, device and medium for improving the activation rate of intelligent driving functions. Background Technology
[0002] User activation rate of intelligent driving functions is a key indicator of technological success. Currently, OEMs mainly improve activation rates by directly pushing function updates or activation prompts to users through the in-vehicle system, but this method has significant limitations. The following technical issues exist: The strategy is simplistic and lacks specificity: all users receive the same activation guidance strategy, which cannot be personalized based on individual user behavior, usage scenarios, and cognitive levels, resulting in low conversion efficiency.
[0003] Limited data analysis capabilities: The computing power and storage of in-vehicle systems are limited, making it difficult to deeply mine and model massive amounts of user behavior data, and thus unable to accurately pinpoint the root causes that hinder user activation.
[0004] Long iteration cycles and slow response: The formulation and verification of optimization strategies rely on offline research and slow OTA iterations, which cannot quickly respond to the characteristics of different user groups or regions, and the cost of trial and error is high.
[0005] Lack of closed-loop verification: It is difficult to quantify and evaluate the actual effects of different guidance strategies, and it is impossible to form a rapid data closed loop of "data -> strategy -> deployment -> feedback -> optimization". Summary of the Invention
[0006] Based on this, it is necessary to provide a method, system, device, and medium for improving the activation rate of intelligent driving functions to address the aforementioned technical problems. This enables precise personalization and rapid iteration of guidance strategies, thereby systematically and efficiently improving the user activation rate of intelligent driving functions.
[0007] Firstly, a method for improving the activation rate of intelligent driving functions is provided, applied in the cloud, the method comprising: Receive user behavior data and vehicle status data sent from the vehicle terminal; User profiles are constructed based on the user behavior data and vehicle status data, and key negative scenarios affecting activation rates are identified. Based on the user profile and the key negative scenarios, generate a smart driving function activation guidance strategy; The intelligent driving function activation guidance strategy is pushed to the vehicle, so that the vehicle can activate the intelligent driving function according to the intelligent driving function activation guidance strategy.
[0008] In some examples, the process of constructing user profiles based on the user behavior data and vehicle status data, and identifying key negative scenarios affecting activation rates, includes: The user behavior data and vehicle status data are aggregated; The aggregated user behavior data and vehicle status data are input into a pre-trained machine learning model to obtain the user profile and identify key negative scenarios that affect the activation rate.
[0009] In some examples, the intelligent driving function activation guidance strategy includes at least one of scenario-triggered guidance strategy, capability-adaptive guidance strategy, and incentive-driven guidance strategy.
[0010] In some examples, the scenario-triggered guidance strategy refers to triggering targeted guidance prompts when the vehicle is predicted or detected to enter a high-frequency scenario where the user has repeatedly tried to activate but failed. The capability-adaptive guidance strategy refers to providing progressive, instructional guidance for one type of user and unlocking guidance for higher-level functions for another type of user based on user profiles. The incentive-driven guidance strategy refers to providing virtual incentives such as points, badges, or personalized settings to users who complete specific guidance steps or successfully activate intelligent driving functions.
[0011] In some examples, it also includes: Different intelligent driving function activation guidance strategies were randomly or according to preset rules assigned to different user groups for testing. Compare the conversion rates of intelligent driving function activation guidance strategies for different user groups; Based on the conversion rate, the optimal intelligent driving function activation guidance strategy is selected for different user groups.
[0012] In some examples, it also includes: When a user executes the intelligent driving function activation guidance strategy, the system determines whether the current scenario matches the execution conditions of the intelligent driving function activation guidance strategy based on the vehicle's location, surrounding environment information, and cabin status.
[0013] In some examples, it also includes: Receive user feedback information after the user executes the intelligent driving function activation guidance strategy; The activation guidance strategy for the intelligent driving function will be optimized based on the user feedback information.
[0014] Secondly, a system for improving the activation rate of intelligent driving functions is provided, applied in the cloud, the system comprising: The receiving module is used to receive user behavior data and vehicle status data sent by the vehicle terminal; The analysis module is used to construct user profiles based on the user behavior data and vehicle status data, and to identify key negative scenarios that affect the activation rate. The generation module is used to generate an intelligent driving function activation guidance strategy based on the user profile and the key negative scenarios. The delivery module is used to push the intelligent driving function activation guidance strategy to the vehicle terminal, so that the vehicle terminal can activate the intelligent driving function according to the intelligent driving function activation guidance strategy.
[0015] Thirdly, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps of the method for increasing the activation rate of the intelligent driving function described in the first aspect and any possible implementation of the first aspect.
[0016] Fourthly, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the steps of the method for increasing the activation rate of the intelligent driving function described in the first aspect and any possible implementation thereof.
[0017] Fifthly, a computer program product is provided, on which a computer program is stored, which, when executed by a processor, implements the steps of the method for increasing the activation rate of the intelligent driving function of the first aspect and any possible implementation thereof.
[0018] The embodiments of this application aggregate and analyze massive amounts of user behavior data to construct user profiles, generate personalized intelligent driving function activation guidance strategies based on these profiles, and distribute these strategies to the vehicle for execution. The vehicle can then execute these strategies through a human-machine interface, while simultaneously feeding back the results to the cloud for strategy optimization. This achieves precise personalization and rapid iteration of the guidance strategy, thereby systematically and efficiently improving the user activation rate of intelligent driving functions. Attached Figure Description
[0019] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 A flowchart illustrating the method for improving the activation rate of intelligent driving functions provided in this application embodiment; Figure 2 This is a structural block diagram of the intelligent driving function activation rate improvement system provided in the embodiments of this application; Figure 3 This is a structural block diagram of a computer device provided in an embodiment of this application. Detailed Implementation
[0020] The present application will now be described in further detail with reference to the embodiments and accompanying drawings. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the application. Furthermore, it should be noted that, for ease of description, only the parts relevant to the application are shown in the accompanying drawings.
[0021] It should be noted that, unless otherwise specified, the embodiments and features of the embodiments in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.
[0022] The following describes in detail, with reference to the accompanying drawings, a method, system, device, and medium for improving the activation rate of intelligent driving functions according to embodiments of this application.
[0023] Figure 1 This is a flowchart of a method for improving the activation rate of intelligent driving functions according to an embodiment of this application. Figure 1 As shown, the method for improving the activation rate of intelligent driving function according to an embodiment of this application includes the following steps: S101: Receives user behavior data and vehicle status data sent by the vehicle terminal.
[0024] For example, receive and aggregate anonymized user behavior data from vehicle terminal systems, build user profiles based on machine learning models, and identify key negative scenarios that affect activation rates.
[0025] User behavior data includes, but is not limited to: the number and scenarios in which users attempt to activate the intelligent driving function but fail, the user's rate of ignoring existing prompts, function usage duration, frequently used driving routes, and function parameters actively set by the user. Vehicle status data includes the vehicle's driving status, etc.
[0026] S102: Construct a user profile based on the user behavior data and vehicle status data, and identify key negative scenarios that affect the activation rate.
[0027] In one embodiment of this application, the step of constructing a user profile based on the user behavior data and vehicle status data, and identifying key negative scenarios affecting the activation rate, includes: aggregating the user behavior data and vehicle status data; inputting the aggregated user behavior data and vehicle status data into a pre-trained machine learning model to obtain the user profile, and identifying key negative scenarios affecting the activation rate.
[0028] Specifically, based on the user profile and key negative scenarios, a personalized intelligent driving function activation guidance strategy is generated, and the strategy is continuously optimized based on feedback data. For example, user feedback information is received after the user executes the intelligent driving function activation guidance strategy; the intelligent driving function activation guidance strategy is optimized based on the user feedback information.
[0029] S103: Generate an intelligent driving function activation guidance strategy based on the user profile and the key negative scenarios.
[0030] In one embodiment of this application, the intelligent driving function activation guidance strategy includes at least one of a scenario-triggered guidance strategy, a capability-adaptive guidance strategy, and an incentive-driven guidance strategy.
[0031] The scenario-triggered guidance strategy refers to triggering targeted guidance prompts when the vehicle is predicted or detected to enter a high-frequency scenario where the user has repeatedly tried to activate but failed. The capability-adaptive guidance strategy refers to providing progressive, instructional guidance for one type of user and unlocking advanced functions for another type of user based on user profiles. The incentive-driven guidance strategy refers to providing virtual incentives such as points, badges, or personalized settings to users who complete specific guidance steps or successfully activate intelligent driving functions.
[0032] Specifically, the generation and optimization of personalized guidance strategies includes at least one of the following: Scene-triggered guidance strategy: When the system predicts or detects that a vehicle has entered a high-frequency scene that the user has tried to activate multiple times but failed, it triggers targeted guidance prompts.
[0033] Capability-adaptive guidance strategy: Based on user profiles, provide novice users with step-by-step tutorial-style guidance, and provide advanced users with guidance on unlocking advanced functions.
[0034] Incentive-driven onboarding strategies: Providing virtual incentives such as points, badges, or personalized settings to users who complete specific onboarding steps or successfully activate features.
[0035] S104: Push the intelligent driving function activation guidance strategy to the vehicle terminal so that the vehicle terminal can activate the intelligent driving function according to the intelligent driving function activation guidance strategy.
[0036] Specifically, different guidance strategies are stored and versioned, and these strategies are then distributed to vehicles based on user profiles or vehicle clusters. Each vehicle collects its own user behavior data and vehicle status data, anonymizes it, and reports it to the cloud. Furthermore, it receives and parses personalized guidance strategies from the cloud, executes these strategies through the in-vehicle human-machine interface, and interacts with the user. It can also monitor user responses to the strategies and collect user behavior data after strategy execution, sending this data back to the cloud.
[0037] Furthermore, when the user executes the intelligent driving function activation guidance strategy, the system determines whether the current scenario matches the execution conditions of the intelligent driving function activation guidance strategy based on the vehicle's location, surrounding environment information, and cabin status.
[0038] In one embodiment of this application, the method for improving the activation rate of intelligent driving functions further includes: randomly or according to preset rules assigning different intelligent driving function activation guidance strategies to different user groups for testing; comparing the execution conversion rates of the intelligent driving function activation guidance strategies for different user groups; and selecting the optimal intelligent driving function activation guidance strategy for different user groups based on the execution conversion rates. In other words, different guidance strategies are randomly or according to preset rules assigned to different user groups, and the optimal strategy is selected in a data-driven manner by comparing the strategy conversion rates of each group.
[0039] In one embodiment of this application, the method for improving the activation rate of intelligent driving functions may further include: when executing a guidance strategy, coordinating with the vehicle's positioning unit, environmental perception unit, and cabin state perception unit to ensure user interaction in a safe and appropriate scenario.
[0040] The method for improving the activation rate of intelligent driving functions in this application embodiment aggregates and analyzes massive amounts of user behavior data to construct user profiles, generates personalized intelligent driving function activation guidance strategies based on these profiles, and sends these strategies to the vehicle for execution. This allows the vehicle to execute the strategy through a human-machine interface, while simultaneously feeding back the results to the cloud for strategy optimization. This achieves precise personalization and rapid iteration of the guidance strategy, thereby systematically and efficiently improving the user activation rate of intelligent driving functions.
[0041] Compared with the prior art, the embodiments of this application have the following significant advantages: Precise and personalized: Through vehicle-cloud collaboration, the system can "tailor-make" activation guidance strategies for each user, greatly improving conversion accuracy and user experience.
[0042] Data-driven decision-making: Leveraging big data and AI capabilities in the cloud to extract insights from massive amounts of data, making strategy formulation based on evidence and avoiding subjective assumptions.
[0043] Rapid Iteration and Optimization: Relying on the cloud-based A / B testing framework and real-time feedback from the vehicle, the guidance strategy can be quickly verified and iterated to optimize, forming an efficient closed loop for improving conversion rates.
[0044] Systematic Improvement: By connecting isolated vehicles into an intelligent network, the activation rate problem is solved through a systematic approach, achieving a leap from "single-vehicle intelligence" to "collective intelligence," and sustainably and scalably improving the overall activation rate of intelligent driving functions for all users.
[0045] Figure 2 This is a structural block diagram of an intelligent driving function activation rate enhancement system according to an embodiment of this application. Figure 2As shown, an intelligent driving function activation rate enhancement system according to an embodiment of this application includes: a receiving module 210, an analysis module 220, a generation module 230, and a distribution module 240, wherein: The receiving module 210 is used to receive user behavior data and vehicle status data sent by the vehicle terminal; Analysis module 220 is used to construct user profiles based on the user behavior data and vehicle status data, and identify key negative scenarios that affect the activation rate; The generation module 230 is used to generate an intelligent driving function activation guidance strategy based on the user profile and the key negative scenarios. The delivery module 240 is used to push the intelligent driving function activation guidance strategy to the vehicle terminal, so that the vehicle terminal can activate the intelligent driving function according to the intelligent driving function activation guidance strategy.
[0046] The intelligent driving function activation rate improvement system according to embodiments of this application aggregates and analyzes massive amounts of user behavior data to construct user profiles, generates personalized intelligent driving function activation guidance strategies based on these profiles, and sends these strategies to the vehicle for execution. The vehicle can then execute these strategies through a human-machine interface, while simultaneously feeding back the results to the cloud for strategy optimization. This achieves precise personalization and rapid iteration of the guidance strategy, thereby systematically and efficiently improving the user activation rate of intelligent driving functions.
[0047] Compared with the prior art, the embodiments of this application have the following significant advantages: Precise and personalized: Through vehicle-cloud collaboration, the system can "tailor-make" activation guidance strategies for each user, greatly improving conversion accuracy and user experience.
[0048] Data-driven decision-making: Leveraging big data and AI capabilities in the cloud to extract insights from massive amounts of data, making strategy formulation based on evidence and avoiding subjective assumptions.
[0049] Rapid Iteration and Optimization: Relying on the cloud-based A / B testing framework and real-time feedback from the vehicle, the guidance strategy can be quickly verified and iterated to optimize, forming an efficient closed loop for improving conversion rates.
[0050] Systematic Improvement: By connecting isolated vehicles into an intelligent network, the activation rate problem is solved through a systematic approach, achieving a leap from "single-vehicle intelligence" to "collective intelligence," and sustainably and scalably improving the overall activation rate of intelligent driving functions for all users.
[0051] Specific limitations regarding the activation rate enhancement system for intelligent driving functions can be found in the limitations of the activation rate enhancement method for intelligent driving functions described above, and will not be repeated here. Each module of the aforementioned activation rate enhancement system for intelligent driving functions can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of the computer device in software form, so that the processor can call and execute the corresponding operations of each module.
[0052] In one embodiment, a computer device is provided. Figure 3 This is a structural block diagram of the computer device provided in the embodiments of this application, with reference to... Figure 3 The computer device includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the aforementioned method embodiment for improving the activation rate of intelligent driving functions. For example, it executes: receiving user behavior data and vehicle status data sent by the vehicle terminal; User profiles are constructed based on the user behavior data and vehicle status data, and key negative scenarios affecting activation rates are identified. Based on the user profile and the key negative scenarios, generate a smart driving function activation guidance strategy; The intelligent driving function activation guidance strategy is pushed to the vehicle, so that the vehicle can activate the intelligent driving function according to the intelligent driving function activation guidance strategy.
[0053] This application also provides a computer-readable storage medium storing a computer program. When the processor executes the computer program, it implements an embodiment of the aforementioned method for improving the activation rate of intelligent driving functions. For example, it executes: receiving user behavior data and vehicle status data sent by the vehicle terminal; User profiles are constructed based on the user behavior data and vehicle status data, and key negative scenarios affecting activation rates are identified. Based on the user profile and the key negative scenarios, generate a smart driving function activation guidance strategy; The intelligent driving function activation guidance strategy is pushed to the vehicle, so that the vehicle can activate the intelligent driving function according to the intelligent driving function activation guidance strategy.
[0054] This application provides a computer program product including instructions that, when executed, cause the method described in this application embodiment to be performed. For example, it can execute... Figure 1 The steps of the method for increasing the activation rate of intelligent driving functions, as shown, are executed as follows: Receive user behavior data and vehicle status data sent from the vehicle terminal; User profiles are constructed based on the user behavior data and vehicle status data, and key negative scenarios affecting activation rates are identified. Based on the user profile and the key negative scenarios, generate a smart driving function activation guidance strategy; The intelligent driving function activation guidance strategy is pushed to the vehicle, so that the vehicle can activate the intelligent driving function according to the intelligent driving function activation guidance strategy.
[0055] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.
[0056] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0057] The above embodiments merely illustrate several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.
Claims
1. A method for improving the activation rate of intelligent driving functions, characterized in that, Applied to the cloud, the method includes: Receive user behavior data and vehicle status data sent from the vehicle terminal; User profiles are constructed based on the user behavior data and vehicle status data, and key negative scenarios affecting activation rates are identified. Based on the user profile and the key negative scenarios, generate a smart driving function activation guidance strategy; The intelligent driving function activation guidance strategy is pushed to the vehicle, so that the vehicle can activate the intelligent driving function according to the intelligent driving function activation guidance strategy.
2. The method for improving the activation rate of intelligent driving functions according to claim 1, characterized in that, The process of constructing user profiles based on user behavior data and vehicle status data, and identifying key negative scenarios affecting activation rates, includes: The user behavior data and vehicle status data are aggregated; The aggregated user behavior data and vehicle status data are input into a pre-trained machine learning model to obtain the user profile and identify key negative scenarios that affect the activation rate.
3. The method for improving the activation rate of intelligent driving functions according to claim 1, characterized in that, The intelligent driving function activation guidance strategy includes at least one of the following: scenario-triggered guidance strategy, capability-adaptive guidance strategy, and incentive-driven guidance strategy.
4. The method for improving the activation rate of intelligent driving functions according to claim 3, characterized in that, in: The scenario-triggered guidance strategy refers to triggering targeted guidance prompts when the vehicle is predicted or detected to enter a high-frequency scenario where the user has repeatedly tried to activate but failed. The capability-adaptive guidance strategy refers to providing progressive, instructional guidance for one type of user and unlocking guidance for higher-level functions for another type of user based on user profiles. The incentive-driven guidance strategy refers to providing virtual incentives such as points, badges, or personalized settings to users who complete specific guidance steps or successfully activate intelligent driving functions.
5. The method for improving the activation rate of intelligent driving functions according to claim 1, characterized in that, Also includes: Different intelligent driving function activation guidance strategies were randomly or according to preset rules assigned to different user groups for testing. Compare the conversion rates of intelligent driving function activation guidance strategies for different user groups; Based on the conversion rate, the optimal intelligent driving function activation guidance strategy is selected for different user groups.
6. The method for improving the activation rate of intelligent driving functions according to any one of claims 1-5, characterized in that, Also includes: When a user executes the intelligent driving function activation guidance strategy, the system determines whether the current scenario matches the execution conditions of the intelligent driving function activation guidance strategy based on the vehicle's location, surrounding environment information, and cabin status.
7. The method for improving the activation rate of intelligent driving functions according to claim 1, characterized in that, Also includes: Receive user feedback information after the user executes the intelligent driving function activation guidance strategy; The activation guidance strategy for the intelligent driving function will be optimized based on the user feedback information.
8. A system for improving the activation rate of intelligent driving functions, characterized in that, The system, applied in the cloud, includes: The receiving module is used to receive user behavior data and vehicle status data sent by the vehicle terminal; The analysis module is used to construct user profiles based on the user behavior data and vehicle status data, and to identify key negative scenarios that affect the activation rate. The generation module is used to generate an intelligent driving function activation guidance strategy based on the user profile and the key negative scenarios. The delivery module is used to push the intelligent driving function activation guidance strategy to the vehicle terminal, so that the vehicle terminal can activate the intelligent driving function according to the intelligent driving function activation guidance strategy.
9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the method for increasing the activation rate of intelligent driving functions according to any one of claims 1-7.
10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method for improving the activation rate of intelligent driving functions according to any one of claims 1-7.