AR-HUD adaptive method and related equipment

Abstract

The invention discloses an AR-HUD self-adaption method and related equipment, relates to the technical field of automotive electronics, and mainly aims to solve the problems that an existing AR-HUD display strategy is static and single and does not meet the real-time requirement of a driver. The method comprises the following steps: acquiring driving behavior data and environment data; calculating a driving behavior risk degree and an environment risk degree based on the driving behavior data, the environment data and a preset weight coefficient; and calling an AR-HUD mode based on the driving behavior risk degree and the environment risk degree.

Description

Technical Field

[0001] This invention relates to the field of automotive electronics technology, and in particular to an AR-HUD adaptive method and related equipment. Background Technology

[0002] As the level of automotive intelligence continues to increase, the functions of in-vehicle infotainment systems are becoming increasingly complex. Although methods such as identifying driving styles based on steering wheel angle sensors can analyze driving behavior to a certain extent and switch vehicle modes through fixed thresholds, and augmented reality navigation display technology can project virtual information onto the windshield to improve the intuitiveness of interaction, these existing technical solutions have inherent limitations: high misjudgment rate due to static and single display strategies, poor environmental adaptability, mismatch between information density and the driver's real-time needs, and low utilization of hardware resources have become key bottlenecks restricting the safety and experience improvement of in-vehicle intelligent interaction systems. Summary of the Invention

[0003] In view of the above problems, the present invention provides an AR-HUD adaptive method and related equipment, the main purpose of which is to solve the problem that the current AR-HUD display strategy is static and singular, and does not match the real-time needs of the driver.

[0004] To address at least one of the aforementioned technical problems, in a first aspect, the present invention provides an AR-HUD adaptive method, the method comprising: Acquire driving behavior data and environmental data; The driving behavior risk level and environmental risk level are calculated based on the driving behavior data, the environmental data, and preset weighting coefficients. The AR-HUD mode is invoked based on the driving behavior risk level and the environmental risk level.

[0005] Optionally, the driving behavior data includes: distraction index and fatigue index, and the environmental data includes: road condition complexity and visibility.

[0006] Optionally, the preset weighting coefficients include: a first set of weights for calculating the driving behavior risk level, and a second set of weights for calculating the environmental risk level, wherein, The first set of weights includes a distraction index weight and a fatigue index weight, the sum of which is 1. The second set of weights includes road condition complexity weights and visibility weights, and the sum of the road condition complexity weights and visibility weights is 1.

[0007] Optionally, the calculation of driving behavior risk and environmental risk based on the driving behavior data, the environmental data, and preset weighting coefficients includes: The risk level of the driving behavior is determined by summing the product of the distraction index weight and the distraction index with the product of the fatigue index weight and the fatigue index. The environmental risk level is determined by summing the product of the road condition complexity weight and the road condition complexity with the product of the visibility weight and the visibility.

[0008] Optional, The risk level of driving behavior is directly proportional to the driver's own risk. The environmental risk level is directly proportional to the risk of the external environment.

[0009] Optionally, the step of invoking the AR-HUD mode based on the driving behavior risk level and the environmental risk level includes: If the driving behavior risk level is greater than a first preset range, or the environmental risk level is greater than a second preset range, the simplified AR-HUD mode will be invoked. If the driving behavior risk level meets the first preset range and the environmental risk level meets the second preset range, the normal AR-HUD mode is invoked. If the risk level of the driving behavior is less than the first preset range, or the risk level of the environment is less than the second preset range, the advanced AR-HUD mode is invoked.

[0010] Optional, The simplified AR-HUD mode is used to display safety information and increase projection brightness; The standard AR-HUD mode displays navigation information, vehicle information, speed limit warnings, and the aforementioned safety information. The advanced AR-HUD mode displays entertainment information, navigation information, vehicle information, speed limit warnings, and safety information.

[0011] Secondly, embodiments of the present invention also provide an AR-HUD adaptive device, comprising: The acquisition unit is used to acquire driving behavior data and environmental data; The calculation unit is used to calculate the driving behavior risk level and environmental risk level based on the driving behavior data, the environmental data, and preset weighting coefficients; The calling unit is used to call the AR-HUD mode based on the driving behavior risk level and the environmental risk level.

[0012] To achieve the above objectives, according to a third aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium comprising a stored program, wherein, when the program is executed by a processor, the steps of the above-described AR-HUD adaptive method are implemented.

[0013] To achieve the above objectives, according to a fourth aspect of the present invention, an electronic device is provided, comprising at least one processor and at least one memory connected to the processor; wherein the processor is configured to invoke program instructions in the memory to execute the steps of the AR-HUD adaptive method described above.

[0014] By employing the aforementioned technical solutions, the AR-HUD adaptive method and related devices provided by this invention address the problem that current AR-HUD display strategies are static and singular, failing to meet the real-time needs of drivers. In this solution, the invention achieves information integration at the data source level by simultaneously acquiring driving behavior data from monitoring devices and environmental data from map APIs and weather sensors, laying the foundation for subsequent collaborative decision-making. Then, preset weighting coefficients are used to weight and calculate the two types of data respectively, resulting in quantified driving behavior risk and environmental risk. This integrates multi-dimensional sensor information into two comparable comprehensive risk indicators, using a weighted model to replace single, rigid threshold judgments, thereby reducing the possibility of misjudgments caused by fluctuations in a single data dimension or interference from specific scenarios. Finally, the corresponding AR-HUD mode is decided and invoked based on the specific numerical combination of these two risk levels.

[0015] Correspondingly, the AR-HUD adaptive device, equipment, and computer-readable storage medium provided in the embodiments of the present invention also have the above-mentioned technical effects.

[0016] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0017] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 A flowchart illustrating an AR-HUD adaptive method provided by an embodiment of the present invention is shown; Figure 2 This diagram illustrates the composition of an AR-HUD adaptive device according to an embodiment of the present invention. Figure 3 A schematic block diagram of an AR-HUD adaptive electronic device provided by an embodiment of the present invention is shown. Detailed Implementation

[0018] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this invention will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.

[0019] To address the problem that current AR-HUD display strategies are static and limited, failing to meet the real-time needs of drivers, this invention provides an adaptive AR-HUD method, such as... Figure 1 As shown, the method includes: S101. Acquire driving behavior data and environmental data; For example, acquiring driving behavior data refers to collecting information reflecting the driver's own state through sensors such as steering wheel angle sensors, eye trackers, and heart rate monitoring devices. This includes distraction index and fatigue index; the distraction index is used to characterize the degree of driver inattention, and the fatigue index is used to characterize the degree of driver fatigue. Acquiring environmental data refers to collecting information reflecting external road and environmental conditions through the Gaode Map API, meteorological sensors, and online weather data. This includes road complexity and visibility; road complexity characterizes complex conditions such as road curvature and congestion, and visibility characterizes the clarity of vision caused by weather conditions.

[0020] In this application, the process of acquiring driving behavior data and environmental data is achieved by collecting multi-dimensional information in real time through various sensors and data interfaces. Driving behavior data is continuously monitored by in-vehicle sensors such as eye trackers and heart rate devices to output distraction index and fatigue index. Environmental data is provided by map service interfaces to provide road condition complexity and by meteorological sensors combined with network data to provide visibility information. For example, when the vehicle is in motion, the system will synchronously acquire data from these sources to ensure that the information on the internal driver status and external environmental conditions is completely captured, providing comprehensive input for subsequent processing.

[0021] By employing the aforementioned technical solution, driving behavior data and environmental data are acquired simultaneously, integrating multi-dimensional information about the driver's internal state and the external environment during the data collection phase. This enables the system to make subsequent decisions based on richer inputs. This integrated acquisition method helps reduce evaluation bias caused by reliance on a single data source and provides solid data support for dynamically adjusting the displayed content, thereby improving the system's comprehensive perception of driving scenarios.

[0022] S102. Calculate the driving behavior risk level and environmental risk level based on the driving behavior data, the environmental data, and preset weighting coefficients; For example, in the steps described above for calculating driving behavior risk and environmental risk based on driving behavior data, environmental data, and preset weighting coefficients, the preset weighting coefficients include a first set of weights for calculating driving behavior risk and a second set of weights for calculating environmental risk. The first set of weights consists of a distraction index weight and a fatigue index weight. The distraction index weight represents the contribution ratio of the distraction index to driving behavior risk, and the fatigue index weight represents the contribution ratio of the fatigue index to driving behavior risk. The sum of the distraction index weight and the fatigue index weight is 1. The second set of weights consists of a road condition complexity weight and a visibility weight. The road condition complexity weight represents the contribution ratio of road condition complexity to environmental risk, and the visibility weight represents the contribution ratio of visibility to environmental risk. The sum of the road condition complexity weight and the visibility weight is 1. Driving behavior risk is a quantitative indicator calculated from comprehensive driving behavior data and is directly proportional to the driver's own risk. Environmental risk is a quantitative indicator calculated from comprehensive environmental data and is directly proportional to the risk of the external environment.

[0023] In this application, the calculation process is implemented by weighting the distraction index and fatigue index in the driving behavior data with the distraction index weight and fatigue index weight in the first set of weights to obtain the driving behavior risk level; at the same time, the road condition complexity and visibility in the environmental data are weighted with the road condition complexity weight and visibility weight in the second set of weights to obtain the environmental risk level; for example, when the system obtains the distraction index and fatigue index, it will adjust the ratio of these two indices according to the preset distraction index weight and fatigue index weight and sum them to output the driving behavior risk level; similarly, for road condition complexity and visibility, the system also performs weighted fusion through corresponding weights to generate the environmental risk level; this calculation method ensures that multi-dimensional data is reasonably integrated into a single risk indicator.

[0024] By employing the aforementioned technical solution, driving behavior data and environmental data are weighted using preset weighting coefficients, enabling risk factors from different sources to be balanced in the assessment based on their actual impact. This weighted fusion method helps reduce judgment bias caused by fluctuations in single data points or scene interference, improving the accuracy and adaptability of risk measurement. At the same time, it simplifies complex data into comparable risk values, providing a clear and consistent input basis for subsequent display mode decisions.

[0025] In one embodiment, the driving behavior data includes a distraction index and a fatigue index, and the environmental data includes road condition complexity and visibility.

[0026] For example, the data types acquired in the above embodiments are specifically defined. Specifically, driving behavior data includes distraction index and fatigue index. The distraction index is a parameter monitored by devices such as eye trackers and used to quantify the degree of driver inattention. The fatigue index is a parameter acquired by devices such as heart rate monitoring devices and used to reflect the driver's mental fatigue state. Environmental data specifically includes road condition complexity and visibility. Road condition complexity refers to parameters obtained from map APIs that describe the complexity of road curvature, traffic congestion, etc. Visibility refers to a parameter reflecting visual clarity derived from a combination of meteorological sensor data and online weather data.

[0027] In this embodiment, the application explicitly defines distraction index and fatigue index as the core assessment criteria for driving behavior risk, and uses road condition complexity and visibility as key inputs for environmental risk, providing a clear and operational data foundation for subsequent risk calculation. During operation, the system continuously collects values ​​of these specific indicators from corresponding dedicated sensors or data interfaces. For example, it tracks line-of-sight deviations using an eye tracker to update the distraction index, determines the fatigue index through heart rate variability analysis, and simultaneously queries map services in real time to obtain information on sharp bends or traffic congestion ahead as road condition complexity, while also calibrating visibility values ​​using data from onboard weather sensors. This explicit definition of data sources ensures that the collected information is directly related to core elements of driving safety.

[0028] By employing the aforementioned technical solutions, driving behavior data is concretized into distraction and fatigue indices, and environmental data is concretized into road condition complexity and visibility. This allows data collection to focus on key dimensions that directly impact driving safety. This specific data definition provides stable and meaningful input for subsequent weighted calculations, enhancing the system's relevance and effectiveness in assessing risk states and laying the necessary data foundation for building reliable risk assessment models.

[0029] In one embodiment, the preset weighting coefficients include: a first set of weights for calculating the driving behavior risk level, and a second set of weights for calculating the environmental risk level, wherein, The first set of weights includes a distraction index weight and a fatigue index weight, the sum of which is 1. The second set of weights includes road condition complexity weights and visibility weights, and the sum of the road condition complexity weights and visibility weights is 1.

[0030] For example, the above embodiments specifically define the composition of the preset weighting coefficients. The preset weighting coefficients include a first set of weights specifically used to calculate driving behavior risk and a second set of weights specifically used to calculate environmental risk. The first set of weights consists of a distraction index weight and a fatigue index weight. The distraction index weight represents the proportion of the distraction index in constituting driving behavior risk, and the fatigue index weight represents the proportion of the fatigue index in constituting driving behavior risk. The sum of the distraction index weight and the fatigue index weight is always one. The second set of weights consists of a road condition complexity weight and a visibility weight. The road condition complexity weight represents the proportion of road condition complexity in constituting environmental risk, and the visibility weight represents the proportion of visibility in constituting environmental risk. The sum of the road condition complexity weight and the visibility weight is also always one.

[0031] In this embodiment, the application establishes a structured proportional relationship for subsequent risk calculation by explicitly dividing the weight coefficients into two groups, each summing to one. During system operation, the distraction index weight and fatigue index weight in the first group are assigned to the distraction index and fatigue index respectively, ensuring that the driving behavior risk can comprehensively reflect the impact of these two aspects according to a preset proportional relationship. Similarly, the road condition complexity weight and visibility weight in the second group are assigned to road condition complexity and visibility respectively, ensuring that the environmental risk can comprehensively reflect the impact of these two aspects according to another preset proportional relationship. This grouping and the setting of internal weights summing to one allows data from different sources and with different dimensions to be organized into a unified measurement system according to their preset importance.

[0032] By employing the aforementioned technical solution, and specifying the pre-defined weighting coefficients into two sets of internal weights that sum to one, different risk factors are assigned clear importance proportions, providing a clear proportional basis for subsequent weighted calculations. This structured weighting helps ensure that the calculation results of driving behavior risk and environmental risk reflect the influence of each component factor in a balanced and reasonable manner, providing an important calculation foundation for generating accurate and reliable risk assessment results.

[0033] In one embodiment, calculating the driving behavior risk level and environmental risk level based on the driving behavior data, the environmental data, and preset weighting coefficients includes: The risk level of the driving behavior is determined by summing the product of the distraction index weight and the distraction index with the product of the fatigue index weight and the fatigue index. The environmental risk level is determined by summing the product of the road condition complexity weight and the road condition complexity with the product of the visibility weight and the visibility.

[0034] For example, the steps described above for calculating driving behavior risk and environmental risk based on driving behavior data, environmental data, and preset weighting coefficients specifically include determining the risk level by multiplying each index by its corresponding weight and then summing the results.

[0035] In this embodiment, the system first acquires real-time updated distraction index and fatigue index as driving behavior data, and simultaneously acquires road condition complexity and visibility as environmental data. Then, the system multiplies the distraction index by its weight to obtain a weighted value for the distraction factor, and multiplies the fatigue index by its weight to obtain a weighted value for the fatigue factor. These two weighted values ​​are then summed to determine the driving behavior risk level. Similarly, the system multiplies road condition complexity by its weight to obtain a weighted value for the road condition factor, and multiplies visibility by its weight to obtain a weighted value for the visibility factor. These two weighted values ​​are then summed to determine the environmental risk level. For example, when the system detects an increase in the driver's distraction index, it calculates its contribution by combining it with the preset distraction index weight and merges it with the impact of the fatigue index to output a comprehensive driving behavior risk level value.

[0036] By employing the above technical solution, the risk level of driving behavior and environmental risk level are calculated using a weighted product summation method. This allows different risk factors to be integrated into a unified metric according to their preset importance ratios. This calculation method helps to balance the influence of various factors during comprehensive assessment, reduces the instability of assessment caused by fluctuations in a single indicator or differences in dimensions, thereby improving the coordination and reliability of risk measurement and providing a more consistent and reasonable input basis for subsequent display mode decisions.

[0037] In one embodiment, The risk level of driving behavior is directly proportional to the driver's own risk. The environmental risk level is directly proportional to the risk of the external environment.

[0038] For example, the above embodiments clarify the mathematical relationship between the two calculated indicators, driving behavior risk and environmental risk, and the actual risks they represent. Driving behavior risk is a quantitative value that combines distraction index and fatigue index, while environmental risk is a quantitative value that combines road condition complexity and visibility.

[0039] In this embodiment, the application establishes a direct proportionality between driving behavior risk and the driver's own risk. This means that as the value of driving behavior risk increases, the operational risk reflected by the driver due to distraction or fatigue also increases. Similarly, environmental risk is directly proportional to the risk of the external environment, meaning that as the value of environmental risk increases, the driving risk reflected by road complexity or poor visibility also increases. For example, when the system calculates that the driving behavior risk increases due to the driver's fatigue index caused by prolonged driving, this increase directly indicates an increase in the risk level brought about by the driver's current state. Correspondingly, when the system calculates an increase in environmental risk due to the vehicle entering a rainstorm area, this value also directly reflects the increased risk brought about by the severity of the external environment.

[0040] By employing the aforementioned technical solution and clearly defining the direct proportional relationship between risk level values ​​and actual risk levels, the calculated driving behavior risk level and environmental risk level can serve as reliable indicators of the true risk level. This direct correspondence ensures that the system's quantitative risk assessment has an intuitive and consistent meaning, providing a logically clear and trustworthy basis for subsequent decisions regarding AR-HUD modes based on these risk level values, and helping to improve the coordination between the system's risk perception and interactive response.

[0041] S103. Invoke the AR-HUD mode based on the driving behavior risk level and the environmental risk level.

[0042] For example, in the above steps of calling AR-HUD mode based on driving behavior risk level and environmental risk level, driving behavior risk level is a quantitative value representing the driver's own risk status, environmental risk level is a quantitative value representing the level of external environmental risk, the first preset interval and the second preset interval are threshold ranges used to divide risk levels and trigger different mode switching, minimalist AR-HUD mode refers to the interface state that only displays safety-related information and increases projection brightness, regular AR-HUD mode refers to the interface state that displays navigation information, vehicle information, speed limit reminders and safety information, and advanced AR-HUD mode refers to the interface state that additionally displays entertainment information on the basis of the former.

[0043] In this application, the invocation process is implemented by comparing the real-time calculated driving behavior risk level and environmental risk level with preset threshold ranges, and deciding on the specific display mode to be activated based on the comparison results. Specifically, when the value of the driving behavior risk level is higher than the first preset range or the value of the environmental risk level is higher than the second preset range, it indicates that the current driver's condition is poor or the external environment is complex. The system will call the simplified AR-HUD mode to focus on key safety information and enhance display visibility. When the values ​​of both risk levels are within their respective preset ranges, it indicates that the risk level is moderate, and the system will call the regular AR-HUD mode to provide basic navigation and driving information. When the driving behavior risk level is lower than the first preset range or the environmental risk level is lower than the second preset range, it indicates that the driving situation is low-risk, and the system will call the advanced AR-HUD mode to allow the display of additional content such as entertainment information while ensuring safety. For example, if the system detects that the driver's fatigue index has increased, causing the driving behavior risk level to exceed the threshold, or the sensor detects that the environmental risk level has increased sharply due to heavy rain, it will immediately switch to the simplified mode to reduce the amount of information on the interface and reduce the cognitive load on the driver.

[0044] By employing the aforementioned technical solution, the risk level indicators obtained from comprehensive assessment are compared in real time with preset ranges, and the corresponding AR-HUD modes are dynamically invoked. This enables the information display strategy to adapt to constantly changing driving scenarios and driver states. This dynamic invocation mechanism based on risk level helps to simplify the interface to reduce information interference in high-risk situations and enrich the display to improve the user experience in low-risk situations. Thus, while ensuring driving safety, it optimizes the adaptability of human-computer interaction and the efficiency of hardware resource allocation.

[0045] In one embodiment, the step of invoking the AR-HUD mode based on the driving behavior risk level and the environmental risk level includes: If the driving behavior risk level is greater than a first preset range, or the environmental risk level is greater than a second preset range, the simplified AR-HUD mode will be invoked. If the driving behavior risk level meets the first preset range and the environmental risk level meets the second preset range, the normal AR-HUD mode is invoked. If the risk level of the driving behavior is less than the first preset range, or the risk level of the environment is less than the second preset range, the advanced AR-HUD mode is invoked.

[0046] For example, the above embodiments specifically specify how to determine which AR-HUD mode to invoke based on the numerical range of driving behavior risk and environmental risk. The first preset range and the second preset range are threshold ranges preset by the system to classify risk levels and trigger mode switching; the minimalist AR-HUD mode refers to an interface state that only displays safety-related information and increases projection brightness; the regular AR-HUD mode refers to an interface state that displays navigation information, vehicle information, speed limit reminders, and safety information; the advanced AR-HUD mode refers to an interface state that displays additional entertainment information on top of the content displayed in the regular mode.

[0047] In this embodiment, the process of mode invocation involves the system continuously comparing the real-time calculated driving behavior risk level with a first preset interval, and simultaneously comparing the environmental risk level with a second preset interval. The corresponding display mode is activated based on a logical combination of the comparison results. Specifically, when the system determines that the driving behavior risk level is higher than the upper limit of the first preset interval, or the environmental risk level is higher than the upper limit of the second preset interval, it indicates a high overall risk. The system immediately invokes the simplified AR-HUD mode, where the interface hides all non-critical information, retaining only safety elements such as collision warning icons and navigation arrows, while automatically increasing projection brightness to ensure clear visibility. When the system determines that the driving behavior risk level is within the first preset interval, and the environmental risk level is also within the second preset interval, it indicates a normal risk level. The system invokes the regular AR-HUD mode, where the interface displays a complete set of driving information, including basic navigation guidance, current vehicle speed, road speed limit reminders, and necessary safety warnings. When the system determines that the value of the driving behavior risk level is lower than the lower limit of the first preset range, or the value of the environmental risk level is lower than the lower limit of the second preset range, it indicates that the driving situation is low-risk. The system will call the advanced AR-HUD mode. At this time, in addition to the regular information, entertainment information such as music playback controls or multimedia notifications will also be displayed in appropriate positions on the interface.

[0048] By employing the aforementioned technical solution, and through real-time comparison of continuously assessed risk indicators with preset threshold ranges, and dynamic switching of display modes according to explicit logical rules, the AR-HUD's interface complexity and information content can adapt to dynamically changing driving scenarios. This risk-level-based decision-making mechanism helps reduce the potential interference from information overload in high-risk situations by simplifying the display, and enhances the completeness of the interactive experience by providing richer information in low-risk situations, thereby optimizing the adaptability and effectiveness of information presentation while ensuring driving safety.

[0049] In one embodiment, The simplified AR-HUD mode is used to display safety information and increase projection brightness; The standard AR-HUD mode displays navigation information, vehicle information, speed limit warnings, and the aforementioned safety information. The advanced AR-HUD mode displays entertainment information, navigation information, vehicle information, speed limit warnings, and safety information.

[0050] For example, the specific display content of the three AR-HUD modes is clearly defined in the above embodiments. The minimalist AR-HUD mode refers to the interface state that only displays safety-related information (such as collision warnings and navigation arrows) and simultaneously increases the projection brightness; the regular AR-HUD mode refers to the complete driving information set that displays navigation information (such as route guidance), vehicle information (such as current speed), speed limit reminders, and safety information; the advanced AR-HUD mode refers to the interface state that adds entertainment information (such as media playback controls) on top of the content displayed in the regular mode.

[0051] In this embodiment, the process of mode invocation involves the system dynamically selecting and activating the corresponding display configuration based on the assessment results of driving behavior risk and environmental risk. When the system decides to invoke the simplified AR-HUD mode, the interface automatically hides all non-critical information, retaining only safety-related elements, and simultaneously adjusts the projection brightness to a higher level to ensure information visibility. When the regular AR-HUD mode is invoked, the interface stably presents core driving information such as navigation, vehicle, speed limits, and safety, meeting general driving needs. When the advanced AR-HUD mode is invoked, the system, while maintaining the display of regular information, additionally loads an entertainment information module to enrich the interactive content. For example, if the system switches to the simplified mode due to a sudden increase in environmental risk, only a high-brightness collision warning icon and directional arrow will appear in the driver's field of vision, eliminating other interference.

[0052] By using the above technical solutions, and by pre-setting differentiated sets of display content for different risk levels, the AR-HUD interface can accurately adjust the information density according to the real-time risk level. This content classification mechanism helps to focus on key security information in high-risk scenarios to reduce cognitive distraction, and provides more comprehensive information support in low-risk scenarios to enhance user experience, thereby improving the contextual adaptability and interaction efficiency of information presentation.

[0053] Furthermore, as a response to the above Figure 1 In addition to the implementation of the method shown, this embodiment of the invention also provides an AR-HUD adaptive device for the above-described method. Figure 1The method shown is implemented accordingly. This device embodiment corresponds to the foregoing method embodiment. For ease of reading, this device embodiment will not repeat the details of the foregoing method embodiment, but it should be clear that the device in this embodiment can implement all the contents of the foregoing method embodiment. Figure 2 As shown, the device includes: an acquisition unit 21, a calculation unit 22, and a recall unit 23, wherein... Acquisition unit 21 is used to acquire driving behavior data and environmental data; The calculation unit 22 is used to calculate the driving behavior risk level and the environmental risk level based on the driving behavior data, the environmental data and the preset weighting coefficients; Calling unit 23 is used to call AR-HUD mode based on the driving behavior risk level and the environmental risk level.

[0054] The processor contains a kernel, which retrieves the corresponding program unit from memory. One or more kernels can be configured, and adjusting kernel parameters can implement an adaptive AR-HUD method, addressing the problem that current AR-HUD display strategies are static and incompatible with the driver's real-time needs.

[0055] This invention provides a computer-readable storage medium including a stored program that, when executed by a processor, implements the AR-HUD adaptive method.

[0056] This invention provides a processor for running a program, wherein the program executes the AR-HUD adaptive method during runtime.

[0057] This invention provides an electronic device, which includes at least one processor and at least one memory connected to the processor; wherein the processor is used to call program instructions in the memory to execute the AR-HUD adaptive method described above. This invention provides an electronic device 30, such as... Figure 3 As shown, the electronic device includes at least one processor 301, and at least one memory 302 and bus 303 connected to the processor; wherein, the processor 301 and the memory 302 communicate with each other through the bus 303; the processor 301 is used to call program instructions in the memory to execute the above-mentioned AR-HUD adaptive method.

[0058] The smart electronic devices mentioned in this article can be PCs, tablets, mobile phones, etc.

[0059] This application also provides a computer program product that, when executed on a process management electronic device, is suitable for executing a program that initializes the steps of the AR-HUD adaptive method described above.

[0060] It should be noted that the descriptions of each embodiment in the above embodiments have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0061] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0062] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0063] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0064] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0065] This application also provides a computer program product, which includes computer software instructions that, when executed on a processing device, cause the processing device to perform actions such as... Figure 1 The control flow of the memory in the corresponding embodiment.

[0066] A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can store or a data storage device such as a server or data center that integrates one or more available media. The available medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0067] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0068] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between apparatuses or units, and may be electrical, mechanical, or other forms.

[0069] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0070] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0071] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0072] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. An AR-HUD adaptive method, characterized in that, include: Acquire driving behavior data and environmental data; The driving behavior risk level and environmental risk level are calculated based on the driving behavior data, the environmental data, and preset weighting coefficients. The AR-HUD mode is invoked based on the driving behavior risk level and the environmental risk level.

2. The method according to claim 1, characterized in that, The driving behavior data includes distraction index and fatigue index, and the environmental data includes road condition complexity and visibility.

3. The method according to claim 2, characterized in that, The preset weighting coefficients include: a first set of weights for calculating the driving behavior risk level, and a second set of weights for calculating the environmental risk level, wherein, The first set of weights includes a distraction index weight and a fatigue index weight, the sum of which is 1. The second set of weights includes road condition complexity weights and visibility weights, and the sum of the road condition complexity weights and visibility weights is 1.

4. The method according to claim 3, characterized in that, The calculation of driving behavior risk and environmental risk based on the driving behavior data, the environmental data, and preset weighting coefficients includes: The risk level of the driving behavior is determined by summing the product of the distraction index weight and the distraction index with the product of the fatigue index weight and the fatigue index. The environmental risk level is determined by summing the product of the road condition complexity weight and the road condition complexity with the product of the visibility weight and the visibility.

5. The method according to claim 4, characterized in that, The risk level of driving behavior is directly proportional to the driver's own risk. The environmental risk level is directly proportional to the risk of the external environment.

6. The method according to claim 1, characterized in that, The step of invoking the AR-HUD mode based on the driving behavior risk level and the environmental risk level includes: If the driving behavior risk level is greater than a first preset range, or the environmental risk level is greater than a second preset range, the simplified AR-HUD mode will be invoked. If the driving behavior risk level meets the first preset range and the environmental risk level meets the second preset range, the normal AR-HUD mode is invoked. If the risk level of the driving behavior is less than the first preset range, or the risk level of the environment is less than the second preset range, the advanced AR-HUD mode is invoked.

7. The method according to claim 6, characterized in that, The simplified AR-HUD mode is used to display safety information and increase projection brightness; The standard AR-HUD mode displays navigation information, vehicle information, speed limit warnings, and the aforementioned safety information. The advanced AR-HUD mode displays entertainment information, navigation information, vehicle information, speed limit warnings, and safety information.

8. An AR-HUD adaptive device, characterized in that, Also includes: The acquisition unit is used to acquire driving behavior data and environmental data; The calculation unit is used to calculate the driving behavior risk level and environmental risk level based on the driving behavior data, the environmental data, and preset weighting coefficients; The calling unit is used to call the AR-HUD mode based on the driving behavior risk level and the environmental risk level.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein, when the program is executed by a processor, it implements the steps of the AR-HUD adaptive method as claimed in any one of claims 1 to 7.

10. An electronic device, characterized in that, The electronic device includes at least one processor and at least one memory connected to the processor; wherein the processor is configured to invoke program instructions in the memory to execute the steps of the AR-HUD adaptive method as described in any one of claims 1 to 7.

Images