Dynamic layered display method, device and electronic equipment

Abstract

This invention discloses a dynamic layered display method, device, and electronic device. The method includes: using multiple sensors in the cockpit to collect multidimensional physiological data of the driver in real time; performing spatiotemporal alignment on the collected raw data to extract key feature vectors; employing a lightweight neural network to perform real-time inference on the key feature vectors to obtain the driver's cognitive load index; and, based on the cognitive load index, calling an adaptive rendering engine to display the information elements of the HUD in a hierarchical manner. The dynamic layered display method, device, and electronic device provided by this invention can solve the problem of the disconnect between the HUD display strategy and the driver's real-time cognitive state in the prior art.

Description

Technical Field

[0001] This application relates to the field of smart cockpit technology, and in particular to dynamic layered display methods, devices and electronic devices. Background Technology

[0002] Existing HUD systems typically employ static or pre-defined information display strategies based on simple scenarios. Mainstream solutions include:

[0003] Fixed information display: Key information such as navigation, vehicle speed, and driver assistance is projected onto the windshield in a fixed layout and size, with information stacked and at a constant density;

[0004] Fixed mode switching: ARHUD typically has four modes: ambient mode, map mode, minimalist mode, and AR mode. You need to manually switch between these modes.

[0005] Passive responses based on vehicle signals: for example, a warning icon pops up temporarily when a collision warning is triggered, but there is a lack of awareness of the driver's own state.

[0006] The main problems with existing technologies are as follows:

[0007] Information overload and distraction risks: Fixed information layouts cannot adapt to dynamically changing driving environments. In complex road conditions (such as urban congestion and inclement weather), excessive AR icons and information streams can significantly increase the driver's cognitive load, thus distracting them from real-world road conditions and creating safety hazards.

[0008] Lack of personalized adaptive capabilities: The system cannot perceive the driver's real-time state (such as fatigue, tension, and concentration). Different drivers or the same driver at different times have different information processing capabilities, and static display strategies cannot adapt to individuals and times, resulting in a poor user experience.

[0009] The interaction logic is rigid and the level of intelligence is low: simple mode switching or responses based on a single signal fail to achieve the fusion analysis and intelligent decision-making of multi-dimensional data (driver status, vehicle dynamics, environment). The system cannot proactively understand the driver's actual needs and processing capabilities like a "caring co-pilot," thus failing to provide appropriate information assistance.

[0010] In summary, the core flaw of existing HUD technology lies in the disconnect between the display strategy and the driver's real-time cognitive state, making it impossible to achieve a dynamic balance between providing rich information and ensuring driving safety. Summary of the Invention

[0011] The purpose of this invention is to provide a dynamic layered display method, device, and electronic device that can solve the problem of the disconnect between the HUD display strategy and the driver's real-time cognitive state in the prior art.

[0012] This invention provides the following solution:

[0013] According to one aspect of the present invention, a dynamic layered display method is provided, the dynamic layered display method comprising:

[0014] Using multiple sensors in the cockpit, the driver's multidimensional physiological data is collected in real time;

[0015] The collected raw data is spatiotemporally aligned to extract key feature vectors;

[0016] A lightweight neural network is used to perform real-time reasoning on key feature vectors to obtain the driver's cognitive load index.

[0017] Based on the cognitive load index, the adaptive rendering engine is invoked to display the information elements of the HUD in a hierarchical manner.

[0018] Optional key feature vectors include: eye-tracking dispersion index, operational complexity score, and environmental threat level;

[0019] Eye movement dispersion index reflects visual search load;

[0020] Operational complexity scores are derived from the frequency and magnitude of steering maneuvers.

[0021] The environmental threat level is determined based on following distance and radius of curvature.

[0022] Optionally, the cognitive load index can be divided into: low load, medium load, and high load;

[0023] Low load indicates that the driver is relaxed and the environment is simple;

[0024] Medium load indicates moderate challenge, but vigilance is still required;

[0025] High load indicates that the processing capacity is exceeded and information needs to be simplified urgently.

[0026] Optionally, based on the cognitive load index, the adaptive rendering engine is invoked to display the information elements of the HUD in a hierarchical manner, including:

[0027] All HUD information elements are categorized into display levels;

[0028] The display will show the cognitive load level associated with the displayed level;

[0029] Based on the current level of cognitive load, the information elements are displayed in a hierarchical manner.

[0030] Optional, also includes:

[0031] The system records the effects of each load assessment and displays the results of adjustments, forming an assessment-adjustment-verification closed loop to continuously optimize strategy parameters.

[0032] Optionally, the system records the effects of each load assessment and displays the results of adjustments, forming an assessment-adjustment-verification closed loop to continuously optimize strategy parameters, including:

[0033] The results were verified through subsequent driver operation smoothness and eye movement patterns.

[0034] Optionally, based on the cognitive load index, the adaptive rendering engine is invoked to display the information elements of the HUD in a hierarchical manner, including:

[0035] When the algorithm detects that the cognitive load index has entered the high load range, it immediately triggers the safety layer mode, automatically hiding non-critical information such as entertainment and POI.

[0036] Optionally, based on the cognitive load index, the adaptive rendering engine is invoked to display the information elements of the HUD in a hierarchical manner, which also includes:

[0037] Under medium to high load conditions, the system prioritizes potential risk warnings visually, using red borders and pulsating animations.

[0038] According to a second aspect of the present invention, a dynamic layered display device is provided, the dynamic layered display device comprising:

[0039] The data acquisition module is used to collect multidimensional physiological data of the driver in real time using multiple sensors in the cockpit.

[0040] The extraction module is used to perform spatiotemporal alignment on the collected raw data and extract key feature vectors.

[0041] The inference module is used to perform real-time inference on key feature vectors using a lightweight neural network to obtain the driver's cognitive load index.

[0042] The display module is used to call the adaptive rendering engine to display the information elements of the HUD in a hierarchical manner based on the cognitive load index.

[0043] According to three aspects of the present invention, an electronic device is provided, the electronic device comprising:

[0044] Processor, communication interface, memory, and communication bus.

[0045] The processor, communication interface, and memory communicate with each other through a communication bus.

[0046] The memory stores a computer program that, when executed by the processor, causes the processor to perform the steps of the dynamic layered display method described above.

[0047] The above solution achieves the following beneficial technical effects:

[0048] Significantly improves driving safety: Focuses on key safety information when the driver is under high load, effectively reducing the probability of accidents caused by information interference, and providing a safer human-machine co-driving interface for advanced intelligent driving;

[0049] Optimize user experience and comfort: The system automatically adjusts to the optimal amount of information, reducing the burden of frequent manual operation and visual search for the driver, and providing a smoother and more personalized interactive experience;

[0050] Enhancing System Intelligence and Added Value: This software framework endows the HUD with "perception-decision" capabilities, upgrading it from a simple information projection screen to an intelligent driving partner, significantly improving the product's technological differentiation and market competitiveness. Attached Figure Description

[0051] Figure 1 This is a flowchart of a dynamic layered display method provided in one or more embodiments of the present invention;

[0052] Figure 2 This is a flowchart of the display operation in the dynamic layered display method provided in one or more embodiments of the present invention;

[0053] Figure 3 This is a flowchart of a dynamic layered display method provided in one or more embodiments of the present invention;

[0054] Figure 4 This is a flowchart of a dynamic layered display method provided in one or more embodiments of the present invention;

[0055] Figure 5 This is a flowchart of a dynamic layered display method provided in one or more embodiments of the present invention;

[0056] Figure 6 This is a structural diagram of a dynamic layered display device provided in one or more embodiments of the present invention;

[0057] Figure 7 This is a structural diagram of an electronic device provided in one or more embodiments of the present invention. Detailed Implementation

[0058] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0059] Figure 1This is a flowchart of a dynamic layered display method provided in one or more embodiments of the present invention. See also... Figure 1 The dynamic layered display method includes the following steps:

[0060] The S11 uses multiple sensors in the cockpit to collect multidimensional physiological data of the driver in real time.

[0061] S12 performs spatiotemporal alignment on the collected raw data and extracts key feature vectors.

[0062] S13 uses a lightweight neural network to perform real-time reasoning on key feature vectors to obtain the driver's cognitive load index.

[0063] S14: Based on the cognitive load index, the adaptive rendering engine is invoked to display the information elements of the HUB in a hierarchical manner.

[0064] With the development of technology, HUDs have been widely used on vehicle windshields.

[0065] However, with the further application of HUDs, their problems have become more apparent. The most significant issue arising during comprehensive application is the disconnect between the content displayed on the HUD and the actual perception of the driver.

[0066] For example, a driver is in a hurry to meet an appointment, feeling very anxious and needing to get there quickly. However, the HUD displays a lot of entertainment information. Understandably, given the driver's current state, he will not only ignore this entertainment, but will also find it distracting from his observation of the road conditions, and will be very annoyed by the information displayed on the HUD.

[0067] For another example, imagine a driver on vacation, completely relaxed. Normally, this would be the perfect time to display entertainment information on the HUD. However, due to insufficient system settings in this regard, very little entertainment information is actually displayed on the HUD.

[0068] The root cause of the above problem lies in the disconnect between the content displayed on the HUD and the actual state of the driver's perception.

[0069] To address the disconnect between HUD display content and the driver's cognitive state, this embodiment provides the following solution.

[0070] First, the cockpit uses different types of sensors to collect physiological data from the driver in various dimensions in real time.

[0071] In this embodiment, the physiological data of the driver collected includes: eye movement trajectory (fixation point, saccade speed), blink frequency, pupil diameter change, heart rate, and muscle tension.

[0072] Among them, eye movement trajectory, blink frequency, and pupil diameter changes are collected by an integrated infrared camera. Heart rate and muscle tension are collected by a steering wheel grip force sensor.

[0073] In addition to the aforementioned physiological parameters, the system further collects dynamic parameters such as vehicle speed, steering angle, yaw rate, acceleration, and brake pedal opening. Furthermore, the system also collects data from the forward-facing camera, millimeter-wave radar, and high-precision map data.

[0074] By fusing the collected data on vehicle speed, steering angle, yaw rate, acceleration, and brake pedal opening, a result reflecting the complexity of vehicle operation and the aggressiveness of driving behavior can be obtained.

[0075] Furthermore, by fusing data from forward-looking cameras, millimeter-wave radar, and high-precision maps, environmental complexity factors such as weather conditions (rain, snow, fog), road type (highway / city), traffic density, and lane line clarity can be identified.

[0076] After the raw data collection is completed, spatiotemporal alignment is performed on the various physical quantities collected.

[0077] The main reason for performing spatiotemporal alignment is to prevent temporal disorder during the processing of raw collected data.

[0078] For example, blood pressure data collected from the driver should be processed along with environmental data collected at the same time. However, if the raw data is not spatiotemporally aligned, it often happens that blood pressure data collected at one moment is treated as if it were collected at the same time, along with environmental data collected at the next moment, and sent to subsequent processing steps. Predictably, such a processing flow will result in a result that is unlikely to accurately reflect the driver's current cognitive state.

[0079] In order to accurately capture the driver's cognitive state at every moment, after collecting all types of raw data, corresponding spatiotemporal alignment is performed on all types of collected data.

[0080] After completing the spatiotemporal alignment operation, the key feature vector extraction operation is performed from the original collected data.

[0081] In this embodiment, the extracted key feature vector includes three parts: eye-tracking dispersion index, operational complexity score, and environmental threat level.

[0082] Among them, the eye movement dispersion index reflects the visual search load.

[0083] The operational complexity score is based on steering frequency and amplitude, and can reflect the complexity of the driver's current driving operation.

[0084] The environmental threat level is based on following distance and radius of curvature, and mainly reflects the degree of threat posed by the vehicle's environment to safe driving.

[0085] After obtaining the key feature vectors mentioned above, inference is performed based on these key feature vectors.

[0086] The result of the reasoning is the driver's current cognitive load index. This reasoning process is performed in real-time via a lightweight neural network.

[0087] In this embodiment, the cognitive load index obtained through reasoning is a score ranging from 1 to 100. When the cognitive load index is between 0 and 30, the driver's cognitive load is considered to be in a low-load state.

[0088] When the cognitive load index is between 31 and 70, the driver's cognitive load index is considered to be at a moderate level.

[0089] When the cognitive load index is between 71 and 100, the driver's cognitive load is considered to be in a high-load state.

[0090] When the cognitive load index is at a low level, the system assumes that the driver is relaxed and the environment is simple.

[0091] When the cognitive load index is at a moderate level, the system considers the driver to be under moderate challenge and needs to remain vigilant.

[0092] When the cognitive load index is at a high level, the system considers it to be close to or beyond the driver's processing capacity and requires urgent simplification of information.

[0093] Next, based on the cognitive load index identified from the driver's cognitive state, the information elements of the HUD are rendered and displayed in a hierarchical manner.

[0094] It's important to note that during tiered rendering and display, in terms of spatial layout, the higher the cognitive load, the more concentrated the information elements should be towards the center of the HUD screen. Conversely, the lower the cognitive load, the more the information elements should be distributed towards the edges of the screen.

[0095] In other words, when the driver's cognitive load is high, the information elements should be displayed centrally on the screen. When the driver's cognitive load is low, the information elements should be distributed across various parts of the screen.

[0096] Regarding the adjustment of visual parameters, when the driver's cognitive load is high, relevant information elements should be displayed in a way that is easier to recognize. Conversely, when the driver's cognitive load is low, the display method of the aforementioned information elements can be adjusted to be less easily recognized.

[0097] Taking icon size as an example, when the driver's cognitive load index is high, relevant icons should be displayed as large icons. When the driver's cognitive load index is not so high, relevant icons can be displayed as relatively small icons.

[0098] Taking color contrast as another example, when the driver's cognitive load index is high, the relevant content should be displayed with a higher color contrast. When the driver's cognitive load index is not so high, a slightly lower color contrast can be used to display the relevant content.

[0099] In terms of interaction logic, a progressive approach is adopted. That is, the HUD interface first displays an icon summary. After the driver's attention is drawn to this icon summary for a predetermined period of time, further information display or interaction is then performed.

[0100] After the above process, the content displayed on the HUD interface will be adjusted according to the driver's current cognitive load index. When the driver's cognitive load index is high, only more urgent information elements will be displayed in an easily identifiable way. When the driver's cognitive load index is low, more information elements can be displayed, and the recognizability of the display can be reduced accordingly. This effectively establishes a correlation between the HUD display content and the current driver's cognitive load index, solving the problem of the disconnect between the display content and the cognitive load status.

[0101] Figure 2 This is a flowchart of the display operation in the dynamic layered display method provided in one or more embodiments of the present invention. See also Figure 2Based on the cognitive load index, the adaptive rendering engine is invoked to display the information elements of the HUB in a hierarchical manner, including the following steps:

[0102] S21, classify all HUD information elements into display levels.

[0103] S22 will display the cognitive load level associated with the displayed level.

[0104] S23, based on the current cognitive load level, perform hierarchical display of information elements.

[0105] This embodiment is based on the foregoing embodiments of the present invention, and further provides specific technical solutions for hierarchical rendering and hierarchical display.

[0106] In this embodiment, the information elements that need to be displayed on the HUD are referred to as HUD information elements.

[0107] Specifically, HUD information elements can be icons, dialog boxes, checkboxes, etc., that need to be displayed. Regardless of the specific type of HUD information element, it should be an element that can be displayed independently on the display interface.

[0108] In order to hierarchically display information elements on the HUD display interface, all HUD information elements that need to be displayed should be divided into certain display levels. Moreover, each specific display level is associated with a certain level of driver cognitive load.

[0109] Since each HUD information element is assigned a specific display level, and each display level is associated with a specific driver cognitive load level, when the driver's cognitive load index is at the corresponding cognitive load level, the system can call the corresponding display rules to determine which HUD information elements should be displayed on the HUD display interface and which HUD information elements should not be displayed on the HUD display interface under the current cognitive load level parameters.

[0110] For example, a HUD information element might be an icon indicating that the current road is closed to traffic. This icon's function is to inform the driver that the current road is impassable. It is assigned the highest display level, which corresponds to the driver's highest cognitive load level. In other words, regardless of whether the displayed cognitive load index is high, medium, or low, the HUD should display this information element according to its own display rules, and should not refuse to display it due to its own display level.

[0111] For another example, a HUD information element might be an icon reminding the driver to maintain a safe distance. This icon's purpose is to remind the driver to keep an appropriate distance from the vehicle in front. This icon is assigned the highest display level, which corresponds to the driver's highest cognitive load level. In other words, regardless of whether the current cognitive load level on the HUD is high, medium, or low, as long as the current road and vehicle conditions determine that the icon's display is necessary, it should be displayed. It should not be denied display simply because of its display level.

[0112] For example, a HUD information element might be a dialog box prompting the driver to turn off the cabin audio. This dialog box is assigned a low display level, which corresponds to the lowest level of driver cognitive load. Therefore, this dialog box will only be displayed on the HUD if the driver is currently at the lowest level of cognitive load. If the driver's cognitive load is at a medium or high level, the dialog box will not be displayed on the HUD.

[0113] By associating each HUD information element with a corresponding display level and linking each display level to the corresponding driver cognitive load level, the HUD can determine whether to push the information element on the HUD under the current circumstances based on the current cognitive load index level and the display level to which the information element is bound, thus further establishing the association between the HUD display content and the driver cognitive load index.

[0114] Figure 3 This is a flowchart of a dynamic layered display method provided in one or more embodiments of the present invention. See also... Figure 3 The dynamic layered display method includes the following steps:

[0115] The S31 uses multiple sensors in the cockpit to collect multidimensional physiological data of the driver in real time.

[0116] S32 performs spatiotemporal alignment on the collected raw data and extracts key feature vectors.

[0117] S33 uses a lightweight neural network to perform real-time reasoning on key feature vectors to obtain the driver's cognitive load index.

[0118] S34, based on the cognitive load index, calls the adaptive rendering engine to display the information elements of the HUB in a hierarchical manner.

[0119] S35: The system records the effect of each load assessment and displays the adjustment, forming an assessment-adjustment-verification closed loop to continuously optimize strategy parameters.

[0120] This embodiment is based on the foregoing embodiments of the present invention, and further provides a specific implementation method for optimizing and adjusting the display strategy.

[0121] It should be understood that the needs of the driver and passengers in the cockpit for the content displayed on the HUD change over time. This is also true in scenarios where a correlation is established between the displayed content and the driver's cognitive load index.

[0122] For example, what was once considered crucial display content may have become less important over time. For instance, when new energy vehicles were less common and charging stations were scarce, it was necessary to immediately notify the driver upon discovering a nearby charging station so they could charge their vehicle promptly. However, with the development of technology and the increasing prevalence of new energy vehicles, more and more public charging stations have been built. Therefore, it's no longer necessary to immediately notify the driver when a usable charging station is found nearby.

[0123] The previous example concerned a specific piece of content for which the display rating needed adjustment. In specific application scenarios, in addition to adjusting the display rating parameters over time, the number of display rating levels, the classification method of the cognitive load index, and the correlation between display ratings and cognitive load levels may also need to be adjusted.

[0124] For example, the simplest display rating is divided into three levels: low, medium, and high. Cognitive load levels are also divided into three levels: low, medium, and high. In this case, a low display rating corresponds to a low cognitive load level, a medium display rating corresponds to a medium cognitive load level, and a high display rating corresponds to a high cognitive load level.

[0125] As time went on, engineers realized that the classification of cognitive load levels was too simplistic and could not meet actual needs, so they needed to add corresponding cognitive load levels. Therefore, the cognitive load levels were changed to five: low, low-medium, medium, high-medium, and high.

[0126] Due to the change in the way cognitive load levels are classified, the correspondence between the new classification and the actual display level also needs to be changed. Under the new cognitive load classification system, low cognitive load levels correspond to low display levels, low-medium, medium, and high-medium cognitive load levels correspond to medium display levels, and high cognitive load levels correspond to high display levels.

[0127] The aforementioned strategy parameter optimization and adjustment process should be based on the actual application effects obtained during the execution of the strategy parameters, and further adjustments should be made accordingly. Specifically, in this embodiment, further adjustments are made based on feedback from cockpit occupants, especially the driver. This feedback may include the frequency with which the driver observes the HUD display, the frequency with which the driver turns off the HUD, the driver's facial expressions when observing the HUD, and so on.

[0128] In one or more typical implementations, the above parameter optimization and adjustment process is verified by subsequent driver operation smoothness and eye movement patterns.

[0129] By obtaining the driver's feedback on the HUD, the feedback is analyzed to determine the direction for further adjustments to the strategy parameters, and then the aforementioned adjustment operations are executed.

[0130] Through the above feedback and adjustment mechanism, a closed loop of evaluation-adjustment-verification of strategy parameters can be established, and the strategy parameters in the HUD display process can be continuously adjusted accordingly, so that the display process of HUD display elements can be continuously optimized over time.

[0131] Figure 4 This is a flowchart of a dynamic layered display method provided in one or more embodiments of the present invention. See also... Figure 4 The dynamic layered display method includes the following steps:

[0132] The S41 uses multiple sensors in the cockpit to collect multidimensional physiological data of the driver in real time.

[0133] S42 performs spatiotemporal alignment on the collected raw data and extracts key feature vectors.

[0134] S43 uses a lightweight neural network to perform real-time reasoning on key feature vectors to obtain the driver's cognitive load index.

[0135] S44. When the algorithm detects that the cognitive load index has entered the high load range, it immediately triggers the security layer mode and automatically hides non-critical information such as entertainment and POI.

[0136] This embodiment is based on the foregoing embodiments of the present invention and further explains the process of hierarchical display.

[0137] Specifically, this embodiment focuses on explaining the display process of relevant information elements by the HUD when the cognitive load index enters the high load range.

[0138] The driver's current cognitive load index is in the high load range, indicating that the driver needs to concentrate on driving. Therefore, the HUD display interface should minimize distractions for the driver and only display the necessary content.

[0139] Specifically, this means immediately activating the HUD's safe mode. In safe mode, the HUD automatically hides less important information such as entertainment and Points of Interest (POIs). This hiding process significantly reduces the number of information elements actually displayed on the HUD. Thus, the HUD display is instantly cleaned up, with only the most essential information elements actually shown.

[0140] In this way, when under high cognitive load, the safety mode is activated, which significantly reduces the number of information elements actually displayed on the HUD. This allows the driver to focus more on driving itself and not be distracted by the content displayed on the HUD, thus ensuring driving safety.

[0141] Under high cognitive load, the HUD display mode corresponds to the safe mode. Conversely, under medium cognitive load, the HUD display mode corresponds to the simplified mode. In simplified mode, although the number of displayed elements is not as small as in safe mode, it is still simplified.

[0142] Under low cognitive load conditions, the HUD's display mode corresponds to full mode. In full mode, all information elements are displayed unconditionally. That is, in full mode, the information elements are not simplified in any way.

[0143] Figure 5 This is a flowchart of a dynamic layered display method provided in one or more embodiments of the present invention. See also... Figure 5 The dynamic layered display method includes the following steps:

[0144] The S51 uses multiple sensors in the cockpit to collect multidimensional physiological data of the driver in real time.

[0145] S52 performs spatiotemporal alignment on the collected raw data and extracts key feature vectors.

[0146] S53 uses a lightweight neural network to perform real-time reasoning on key feature vectors to obtain the driver's cognitive load index.

[0147] S54, under medium to high load conditions, the system prioritizes the visual warnings of potential risks in advance, using a red border and pulsating animation.

[0148] This embodiment is based on the foregoing embodiments of the present invention, and further describes the implementation of the hierarchical display.

[0149] More specifically, this embodiment focuses on the process of displaying potential risk warnings by the system under medium to high load conditions.

[0150] It should be understood that highlighting potential risks during driving is crucial for improving driving safety. However, in actual HUD displays, these potential risk warnings are often mixed in with various other displayed information, making it difficult to truly capture the attention of the driver and other occupants.

[0151] In response to the above situation, this embodiment provides a display method for potential risk warnings, which enables occupants, including the driver, to quickly observe the information displayed on the HUD, thereby accelerating the identification of potential risks.

[0152] In this embodiment, potential risk warnings displayed on the HUD are given higher priority. Specifically, a red border is added to the display elements of potential risk warnings. With the addition of the red border, the potential risk warnings become more prominent than other display elements on the interface, making them more likely to attract the attention of the driver and other occupants of the vehicle.

[0153] In addition to the red border, the potential risk warning also features a pulsating animation. Compared to traditional display methods, the pulsating animation is a more novel approach and is more likely to attract attention.

[0154] By adding a red border and a pulsating animation, potential risk warnings can more easily attract the attention of occupants, including the driver, thereby improving their ability to recognize potential risks and enhancing driving safety.

[0155] Figure 6 This is a structural diagram of a dynamic layered display device provided in one or more embodiments of the present invention. See also... Figure 6 The dynamic layered display device includes:

[0156] The acquisition module 61 is used to collect multidimensional physiological data of the driver in real time using multiple sensors in the cockpit.

[0157] The extraction module 62 is used to perform spatiotemporal alignment on the collected raw data and extract key feature vectors.

[0158] The reasoning module 63 is used to perform real-time reasoning on key feature vectors using a lightweight neural network to obtain the driver's cognitive load index.

[0159] Display module 64 is used to call the adaptive rendering engine to display the information elements of the HUD in a hierarchical manner based on the cognitive load index.

[0160] It is worth noting that although only some basic functional modules are disclosed in the embodiments of this invention, it does not mean that the composition of this system is limited to the above-mentioned basic functional modules. On the contrary, what this embodiment intends to express is that, based on the above-mentioned basic functional modules, those skilled in the art can arbitrarily add one or more functional modules in combination with existing technology to form an infinite number of embodiments or technical solutions. That is to say, this system is open rather than closed. The fact that this embodiment only discloses a few basic functional modules should not be considered as the scope of protection of the claims of this invention being limited to the disclosed basic functional modules. At the same time, for the convenience of description, the above device is described separately according to its functions as various units and modules. Of course, in implementing this invention, the functions of each unit and module can be implemented in one or more software and / or hardware.

[0161] like Figure 7 As shown, the present invention also provides an electronic device, including: a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the dynamic layered display method.

[0162] Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. For example... Figure 7 The structure shown in this embodiment of the invention includes an electronic device comprising one or more processors 710 and a memory 720; the processors 710 in this electronic device may be one or more. Figure 7 Taking a processor 710 as an example; a memory 720 is used to store one or more programs; the one or more programs are executed by the one or more processors 710, so that the one or more processors 710 implement the dynamic layered display method as described in any one embodiment of the present invention.

[0163] The electronic device may also include an input device 730 and an output device 740.

[0164] The processor 710, memory 720, input device 730, and output device 740 in this electronic device can be connected via a bus or other means. Figure 7 Taking the example of a connection between China and Israel via a bus.

[0165] The memory 720 in this electronic device serves as a computer-readable storage medium, capable of storing one or more programs. These programs can be software programs, computer-executable programs, or modules, such as the program instructions / modules corresponding to the dynamic layered display method provided in this embodiment. The processor 710 executes various functional applications and data processing of the electronic device by running the software programs, instructions, and modules stored in the memory 720, thereby implementing the dynamic layered display method described in the above embodiment.

[0166] The memory 720 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the electronic device. Furthermore, the memory 720 may include high-speed random access memory and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some instances, the memory 720 may further include memory remotely located relative to the processor 710, which can be connected to the device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0167] Input device 730 can be used to receive input digital or character information, and to generate key signal inputs related to user settings and function control of the electronic device. Output device 740 may include display devices such as a display screen.

[0168] The present invention also provides a computer-readable storage medium, comprising: storing a computer program executable by a vehicle, wherein when the computer program is run on the vehicle, the vehicle performs the steps of the dynamic layered display method.

[0169] Specifically, the computer storage medium in this embodiment of the invention can be any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. For example, a computer-readable storage medium can be—but is not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this embodiment, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0170] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention 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 or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A dynamic layered display method, characterized in that, The dynamic layered display method includes: Using multiple sensors in the cockpit, the driver's multidimensional physiological data is collected in real time; The collected raw data is spatiotemporally aligned to extract key feature vectors; A lightweight neural network is used to perform real-time reasoning on key feature vectors to obtain the driver's cognitive load index. Based on the cognitive load index, the adaptive rendering engine is invoked to display the information elements of the HUD in a hierarchical manner.

2. The method according to claim 1, characterized in that, Key feature vectors include: eye-tracking dispersion index, operational complexity score, and environmental threat level; Eye movement dispersion index reflects visual search load; Operational complexity scores are derived from the frequency and magnitude of steering maneuvers. The environmental threat level is determined based on following distance and radius of curvature.

3. The method according to claim 1, characterized in that, Cognitive load index is divided into: low load, medium load, and high load; Low load indicates that the driver is relaxed and the environment is simple; Medium load indicates moderate challenge, but vigilance is still required; High load indicates that the processing capacity is exceeded and information needs to be simplified urgently.

4. The method according to claim 1, characterized in that, Based on the cognitive load index, the adaptive rendering engine is invoked to display the HUD information elements in a hierarchical manner, including: All HUD information elements are categorized into display levels; The display will show the cognitive load level associated with the displayed level; Based on the current level of cognitive load, the information elements are displayed in a hierarchical manner.

5. The method according to claim 1, characterized in that, Also includes: The system records the effects of each load assessment and displays the results of adjustments, forming an assessment-adjustment-verification closed loop to continuously optimize strategy parameters.

6. The method according to claim 5, characterized in that, The system records the effects of each load assessment and adjustment, forming an assessment-adjustment-verification closed loop to continuously optimize strategy parameters, including: The results were verified through subsequent driver operation smoothness and eye movement patterns.

7. The method according to claim 1, characterized in that, Based on the cognitive load index, the adaptive rendering engine is invoked to display the HUD information elements in a hierarchical manner, including: When the algorithm detects that the cognitive load index has entered the high load range, it immediately triggers the safety layer mode, automatically hiding non-critical information such as entertainment and POI.

8. The method according to claim 1, characterized in that, Based on the cognitive load index, the adaptive rendering engine is invoked to display the HUD information elements in a hierarchical manner, including: Under medium to high load conditions, the system prioritizes potential risk warnings visually, using red borders and pulsating animations.

9. A dynamic layered display device, characterized in that, The dynamic layered display device includes: The data acquisition module is used to collect multidimensional physiological data of the driver in real time using multiple sensors in the cockpit. The extraction module is used to perform spatiotemporal alignment on the collected raw data and extract key feature vectors. The inference module is used to perform real-time inference on key feature vectors using a lightweight neural network to obtain the driver's cognitive load index. The display module is used to call the adaptive rendering engine to display the information elements of the HUD in a hierarchical manner based on the cognitive load index.

10. An electronic device, characterized in that, The electronic device includes: Processor, communication interface, memory, and communication bus. The processor, communication interface, and memory communicate with each other through a communication bus. The memory stores a computer program that, when executed by the processor, causes the processor to perform the steps of the dynamic layered display method according to any one of claims 1 to 8.

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