Vehicle head-up display adjustment method, electronic device, storage medium and program product

Abstract

The application provides a vehicle head-up display adjustment method, an electronic device, a storage medium and a program product, relates to the technical field of vehicle control, and is used for predicting the motion sickness state of a driver to adjust the display effect of the head-up display in advance, thereby relieving or preventing the motion sickness reaction of the driver. The method comprises the following steps: acquiring physiological characteristics of the driver and driving data of the vehicle; the physiological characteristics comprise electroencephalogram signals, electrooculogram signals and electromyogram signals of the driver, and the driving data comprises the driving speed and driving trajectory of the vehicle; inputting the physiological characteristics and the driving data into a preset prediction model to predict the motion sickness state of the driver and obtain a prediction result; the prediction result comprises the motion sickness type of the driver and the corresponding motion sickness probability; determining a corresponding control strategy based on the prediction result, and adjusting the display effect of the head-up display of the vehicle based on the control strategy; wherein the adjusted head-up display is used for weakening the visual stimulation that induces the driver to produce the motion sickness reaction.

Description

Technical Field

[0001] This application relates to the field of vehicle control technology, and in particular to a vehicle head-up display adjustment method, electronic device, storage medium, and program product. Background Technology

[0002] Augmented reality head-up display (AR-HUD) technology, as the core interactive carrier of smart cockpits, accurately overlays virtual information such as navigation guidance, vehicle parameters, and environmental warnings onto the driver's real-world field of vision, and has been widely used in aerospace, high-end automobiles, and other fields. Its core value lies in allowing the driver to maintain a head-up position at all times, obtaining key information without having to look down to switch their gaze, thereby significantly improving operational convenience and driving safety.

[0003] However, with the increasing immersion and information complexity of AR-HUDs, motion sickness has become a major bottleneck restricting the widespread adoption of this technology. Motion sickness stems from "information conflict" within the human sensory system. Specifically, when the signals received by the inner ear's vestibular system are inconsistent with those received by the visual system, the brain cannot integrate this contradictory information, triggering a defensive physiological response that manifests as dizziness, nausea, cold sweats, or even vomiting. Summary of the Invention

[0004] This application provides a vehicle head-up display adjustment method, electronic device, storage medium, and program product. By acquiring the driver's physiological characteristics and vehicle driving data, the method predicts the driver's motion sickness state and then adjusts the display effect of the vehicle's head-up display based on the prediction results. This reduces the visual stimulation that causes motion sickness before the driver experiences severe motion sickness, thereby preventing or alleviating motion sickness.

[0005] In a first aspect, this application provides a method for adjusting a vehicle head-up display, comprising: acquiring the driver's electroencephalogram (EEG), electrooculogram (EOG), and electromyogram (EMG) signals, and driving data including the vehicle's speed and trajectory; inputting the physiological characteristics and driving data into a preset prediction model to predict the driver's motion sickness state and obtain a prediction result; the prediction result includes the driver's motion sickness type and the corresponding motion sickness probability, wherein the motion sickness type characterizes the dominant cause that induces the driver's motion sickness reaction; determining a corresponding control strategy based on the prediction result, and adjusting the display effect of the vehicle's head-up display based on the control strategy; wherein the adjusted head-up display is used to weaken the visual stimuli that induce the driver's motion sickness reaction.

[0006] The technical solution provided in this application offers at least the following beneficial effects: First, by collecting multimodal physiological characteristics of the driver, such as electroencephalogram (EEG), electrooculogram (EOG), and electromyogram (EMG), and combining this with vehicle speed and trajectory data, the driver's physiological state can be comprehensively captured from dimensions such as neurocognition, visual behavior, and muscle tension, solving the problem of the one-sided perception caused by traditional technologies relying solely on a single external parameter. Second, in the prediction stage, multidimensional physiological characteristics and vehicle driving data are input into a preset prediction model. This model can determine the relationship between the changing trends of physiological signals and the future driving state of the vehicle. The system outputs the type and probability of motion sickness, enabling a shift from reactive response to proactive prediction, significantly improving the timeliness of motion sickness prevention intervention. Finally, during the adjustment phase, differentiated control strategies are determined based on the prediction results. Parameters such as the information density, refresh rate, fusion degree, and visual anchor point of the head-up display are adjusted for different types of motion sickness. This alleviates motion sickness while avoiding oversimplification of the displayed content, which could affect driving safety. Ultimately, a multi-objective balance is achieved between motion sickness prevention, driver attention load, and information display efficiency, effectively solving the problems of adjustment lag, single strategy, and safety hazards currently existing in AR-HUD technology.

[0007] In one possible implementation, a corresponding control strategy is determined based on the prediction results, and the display effect of the vehicle's head-up display is adjusted based on the control strategy, including: when the motion sickness probability is greater than a first preset probability and less than a second preset probability, adjusting the information density of the head-up display to be less than or equal to the first preset density, and adjusting the refresh rate of the head-up display to be greater than or equal to the first preset refresh rate, wherein the information density is negatively correlated with the motion sickness probability, and the refresh rate is positively correlated with the motion sickness probability.

[0008] Based on the above implementation method, firstly, by setting a specific trigger range where the motion sickness probability is greater than a first preset probability and less than a second preset probability, the driver's motion sickness state is identified, thereby initiating a gradual, mild intervention. This avoids the drastic changes in the head-up display content caused by directly taking extreme concealment measures, effectively preventing psychological discomfort and new safety hazards caused by visual abruptness, and improving the smoothness of human-computer interaction. Secondly, a continuous mapping relationship between parameters and probabilities is introduced in the adjustment mechanism. On the one hand, it is clear that information density is negatively correlated with motion sickness probability, that is, as the risk of motion sickness increases, unnecessary visual information is dynamically reduced proportionally, directly reducing the processing load and cognitive pressure of the visual system, weakening the visual stimuli that induce motion sickness from the source. On the other hand, it is clear that refresh rate is positively correlated with motion sickness probability, that is, as the risk of motion sickness increases, the screen update speed is increased synchronously, thereby effectively reducing image ghosting and visual delay, enhancing the synchronization between virtual information and real physical movement, and alleviating motion sickness reaction.

[0009] In one implementation, a corresponding control strategy is determined based on the prediction results, and the display effect of the vehicle's head-up display is adjusted based on the control strategy, including: when the motion sickness probability is greater than or equal to a second preset probability and the motion sickness type is visually dominant motion sickness, adjusting the information density of the head-up display to be less than or equal to a second preset density, and adjusting the fusion degree of the head-up display to be greater than or equal to a first preset fusion degree; the second preset density is less than the first preset density, and the fusion degree characterizes the fit between the head-up display and the vehicle's external environment, and the fusion degree is positively correlated with the motion sickness probability.

[0010] Based on the above implementation, firstly, by setting a trigger condition where the motion sickness probability is greater than or equal to a second preset probability and the type is visually dominant, severe visual conflict states are identified, achieving dual-dimensional hierarchical control based on the severity and type of motion sickness. Secondly, the information density is adjusted to be less than or equal to a second preset density, and this density is less than a first preset density, thereby reducing the amount of visual information in the head-up display in severe visually dominant motion sickness scenarios, reducing the visual processing load, and eliminating the core stimulus that induces motion sickness at its root. Thirdly, a fusion parameter is introduced and adjusted to be greater than or equal to a first preset fusion degree, and the fusion degree is positively correlated with the motion sickness probability; as the motion sickness risk increases, the fit between the head-up display and the external environment is dynamically enhanced, making the virtual information closely fit the real road, effectively reducing the difference between vision and real physical movement, and alleviating sensory conflict. Finally, through the control strategy of reducing information density and increasing environmental fusion, environmental fit replaces information stacking in visually dominant motion sickness scenarios, effectively suppressing motion sickness reactions while ensuring the driver's basic perception of road conditions, achieving a reasonable balance between anti-motion sickness effect and driving safety.

[0011] In one implementation, a corresponding control strategy is determined based on the prediction result, and the display effect of the vehicle's head-up display is adjusted based on the control strategy, including: when the motion sickness probability is greater than or equal to a second preset probability, and the motion sickness type is vestibular dominant motion sickness, adjusting the refresh rate of the head-up display to be greater than or equal to the second preset refresh rate, and adjusting the visual anchor point of the head-up display based on the electrooculogram signal; the second preset refresh rate is greater than the first preset refresh rate.

[0012] Based on the above implementation, firstly, by setting a trigger condition where the motion sickness probability is greater than or equal to a second preset probability and the type is vestibular dominant, severe vestibular conflict is identified, achieving dual-dimensional hierarchical control based on the severity and type of motion sickness. Secondly, the refresh rate is adjusted to be greater than or equal to a second preset refresh rate, which is also greater than a first preset refresh rate. This improves the refresh speed of the head-up display in severe vestibular dominant motion sickness scenarios, reduces image ghosting and visual delay, and enhances the synchronization between virtual information and real physical motion. Thirdly, the visual anchor point of the head-up display is dynamically adjusted based on electrooculogram (EOG) signals. By tracking the driver's gaze focus and eye movement in real time using EOG signals, the visual anchor point is precisely matched to the driver's current gaze position, providing a stable visual reference benchmark for the vestibular system. This effectively reduces the difference in motion information between visual perception and inner ear vestibular perception, fundamentally alleviating vestibular dominant motion sickness. Ultimately, by increasing the refresh rate and setting dynamic visual anchor points, the strategy of enhancing visual-vestibular consistency instead of simple information adjustment was adopted in severe vestibular-dominant motion sickness scenarios. This effectively suppressed motion sickness reactions while ensuring the driver's continuous access to key information, achieving a reasonable balance between anti-motion sickness effect and driving safety.

[0013] In one implementation, adjusting the visual anchor point of the head-up display based on electrooculogram (EOG) signals includes: identifying the driver's gaze point based on the EOG signals; generating visual anchor points with a preset radius around the gaze point; and determining the content of the visual anchor points based on driving data.

[0014] Based on the above implementation, firstly, the driver's gaze point is identified based on electrooculography (EOG) signals to obtain the location of the driver's gaze, providing objective physiological data for the dynamic positioning of visual anchor points. This avoids the problem of heightened sensory conflict caused by the mismatch between fixed or preset anchor points and the driver's actual gaze area. Secondly, visual anchor points with a preset radius are generated around the gaze point, allowing the anchor points to dynamically adjust their positions as the driver's gaze moves. This provides the vestibular system with a stable visual reference benchmark consistent with the driver's current visual focus area, effectively reducing the information difference between the physical motion perceived by the inner ear vestibular system and the virtual motion perceived by vision, fundamentally alleviating motion sickness. Thirdly, the content of the visual anchor points is determined based on driving data; that is, the displayed content of the anchor points is related to the vehicle's current speed and trajectory. This ensures that the anchor point information not only has a visual stabilizing effect but also provides the driver with auxiliary references related to the current driving state, avoiding the problem of anchor point content being out of touch with actual road conditions and thus distracting the driver's attention. Ultimately, through the linkage mechanism of gaze point recognition, anchor point following, and content-related driving data, the visual anchor point can be synchronized with the driver's line of sight in spatial position and consistent with the vehicle's movement state in terms of information content. This achieves the precision, dynamism, and contextualization of the visual anchor point, effectively improving the mitigation of motion sickness and ensuring driving safety.

[0015] In one implementation, after adjusting the display effect of the vehicle's head-up display based on a control strategy, the method further includes: when the driving speed is greater than or equal to a first preset speed and the probability of motion sickness is greater than a second preset probability, adjusting the refresh rate of the head-up display to be greater than or equal to a second preset refresh rate; when the driving speed is less than or equal to a second preset speed and the probability of motion sickness is greater than a second preset probability, adjusting the blending degree of the head-up display to be greater than or equal to a second preset blending degree; the second preset blending degree is greater than the first preset blending degree, and the second preset speed is less than the first preset speed.

[0016] Based on the above implementation, firstly, after executing the control strategy based on the prediction results, the combined state of driving speed and motion sickness probability is continuously monitored. In high-speed, severe motion sickness scenarios where the driving speed is greater than or equal to a first preset speed and the motion sickness probability is greater than a second preset probability, the refresh rate is increased to greater than or equal to the second preset refresh rate. By increasing the screen update speed, the ghosting and delay of virtual information under high-speed movement are reduced, enhancing the synchronization between virtual information and the rapidly changing real scene, effectively alleviating the increased motion sickness caused by visual lag in high-speed scenarios. Secondly, in low-speed, severe motion sickness scenarios where the driving speed is less than or equal to the second preset speed and the motion sickness probability is greater than the second preset probability, the fusion degree is increased to greater than or equal to the second preset fusion degree. By enhancing the fit between the head-up display and the external environment, the virtual information is more closely aligned with the real road in complex road conditions such as low-speed crawling or frequent starts and stops, providing the driver with a more stable visual reference and alleviating motion sickness caused by sensory information mismatch in low-speed scenarios.

[0017] In one implementation, the electrooculogram (EOG) signal includes gaze duration distribution and blink interval. After adjusting the display effect of the vehicle's head-up display based on the control strategy, the method further includes: acquiring the information loss rate; the information loss rate characterizes the degree of reduction in the information density of the head-up display; determining the driver's attentional distraction level based on the gaze duration distribution and blink interval; increasing the information density of the head-up display when the information loss is greater than or equal to a preset loss rate; and increasing the number of visual anchor points when the attentional distraction level is greater than or equal to a preset distraction level.

[0018] Based on the above implementation, firstly, after executing the control strategy, the information loss rate is obtained to quantify the degree of reduction in the head-up display's information density. Simultaneously, the fixation duration distribution and blink interval in the electrooculogram (EOG) signal are used to determine the driver's level of distraction, thereby enabling real-time monitoring and evaluation of the actual driving state after anti-motion sickness adjustment. Secondly, when the information loss rate is greater than or equal to a preset loss rate, the information density of the head-up display is proactively increased to prevent the driver from being unable to obtain necessary driving information due to excessive information reduction in anti-motion sickness adjustment, thus avoiding negative impacts on driving safety caused by the anti-motion sickness strategy. Thirdly, when the level of distraction is greater than or equal to a preset level of distraction, the number of visual anchor points is increased to provide more visual reference points for drivers with inattentiveness, guiding their gaze back to a stable area.

[0019] Secondly, this application provides a vehicle head-up display adjustment device, comprising: The acquisition module is used to acquire the driver's physiological characteristics and the vehicle's driving data; the physiological characteristics include the driver's electroencephalogram (EEG), electrooculogram (EOG), and electromyogram (EMG) signals, and the driving data includes the vehicle's speed and trajectory. The processing module is used to input physiological characteristics and driving data into a preset prediction model to predict the driver's motion sickness state and obtain prediction results. The prediction results include the driver's motion sickness type and the corresponding motion sickness probability. The motion sickness type represents the dominant cause that induces the driver's motion sickness reaction. The control module is used to determine the corresponding control strategy based on the prediction results, and adjust the display effect of the vehicle's head-up display based on the control strategy; the adjusted head-up display is used to reduce the visual stimuli that induce motion sickness in the driver.

[0020] In one implementation, the control module is further configured to, when the motion sickness probability is greater than a first preset probability and less than a second preset probability, adjust the information density of the head-up display to be less than or equal to the first preset density and adjust the refresh rate of the head-up display to be greater than or equal to the first preset refresh rate, wherein the information density is negatively correlated with the motion sickness probability and the refresh rate is positively correlated with the motion sickness probability.

[0021] In one implementation, the control module is further configured to, when the motion sickness probability is greater than or equal to a second preset probability and the motion sickness type is visually dominant motion sickness, adjust the information density of the head-up display to be less than or equal to a second preset density and adjust the fusion degree of the head-up display to be greater than or equal to a first preset fusion degree; the second preset density is less than the first preset density, and the fusion degree characterizes the fit between the head-up display and the vehicle's external environment, and the fusion degree is positively correlated with the motion sickness probability.

[0022] In one implementation, the control module is further configured to, when the motion sickness probability is greater than or equal to a second preset probability and the motion sickness type is vestibular dominant motion sickness, adjust the refresh rate of the head-up display to be greater than or equal to the second preset refresh rate, and adjust the visual anchor point of the head-up display based on the electrooculogram signal; the second preset refresh rate is greater than the first preset refresh rate.

[0023] In one implementation, the control module is further configured to: identify the driver's gaze point based on electrooculography (EOG) signals; generate visual anchor points with a preset radius around the gaze point; and determine the content of the visual anchor points based on driving data.

[0024] In one implementation, the control module is further configured to: adjust the refresh rate of the head-up display to be greater than or equal to the second preset refresh rate when the driving speed is greater than or equal to the first preset speed and the probability of dizziness is greater than the second preset probability; and adjust the blending degree of the head-up display to be greater than or equal to the second preset blending degree when the driving speed is less than or equal to the second preset speed and the probability of dizziness is greater than the second preset probability; the second preset blending degree is greater than the first preset blending degree and the second preset speed is less than the first preset speed.

[0025] In one implementation, the control module is further configured to: acquire the information loss rate; the information loss rate characterizes the degree of reduction in the information density of the head-up display; determine the driver's attentional distraction level based on the gaze duration distribution and blink interval; increase the information density of the head-up display when the information loss is greater than or equal to the preset loss rate; and increase the number of visual anchor points when the attentional distraction level is greater than or equal to the preset distraction level.

[0026] Thirdly, this application provides an electronic device, including: a processor and a memory, the processor being coupled to the memory; the memory being used to store computer instructions, which are loaded and executed by the processor to enable the computer device to implement the method described in the first aspect.

[0027] Fourthly, this application provides a computer-readable storage medium comprising: computer software instructions; which, when executed in an electronic device, cause the electronic device to implement the method described in the first aspect.

[0028] Fifthly, this application provides a computer program product comprising a computer program; when the computer program is run in an electronic device, it causes the electronic device to implement the method described in the first aspect.

[0029] The beneficial effects of the second to fifth aspects mentioned above are described in the corresponding description of the first aspect and will not be repeated here. Attached Figure Description

[0030] Figure 1A schematic flowchart illustrating a vehicle head-up display adjustment method provided in an embodiment of this application; Figure 2 A schematic flowchart illustrating another vehicle head-up display adjustment method provided in this application embodiment; Figure 3 A schematic flowchart illustrating another vehicle head-up display adjustment method provided in this application embodiment; Figure 4 This is a schematic diagram of the structure of a vehicle head-up display adjustment device provided in an embodiment of this application; Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0031] In this article, the term "and / or" is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone.

[0032] The terms "first" and "second," etc., used in the specification and drawings of this application are used to distinguish different objects or to distinguish different treatments of the same object, rather than to describe a specific order of objects.

[0033] Furthermore, the terms "comprising" and "having," and any variations thereof, used in the description of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the steps or units listed, but may optionally include other steps or units not listed, or may optionally include other steps or units inherent to such process, method, product, or apparatus.

[0034] It should be noted that in the embodiments of this application, the words "exemplary" or "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of the words "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0035] To facilitate a clear description of the technical solutions of the embodiments of this application, the terms "first" and "second" are used in the embodiments of this application to distinguish the same or similar items with essentially the same function and effect. Those skilled in the art can understand that the terms "first" and "second" are not intended to limit the quantity or execution order.

[0036] In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0037] Augmented reality head-up display (AR-HUD) technology overlays virtual information (such as navigation guidance, vehicle parameters, and environmental warnings) onto the real-world field of view. It is widely used in automotive, aerospace, and industrial equipment applications. Its core advantage is that users do not need to look down at the screen, improving ease of operation and safety. However, AR-HUD technology currently suffers from significant motion sickness, severely hindering its widespread adoption.

[0038] Motion sickness primarily stems from the body's misperception of motion. When the inner ear's vestibular system (responsible for balance and spatial positioning) receives signals inconsistent with the visual system (processing AR-HUD image information), motion sickness is triggered, manifesting as dizziness, nausea, paleness, and cold sweats. Current AR-HUD display strategies are mostly static and fixed, only subject to simple adjustments based on external parameters such as ambient brightness and vehicle speed. They fail to perceive the real-time physiological differences of individual users, leading to varying degrees of motion sickness among different users. This is especially pronounced during prolonged use or in complex road conditions, where motion sickness symptoms can worsen.

[0039] Brain-computer interface (BCI) technology can collect physiological signals such as electroencephalogram (EEG) and electrooculogram (EOG) from a user's brain through sensors, enabling direct information interaction between the brain and external devices. It has been applied in scenarios such as motion sickness relief in virtual reality (VR). However, current anti-motion sickness solutions based on adjusting head-up displays have the following drawbacks: 1. Delayed adjustment: Current solutions adjust based on the current state after motion sickness occurs, and cannot prevent motion sickness in advance. When severe motion sickness is detected, the driver has already experienced obvious discomfort, affecting driving safety.

[0040] 2. Limited adjustment strategy: The underlying cause of motion sickness is not distinguished, and a uniform adjustment method is used for all types of motion sickness, resulting in significant differences in the relief effect for motion sickness responses caused by different reasons.

[0041] 3. Safety hazards exist: Overly pursuing anti-motion sickness effects by simplifying information display or adding fixed horizon anchors can distract the driver and increase driving risks.

[0042] Based on this, this application provides a method for adjusting a vehicle head-up display (HUD). This method predicts motion sickness based on the driver's physiological characteristics and vehicle driving data, and then adaptively adjusts the HUD's display effect. By reducing visual stimuli that trigger motion sickness before severe symptoms appear, it achieves proactive prevention and effective relief of the driver's motion sickness response.

[0043] The embodiments provided in this application will now be described in detail with reference to the accompanying drawings.

[0044] The vehicle head-up display adjustment method provided in this application can be applied to electronic devices. These electronic devices can be servers, such as a server cluster consisting of multiple servers, a single server, a computer, or a processor or processing chip within a server or computer. For example, the electronic device can also be a terminal, such as a mobile phone, tablet computer, wearable device, in-vehicle device, laptop computer, or ultra-mobile personal computer. Mobile personal computers (UMPCs), netbooks, personal digital assistants (PDAs), etc. It should be noted that the embodiments of this application do not limit the specific form of the electronic device.

[0045] Figure 1 This is a flowchart illustrating a vehicle head-up display adjustment method provided in an embodiment of this application. Figure 1 As shown, the vehicle head-up display adjustment method provided in this application can be implemented by the aforementioned electronic device, specifically including S101~S103.

[0046] S101. Obtain the driver's physiological characteristics and the vehicle's driving data.

[0047] Motion sickness is essentially an information conflict between the human sensory systems (vision, vestibular system, and proprioception), and this conflict is directly reflected in changes in the driver's physiological signals. Meanwhile, the vehicle's motion is the external physical cause of motion sickness. Therefore, collecting data on the driver's internal physiological state and the vehicle's external motion state can provide a comprehensive and reliable data foundation for subsequent motion sickness prediction.

[0048] The physiological characteristics include the driver's electroencephalogram (EEG), electrooculogram (EOG), and electromyogram (EMG) signals, while the driving data includes the vehicle's speed and trajectory. The EEG signals reflect the electrical activity of neurons in the cerebral cortex and can be used to identify the driver's fatigue level, cognitive load, and abnormal activation patterns in brain regions during the early stages of motion sickness. The EOG signals reflect eye movements and blinking behavior and can be used to track motion sickness-related features such as gaze direction, gaze stability, and nystagmus. The EMG signals reflect muscle electrical activity and can be used to detect changes in muscle tension caused by motion sickness (such as abnormal contractions of neck and facial muscles).

[0049] Specifically, EEG signals are collected via a flexible dry electrode array in the car seat headrest (contacting only the back of the driver's head); EOG signals are collected via an infrared eye-tracking camera integrated into the head-up display optical module; EMG signals are collected via an electromyography (EMG) sensor integrated into the steering wheel grip (sensing the tension of hand and neck muscles); driving speed and trajectory are directly read from the vehicle's CAN bus. Other sensors can also be used, as long as they can capture the physiological characteristics related to driver motion sickness.

[0050] Meanwhile, automotive-grade electromagnetic shielding materials are used to filter electromagnetic interference from the engine and motor; motion compensation algorithms are used to filter signal noise caused by vehicle vibration.

[0051] For example, firstly, algorithms such as bandpass filtering, denoising, and baseline correction are used to filter out vehicle environmental noise, power frequency interference, and motion artifacts during driving from the original signal to obtain a pure physiological signal; among them, the filtering range of the EEG signal is set to 0.5-30Hz, with a focus on preserving the alpha, beta, and theta wave features related to motion sickness.

[0052] Then, the electronic device extracts motion sickness features specific to the automotive scene through time-domain and frequency-domain analysis. Specifically, for EEG: the phase synchronization of theta waves (4-8Hz) and alpha waves (8-13Hz) is extracted (this indicator has a much higher correlation with motion sickness than the single power percentage during vehicle acceleration and deceleration); for EOG: the acceleration peak of eye saccades is extracted (unconscious eye movement characteristics caused by driver visual-vestibular conflict); for EMG: the high-frequency burst signal of the sternocleidomastoid muscle in the neck is extracted (a specific indicator of compensatory tension of neck muscles during vehicle turning).

[0053] Furthermore, the electronic device normalizes the extracted feature parameters to eliminate feature fluctuations caused by differences in the physiological characteristics of different users and to unify the feature dimensions. Finally, the preprocessed physiological signals are aligned with the acceleration and angular velocity signals output from the vehicle's CAN bus at the millisecond level to filter out physiological noise unrelated to vehicle motion (such as electromyographic interference caused by the driver's cough or speech).

[0054] S102. Input physiological characteristics and driving data into a preset prediction model to predict the driver's motion sickness state and obtain the prediction result.

[0055] Raw physiological signals and driving data cannot be directly used for decision-making. By fusing and analyzing these multimodal data through predictive models, motion sickness-related feature patterns can be extracted, and predictive results with clear semantics can be output, providing a quantitative basis for subsequent control strategy selection.

[0056] The prediction model is a trained data processing model (such as a machine learning classifier or a deep learning network). Its input is a fusion vector of multimodal physiological features and driving data, and its output is the motion sickness type and motion sickness probability. This model is trained using a large number of labeled samples (containing physiological signals and driving data under different motion sickness states). The prediction results include the driver's motion sickness type and corresponding motion sickness probability. The motion sickness type represents the dominant cause inducing the driver's motion sickness response, mainly including categories such as "visual-dominated" (caused by inconsistency between motion perceived by the visual system and motion perceived by the vestibular system) and "vestibular-dominated" (caused by a mismatch between acceleration changes perceived by the vestibular system and visual information). The motion sickness probability is a quantitative value between 0 and 1, representing the likelihood that the driver is currently in a motion sickness state, and is used to measure the severity of motion sickness.

[0057] Specifically, the collected EEG, EEG, and EMG signals are preprocessed (filtering, denoising, and feature extraction), and then concatenated with data such as driving speed and driving trajectory to form a unified multimodal feature vector. This vector is then input into the prediction model for forward inference, and the resulting motion sickness type classification and corresponding motion sickness probability value are output.

[0058] For example, the normalized slope of physiological characteristic changes, the current driving trajectory, and the vehicle driving trajectory data (turning radius, acceleration and deceleration amplitude, tunnel entrance, etc.) provided by the navigation system for the next 3-10 seconds are input into the prediction model. The model outputs the possible types of motion sickness for the driver in the next 3-10 seconds and the corresponding motion sickness probability.

[0059] S103. Determine the corresponding control strategy based on the prediction results, and adjust the display effect of the vehicle's head-up display based on the control strategy.

[0060] Different types and probabilities of motion sickness correspond to different triggers and severity levels, requiring differentiated interventions. By establishing a mapping relationship between prediction results and control strategies, electronic devices can select the optimal adjustment scheme for different scenarios, achieving precise intervention.

[0061] Among them, the control strategy refers to a set of adjustment rules pre-set according to the type and probability of motion sickness, which specifies how to adjust various display parameters of the head-up display (such as information density, refresh rate, blending degree, visual anchor point, etc.) under specific prediction results; the display effect refers to the comprehensive performance of the visual information presented to the driver by the head-up display, including the amount of information, the screen update speed, the degree of fit between virtual information and the real environment, the position of visual reference points, etc. The adjusted head-up display is used to weaken the visual stimuli that induce motion sickness in the driver.

[0062] Specifically, the electronic device internally presets a strategy mapping table or decision logic. For example, when the motion sickness probability is in the mild range, the information density is moderately reduced; when the motion sickness probability is in the severe range and the type is visually dominant, the information density is significantly reduced and the fusion is increased; when the type is vestibular dominant, the refresh rate is increased and the visual anchor point is adjusted. After the strategy is determined, the corresponding display parameters are adjusted through the head-up display controller.

[0063] For example, when the predicted probability of motion sickness is ≥60%, an early warning intervention is triggered; when the probability of motion sickness is ≥80%, a severe intervention is triggered.

[0064] In one implementation, the electronic device can also continuously optimize the accuracy of the prediction model based on feedback from the driver during use (such as manually marking motion sickness sensations or manually adjusting image parameters), adapting to the physiological differences and driving habits of different drivers.

[0065] Specifically, early warning intervention (probability of motion sickness 60%-80%): initiate mild adjustments in advance, reduce information density by 10%, and increase refresh rate by 10Hz to prevent motion sickness from actually occurring.

[0066] Visually-driven motion sickness (probability of motion sickness ≥80%): Prioritize adjusting information density (hiding unnecessary entertainment / auxiliary information) and fusion (increasing to over 70%) to reduce the complexity of virtual images.

[0067] Vestibular-dominant motion sickness (probability ≥80%): Prioritize adjusting dynamic visual anchor points and refresh rates (increase to 120Hz or higher) to enhance visual-vestibular consistency.

[0068] In one implementation, S103 can be implemented using S103A: S103A. When the probability of motion sickness is greater than the first preset probability and less than the second preset probability, adjust the information density of the head-up display to be less than or equal to the first preset density, and adjust the refresh rate of the head-up display to be greater than or equal to the first preset refresh rate.

[0069] Because the severity of motion sickness varies among drivers, this application provides specific adjustment parameters and directions for a particular scenario involving low to moderate motion sickness risk (i.e., motion sickness probability between a first preset probability and a second preset probability). The aim is to take appropriate, rather than excessive, intervention measures before the motion sickness reaction becomes severe, effectively alleviating the motion sickness trend while avoiding sudden changes in the head-up display effect due to excessive adjustment, which could interfere with the driver.

[0070] Information density is negatively correlated with motion sickness probability, while refresh rate is positively correlated. That is, as the probability of motion sickness increases, the information density of the head-up display gradually decreases, while the refresh rate gradually increases. The first and second preset probabilities are two pre-defined motion sickness probability thresholds used to classify the risk levels of motion sickness. For example, the first preset probability is the dividing point between mild and moderate, and the second preset probability is the dividing point between moderate and severe. Information density refers to the number of information elements or the amount of information presented in the head-up display per unit display area. Excessive information density increases the driver's visual processing load and exacerbates the perceptual conflict between the visual and vestibular systems. Refresh rate refers to the number of times the head-up display screen updates per second, measured in Hz. Insufficient refresh rate can cause image ghosting and delays, making the movement of virtual information asynchronous with real physical movement, thus inducing or exacerbating motion sickness.

[0071] Specifically, the electronic system determines whether the motion sickness probability falls within the range of "greater than a first preset probability and less than a second preset probability." If the condition is met, the corresponding control strategy is triggered: on the one hand, the information density of the head-up display is reduced to less than or equal to the first preset density by hiding non-critical information (such as entertainment information, secondary navigation prompts, etc.) and simplifying display elements to reduce visual load; on the other hand, the refresh rate of the head-up display is increased to greater than or equal to the first preset refresh rate by accelerating the screen update frequency to reduce visual delay and ghosting. The two adjustments are executed simultaneously or sequentially, synergistically weakening the visual stimuli that induce motion sickness from two dimensions: "reducing information input" and "improving screen smoothness," achieving early intervention in the moderate motion sickness risk stage and preventing the motion sickness reaction from worsening further.

[0072] Based on the implementation of S103A, firstly, a specific trigger range is set where the motion sickness probability is greater than a first preset probability and less than a second preset probability to identify the driver's motion sickness state. This initiates a gradual, mild intervention, avoiding drastic changes in the head-up display content caused by directly taking extreme concealment measures. This effectively prevents psychological discomfort and new safety hazards caused by visual abruptness, improving the smoothness of human-computer interaction. Secondly, a continuous mapping relationship between parameters and probabilities is introduced in the adjustment mechanism. On the one hand, it is clear that information density is negatively correlated with motion sickness probability. That is, as the risk of motion sickness increases, unnecessary visual information is dynamically reduced proportionally, directly reducing the processing load and cognitive pressure of the visual system, weakening the visual stimuli that induce motion sickness from the source. On the other hand, it is clear that refresh rate is positively correlated with motion sickness probability. That is, as the risk of motion sickness increases, the screen update speed is increased synchronously, effectively reducing image ghosting and visual delay, enhancing the synchronization between virtual information and real physical movement, and alleviating motion sickness.

[0073] In one implementation, S103 can also be implemented via S103B: When the motion sickness probability is greater than or equal to the second preset probability and the motion sickness type is visually dominant motion sickness, adjust the information density of the head-up display to be less than or equal to the second preset density, and adjust the blending degree of the head-up display to be greater than or equal to the first preset blending degree.

[0074] For scenarios with severe motion sickness risk and a visually dominant trigger, more robust intervention measures are needed. This is because the core cause of visually dominant motion sickness lies in the significant conflict between motion information perceived by the visual system and physical motion information perceived by the vestibular system, and the content displayed on the head-up display is one of the main sources of visual information. When the probability of motion sickness reaches the severe range, simply reducing information density and increasing the refresh rate is insufficient to effectively alleviate the conflict; more aggressive interventions are required to effectively curb the further deterioration of severe motion sickness.

[0075] Among them, the second preset density is less than the first preset density. The fusion degree is a parameter that characterizes the degree of fit between the virtual information displayed in the head-up display and the real scene of the vehicle's external environment. The higher the fusion degree, the closer the virtual information fits the real road environment in terms of position, perspective relationship, and motion synchronization. The smaller the difference between the virtual and real perceived by the driver, the lower the fusion degree is. The fusion degree is positively correlated with the probability of motion sickness. Visual-dominant motion sickness is a type of motion sickness. It refers to the fact that the dominant cause of motion sickness inducing the driver's motion sickness reaction is the perceptual conflict between the visual system and the vestibular system. Specifically, the motion state perceived by the driver through vision (such as the motion of the virtual information displayed in the head-up display) is inconsistent with the actual physical motion perceived by the inner ear vestibular system, thereby causing motion sickness symptoms such as dizziness and nausea.

[0076] Specifically, the electronic device first determines whether the probability of motion sickness is greater than or equal to a second preset probability, and simultaneously determines whether the motion sickness type is vision-dominant. If both conditions are met, a heavily vision-dominant control strategy is triggered: on the one hand, the information density of the head-up display is reduced to less than or equal to the second preset density, by hiding most non-critical information (only retaining core safety information such as vehicle speed and collision warning), minimizing the information processing load on the vision system; on the other hand, the fusion degree of the head-up display is increased to greater than or equal to the first preset fusion degree, by enhancing the perspective accuracy, positional fit, and motion synchronization of virtual information, making the content displayed on the head-up display more visually integrated into the real road scene, effectively reducing the difference between the virtual motion perceived by the driver's visual system and the real physical motion perceived by the vestibular system, fundamentally alleviating visual-vestibular sensory conflict.

[0077] Based on the implementation of S103B described above, firstly, by setting a trigger condition where the motion sickness probability is greater than or equal to a second preset probability and the type is visually dominant, severe visual conflict states are identified, achieving dual-dimensional hierarchical control based on the severity and type of motion sickness. Secondly, the information density is adjusted to be less than or equal to a second preset density, and this density is less than a first preset density, thereby reducing the amount of visual information in the head-up display in severe visually dominant motion sickness scenarios, reducing the visual processing load, and eliminating the core stimulus source that induces motion sickness at its root. Thirdly, a fusion parameter is introduced and adjusted to be greater than or equal to a first preset fusion degree, and the fusion degree is positively correlated with the motion sickness probability; as the motion sickness risk increases, the fit between the head-up display and the external environment is dynamically enhanced, making the virtual information closely fit the real road, effectively reducing the difference between vision and real physical movement, and alleviating sensory conflict. Finally, through the control strategy of reducing information density and increasing environmental fusion, environmental fit replaces information stacking in visually dominant motion sickness scenarios, effectively suppressing motion sickness reactions while ensuring the driver's basic perception of road conditions, achieving a reasonable balance between anti-motion sickness effect and driving safety.

[0078] In one implementation, S103 can also be implemented via S103C: S103C: When the motion sickness probability is greater than or equal to the second preset probability and the motion sickness type is vestibular dominant motion sickness, adjust the refresh rate of the head-up display to be greater than or equal to the second preset refresh rate, and adjust the visual anchor point of the head-up display based on the electrooculogram signal.

[0079] The core cause of vestibular-dominant motion sickness lies in the significant mismatch between the acceleration changes perceived by the inner ear vestibular system and the motion information received by the visual system. The crux of the problem is not excessive information, but rather the instability of the visual reference and the insufficient synchronization between virtual information and physical motion. Therefore, following the control strategy for visually dominant motion sickness described in S103B, this application further provides a control strategy for vestibular-dominant motion sickness.

[0080] The second preset refresh rate is greater than the first preset refresh rate. Visual anchors refer to fixed or semi-fixed visual elements in the head-up display that provide a stable visual reference for the driver. In vestibular-dominant motion sickness scenarios, the role of visual anchors is to provide the vestibular system with a visual reference benchmark consistent with the physical motion state, helping the brain calibrate the perceptual differences between vision and the vestibular system.

[0081] Specifically, the electronic device first determines whether the probability of motion sickness is greater than or equal to a second preset probability, and simultaneously determines whether the type of motion sickness is vestibular dominant. If both conditions are met, a severe vestibular dominant intervention strategy is triggered: on the one hand, the refresh rate of the head-up display is increased to be greater than or equal to the second preset refresh rate. By significantly increasing the screen update frequency, the movement changes of virtual information are kept highly synchronized with the actual physical movements of the vehicle, such as acceleration and steering, eliminating visual-vestibular information misalignment caused by screen delay and reducing sensory conflict in the time dimension; on the other hand, the driver's electrooculogram (EOG) signals are collected in real time to extract eye movement trajectory and gaze direction information. Based on this, the display position of the visual anchor point is dynamically adjusted so that the anchor point always follows the driver's gaze focus, providing the vestibular system with a stable reference benchmark consistent with the driver's current visual focus area, and helping the brain calibrate the perceptual differences between vision and vestibular systems in the spatial dimension.

[0082] Based on the implementation of S103C described above, firstly, by setting a trigger condition where the motion sickness probability is greater than or equal to a second preset probability and the type is vestibular dominant, severe vestibular conflict is identified, achieving dual-dimensional hierarchical control based on the severity and type of motion sickness. Secondly, the refresh rate is adjusted to be greater than or equal to a second preset refresh rate, which is also greater than a first preset refresh rate. This improves the refresh rate of the head-up display in severe vestibular dominant motion sickness scenarios, reduces image ghosting and visual delay, and enhances the synchronization between virtual information and real physical motion. Thirdly, the visual anchor point of the head-up display is dynamically adjusted based on electrooculogram (EOG) signals. By tracking the driver's gaze focus and eye movement in real time using EOG signals, the visual anchor point is precisely matched to the driver's current gaze position, providing a stable visual reference benchmark for the vestibular system. This effectively reduces the difference in motion information between visual perception and inner ear vestibular perception, fundamentally alleviating vestibular dominant motion sickness. Ultimately, by increasing the refresh rate and setting dynamic visual anchor points, the strategy of enhancing visual-vestibular consistency instead of simple information adjustment was adopted in severe vestibular-dominant motion sickness scenarios. This effectively suppressed motion sickness reactions while ensuring the driver's continuous access to key information, achieving a reasonable balance between anti-motion sickness effect and driving safety.

[0083] In one implementation, the adjustment of the visual anchor point of the head-up display based on the electrooculogram signal in S103C can be achieved through S103C1-S103C2: S103C1, Identifying the driver's gaze point based on electrooculogram (EOG) signals.

[0084] The core of intervention for vestibular-dominant motion sickness lies in providing a stable visual reference point, and visual anchor points only achieve maximum effectiveness when placed in the area the driver is currently focusing on. If the anchor point is off-center from the driver's line of sight, it not only fails to provide an effective visual reference but may also become a source of visual distraction. Therefore, it is essential to accurately determine the driver's current line of sight first.

[0085] The fixation point represents the two-dimensional mapping position of the driver's current three-dimensional spatial coordinates on the display plane of the head-up display. It represents the driver's current visual attention center.

[0086] The electronic device continuously collects electrooculography (EOG) signals from the driver's face and the area around their eyes using sensors integrated within the head-up display (HUD) optical module. After bandpass filtering and noise reduction of the raw signals, characteristic waveforms related to horizontal and vertical eye movements are extracted. Using a pre-calibrated conversion matrix between the EOG signal amplitude and the eye deflection angle, the eye deflection angle is calculated in real time. Then, combining this with the geometric relationship between the driver's eye position and the HUD projection plane, the deflection angle is converted into gaze point coordinates in the HUD coordinate system.

[0087] S103C2: Generate visual anchor points with a preset radius around the gaze point.

[0088] Because the human eye exhibits minute involuntary saccades (microsaccades) during natural fixation, the fixation point is a dynamically fluctuating area rather than an absolutely fixed point. If the visual anchor point is precisely aligned with the instantaneous fixation point, it will cause frequent jittering within the visual field, exacerbating visual discomfort. By generating anchor points around the fixation point with a preset radius, a tolerance buffer is provided for the visual reference, allowing the anchor points to stably cover the driver's actual fixation area, providing a smooth and stable visual reference benchmark.

[0089] After acquiring the coordinates of the current gaze point, the electronic device generates a circular, cross-shaped, or dot-matrix visual anchor point graphic in the head-up display rendering engine, using these coordinates as the center and a preset radius. This graphic has sufficient contrast and recognizability to be quickly identified as a stable spatial reference by the driver's vestibular-visual integration system in complex road backgrounds. When the gaze point moves, the anchor point smoothly transitions, avoiding abrupt changes in position.

[0090] Furthermore, the content of the visual anchor points is determined based on driving data. Specifically, electronic devices read data such as current driving speed, acceleration, and steering angle in real time from the vehicle's CAN bus or chassis domain controller. The head-up display rendering engine dynamically generates anchor point content based on this data: for example, when the vehicle is detected to be turning left, the anchor point content is automatically adjusted to an arc marker with left-turn curvature; when the vehicle accelerates rapidly, a longitudinal acceleration indicator element is added to the anchor point. By mapping driving data to the visual representation of anchor points in real time, the driver can simultaneously receive visual motion feedback that is completely consistent with the body's vestibular perception when looking at the anchor point, thereby effectively calibrating the visual-vestibular perception difference and alleviating vestibular-dominant motion sickness.

[0091] For example, the electronic device can track the driver's gaze point in real time based on the EOG signal and generate a semi-transparent stable anchor point with a radius of 5cm around the gaze point; the content of the anchor point is synchronized with the vehicle's driving status (such as displaying a steering direction arrow when turning), providing visual reference without distracting attention.

[0092] Based on the implementation of S103C1-S103C2 described above, firstly, the driver's gaze point is identified based on electrooculography (EOG) signals to obtain the location of the driver's gaze. This provides objective physiological data for the dynamic positioning of visual anchor points, avoiding the problem of heightened sensory conflict caused by the mismatch between fixed or preset anchor points and the driver's actual gaze area. Secondly, visual anchor points with a preset radius are generated around the gaze point, allowing the anchor points to dynamically adjust their positions as the driver's gaze moves. This provides the vestibular system with a stable visual reference consistent with the driver's current visual focus area, effectively reducing the information difference between the physical motion perceived by the inner ear vestibular system and the virtual motion perceived by vision, fundamentally alleviating motion sickness. Thirdly, the content of the visual anchor points is determined based on driving data; that is, the displayed content of the anchor points is related to the vehicle's current speed and trajectory. This ensures that the anchor point information not only has a visual stabilizing effect but also provides the driver with auxiliary references related to the current driving state, avoiding the problem of anchor point content being out of touch with actual road conditions and thus distracting the driver's attention. Ultimately, through the linkage mechanism of gaze point recognition, anchor point following, and content-related driving data, the visual anchor point can be synchronized with the driver's line of sight in spatial position and consistent with the vehicle's movement state in terms of information content. This achieves the precision, dynamism, and contextualization of the visual anchor point, effectively improving the mitigation of motion sickness and ensuring driving safety.

[0093] Based on the implementation methods of S101-S103 described above, firstly, by collecting multimodal physiological characteristics of the driver, such as electroencephalogram (EEG), electrooculogram (EOG), and electromyogram (EMG), and combining them with vehicle speed and trajectory data, the driver's physiological state can be comprehensively captured from dimensions such as neurocognition, visual behavior, and muscle tension, solving the problem of perception bias caused by traditional technologies relying on only a single external parameter. Secondly, in the prediction stage, the multidimensional physiological characteristics and vehicle driving data are input into a preset prediction model. This model can predict the future driving state of the vehicle based on the correlation between the changing trends of physiological signals and the future driving state of the vehicle. The system outputs the type and probability of motion sickness, enabling a shift from reactive response to proactive prediction, significantly improving the timeliness of motion sickness prevention intervention. Finally, during the adjustment phase, differentiated control strategies are determined based on the prediction results. Parameters such as the information density, refresh rate, fusion degree, and visual anchor point of the head-up display are adjusted for different types of motion sickness. This alleviates motion sickness while avoiding oversimplification of the displayed content, which could affect driving safety. Ultimately, a multi-objective balance is achieved between motion sickness prevention, driver attention load, and information display efficiency, effectively solving the problems of adjustment lag, single strategy, and safety hazards currently existing in AR-HUD technology.

[0094] In actual driving, adjusting the head-up display needs to consider not only the driver's motion sickness but also the actual driving status of the vehicle. Therefore, in one implementation, after S103, the method also includes S104-S105: S104. When the driving speed is greater than or equal to the first preset speed and the probability of dizziness is greater than the second preset probability, adjust the refresh rate of the head-up display to be greater than or equal to the second preset refresh rate.

[0095] In high-speed driving scenarios, the relative speed between the vehicle and its surroundings is extremely fast, and the scene changes frequently. If the HUD refresh rate is insufficient, virtual information will produce noticeable ghosting and delay, causing the virtual motion perceived by the visual system to lag significantly behind the real physical motion perceived by the vestibular system, thus drastically exacerbating motion sickness. Therefore, after implementing existing control strategies, introducing a dynamic correction mechanism based on speed can ensure that the refresh rate is adequately guaranteed in high-speed, severe motion sickness scenarios.

[0096] After executing the control strategies outlined above, the electronic device continuously monitors the vehicle's speed and motion sickness probability. When the speed is detected to be greater than or equal to a first preset speed and the motion sickness probability is greater than a second preset probability, regardless of the previous control strategy, the head-up display's refresh rate is increased to be greater than or equal to the second preset refresh rate. This adjustment is achieved through the timing-driven module of the head-up display controller, specifically including increasing the frame generation frequency, shortening the frame buffer latency, and optimizing the rendering pipeline priority, ensuring that the movement rhythm of virtual information remains highly synchronized with real physical movement in high-speed, severe motion sickness scenarios.

[0097] S105. When the driving speed is less than or equal to the second preset speed and the probability of dizziness is greater than the second preset probability, adjust the blending degree of the head-up display to be greater than or equal to the second preset blending degree.

[0098] Unlike high-speed scenarios, in low-speed scenarios (such as congested traffic, frequent starts and stops, and U-turns on narrow roads), the vehicle's movement changes frequently but at low speeds. In these situations, the main factor inducing motion sickness is not screen delay, but rather the spatial misalignment between virtual information and the real environment. For example, when turning or changing lanes at low speeds, if the virtual information displayed in the head-up view fails to accurately match the perspective and positional changes of the real road, the driver will experience a noticeable "floating" and "displacement" sensation. This mismatch between the virtual and real worlds is particularly pronounced in complex low-speed driving scenarios. Therefore, for low-speed, severe motion sickness scenarios, the focus of adjustment shifts from refresh rate to blending accuracy, alleviating motion sickness by enhancing the fit between the virtual and real worlds.

[0099] Among them, the second preset fusion degree is greater than the first preset fusion degree, and the second preset speed is less than the first preset speed.

[0100] After executing the control strategy described in the preceding steps, the electronic device continuously monitors the vehicle's speed and the probability of motion sickness. When the speed is detected to be less than or equal to a second preset speed, and the probability of motion sickness is greater than or equal to a second preset probability, the electronic device forcibly increases the head-up display blending degree to be greater than or equal to a second preset blending degree. This adjustment is achieved through the augmented reality rendering module of the head-up display, specifically including: improving the perspective projection accuracy of virtual information, enhancing the geometric alignment between virtual elements and real road features (such as lane lines and curbs), optimizing the virtual-real occlusion relationship, and improving the environmental adaptability of color and brightness, so that virtual information is visually integrated into the real road scene almost seamlessly.

[0101] For example, when the probability of motion sickness is ≥80% on a straight highway (vehicle speed ≥80km / h), the refresh rate is increased to 120Hz while the blending degree remains at 40%; when the probability of motion sickness is ≥80% in urban congestion (vehicle speed ≤40km / h), the blending degree is increased to 70% while the refresh rate remains at 60Hz; and the brightness adjustment range is reduced by 30% for nighttime driving to avoid strong light stimulation.

[0102] Based on the implementation of S104-S105 above, firstly, after executing the control strategy based on the prediction results, the combined state of driving speed and motion sickness probability is continuously monitored. In high-speed, severe motion sickness scenarios where the driving speed is greater than or equal to a first preset speed and the motion sickness probability is greater than a second preset probability, the refresh rate is increased to greater than or equal to the second preset refresh rate. By increasing the screen update speed, the ghosting and delay of virtual information under high-speed motion are reduced, enhancing the synchronization between virtual information and the rapidly changing real scene, effectively alleviating the increased motion sickness caused by visual lag in high-speed scenarios. Secondly, in low-speed, severe motion sickness scenarios where the driving speed is less than or equal to the second preset speed and the motion sickness probability is greater than the second preset probability, the fusion degree is increased to greater than or equal to the second preset fusion degree. By enhancing the fit between the head-up display and the external environment, the virtual information is more closely aligned with the real road in complex road conditions such as low-speed crawling or frequent starts and stops, providing the driver with a more stable visual reference and alleviating motion sickness caused by sensory information mismatch in low-speed scenarios.

[0103] For similar reasons, in actual driving, considering only the degree of relief of the driver's motion sickness is one-sided. It is also necessary to pay attention to the basic function of the head-up display, namely, displaying driving-related information. Therefore, in one implementation, the electrooculogram signal includes the gaze duration distribution and blink interval. After S103, the method may also include S106-S109: S106, Information loss rate.

[0104] Since the aforementioned steps all involve reducing the information density of the head-up display to alleviate motion sickness, excessive reduction in information density may prevent drivers from obtaining critical driving information (such as vehicle speed, navigation prompts, collision warnings, etc.), thereby affecting driving safety. Therefore, it is necessary to quantitatively assess the degree of information density reduction, i.e., the "information loss rate," as a basis for subsequent safety baseline adjustments.

[0105] The information loss rate is a quantitative indicator representing the degree of reduction in the information density of the head-up display. It can be calculated as (original information content - currently displayed information content) / original information content × 100%. The higher the information loss rate, the more information is hidden or reduced, and the less driving-related information the driver can obtain.

[0106] Specifically, after each information density adjustment operation, the electronic device counts the number of information elements (or information bits) currently displayed, compares it with the baseline information amount before the adjustment, calculates the current information loss rate, and continuously updates this value for subsequent judgment.

[0107] S107. Determine the driver's level of distraction based on gaze duration distribution and blink interval.

[0108] When the information density of a head-up display is significantly reduced, drivers may frequently shift their gaze to the dashboard or other locations due to the lack of crucial information, leading to distraction. By using two physiological indicators from electrooculography (EOG) signals—fixation duration distribution and blink interval—the driver's attention state can be objectively assessed, providing a basis for decisions regarding whether to restore information density or add visual anchors.

[0109] Among them, fixation duration distribution refers to the statistical distribution of the duration of a driver's gaze at various areas within a unit of time. When attention is concentrated, the fixation duration distribution shows the characteristic of "high proportion of primary fixation area and low proportion of other areas"; when attention is distracted, the fixation duration distribution tends to be more uniform, and the proportion of primary fixation area decreases; blink interval refers to the time interval between two consecutive blinks. When attention is highly concentrated, the blink frequency decreases (the interval becomes longer), while when attention is distracted or fatigued, the blink frequency increases (the interval becomes shorter); the degree of attention distraction is a quantitative indicator calculated by combining the entropy value of the fixation duration distribution and the statistical characteristics of the blink interval, used to characterize the degree to which the driver's attention deviates from the primary driving task.

[0110] Specifically, the electronic device continuously acquires the driver's electrooculogram (EOG) signals, extracting the gaze point coordinate sequence and blink event timestamps. The gaze point coordinates are then divided into regions for statistical analysis, and the Shannon entropy value of the gaze duration distribution is calculated (a higher entropy value indicates more scattered gaze). Simultaneously, the average blink interval per unit time is calculated. The gaze duration distribution entropy value and blink interval are weighted and fused to obtain a comprehensive score of the degree of attentional distraction.

[0111] S108. When the information loss rate is greater than or equal to the preset loss rate, increase the information density of the head-up display.

[0112] When motion sickness intervention strategies result in excessive reduction of information density (information loss rate reaches a preset threshold), electronic devices need to actively recover some key information to prevent driving safety hazards caused by information loss.

[0113] Specifically, the electronic device continuously monitors the information loss rate. When the detected information loss rate is greater than or equal to a preset loss rate, it restores the display layer by layer according to a preset information priority queue, starting with the highest priority information category, until the information loss rate falls below the preset loss rate. The restoration process uses a gradual animation transition to avoid visual shock to the driver from a sudden surge of information.

[0114] S109. If the degree of attentional distraction is greater than or equal to the preset degree of distraction, increase the number of visual anchor points.

[0115] When excessive driver inattention is detected, it indicates that the current head-up display is failing to effectively guide the driver's visual attention. Increasing the number of visual anchor points in this situation provides the driver with more stable visual reference points, helping them refocus their attention on the road ahead. This not only helps alleviate motion sickness exacerbated by inattention but also improves driving safety.

[0116] Specifically, the electronic device continuously monitors the degree of attentional distraction. When the detected level of distraction is greater than or equal to a preset level, it dynamically increases the number of visual anchors based on a preset spatial layout strategy (such as evenly spaced distribution along the road's direction or adding auxiliary anchors at the four corners of the field of vision). The newly added anchors adopt the same style as the original anchors but with slightly lower visual weight to avoid being overly eye-catching. At the same time, the electronic device continues the electrooculogram (EOG) signal tracking mechanism from the previous steps to ensure that the position of the newly added anchors maintains a reasonable correlation with the driver's gaze area.

[0117] For example, the driver's attentional distraction can be determined by the "gaze duration distribution" and "blink interval" of the EOG signal, and then a multi-objective optimization function can be established: Adjustment effect = 0.6 × motion sickness relief rate - 0.3 × attentional distraction - 0.1 × information loss rate. The driver's physiological characteristics after adjustment are obtained, and the adjustment effect is calculated. If motion sickness is relieved but attentional distraction increases beyond a threshold, parameters are adjusted (e.g., reducing information hiding amount, increasing dynamic anchor points); if the information loss rate is too high (core navigation information is hidden), core information is prioritized for preservation, and adjustment methods that increase the refresh rate are used instead.

[0118] In addition, it can record the driver's physiological characteristics, motion sickness prediction records, corresponding image adjustment parameters, and the vehicle's driving status at the time, forming a personalized database for the driver. In subsequent use, the optimal adjustment parameters can be directly called, shortening the response time.

[0119] Based on the implementation of S106-S109 above, firstly, after executing the control strategy, the information loss rate is obtained to quantify the degree of reduction in the head-up display information density. Simultaneously, the fixation duration distribution and blink interval in the electrooculogram (EOG) signal are used to determine the driver's level of distraction, thereby enabling real-time monitoring and evaluation of the actual driving state after anti-motion sickness adjustment. Secondly, when the information loss rate is greater than or equal to a preset loss rate, the information density of the head-up display is actively increased to prevent the driver from being unable to obtain necessary driving information due to excessive information reduction caused by anti-motion sickness adjustment, thus avoiding negative impacts on driving safety from the anti-motion sickness strategy. Thirdly, when the level of distraction is greater than or equal to a preset level of distraction, the number of visual anchor points is increased to provide more visual reference points for the driver with distracted attention, guiding the gaze back to a stable area.

[0120] In another implementation, the method provided in this application can also interact with the vehicle's advanced driver assistance systems (ADAS). When the ADAS detects an emergency (such as a collision warning or lane departure warning), it immediately pauses the head-up display adjustment and restores the full information display of the AR-HUD to prioritize driving safety.

[0121] The following section describes the vehicle head-up display adjustment method provided in this application in conjunction with practical applications. (See also...) Figure 2 : S201. Initialize the system. The multimodal physiological acquisition module, time-series feature processing module, motion sickness prediction module, scenario-based adjustment module, and multi-target closed-loop module are started, and the device self-test and parameter calibration are completed. These modules may be part of the electronic device provided in this application.

[0122] S202, the multimodal physiological acquisition module automatically starts, and collects EEG, EOG and EMG signals in real time through headrest electrodes, eye-tracking camera and steering wheel sensor, with a sampling frequency of 256Hz or higher.

[0123] S203, the time-series feature processing module preprocesses the original signal, extracts the driver's physiological features, and aligns them with the vehicle driving data at the millisecond level.

[0124] S204 The motion sickness prediction module integrates the trend of physiological characteristic changes with the navigation system's driving trajectory data for the next 3-10 seconds to predict the probability and type of motion sickness.

[0125] S205. The scenario-based adjustment module executes corresponding early warning interventions or differentiated adjustment strategies based on the prediction results.

[0126] S206. The multi-objective closed-loop module continuously collects physiological characteristics and attention data after adjustment, and iteratively optimizes the adjustment parameters according to the multi-objective optimization function until the adjustment effect reaches the optimal level.

[0127] S207. After the user parks and turns off the engine, save the physiological characteristics, motion sickness prediction records and adjustment parameters of this use, and update the driver's personalized database.

[0128] In conjunction with the above embodiments, this application also provides a method for adjusting a vehicle head-up display, see below. Figure 3 Including S301-S308: S301, Driver's physiological characteristics and vehicle driving data.

[0129] The system acquires the driver's real-time physiological characteristics and the vehicle's real-time driving data through onboard sensors, as well as the prediction results of the vehicle's route. The specific implementation method can also refer to the content given in S101 above.

[0130] S302, Prediction Model.

[0131] The data obtained in S301 is input into the preset prediction model to obtain the prediction result corresponding to S303.

[0132] S303, Prediction Results.

[0133] Based on the prediction results of the driver's motion sickness type and probability, the corresponding head-up display effect adjustment strategy can be determined, and the content of S102-S103 above can be referred to.

[0134] S304, Control Strategy One.

[0135] S305, Control Strategy Two.

[0136] S306, Control Strategy Three.

[0137] Strategy 1 corresponds to the content of S103A above, Strategy 2 corresponds to the content of S103B above, and Strategy 3 corresponds to the content of S103C above. The corresponding control strategy is executed in combination with the prediction results given in S303.

[0138] S307, Display effect is adjusted based on vehicle speed and motion sickness probability.

[0139] S308, Adjust the display effect based on information loss rate and driver attention distraction level.

[0140] After executing the corresponding control strategy, the display effect can be adjusted by combining vehicle speed and motion sickness probability, or by combining information loss rate and attention distraction level. The specific implementation of S307 can be found in the aforementioned S104-S105; the specific implementation of S308 can be found in the aforementioned S106-S109.

[0141] Furthermore, after implementing different control strategies, and after adjusting the display effect again after implementing the control strategies, the adjusted parameters can be used for model optimization to improve the accuracy of model prediction.

[0142] This application embodiment can divide the following device into functional modules based on the above method example. For example, each function can be divided into its own functional module, or two or more functions can be integrated into one processing module. The integrated module can be implemented in hardware or as a software functional module. Optionally, the module division in this application embodiment is illustrative and only represents one logical functional division; other division methods may be used in actual implementation.

[0143] In some embodiments, this application also provides a vehicle head-up display adjustment device. This device may include one or more functional modules for implementing the methods described in the above embodiments. For example, Figure 4 This is a schematic diagram of the structure of a vehicle head-up display adjustment device 400 provided in an embodiment of this application. Figure 4 As shown, the vehicle head-up display adjustment device 400 includes: The acquisition module 410 is used to acquire the driver's physiological characteristics and the vehicle's driving data; the physiological characteristics include the driver's electroencephalogram (EEG), electrooculogram (EOG), and electromyogram (EMG) signals, and the driving data includes the vehicle's driving speed and driving trajectory. The processing module 420 is used to input physiological characteristics and driving data into a preset prediction model to predict the driver's motion sickness state and obtain prediction results. The prediction results include the driver's motion sickness type and the corresponding motion sickness probability. The motion sickness type represents the dominant cause that induces the driver's motion sickness reaction. The control module 430 is used to determine the corresponding control strategy based on the prediction results, and adjust the display effect of the vehicle's head-up display based on the control strategy; wherein, the adjusted head-up display is used to reduce the visual stimuli that induce motion sickness in the driver.

[0144] In one implementation, the control module 430 is further configured to, when the motion sickness probability is greater than a first preset probability and less than a second preset probability, adjust the information density of the head-up display to be less than or equal to the first preset density and adjust the refresh rate of the head-up display to be greater than or equal to the first preset refresh rate, wherein the information density is negatively correlated with the motion sickness probability and the refresh rate is positively correlated with the motion sickness probability.

[0145] In one implementation, the control module 430 is further configured to, when the motion sickness probability is greater than or equal to a second preset probability and the motion sickness type is visually dominant motion sickness, adjust the information density of the head-up display to be less than or equal to a second preset density and adjust the fusion degree of the head-up display to be greater than or equal to a first preset fusion degree; the second preset density is less than the first preset density, the fusion degree represents the degree of fit between the head-up display and the external environment of the vehicle, and the fusion degree is positively correlated with the motion sickness probability.

[0146] In one implementation, the control module 430 is further configured to, when the motion sickness probability is greater than or equal to a second preset probability and the motion sickness type is vestibular dominant motion sickness, adjust the refresh rate of the head-up display to be greater than or equal to the second preset refresh rate, and adjust the visual anchor point of the head-up display based on the electrooculogram signal; the second preset refresh rate is greater than the first preset refresh rate.

[0147] In one implementation, the control module 430 is further configured to: identify the driver's gaze point based on electrooculogram signals; generate visual anchor points with a preset radius around the gaze point; and determine the content of the visual anchor points based on driving data.

[0148] In one implementation, the control module 430 is further configured to: adjust the refresh rate of the head-up display to be greater than or equal to the second preset refresh rate when the driving speed is greater than or equal to the first preset speed and the probability of dizziness is greater than the second preset probability; and adjust the blending degree of the head-up display to be greater than or equal to the second preset blending degree when the driving speed is less than or equal to the second preset speed and the probability of dizziness is greater than the second preset probability; the second preset blending degree is greater than the first preset blending degree and the second preset speed is less than the first preset speed.

[0149] In one implementation, the control module 430 is further configured to: acquire an information loss rate; the information loss rate characterizes the degree of reduction in the information density of the head-up display; determine the driver's attentional distraction level based on the gaze duration distribution and blink interval; increase the information density of the head-up display when the information loss is greater than or equal to a preset loss rate; and increase the number of visual anchor points when the attentional distraction level is greater than or equal to a preset distraction level.

[0150] When the methods of the above embodiments are implemented in hardware, this embodiment of the invention provides a possible structural schematic diagram of the electronic device involved in the above embodiments. For example... Figure 5 As shown, the electronic device 500 includes: a processor 502, a communication interface 503, and a bus 504. Optionally, the electronic device 500 may also include a memory 501.

[0151] Processor 502 may implement or execute various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 502 may be a central processing unit, a general-purpose processor, a digital signal processor, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It may implement or execute various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 502 may also be a combination that implements computing functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0152] Communication interface 503 is used to connect to other devices via a communication network. This communication network can be Ethernet, wireless access network, wireless local area network (WLAN), etc.

[0153] The memory 501 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but is not limited thereto.

[0154] In one possible implementation, the memory 501 can exist independently of the processor 502. The memory 501 can be connected to the processor 502 via a bus 504 and is used to store instructions or program code. When the processor 502 calls and executes the instructions or program code stored in the memory 501, it can implement the method provided in the embodiments of the present invention.

[0155] In another possible implementation, the memory 501 can also be integrated with the processor 502.

[0156] Bus 504 can be an Extended Industry Standard Architecture (EISA) bus, etc. Bus 504 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 5 The symbol is represented by only one line, but this does not mean that there is only one bus or one type of bus.

[0157] Through the above description of the embodiments, those skilled in the art can clearly understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the above device is divided into different functional modules to complete all or part of the functions described above.

[0158] This application also provides a computer-readable storage medium. All or part of the processes in the above method embodiments can be executed by computer instructions instructing related hardware. The program can be stored in the computer-readable storage medium, and when executed, it can include the processes of the above method embodiments. The computer-readable storage medium can be any of the foregoing embodiments or memory. The computer-readable storage medium can also be an external storage device of the above-described device, such as a plug-in hard drive, Smart Media Card (SMC), Secure Digital (SD) card, Flash Card, etc., equipped on the above-described device. Further, the computer-readable storage medium can include both internal storage units and external storage devices of the above-described device. The computer-readable storage medium is used to store the above-described computer program and other programs and data required by the above-described device. The computer-readable storage medium can also be used to temporarily store data that has been output or will be output.

[0159] This application also provides a computer program product comprising a computer program that, when run on a computer, causes the computer to perform the methods provided in the above embodiments.

[0160] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for adjusting a vehicle head-up display, characterized in that, The method includes: Acquire the driver's physiological characteristics and the vehicle's driving data; the physiological characteristics include the driver's electroencephalogram (EEG), electrooculogram (EOG), and electromyogram (EMG) signals, and the driving data includes the vehicle's speed and trajectory; The physiological characteristics and driving data are input into a preset prediction model to predict the driver's motion sickness state and obtain a prediction result. The prediction result includes the driver's motion sickness type and the corresponding motion sickness probability. The motion sickness type represents the dominant cause that induces the driver's motion sickness reaction. Based on the prediction results, a corresponding control strategy is determined, and the display effect of the vehicle's head-up display is adjusted based on the control strategy; wherein, the adjusted head-up display is used to reduce the visual stimuli that induce motion sickness in the driver.

2. The method according to claim 1, characterized in that, The step of determining the corresponding control strategy based on the prediction result and adjusting the display effect of the vehicle's head-up display based on the control strategy includes: When the motion sickness probability is greater than a first preset probability and less than a second preset probability, the information density of the head-up display is adjusted to be less than or equal to the first preset density, and the refresh rate of the head-up display is adjusted to be greater than or equal to the first preset refresh rate. The information density is negatively correlated with the motion sickness probability, and the refresh rate is positively correlated with the motion sickness probability.

3. The method according to claim 2, characterized in that, The step of determining the corresponding control strategy based on the prediction result and adjusting the display effect of the vehicle's head-up display based on the control strategy includes: When the motion sickness probability is greater than or equal to the second preset probability, and the motion sickness type is visually dominant motion sickness, the information density of the head-up display is adjusted to be less than or equal to the second preset density, and the fusion degree of the head-up display is adjusted to be greater than or equal to the first preset fusion degree; the second preset density is less than the first preset density, and the fusion degree characterizes the degree of fit between the head-up display and the external environment of the vehicle, and the fusion degree is positively correlated with the motion sickness probability.

4. The method according to claim 2, characterized in that, The step of determining the corresponding control strategy based on the prediction result and adjusting the display effect of the vehicle's head-up display based on the control strategy includes: When the motion sickness probability is greater than or equal to the second preset probability, and the motion sickness type is vestibular dominant motion sickness, the refresh rate of the head-up display is adjusted to be greater than or equal to the second preset refresh rate, and the visual anchor point of the head-up display is adjusted based on the electrooculogram signal; the second preset refresh rate is greater than the first preset refresh rate.

5. The method according to claim 4, characterized in that, The adjustment of the visual anchor point of the head-up display based on the electrooculogram signal includes: The driver's gaze point is identified based on the electrooculogram signal; Visual anchor points with a preset radius are generated around the gaze point; the content of the visual anchor points is determined based on the driving data.

6. The method according to any one of claims 1-5, characterized in that, After adjusting the display effect of the vehicle's head-up display based on the control strategy, the method further includes: When the driving speed is greater than or equal to the first preset speed and the probability of dizziness is greater than the second preset probability, the refresh rate of the head-up display is adjusted to be greater than or equal to the second preset refresh rate. When the driving speed is less than or equal to the second preset speed and the motion sickness probability is greater than the second preset probability, the blending degree of the head-up display is adjusted to be greater than or equal to the second preset blending degree; the second preset blending degree is greater than the first preset blending degree, and the second preset speed is less than the first preset speed.

7. The method according to any one of claims 1-5, characterized in that, The electrooculogram (EOG) signals include fixation duration distribution and blink interval. After adjusting the display effect of the vehicle's head-up display based on the control strategy, the method further includes: The information loss rate is obtained; the information loss rate characterizes the degree of reduction in the information density of the head-up display. Based on the gaze duration distribution and blink interval, the driver's level of attentional distraction is determined; If the information loss is greater than or equal to a preset loss rate, the information density of the head-up display is increased; If the degree of attention distraction is greater than or equal to a preset degree of distraction, the number of visual anchor points is increased.

8. An electronic device, characterized in that, It includes a processor and a memory, the processor being coupled to the memory; the memory is used to store computer instructions, which are loaded and executed by the processor to enable the computer device to perform the method as described in any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes computer-executable instructions that, when executed on a computer, cause the computer to perform the method as described in any one of claims 1 to 7.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when run on an electronic device, causes the electronic device to perform the method as described in any one of claims 1 to 7.

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