Display control device, method, head-up display apparatus, and computer storage medium

Abstract

The present disclosure provides a display control device, method, head-up display device and computer storage medium, the display control device comprises a memory and a processor, wherein the memory is configured to store navigation data containing predetermined path characteristics, and dynamic scene data reflecting the current vehicle driving state and / or the vehicle surrounding environment; the processor is configured to adjust the display state of the display content on the display device according to the navigation data and the dynamic scene data.

Description

Technical Field

[0001] This disclosure relates to the field of display control technology, and in particular to a display control device, method, head-up display device, and computer storage medium. Background Technology

[0002] While current head-up display (AR-HUD) technology has achieved the fusion projection of road information and real-world scenes, it still has significant shortcomings in adaptability to complex environments.

[0003] The dynamic navigation indication methods used in existing HUDs typically rely on fixed distance thresholds to trigger animation displays, which lack scene adaptability, accuracy and precision of dynamic navigation displays, and can lead to increased fatigue from excessive dynamic navigation display animations. Summary of the Invention

[0004] This disclosure aims to provide a display control device, method, head-up display device, and computer storage medium that can solve the technical problem that dynamic navigation in the prior art cannot adapt to different scenarios.

[0005] The technical solution of this disclosure embodiment is implemented as follows: In a first aspect, embodiments of this disclosure provide a display control device, including: The memory is configured to store navigation data containing features of a predetermined path, as well as dynamic scene data reflecting the current driving status of the vehicle and / or the surrounding environment of the vehicle. The processor is configured to adjust the display state of the content displayed on the display device based on the navigation data and the dynamic scene data.

[0006] Secondly, embodiments of this disclosure provide a display control method, including: Acquire navigation data containing features of the predetermined route, as well as dynamic scene data reflecting the current vehicle driving status and / or the vehicle's surrounding environment; The display status of the content displayed on the display device is adjusted based on the navigation data and the dynamic scene data.

[0007] Thirdly, embodiments of this disclosure provide a head-up display device, including: The first aspect is the display control device; The projection component is communicatively connected to the processor and is used to project the image to be displayed corresponding to the content to be displayed after the display state has been adjusted. The projected component is used to display the image to be displayed; An optical path component is used to transmit the image to be displayed to the projected component.

[0008] Fourthly, embodiments of this disclosure provide a computer storage medium storing at least one instruction, which is executed by a processor to implement the display control method as described in the second aspect.

[0009] This disclosure provides a display control device, method, head-up display (HUD) device, and computer storage medium. On one hand, by utilizing navigation data containing predetermined path characteristics, it can predict semantic information such as the complexity and curvature of the road ahead, rather than relying solely on a single distance parameter. Adjusting the display state is based on a deep understanding of the navigation task itself, improving the matching degree between navigation instructions and actual driving needs, avoiding problems such as premature prompts on mountain roads or unclear instructions on highway ramps, and improving navigation accuracy. On the other hand, by utilizing dynamic scene data, it can perceive the vehicle's current driving environment in real time, such as changes in lighting, weather conditions, or traffic flow. Dynamically adjusting the display state based on this real-time environmental information ensures that the displayed content maintains optimal visibility and clarity under various conditions, overcoming the information recognition difficulties caused by the use of fixed display parameters in existing technologies, and ensuring driving safety. Furthermore, it integrates the needs of the navigation task (from navigation data) with the immediate constraints of the driving environment (from dynamic scene data). It avoids interference from invalid or untimely information, significantly reduces cognitive load and visual fatigue caused by information overload or improper display methods, and ultimately brings a safer and more comfortable driving experience. Attached Figure Description

[0010] Figure 1 This is an example diagram of a system architecture for implementing a display control method, provided as an embodiment of the present disclosure.

[0011] Figure 2 This is a schematic diagram of the system architecture of a HUD device to which embodiments of the present disclosure can be applied.

[0012] Figure 3 This is a schematic diagram of a display control device provided in an embodiment of the present disclosure.

[0013] Figure 4 This is a schematic diagram illustrating one type of content display provided in this disclosure.

[0014] Figure 5 This is a schematic diagram illustrating the geometric meaning of a triangular membership function provided in this disclosure.

[0015] Figure 6 This is a schematic diagram illustrating the geometric meaning of another triangular membership function provided in this disclosure.

[0016] Figure 7 This is a schematic diagram illustrating the geometric meaning of another triangular membership function provided in this disclosure.

[0017] Figure 8 This is a schematic diagram of a dynamic display mode provided in this disclosure.

[0018] Figure 9 This is a schematic diagram of a static display mode provided in this disclosure.

[0019] Figure 10 This is a schematic diagram of a hybrid display mode provided in this disclosure.

[0020] Figure 11 This is a schematic diagram of a scene switching display provided in this disclosure.

[0021] Figure 12 This is a schematic diagram illustrating the reduction in distance to an obstacle, as provided in this disclosure.

[0022] Figure 13 A flowchart of a processor control logic provided in this disclosure.

[0023] Figure 14 A flowchart of the display control method provided in this disclosure. Detailed Implementation

[0024] The technical solutions in this disclosure will now be clearly and completely described with reference to the accompanying drawings.

[0025] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that this disclosure will be more comprehensive and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0026] Furthermore, the accompanying drawings are merely illustrative of this disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0027] Head-up display (HUD) technology, especially augmented reality (AR) HUD technology, has been applied in vehicles, enabling the fusion and projection of road navigation information with the real-world scene from the driver's perspective. In current implementations, the triggering mechanism for dynamic navigation instructions typically relies on a fixed distance threshold. For example, when the vehicle is less than 100 meters from the next turn, the system triggers a corresponding navigation animation. Furthermore, the display parameters of existing systems, such as color coordinates (color temperature, color gamut offset) and brightness, are usually set to fixed preset values ​​and cannot be dynamically adjusted according to changes in the external environment.

[0028] The fixed and standardized control strategies used in existing technologies have revealed significant technical shortcomings in complex and ever-changing real-world driving environments.

[0029] First, the triggering mechanism relying on a fixed distance threshold lacks scenario adaptability, resulting in insufficient timeliness and accuracy of navigation instructions. This static logic can have negative effects under different road conditions. Specifically, on winding mountain roads, distance-based prompts may trigger too early, causing unnecessary information interference; while in high-speed, complex highway ramp switching scenarios, the same distance threshold may lead to the provision of instructions too late, resulting in unclear guidance and even causing drivers to miss key intersections. The flaw of this control method lies in the one-dimensionality of its decision-making process, that is, using only distance as a single variable to control a complex human-computer interaction task involving path geometry, vehicle dynamics, and driver cognitive load. It ignores the multidimensional complexity of driving tasks, and therefore performs reasonably well only under a few standard conditions, while failing to provide an optimal guidance experience in most real-world scenarios.

[0030] Secondly, existing systems generally lack the ability to deeply analyze the characteristics of driving scenarios, and their animation libraries are relatively limited, resulting in mechanical and repetitive presentation of navigation animations. During long drives, this monotonous prompting method can easily cause visual fatigue in drivers, and may even make them less sensitive to important navigation warnings, thus creating potential safety hazards.

[0031] Furthermore, a fixed display parameter scheme cannot adapt to varying external lighting and weather conditions, severely impacting information readability and driving safety. For example, in rainy or foggy weather, the enhanced ambient light scattering effect causes content with fixed colors and contrasts to become blurry, making critical information difficult to identify. When entering or exiting tunnels, the drastic change in light intensity means that a fixed display brightness may cause the image to be momentarily overexposed, resulting in glare, or become too dark to see clearly, all of which directly pose safety risks.

[0032] In summary, existing dynamic navigation systems cannot effectively adapt to specific driving scenarios, resulting in technical problems such as low navigation accuracy, mechanical display effects, and poor information readability in complex environments, which urgently need to be solved.

[0033] Based on this, this disclosure first proposes a display control device that can be applied in, for example... Figure 1 In the illustrated display control system, display control system 10 is shown with an exemplary vehicle 11. Although a bus is illustrated, it should be understood that vehicle 11 can be any type of vehicle without departing from the scope of this disclosure. System 10 generally includes display control device 12, vehicle sensors 13, head-up display device 14, and human-machine interface (HMI) 15.

[0034] Display control device 12 is used to display information about the current vehicle and its surroundings on the windshield 16 of vehicle 11, as described below. Display control device 12 includes at least one processor 121 and memory 122, the memory being a computer-readable storage device or medium. Processor 121 may be a custom or commercially available processor, central processing unit (CPU), graphics processing unit (GPU), an auxiliary processor among several processors associated with display control device 12, a semiconductor-based microprocessor (in the form of a microchip or chipset), a macroprocessor, a combination thereof, or generally a device for executing instructions. Computer-readable storage device or medium may include, for example, volatile and non-volatile memory in read-only memory (ROM), random access memory (RAM), and keep-alive memory (KAM). KAM is persistent or non-volatile memory that can be used to store various operational variables when processor 121 is powered off. Computer-readable storage devices or media may be implemented using multiple storage devices (such as PROM (Programmable Read-Only Memory), ePROM (Electrically Powered PROM), EEPROM (Electrically Erasable PROM), flash memory, or other electrical, magnetic, optical, or combined storage devices capable of storing data), some of which represent executable instructions used by the display control device 12 to control various systems of the vehicle 11. The display control device 12 may also consist of multiple processors that are electrically in communication with each other.

[0035] The display control unit 12 communicates electrically with the vehicle sensors 13, the head-up display 14, and the HMI 15. Electrical communication can be established using, for example, a CAN bus, Wi-Fi network, or cellular data network. It should be understood that various other wired and wireless technologies and communication protocols used for communicating with the display control unit 12 are within the scope of this disclosure.

[0036] Vehicle sensor 13 is used to acquire information about the environment surrounding vehicle 11, i.e., to acquire external environmental parameters of vehicle 11. In an exemplary embodiment, vehicle sensor 13 includes an external camera 131, a vehicle communication system 132, and an electronic distance sensor 133. It should be understood that, without departing from the scope of this disclosure, vehicle sensor 13 may include additional sensors for determining characteristics of vehicle 11, such as vehicle speed, road curvature, and / or vehicle steering. As discussed above, vehicle sensor 13 is in electrical communication with display control device 12.

[0037] An external camera 131 is used to capture images and / or videos of the environment surrounding the vehicle 11. In an exemplary embodiment, the external camera 131 is a photographic and / or video camera positioned to observe the environment in front of the vehicle 11. In one example, the external camera 131 is fixed inside the vehicle 11 (e.g., in the roof lining of the vehicle 11) and has a field of view through the windshield 16. In another example, the external camera 131 is fixed outside the vehicle 11, for example, on the roof of the vehicle 11, and has a view of the environment in front of the vehicle 11. It should be understood that cameras with various sensor types, including, for example, charge-coupled device (CCD) sensors, complementary metal-oxide-semiconductor (CMOS) sensors, and / or high dynamic range (HDR) sensors, are all within the scope of this disclosure. Furthermore, cameras with various lens types, including, for example, wide-angle lenses and / or narrow-angle lenses, are also within the scope of this disclosure.

[0038] The display control unit 12 uses the vehicle communication system 132 to communicate with other systems outside the vehicle 11. For example, the vehicle communication system 132 includes the ability to communicate with vehicles, infrastructure, remote call centers, and / or personal devices. The vehicle communication system 132 may include one or more antennas and / or communication transceivers for receiving and / or transmitting signals, such as Coordinating Sensing Messages (CSM). The vehicle communication system 132 is configured to wirelessly transmit information between the vehicle 11 and another vehicle. Furthermore, the vehicle communication system 132 is configured to wirelessly transmit information between the vehicle 11 and infrastructure or other vehicles.

[0039] Electronic distance sensor 133 is used to determine the range (i.e., distance) between vehicle 11 and objects in the environment surrounding the vehicle. Electronic distance sensor 133 may utilize electromagnetic waves (e.g., radar), sound waves (e.g., ultrasound), and / or light (e.g., lidar) to determine the distance. Figure 1 In the exemplary embodiments shown, the electronic ranging sensor 133 is a lidar sensor. It should be understood that embodiments in which the electronic ranging sensor 133 includes radar sensors, ultrasonic sensors, lidar sensors, and / or other sensors configured to determine range (i.e., distance) fall within the scope of this disclosure.

[0040] refer to Figure 2 This diagram illustrates a system diagram of a head-up display device 14 used by an exemplary occupant 21. Within the scope of this disclosure, in a non-limiting example, occupant 21 includes the driver, passengers, and / or any other person in the vehicle 11. The head-up display device 14 is used to display a projected image (i.e., a notification symbol providing visual information to the occupant 21) on the windshield 16 of the vehicle 11. The head-up display device 14 includes a projection component 141 and an occupant status acquisition device 142. As discussed above, the head-up display device 14 is in electrical communication with a display control device 12.

[0041] Projection component 141 is used to project a projected image onto a projected component 16 of the vehicle 11, typically the windshield. It should be understood that various devices designed for projecting images, including, for example, optical collimators, laser projectors, digital light projectors (DLP), etc., are within the scope of this disclosure.

[0042] The occupant status acquisition device 142 is used to determine the position of the occupant 21 in the vehicle 11 and the driver's visual state parameters. For example, the occupant status acquisition device 142 can track the position of the occupant 21's head 211 or eyes 212, as well as the driver's facial expressions. The position and visual state parameters of the occupant 21 in the vehicle 11 obtained from the occupant status acquisition device 142 are used to locate a projected image on the windshield of the vehicle 11 and adjust the display color of the projected image. In an exemplary embodiment, the occupant status acquisition device 142 is one or more cameras disposed in the vehicle 11.

[0043] To operate the head-up display 14, the processor 121 in the display control unit 12 may include multiple software modules, including a system manager 144. During operation of the system 10, the system manager 144 receives at least a first input 24, a second input 23, and a third input 22. The first input 24 indicates the location of the vehicle 11 in space (i.e., the geographic location of the vehicle 11), the second input 23 indicates the location of the vehicle occupant 21 within the vehicle 11 and visual state parameters (e.g., the position of the occupant 21's eyes and / or head within the vehicle 11), and the third input 22 is data relating to the expected lighting state of at least one indicator of a distant vehicle, which will be discussed in more detail below. The first input 24 may include data such as GNSS data (e.g., GPS data), vehicle speed, road curvature, and vehicle steering, and this data is collected from vehicle sensors 13. The second input 23 is received from an occupant state acquisition device 142. The third input 22 is vehicle external environment parameters concerning the distant vehicle in the environment surrounding the vehicle 11. System manager 144 is configured to determine (e.g., calculate) the type, size, shape, and color of the projected image to be displayed using projection component 141 based on a first input 24 (i.e., vehicle position in the environment), a second input 23 (e.g., the position of the eyes 212 and / or head 211 of the occupant 21 in vehicle 11), and a third input 22. System manager 144 instructs image engine 143 to display the projected image using projection component 141. Image engine 143 is a software module or integrated circuit of projection component 141 or display control device 12. Image engine 143 displays the projected image on the windshield 16 of vehicle 11 using projection component 141 based on the type, size, shape, and color of the projected image determined by system manager 144. When the head-up display device is an AR-HUD, the projected image is projected onto the windshield 16 by projection component 141 to display the projected image along the road surface 26.

[0044] In some exemplary embodiments of this disclosure, the above-described display control device 12 and head-up display device 14 are combined to constitute the head-up display device of this disclosure.

[0045] In some examples of this disclosure, refer to Figure 3 The display control device may include a memory 122 and a processor 121, wherein the memory 122 is configured to store navigation data including predetermined path features and dynamic scene data reflecting the current vehicle driving status and / or the vehicle's surrounding environment; the processor 121 is configured to adjust the display status of the content displayed on the display device according to the navigation data and the dynamic scene data.

[0046] In some exemplary embodiments of this disclosure, memory 122 is configured to store two types of data that are functionally complementary. The first type is navigation data containing predetermined path features. This navigation data includes the vehicle's driving plan and the inherent attributes of the path, and is pre-calculated based on high-precision maps and navigation path planning. The predetermined path features are specific indicators derived through quantitative analysis, such as an intersection complexity index used to assess the difficulty of navigating intersections, path curvature features describing the degree of road curvature, and distances to key decision points indicating the timing of operations. The specific types of predetermined path features can also be customized according to the needs of subsequent calculations, which will not be elaborated here.

[0047] The second type of data is dynamic scene data reflecting the current vehicle's driving status and / or its surrounding environment. Dynamic scene data is real-time information about the vehicle's operating status and its surrounding environment at any given moment, collected and processed in real time by devices such as onboard cameras, radar, and ambient light sensors. Dynamic scene data includes vehicle driving status such as speed and acceleration, as well as data on the vehicle's surrounding environment such as light intensity, weather conditions, and surrounding obstacles.

[0048] The processor 121 is configured to adjust the display state of the content displayed on the display device according to the navigation data and the dynamic scene data. The display state covers various ways of presenting navigation information, such as whether to use dynamic icons or static icons, as well as the brightness, size and other attributes of these visual elements.

[0049] In some examples, the display status is adjusted based on control parameters; these control parameters characterize the navigation information display priority in the current driving context and are generated based on the navigation data and the dynamic scene data.

[0050] During the parameter generation phase, processor 121 performs a dual-path parallel analysis task to generate a control parameter that can comprehensively characterize the navigation information display priority in the current driving situation.

[0051] First, processor 121 processes navigation data, analyzing predetermined path characteristics such as intersection complexity index and path curvature features. Using an internal fuzzy logic decision model, processor 121 calculates and infers from these path characteristics, assessing the inherent importance and complexity of the upcoming navigation task. The final output control parameters represent task priority, such as a quantified weighted dynamic decision. A higher weight value indicates that processor 121 determines the current navigation task has a higher display priority.

[0052] Meanwhile, processor 121 processes real-time dynamic scene data. It analyzes information from various sensors regarding the vehicle's surrounding environment and driving status, such as light intensity, obstacle distance, and vehicle speed. Through an internal scene recognition and evaluation model (such as the LightGBM classifier), processor 121 quantifies the safety, visibility, and other conditions of the current driving environment. The final output control parameters represent the portion that characterizes the environmental adaptability priority.

[0053] The processor 121 integrates the generated indicators (such as animation decision parameters, environmental threat level, optical interference coefficient, etc.) into a complete, multi-dimensional control parameter. Finally, the processor 121 generates the control parameter to adjust the display state of the displayed content.

[0054] In some examples, the displayed content may include driver assistance information and vehicle driving information. For example, refer to Figure 4 The displayed content can include navigation guidance, such as lane lines 402, turn signs 404, speed limit signs 406, etc., as well as vehicle driving information such as vehicle speed information 408, vehicle driving mode 410, etc. The specific content of the displayed content can also be customized according to needs, which will not be elaborated here.

[0055] In some examples, after receiving the aforementioned navigation data and dynamic scene data, the data can be preprocessed. The processor 121 is configured to perform moving average standard deviation filtering on the collected track point coordinates to address anomalous point jumps caused by GPS data being susceptible to multipath drift in urban areas or tunnels, signal reflection errors under viaducts, and other factors.

[0056] Specifically, first, the sliding window for the moving average and standard deviation is determined. For example, processor 121 sets the sliding window using the coordinates of the most recent 5 frames of data. Outlier removal is performed on the collected track points (x, y coordinates in meters). The removal method is to first calculate the moving average (μ) and standard deviation (σ) of the 5 frames of data, as shown in the following formula:

[0057] in This represents the average value of the x-axis. This represents the average value of the y-axis. Since 99.7% of the data falls within the normal distribution, this is because... 3 Within the range, and for typical σ values ​​of GPS data for urban roads, the horizontal distance is 1 to 3 meters (open area). Therefore, if the x and y coordinates of the current frame meet any of the following rejection conditions, the data will be rejected. This includes filtering out abnormal points such as multipath drift where the coordinates of the track ahead in the tunnel suddenly jump by more than 10 meters and signal reflection errors under the overpass. Then, the rejected data will be completed by linear interpolation.

[0058] The exclusion criteria are as follows: or

[0059] Determine whether the x and y data of the current frame need to be removed, where This represents the x-coordinate of the current frame. The line representing the average value of the y-axis. This represents the y-coordinate of the current frame.

[0060] The linear interpolation method is as follows: =

[0061] The geometric curvature value of the radius of curvature refers to the magnitude of curvature of the road centerline at a certain point. It is defined as the rate of change of the tangent direction angle with respect to the arc length at that point, quantifying the bending characteristics of the road centerline at that point. The collected radii of curvature can be normalized using Min-Max, converting the original, large-range values ​​into a unified, standardized input suitable for fuzzy logic decision-making.

[0062]

[0063] in, Represents the normalized radius of curvature, where X is the collected radius of curvature; The maximum radius of curvature is set. This is the set minimum radius of curvature.

[0064] For example, suppose =3,000 meters (covering most highways) and =10 meters (covering the sharpest curves), for straight sections set to The value (normalized to 1) is less than The value is truncated to (Normalized result is 0). The range of curvature radius values ​​can represent road conditions such as highways (1000-3000 meters), urban arterial roads (300-1000 meters), ordinary urban roads (100-300 meters), sharp curves (15-100 meters), and U-turns (10-15 meters). The smaller the curvature radius value after Min-Max normalization, the sharper the curve. The road conditions that the range of curvature radius values ​​can represent can also be customized according to actual calculation needs, which will not be elaborated here.

[0065] In some examples disclosed herein, the predetermined path features may include intersection complexity index, path curvature features, distance to key decision points, and traffic flow features, which can be set based on requirements and will not be elaborated here.

[0066] In some examples, the intersection complexity index is a comprehensive metric used to quantify the driving difficulty, risk, and uncertainty faced by vehicles at a specific intersection. It can include fixed or slowly changing conditions such as the intersection's geometry (e.g., number of lanes, width, intersection angle), traffic facilities (traffic lights, signs and markings), and visibility (affected by weather and lighting). Path curvature is a physical quantity describing the geometry of a curve, precisely measuring the degree of curvature of a path at a given point. Its physical meaning can be intuitively understood as the reciprocal of the radius of the circle that best fits the path curve at that point (i.e., the "closest circle"). Critical decision point distance is the distance on a vehicle's path from a specific geographical location or time point where critical, tactical driving actions are required. Critical decision points mark the end of one state and the beginning of another in a driving task. Traffic flow characteristics are physically defined as the number of vehicles present per unit length of road at a specific moment.

[0067] In some examples, the intersection complexity index can be calculated using an intersection complexity model. The collected data on the number of lanes, turning angles, and conflict points can be input into the complexity quantification model to calculate the intersection complexity index. The intersection complexity model is as follows:

[0068] in, This represents the complexity index of the aforementioned intersection. , , As weight, This represents the change in the number of lanes (e.g., from 3 lanes to 2 lanes, the value is 1). The absolute value of the steering angle. The number of conflict points is calculated based on road network topology and traffic flow model using high-precision maps. Conflict points refer to the areas where different traffic flow trajectories intersect at intersections. Basic conflict points include motor vehicle-motor vehicle conflicts (such as intersections, merging, and diverging), motor vehicle-non-motor vehicle conflicts (such as intersections between bicycle lanes and motor vehicle lanes), and pedestrian crossing conflicts (such as pedestrian crossings without traffic lights).

[0069] When calculating the above weight values, you can first prepare multiple intersection samples, each sample containing the intersection ID and the number of lanes. Steering angle Number of conflict points The square root of the intersection's complexity and the actual complexity score Y are used. For example, the Y score ranges from 0 to 100. The intersection's score is calculated by comprehensively considering three dimensions: the intersection's complexity, its historical accident rate, and traffic delay time (average vehicle transit time). Then, the changes in the number of lanes, turning angles, and number of conflict points are normalized to ensure the data falls within the same range. The normalized change in the number of lanes is obtained by dividing the change in the number of lanes by the largest change in the domestic intersection scenario. The turning angle is divided by the largest turning angle of 180 degrees, and the number of conflict points is divided by the square root of the largest number of conflict points in the domestic intersection scenario. Then, each data point is substituted into the following formula for multiple linear regression, where the weights are determined by reference to... + + Given the constraint of 1, the calculated weight values ​​are averaged to obtain the final weight values.

[0070] in, , , .

[0071] In some examples, when determining the curvature characteristics of the aforementioned paths, the curvature radius (degree of road curvature) can be used to classify risks. Road sections with a curvature radius greater than 500 meters can be classified as gentle curves or straight extensions of highways and are considered low-risk. Road sections with a curvature radius greater than or equal to 200 meters and less than or equal to 500 meters can be classified as medium-risk, falling between the highway limit and urban arterial roads. Road sections with a curvature radius less than 200 meters can be classified as high-risk, approaching the limit of urban secondary arterial roads.

[0072] After completing the risk classification, the risk level can be determined by correcting the radius of curvature R based on the current vehicle speed v (unit: km / h, needs to be converted to m / s). At the intersection of a curve, the lateral acceleration of an average driver exceeding... At speeds of 0.4g to 0.5g (approximately 3.92 to 4.91 m / s²), handling becomes difficult and the accident rate is high. Therefore, for safety reasons, a lower risk threshold can be adopted. =0.2g (1.96 m / s², where g is the standard gravitational acceleration), according to the centrifugal force formula:

[0073] Where m is the vehicle mass (kg) Vehicle speed (m / s) Let be the radius of curvature (m).

[0074] lateral acceleration The calculation formula is The calculated acceleration is used to determine whether it exceeds the threshold acceleration. If the curvature radius exceeds the threshold, the risk level is increased, and finally the path curvature feature is obtained.

[0075] In some examples, when determining the distance to critical decision points, the distance to turning points in the real-time navigation path, i.e., the straight-line distance (in meters) between the next point in the navigation path where a turn is required (such as a left turn or a right turn) and the current vehicle position, can be directly used as the distance to critical decision points. When calculating traffic flow characteristics, real-time traffic flow (Q, vehicles / hour) and the number of lanes on a high-precision map (L) can be used, and the following formula can be used for calculation:

[0076] in, Indicates traffic flow characteristics, This indicates the saturation flow rate, and the default value can be set to 1500 vehicles / hour / lane.

[0077] In some examples, it can be <0.3 indicates that there are few vehicles at the current intersection. The value is moderate in the range of 0.3 to 0.7. >0.7 represents the density level, outputting a normalized density value (0-1). In some examples, after obtaining the aforementioned predetermined path features, these features can be fuzzified. Specifically, a fuzzy rule base can be established first, followed by defuzzification output, ultimately yielding the decision result of the dynamic-static animation.

[0078] Fuzzification is the process of fuzzifying the features of a predetermined path, bridging the gap between the precise physical world and fuzzy logic concepts. The core task at this stage is to use membership functions (MFs) to map each precise input value to one or more fuzzy sets and determine its membership degree. A membership function is a curve or function that defines how each point in the input space (also called the "universe of discourse") maps to a membership value between 0 and 1. The membership degree represents the degree to which the input point truly belongs to a particular fuzzy set. For example, for an input value "vehicle speed is 60 km / h", the membership function can calculate its membership degree as 0.1 in the "slow" set, 0.8 in the "medium" set, and 0.2 in the "fast" set. This process of generating membership values ​​from precise values ​​is fuzzification.

[0079] Specifically, the intersection complexity index is divided into {low, medium, high}, the path curvature feature is divided into {straight, gentle curve, sharp curve}, the distance to key decision points is divided into {far (300m), medium (100~300m), near (<100m)}, and the traffic flow density is divided into {sparse, medium, congested}, resulting in the parameter fuzzification comparison table 1.

[0080]

[0081] In some examples, membership functions can be used to convert precise values ​​to fuzzy sets. For instance, the intersection complexity index can be used to divide fuzzy sets into low, medium, and high levels, given a... , , The range of the input standard data is determined by the triangular membership function. This determines whether the current input standard data falls within the membership range of the low-level fuzzy set. The intersection complexity index membership varies within different ranges, and the corresponding function formula differs for different fuzzy sets. The parameter 'c' defines the endpoint where the membership function value is 0.

[0082] The following explanation uses the intersection complexity index mentioned above as an example.

[0083] For example, in the fuzzy set distinction of intersection complexity index, the intersection complexity index of the "low" fuzzy set uses a right-descent type, and the membership function used is as follows:

[0084] The geometric shape of this function is such that the membership degree decreases linearly from 1 at the vertex to 0 at the vertices. The vertex coordinates are (…). =0, =0.3, =0.6), with an exponent range of [0,1]. The geometric meaning of the triangular membership function is as follows: Figure 5 as shown.

[0085] Specifically, when , the intersection complexity index completely falls into the low category, , the intersection complexity partially belongs to the low category and decreases linearly at this level. When , the intersection complexity does not belong to the low category at all.

[0086] For example, assume =0.0, =0.3, c=0.6. If , it completely belongs to low complexity, , it does not belong to low complexity at all, , then , then 50% of it belongs to low complexity.

[0087] The fuzzy set "medium" can adopt a symmetric triangular membership function, whose mathematical expression is:

[0088] The vertex coordinates of the "medium" fuzzy set , , are (0.3, 0.6, 0.9), the index range is [0,1], and then the fuzzy membership degree is judged. The geometric meaning of the triangular membership function is as Figure 6 shown.

[0089] For example, assume =0.3, =0.6, =0.9. If , , it does not belong to medium complexity at all, , then 50% of it belongs to medium complexity, , then , it completely belongs to medium complexity, , then , then 50% of it belongs to medium complexity, , then , it does not belong to medium complexity at all.

[0090] The fuzzy set "high" adopts a left-rising triangular membership function, whose mathematical expression is:

[0091] The vertex coordinates of the "high" fuzzy set , , are ( =0.6, =0.9, =1.0), the exponent range is [0,1], then the fuzzy membership degree is determined, and the geometric meaning of the triangular membership function is as follows: Figure 7 As shown.

[0092] For example, suppose =0.6, =0.9, c=1.0 if , Therefore, it does not belong to high complexity at all. , Then 50% belong to high complexity. ,but Then it belongs to high complexity.

[0093] In some examples, the path curvature features are modeled after the intersection complexity index, defining right-descending, symmetrical triangular, and left-ascending membership functions for "straight," "gentle curve," and "sharp curve," respectively, and setting appropriate vertex coordinates to cover the entire curvature domain. The fuzzification of key point decision distances and traffic flow features can refer to the fuzzification of the intersection complexity index described above, and will not be elaborated upon here.

[0094] The reason for using triangular membership functions to achieve fuzzification is that it can improve the calculation speed and is more suitable for the 10-100ms decision cycle of vehicle systems. Furthermore, by setting overlap parameters, the entire input domain is covered, avoiding decision blind spots.

[0095] In some examples, the display status of the above-mentioned content includes display animation, display color, display size, and display brightness. The display animation mode is determined based on predetermined path characteristics in the navigation data.

[0096] Specifically, the display mode of the animation is determined based on the animation decision parameters; the animation decision parameters are determined based on the pre-allocated weights of the current path characteristics and path rules; the path rules are determined based on the current path characteristics and the predetermined path characteristics; and the triggered path rules are determined based on the current path characteristics and the predetermined path characteristics.

[0097] The display modes for animations can include static display mode, dynamic display mode, and mixed display mode. The specific mode division can also be customized according to user needs, which will not be elaborated in this example implementation.

[0098] After obtaining the states of the aforementioned multiple predetermined path features, these states can be arranged and combined to obtain the corresponding path rules. Specifically, taking the example that each of the four predetermined path features has three states, 81 rules can be arranged and combined, as shown in Table 2. Table 2

[0099] After obtaining the aforementioned path rules, weights can be pre-assigned to them to achieve a safer driving experience. Weights are assigned based on characteristics such as driving safety and user comfort. For example, in high-risk scenarios such as approaching highway ramps (high complexity, sharp curves, short distances) or congested urban intersections (high complexity, congestion), the corresponding path rules will be given higher weights to ensure the system prioritizes and enhances dynamic navigation prompts. Conversely, on simple straight road sections, the corresponding rules have lower weights, and the system tends to use static or low-intensity displays to reduce unnecessary interference.

[0100] After determining the membership function and establishing the fuzzy rule base, the processor 121 performs fuzzy inference and defuzzification calculations to obtain an accurate output value that can be used for decision-making.

[0101] First, the activation strength of the rules is calculated. For a set of precise path feature values ​​input at any given time (e.g., intersection complexity index of 0.8, path curvature feature of 0.25, critical decision point distance of 80 meters, and traffic flow feature of 30 vehicles / km), processor 121 first calculates the membership degree of these precise values ​​in each fuzzy set using a membership function. For example, it might obtain: a membership degree of 0.8 for intersection complexity belonging to "high", a membership degree of 0.6 for path curvature belonging to "gentle curve", a membership degree of 0.9 for critical decision point distance belonging to "near", and a membership degree of 0.3 for traffic flow density belonging to "medium". Subsequently, for each path rule in the fuzzy rule base, its activation strength is obtained by multiplying (or taking the minimum value, multiplication is used in this embodiment) the membership degrees of each fuzzy set in its preconditions. For example, if the intersection complexity is high, the path curvature is gentle curve, the critical decision point distance is near, and the traffic flow is sparse, its activation strength is the product of the membership degrees corresponding to these four conditions.

[0102] Secondly, deblurring is performed to obtain the animation decision parameters. This disclosure exemplarily employs the Center of Gravity method for deblurring, which comprehensively considers the strength of all activated rules and their corresponding output weights. Specifically, the animation decision parameters here refer to the weighting of dynamic decisions, calculated using the following formula:

[0103] in, This represents the total number of activated path rules. Let be the activation strength of the i-th rule. The pre-assigned weights for the i-th rule. Continuing the example above, suppose the current input feature activates three rules with activation strengths of 0.25, 0.6, and 0.1, corresponding to rule weights of 0.9, 0.07, and 0.4. Substituting these values ​​into the formula, we can calculate... .

[0104] Finally, the display mode is determined based on the animation decision parameters. The processor 121 will calculate the values... The final display mode is determined by comparing it with a preset threshold. For example, a threshold can be set: when... When using a dynamic display mode, take the navigation arrow in the navigation guide as an example; see the specific display for details. Figure 8 ;when At that time, a mixed display mode is used; for specific display details, please refer to [reference needed]. Figure 9 ;when At this time, a static display mode is used; please refer to the specific display method. Figure 10 Therefore, regarding the calculation results The processor 121 will make decisions using a dynamic display mode, such as displaying dynamic navigation arrows. By employing a threshold-based mechanism, the display mode can be effectively prevented from switching frequently when the value fluctuates slightly near the decision boundary, thus preventing visual flicker and improving the stability of the user experience.

[0105] In some examples of this disclosure, the displayed color is determined based on the scene type in dynamic scene data; the scene type is determined based on the vehicle's surrounding environment. (See also...) Figure 11 As shown, different scene types display different colors. Figure 11 Different shading methods define different display colors. Figure 11 In the game, when the scene type is changed from sunny to rainy / snowy weather, the displayed colors will change.

[0106] In parallel with the aforementioned path feature processing flow based on navigation data, processor 121 also executes a scene parameter processing flow, which analyzes the vehicle's real-time driving environment. Processor 121 collects dynamic scene data such as lane line recognition confidence and traffic sign detection results from camera sensors, nearest obstacle distance and relative speed vector from radar sensors, and illumination intensity (lux) and color temperature (K) from ambient light sensors. This multi-source, heterogeneous sensor data is input into a LightGBM classifier model pre-trained on a server using a large amount of labeled driving data. This model can learn and master the complex mapping relationship between sensor data patterns and real-world scene labels. On the vehicle side, the model processes the input sensor data in real time and outputs a probability distribution representing the probability that the current data belongs to each predefined scene label. Processor 121 selects the label with the highest probability as the final scene classification output.

[0107] Predefined scene labels can include: urban roads, highways, tunnels, ramps, rainy / foggy weather, nighttime, construction areas, etc. The processor 121 encodes the output scene labels to generate a structured scene feature vector, such as: [scene type, environmental threat level, optical interference coefficient].

[0108] In some examples, the display size is determined based on the environmental threat level; the environmental threat level is determined based on at least one of the scene type, the predicted collision time between the vehicle and the obstacle, and the ambient light level in the vehicle's driving environment; wherein the predicted collision time between the vehicle and the obstacle is determined based on the vehicle's driving state and the distance between the vehicle and the obstacle.

[0109] Specifically, environmental threat level is a quantitative indicator ranging from 0 to 1. It is calculated by taking the maximum value among the base threat value, the object threat value, and the visibility threat value.

[0110] in, For environmental threat level, Based on the threat value, Threat value of the object. The visibility threat value is a basic risk level assigned based on the scene label. For example, the basic threat value of a construction area (e.g., 0.8) will be higher than that of a regular highway (e.g., 0.2). For another example, urban roads can be set to 0.3, tunnels to 0.6, and nighttime to 0.5, etc. Specific settings can be customized based on user needs, and will not be elaborated here.

[0111] The object threat value is determined based on the estimated time-to-collision (TTC) between the vehicle and the nearest obstacle, calculated from radar data. When the TTC is below a preset safety threshold, the object threat value increases significantly. The visibility threat value is determined by combining the confidence level of the camera in recognizing elements such as lane lines with the readings from the ambient light sensor. This value increases accordingly in rain, fog, or low-light conditions.

[0112] In some examples, display brightness is determined based on the ambient light intensity in the vehicle's surrounding environment. More specifically, display brightness can be dynamically adjusted based on the optical interference coefficient. The optical interference coefficient is also a quantitative indicator, calculated by taking the maximum of the glare factor and the low light factor; these two factors are usually mutually exclusive. The glare factor primarily addresses situations such as strong daylight, oncoming headlights, or ground reflections, and its value is calculated using a formula that comprehensively considers ambient light intensity (lux) and color temperature (K), for example:

[0113] in, For reference light intensity (e.g., 50,000 lux, a typical value on a sunny day). The color temperature variation range (e.g., 2000 K) is used to normalize the color temperature difference. For glare factor, Light intensity, Color temperature Standard daylight color temperature, and These are the weighting coefficients, and When the light intensity and color temperature significantly deviate from the normal range, the glare factor increases. The low-light factor is used to address dimly lit environments such as nighttime or tunnels, preventing the HUD image from appearing too abrupt, glaring, or having an unbalanced contrast with the background in dark environments. Its judgment criteria and calculation method are as follows: when the ambient light intensity... Below the preset threshold (For example, at 200 lux), the dimness factor is calculated using the following formula; otherwise, the dimness factor is 0, and the formula for calculating the dimness factor is:

[0114] In the animation matching process, the processor 121 fuses the outputs of the aforementioned parallel processes to generate the final rendering instructions. The inputs to the rendering process are the display mode of the animation output from the dynamic-static decision process and the scene feature vector (e.g., [city road, environmental threat level = 0.5, optical interference coefficient = 0.3]) output from the scene parameter processing process.

[0115] The processor 121 first retrieves the most suitable animation template from the pre-stored animation template library based on the display mode and scene type. The animation template library is pre-divided into three categories: dynamic templates, such as 3D arrow light effects and lane line breathing effects; static templates, such as fixed sign projections and distance number displays; and hybrid templates, such as static information layers overlaid on a dynamic base. The scene type determines the specific style of the template; for example, conventional blue or green arrows are used on city roads, while a more technologically advanced style might be used on highways.

[0116] After selecting an animation template, processor 121 dynamically adjusts the template's display attributes using other parameters in the scene feature vector. This ensures that the displayed content is not only logically correct but also visually optimal. The display size is adjusted based on the environmental threat level. For example, the scaling ratio Z of the displayed elements can be calculated using the following formula:

[0117] in, Scaling ratio This is an adjustment factor (e.g., 0.2). When the environmental threat level is high (e.g., there is a risk of collision), the size of the navigation arrow or light carpet will be enlarged accordingly to provide a stronger warning effect. The display brightness is adjusted according to the optical interference factor to counteract interference from ambient light. The final display brightness calculation formula is: final brightness equals base brightness multiplied by 1 plus the optical interference factor.

[0118] For example, refer to Figure 12 When the distance between the vehicle and the obstacle decreases, it means the environmental threat level increases. Therefore, it's possible to enlarge certain display elements within the content, such as... Figure 12 The guide arrows in the middle of the lanes.

[0119] In bright light environments (with a positive optical interference coefficient), brightness is increased to ensure visibility; in low light environments (with a negative optical interference coefficient or adjusted by a low light factor), brightness is appropriately reduced to avoid glare.

[0120] Finally, the animation matching module outputs the final rendering instructions containing information such as [animation type, duration, and micro-motion effects], which are then executed by the rendering and display module to present scene-adaptive optimized display content on the head-up display device.

[0121] To illustrate the technical solution of this disclosure more specifically, an exemplary application scenario is given below. Assume a vehicle is traveling at 60 km / h on a three-lane urban road in sunny weather with strong sunlight. The navigation route indicates a complex intersection 80 meters ahead requiring a sharp right turn. Simultaneously, the millimeter-wave radar in front of the vehicle detects a sudden deceleration of another vehicle, resulting in a low calculated TTC (Traffic Tranche Cost). In this scenario, referring to... Figure 13 The system's data processing flow is as follows: Step S1310 can be executed first, path feature processing. The processor 121 receives navigation data and analyzes it to find that the intersection complexity index is high (e.g., 0.8), the path curvature feature is a sharp bend (e.g., 0.9), the distance to the key decision point is close (80 meters), and the traffic flow is medium.

[0122] Then, in the insufficient execution of S1320, a dynamic-static decision-making process is adopted. Based on the aforementioned path characteristics, the fuzzy logic decision module performs inference calculations and obtains a high animation decision parameter (e.g., 0.85), which exceeds the preset dynamic display mode threshold (0.7). Therefore, the processor 121 decides to adopt the dynamic display mode.

[0123] Simultaneously, step S1330 is executed to process scene parameters. The vehicle sensor data is input into the LightGBM classifier, and the output scene label is "urban road." Due to the detection of a vehicle braking suddenly ahead, the object threat value is high, resulting in a high final "environmental threat level" (e.g., 0.8). At the same time, the ambient light sensor detects strong sunlight, and the calculated "optical interference coefficient" is also high (e.g., 0.4).

[0124] Finally, step S1340 is executed, involving animation matching and rendering. The animation matching module receives the dynamic display mode and scene feature vector. It first selects a "Dynamic Arrow Flowing Light" template (e.g., a blue flowing light arrow) suitable for "urban roads" from the template library. Then, based on the environmental threat level (0.8), the arrow's display size is significantly enlarged from its normal size. Next, based on the optical interference coefficient (0.4), the arrow's display brightness is increased. Ultimately, the driver sees a large, bright, blue arrow with a dynamic flowing light effect on the windshield, clearly indicating the upcoming right turn. Even under strong sunlight interference and urgent traffic conditions ahead, this navigation indication remains prominent and easily identifiable, effectively ensuring the driver does not miss key intersections, thus significantly improving navigation accuracy and driving safety.

[0125] The display control device provided in this example embodiment constructs a multi-dimensional path feature model (including intersection complexity, path curvature, distance to key decision points, and traffic flow characteristics) and uses a fuzzy logic decision algorithm for comprehensive judgment, enabling it to accurately assess the true difficulty and urgency of the navigation task ahead. This allows the system to provide early and enhanced dynamic prompts at complex and easily missed intersections such as highway ramps, while avoiding premature and ineffective interference on non-critical road sections such as winding mountain roads, thereby fundamentally improving the accuracy of navigation guidance and ensuring the correct execution of the driving route. A parallel scene parameter processing flow is introduced, which analyzes multi-source data from cameras, radar, and ambient light sensors in real time to accurately profile the vehicle's immediate driving environment. The system can dynamically adjust the visual attributes of the displayed content to adapt to changes in the external environment. Specifically, it dynamically scales the size of the navigation icons based on a comprehensive assessment of the environmental threat level (combined with scene type, collision risk TTC, etc.). When potential hazards are identified (such as sudden braking ahead or construction areas), the displayed elements are enlarged to provide an effective warning. By calculating the optical interference coefficient, it accurately responds to strong light, glare, or low-light environments such as tunnels and nighttime. The system automatically increases display brightness to combat sunlight interference or decreases brightness to avoid glare in the dark, ensuring optimal readability and comfort of navigation information under any lighting conditions, directly improving driving safety. Through scene recognition, the system categorizes road types (such as city streets, highways, and tunnels) and combines dynamic-static decision-making results to match the most suitable display style from a rich animation template library. This makes navigation animations no longer monotonous repetitions, but rather deeply coupled with the driving scenario and full of variation. It effectively avoids visual fatigue caused by monotonous animations, allowing drivers to maintain sensitivity to key information even during long drives, significantly improving the user experience.

[0126] Furthermore, this disclosure also provides a display control method, referring to... Figure 14 This can include steps S1410 to S1420.

[0127] In step S1410, navigation data containing predetermined path features and dynamic scene data reflecting the current vehicle driving status and / or the vehicle's surrounding environment are acquired.

[0128] In step S1420, the display status of the content displayed on the display device is adjusted according to the navigation data and dynamic scene data.

[0129] It should be noted that the specific control logic of the above display control method can be referred to the above description of the display control device, and will not be repeated here.

[0130] This disclosure also provides a computer-readable storage medium storing at least one instruction that is executed by a processor to implement the display control method described in the above embodiments.

[0131] This disclosure also provides a computer program product including computer instructions stored in a computer-readable storage medium; a processor of a computing device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computing device to perform the display control method described in the above embodiments.

[0132] Those skilled in the art will recognize that the functions described in this disclosure in one or more of the examples above can be implemented using hardware, software, firmware, or any combination thereof. When implemented in software, these functions can be stored in a computer-readable medium or transmitted as one or more instructions or code on a computer-readable medium. Computer-readable media include computer storage media and communication media, wherein communication media include any medium that facilitates the transfer of a computer program from one place to another. Storage media can be any available medium accessible to a general-purpose or special-purpose computer.

[0133] It should be noted that the technical solutions described in this disclosure can be combined arbitrarily as long as they do not conflict.

[0134] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A display control device, characterized in that, include: The memory is configured to store navigation data containing features of a predetermined path, as well as dynamic scene data reflecting the current driving status of the vehicle and / or the surrounding environment of the vehicle. The predetermined path features include path curvature features; The path curvature feature is obtained by correcting the initial risk level determined based on the curvature radius of the road. The correction includes determining the lateral acceleration of the vehicle based on the current vehicle speed and the curvature radius in the dynamic scene data, and upgrading the initial risk level if the lateral acceleration exceeds a preset lateral acceleration threshold. The processor is configured to adjust the display state of the content displayed on the display device based on the navigation data and the dynamic scene data.

2. The display control device according to claim 1, characterized in that, The display status is adjusted based on control parameters; The control parameters are used to characterize the display priority of the content displayed in the current driving situation, and the control parameters are generated based on the navigation data and the dynamic scene data.

3. The display control device according to claim 1, characterized in that, The display state includes a display animation, and the display mode of the display animation is determined based on predetermined path features in the navigation data.

4. The display control device according to claim 3, characterized in that, The display mode of the animation is determined based on the animation decision parameters; The animation decision parameters are determined based on the pre-assigned weights of the current path characteristics and the already triggered path rules; The triggered path rules are determined based on the current path characteristics and the predetermined path characteristics.

5. The display control device according to claim 4, characterized in that, The animation decision parameters are generated by defuzzification calculation based on the activation intensity and pre-assigned weights of each path rule. The activation intensity is determined based on the membership degree of the current path feature in multiple preset fuzzy sets and preset path rules; The membership degree is determined by fuzzing the current path features.

6. The display control device according to claim 1, characterized in that, The display state includes the display color, which is determined based on the scene type in the dynamic scene data; The scenario type is determined based on the vehicle's surrounding environment.

7. The display control device according to claim 1, characterized in that, The display status includes the display size, which is determined based on the level of environmental threat. The environmental threat level is determined based on at least one of the following: scene type, predicted collision time between the vehicle and the obstacle, and ambient light intensity in the vehicle's driving environment. The predicted collision time between the vehicle and the obstacle is determined based on the vehicle's driving state and the distance between the vehicle and the obstacle.

8. The display control device according to claim 7, characterized in that, The environmental threat level is the maximum value among the base threat value, object threat value, and visibility threat value; The basic threat value includes predefined threat values ​​for each of the aforementioned scenario types; The object threat value is determined based on the predicted collision time between the vehicle and the obstacle; The visibility threat value is the maximum value between the glare factor and the low light factor; The glare factor is determined by the color temperature and intensity of the ambient light, while the dim light factor is determined based on the brightness of the ambient light.

9. The display control device according to claim 1, characterized in that, The display status includes display brightness, which is determined based on the ambient light brightness in the vehicle's surrounding environment.

10. The display control device according to claim 1, characterized in that, Before adjusting the display state of the content displayed on the display device, the processor is further configured to: A moving average standard deviation filter is applied to the waypoint coordinates in the navigation data to remove outlier coordinates. The path curvature radius in the navigation data is normalized using a minimum-maximum method.

11. A display control method, characterized in that, include: Acquire navigation data containing features of the predetermined route, as well as dynamic scene data reflecting the current vehicle driving status and / or the vehicle's surrounding environment; The predetermined path features include path curvature features; The path curvature feature is obtained by correcting the initial risk level determined based on the curvature radius of the road. The correction includes determining the lateral acceleration of the vehicle based on the current vehicle speed and the curvature radius in the dynamic scene data, and upgrading the initial risk level if the lateral acceleration exceeds a preset lateral acceleration threshold. The display status of the content displayed on the display device is adjusted based on the navigation data and the dynamic scene data.

12. A head-up display device, characterized in that, The head-up display device includes: The display control device according to any one of claims 1 to 10; The projection component is communicatively connected to the processor and is used to project the image to be displayed corresponding to the content to be displayed after the display state has been adjusted. The projected component is used to display the image to be displayed; An optical path component is used to transmit the image to be displayed to the projected component.

13. A computer storage medium, characterized in that, The computer storage medium stores at least one instruction, which is executed by a processor to implement the display control method as described in claim 11.

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