A display control device, method, and storage medium

Abstract

The present disclosure provides a display control device, method and storage medium. The display control device comprises a processor and a memory; the memory is configured to store vehicle operation data of a vehicle; the processor is configured to determine a lane changing intention of the vehicle and a lane changing direction to which the lane changing intention points according to the vehicle operation data; based on the lane changing intention, the viewing angle parameter of a virtual image rendering camera is pre-adjusted towards the opposite direction of the lane changing direction; and an augmented reality virtual image superimposed on the road surface in front of the vehicle is projected using the pre-adjusted viewing angle parameter.

Description

Technical Field

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

[0002] Augmented Reality Head-Up Display (AR-HUD) systems project augmented reality (AR) elements such as lane lines, light carpets, objects, or navigation prompts into the driver's field of vision, overlaying the augmented reality image onto the road environment in front of the vehicle. This type of display improves the intuitiveness of driving guidance and reduces the frequency with which the driver switches their gaze between the instrument panel, the center console screen, and the road.

[0003] During lane changes, information such as vehicle posture, yaw angle, driver's line of sight, turn signal status, and navigation path dynamically changes. Affected by factors such as sensor latency, inertial measurement data noise, and the offset between the rendered viewpoint and the actual road surface, augmented reality (AR) images are prone to lateral shifts relative to the real road surface during lane changes, causing AR lane lines, AR objects, or light carpets to not align with real road conditions. AR images are also prone to shifting, experiencing response lag, and increasing computational burden during lane changes. Summary of the Invention

[0004] A first aspect of this disclosure provides a display control device. The display control device includes a processor and a memory. The memory is configured to store vehicle operation data. The processor is configured to determine the vehicle's lane-changing intention and the direction of the lane-changing intention based on the vehicle operation data, pre-adjust the viewing angle parameters of a virtual image rendering camera in the opposite direction of the lane-changing intention, and project an augmented reality virtual image superimposed on the road surface in front of the vehicle using the pre-adjusted viewing angle parameters.

[0005] A second aspect of this disclosure provides a display control method. The method includes determining the vehicle's lane-changing intention and the direction of the lane-changing intention based on vehicle operation data; pre-adjusting the viewing angle parameters of a virtual image rendering camera in the opposite direction of the lane-changing intention based on the lane-changing intention; and projecting an augmented reality virtual image superimposed on the road surface in front of the vehicle using the pre-adjusted viewing angle parameters.

[0006] A third aspect of this disclosure provides a computer-readable storage medium. This computer-readable storage medium stores a computer program that, when executed by a processor, implements the aforementioned display control method. Attached Figure Description

[0007] Figure 1 This is a schematic diagram of the overall layout of the head-up display system provided in this disclosure; Figure 2 This is a system schematic diagram of the head-up display device provided in this disclosure; Figure 3 This is a schematic diagram of the structure of the display control device provided in this disclosure; Figure 4 This is a schematic diagram of the outline of the display control method provided in this disclosure; Figure 5 This is a schematic diagram illustrating the principle of pre-deflection of the virtual image rendering camera viewpoint parameters provided in this disclosure. Figure 6 This is a schematic diagram of the functional architecture of the lane change augmented reality scene fusion system provided in this disclosure. Figure 7 A detailed flowchart illustrating the display control method provided in this disclosure; Figure 8 This is a schematic diagram illustrating the effect of augmented reality image fitting with the road surface in front of the vehicle when changing lanes, as provided in this disclosure. Figure 9 This is a schematic diagram illustrating the effect of the augmented reality image deviating from the road surface in front of the vehicle when it changes lanes, as shown in existing technologies. Figure 10 A schematic diagram of the sub-processes for multi-source intent prediction and pre-deflection processing provided in this disclosure; Figure 11 This is a geometric diagram illustrating the lane-changing scenario provided in this disclosure. Detailed Implementation

[0008] The technical solutions in this disclosure will now be described with reference to the accompanying drawings.

[0009] During vehicle operation, head-up displays (HUDs) typically project augmented reality (AR) images along the road surface in front of the vehicle to provide drivers with guidance information such as lane lines, objects, light carpets, navigation arrows, and hazard warning signs. The inventors discovered that during lane changes, due to factors such as dynamic changes in vehicle posture, sensor data delays, and discrepancies between the rendered viewpoint and the actual road surface, AR images—such as lane lines, light carpets, and objects—are prone to lateral shifts relative to the real road surface, resulting in a misalignment between the AR image and the road surface. This phenomenon not only reduces the reliability of the guidance information but also easily distracts the driver.

[0010] Based on the above problems, the display control device proposed in this disclosure can be applied to, for example... Figure 1The head-up display system shown is a head-up display system 10 installed in a vehicle 11. The vehicle 11 can be a bus, passenger car, commercial vehicle, or other vehicle suitable for configuring a head-up display system. The system 10 may include a display control device 12, vehicle sensors 13, a head-up display device 14, and an external communication interface 15. The display control device 12 controls the head-up display device 14 to display information about the current vehicle and its surrounding environment on the projection component 16 of the vehicle 11.

[0011] The display control device 12 includes at least one processor 121 and a memory 122 communicatively connected to the processor 121. The memory 122 may be a computer-readable storage device or medium. The processor 121 may be a field-programmable gate array (FPGA) chip, a central processing unit, a graphics processing unit, a microcontroller, a display domain controller, an in-vehicle computing platform, an application-specific integrated circuit (ASIC), or a combination thereof. The memory 122 may include read-only memory, random access memory, non-volatile memory, flash memory, or other storage devices capable of storing data and instructions. The display control device 12 may also consist of multiple processors communicating with each other.

[0012] The display control unit 12 communicates electrically with the vehicle sensors 13, the head-up display 14, and the external communication interface 15. This electrical communication can be established via a Controller Area Network (CAN) bus, in-vehicle Ethernet, Wi-Fi network, cellular data network, serial communication interface, or other wired or wireless communication methods.

[0013] Vehicle sensor 13 is used to acquire information about the environment surrounding vehicle 11 and vehicle operating status information. In some examples, vehicle sensor 13 includes an external camera 131, a vehicle communication system 132, and an electronic distance sensor 133. Vehicle sensor 13 may also include sensors for determining vehicle speed, road curvature, vehicle steering status, vehicle yaw angle, vehicle acceleration, or lane line information.

[0014] External camera 131 is used to capture images or videos of the environment surrounding vehicle 11. In one example, external camera 131 is located inside vehicle 11 and has a field of view of the environment in front of the vehicle through projection component 16.

[0015] refer to Figure 2The head-up display 14 can be used by occupants of the vehicle 11. Occupants may include the driver, passengers, or other persons in the vehicle. The head-up display 14 is used to display projected images, such as accident area outlines, construction area warning frames, AR lane lines, light carpets, targets, or navigation indicator symbols, on the projection component 16 of the vehicle 11. The head-up display 14 may include a projection component 141 and an occupant status acquisition device 142.

[0016] Projection component 141 is used to project a projected image onto a projected component 16 of vehicle 11. The projected component 16 may be a windshield, a combiner, or other component suitable for forming a virtual image. Projection component 141 may include an optical collimator, a laser projector, a digital light processing projector, a liquid crystal display, a micro-light-emitting diode display, or other devices for projecting images.

[0017] The projection component 141 may also include an adjustment control module. The adjustment control module may include optical path components, such as a large reflector, a small reflector, or other reflective or refractive components, for adjusting the projection position of the projected image and the distance to the virtual image.

[0018] The occupant status acquisition device 142 is used to determine the position of the occupant 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's head or eyes and acquire the driver's head posture, gaze direction, facial expression, eyelid state, or gaze duration. The occupant position and visual state parameters from the occupant status acquisition device 142 can be used to locate the projected image on the projected component 16, and can also be used for lane change intention prediction in this disclosure.

[0019] In some examples, the occupant status acquisition device 142 is one or more cameras installed in the vehicle 11, and its gaze data sampling frequency can be set to 10Hz. The specific sampling frequency can be set according to system requirements, vehicle model calibration, or hardware performance.

[0020] To operate the head-up display 14, the processor 121 in the display control unit 12 may include multiple software modules, such as a system manager 144 and an image engine 143. During operation of the system 10, the system manager 144 receives a first input IN1, a second input IN2, and a third input IN3. The first input IN1 may indicate the position of the vehicle 11 in space, such as the vehicle's geographic location, Global Navigation Satellite System (GNSS) data, vehicle speed, road curvature, and vehicle steering data. The second input IN2 may indicate the position of the occupants in the vehicle 11 and visual state parameters, such as the position of the occupants' eyes or heads. The third input IN3 may include external environmental parameters of the vehicle 11's surroundings, such as the status of distant vehicles, road targets, or traffic facility information.

[0021] System manager 144 can determine the type, size, shape, color, and position of the projected image displayed by projection unit 141 based on first input IN1, second input IN2, and third input IN3. System manager 144 can instruct image engine 143 to generate the projected image. Image engine 143 can be a software module, graphics processing module, or integrated circuit in projection unit 141 or display control device 12. Based on the projected image parameters determined by system manager 144, image engine 143 uses projection unit 141 to display the projected image on projected unit 16. When head-up display device 14 is an AR-HUD, the projected image is projected onto projected unit 16 and forms an augmented reality virtual image 25 along road surface 26.

[0022] Reference Figure 3 and Figure 4 The display control device 12 may include a memory 122 and a processor 121. The memory 122 is configured to store vehicle operation data and image frame data generated by the image generation unit based on vehicle status signals. The processor 121 is configured to execute a display control method. This display control method revolves around the pre-deflection, dynamic adjustment, and reset of the augmented reality virtual image 25 in a vehicle lane change scenario, and may specifically include steps S410 to S430.

[0023] In step S410, the vehicle's lane-changing intention and the direction of lane-changing are determined based on the vehicle operation data.

[0024] Specifically, the processor 121 can acquire vehicle operation data from the data acquisition module. The vehicle operation data may include path guidance data from the navigation device, driver status data from the driver monitoring device, and the vehicle speed. The processor 121 determines candidate lane-changing directions based on the path guidance data. For example, when the path guidance data indicates "please change lanes to the left ahead," the candidate lane-changing direction is to the left; when the path guidance data indicates "please change lanes to the right ahead," the candidate lane-changing direction is to the right. The processor 121 then verifies the candidate lane-changing directions based on the driver's observation direction represented by the driver status data. When the observation direction represented by the driver's status data matches the candidate lane-changing direction, it is determined that there is a lane-changing intention, and this candidate lane-changing direction is taken as the lane-changing direction indicated by the intention.

[0025] Since the lane change intention is predetermined based on multi-source data such as path guidance data and driver status data, the processor 121 can collect the lane change trend before the driver actually operates the vehicle to change lanes, or even before the turn signal is turned on. This provides a decision for the pre-adjustment of subsequent view parameters and reduces the delay caused by responding only after the turn signal is turned on.

[0026] In step S420, based on the lane change intention, the viewing angle parameters of the virtual image rendering camera are pre-adjusted in the opposite direction of the lane change direction.

[0027] Reference Figure 5 The virtual image rendering camera 31 is a virtual camera in the AR rendering engine that generates augmented reality virtual images. Its view parameters are used to characterize the optical axis direction, view matrix, projection matrix, or other parameters that affect the projection position of the virtual image. When the lane change intention is not determined, the view parameters of the virtual image rendering camera 31 correspond to the default view direction, which is the straight direction consistent with the current driving direction of the vehicle. When the lane change intention is determined, the processor 121 controls the view parameters of the virtual image rendering camera 31 to pre-deflect by a pre-deflection angle θ in the opposite direction of the lane change direction, so that the optical axis of the virtual image rendering camera 31 changes from the default view direction to the reversed view direction, thereby giving the rendered augmented reality virtual image a pre-compensation in the opposite direction of the target lane.

[0028] The reason for using a reverse pre-deflection in the opposite direction of the lane change is that when a vehicle enters the lane change process, the vehicle coordinate system will deflect as the vehicle's front rotates. The rendering position of the augmented reality virtual image is usually calculated based on the vehicle coordinate system or the virtual image rendering camera coordinate system. If the viewing angle parameters of the virtual image rendering camera 31 deflect directly in the same direction as the vehicle's lane change direction, the augmented reality virtual image is prone to excessive lateral offset as the vehicle's front rotates, causing display elements such as AR lane lines, light carpets, or target objects to deviate from their positions relative to the real road. By pre-deflecting the virtual image rendering camera 31 in the opposite direction of the lane change, the display offset caused by vehicle yaw, changes in vehicle posture, and the rotation of the rendering coordinate system with the vehicle can be offset in advance. This ensures that the augmented reality virtual image tends to remain in the position corresponding to the target lane or target road surface area during the lane change process, thereby improving the fit between the augmented reality virtual image and the real road surface and reducing display lag and visual jumps caused by rapid changes in vehicle posture at the beginning of the lane change.

[0029] For example, when a vehicle changes lanes to the left, the yaw of the vehicle may cause the uncompensated augmented reality image to shift to the left along with the vehicle; the processor 121 can pre-shift the virtual image rendering camera 31 to the right to counteract the aforementioned shift. When a vehicle changes lanes to the right, the processor 121 can pre-shift the virtual image rendering camera 31 to the left.

[0030] Since the deflection is performed in advance based on the lane change intention before the vehicle actually produces a significant change in yaw attitude, the augmented reality image can better match the direction of the target lane and the road surface in front of the vehicle at the beginning of the lane change action, reducing the inherent adjustment lag of traditional feedback adjustment.

[0031] In step S430, using the pre-adjusted viewing angle parameters, an augmented reality virtual image superimposed on the road surface in front of the vehicle is projected.

[0032] Specifically, the processor 121 generates AR elements, including AR lane lines, light carpets, target objects, and navigation prompt symbols, by the image element generation unit based on pre-adjusted viewing angle parameters, and configures the parameters of the AR image and the virtual image rendering camera 31 by the AR parameter configuration unit. The image display unit receives the generated display elements and generates a real image. The optical display unit, such as the projection component 141, projects the real image onto the projected component 16, thereby forming an augmented reality virtual image 25 superimposed on the road surface 26. Since the augmented reality virtual image 25 is rendered and projected based on pre-adjusted viewing angle parameters, the augmented reality virtual image 25 projected during lane changing can maintain a good fit with the real road conditions in front of the vehicle, improving the relevance of guidance information and the stability of the displayed image.

[0033] To achieve the above processing procedure, refer to Figure 6The display control device 12 can be functionally divided into a data acquisition module 101, a data processing module 102, an AR element generation module 103, and a display module 104, which communicate via electrical or data communication in sequence.

[0034] The data acquisition module 101 is used to acquire vehicle operation data such as vehicle, navigation, and sensors when the driver is driving the vehicle in the lane. The data sources may include vehicle sensors 13, driver monitoring devices, navigation devices, inertial measurement units, ADAS systems, and vehicle communication systems.

[0035] The data processing module 102 is used to calculate the viewing angle parameters that the virtual image rendering camera 31 needs to adjust. The data processing module 102 may include a prediction unit 1021 and a lane-change processing unit 1022. The prediction unit 1021 is used to determine the lane-change intention from the received raw data and to predict and correct the inertial measurement data. The lane-change processing unit 1022 is used to determine and calculate the deflection angle based on the acquired data during the lane-change process.

[0036] The AR element generation module 103 is used to generate augmented reality virtual images 25. The AR element generation module 103 may include a scene element generation unit 1031 and an AR parameter configuration unit 1032. The scene element generation unit 1031 is used to generate AR elements including lane lines, light carpets, target objects, navigation prompts, etc. The AR parameter configuration unit 1032 is used to configure the parameters of the AR scene and the virtual image rendering camera 31.

[0037] Display module 104 is used to display augmented reality virtual image 25. Display module 104 may include image display unit 1041 and optical display unit 1042. Image display unit 1041 is used to receive display elements generated by AR element generation module 103 and generate real images corresponding to the display elements. Optical display unit 1042 is used to display the image generated by image display unit 1041 on a screen and project it into the driver's field of vision in the form of a projection.

[0038] The above functional modules 101 to 104 and Figure 1 The system includes vehicle sensor 13, display control device 12, image engine 143, system manager 144, and head-up display device 14. Data acquisition module 101 corresponds to the data acquisition function of vehicle sensor 13, driver monitoring device, and navigation device; data processing module 102 corresponds to the viewpoint parameter calculation function of processor 121; AR element generation module 103 corresponds to the AR element generation and parameter configuration function of image engine 143 and system manager 144; and display module 104 corresponds to the image display and optical projection function of head-up display device 14.

[0039] Reference Figure 7The display control method executed by the processor 121 may include steps S701 to S718.

[0040] Step S701: Determine if AR rendering is enabled. After the in-vehicle AR-HUD system is powered on, it first enters the initialization state and continuously monitors the enabling status of the AR rendering function. The system confirms whether the current vehicle is in a driving mode that supports AR-HUD, such as intelligent driving assistance mode or navigation enabled, and confirms whether the AR rendering function is enabled. If AR rendering is not enabled, the process jumps directly to the end without executing subsequent view control logic; if AR rendering is enabled, the process proceeds to step S702.

[0041] Step S702: Determine if a single-sided turn signal is on. The system reads the vehicle's turn signal and determines whether a single-sided turn signal, i.e., the left or right turn signal, is activated. If the single-sided turn signal is not on, proceed to step S704, where intent analysis is performed only based on the vehicle's driving status, driver status, and navigation signals, without activating the view control branch associated with the turn signal; if the single-sided turn signal is on, proceed to step S703.

[0042] Step S703: Determine if the single-sided turn signal is being activated for the first time. When the single-sided turn signal is activated, the system determines whether this is the first trigger of the turn signal, distinguishing between initial activation and continuous activation. If it is the first activation, proceed to step S708 to execute the initial yaw angle acquisition process of the inertial measurement unit, providing reference data for subsequent view control; if it is continuous activation, proceed to step S709 to continue monitoring and analyzing the yaw angle data.

[0043] Step S704, Multi-source Intent Prediction. The system combines vehicle data, driver status, and navigation data, and performs fusion analysis on each data source through a preset intent prediction model to comprehensively predict the driver's lane change intent, outputting the driver's lane change intent value to provide a decision-making basis for subsequent view control.

[0044] Step S705: Calculate the lane change trend score S. Based on the multi-source intent prediction results from step S704, the system calculates the lane change trend score S, which measures the likelihood of the driver performing a lane change maneuver. This lane change trend score S can also be referred to as the lane change intent value or the lane change intent confidence level.

[0045] Step S706: Determine whether the lane change trend score S is greater than or equal to the intent determination threshold Threshold. The system compares the lane change trend score S output in step S705 with the preset intent determination threshold Threshold. If S is greater than or equal to Threshold, proceed to step S707 to perform small-amplitude pre-deflection control of the virtual image rendering camera 31; if S is less than Threshold, jump to step S718 to maintain or restore the default configuration of the virtual image rendering camera 31.

[0046] In step S707, the viewing angle parameters of the virtual image rendering camera are slightly pre-deflected. When a clear lane change intention is determined, the system slightly adjusts the viewing angle parameters of the virtual image rendering camera 31 to compensate the augmented reality virtual image 25 in the opposite direction of the target lane. The deflection angle is dynamically adjusted according to the lane change trend score S and the vehicle speed.

[0047] Step S708: Obtain the yaw angle startAngle from the inertial measurement unit. When a turn signal on one side is activated for the first time, the system reads the initial yaw angle data startAngle from the vehicle's inertial measurement unit, which serves as the reference yaw angle for subsequent lane changes. startAngle is the reference yaw angle at the moment the turn signal is activated.

[0048] Step S709: Determine if the turn signal on the same side is off. The system continuously monitors the turn signal status and determines whether the previously turned-on turn signal on one side has been turned off. If the turn signal is off, proceed to step S718 and execute the default view parameter restoration process; if the turn signal is not off, proceed to step S710.

[0049] Step S710: Obtain the current yaw angle currentAngle from the inertial measurement unit. During the continuous activation of the turn signal, the system periodically reads the current yaw angle data currentAngle output by the inertial measurement unit to reflect the current vehicle body attitude.

[0050] Step S711, predict the yaw angle (predictAngle). Based on the current vehicle state and historical inertial measurement unit data, the system uses a Kalman filter algorithm or other prediction correction algorithm to filter, denoise, and smooth the vehicle's yaw angle to obtain the predicted yaw angle value (predictAngle) at the current moment, thereby obtaining more stable and accurate vehicle attitude data.

[0051] Step S712: Calculate the yaw angle difference ΔAngle. The system calculates the difference between the predicted yaw angle (predictAngle) and the reference yaw angle (startAngle), and takes the absolute value to obtain the vehicle attitude change ΔAngle, i.e., the yaw angle difference.

[0052] Step S713: Determine whether the single-frame change in yaw angle difference ΔAngle exceeds the abrupt change threshold. If the single-frame change in ΔAngle exceeds the abrupt change threshold, for example, exceeding 5° / frame, it indicates that there may be an anomaly in the inertial measurement unit data. In this case, proceed to step S718 and set the viewing angle parameters of the virtual image rendering camera 31 to the default value to avoid abnormal adjustments causing image chaos; if it does not exceed the threshold, proceed to step S714.

[0053] Step S714: Determine the turn signal activation duration.

[0054] If the turn signal is on for a duration exceeding the specified threshold, and the yaw angle difference ΔAngle remains below or equal to the third threshold T3, it indicates that a lane change may not have been performed for a long time or the turn signal may have been accidentally activated. In this case, proceed to step S718 and set the viewpoint parameters of the virtual image rendering camera 31 to the default value; otherwise, proceed to step S715.

[0055] Step S715: Determine the offset distance between the vehicle's current position and the center line of the target lane. The system calculates the offset distance between the vehicle's current position and the center line 29 of the target lane, and determines whether the offset distance is less than the set offset threshold L. If the offset distance is less than the offset threshold L, it is considered that the vehicle has approached the center line of the target lane or the lane change has been corrected, and the process jumps to step S718, restoring the viewpoint parameters of the virtual image rendering camera 31 to their default values; otherwise, proceed to step S716.

[0056] Step S716: Determine if the yaw angle difference ΔAngle is within the adjustment range. The system compares the yaw angle difference ΔAngle with a first threshold T1 and a second threshold T2. The first threshold T1 is the minimum threshold for adjusting the viewpoint parameters of the virtual image rendering camera 31, and the second threshold T2 is the upper limit of the normal yaw angle variation range during lane changing. If T1 is less than ΔAngle and ΔAngle is not greater than T2, proceed to step S717 to adjust the viewpoint parameters according to the yaw angle difference ΔAngle; if ΔAngle is not greater than T1 or ΔAngle is greater than T2, jump to step S718 to restore the viewpoint parameters of the virtual image rendering camera 31 to their default values. When ΔAngle is greater than the second threshold T2, it can be considered that the vehicle is in a turning or U-turn scenario, and the vehicle's turning amplitude is greater than the normal lane change angle. At this time, the viewpoint parameters are restored to the default center angle to avoid image clutter caused by excessive viewpoint offset.

[0057] Step S717: Adjust the viewing angle parameters of the virtual image rendering camera according to the yaw angle difference ΔAngle. The system receives the viewing angle adjustment command from the AR rendering engine based on the size of the yaw angle difference ΔAngle, and updates the viewing angle parameters of the virtual image rendering camera 31 to ensure that the augmented reality virtual image 25 closely matches the actual road conditions.

[0058] Step S718: The view parameters of the virtual image rendering camera are restored to their default values. When the lane change intention disappears, the turn signal is turned off, the offset distance between the vehicle and the center line 29 of the target lane is less than the offset threshold, the vehicle is in a turning or U-turn scenario, or an abnormal situation occurs, the system restores the view parameters of the virtual image rendering camera 31 to their default state.

[0059] As can be seen from steps S701 to S718 above, this disclosure forms a closed-loop control of adjustment, monitoring, feedback, and reset. The system collects the reference yaw angle startAngle when the turn signal is first turned on. In subsequent monitoring, the system calculates the yaw angle difference ΔAngle based on this reference yaw angle, thereby distinguishing normal lane change, turning and U-turn, straightening and abnormal scenarios according to the size of ΔAngle, and performing synchronous adjustment or reset of the viewing angle parameters to achieve real-time fitting of the augmented reality virtual image 25 throughout the lane change process.

[0060] like Figure 8 As shown, under the control of this disclosure, when vehicle 11 changes lanes from the current lane to the target lane, processor 121 performs reverse pre-deflection and follow-up adjustment of the viewing angle parameters of the virtual image rendering camera 31 based on the lane change intention, the yaw angle difference ΔAngle, and the offset distance L between vehicle 11 and the centerline 29 of the target lane. Therefore, the display trajectory of the augmented reality virtual image 25 can continuously change with the lane change process of vehicle 11 and remain basically consistent with the target contact area on the road surface 26. In other words, during the process of vehicle 11's front turning and the vehicle coordinate system deflecting, the augmented reality virtual image 25 will not significantly drift excessively with the direction of vehicle front turning, but will remain at the position corresponding to the target lane or target road surface area through viewing angle parameter compensation.

[0061] In comparison, such as Figure 9 As shown, in existing technologies without the aforementioned reverse pre-deflection, attitude data fusion, and closed-loop reset control, there is a significant lateral deviation between the display trajectory of the augmented reality virtual image 25 and the target contact area corresponding to the road surface 26. The interval between the two can characterize the display deviation of the augmented reality virtual image 25 relative to the real road surface. This deviation can cause display elements such as AR lane lines, light carpets, or targets to not be accurately superimposed on the real road conditions, thereby reducing the accuracy and stability of driving guidance. Therefore, this disclosure can reduce response lag in the initial stage of lane change, suppress lateral drift of the augmented reality virtual image 25 during lane change, and reset the viewing angle parameters in a timely manner when the vehicle returns to center or in abnormal scenarios, thereby improving the fit between the augmented reality virtual image 25 and the real road surface, the display continuity, and the reliability of driving guidance.

[0062] The multi-source intent prediction in the above process is mainly used to identify lane-changing trends in advance before a vehicle makes a clear lane change. Some drivers may only activate their turn signals after a lane change has already begun. If the viewing angle parameters are adjusted based on the activation of the turn signal at this time, a certain delay will occur. Therefore, this disclosure utilizes multi-source data to perceive lane-changing intent in advance, realizing pre-preparation of the viewing angle response. Its core logic is to quantify the path guidance data, driver status data, and vehicle speed into lane-changing intent values ​​through standardized processing, providing a basis for pre-adjustment of viewing angle parameters for the augmented reality virtual image 25.

[0063] Reference Figure 10 The multi-source intent prediction and pre-deflection processing can specifically include steps S1010 to S1050.

[0064] In step S1010, the direction of the lane change intention is determined.

[0065] Vehicle operation data includes route guidance data from the navigation device, driver status data from the driver monitoring device, and vehicle speed. Route guidance data is used to identify lane change-related instructions, such as "Change lanes left ahead," "Change lanes right ahead," and "Keep in your current lane." NavL=1 indicates that the route guidance data indicates a left lane change, NavR=1 indicates that the route guidance data indicates a right lane change, and Nav0=1 indicates no lane change instruction or a straight-ahead instruction.

[0066] Driver status data can include the head horizontal yaw angle (Yaw) acquired by the driver monitoring device, i.e., the left and right head turning angle, and the duration t of the head posture. The preset observation conditions can be defined as follows: a head turn to the left greater than or equal to 10° and lasting longer than 0.8s is recorded as looking at the left lane (HeadL=1); a head turn to the right greater than or equal to 10° and lasting longer than 0.8s is recorded as looking at the right lane (HeadR=1); and a centered head or a turn less than 10° is recorded as Head0=1. Vehicle speed is used to dynamically adjust the pre-yaw angle.

[0067] The lane change direction is determined using a fusion rule that prioritizes navigation and verifies head posture. Candidate lane change directions are determined based on path guidance data. When the driver's observation direction, as represented by the driver's state data, matches the candidate lane change direction, a lane change intention is confirmed, and this candidate lane change direction is adopted as the lane change direction. Specifically, if NavL=1 and HeadL=1, it is determined to be a left lane change intention; if NavR=1 and HeadR=1, it is determined to be a right lane change intention; if the direction indicated by the path guidance data does not match the direction represented by the head posture, it is determined to be an intention conflict, and no pre-deflection is triggered or only a low-confidence intention is output; if there is no path guidance lane change instruction but the driver's single-sided observation duration is greater than 1 second, it is determined to be an autonomous lane change tendency, and the driver enters a low-amplitude pre-deflection mode.

[0068] In step S1020, the lane change intention value is calculated.

[0069] The lane change intention value S can range from 0 to 100. This value S is obtained by a weighted sum of the path guidance component, driver state component, and timing component. The weighting is determined based on the intention reliability level of each component, with the path guidance component having a greater weight than the driver state component, and the driver state component having a greater weight than the timing component. In some examples, the lane change intention value S is calculated using the following formula: S = 0.5·S nav + 0.3·S head + 0.2·S time Among them, S nav This is the path guidance component, with a value of 100 when the path guidance data indicates a lane change, and a value of 0 when no lane change is indicated; S head The driver state component takes a value of 100 when the driver state data meets preset observation conditions, and a value of 0 when the conditions are not met; S time This is a time-series component, which increases with the duration for which the driver's state data meets the preset observation conditions. For example, it takes a value of 60 when the observation lasts from 0.8s to 1.5s, a value of 100 when it lasts longer than 1.5s, and a value of 0 otherwise. In the above formula, S... nav S head S time The subscripts nav, head, and time represent path guidance, driver status, and time sequence, respectively.

[0070] In step S1030, it is determined whether the lane change intention value is greater than or equal to Threshold.

[0071] The default value for the intent determination threshold, Threshold, is 60. When the intent value S is greater than or equal to Threshold, step S1040 is executed, and the intent is determined to be a valid lane change, and the viewpoint parameters of the virtual image rendering camera are pre-adjusted. When the intent value S is less than Threshold, step S1050 is executed, and the intent is determined to be an unintentional lane change or interference action, and no pre-adjustment is performed.

[0072] Since the path guidance component has the largest weight, it serves as the primary decision-making source when the path guidance data clearly indicates a lane change. At the same time, by introducing multi-source verification based on the driver's observation direction and duration, the intent value is more likely to reach the intent determination threshold only when the driver's status data and path guidance data corroborate each other for a sufficient duration, thereby reducing the probability of false triggering.

[0073] After multi-source intent prediction is completed, the system further calculates the pre-deflection angle based on the intent value and vehicle speed. The viewing angle parameters are pre-adjusted in the opposite direction of the lane change, including adjusting the viewing angle parameters according to the deflection angle θ, so that the optical axis of the virtual image rendering camera 31 is deflected from the default direction to the pre-deflected viewing angle direction in the opposite direction of the lane change. The deflection angle θ is the sum of the base deflection amount and the adjusted deflection amount, and the deflection angle θ is within a preset angle range.

[0074] This disclosure employs a segmented speed adaptive model to avoid insufficient deflection at high speeds or excessive deflection at low speeds. The overall pre-deflection angle limit constraint can be 0.5° ≤ θ ≤ 3.0°. In some examples, the deflection angle θ is calculated according to the following formula: θ = θ base + k v ·(S Threshold·f(v) Where, θ base The base deflection can be taken as 0.5°; k v θ is the angle gain coefficient, which can be taken as 0.04; S is the intention value; Threshold is the intention determination threshold, which can be taken as 60 by default; f(v) is the speed gain coefficient corresponding to the vehicle speed. In the above formula, θ base k v The subscripts base and v represent the base quantity and vehicle speed, respectively.

[0075] In the above calculation formula, the adjustment deflection is k. v ·(S The adjustment deflection amount increases as the difference between the arbitrary graph value S and the intention determination threshold Threshold increases, and also increases as the velocity gain coefficient f(v) increases.

[0076] The segmented values ​​of the speed gain coefficient f(v) can be as follows: when 30km / h < v ≤ 60km / h, f(v) = 0.6, corresponding to low-speed small-angle gain; when 60km / h < v ≤ 100km / h, f(v) = 1.0, corresponding to medium-speed normal-angle gain; when 100km / h < v ≤ 120km / h, f(v) = 1.3, corresponding to high-speed enhanced-angle gain; when v ≤ 30km / h or v > 120km / h, f(v) = 0.3, to limit the angle and avoid visual disturbance.

[0077] The range of 60 km / h to 100 km / h can be used as an example of a preset operating speed range. When the vehicle speed is within the preset operating speed range, the speed gain coefficient increases with the increase of vehicle speed; when the vehicle speed is below or above the preset operating speed range, the speed gain coefficient is less than the speed gain coefficient when the vehicle speed is within the preset operating speed range.

[0078] Since the deflection angle θ is determined by the basic deflection amount θ base Together with the adjustment deflection amount, the pre-deflection angle is formed, and the difference between the arbitrary graph value and the judgment threshold, as well as the speed gain coefficient, increase simultaneously. Therefore, the more obvious the lane change intention and the greater the vehicle speed gain, the larger the pre-deflection angle. Furthermore, because an angle limit range is set to restrict the deflection angle θ, the visual disturbance caused by excessive viewpoint shift can be reduced in both low-speed and ultra-high-speed ranges, thus ensuring the stability of the displayed image while maintaining the pre-deflection response. Based on the above calculations and logical processing, the system outputs the pre-adjustment amount of the viewpoint parameters for the virtual image rendering camera 31.

[0079] In addition to the aforementioned multi-source intent prediction and pre-deflection processing, this disclosure also dynamically monitors the vehicle attitude during lane changes through yaw angle data fusion. The display control device 12 may further include an inertial measurement unit. The inertial measurement unit is configured to acquire yaw angle observations and angular velocity observations. The current yaw angle is obtained by updating the predicted yaw angle value at the current moment based on the yaw angle observations; the predicted yaw angle value at the current moment is predicted based on the yaw angle observations and angular velocity observations from the previous moment.

[0080] Yaw angle data fusion addresses the issues of high noise, insufficient real-time performance, or drift in raw inertial measurement unit (IMU) data by performing a "prediction-correction" fusion process. It accurately obtains the vehicle's yaw angle difference, providing attitude data support for viewing angle parameter adjustment. Essentially, it utilizes the predictive capability of Kalman filtering to compensate for data delay and uses yaw angle observations to correct prediction biases, achieving a complementary advantage between prediction and observation.

[0081] Kalman filtering can be divided into a prediction step and an update step. When used for yaw angle prediction, the state equation is: X(k) = A·X(k-1) + B·u(k) + w(k) Where X(k) is the predicted yaw angle at the current moment, X(k-1) is the observed yaw angle at the previous moment, A is the state transition matrix, B is the control matrix, u(k) is the observed angular velocity of the current inertial measurement unit, and w(k) is the process noise. A can be set to 1 to correspond to continuous yaw angle changes; B can be set to 0.1 to correspond to the influence of angular velocity on the yaw angle; w(k) can be set to 0.01 or determined according to actual calibration.

[0082] The observation equation is: Z(k) = H·X(k) + v(k) Where Z(k) is the current yaw angle observation value of the inertial measurement unit, H is the observation matrix, and v(k) is the observation noise. H can be set to 1, and v(k) can be set to 0.02 or determined according to the actual calibration.

[0083] This algorithm can predict the yaw angle of the next 1 to 2 frames, compensate for data delay, and obtain the predicted yaw angle value `predictAngle` for the current moment. In some examples, `predictAngle` can be represented as: predictAngle=X(k 1)+u(k)·T Where T is the frame period.

[0084] The output of the update step, after a delay, is used as the estimated value X(k) from the previous time step. 1) Feedback is sent to the prediction step to form a closed loop of prediction and correction.

[0085] After obtaining the predicted yaw angle value predictAngle, the yaw angle startAngle at the moment the turn signal is first activated is used as the reference yaw angle. The difference between the predicted yaw angle value predictAngle and the reference yaw angle startAngle is calculated and its absolute value is taken to obtain the yaw angle difference ΔAngle: ΔAngle = |predictAngle startAngle| In some methods, when the current yaw angle (currentAngle) has been corrected by the filtered wave or updated by fusion, the difference between the current yaw angle (currentAngle) and the startAngle (startAngle) after fusion correction can also be used to calculate the yaw angle difference: ΔAngle = |currentAngle startAngle| The magnitude of the yaw angle difference ΔAngle directly corresponds to the vehicle's steering amplitude. A smaller ΔAngle indicates a smaller steering amplitude; when ΔAngle is within the normal lane-change threshold range, the vehicle may be in the process of changing lanes; when ΔAngle is too large, the vehicle may be in a turning or U-turn scenario. Because Kalman filtering is used to predict the yaw angle and correct it using observed yaw angle values, relatively stable yaw angle predictions can still be obtained even when the original data from the inertial measurement unit contains noise and drift, thus providing a reliable basis for subsequent scene judgments.

[0086] Reference Figure 11 Vehicle 11 changes lanes from its current lane 27 to the target lane 28. The yaw angle of vehicle 11 when the turn signal is first activated is used as the reference yaw angle startAngle, and the current yaw angle of vehicle 11 is used as currentAngle. The absolute value of the difference between the two is the yaw angle difference ΔAngle, which represents the turning magnitude of vehicle 11 relative to the moment the turn signal is activated. The lateral distance between the current position of vehicle 11 and the center line 29 of the target lane is the offset distance L, used to represent the degree to which vehicle 11 has entered the target lane 28.

[0087] Scene recognition can be achieved through a combination of thresholds and states for the yaw angle difference ΔAngle. For turning and U-turn scenarios, when ΔAngle is greater than the second threshold T2, for example, when the second threshold T2 is 15°, the vehicle's steering angle is greater than the normal lane change angle. In this case, the viewing angle parameter is restored to the default center angle to avoid excessive viewing angle shift that could cause visual clutter.

[0088] For normal lane-changing scenarios, when the first threshold T1 is less than ΔAngle and ΔAngle is not greater than the second threshold T2 (e.g., 3° ​​< ΔAngle ≤ 15°), the viewing angle parameter adjusts synchronously with ΔAngle, and the adjustment amount is proportional to ΔAngle. In some examples, the adjustment amount of the viewing angle parameter is determined by the following formula: Viewpoint parameter adjustment amount = k·ΔAngle Where k is the scaling factor. The scaling factor k can be adjusted according to the AR calibration parameters and the HUD installation position to ensure that the viewing angle is synchronized with the vehicle's attitude. The adjustment direction, i.e., the deflection direction, can be determined by the opposite direction of the vehicle's turn signal indication, or by the opposite direction of the lane change direction, the opposite direction of the steering wheel angle, the opposite direction of the inertial measurement unit's steering direction, or the opposite direction of the navigation direction.

[0089] For lane change recovery scenarios, during the lane change process, the offset distance L between the current position of vehicle 11 and the centerline 29 of the target lane is obtained in real time based on map data and ADAS data. When the offset distance L is lower than the offset threshold, it is confirmed that vehicle 11 has stably entered the area near the centerline of the target lane 28, and the vehicle is judged to have recovered. In addition, if the turn signal is off or the yaw angle difference ΔAngle is not greater than the first threshold T1, the vehicle can also be judged to have recovered, and the view parameters are reset. The offset threshold can be adaptively adjusted according to the vehicle speed.

[0090] The first threshold T1 can be 3°, the second threshold T2 can be 15°, and the third threshold T3 can be 1°. The third threshold T3 is less than the first threshold T1. If the first threshold T1 is calibrated separately according to the vehicle model or operating condition, the third threshold T3 will be adjusted according to the first threshold T1 and will remain less than the first threshold T1.

[0091] Because the viewing angle parameter is adjusted proportionally to ΔAngle when the first threshold T1 is less than ΔAngle and ΔAngle is not greater than the second threshold T2, the augmented reality image 25 can rotate synchronously with the vehicle's posture during normal lane changes, maintaining its fit with the real road surface. Furthermore, because the viewing angle parameter is reset when ΔAngle is greater than the second threshold T2, the offset distance L between the vehicle and the target lane centerline 29 is less than the offset threshold, the turn signal is off, or ΔAngle is not greater than the first threshold T1, the augmented reality image 25 can promptly return to its default display state in scenarios such as turning, U-turns, and lane change recovery, avoiding visual clutter.

[0092] During vehicle operation, the system also identifies abnormal situations. First, when the single-frame change in yaw angle difference ΔAngle exceeds a sudden change threshold (e.g., a single-frame change exceeding 5° / frame), the system determines the inertial measurement unit (IMU) data is abnormal. In this case, the viewing angle parameters are reset to prevent sudden jumps in IMU data from causing significant adjustments to the augmented reality image. Second, when the turn signal's activation duration exceeds a duration threshold (e.g., more than 30 seconds after activation) and the yaw angle difference ΔAngle remains below or equal to the third threshold T3, the system determines it as a mis-activation of the turn signal or no actual lane change. The third threshold T3 can be 1° and is less than the first threshold T1. In this case, the viewing angle parameters are reset to reduce visual offset in scenarios of mis-activation of the turn signal or prolonged periods without an actual lane change. Third, when multiple consecutive frames of vehicle operation data are lost, or when communication with critical data sources is interrupted, the system determines it as a communication anomaly. In this case, the viewing angle parameters are reset or maintained at their default state to ensure display stability.

[0093] Because an abnormality in the inertial measurement unit (IMU) data is identified and the viewpoint parameters are reset when the single-frame change in the yaw angle difference ΔAngle exceeds the abrupt change threshold, it avoids significant adjustments to the augmented reality virtual image 25 caused by sudden jumps in IMU data. Furthermore, because a mis-activated turn signal or failure to change lanes is identified and the viewpoint parameters are reset when the turn signal duration exceeds the duration threshold and the yaw angle difference ΔAngle remains below or equal to the third threshold T3, it reduces display disturbances in accidental triggering scenarios, thereby reducing unnecessary resource consumption and maintaining image stability.

[0094] The aforementioned multi-source intent prediction, pre-deflection angle calculation, yaw angle data fusion, scenario-based adjustment, and anomaly reset can all be implemented using replacement methods. For multi-source intent prediction, the driver's head posture can be replaced with one or a combination of the driver's line of sight, eyelid state, and hand operation actions. Lane change intent can be determined by recognizing the driver's line of sight trajectory when observing the side lane or the hand's pre-steering action. Path guidance data can be replaced with lane-level guidance information from a high-precision vehicle map, lane keeping assist signals from an advanced driver assistance system, and lane suggestion information from a roadside perception unit, still using road guidance information as the main image source. Vehicle speed signals can be replaced with wheel speed, acceleration, or gear signals to characterize the vehicle's driving state and adjust the pre-deflection angle, achieving the same control effect of vehicle speed changes affecting the deflection angle.

[0095] The calculation of lane change intention value can be replaced by linear weighted summation, fuzzy logic reasoning, Bayesian probability estimation, neural network classification, etc., while the output remains a quantified value of lane change intention. Directional consistency verification can be replaced by a priority arbitration mechanism. When path guidance data conflicts with the driver's observed direction, delayed confirmation, continuous multi-frame verification, or weighted voting mechanisms can be used, still reflecting the concept of multi-source mutual verification to reduce false triggering. The effective observation duration threshold can be selected within the range of 0.5s to 1.5s, not limited to 0.8s; the head deflection angle threshold can be adjusted within the range of 10° to 20°; the intention determination threshold can adaptively float according to the driving scenario, such as highway, urban area, or parking, and is not fixed at 60°.

[0096] The vehicle speed gain function in the pre-deflection angle can be replaced with a continuous nonlinear function, an exponential function, or a quadratic function, or a vehicle speed integral term or a filter lag term can be introduced. As long as the core control law of vehicle speed change affecting angle gain is maintained, any replacement scheme can be used. The angle limiting method can be replaced with a fixed threshold and a soft limiting that is dynamically adjusted based on arbitrary map values, or the maximum angle can be adaptively adjusted according to the vehicle model, camera installation position, and HUD calibration parameters. In the low-speed or ultra-high-speed range, in addition to using a speed gain coefficient of 0.3 for angle suppression, angle constraints can also be achieved by using fixed small angle output, slope limitation, or step-by-step growth. The purpose of all these methods is to avoid visual disturbance caused by excessive viewing angle shift.

[0097] In yaw angle data fusion algorithms, Kalman filtering can be replaced by extended Kalman filtering, unscented Kalman filtering, complementary filtering, or particle filtering, all of which are alternative methods for predicting and correcting attitude signals. Yaw angle can be replaced by information such as steering wheel angle, ADAS trajectory lines, GPS heading change rate, wheel speed difference calculation results, and visual SLAM attitude. The method of dynamically adjusting the noise matrix can be replaced by adaptive adjustment based on the magnitude of the yaw angle difference ΔAngle, steering rate, or intended value. The state equation can be simplified to a first-order linear extrapolation model, combined with correction based on yaw angle observations.

[0098] Viewpoint parameter rendering control can be implemented in rendering engines or graphics frameworks such as Kanzi, Unity, Unreal, Android Canvas, OpenGL ES, and Vulkan. Rendering layer processing logic such as viewpoint smoothing, interpolation, and amplitude limiting can be implemented using algorithms such as linear interpolation, first-order hysteresis filtering, exponential smoothing, and stepped amplitude limiting, with the aim of avoiding screen jumps and improving display comfort.

[0099] The values ​​of the first threshold T1, the second threshold T2, the offset threshold, and the single-frame mutation threshold can be adjusted within a reasonable range according to the vehicle model, calibration parameters, and driving conditions. Dynamic adaptive thresholds can also be used instead of fixed thresholds. The lane change completion determination logic can be replaced by timing filtering, lane change completion probability estimation, or navigation lane change end event determination methods, as long as the effect of resetting the viewpoint parameters after the vehicle returns to center is achieved. The direct proportionality between the viewpoint parameter adjustment and ΔAngle can be replaced by piecewise linear, nonlinear gain, first-order inertial integral, and amplitude-limiting filtering control methods. The proportional coefficient k can also be dynamically adjusted according to vehicle speed, intention value, or vehicle attitude. The deflection direction can be obtained not only from the reverse direction of the turn signal indication or the reverse direction of the lane change intention, but also directly from the reverse direction of the steering wheel angle, the reverse direction of the inertial measurement unit's steering, or the reverse direction of the navigation.

[0100] The anomaly detection criteria can be adjusted based on vehicle model, sensor accuracy, or vehicle network status. For example, anomaly thresholds, detection duration, and consecutive frame drops can all be adaptively set. CAN signal verification, sensor health status, and inertial measurement unit self-diagnosis can also be added as criteria for anomaly detection. The viewing angle parameter reset method can employ uniform reset, gradient decay reset, or exponential smooth reset to reduce visual jumps during the reset process.

[0101] Compared to traditional AR-HUDs, which are prone to misalignment between AR elements and actual road conditions when vehicles change lanes, this invention can dynamically adapt to changes in vehicle attitude, making AR guidance more stably conform to the real road environment, improving the stability and reliability of AR display, and reducing interference with the driver's normal driving.

[0102] This disclosure identifies lane-changing trends in advance through multi-source intent prediction and pre-deflects the virtual image rendering camera 31 before the vehicle undergoes a significant change in yaw attitude. Compared with display control methods that rely solely on vehicle attitude feedback, this reduces the adjustment lag of the augmented reality virtual image 25. At the same time, by fusing multi-source information such as path guidance data, driver status data, vehicle speed, and turn signal status to determine lane-changing intent, the probability of false triggering from a single data source can be reduced, thereby improving the reliability of lane-changing intent recognition. Furthermore, this disclosure obtains the yaw angle difference through prediction and correction processing of inertial measurement data, which can reduce the impact of noise, drift, or delay in the raw data of the inertial measurement unit on the control of the viewing angle parameters, thereby improving the stability of the augmented reality virtual image 25 in conforming to the real road environment. In addition, by identifying normal lane changes, turns, U-turns, straightening, and abnormal scenarios through the yaw angle difference, the offset distance of the target lane centerline, the turn signal status, and abnormal conditions, and performing dynamic adjustments or resets in different scenarios, unnecessary viewing angle shifts in non-lane change scenarios can be reduced, and the risk of image confusion can be lowered. Moreover, timely reset of the viewing angle parameters of the virtual image rendering camera 31 in the event of sudden changes in inertial measurement data, accidental turn signal activation, or communication abnormalities can improve the stability of system operation and reduce unnecessary computing power and resource consumption.

[0103] This disclosure also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described display control method. The computer-readable storage medium may include read-only memory (ROM), random access memory (RAM), flash memory, programmable memory, magnetic storage medium, optical storage medium, or other media capable of storing program code.

[0104] The solutions disclosed herein can be provided as methods, apparatus, or computer program products. This disclosure can be implemented in hardware, software, or a combination of both. The specific methods described above are merely illustrative of the technical solutions of this disclosure and do not constitute a limitation on the scope of protection of this disclosure. Equivalent substitutions, modifications, or improvements made by those skilled in the art to the above content without departing from the technical concept of this disclosure may fall within the scope of protection of this disclosure.

Claims

1. A display control device, characterized in that, Includes a processor and a memory communicatively connected to the processor; The memory is configured to store vehicle operation data; The processor is configured as follows: The lane-changing intention of the vehicle and the direction of the lane-changing intention are determined based on the vehicle operation data. Based on the lane change intention, the viewing angle parameters of the virtual image rendering camera are pre-adjusted in the opposite direction of the lane change direction; Using the pre-adjusted viewing parameters, an augmented reality image is projected and superimposed on the road surface in front of the vehicle.

2. The display control device according to claim 1, characterized in that, The vehicle operation data includes driver status data from the driver monitoring device and route guidance data from the navigation device; When the observation direction represented by the driver's state data is consistent with the candidate lane change direction, it is determined that there is a lane change intention, and the candidate lane change direction is taken as the lane change direction. The candidate lane change direction is determined based on the path guidance data.

3. The display control device according to claim 2, characterized in that, When the metric value of the lane change intention is greater than the intention determination threshold, the viewing angle parameter is pre-adjusted in the opposite direction of the lane change direction.

4. The display control device according to claim 3, characterized in that, The vehicle operation data also includes the vehicle speed; The metric is obtained by weighted summation of the path guidance component, the driver state component, and the time series component. The weight of the path guidance component is greater than the weight of the driver state component, and the weight of the driver state component is greater than the weight of the time series component.

5. The display control device according to claim 4, characterized in that, The value of the path guidance component when the path guidance data indicates a lane change is greater than the value when the path guidance data does not indicate a lane change. The value of the driver state component when the driver state data meets the preset observation conditions is greater than the value when the driver state data does not meet the preset observation conditions. The temporal component increases as the duration for which the driver's state data meets the preset observation conditions increases.

6. The display control device according to claim 3, characterized in that, The vehicle operation data also includes the vehicle speed; The pre-adjustment of the viewing angle parameters in the opposite direction to the lane change direction includes: The viewing angle parameter is adjusted according to the deflection angle, wherein the deflection angle is the sum of the base deflection and the adjustment deflection, and the deflection angle is within a preset angle range. The adjustment deflection amount is determined based on the difference between the metric value and the intent determination threshold, and the speed gain coefficient corresponding to the vehicle speed. The adjustment deflection amount increases as the difference increases, and also increases as the speed gain coefficient increases.

7. The display control device according to claim 6, characterized in that, The speed gain coefficient is determined based on the speed range in which the vehicle speed is located; When the vehicle speed is within the preset operating speed range, the speed gain coefficient increases as the vehicle speed increases; When the vehicle speed is lower than or higher than the preset operating speed range, the speed gain coefficient is less than the speed gain coefficient when the vehicle speed is within the preset operating speed range.

8. The display control device according to claim 1, characterized in that, When the vehicle's turn signal is on, the viewing angle parameter is determined based on the yaw angle difference, wherein the yaw angle difference is the difference between the vehicle's current yaw angle and the reference yaw angle at the time the turn signal is on.

9. The display control device according to claim 8, characterized in that, The display control device further includes: An inertial measurement unit is configured to acquire yaw angle and angular velocity observations. The current yaw angle is obtained by updating the predicted yaw angle value at the current moment based on the observed yaw angle value; The predicted yaw angle at the current moment is obtained by predicting the observed yaw angle and the observed angular velocity at the previous moment.

10. The display control device according to claim 8, characterized in that, When the yaw angle difference is greater than the first threshold and not greater than the second threshold, the viewing angle parameter is adjusted by an adjustment amount proportional to the yaw angle difference, and the adjustment direction is determined by the direction indicated by the vehicle's turn signal. When the yaw angle difference is greater than the second threshold, the viewing angle parameter is reset; Wherein, the second threshold is greater than the first threshold.

11. The display control device according to claim 10, characterized in that, When the offset distance between the vehicle and the center line of the target lane is less than the offset threshold, the viewpoint parameter is reset; The viewpoint parameters are reset when at least one of the following conditions occurs: The single-frame change in the yaw angle difference is greater than the abrupt change threshold; The turn signal activation duration is greater than the duration threshold, and the yaw angle difference is consistently no greater than the third threshold, wherein the third threshold is less than the first threshold.

12. A display control method, characterized in that, include: The lane-changing intention of the vehicle and the direction of the lane-changing intention are determined based on the vehicle operation data. Based on the lane change intention, the viewing angle parameters of the virtual image rendering camera are pre-adjusted in the opposite direction of the lane change direction; Using the pre-adjusted viewing parameters, an augmented reality image is projected and superimposed on the road surface in front of the vehicle.

13. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method as described in claim 12.

Images