AR-HUD light carpet display method, device, vehicle and storage medium

Abstract

This application discloses a method, device, vehicle, and storage medium for displaying a light carpet in an AR-HUD. The light carpet display method includes acquiring target slope data of the vehicle in its own coordinate system; finding the current pose data based on the timestamp of the target slope data and the vehicle pose information set; associating the current pose data and the target slope data to construct a slope sequence distributed along the vehicle's driving path; obtaining actual slope features to characterize changes in road surface slope based on the slope sequence and pre-stored historical slope information; calculating the vehicle pitch angle based on the geometric features of the actual slope features; decoupling the target slope data based on the vehicle pitch angle to obtain decoupled road surface slope data; and displaying the light carpet based on the road surface slope data. This light carpet display method eliminates vehicle pitch interference, enabling the AR-HUD light carpet to conform stably to the actual road surface in real time, significantly improving projection accuracy and driving experience under dynamic slopes.

Description

Technical Field

[0001] This application relates to the field of vehicle technology, and in particular to an AR-HUD light carpet display method, light carpet display device, vehicle, and computer-readable storage medium. Background Technology

[0002] Onboard sensors collect data in real time and calculate ego motion parameters. The vehicle system can dynamically adjust the position and angle of the virtual information projected by the onboard augmented reality head-up display (AR-HUD) based on these ego motion parameters. These ego motion parameters include the vehicle's speed, steering angle, and pitch angle.

[0003] In related technologies, the slope data for autonomous driving suffers from high latency, meaning that the slope data updates lag behind the actual vehicle motion, causing visual discrepancies between the AR-HUD virtual information and the real road scene. Furthermore, both inertial measurement unit (IMU) stabilization and slope stabilization rely on vehicle attitude parameters, preventing the AR-HUD virtual information from achieving accurate alignment within a 70ms delay.

[0004] For example, when a vehicle travels over a continuous, undulating slope, IMU-based image stabilization relies on the vehicle's angular velocity and roll angle to determine the magnitude of the bumps and adjusts the navigation lines in the opposite direction to counteract the shaking. In other words, slope stabilization requires adjusting the navigation line angle based on the vehicle's pitch angle to match the slope. However, the raw IMU data needs filtering and calibration, and calculating the compensation for the two stabilization schemes also takes time. Even slight fluctuations in computing power can exceed 70ms, meaning the vehicle's attitude may have already changed (e.g., from a gentle slope to a steep one, from slight shaking to significant swaying), but the stabilization system is still compensating based on the attitude parameters of the previous frame. This results in the image not keeping up with the actual vehicle state and failing to accurately match the stabilization. The navigation lines refer to the guide light carpet projected onto the road surface in front of the vehicle via AR-HUD, used to indicate the vehicle's driving path to the driver.

[0005] Therefore, how to correct the IMU stabilization scheme based on high-latency slope data in order to achieve accurate fitting of AR-HUD virtual information has become a technical problem that urgently needs to be solved in this field. Summary of the Invention

[0006] In view of this, this application provides an AR-HUD light carpet display method, light carpet display device, vehicle, and non-volatile computer-readable storage medium.

[0007] The AR-HUD light carpet display method according to the embodiments of this application includes: Obtain the target slope data of the vehicle in its own coordinate system; Based on the timestamp of the target slope data and the vehicle pose information set, the current pose data is found; The current pose data and the target slope data are correlated to construct a slope sequence distributed along the vehicle's driving path; Based on the slope sequence and pre-stored historical slope information, obtain the actual slope characteristics used to characterize changes in road surface slope; The vehicle pitch angle is calculated based on the geometric characteristics of the actual slope features. Based on the vehicle pitch angle, the target slope data is decoupled to obtain the decoupled road slope data; The light carpet is displayed based on the road slope data.

[0008] In some implementations, associating the current pose data and the target slope data to construct a slope sequence distributed along the vehicle's travel path includes: The current pose data and the target slope data are mapped to the same spatial coordinate system to construct a slope spatial equation that describes the slope distribution along the path.

[0009] In some implementations, obtaining the actual slope characteristics used to characterize changes in road surface slope based on the slope sequence and pre-stored historical slope information includes: Based on the historical slope information, a historical slope location equation is constructed, and the historical slope location equation and the slope spatial equation are located in the same spatial coordinate system; Based on the slope space equation and the historical slope location equation, the slope change curve of the slope space equation on the vertical plane is solved. The slope change curve is used to characterize the trend of road slope change along the driving direction.

[0010] In some embodiments, the step of solving the slope variation curve in the vertical plane based on the slope space equation and the historical slope location equation, wherein the slope variation curve is used to characterize the changing trend of road surface slope along the driving direction, includes: Determine the projected distance from each point in the historical slope location equation to the slope space equation; Based on the projection distance, the target slope data corresponding to the slope space equation is weighted and fused to obtain effective slope data for fitting the slope change curve.

[0011] In some embodiments, the step of weighted fusion of the target slope data corresponding to the slope space equation based on the projection distance to obtain effective slope data for fitting the slope change curve includes: The projection distance that is less than a preset threshold is used as the weight; The target slope data is weighted and averaged based on the weights to obtain effective slope data for fitting.

[0012] In some embodiments, calculating the vehicle pitch angle based on the geometric features of the actual slope characteristics includes: Fit the slope variation curve; Calculate the slope of the tangent line to the fitted slope change curve at the current vehicle position; The vehicle pitch angle is determined based on the slope of the tangent.

[0013] In some embodiments, fitting the slope variation curve includes: The slope change curve is obtained by fitting the slope change curve using the least squares method.

[0014] In some implementations, the decoupling of the target slope data based on the vehicle pitch angle to obtain decoupled road slope data includes: The target slope data is obtained by inverse parameter decomposition using the vehicle pitch angle. The coupling interference caused by the vehicle pitch motion is removed from the target slope data to separate the decoupled road slope data.

[0015] In some embodiments, the light carpet display method further includes: The slope information in the slope space equation is corrected using the decoupled road slope data to update the slope space equation.

[0016] The AR-HUD light carpet display device according to the embodiments of this application includes: The acquisition module is used to acquire the target slope data of the vehicle in its own coordinate system. The determination module is used to find the current pose data based on the timestamp of the target slope data and the vehicle pose information set; A construction module is used to associate the current pose data and the target slope data to construct a slope sequence distributed along the vehicle's driving path; The solution module is used to obtain the actual slope characteristics that characterize the changes in road surface slope based on the slope sequence and the pre-stored historical slope information. The calculation module is used to calculate the vehicle pitch angle based on the geometric characteristics of the actual slope features; The decoupling module is used to decouple the target slope data based on the vehicle pitch angle to obtain decoupled road slope data; The display module is used to display the light carpet based on the road slope data.

[0017] The vehicle according to the embodiments of this application includes a processor and a memory, the memory storing a computer program, which, when executed by the processor, causes the processor to perform the AR-HUD light carpet display method.

[0018] The non-volatile computer-readable storage medium of this application includes a computer program that, when executed by a processor, causes the processor to perform the AR-HUD light carpet display method.

[0019] In the AR-HUD light carpet display method, light carpet display device, vehicle, and readable storage medium of this application, a slope sequence distributed along the driving path is constructed by associating target slope data with current pose data. The actual slope features representing real changes in the road surface are extracted by combining historical slope information. Then, the vehicle pitch angle is accurately calculated using the geometric relationship of these features. Finally, coupling interference of vehicle posture is removed from the original slope data to obtain pure road slope data. This effectively solves the problem of poor adhesion between the AR-HUD projection light carpet and the road surface and display jitter caused by vehicle pitch motion. This allows the light carpet to always stably and accurately adhere to the actual slope, significantly improving the dynamic adaptability and driving experience of augmented reality navigation lines.

[0020] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0021] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart illustrating a method for displaying a light carpet in an AR-HUD according to certain embodiments of this application; Figure 2 This is a schematic diagram of the light carpet display device of AR-HUD according to certain embodiments of this application; Figure 3 This is a schematic diagram showing the relationship between target slope data and vehicle pitch angle and actual road slope in certain embodiments of this application; Figure 4 This is a flowchart illustrating a method for displaying a light carpet in an AR-HUD according to certain embodiments of this application; Figure 5 This is a schematic diagram of the slope space equation in the station center coordinate system according to certain embodiments of this application; Figure 6 This is a schematic diagram of the slope information equation in some embodiments of this application; Figure 7-8This is a flowchart illustrating a method for displaying a light carpet in an AR-HUD according to certain embodiments of this application; Figure 9 This is a schematic diagram of a scenario in which the projection distance is used to perform weighted fusion with the slope spatial equation according to certain embodiments of this application; Figure 10-13 This is a flowchart illustrating a method for displaying a light carpet in an AR-HUD according to certain embodiments of this application; Figure 14 This is a schematic diagram of the light carpet display device of AR-HUD according to certain embodiments of this application. Detailed Implementation

[0022] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0023] During vehicle operation (such as acceleration, braking, turning, and bumpy conditions), onboard cameras, inertial measurement units (IMUs), radar, and other sensors collect environmental and vehicle motion data in real time. Algorithms are used to calculate the vehicle's ego motion parameters, including speed, steering angle, and pitch angle. The onboard augmented reality head-up display (AR-HUD) system dynamically adjusts the projection position and angle of virtual information such as navigation arrows and warning signs based on these parameters to ensure that the virtual information accurately matches the real road scene and avoids offset or misalignment due to changes in vehicle posture.

[0024] In practical applications of AR-HUD, relying solely on self-motion parameters cannot meet the fitting requirements under all working conditions. When the vehicle's posture changes, causing optomechanical offset—for example, on a level road, the virtual human image of the AR-HUD optomechanical system can accurately fit the real human image; however, when the vehicle accelerates or drives over speed bumps, the optomechanical system rises synchronously with the vehicle body, directly causing misalignment between the two; when the vehicle travels to non-level road sections such as halfway up a mountain, the vehicle's pitch angle increases, which also causes fitting deviations. This requires a dedicated anti-shake solution for compensation and correction.

[0025] Currently, AR-HUD-related image stabilization solutions mainly include Inertial Measurement Unit (IMU) stabilization, slope stabilization, and eye-box stabilization. IMU stabilization addresses the issue of the optical engine tilting upwards with the vehicle body during acceleration and speed bumps. It calculates a precise vehicle pitch angle by fusing IMU and Ego Motion data, controlling the virtual information to compensate downwards, thus restoring the fit between the virtual and real-world images. Slope stabilization sets a baseline height for virtual information (such as a virtual avatar), laying the foundation for a good fit. It also adjusts the required slope compensation value when the vehicle is on uneven roads, accelerating rapidly, or experiencing bumps, adapting to dynamic changes in vehicle posture and helping to maintain a stable fit between the virtual information and the real-world scene. Eye-box stabilization adapts to changes in the driver's eye-box position, specifically adjusting the presentation angle and range of the virtual information to ensure the driver can clearly recognize the virtual information from different viewing angles. The eye-box stabilization solution does not operate independently; its implementation relies entirely on the virtual information reference height provided by the slope stabilization solution and the dynamic attitude compensation of the MU stabilization solution.

[0026] However, both IMU (In-Vehicle Measure) and slope stabilization solutions rely on vehicle attitude parameters and cannot be separated independently. Furthermore, the slope data output by existing autonomous driving systems suffers from high latency, making it impossible to accurately correct the IMU stabilization solution based on this high-latency slope data. Therefore, how to correct the IMU stabilization solution based on high-latency slope data to achieve high-precision, low-latency alignment of AR-HUD virtual information has become a pressing technical challenge in this field.

[0027] In view of this, please refer to Figure 1 This application provides a method for displaying a light carpet in an AR-HUD, the method comprising: 01. Obtain the target slope data of the vehicle in its own coordinate system; 02. Based on the timestamp of the target slope data and the vehicle pose information set, find the current pose data; 03. Associate the current pose data and the target slope data to construct a slope sequence distributed along the vehicle's driving path; 04. Based on the slope sequence and pre-stored historical slope information, obtain the actual slope characteristics used to characterize changes in road surface slope; 05. Calculate the vehicle pitch angle based on the geometric characteristics of the actual slope. 06. Decouple the target slope data based on the vehicle pitch angle to obtain the decoupled road slope data; 07. Display the light carpet based on road slope data.

[0028] Please see Figure 2 This application provides an AR-HUD light carpet display device 10. The light carpet display device 10 includes an acquisition module 11, a determination module 12, a construction module 13, a solution module 14, a calculation module 15, a decoupling module 16, and a display module 17. Step 01 can be implemented by the acquisition module 11, step 02 by the determination module 12, step 03 by the construction module 13, step 04 by the solution module 14, step 05 by the calculation module 15, step 06 by the decoupling module 16, and step 07 by the display module 17.

[0029] Alternatively, the acquisition module 11 can be used to acquire the target slope data of the vehicle in its own coordinate system; the determination module 12 can be used to find the current pose data based on the timestamp of the target slope data and the vehicle pose information set; the construction module 13 can be used to associate the current pose data and the target slope data to construct a slope sequence distributed along the vehicle's driving path; the solution module 14 can be used to acquire the actual slope features used to characterize the changes in road surface slope based on the slope sequence and pre-stored historical slope information; the calculation module 15 can be used to calculate the vehicle pitch angle based on the geometric features of the actual slope features; the decoupling module 16 can be used to decouple the target slope data based on the vehicle pitch angle to obtain the decoupled road surface slope data; and the display module 17 can be used to display a light carpet based on the road surface slope data.

[0030] This application also provides a vehicle, which includes a processor and a memory. The memory stores a computer program. When the computer program is executed by the processor, the processor implements the aforementioned light carpet display method. That is, the processor acquires target slope data in the vehicle coordinate system output by the autonomous driving system; determines the current pose data based on the target slope data; constructs a slope space equation based on the current pose data and the target slope data; solves the slope change curve of the slope space equation in the vertical plane based on the slope space equation and the pre-stored historical slope position equation; calculates the vehicle pitch angle based on the geometric characteristics of the slope change curve; and decouples the target slope data based on the vehicle pitch angle to obtain decoupled road slope data.

[0031] In the AR-HUD light carpet display method, light carpet display device, and vehicle of this application, a slope sequence distributed along the driving path is constructed by associating target slope data with current pose data. The actual slope features representing real changes in the road surface are extracted by combining historical slope information. Then, the geometric relationship of these features is used to accurately calculate the vehicle pitch angle. Finally, the coupling interference of vehicle attitude is removed from the original slope data to obtain pure road slope data. This effectively solves the problem of poor adhesion between the AR-HUD projection light carpet and the road surface and display jitter caused by vehicle pitch motion. It enables the light carpet to always stably and accurately adhere to the actual slope surface, significantly improving the dynamic adaptability of augmented reality navigation lines and driving experience.

[0032] In some embodiments, the light carpet display device 10 may be part of a vehicle. Or, the vehicle may include the light carpet display device 10.

[0033] In some embodiments, the light carpet display device 10 may be a discrete component assembled in a certain way to have the aforementioned functions, or a chip having the aforementioned functions in the form of an integrated circuit, or a computer software code segment that enables the computer to have the aforementioned functions when run on a computer.

[0034] In some implementations, the light carpet display device 10 can be installed in the vehicle as hardware, either independently or as an additional peripheral component. The light carpet display device 10 can also be integrated into the vehicle; for example, when it is part of the vehicle, it can be integrated into the processor.

[0035] It should be noted that the target slope data can be the slope value in the vehicle coordinate system of the current frame, identified by the autonomous driving system through video. This value describes the longitudinal tilt of the road surface the vehicle is currently traveling on. This longitudinal tilt is referenced to the longitudinal horizontal reference of the vehicle coordinate system. The target slope data is the slope data of the current frame. The vehicle coordinate system is defined with the vehicle center as the origin, the front direction as the positive X-axis, the left side as the positive Y-axis, and the vertically upward Z-axis as the positive Z-axis. The target slope data is presented as a set of points, with each point corresponding to the slope value at a specific location on the road surface. The target slope data for the current frame is represented as [x0, z0; x1, z1; x2, z2; ..., xn, zn], where x represents the distance coordinate along the longitudinal direction in front of the vehicle, and z represents the vertical height coordinate of the corresponding location. Each target slope data point has a precise timestamp for subsequent spatiotemporal alignment with the pose data.

[0036] The target slope data contains coupled information between the actual road surface slope and the vehicle's pitch motion. For example, please refer to... Figure 3 At the current moment, the target slope of the vehicle is A, the vehicle's pitch angle is B, and the actual slope of the road surface is C. Then, A = B + C.

[0037] It should also be noted that the vehicle also records vehicle pose data in real time, including the x-coordinate, y-coordinate, and yaw angle, to obtain a vehicle pose information set. This vehicle pose information set contains the vehicle pose data recorded for each frame. The vehicle pose information set is pre-stored in the vehicle's onboard memory.

[0038] After acquiring the target slope data for the current frame, the current pose data corresponding to the target slope data can be found from the vehicle pose information set based on the timestamp of the target slope data for the current frame. It's worth noting that the timestamp of the current pose data can be a timestamp obtained by delay compensation of the timestamp of the target slope data. That is, the current pose data is the vehicle pose data obtained by delay compensation of the timestamp of the target slope data. For example, the vehicle pose data at the calculated time can be used as the current pose data, calculated by extrapolating 200ms from the sensor data. Here, extrapolation refers to deducing the vehicle's actual pose 200ms ago based on the sensor acquisition delay and calculation time. This ensures that the current pose data corresponds to the target slope data, effectively avoiding timing misalignment problems caused by asynchronous acquisition and transmission, and guaranteeing that the vehicle body posture and target slope data are accurately matched at the same driving moment.

[0039] Next, the current pose data (x, y, and yaw angles) is correlated with the target slope data, meaning that each slope value is assigned a corresponding spatial coordinate. Then, these coordinates are arranged according to the vehicle's travel time (i.e., mileage along the travel path) to form a slope-position curve that accurately reflects the continuous undulations of the road surface. This provides a data foundation for subsequent extraction of road slope features and decoupling of vehicle attitude.

[0040] The currently constructed gradient sequence distributed along the driving path is then compared and fused with pre-stored historical gradient information for the same road segment. The statistical regularities of historical data are used to suppress random noise and accidental errors in single measurements, thereby extracting the actual gradient characteristics that stably reflect the inherent undulations of the road surface. This ensures that the gradient information used to calculate the vehicle's pitch angle is the accurate true road gradient, rather than a measurement value affected by instantaneous interference. Historical gradient information refers to the road gradient data recorded and accumulated when the vehicle passes through the same road segment. This data can be preprocessed (e.g., outlier removal, coordinate system 1) and stored in the form of a position-gradient mapping.

[0041] The actual slope characteristics can be mathematically represented as a continuous curve; by fitting this continuous curve, a smooth slope function can be obtained. Since the road slope is continuous in space, the slope of the tangent line at the current vehicle position is the slope of the curve at the current point, and the pitch angle of the current vehicle can be obtained.

[0042] By decoupling the target slope data using the vehicle's pitch angle, decoupled road slope data is obtained. In essence, decoupling involves inverse coordinate transformation, mapping the pitch angle from the stationary coordinate system back to the vehicle's coordinate system. This pitch angle is then used to perform vector decomposition on the target slope data, stripping away the vehicle's own attitude disturbance components and retaining only the pure road geometric slope components. This achieves precise decoupling between slope and vehicle attitude. Therefore, the decoupled road slope data possesses high stability and physical consistency, directly driving the longitudinal attitude rendering of virtual lane lines in AR-HUD, ensuring the geometric realism of road projection in the virtual-real fusion scene.

[0043] Finally, the projection angle and position of the light carpet can be dynamically adjusted based on the decoupled road slope data. Specifically, when the vehicle is driving uphill, the system tilts the light carpet upwards at a certain angle to conform to the slope; when the vehicle is driving downhill, the system tilts the light carpet downwards; and when the road surface is level, the light carpet maintains a horizontal projection. Through real-time adjustments, the light carpet consistently and accurately conforms to the actual road surface, eliminating projection jitter and deviation caused by vehicle pitch motion, significantly improving the dynamic adaptability and driving experience of augmented reality navigation lines.

[0044] Please see Figure 4 In some implementations, step 03 includes: 031. Map the current pose data and the target slope data to the same spatial coordinate system to construct a slope spatial equation that describes the slope distribution along the path.

[0045] In some implementations, sub-step 031 can be implemented by the construction module 13, or the construction module 13 can be used to construct a slope space equation based on the current pose data and the target slope data, the slope space equation being used to describe the distribution of the target slope data along the driving path.

[0046] In some implementations, the processor can be used to construct a slope space equation based on the current pose data and the target slope data, the slope space equation being used to describe the distribution of the target slope data along the driving path.

[0047] In this embodiment, the current pose data and the target slope data are mapped to a station-centered coordinate system. The station-centered coordinate system uses the east direction as the X-axis, the north direction as the Y-axis, and the sky direction as the Z-axis. The origin can be set as the vehicle's starting point or any fixed reference point. Understandably, since the vehicle's coordinate system is based on the vehicle itself, it shifts synchronously with dynamic attitude changes such as acceleration, braking, and bumps, causing instability in the reference reference for the current pose data and slope data. The station-centered coordinate system, however, has a fixed spatial reference characteristic, unaffected by changes in the vehicle's own attitude. It provides a unified reference dimension for the current pose data and slope data, effectively eliminating data correlation deviations caused by the offset of the vehicle's coordinate system reference, allowing the converted current pose data to accurately correspond to the vehicle's position and attitude in real space. Of course, in other embodiments, a Universal Transverse Mercator Projection Coordinate System (UTM coordinate system) or a local Cartesian coordinate system defined by the vehicle's starting point can also be used as the same spatial coordinate system. The choice of coordinate system does not affect the core concept of this invention; it is only necessary to ensure that the current pose data and the target slope data are converted to the same coordinate system.

[0048] Please see Figure 5 and Figure 6 The slope space equation is a spatial parametric equation in a station-centered coordinate system, used to characterize the slope distribution along a path in that coordinate system. The slope space equation is a continuous function obtained by fitting a slope sequence, representing an ordered and structured expression of the slope distribution.

[0049] The slope spatial equation can be divided into two parts: the position equation of the horizontal part (xy plane) and the slope information equation of the vertical part (xz plane). The position equation of the horizontal part represents the position information of the slope along the path in the xy plane (as a straight line), determined by the current pose data (xy position and yaw angle). The slope information equation of the vertical part represents a slope variation curve, which fully reflects the continuous change of slope with position. This slope variation curve is formed by the target slope data {(x0,z0),(x1,z1),…,(x...z1)}. n ,z n The results were obtained by fitting the data.

[0050] This ensures the correlation and consistency of the data parameters in the equation, avoids deviations in equation construction due to inconsistent benchmarks, and lays a precise and reliable model foundation for subsequent steps such as solving the slope change curve and calculating the vehicle pitch angle.

[0051] Please see Figure 7 In some implementations, step 04 includes: 041. Based on historical slope information, a historical slope location equation is constructed. The historical slope location equation and the slope spatial equation are located in the same spatial coordinate system. 042. Based on the slope space equation and the historical slope location equation, solve for the slope change curve of the slope space equation on the vertical plane. The slope change curve is used to characterize the changing trend of the road surface slope along the driving direction.

[0052] Please combine Figure 2 In some implementations, sub-steps 041-042 can be implemented by the solution module 14. In other words, the solution module 14 can be used to construct a historical slope location equation based on historical slope information. The historical slope location equation and the slope space equation are located in the same spatial coordinate system. The target slope data corresponding to the slope space equation are weighted and fused to obtain effective slope data for fitting the slope change curve.

[0053] In some implementations, the processor can be used to solve the slope change curve of the slope space equation on the vertical plane based on the slope space equation and the historical slope location equation. The slope change curve is used to characterize the trend of road slope change along the driving direction.

[0054] Please see Figure 5 Historical slope position equations refer to line segment equations on the xy plane of the station center coordinate system, without slope information, used to characterize position and yaw angle, and with an effective range. There can be multiple historical slope position equations, which are obtained by fitting the corresponding historical frame slope data. That is, each frame of historical frame slope data corresponds to an independent historical slope position equation.

[0055] Since historical slope information comes from multiple measurements taken at different times and in different driving directions, directly using historical slope data for fusion would result in excessive computation and information redundancy. Therefore, this embodiment first transforms the historical slope information into a historical slope location equation, making it have the same mathematical form as the current slope spatial equation.

[0056] In step 042, the historical slope location equation and the slope space equation can be spatially geometrically matched. Based on the spatial relationship between the historical slope location equation and the current slope space equation, the slope information equation representing the slope change curve in the vertical plane of the slope space equation is fitted, and the slope information equation in the vertical plane of the slope space equation is solved.

[0057] For example, spatial consistency matching can be performed between the historical slope location equation and the slope spatial equation to filter out those whose angle with the current frame location equation is less than a threshold θ. t And the overlap length is greater than L tThe historical slope location equations are used; the selected historical slope location equations are weighted and fused, and the slope space equations are weighted on the xz plane according to the weights, and finally a smooth and continuous current slope change curve (i.e., the slope change curve of the slope space equations on the vertical plane) is generated by fitting.

[0058] Understandably, in the vehicle coordinate system, single-frame slope data is susceptible to interference from vehicle posture and contains errors, and the benchmarks for multiple frames of slope data are not consistent, making averaging impossible. However, after switching to a station-centered coordinate system that closely matches the ground slope, multiple frames of slope data have a unified benchmark. By utilizing the fixed characteristic of the actual ground slope, the errors of a single frame can be offset by averaging multiple frames, thus accurately calculating the actual road surface slope. Therefore, the slope change curve, being a smooth xz curve reflecting the inherent geometric characteristics of the road generated through weighted fusion and geometric consistency constraints of multiple frames of historical data, eliminates vehicle dynamic posture disturbances and can accurately represent the current actual road surface slope.

[0059] Please see Figure 8 In some implementations, step 042 includes: 0421, Determine the projected distance from each point in the historical slope location equation to the slope space equation; 0422. Based on the projection distance, the target slope data corresponding to the slope space equation is weighted and fused to obtain effective slope data for fitting the slope change curve.

[0060] Please combine Figure 2 In some implementations, sub-steps 0421-0422 can be implemented by the solution module 14. In other words, the solution module 14 can be used to determine the projection distance from each point of the historical slope location equation to the slope space equation; based on the projection distance, the target slope data corresponding to the slope space equation is weighted and fused to obtain effective slope data for fitting the slope change curve.

[0061] In some implementations, the processor can be used to determine the projected distance from each point of the historical slope location equation to the slope space equation; based on the projected distance, the target slope data corresponding to the slope space equation is weighted and fused to obtain effective slope data for fitting the slope change curve.

[0062] It is worth noting that the historical slope position equation is generated by fitting a set of effective slope data collected during the vehicle's past driving process. It can accurately reflect the historical change pattern, continuous trend and inherent characteristics of the slope of the corresponding driving section. Each sampling point on the historical slope position equation has real road slope reference value.

[0063] Specifically, in order to integrate current measurement data with historical data, it is necessary to establish a spatial correspondence between the two. This is achieved by introducing the concept of projection distance to measure the difference between points on the historical slope location equation and the current slope spatial equation.

[0064] Please see Figure 9 In step 0421, the deviation between the target slope data corresponding to the current slope spatial equation and the historical slope characteristics can be quantified by calculating the projection distance from each sampling point to the current slope spatial equation. The smaller the projection distance, the higher the fit between the current target slope data and the historical slope change trend, and the stronger the data credibility. Conversely, the larger the projection distance, the higher the probability of abnormal interference in the current target slope data.

[0065] In step 0422, the target slope data corresponding to the slope space equation is weighted and fused based on the projection distance. The projection distance is used as the core weight allocation basis. Target slope data with small projection distance and high consistency with historical slope are given higher weights, while target slope data with large projection distance and deviation from historical slope trends are given lower weights.

[0066] In this way, by correcting the target slope data through weighted fusion, the corrected slope data retains effective information that is consistent with historical slope trends and has high reliability, while filtering out noise and abnormal deviations in the target slope data caused by vehicle dynamics (such as acceleration and bumps), ultimately obtaining effective slope data with high accuracy, strong stability and good continuity.

[0067] Please see Figure 10 In some implementations, step 0422 includes: 04221, use projection distances less than a preset threshold as weights; 04222, weighted average of the target slope data is performed based on the weights to obtain effective slope data for fitting.

[0068] Please combine Figure 2 In some implementations, sub-steps 04221-04222 can be implemented by the solution module 14. In other words, the solution module 14 can be used to take the projection distance less than a preset threshold as a weight, and perform a weighted average on the target slope data based on the weight to obtain effective slope data for fitting.

[0069] In some implementations, the processor can be used to take the projected distance less than a preset threshold as a weight, and perform a weighted average of the target slope data based on the weight to obtain effective slope data for fitting.

[0070] To avoid the negative impact of abnormal historical data on the fusion results, this embodiment further introduces a preset threshold to filter the projection distance. Only historical data points with a projection distance less than the preset threshold are included in the fusion calculation. Specifically, the preset threshold is used as the criterion to filter out projection distances from historical slope location equation sampling points to the current slope spatial equation that are less than the preset threshold. These qualified projection distances are then directly used as the basis for weight allocation. The preset threshold can be an engineering value, for example, 10 meters. The preset threshold can accurately filter out effective projection distances that have a high degree of fit with the current slope spatial equation and a small deviation, while excluding invalid data with excessively large projection distances or significant deviations from the current slope trend, ensuring that the core basis for weight allocation has high credibility and reference value.

[0071] Next, based on the weights corresponding to the projection distances, a weighted average calculation is performed on the target slope data corresponding to the slope space equation. During the calculation, the magnitude of the projection distance maps the reliability of the data. The smaller the projection distance, the higher the weight ratio, and the greater the contribution of the target slope data to the effective slope data. Conversely, the larger the projection distance, the smaller the contribution.

[0072] In this way, by using a weighted average calculation method, the core effective information in the target slope data that conforms to the historical slope change pattern and is less affected by vehicle dynamics is preserved, while the interference of abnormal data is further reduced. The result is effective slope data with high accuracy, small fluctuations and strong continuity. This provides high-quality data support for the accurate fitting of the slope change curve in the vertical plane, ensuring that the fitted slope change curve can truly reflect the actual slope characteristics of the road. This, in turn, supports the accurate calculation of the vehicle pitch angle and the effective decoupling of the target slope data.

[0073] Please see Figure 11 In some implementations, step 05 includes: 051, Fit the slope change curve; 052, Calculate the slope of the tangent line of the fitted slope change curve at the current vehicle position; 053, Determine the vehicle body pitch angle based on the tangent slope.

[0074] Please combine further Figure 2 In some implementations, sub-steps 051-053 can be implemented by the calculation module 15. In other words, the calculation module 15 is used to fit the slope change curve, calculate the tangent slope of the fitted slope change curve at the current vehicle position, and determine the vehicle pitch angle based on the tangent slope.

[0075] In some implementations, the processor is used to fit the slope change curve, calculate the tangent slope of the fitted slope change curve at the current vehicle position, and determine the vehicle pitch angle based on the tangent slope.

[0076] Specifically, a pre-defined fitting algorithm can be used to fit the slope variation curve on the vertical plane based on the effective slope data. This pre-defined fitting algorithm can be, for example, the least squares method or polynomial fitting; for instance, the least squares method can be used to fit the effective slope data to obtain the slope variation curve. Those skilled in the art will understand that the least squares method is a classic and robust curve fitting method whose goal is to minimize the sum of the squares of the vertical distances from each data point to the fitted curve, thereby obtaining the optimal approximation solution in a statistical sense. The least squares method effectively suppresses noise interference and the influence of outliers, ensuring that the slope variation curve closely matches the geometric trend of the actual slope data.

[0077] In step 052, the tangent slope of the slope change curve at the current vehicle position can be accurately obtained by solving the derivative or the slope calculation algorithm. The tangent slope can be converted into the corresponding vehicle pitch angle by using the preset slope-pitch angle mapping relationship or conversion algorithm.

[0078] In this way, by first fitting the effective slope data to obtain the slope change curve, the interference of fluctuations in scattered slope data is avoided, and the slope change curve can smoothly reflect the continuous change characteristics of the road slope. Then, the tangent slope of the fitted slope change curve at the current vehicle position is accurately calculated, and the vehicle pitch angle is determined based on the tangent slope. This makes the calculation of the vehicle pitch angle no longer dependent on a single slope sampling point, but fits the real-time change trend of the current road slope. It effectively filters out the pitch angle calculation deviation caused by bumps and instantaneous changes in attitude during vehicle driving, and further improves the stability and accuracy of the vehicle pitch angle.

[0079] Please see Figure 12 In some implementations, step 06 includes: 061, Use the vehicle pitch angle to perform inverse parameter decomposition on the target slope data; 062, Remove coupling interference caused by vehicle pitch motion in the target slope data to separate the decoupled road slope data.

[0080] Please combine further Figure 2 In some implementations, sub-steps 061 and 062 can be implemented by the decoupling module 16. In other words, the decoupling module 16 is used to perform parameter inverse decomposition on the target slope data using the vehicle pitch angle, and remove the coupling interference caused by the vehicle pitch motion in the target slope data to separate the decoupled road slope data.

[0081] In some implementations, the processor is used to perform inverse parameter decomposition on the target slope data using the vehicle pitch angle, and to remove coupling interference caused by the vehicle pitch motion in the target slope data, so as to separate the decoupled road slope data.

[0082] Specifically, the vehicle pitch angle is first used to perform inverse parameter decomposition on the target slope data in the vehicle coordinate system. Since the vehicle pitch angle has been stripped of vehicle dynamic interference, it can accurately represent the true relative attitude between the vehicle and the road. Therefore, during the inverse parameter decomposition process, a quantitative correlation between the target slope data and the vehicle pitch angle can be established, clarifying the proportion and correlation logic of the actual road slope component and the vehicle pitch motion coupling component contained in the target slope data. Then, based on the results of the inverse parameter decomposition, the coupling interference components generated by the vehicle pitch motion under conditions such as vehicle acceleration, braking, and bumps are identified and removed from the target slope data, completely separating the decoupled road slope data that only reflects the actual terrain features of the road.

[0083] In this way, by performing inverse parameter decomposition on the target slope data in the vehicle coordinate system through the vehicle pitch angle, the coupling interference components of vehicle pitch motion caused by vehicle driving bumps, acceleration and deceleration are specifically eliminated from the target slope data. This achieves accurate separation between the real road slope data and the vehicle attitude interference data, effectively avoiding the interference of vehicle attitude changes on the road slope detection results. Finally, the decoupled road slope data can truly and accurately reflect the actual slope characteristics of the current driving road.

[0084] Please see Figure 13 In some embodiments, the light carpet display method further includes: 08. Using the decoupled road slope data, the slope information in the slope space equation is corrected to update the slope space equation.

[0085] Please combine further Figure 14 In some embodiments, the light carpet display device 10 further includes an update module 18. Step 07 can be implemented by the update module 18. In other words, the update module 18 can be used to correct the slope information in the slope space equation using the decoupled road slope data, so as to update the slope space equation.

[0086] In some implementations, the processor can also be used to modify the slope information in the slope space equation using the decoupled road slope data, so as to update the slope space equation.

[0087] Specifically, the slope information equation of the slope space equation can be reconstructed based on the road slope data. That is, the slope information equation constructed from the road slope data is used as the slope space equation, so that the updated slope space equation can reflect the true distribution of the longitudinal undulation of the road.

[0088] In this way, by decoupling and accurately matching the actual slope data of the road surface to the slope space equation, the slope information of the slope space equation can be dynamically updated, allowing the slope space equation to match the real slope changes of the road surface where the vehicle is currently driving in real time, avoiding deviations in matching with the real road surface due to lag or distortion of slope information.

[0089] This application also provides a non-volatile computer-readable storage medium storing a computer program that, when executed by a processor, causes the processor to perform the aforementioned light carpet display method.

[0090] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any other combination. When implemented in software, it can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., digital video discs (DVDs)), or semiconductor media (e.g., solid-state disks (SSDs)).

[0091] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0092] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0093] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0094] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for displaying a light carpet in an AR-HUD, characterized in that, include: Obtain the target slope data of the vehicle in its own coordinate system; Based on the timestamp of the target slope data and the vehicle pose information set, the current pose data is found; The current pose data and the target slope data are correlated to construct a slope sequence distributed along the vehicle's driving path; Based on the slope sequence and pre-stored historical slope information, obtain the actual slope characteristics used to characterize changes in road surface slope; The vehicle pitch angle is calculated based on the geometric characteristics of the actual slope features. Based on the vehicle pitch angle, the target slope data is decoupled to obtain the decoupled road slope data; The light carpet is displayed based on the road slope data.

2. The light carpet display method according to claim 1, characterized in that, The step of associating the current pose data and the target slope data to construct a slope sequence distributed along the vehicle's driving path includes: The current pose data and the target slope data are mapped to the same spatial coordinate system to construct a slope spatial equation that describes the slope distribution along the path.

3. The light carpet display method according to claim 2, characterized in that, The step of obtaining actual slope characteristics to characterize road surface slope changes based on the slope sequence and pre-stored historical slope information includes: Based on the historical slope information, a historical slope location equation is constructed, and the historical slope location equation and the slope spatial equation are located in the same spatial coordinate system; Based on the slope space equation and the historical slope location equation, the slope change curve of the slope space equation on the vertical plane is solved. The slope change curve is used to characterize the trend of road slope change along the driving direction.

4. The light carpet display method according to claim 3, characterized in that, The process involves solving the slope variation curve in the vertical plane based on the slope space equation and the historical slope location equation. This slope variation curve characterizes the trend of road surface slope change along the driving direction, including: Determine the projected distance from each point in the historical slope location equation to the slope space equation; Based on the projection distance, the target slope data corresponding to the slope space equation is weighted and fused to obtain effective slope data for fitting the slope change curve.

5. The light carpet display method according to claim 4, characterized in that, The step of weighted fusion of the target slope data corresponding to the slope space equation based on the projection distance to obtain effective slope data for fitting the slope change curve includes: The projection distance that is less than a preset threshold is used as the weight; The target slope data is weighted and averaged based on the weights to obtain effective slope data for fitting.

6. The light carpet display method according to claim 3, characterized in that, The calculation of the vehicle pitch angle based on the geometric characteristics of the actual slope features includes: Fit the slope variation curve; Calculate the slope of the tangent line to the fitted slope change curve at the current vehicle position; The vehicle pitch angle is determined based on the slope of the tangent.

7. The light carpet display method according to claim 6, characterized in that, The fitting of the slope change curve includes: The slope change curve is obtained by fitting the slope change curve using the least squares method.

8. The light carpet display method according to claim 1, characterized in that, The process of decoupling the target slope data based on the vehicle pitch angle to obtain decoupled road slope data includes: The target slope data is obtained by inverse parameter decomposition using the vehicle pitch angle. The coupling interference caused by the vehicle pitch motion is removed from the target slope data to separate the decoupled road slope data.

9. The light carpet display method according to claim 2, characterized in that, The light carpet display method also includes: The slope information in the slope space equation is corrected using the decoupled road slope data to update the slope space equation.

10. A light carpet display device for AR-HUD, characterized in that, The light carpet display device includes: The acquisition module is used to acquire the target slope data of the vehicle in its own coordinate system. The determination module is used to find the current pose data based on the timestamp of the target slope data and the vehicle pose information set; A construction module is used to associate the current pose data and the target slope data to construct a slope sequence distributed along the vehicle's driving path; The solution module is used to obtain the actual slope characteristics that characterize the changes in road surface slope based on the slope sequence and the pre-stored historical slope information. The calculation module is used to calculate the vehicle pitch angle based on the geometric characteristics of the actual slope features; The decoupling module is used to decouple the target slope data based on the vehicle pitch angle to obtain decoupled road slope data; The display module is used to display the light carpet based on the road slope data.

11. A vehicle, characterized in that, It includes a processor and a memory, the memory storing a computer program that, when executed by the processor, causes the processor to implement the light carpet display method as described in any one of claims 1-9.

12. A computer-readable storage medium containing a computer program, characterized in that, When the computer program is executed by the processor, it implements the light carpet display method as described in any one of claims 1 to 9.

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