Lane line display method and device, electronic equipment, vehicle and medium
By utilizing the location points of vehicle information for displacement processing and fitting in the lane line display method, the problem of discontinuous lane line display was solved, achieving synchronized display of lane lines and map images, thus improving the user experience.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- 合肥疆程技术有限公司
- Filing Date
- 2023-06-15
- Publication Date
- 2026-07-03
Smart Images
Figure CN116753978B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the fields of intelligent transportation and vehicle networking in artificial intelligence technology, and in particular to a lane line display method, device, electronic device, vehicle and medium. Background Technology
[0002] A car infotainment system is a type of in-vehicle information product installed in a vehicle, which can have functions such as radio, map navigation, and telephone. Currently, when a car infotainment system performs map navigation, it can display a map and virtual vehicles on the screen. Figure 1 Generally, it can include drawn maps or real-world maps. For drawn maps or real-world maps, the map can include lane lines, which are obtained by directly displaying information transmitted from the vehicle's infotainment system.
[0003] However, in practical applications, the frequency of information input from the vehicle's infotainment system is lower than the map's refresh frame rate. This results in the lane lines displayed on the map not being refreshed in a timely manner, causing significant lag in the displayed lane lines and reducing the user experience. Summary of the Invention
[0004] This application provides a lane line display method, apparatus, electronic device, vehicle, and medium to solve the problem that the refresh rate of map image frames is higher than the refresh rate of lane lines, resulting in severe stagnation in the display of lane lines and asynchrony between the display of map images and lane lines.
[0005] In a first aspect, this application provides a lane line display method, including:
[0006] Obtain the vehicle information corresponding to the vehicle information system and determine the first moment of receiving the vehicle information system, wherein the vehicle information system includes multiple location points collected with the vehicle as the center;
[0007] Based on the time period corresponding to the first moment and the second moment when the vehicle information is received next, the target map image to be rendered within the time period is determined.
[0008] By using multiple of the aforementioned location points, lane line shifting processing is performed to obtain lane line prediction points corresponding to the target map image;
[0009] Based on the lane line prediction points corresponding to the target map image, lane lines are fitted to obtain the target lane lines, which are then rendered at the corresponding positions in the target map image and displayed in the vehicle's infotainment system.
[0010] Secondly, this application provides a lane line display device, comprising:
[0011] An information receiving unit is used to receive vehicle information and determine the first moment of receiving the vehicle information, wherein the vehicle information includes multiple location points collected with the vehicle as the center;
[0012] The map determination unit is used to determine the target map image to be rendered within the time period based on the time period corresponding to the first time and the second time when the vehicle information is received next.
[0013] The lane prediction unit is used to perform lane line shifting processing using multiple location points to obtain lane line prediction points corresponding to the target map image.
[0014] The map display unit is used to perform lane line fitting based on the lane line prediction points corresponding to the target map image to obtain the target lane line, and then render and display the target lane line at the corresponding position in the target map image.
[0015] Thirdly, this application provides an electronic device, including: a processor, and a memory communicatively connected to the processor;
[0016] The memory stores computer-executed instructions;
[0017] The processor executes computer execution instructions stored in the memory to implement the method as provided in the first aspect.
[0018] Fourthly, this application provides a vehicle, including: a vehicle-mounted system, the vehicle-mounted system including a processor and a memory communicatively connected to the processor;
[0019] The memory stores computer-executed instructions;
[0020] The processor executes computer execution instructions stored in the memory to implement the method as provided in the first aspect.
[0021] Fifthly, this application provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, are used to implement the method provided in the first aspect.
[0022] In the solution provided in this application, the electronic device can receive and acquire vehicle information corresponding to the vehicle's infotainment system, and determine the first moment of receiving the information. The vehicle information at the first moment can be directly rendered into the corresponding map image. However, for the time period corresponding to the second moment of receiving vehicle information and the first moment, the target map image to be rendered within the time period can be determined, and the map image to be rendered can be acquired in real time. For multiple location points in the acquired vehicle information, lane line shifting processing can be performed. Through lane line shifting processing, lane line prediction points corresponding to the target map image can be obtained, making the position of the lane line prediction points close to the actual lane lines in the target map image. Then, lane line fitting can be performed using the lane line prediction points corresponding to the target map image to obtain the target lane lines, so that the target lane lines and the target map image can be rendered and displayed. During the interval between two receptions of vehicle information, lane lines in the map image to be rendered can be predicted to obtain lane line prediction points that better match the map image, realizing synchronous shifting of lane lines and images, making the display of lane lines and images more synchronized, avoiding the phenomenon of segmented lane line display, achieving continuous display, and improving user experience. Attached Figure Description
[0023] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.
[0024] Figure 1 This application provides an example system diagram for implementing a lane line display method.
[0025] Figure 2 A flowchart illustrating one embodiment of a lane line display method provided in this application;
[0026] Figure 3 An example diagram showing a map image provided in an embodiment of this application;
[0027] Figure 4 A flowchart illustrating yet another embodiment of a lane display method provided in this application;
[0028] Figure 5 A flowchart illustrating yet another embodiment of a lane display method provided in this application;
[0029] Figure 6 A schematic diagram of an embodiment of a lane display device provided in this application;
[0030] Figure 7 This is a block diagram of an electronic device for implementing a lane line display method, provided as an embodiment of this application.
[0031] The accompanying drawings have illustrated specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to specific embodiments. Detailed Implementation
[0032] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.
[0033] The lane line display method, device, vehicle-mounted system, vehicle, and medium provided in this application embodiment can be applied to the fields of intelligent transportation and vehicle networking in artificial intelligence technology. This embodiment does not impose too many limitations on the application scenarios of the lane line display method, device, vehicle-mounted system, vehicle, and medium.
[0034] In related technologies, in-vehicle infotainment systems (IVS) can be multi-functional terminal devices, including various sensors and output devices. Sensors, cameras, or radar on the IVS can collect IVS information, which may refer to the position information of lane lines in front of the vehicle. Of course, there are various ways to acquire IVS information, and this embodiment does not limit the specific method of acquisition. Lane lines in map image frames can be drawn using IVS information. However, because the acquisition frequency of IVS information is much lower than the update frequency of map image frames (for example, the update frequency of map image frames is generally 24 frames / second, while the acquisition frequency of IVS information is 2 times / second), the refresh frequencies of image frames and lane lines are inconsistent. The refresh frequency of lane lines is much lower than that of image frames, causing the road in the map image to shift while the lane lines do not, resulting in discontinuous or segmented display of lane lines, thus reducing the user experience.
[0035] To address the aforementioned issues, while the reception time of vehicle-mounted information may vary, the reception frequency and image switching frequency remain constant. The first moment of receiving vehicle-mounted information and the second moment of receiving it next can be determined. Within these two moments, the display switching between map images and lane lines can be performed. For the target map image to be rendered within a given time period, multiple location points can be used for lane line shifting. This shifting simulates lane line changes during vehicle movement, allowing lane lines to change with the image. The changed lane lines enable synchronized display switching with the image, avoiding jumps or pauses in lane line display during frequent image switching. This ensures synchronized and continuous display changes between lane lines and map images, improving display continuity.
[0036] In this embodiment, the electronic device can receive and acquire vehicle information corresponding to the vehicle information system and determine the first moment of receiving the information. The vehicle information at the first moment can be directly rendered in the corresponding map image. However, for the time period corresponding to the second moment of receiving vehicle information and the first moment, the target map image to be rendered within the time period can be determined, and the map image to be rendered can be acquired in real time. For multiple location points in the acquired vehicle information, lane line shifting processing can be performed. Through lane line shifting processing, lane line prediction points corresponding to the target map image can be obtained, making the position of the lane line prediction points close to the actual lane lines in the target map image. Then, lane line fitting can be performed using the lane line prediction points corresponding to the target map image to obtain the target lane lines, so that the target lane lines and the target map image can be rendered and displayed. During the interval between two receptions of vehicle information, lane lines in the map image to be rendered can be predicted to obtain lane line prediction points that better match the map image, realizing synchronous shifting of lane lines and images, making the display of lane lines and images more synchronized, avoiding the phenomenon of segmented lane line display, achieving continuous display, and improving user experience.
[0037] The lane line display method provided in this application aims to solve the above-mentioned technical problems of the prior art.
[0038] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.
[0039] like Figure 1The diagram shown is an example of a system for implementing a lane line display method according to an embodiment of this application. The system may include a vehicle-mounted system 1 and an electronic device 2 that is communicatively connected to the vehicle-mounted system 1. The electronic device 2 may be, for example, a cloud server.
[0040] The vehicle-mounted unit 1 can be located on the vehicle 3. Other devices can also be configured in the vehicle 3; please refer to relevant technical records for details, which will not be elaborated upon here. The vehicle-mounted unit 1 can periodically report vehicle information to the electronic device 2. This vehicle information may include multiple location points collected centered on the vehicle.
[0041] The electronic device 2 can be configured with the lane line display method provided in this disclosure. This method allows for the confirmation of a time interval between the first moment of receiving information from the vehicle's infotainment system and the second moment of receiving information from the vehicle's infotainment system again. Within this time interval, the target map image to be rendered can utilize multiple location points from the vehicle's infotainment system information received at the first moment to perform lane line shifting processing, obtaining lane line prediction points corresponding to the target map image. These lane line prediction points can then be used for lane line fitting to obtain target lane lines with higher adaptability to the target map image. This achieves continuous display of the target lane lines and the target map image, avoiding segmented display or display asynchrony, and improving the user experience.
[0042] like Figure 2 The diagram shown is a flowchart of one embodiment of a lane line display method provided in this application. This lane line display method can be configured as a lane line display device, which can be located in an electronic device. The lane line display method may include the following steps:
[0043] S201. Obtain the vehicle information corresponding to the vehicle information system and determine the first moment of receiving the vehicle information system. The vehicle information system includes multiple location points collected with the vehicle as the center.
[0044] Optionally, the vehicle's infotainment system or various sensors that communicate with it, such as radar, cameras, and position sensors, can detect the location points of vehicle accessories and determine the location points of lane lines among them. In this embodiment, the multiple location points in the vehicle's infotainment information may include the location points of lane lines.
[0045] For example, the vehicle's infotainment system can collect data centered on the vehicle and determine multiple location points of the lane lines from the data. These multiple location points can be used to draw the lane lines. For instance, lane lines can be extracted from data collected by sensors such as radar and cameras. Taking a camera as an example, it can capture images in the vehicle's direction of travel and use lane line extraction algorithms to extract the location points from the images.
[0046] The first moment can be the moment when the vehicle information is received. Upon receiving the vehicle information at the first moment, the map image to be rendered at the first moment can be determined, and the lane lines corresponding to the vehicle information can be determined. The lane lines and the map image at the first moment are then used for rendering so that the lane lines are displayed normally in the map image. In this application, lane line displacement processing is performed on the map image to be rendered after the first moment.
[0047] S202. Based on the time period corresponding to the first moment and the second moment when the vehicle information is received next, determine the target map image to be rendered within the time period.
[0048] Optionally, at least one map image may be rendered between the first and second time points, and typically multiple images may be rendered.
[0049] The second moment can be calculated using the transmission frequency of the vehicle information system and the first moment. Specifically, the transmission time interval of the vehicle information system can be calculated based on the transmission frequency, and the second moment can be obtained by summing the first moment and the transmission time interval.
[0050] The target map image within the time period corresponding to the first and second moments can be determined based on the image refresh rate. For example, if the image refresh rate is 24 frames per second, and assuming the time period between the first and second moments is 0.5 seconds, the map image to be rendered within this time period can be determined to be 12 frames.
[0051] Multiple map images to be rendered within a time period can be used sequentially as target map images to be rendered.
[0052] S203. Using multiple location points, lane line shifting processing is performed to obtain the lane line prediction points corresponding to the target map image.
[0053] In one possible design, lane shifting processing refers to using multiple location points to predict the location points of the lane lines corresponding to the target map image, and then optimizing the predicted location points to obtain the predicted lane line points corresponding to the target map image.
[0054] Optionally, a lane line location prediction model can also be trained. This model takes lane line location points from the previous map image as input and outputs predicted lane line location points from the next map image. The trained model can directly use multiple location points from the previous map image as input to calculate predicted lane line points for the next map image. The previous and next map images corresponding to the model can be two adjacent images. In other words, the model can predict lane line location points from the previous map image. Specifically, during the training process, the training data can be lane line location points from the previous map image, the labels can be the actual lane line location points from the next map image, and the training objective is to ensure that the predicted lane line location points match the actual lane line location points.
[0055] S204. Based on the lane line prediction points corresponding to the target map image, perform lane line fitting to obtain the target lane line, so as to render the target lane line at the corresponding position in the target map image and display it in the vehicle system.
[0056] Optionally, the lane line prediction points can be discrete points. These prediction points can be connected, and the resulting points determine the lane line's position, shape, length, and width. The target lane line can then be determined based on this information. Determining the target lane line based on its position, shape, length, and width can include drawing the target lane line, such as a rectangular or trapezoidal lane line. Alternatively, the target lane line can be obtained by connecting the lane line prediction points and then smoothing the connection.
[0057] Rendering the target lane line at its corresponding location in the target map image and displaying it in the vehicle information can include: rendering the target lane line onto the target map image according to its position in the target map image, so that the target map image contains the target lane line. Displaying the target lane line in the vehicle can include: sending the target lane line and the target map image to the vehicle to control the vehicle to display the target lane line. If the electronic device is a vehicle, it can directly display the target map image containing the target lane line.
[0058] In some embodiments, the electronic device can be a vehicle including an in-vehicle infotainment system. The vehicle can be equipped with a processor and a memory. The memory can store computer instructions to enable the processor to execute the lane line display method of this application. The vehicle's memory and processor can transmit data with the in-vehicle infotainment system to transmit in-vehicle infotainment system information, target map images, and target lane lines. Alternatively, the electronic device can also refer to an in-vehicle infotainment system, which can be equipped with a processor and a memory. The memory can store computer instructions to enable the processor to execute the lane line display method of this application and directly execute step 204 to display the target map image and target lane lines. To reduce the processing load on vehicles, in-vehicle infotainment systems, and other devices, the technical solution of this application can be configured in a cloud server. The cloud server interacts with the in-vehicle infotainment system to achieve the transmission of in-vehicle infotainment system information, target map images, and target lane lines.
[0059] In this embodiment, the vehicle or in-vehicle system can receive vehicle information and determine the first moment of receiving the information. The vehicle information at the first moment can be directly rendered in the corresponding map image. However, for the time period corresponding to the second moment of receiving vehicle information and the first moment, the target map image to be rendered within the time period can be determined, and the map image to be rendered can be acquired in real time. For multiple location points in the obtained vehicle information, lane line shifting processing can be performed. Through lane line shifting processing, lane line prediction points corresponding to the target map image can be obtained, making the position of the lane line prediction points close to the actual lane lines in the target map image. Then, lane line fitting can be performed using the lane line prediction points corresponding to the target map image to obtain the target lane lines, so that the target lane lines and the target map image can be rendered and displayed. During the interval between two receiving vehicle information, lane lines in the map image to be rendered can be predicted to obtain lane line prediction points that better match the map image, realizing synchronous shifting of lane lines and images, making the display of lane lines and images more synchronized, avoiding the phenomenon of segmented lane line display, achieving continuous display, and improving user experience.
[0060] As one embodiment, based on the time period corresponding to the first moment and the second moment of receiving vehicle information, the target map image to be rendered within the time period is determined, including:
[0061] Based on the time period corresponding to the first moment and the second moment when the vehicle information is received next, each map image to be rendered is sequentially determined as the target map image, starting from the first map image to be rendered after the first moment.
[0062] For ease of understanding, such as Figure 3The example map image shown illustrates how, for the time intervals corresponding to the two vehicle-to-everything (V2X) information updates, the V2X information at the first and second moments can be determined. The map images to be rendered between the first and second moments are assumed to be map image 1, map image 2, map image 3, ..., map image n. Each map image can have its corresponding lane displacement determined; for example, lane line prediction point 1 for map image 1, lane line prediction point 2 for map image 2, lane line prediction point 3 for map image 3, lane line prediction point 4 for map image 4, and so on, up to the lane line prediction point n for the last map image n. By determining the corresponding lane line prediction points for each map image, the lane lines in each map image can achieve a higher degree of matching with the map, resulting in stronger display continuity.
[0063] For example, starting with the first map image to be rendered after the first moment, each map image to be rendered is determined as the target map image frame by frame. Of course, to improve rendering efficiency and avoid the lane line prediction speed being slower than the image rendering speed, image sampling can be used to sequentially determine each map image to be rendered as the target map image, starting with each map image to be rendered after the first moment, based on the sampling frequency. To achieve synchronized displacement of the map image and lane lines, the sampling frequency can be set to 2 or 3 frames, so that when switching between two or three map images, the lane lines remain unchanged, ensuring their continuity and stable display across multiple consecutive map images without segmentation.
[0064] In this embodiment, for the time period corresponding to the first time and the second time, starting from the first map image to be rendered, each map image to be rendered can be sequentially determined as a target map image, so as to realize the sequential and coherent update of the target map images. In the process of continuous updating of the target map images, the lane lines of each target map image can be predicted, thereby improving the correlation between the target map image and the target lane lines and realizing the continuous display of lane lines.
[0065] Furthermore, based on any of the above embodiments, lane line shifting processing is performed using multiple location points to obtain lane line prediction points corresponding to the target map image, including:
[0066] Using the first lane lines corresponding to multiple location points as the fitting curve, determine the first fitting formula corresponding to the fitting curve.
[0067] Obtain the second fitting formula corresponding to the vehicle information received at the first moment.
[0068] Based on the parameter differences between the first and second fitting formulas, a third fitting formula is determined for the lane lines in the target map image.
[0069] Based on the third fitting formula and the range of position values corresponding to multiple location points, the lane line prediction points of the target map image are determined.
[0070] Optionally, the first fitting formula, the second fitting formula, and the third fitting formula can be polynomials, for example, y = ax 4 +bx 3 +cx 2 +dx+k, where a, b, c, d, and k can be parameters of the polynomial, and (x, y) can be the location points of the lane lines, meaning that inputting a value for x can calculate the value for y. Of course, the above polynomial is merely an example and should not constitute a limitation on the specific formula structure of the fitting formula.
[0071] The first, second, and third fitting formulas can have the same structure, but the parameter values can differ. For example, the first fitting formula might have parameters a = 1, b = 2, c = 3, d = 4, and k = 0 for the polynomial. The second fitting formula might have parameters a = 4, b = 5, c = 6, d = 7, and k = 5 for the polynomial. Multiple third fitting formulas can be included, determined by the parameter differences between the first and second fitting formulas. Parameter differences refer to the parameter values within the same monomial; for example, in x... 4 For different values of the parameter 'a' in this monomial, the first fitting formula takes a value of 1, and the second fitting formula takes a value of 4. In x 3 The parameter 'b' of this monomial takes different values: 4 in the first fitting formula, 5 in the second, and so on. Of course, in practical applications, the structure of the fitting formula may be more complex, but the parameter difference between different monomials is simply the difference in parameter values across different fitting formulas. For example, the parameter difference for 'a' in two fitting formulas is 4-1=3, and the parameter difference for 'b' is 5-2=3. Therefore, referring to the above description, the parameter difference between the first and second fitting formulas can include at least one parameter corresponding to a parameter difference. For the third fitting formula, the value of the corresponding parameter can be determined based on the parameter difference corresponding to different parameters.
[0072] In this embodiment, multiple location points can be analyzed using curve fitting to obtain a first fitting formula corresponding to each location point. Furthermore, a second fitting formula corresponding to the previous vehicle-to-everything (V2X) information can be determined. The first fitting formula represents the geometric representation of multiple location points at a first moment, while the second fitting formula represents the geometric representation of multiple location points at the moment preceding the first moment. The parameter difference between the first and second fitting formulas reflects the positional changes between the two moments. Therefore, by using this parameter difference and combining it with the first fitting formula, a third fitting formula can be determined for the next moment. This third fitting formula can then geometrically represent the location points after the first moment. Furthermore, by using the third fitting formula and combining it with the positional value range of multiple location points, the lane line prediction points of the target map image can be determined. The obtained lane line prediction points satisfy the third fitting formula, accurately mapping the lane line location points of the target map image and improving the accuracy and precision of the lane line prediction points.
[0073] Furthermore, based on any of the above embodiments, a third fitting formula corresponding to the lane lines of the target map image is determined based on the parameter differences between the first fitting formula and the second fitting formula, including:
[0074] Based on the parameter differences between the second fitting formula and the first fitting formula, determine the parameter changes corresponding to the target map image;
[0075] Based on the first fitting formula and the parameter changes, the third fitting formula corresponding to the lane lines of the target map image is determined.
[0076] The third fitting formula can be determined based on the first fitting formula and the parameter changes.
[0077] As described above, the first, second, and third fitting formulas have the same formula structure, meaning the monomials of the polynomials are the same, but the parameter values of the monomials differ. Parameter differences can include at least one parameter corresponding to a parameter difference. The first parameter value corresponding to at least one parameter in the first fitting formula can be determined. The first parameter value and the parameter difference of each parameter are then summed to obtain the target parameter value for each parameter, thus determining the target parameter value corresponding to at least one parameter. Substituting the target parameter value corresponding to at least one parameter into their respective monomials yields the third fitting formula.
[0078] For example, assuming the parameter variations of a target map image are a = 1.5, b = 1.5, c = 1.5, d = 1.5, and k = 2.5, then combining the first fitting formula, we can calculate a = 1 + 1.5, b = 2 + 1.5, c = 3 + 1.5, d = 4 + 1.5, and k = 0 + 2.5. The target parameter values obtained for the third fitting formula are: a = 2.5, b = 3.5, c = 4.5, d = 5.5, and k = 2.5. That is, the third fitting formula can be expressed as: y = 2.5 * x 4 +3.5*x 3 +4.5*x 2 +5.5*x+2.5.
[0079] In this embodiment, the third fitting formula can be confirmed by the parameter change. The third fitting formula is more accurate because it is precisely affected by the parameter change.
[0080] Further, optionally, based on the parameter differences between the second fitting formula and the first fitting formula, the parameter changes corresponding to the target map image are determined, including:
[0081] The number of lane line offsets is determined based on the number of map images that need to be rendered within the time intervals of the first and second moments.
[0082] Based on the number of lane line deviations, the step size is calculated to determine the parameter difference between the first and second fitting formulas, thus obtaining the adjustment step size.
[0083] The parameter changes corresponding to the target map image are determined based on the adjustment step size and the rendering order of the target map image within the time period.
[0084] Optionally, the parameter difference may include the parameter difference corresponding to at least one parameter, and the step size may be calculated for each parameter. The adjustment step size may include the step size corresponding to at least one parameter.
[0085] The step size of a parameter can be obtained by calculating the quotient of the parameter difference and the number of map images.
[0086] Assuming the lane line deviates 10 times, and using the above fitting formula example where the parameter difference for a is 3, the parameter difference for b is 3, the parameter difference for c is 3, the parameter difference for d is 3, and the parameter difference for k is 5, then the step size for parameters a, b, c, and d is 3 / 10 = 0.3, and the step size for parameter k is 5 / 10 = 0.5.
[0087] The rendering order of the target map images within a time period refers to the order in which each rendered map image is recorded after the first moment, with the target map image being the order of the map images currently to be rendered. Assuming that 5 map images have been rendered since the first moment, the rendering order of the target map image to be rendered is 6. This rendering order can be calculated by multiplying it by the step size of each parameter to obtain the adjustment step size for each parameter. That is, the adjustment step size for parameters a, b, c, and d in the target map image is 0.3 * 5 = 1.5, and the step size for parameter k is 0.5 * 5 = 2.5.
[0088] In this embodiment, the number of map images to be rendered in the first and second time periods can be used as the number of lane line offsets. That is, the parameter change amount of different target map images is determined by the number of map images, so that the parameter offset is related to the rendering order of the target map images, realizing a step-like parameter change. This makes the parameter change amount obtained by different rendering orders consistent with the rendering order of the map images. Then, the third fitting formula determined by the parameter change amount can more accurately predict the lane line position points of the corresponding target map images, improving the position prediction accuracy of the third fitting formula.
[0089] Further, optionally, based on the third fitting formula and combined with the position value range corresponding to multiple location points, the lane line prediction points of the target map image are determined, including:
[0090] Determine the range of values for the first fitting formula at multiple location points.
[0091] Based on the parameter changes, the range of position values is adjusted to obtain the target range of the third fitting formula.
[0092] From the target value range, select the lane line location points that satisfy the third fitting formula and correspond to the target map image.
[0093] Optionally, the range of position values refers to the range of values of the position point on the X-axis or the Y-axis. Since the third fitting formula represents the mapping relationship between the x-coordinate point and the y-coordinate point, the range of values can be selected from either the X-axis or the Y-axis.
[0094] If we assume that the range of values for the first fitting formula at multiple points corresponds to the range of values along the X-axis, then the target range is the range of values for x along the X-axis in the third fitting formula. Alternatively, if we assume that the range of values for the first fitting formula at multiple points corresponds to the range of values along the Y-axis, then the target range is the range of values for y along the Y-axis in the third fitting formula.
[0095] During the transition from the first to the third fitting formula, the parameter values of each monomial change. Therefore, the range of values can be adjusted accordingly. Assuming the first fitting formula has a range of (0-1) on the X-axis, and the parameter changes are 0.3*5 = 1.5 for parameters a, b, c, and d, and 0.5*5 = 2.5 for parameter k, the adjusted parameter range can be determined to be (2.5-4). Since parameter k is 2.5, the minimum value is adjusted from 0 to 2.5. And since parameters a, b, c, and d are all 0.3*5 = 1.5, then 1.5 + 2.5 = 4. Of course, the above method is merely an example and not a specific limitation on how the range of values can be adjusted.
[0096] In this embodiment, when determining the lane line prediction points of the target map image using the third fitting formula, the target value range of the third fitting formula can be determined based on the position value range of multiple location points and the parameter variation. The lane line location points that satisfy the third fitting formula and do not exceed the target value range can be selected using the target value range. This achieves effective selection of lane line location points and avoids invalid selection due to parameters exceeding the range. Invalid lane line location points would not participate in the subsequent fitting process of the target lane line, resulting in a decrease in the displacement accuracy of the lane line.
[0097] like Figure 4 The flowchart shown is for another embodiment of a lane display method provided in this application. The difference from the previous embodiments is that step 204, which involves fitting lane lines based on the lane line prediction points corresponding to the target map image to obtain the target lane line, may include:
[0098] S401: Collect vehicle movement data between the target map image and its previous map image.
[0099] S402: Based on vehicle operation data, optimize the position of the lane line prediction points corresponding to the target map image to obtain the optimized target position points.
[0100] S403: Based on the target location point, perform lane line fitting processing to obtain the target lane line.
[0101] Vehicle operation data refers to the data corresponding to the speed and angle of a vehicle's movement. Based on vehicle operation data, lane line prediction points can be optimized from both speed and angle dimensions to obtain optimized target location points.
[0102] In this embodiment, vehicle movement data can be collected between the target map image and its previous map image. Using this vehicle movement data, the predicted lane line points corresponding to the target map image are optimized to obtain optimized target position points. Lane line fitting processing is then performed on these target position points to obtain the target lane line. Optimizing the position of the fitted lane line prediction points using vehicle movement data makes the target position points more closely match the vehicle's operating state, more accurately representing lane line changes and improving the accuracy of the target lane line.
[0103] As one possible implementation, vehicle movement data is collected between a target map image and its previous map image, including:
[0104] During the time interval between the target map image and its previous rendered map image, acquire at least one speed and at least one steering angle of the vehicle.
[0105] Calculate the average speed based on at least one speed, and calculate the average steering angle based on at least one steering angle;
[0106] The average speed and average steering angle are determined as vehicle operating data.
[0107] Optionally, the total speed value can be obtained by summing at least one speed, and the quotient of the total speed value and the number of speeds can be calculated. This quotient is the average speed. Alternatively, at least one steering angle can be summed to obtain the total angle, and the quotient of the total angle and the number of angles can be calculated. This quotient is the average steering angle.
[0108] In some embodiments, the average speed may also be the median of at least one speed, and the average steering angle may also be the median of at least one steering angle. The median speed refers to the speed value located in the middle of a sequence of at least one speed arranged in ascending order; similarly, the median angle refers to the steering angle located in the middle of a sequence of at least one steering angle arranged in ascending order; similarly, the median steering angle is the steering angle at the middle of a sequence of at least one steering angle.
[0109] In this embodiment, at least one speed and at least one steering angle of the vehicle can be collected within the time interval corresponding to the target map image and its previous rendered map image. The at least one speed can be used to calculate the average speed within that time interval, and the at least one steering angle can be used to calculate the average steering angle within that time interval. Using the average speed and average steering angle as vehicle operation data, the lane line prediction points can be optimized from both distance and angle dimensions, resulting in higher accuracy of the obtained target location points and a higher degree of fit between the obtained target lane lines and the vehicle's operating state, making the prediction more accurate.
[0110] Further, optionally, based on vehicle operation data, the predicted lane lines corresponding to the target map image are positionally optimized to obtain optimized target location points, including:
[0111] Based on the average speed and average steering angle, the lane line prediction points in the target map image are calculated by coordinate translation to obtain the intermediate prediction points after the vehicle body is translated.
[0112] Based on the average turning angle, coordinate rotation calculations are performed on the intermediate prediction points to obtain the optimized target position.
[0113] In this embodiment, coordinate translation calculations can be performed based on the average speed and the evaluation steering angle to obtain intermediate prediction points. Then, coordinate selection calculations can be performed on the intermediate prediction points based on the average steering angle to obtain the optimized target position point. This achieves optimization from both distance and angle dimensions, improving optimization accuracy and efficiency.
[0114] Furthermore, based on any of the above embodiments, according to the average speed and average steering angle, the lane line prediction points of the target map image are subjected to coordinate translation calculation to obtain the intermediate prediction points after vehicle translation, including:
[0115] Calculate the product of the average speed and the sine of the average steering angle to obtain the first offset distance;
[0116] Calculate the product of the average speed and the cosine of the average steering angle to obtain the second offset distance;
[0117] Subtract the x-coordinate of the lane line prediction point from the first offset distance to obtain the lateral value of the vehicle body;
[0118] Subtract the longitudinal coordinate of the lane line prediction point from the second offset distance to obtain the longitudinal value of the vehicle body;
[0119] Based on the lateral and longitudinal values of the vehicle body, the intermediate prediction point after the vehicle body is translated is determined.
[0120] Optionally, the sine of the average steering angle can be expressed as sin(a), and the cosine of the average steering angle can be expressed as cos(a). Where a is the average steering angle.
[0121] The first offset distance can be expressed as: S*sin(a), and the second offset distance can be expressed as: S*cos(a).
[0122] Assuming the x and y coordinates of the lane line prediction point are (x, y), the lateral values of the vehicle body can be: x' = x - sin(a), and the lateral values can be: y' = y - cos(a). The intermediate prediction point is (x', y').
[0123] In this embodiment, the offset of the distance dimension can be calculated by using the sine and cosine values of the average speed and average steering angle to obtain an accurate intermediate prediction point. This ensures that the optimized intermediate prediction point corresponds to the displacement of the vehicle during its driving process, thereby improving the accuracy of the intermediate prediction point.
[0124] Further, optionally, the intermediate prediction point includes lateral and longitudinal values. Based on the average turning angle, the intermediate prediction point is subjected to coordinate rotation calculation to obtain the optimized target position point, which may include:
[0125] Determine the rotation matrix based on the sine and cosine values of the average steering angle;
[0126] Calculate the product of the rotation matrix and the matrix corresponding to the horizontal and vertical values of the intermediate prediction point to obtain the optimized target position point.
[0127] Alternatively, the rotation matrix can be represented as:
[0128] The target location point can be represented as:
[0129] In this embodiment, the intermediate prediction point is optimized based on the rotation matrix determined by the sine and cosine values of the average steering angle. The matrix calculation is more efficient, and the optimized target position point corresponds to the vehicle's changing angle. This achieves optimization of the prediction point from the angle direction, resulting in a more accurate target position point.
[0130] To facilitate understanding of the technical solution of this application, such as Figure 5 The diagram shown is an application example of a lane line display method provided in this application embodiment. Taking a cloud server as an example, the vehicle-mounted system can collect vehicle information during vehicle operation.
[0131] Electronic devices can perform the following steps:
[0132] S501: Obtain vehicle information from the vehicle's infotainment system, which may include multiple location points collected centered on the vehicle. These location points can be lane line location points of the vehicle.
[0133] S502: Determine the second moment for receiving vehicle information next.
[0134] S503: Based on the time period corresponding to the first and second time periods, determine the target map image to be rendered within the time period.
[0135] S504: Using the first lane lines corresponding to multiple location points as the fitting curve, determine the first fitting formula corresponding to the fitting curve.
[0136] S505: Obtain the second fitting formula corresponding to the vehicle information received before the first moment.
[0137] S506: Based on the parameter differences between the first and second fitting formulas, determine the third fitting formula corresponding to the lane lines of the target map image.
[0138] S507: Based on the third fitting formula and the range of position values corresponding to multiple location points, determine the lane line prediction points of the target map image.
[0139] S508: Collect vehicle movement data between the target map image and its previous map image.
[0140] S509: Based on vehicle operation data, optimize the position of the lane line prediction points corresponding to the target map image to obtain the optimized target position points.
[0141] S510: Based on the target location point, perform lane line fitting processing to obtain the target lane line.
[0142] In this embodiment, during the interval between receiving vehicle information twice, lane lines in the map image to be rendered can be predicted to obtain lane line prediction points that better match the map image. This enables synchronous shifting of lane lines and images, making the display of lane lines and images more synchronized, avoiding the phenomenon of segmented lane line display, achieving continuous display, and improving user experience.
[0143] like Figure 6 The diagram shown is a structural schematic of an embodiment of a lane display device provided in this application. This lane display device can be used to implement the above-described lane display method and can be located in an electronic device. The lane display device 600 may include the following units:
[0144] Information receiving unit 601: used to acquire vehicle information corresponding to the vehicle information unit and determine the first moment of receiving the vehicle information unit information, which includes multiple location points collected with the vehicle as the center.
[0145] Map determination unit 602: used to determine the target map image to be rendered within the time period based on the time period corresponding to the first time and the second time when the vehicle information is received next.
[0146] Lane prediction unit 603: Used to perform lane line shifting processing using multiple location points to obtain lane line prediction points corresponding to the target map image.
[0147] Map display unit 604: Used to perform lane line fitting based on the lane line prediction points corresponding to the target map image, obtain the target lane line, and render and display the target lane line at the corresponding position in the target map image.
[0148] As one embodiment, the map determination unit may include:
[0149] The image determination module is used to determine each map image to be rendered as the target map image sequentially, starting from the first map image to be rendered after the first time, based on the time period corresponding to the first time and the second time when the vehicle information is received next.
[0150] As another embodiment, the lane prediction unit includes:
[0151] The first fitting module is used to use the first lane lines corresponding to multiple location points as fitting curves and determine the first fitting formula corresponding to the fitting curves.
[0152] The second fitting module is used to obtain the second fitting formula corresponding to the vehicle information received before the first moment of receiving vehicle information.
[0153] The third fitting module is used to determine the third fitting formula corresponding to the lane lines of the target map image based on the parameter differences between the first fitting formula and the second fitting formula.
[0154] The lane prediction module is used to determine the lane line prediction points of the target map image based on the third fitting formula and the position value range corresponding to multiple location points.
[0155] As yet another embodiment, the third fitting module includes:
[0156] The difference determination submodule is used to determine the parameter change amount corresponding to the target map image based on the parameter difference between the second fitting formula and the first fitting formula.
[0157] The formula determination submodule is used to determine the third fitting formula corresponding to the lane lines of the target map image based on the first fitting formula and the parameter change.
[0158] As yet another example, the difference determination submodule can specifically be used for:
[0159] The number of lane line offsets is determined based on the number of map images that need to be rendered within the time intervals of the first and second moments.
[0160] Based on the number of lane line deviations, the step size is calculated to determine the parameter difference between the first and second fitting formulas, thus obtaining the adjustment step size.
[0161] The parameter changes corresponding to the target map image are determined based on the adjustment step size and the rendering order of the target map image within the time period.
[0162] As another embodiment, the lane prediction module may specifically include:
[0163] The range determination submodule is used to determine the range of values for the first fitting formula at multiple location points.
[0164] The range adjustment submodule is used to adjust the range of position values according to the parameter change to obtain the target range of the third fitting formula.
[0165] The location selection submodule is used to select lane line location points that satisfy the third fitting formula and correspond to the target map image from the target value range.
[0166] As another embodiment, the map display unit includes:
[0167] Run the data acquisition module to collect vehicle movement data between the target map image and its previous map image;
[0168] The location optimization module is used to optimize the location of the lane line prediction points corresponding to the target map image based on vehicle operation data, so as to obtain the optimized target location point.
[0169] The lane determination module is used to perform lane line fitting based on the target location point to obtain the target lane line.
[0170] As another example, the data acquisition module is operated, including:
[0171] The data acquisition submodule is used to acquire at least one speed and at least one steering angle of the vehicle within the time interval between the target map image and the previous rendered map image.
[0172] The mean calculation submodule is used to calculate the average speed based on at least one speed and the average steering angle based on at least one steering angle.
[0173] The determination submodule is used to determine the average speed and average steering angle as vehicle operating data.
[0174] As another embodiment, the location optimization module may include:
[0175] The first prediction submodule is used to perform coordinate translation calculations on the lane line prediction points of the target map image based on the average speed and average steering angle, so as to obtain the intermediate prediction points after the vehicle body is translated.
[0176] The second prediction submodule is used to perform coordinate rotation calculations on the intermediate prediction points based on the average turning angle to obtain the optimized target position point.
[0177] As yet another embodiment, the first prediction submodule can specifically be used for:
[0178] Calculate the product of the average speed and the sine of the average steering angle to obtain the first offset distance;
[0179] Calculate the product of the average speed and the cosine of the average steering angle to obtain the second offset distance;
[0180] Subtract the x-coordinate of the lane line prediction point from the first offset distance to obtain the lateral value of the vehicle body;
[0181] Subtract the longitudinal coordinate of the lane line prediction point from the second offset distance to obtain the longitudinal value of the vehicle body;
[0182] Based on the lateral and longitudinal values of the vehicle body, the intermediate prediction point after the vehicle body is translated is determined.
[0183] As yet another embodiment, the second prediction submodule is specifically used for:
[0184] Determine the rotation matrix based on the sine and cosine values of the average steering angle;
[0185] Calculate the product of the rotation matrix and the matrix corresponding to the horizontal and vertical values of the intermediate prediction point to obtain the optimized target position point.
[0186] Figure 7 This is a block diagram illustrating a terminal device according to an exemplary embodiment. The device may be a mobile phone, computer, digital broadcasting terminal, messaging device, game console, tablet device, medical device, fitness device, personal digital assistant, etc.
[0187] The device 700 may include one or more of the following components: processing component 702, memory 704, power supply component 706, multimedia component 708, audio component 710, input / output (I / O) interface 712, sensor component 714, and communication component 716.
[0188] Processing component 702 typically controls the overall operation of device 700, such as operations associated with display, telephone calls, data communication, camera operation, and recording. Processing component 702 may include one or more processors 720 to execute instructions to complete all or part of the steps of the methods described above. Furthermore, processing component 702 may include one or more modules to facilitate interaction between processing component 702 and other components. For example, processing component 702 may include a multimedia module to facilitate interaction between multimedia component 708 and processing component 702.
[0189] Memory 704 is configured to store various types of data to support the operation of device 700. Examples of this data include instructions for any application or method operating on device 700, contact data, phonebook data, messages, pictures, videos, etc. Memory 704 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.
[0190] Power supply assembly 706 provides power to various components of device 700. Power supply assembly 706 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to device 700.
[0191] Multimedia component 708 includes a screen that provides an output interface between device 700 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may sense not only the boundaries of touch or swipe actions but also the duration and pressure associated with the touch or swipe operation. In some embodiments, multimedia component 708 includes a front-facing camera and / or a rear-facing camera. When device 700 is in an operating mode, such as a shooting mode or a video mode, the front-facing camera and / or rear-facing camera may receive external multimedia data. Each front-facing camera and rear-facing camera may be a fixed optical lens system or have focal length and optical zoom capabilities.
[0192] Audio component 710 is configured to output and / or input audio signals. For example, audio component 710 includes a microphone (MIC) configured to receive external audio signals when device 700 is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 704 or transmitted via communication component 716. In some embodiments, audio component 710 also includes a speaker for outputting audio signals.
[0193] I / O interface 712 provides an interface between processing component 702 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, power buttons, and lock buttons.
[0194] Sensor assembly 714 includes one or more sensors for providing state assessments of various aspects of device 700. For example, sensor assembly 714 may detect the on / off state of device 700, the relative positioning of components such as the display and keypad of device 700, changes in the position of device 700 or a component of device 700, the presence or absence of user contact with device 700, the orientation or acceleration / deceleration of device 700, and temperature changes of device 700. Sensor assembly 714 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 714 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, sensor assembly 714 may also include an accelerometer, a gyroscope, a magnetometer, a pressure sensor, or a temperature sensor.
[0195] Communication component 716 is configured to facilitate wired or wireless communication between device 700 and other devices. Device 700 can access wireless networks based on communication standards, such as WiFi, 2G, or 3G, or combinations thereof. In one exemplary embodiment, communication component 716 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 716 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.
[0196] In an exemplary embodiment, the apparatus 700 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the methods described above.
[0197] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 704 including instructions, which can be executed by a processor 720 of the device 700 to perform the above-described method. For example, the non-transitory computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.
[0198] This embodiment also provides an electronic device, including: a processor, and a memory communicatively connected to the processor;
[0199] The memory stores the instructions that the computer executes;
[0200] The processor executes computer execution instructions stored in memory to implement the method as described in any of the above embodiments.
[0201] This embodiment also provides a vehicle, including: a vehicle-mounted unit, the vehicle-mounted unit including a processor and a memory communicatively connected to the processor;
[0202] The memory stores the instructions that the computer executes;
[0203] The processor executes computer execution instructions stored in memory to implement the method as described in any of the above embodiments.
[0204] This embodiment also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the methods provided in any of the above embodiments.
[0205] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the following claims.
[0206] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.
Claims
1. A lane line display method characterized by, include: Obtain the vehicle information corresponding to the vehicle information system and determine the first moment of receiving the vehicle information system, wherein the vehicle information system includes multiple location points collected with the vehicle as the center; Based on the time period corresponding to the first moment and the second moment when the vehicle information is received next, the target map image to be rendered within the time period is determined. Using the first lane lines corresponding to multiple location points as fitting curves, determine the first fitting formula corresponding to the fitting curves; Obtain the second fitting formula corresponding to the vehicle information received before the first moment when the vehicle information was received; Based on the parameter differences between the second fitting formula and the first fitting formula, the parameter change corresponding to the target map image is determined; Based on the first fitting formula and the parameter change, a third fitting formula corresponding to the lane lines of the target map image is determined. Based on the third fitting formula and the position value range corresponding to multiple position points, the lane line prediction points of the target map image are determined. Based on the lane line prediction points corresponding to the target map image, lane lines are fitted to obtain the target lane lines, which are then rendered at the corresponding positions in the target map image and displayed in the vehicle's infotainment system.
2. The method of claim 1, wherein, The determination of the target map image to be rendered within the time period corresponding to the first time and the second time of receiving vehicle information includes: Based on the time period corresponding to the first moment and the second moment when the vehicle information is received next, each map image to be rendered is sequentially determined as the target map image, starting from the first map image to be rendered after the first moment.
3. The method of claim 1, wherein, The step of determining the parameter change amount corresponding to the target map image based on the parameter difference between the second fitting formula and the first fitting formula includes: The number of lane line offsets is determined based on the number of map images that need to be rendered within the time intervals of the first and second time points. Based on the number of lane line deviations, the step size is calculated for the parameter differences between the first fitting formula and the second fitting formula to obtain the adjustment step size; Based on the adjustment step size and the rendering order of the target map image within the time period, the parameter change amount corresponding to the target map image is determined.
4. The method of claim 1, wherein, The step of determining the lane line prediction points of the target map image based on the third fitting formula and the position value range corresponding to multiple position points includes: Determine the range of values for the first fitting formula at the corresponding positions of the multiple said positions; Based on the change in the parameter, the range of the position values is adjusted to obtain the target range of the third fitting formula; From the target value range, select the lane line location point that satisfies the third fitting formula and corresponds to the target map image.
5. The method of claim 1, wherein, The step of fitting lane lines to obtain target lane lines based on lane line prediction points corresponding to the target map image includes: Collect vehicle movement data between the target map image and its previous map image; Based on the vehicle operation data, the predicted lane line points corresponding to the target map image are optimized to obtain the optimized target location points. Based on the target location point, lane line fitting processing is performed to obtain the target lane line.
6. The method of claim 5, wherein, The process of collecting vehicle movement data between the target map image and its previous map image includes: During the time interval between the target map image and its previous rendered map image, at least one speed and at least one steering angle of the vehicle are acquired; Calculate the average speed based on at least one of the speeds, and calculate the average steering angle based on at least one of the steering angles; The average speed and the average steering angle are determined as the vehicle operating data.
7. The method of claim 6, wherein, The step of optimizing the predicted lane lines corresponding to the target map image based on the vehicle operation data to obtain the optimized target location points includes: Based on the average speed and the average steering angle, coordinate translation calculations are performed on the lane line prediction points of the target map image to obtain the intermediate prediction points after the vehicle body translation. Based on the average turning angle, coordinate rotation calculations are performed on the intermediate prediction point to obtain the optimized target position point.
8. The method of claim 7, wherein, The step of performing coordinate translation calculations on the lane line prediction points of the target map image based on the average speed and the average steering angle to obtain the intermediate prediction points after vehicle translation includes: Calculate the product of the average speed and the sine of the average steering angle to obtain the first offset distance; The second offset distance is obtained by calculating the product of the average speed and the cosine of the average steering angle. Subtract the x-coordinate of the lane line prediction point from the first offset distance to obtain the lateral value of the vehicle body; Subtract the longitudinal coordinate of the lane line prediction point from the second offset distance to obtain the longitudinal value of the vehicle body; Based on the lateral and longitudinal values of the vehicle body, the intermediate prediction point after the vehicle body is translated is determined.
9. The method of claim 7, wherein, The intermediate prediction point includes horizontal and vertical values. The step of performing coordinate rotation calculations on the intermediate prediction point based on the average turning angle to obtain the optimized target position point includes: The rotation matrix is determined based on the sine and cosine values of the average steering angle. The optimized target position point is obtained by multiplying the rotation matrix with the matrix corresponding to the horizontal and vertical values of the intermediate prediction point.
10. A lane line display device characterized by comprising: include: An information receiving unit is used to receive vehicle information and determine the first moment of receiving the vehicle information, wherein the vehicle information includes multiple location points collected with the vehicle as the center; The map determination unit is used to determine the target map image to be rendered within the time period based on the time period corresponding to the first time and the second time when the vehicle information is received next. The lane prediction unit is used to perform lane line shifting processing using multiple location points to obtain lane line prediction points corresponding to the target map image. The map display unit is used to perform lane line fitting based on the lane line prediction points corresponding to the target map image to obtain the target lane line, and to render and display the target lane line at the corresponding position in the target map image. The lane prediction unit includes: The first fitting module is used to use the first lane lines corresponding to the multiple location points as fitting curves to determine the first fitting formula corresponding to the fitting curves. The second fitting module is used to obtain the second fitting formula corresponding to the vehicle information received before the first moment when the vehicle information was received. The third fitting module is used to determine the third fitting formula corresponding to the lane lines of the target map image based on the parameter differences between the first fitting formula and the second fitting formula. The lane prediction module is used to determine the lane line prediction points of the target map image based on the third fitting formula and the position value range corresponding to multiple location points. The third fitting module includes: The difference determination submodule is used to determine the parameter change amount corresponding to the target map image based on the parameter difference between the second fitting formula and the first fitting formula; The formula determination submodule is used to determine the third fitting formula corresponding to the lane lines of the target map image based on the first fitting formula and the parameter change amount.
11. An electronic device comprising: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of claims 1-9.
12. A vehicle comprising: The vehicle infotainment system includes a processor and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of claims 1-9.
13. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1 to 9.