Data visualization method and device, equipment and storage medium

Abstract

The invention discloses a data visualization method, device and equipment and a storage medium, and relates to the technical field of data processing, and the method comprises the steps: obtaining vehicle driving data which comprises a plurality of single-frame driving data; based on pre-configured visualization parameters, data visualization is carried out on the multiple pieces of single-frame driving data under a self-vehicle coordinate system, and multiple pieces of single-frame visualization data are obtained; and splicing the plurality of single-frame visual data to obtain a vehicle driving visual video. According to the method, the video visualization of the intelligent driving data can be realized, so that the dynamic change of various data in the intelligent driving data can be intuitively known.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a data visualization method, apparatus, device, and storage medium. Background Art

[0002] Intelligent driving data contains complex and diverse content, including not only map information, positioning information, vehicle information, and driving status information, but also dynamic information such as traffic lights. This diverse information changes over time. Existing data visualization methods often focus on a single piece of intelligent driving data, such as the associated vehicle behavior within the ego vehicle's lane. This lacks flexibility and is unable to present multiple data points within intelligent driving data and display them in a modular manner.

[0003] In order to more intuitively understand the detailed information of intelligent driving data, understand the binding information between vehicles and roads, understand the interaction information between vehicles, and better present the data mining results, it is necessary to propose a data visualization method to flexibly display intelligent driving data, so as to solve the problem that the existing methods are not flexible enough and cannot fully and detailedly present multiple data in intelligent driving data.

[0004] The above content is only used to assist in understanding the technical solution of this application and does not constitute an admission that the above content is prior art. Summary of the Invention

[0005] The main purpose of this application is to provide a data visualization method, device, equipment and storage medium, aiming to solve the technical problem that the existing methods are insufficiently flexible and cannot fully and detailedly present multiple data in intelligent driving data.

[0006] To achieve the above objectives, this application proposes a data visualization method, which includes:

[0007] Acquiring vehicle driving data, wherein the vehicle driving data includes a plurality of single-frame driving data;

[0008] Based on preconfigured visualization parameters, the plurality of single-frame driving data are visualized in a vehicle coordinate system to obtain a plurality of single-frame visualization data;

[0009] The plurality of single-frame visualization data are spliced together to obtain a vehicle driving visualization video.

[0010] In one embodiment, the preconfigured visualization parameters include a data frame validity flag, image pixels, and drawing elements. The step of visualizing the plurality of single-frame driving data in a vehicle coordinate system based on the preconfigured visualization parameters to obtain the plurality of single-frame visualization data includes:

[0011] Extracting vehicle information, map information, and dynamic and static obstacle information from the single-frame driving data;

[0012] Drawing a single-frame basic canvas according to the data frame validity identifier and the image pixels;

[0013] Determining a drawing target on the single-frame basic canvas according to the drawing element, wherein the drawing target is at least one of a target vehicle, a lane centerline, a lane boundary, a road boundary, a crosswalk, and a dynamic or static obstacle;

[0014] Obtaining the vehicle positioning data according to the vehicle information, and drawing the target vehicle on the single-frame basic canvas;

[0015] Drawing lane centerlines, lane boundaries, road boundaries, and crosswalks on the single-frame base canvas based on the map information and the vehicle positioning data;

[0016] Drawing the dynamic and static obstacles on the single-frame basic canvas according to the dynamic and static obstacle information and the vehicle positioning data;

[0017] The drawn target vehicle, lane centerline, lane boundary, road boundary, crosswalk, dynamic and static obstacles are integrated through the single-frame basic canvas to obtain single-frame visualization data.

[0018] In one embodiment, the preconfigured visualization parameters include a historical frame number, a future frame number, and a zoom factor, and the drawn target further includes a dynamic target trajectory. After the steps of obtaining the vehicle positioning data based on the vehicle information and drawing the target vehicle on the single-frame base canvas, the method further includes:

[0019] According to the historical frame number and the future frame number, obtaining historical multi-frame driving data and future multi-frame driving data of corresponding frame numbers;

[0020] Extracting the dynamic target trajectory coordinates from the historical multi-frame driving data and forming a historical trajectory point sequence of several dynamic targets;

[0021] Extracting the dynamic target trajectory coordinates from the future multi-frame driving data and forming a sequence of future trajectory points of multiple dynamic targets;

[0022] According to the vehicle positioning data, the coordinates of each trajectory point in the historical trajectory point sequence and the future trajectory point sequence are converted into trajectory point coordinates in the vehicle coordinate system to obtain the historical trajectory point sequence and the future trajectory point sequence in the vehicle coordinate system;

[0023] Based on the historical trajectory point sequence and the future trajectory point sequence in the vehicle coordinate system, the pixel coordinates of the historical trajectory point sequence and the future trajectory point sequence are calculated according to the scaling factor and the vehicle positioning data;

[0024] According to the pixel coordinates of the historical trajectory point sequence and the future trajectory point sequence, the trajectory of the dynamic target is drawn on the single-frame basic canvas, and different types of trajectories of different dynamic targets are given different colors.

[0025] In one embodiment, the preconfigured visualization parameters include a core area size and a secondary core area size, the drawing target also includes a core area and a secondary core area, and after the steps of obtaining vehicle positioning data based on the vehicle information and drawing the target vehicle on the single-frame basic canvas, the method further includes:

[0026] Calculating vertex coordinates of the core area rectangle and the sub-core area rectangle in the vehicle coordinate system based on the core area size, the sub-core area size, and the vehicle positioning data;

[0027] Based on the vertex coordinates of the core rectangular area and the sub-core rectangular area in the vehicle coordinate system, the pixel coordinates of the core rectangular area and the sub-core rectangular area are calculated according to the scaling factor and the vehicle positioning data;

[0028] According to the pixel coordinates of the core rectangular area and the sub-core rectangular area, the core rectangular area and the sub-core rectangular area are interpolated and drawn respectively on the single-frame basic canvas, and different colors are given to the core rectangular area and the sub-core rectangular area.

[0029] In one embodiment, the step of drawing the lane centerline on the single-frame base canvas based on the map information and the vehicle positioning data includes:

[0030] According to the map information, obtaining lane information of the current frame, wherein the lane information includes lane centerline information, lane boundary information, and lane type;

[0031] Obtaining a background area of the lane centerline according to the lane centerline information and the lane boundary information, and assigning a background color to the background area of the lane centerline for drawing;

[0032] Obtaining lane centerline coordinate data from the lane centerline information, and performing coordinate conversion and pixel conversion on the lane centerline coordinate data in a vehicle coordinate system to obtain lane centerline pixel coordinate data;

[0033] Extracting lane centerline segment points from the lane centerline pixel coordinate data based on a preset sampling interval;

[0034] According to the lane centerline segment points, lane centerline segments are drawn on the single-frame basic canvas, and different types of lane centerlines are given different colors, as well as different starting point identifiers and end point identifiers.

[0035] In one embodiment, the step of drawing the dynamic and static obstacles on the single-frame basic canvas based on the dynamic and static obstacle information and the vehicle positioning data includes:

[0036] According to the dynamic and static obstacle information, dynamic obstacle information and static obstacle information of the current frame are obtained, wherein the dynamic obstacle information includes vehicle lane change information, vehicle cut-in information, and preset label information;

[0037] Obtaining static obstacle coordinate data from the static obstacle information, and performing coordinate conversion and pixel conversion on the static obstacle coordinate data in a vehicle coordinate system to obtain static obstacle pixel coordinate data;

[0038] Drawing an obstacle bounding box for the static obstacle on the single-frame basic canvas according to the pixel coordinate data of the static obstacle, and assigning different colors to different types of static obstacles;

[0039] Obtaining dynamic obstacle coordinate data from the dynamic obstacle information, and performing coordinate conversion and pixel conversion on the dynamic obstacle coordinate data in a vehicle coordinate system to obtain dynamic obstacle pixel coordinate data;

[0040] Drawing an obstacle bounding box for the dynamic obstacle on the single-frame basic canvas according to the pixel coordinate data of the dynamic obstacle, and assigning different colors to different types of dynamic obstacles;

[0041] A preset logo is drawn for a dynamic obstacle associated with at least one of vehicle lane change information, vehicle cut-in information, and preset label information, and is assigned a different logo color.

[0042] In one embodiment, the preconfigured visualization parameters include an image scale adjustment factor, and the method further includes:

[0043] Adjusting the image pixels according to the image adjustment factor to obtain adjusted image pixels;

[0044] The single-frame basic canvas is adjusted based on the adjusted image pixels.

[0045] In addition, to achieve the above-mentioned purpose, the present application also proposes a data visualization device, which includes:

[0046] An acquisition module, configured to acquire vehicle driving data, wherein the vehicle driving data includes a plurality of single-frame driving data;

[0047] A data visualization module is used to visualize the plurality of single-frame driving data in a vehicle coordinate system based on preconfigured visualization parameters to obtain a plurality of single-frame visualization data;

[0048] The splicing module is used to splice the plurality of single-frame visualization data to obtain a vehicle driving visualization video.

[0049] In addition, to achieve the above-mentioned purpose, the present application also proposes a data visualization device, which includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the computer program is configured to implement the steps of the data visualization method described above.

[0050] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by the processor, the steps of the data visualization method described above are implemented.

[0051] One or more technical solutions proposed in this application have at least the following technical effects:

[0052] The data visualization method, apparatus, device, and storage medium proposed in the embodiments of the present application specifically obtain vehicle driving data, where the vehicle driving data includes multiple single-frame driving data; based on preconfigured visualization parameters, the multiple single-frame driving data are visualized in a vehicle coordinate system to obtain multiple single-frame visualization data; and the multiple single-frame visualization data are spliced to obtain a vehicle driving visualization video.

[0053] The present application obtains vehicle driving data including multiple single-frame driving data, and then visualizes each frame of the vehicle driving data in the vehicle coordinate system based on pre-configured visualization parameters, thereby obtaining a plurality of single-frame visualization data; the plurality of single-frame visualization data are spliced to obtain a vehicle driving visualization video, thereby realizing video visualization of the vehicle driving data. At the same time, the single-frame data visualization can display the data content in the vehicle intelligent driving data in detail and fully, so as to intuitively understand the dynamic changes of various data in the intelligent driving data. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0055] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0056] Figure 1 A flowchart of the first embodiment of the data visualization method of this application is provided;

[0057] Figure 2 A flowchart of the second embodiment of the data visualization method of this application is provided;

[0058] Figure 3 A flowchart of the third embodiment of the data visualization method of this application is provided;

[0059] Figure 4 A flowchart of the fourth embodiment of the data visualization method of this application is provided;

[0060] Figure 5 A flowchart of a data visualization method according to an embodiment of the present application;

[0061] Figure 6 This is a schematic diagram of the module structure of the data visualization device according to an embodiment of the present application;

[0062] Figure 7 Schematic diagram of the device structure of the hardware operating environment involved in the data visualization method in the embodiment of the present application.

[0063] The purpose, features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0064] It should be understood that the specific embodiments described herein are merely used to explain the technical solutions of the present application and are not intended to limit the present application.

[0065] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0066] The main solution of the embodiment of the present application is: obtaining vehicle driving data, wherein the vehicle driving data includes a plurality of single-frame driving data; based on preconfigured visualization parameters, visualizing the plurality of single-frame driving data in the vehicle coordinate system to obtain a plurality of single-frame visualization data; and splicing the plurality of single-frame visualization data to obtain a vehicle driving visualization video.

[0067] Existing data visualization methods often only focus on a certain data in intelligent driving data, such as only focusing on the associated vehicle behavior of the ego vehicle lane in intelligent driving data. They lack flexibility and cannot present multiple data in intelligent driving data and display data content in a modular manner.

[0068] Therefore, it is necessary to propose a data visualization method to flexibly display intelligent driving data to solve the problem that the existing methods are not flexible enough and cannot fully and detailedly present multiple data in intelligent driving data.

[0069] The present application provides a solution, which obtains vehicle driving data including multiple single-frame driving data, and then visualizes each frame of the vehicle driving data in the vehicle coordinate system based on pre-configured visualization parameters, thereby obtaining multiple single-frame visualization data; the multiple single-frame visualization data are spliced to obtain a vehicle driving visualization video, thereby realizing video visualization of the vehicle driving data. At the same time, the single-frame data visualization can display the data content in the vehicle intelligent driving data in detail and fully, so as to intuitively understand the dynamic changes of various data in the intelligent driving data.

[0070] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, mobile phone, etc., or a data visualization device capable of implementing the above functions. The following uses a data visualization device as an example to illustrate this embodiment and the following embodiments.

[0071] Based on this, the embodiment of the present application provides a data visualization method, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the data visualization method of this application.

[0072] In this embodiment, the data visualization method includes steps S110 to S130:

[0073] Step S110, obtaining vehicle driving data, wherein the vehicle driving data includes a plurality of single-frame driving data;

[0074] Specifically, the data visualization device first needs to receive vehicle driving data, and the vehicle driving data should include several single-frame driving data, so as to subsequently visualize the detailed data content of the single-frame driving data, thereby realizing video visualization of the vehicle driving data.

[0075] Among them, vehicle driving data usually refers to intelligent vehicle driving data, which is obtained through various on-board sensors (such as speedometers, accelerometers, gyroscopes, radars, infrared rays, on-board GPS (Global Positioning System) and other sensors) to obtain information about surrounding roads, other vehicles and obstacles (such as relative distance, relative speed, size, shape contour, etc.), as well as the vehicle's driving status information (such as speed, acceleration, steering angle, mileage, etc.).

[0076] Step S120: Visualizing the plurality of single-frame driving data in a vehicle coordinate system based on pre-configured visualization parameters to obtain a plurality of single-frame visualization data;

[0077] It should be noted that pre-configured visualization parameters refer to global visualization parameters pre-created by relevant personnel based on the visualization requirements of vehicle driving data. Among them, global visualization parameters are a set of parameter configurations pre-set to ensure the consistency and accuracy of all relevant visualization elements when processing and displaying vehicle driving data. Global visualization parameters define how to convert raw data into graphical representations so that data from different sources or types can be correctly interpreted within the same visual framework. Global visualization parameters can generally include the pixel size of a single frame, the grid spacing of a single frame, the zoom factor, the drawing elements, the color configuration scheme of the drawing elements, the font style, the coordinate system and direction (for example, the vehicle's forward direction is always facing directly above the map), the number of video frames, etc.

[0078] Specifically, according to the preconfigured visualization parameters, the coordinate data of all drawing elements in each frame of vehicle driving data are converted into relevant coordinate data in the vehicle coordinate system. Then, based on the relevant coordinate data in the vehicle coordinate system, combined with other data of the drawing elements (such as size, speed, etc.), data visualization is performed to obtain several single-frame visualization data.

[0079] In a feasible implementation, the preconfigured visualization parameters include a data frame validity flag, image pixels, and drawing elements. Step S120 may include steps A01 to A07:

[0080] Step A01, extracting vehicle information, map information, and dynamic and static obstacle information from the single-frame driving data;

[0081] Specifically, vehicle information, map information, and dynamic and static obstacle information are first extracted from single-frame driving data.

[0082] Vehicle information refers to all data related to the vehicle and its status, including but not limited to coordinates, heading angle, speed and acceleration, and sensor data (such as wheel speed, steering angle, IMU data, and brake status). Map information includes lane centerline information, lane boundary information, road intersections and junctions, traffic light locations and status, road signs, and crosswalk information. Dynamic and static obstacle information includes the location, speed, size, and outline of dynamic obstacles on the map surrounding the vehicle, as well as the location, size, and outline of static obstacles.

[0083] Step A02: drawing a single-frame basic canvas according to the data frame validity flag and the image pixels;

[0084] It should be noted that the data frame validity flag is used to identify whether the current single-frame driving data is valid. It should be understood that invalid driving data is data that lacks important information or has invalid important information, such as missing vehicle coordinates or a significant difference between the vehicle coordinates of the previous frame.

[0085] Specifically, in order to visually display whether the current single-frame driving data is valid, the data visualization device first determines the background color of the single-frame basic canvas based on the data frame validity identifier, and highlights the abnormality of the frame data by drawing different background colors, such as using a bright background color, so that relevant personnel can quickly distinguish abnormal frames from normal frames based on the video after data visualization, intuitively feel the proportion of abnormal frames in the vehicle driving data, and the continuity of normal frames, and quickly locate abnormal frames.

[0086] Then, the data visualization device generates a basic canvas of corresponding pixel size according to the image pixels, and draws a single-frame basic canvas based on the background color determined by the data frame validity identifier.

[0087] It should be understood that a grid can also be drawn on a single-frame base canvas based on a grid spacing. The grid spacing is used to provide a coordinate reference and enhance the readability of the vehicle driving data. The grid spacing represents the size of the background grid representation space in the single-frame base canvas, in meters.

[0088] Step A03: determining a drawing target on the single-frame basic canvas based on the drawing element, wherein the drawing target is at least one of a target vehicle, a lane centerline, a lane boundary, a road boundary, a crosswalk, and a dynamic or static obstacle;

[0089] Step A04: acquiring the vehicle positioning data according to the vehicle information, and drawing the target vehicle on the single-frame basic canvas;

[0090] Step A05: drawing lane centerlines, lane boundaries, road boundaries, and crosswalks on the single-frame basic canvas based on the map information and the vehicle positioning data;

[0091] Step A06: drawing the dynamic and static obstacles on the single-frame basic canvas according to the dynamic and static obstacle information and the vehicle positioning data;

[0092] Step A07: Integrate the drawn target vehicle, lane centerline, lane boundary, road boundary, crosswalk, and dynamic and static obstacles through the single-frame basic canvas to obtain single-frame visualization data.

[0093] Specifically, the data visualization device first needs to determine the drawing target that needs to be drawn on the current single-frame basic canvas based on the drawing elements, where the drawing target can be any combination of target vehicles, lane centerlines, lane boundaries, road boundaries, crosswalks, dynamic and static obstacles.

[0094] When drawing targets including the target vehicle, lane centerline, lane boundary, road boundary, crosswalk, and dynamic and static obstacles, the data visualization device obtains the current frame's ego vehicle positioning data based on the ego vehicle information. Using the ego vehicle's size information, the device then determines the coordinates of the four vertices of the ego vehicle's rectangular frame. These vertex coordinates are converted to the origin of the ego vehicle coordinate system. Pixel mapping is then performed based on the mapping relationship between pixels and real space to determine the pixel coordinates of the converted ego vehicle rectangular frame. Based on these pixel coordinates, the target vehicle's outline is drawn on the single-frame base canvas. The ego vehicle positioning data includes the ego vehicle's coordinate data in a relative coordinate system with the ego vehicle's start-up coordinates as the origin. The pixel coordinates corresponding to the ego vehicle's start-up coordinates are typically the midpoint of the lower boundary of the single-frame base canvas.

[0095] It should be noted that because the accuracy of on-board GPS is usually between 5 and 30 meters and is affected by the network and environment, when visualizing single-frame driving data, the coordinate data of each dynamic and static object is usually selected in a relative coordinate system with the coordinates at the time of vehicle startup as the origin.

[0096] Then, refer to Figure 5 Based on the lane centerline information (lane centerline width, type, length, coordinate data, etc.) and lane boundary information (lane boundary width, type, length, coordinate data, etc.) in the map information, combined with the vehicle positioning data and the current pixel coordinates of the vehicle in the single-frame basic canvas, the lane centerline background, lane centerline, and lane boundary are drawn on the single-frame basic canvas.

[0097] Then, refer to Figure 5According to the road boundary information in the map information (such as road boundary coordinate data, type, etc.), combined with the vehicle positioning data and the pixel coordinates of the current vehicle in the single-frame basic canvas, the road boundary coordinate data is first subjected to coordinate conversion and pixel conversion in the vehicle coordinate system to obtain the road boundary pixel coordinate data. Then, it is determined whether the road boundary is a soft boundary or a hard boundary, and different drawing colors are used for soft boundaries and hard boundaries. Finally, the road boundary is drawn on the single-frame basic canvas based on the road boundary pixel coordinate data. Among them, a hard boundary refers to a road boundary with a clear and fixed physical obstacle, and a soft boundary refers to a road boundary without obvious physical obstacles but still with some form of restriction or suggestion, such as a dotted line, speed bump, green belt, etc. In addition, for hard boundaries, the data visualization device can also perform special markings, such as highlighting or widening the road boundary.

[0098] Then, refer to Figure 5 Since a crosswalk is typically composed of multiple line segments forming a rectangular area, the data visualization device obtains the coordinate point data for each line segment of the crosswalk rectangular area from the crosswalk information in the map (e.g., crosswalk coordinate data). Combined with the vehicle positioning data and the current pixel coordinates of the vehicle on the single-frame base canvas, the coordinate point data for each line segment of the crosswalk rectangular area is first converted to the vehicle coordinate system and then to pixels to obtain the pixel coordinate point data for the crosswalk rectangular area. Finally, the crosswalk rectangular area is drawn on the single-frame base canvas based on this pixel coordinate point data.

[0099] For example, crosswalk rectangular areas at different locations may be assigned different colors for drawing.

[0100] In addition, the data visualization device may also use special elements, such as small circles, to mark each vertex of the crosswalk rectangular area, so as to further intuitively display the crosswalk rectangular area.

[0101] Then, based on the dynamic and static obstacle information in the map information (the obstacle's outline, size, speed, coordinate data, etc.), combined with the vehicle positioning data and the current pixel coordinates of the vehicle on the single-frame basic canvas, the outline and position of the dynamic and static obstacles are drawn on the single-frame basic canvas.

[0102] Finally, the various drawn targets are integrated through a single-frame basic canvas, including the target vehicle, lane centerline, lane boundary, road boundary, crosswalk, dynamic and static obstacles, to obtain single-frame visualization data.

[0103] In this embodiment, by separately drawing the target vehicle, lane centerline, lane boundary, road boundary, crosswalk, dynamic and static obstacles, a modular display of multiple data contents in the vehicle driving data can be achieved.

[0104] Step S130: splicing the plurality of single-frame visualization data to obtain a vehicle driving visualization video.

[0105] Specifically, after visualizing all single-frame driving data in the vehicle driving data in the vehicle coordinate system, all single-frame visualization data are spliced according to the number of video frames in the global visualization parameter to obtain a vehicle driving visualization video that meets the visualization requirements.

[0106] Furthermore, the pre-configured visualization parameters include a map adjustment factor. After the above step A02, steps B01 to B02 are also included:

[0107] Step B01, adjusting the image pixels according to the image adjustment factor to obtain adjusted image pixels;

[0108] Step B02: adjusting the single-frame basic canvas based on the adjusted image pixels.

[0109] Specifically, after step A02, to facilitate adaptive adjustment of the size of the single-frame base canvas, the width and height of the single-frame base canvas are calculated by multiplying the base map pixels by the map adjustment factor. The base map pixels refer to the default map size, which can be pre-set by relevant personnel, for example, to 900*600 pixels. Therefore, the data visualization device can adjust the map pixels based on the map adjustment factor to obtain adjusted map pixels. The adjusted map pixels are then used to adjust the width and height of the single-frame base canvas, thereby adjusting the position of the vehicle and other drawn objects on the single-frame base canvas to adjust the visualization content for different driving situations.

[0110] This embodiment provides a data visualization method, which obtains vehicle driving data, wherein the vehicle driving data includes multiple single-frame driving data; based on preconfigured visualization parameters, visualizes the multiple single-frame driving data in a vehicle coordinate system to obtain multiple single-frame visualization data; and splices the multiple single-frame visualization data to obtain a vehicle driving visualization video.

[0111] The present application obtains vehicle driving data including multiple single-frame driving data, and then visualizes each frame of the vehicle driving data in the vehicle coordinate system based on pre-configured visualization parameters, thereby obtaining a plurality of single-frame visualization data; the plurality of single-frame visualization data are spliced to obtain a vehicle driving visualization video, thereby realizing video visualization of the vehicle driving data. At the same time, the single-frame data visualization can display the data content in the vehicle intelligent driving data in detail and fully, so as to intuitively understand the dynamic changes of various data in the intelligent driving data.

[0112] Based on the first embodiment of this application, in the second embodiment of this application, the same or similar content as the above embodiment 1 can refer to the above introduction and will not be repeated hereafter. On this basis, the pre-configured visualization parameters include the number of historical frames, the number of future frames, the scaling factor, and the drawing target also includes the dynamic target trajectory. Please refer to Figure 2 After step A04, steps S210 to S260 are included:

[0113] Step S210, acquiring historical multi-frame driving data and future multi-frame driving data of corresponding frames according to the historical frame number and the future frame number;

[0114] Step S220, extracting the dynamic target trajectory coordinates in the historical multi-frame driving data, and forming a sequence of historical trajectory points of several dynamic targets;

[0115] Step S230, extracting the dynamic target trajectory coordinates in the future multi-frame driving data and forming a sequence of future trajectory points of several dynamic targets;

[0116] In this embodiment, refer to Figure 5 When the drawn target also includes a dynamic target trajectory, according to the historical frame number and the future frame number, the historical multi-frame driving data including the historical frame number before the current single-frame driving data and the future multi-frame driving data including the future frame number after the current single-frame driving data are obtained.

[0117] Then, based on the historical multi-frame driving data, the trajectory coordinates of dynamic targets in different frames from the current frame to the historical moment are extracted to form a historical trajectory point sequence. Dynamic targets are objects that move in the historical multi-frame driving data, including the vehicle itself, other vehicles, or other dynamic obstacles.

[0118] Usually, there are multiple dynamic targets in vehicle driving data, so a historical trajectory point sequence of several dynamic targets can be generated based on historical multi-frame driving data.

[0119] Similarly, based on multiple frames of future driving data, the trajectory coordinates of the dynamic target in different frames from the current frame to the future moment are extracted to form a future trajectory point sequence. Typically, there are multiple dynamic targets in vehicle driving data, so the future trajectory point sequences of several dynamic targets can be generated based on multiple frames of future driving data.

[0120] Step S240 , converting the coordinates of each trajectory point in the historical trajectory point sequence and the future trajectory point sequence into trajectory point coordinates in the vehicle coordinate system according to the vehicle positioning data, thereby obtaining the historical trajectory point sequence and the future trajectory point sequence in the vehicle coordinate system;

[0121] Step S250, based on the historical trajectory point sequence and the future trajectory point sequence in the vehicle coordinate system, according to the scaling factor and the vehicle positioning data, calculate the pixel coordinates of the historical trajectory point sequence and the future trajectory point sequence;

[0122] Step S260 , drawing the trajectory of the dynamic target on the single-frame basic canvas according to the pixel coordinates of the historical trajectory point sequence and the future trajectory point sequence, and assigning different colors to different types of trajectories of different dynamic targets.

[0123] Specifically, according to the vehicle positioning data, the coordinates of each trajectory point in the historical trajectory point sequence and the future trajectory point sequence are converted into the trajectory point coordinates in the vehicle coordinate system, thereby obtaining the historical trajectory point sequence and the future trajectory point sequence in the vehicle coordinate system.

[0124] The scaling factor is then used to determine the mapping relationship between pixels and real space. The pixel coordinates of the ego vehicle in the current frame are then determined based on the ego vehicle positioning data and the pixel coordinates corresponding to the coordinates at the time of ego vehicle startup. Based on the mapping relationship between pixels and real space and the pixel coordinates of the ego vehicle in the current frame, a pixel conversion is performed on the historical and future trajectory point sequences in the ego vehicle coordinate system to calculate the pixel coordinates of the historical and future trajectory point sequences. The scaling factor represents the actual spatial size represented by the distance between adjacent pixels. For example, a scaling factor of 0.1 means the distance between two adjacent pixels represents 0.1 meters in space.

[0125] According to the pixel coordinates of the historical trajectory point sequence and the future trajectory point sequence, the historical trajectory and future trajectory of the dynamic target are drawn on a single-frame basic canvas.

[0126] The calculation method for converting the trajectory point coordinates into the trajectory point coordinates in the vehicle coordinate system based on the vehicle positioning data is as follows:

[0127] Let the coordinates of a certain coordinate point P be (global_x, global_y), the coordinates of the ego vehicle ego be (base_x, base_y), and the heading angle of the ego vehicle be base_theta. The formula for calculating the coordinate position of any coordinate point P in the ego vehicle coordinate system is as follows:

[0128] x_diff=global_x-base_x

[0129] y_diff = global_y - base_y

[0130] angle_cos = cos(base_theta)

[0131] angle_sin=sin(base_theta)

[0132] local_x=x_diff*angle_cos-y_diff*angle_sin

[0133] local_y=x_diff*angle_sin+y_diff*angle_cos

[0134] Among them, (local_x, local_y) is the coordinate of the coordinate point P in the vehicle coordinate system.

[0135] To clearly represent the distance of the dynamic target's trajectory points from the ego vehicle, the color of each historical coordinate point of the dynamic target is mapped based on the difference between the historical frame number corresponding to the coordinate point and the current frame. This mapping generates the color information of each historical coordinate point and plots the historical trajectory of the dynamic target.

[0136] To clearly represent the distance of the dynamic target's trajectory points from the ego vehicle, the color of each future coordinate point of the dynamic target is mapped based on the difference between the future frame corresponding to the coordinate point and the current frame. This mapping generates the color information of each future coordinate point and plots the dynamic target's future trajectory.

[0137] Furthermore, in order to intuitively display the area with high correlation with the vehicle, the pre-configured visualization parameters include the core area size and the secondary core area size, and the drawing target also includes the core area and the secondary core area. After step A04, steps S270 to S290 are included:

[0138] Step S270 , calculating vertex coordinates of the core area rectangle and the sub-core area rectangle in the vehicle coordinate system based on the core area size, the sub-core area size, and the vehicle positioning data;

[0139] Step S280 , calculating pixel coordinates of the core rectangular area and the sub-core rectangular area based on the vertex coordinates of the core rectangular area and the sub-core rectangular area in the vehicle coordinate system, according to the scaling factor and the vehicle positioning data;

[0140] Step S290 , interpolating and drawing the core rectangular area and the sub-core rectangular area on the single-frame basic canvas according to the pixel coordinates of the core rectangular area and the sub-core rectangular area, and assigning different colors to the core rectangular area and the sub-core rectangular area.

[0141] Specifically, the core area and the sub-core area are set according to the distance from the vehicle, and the visualization importance of various elements in the core area, the sub-core area, and the area outside the sub-core area is different.

[0142] Among them, the core area and the secondary core area are both rectangular areas surrounding the vehicle.

[0143] Based on the core area and sub-core area sizes, the distances of the core area and sub-core area from the front, rear, left, and right of the vehicle are determined, and the coordinates of the four vertices of the rectangular area are further calculated. Then, combined with the vehicle positioning data, the coordinates of the four vertices of the core area and sub-core area are calculated in the vehicle coordinate system.

[0144] According to the vertex coordinates of the core rectangular area and the sub-core rectangular area in the vehicle coordinate system, the pixel coordinates of the core rectangular area and the sub-core rectangular area are calculated according to the scaling factor and the vehicle positioning data.

[0145] Finally, according to the pixel coordinates of the core area rectangle and the sub-core rectangular area, the core area and the sub-core rectangular area with the pixel coordinates as the vertices are obtained. The edges of the two rectangular areas are interpolated to obtain the interpolation coordinate points, and the dotted rectangular frame is drawn according to the interpolated coordinate points.

[0146] In addition, in order to distinguish the core rectangular area from the sub-core rectangular area, different colors are used when drawing the core rectangular area and the sub-core rectangular area.

[0147] Through the above scheme, this embodiment obtains the historical trajectory of the dynamic target based on historical multi-frame driving data, obtains the future trajectory of the dynamic target based on future multi-frame driving data, and plots the historical trajectory of the dynamic target and the future trajectory of the dynamic target. This allows relevant personnel to intuitively understand the trajectory of dynamic objects in vehicle driving data and understand the dynamic changes of various data in intelligent driving data.

[0148] Based on the first and / or second embodiments of the present application, in the third embodiment of the present application, the same or similar contents as those in the first and second embodiments can be referred to above and will not be described in detail. Figure 3 The step of drawing the lane centerline on the single-frame basic canvas according to the map information and the vehicle positioning data includes steps S310 to S350:

[0149] Step S310: acquiring lane information of the current frame based on the map information, wherein the lane information includes lane centerline information, lane boundary information, and lane type;

[0150] Step S320: obtaining a background area of the lane centerline based on the lane centerline information and the lane boundary information, and assigning a background color to the background area of the lane centerline for drawing;

[0151] Step S330: obtaining lane centerline coordinate data from the lane centerline information, and performing coordinate conversion and pixel conversion on the lane centerline coordinate data in the vehicle coordinate system to obtain lane centerline pixel coordinate data;

[0152] Step S340: extracting lane centerline segment points from the lane centerline pixel coordinate data based on a preset sampling interval;

[0153] Step S350: Draw lane centerline segments on the single-frame basic canvas based on the lane centerline segment points, and assign different colors, start point markers, and end point markers to different types of lane centerlines.

[0154] Specifically, first, the lane information of the current frame is obtained according to the map information of the current frame, wherein the lane information includes lane centerline information, lane boundary information, and lane type.

[0155] Reference Figure 5 The data visualization device first needs to draw the background area of the lane centerline. Specifically, the lane centerline information and lane boundary information are used to determine the lane area where the lane centerline is located, and then the background area of the lane centerline is obtained. At the same time, the background area of the lane centerline is represented by a polygon.

[0156] The coordinates of each point in the polygon are then converted to the vehicle coordinate system centered on the vehicle's center point. Finally, based on the vehicle's pixel position within the image, the polygon coordinates converted to the vehicle coordinate system are further converted to pixel coordinates. Based on these converted pixel coordinates, a specific lane centerline polygon is drawn as the lane centerline background area. Furthermore, different lane centerline background areas are assigned different background colors.

[0157] Then draw the lane centerline. First, convert the coordinates of each coordinate point on the lane centerline into the coordinates of the vehicle coordinate system centered on the vehicle center point, and further convert them into pixel coordinates to obtain the lane centerline pixel coordinate data.

[0158] Lane centerline segment points are then extracted from the lane centerline pixel coordinate data based on a preset sampling interval. The preset sampling interval is a sampling interval predetermined by the relevant personnel based on the lane type. These lane centerline segment points are used to draw multiple lane centerline segments within a single-frame base canvas.

[0159] Lane centerline segments are drawn on a single-frame canvas based on lane centerline segment points, with different colors assigned to different lane centerline types. Additionally, special markers (such as circles) are drawn for the start and end coordinates of the entire lane centerline to identify them as belonging to the same lane centerline and to distinguish between different lane centerlines.

[0160] Through the above scheme, this embodiment obtains the lane information of the current frame based on the map information of the current frame and draws the lane centerline in the current frame. This can fully and detailedly display the lane information in the vehicle intelligent driving data, so as to intuitively understand the dynamic changes of various objects in the intelligent driving data.

[0161] This embodiment is based on the third embodiment of this application. In the fourth embodiment of this application, the same or similar contents as those in the third embodiment can be referred to above and will not be described in detail. Figure 4 The step A06 further includes steps S410 to S460:

[0162] Step S410: acquiring dynamic obstacle information and static obstacle information of the current frame based on the dynamic and static obstacle information, wherein the dynamic obstacle information includes vehicle lane change information, vehicle cut-in information, and preset tag information;

[0163] Step S420: Obtain static obstacle coordinate data from the static obstacle information, and perform coordinate conversion and pixel conversion on the static obstacle coordinate data in the vehicle coordinate system to obtain static obstacle pixel coordinate data;

[0164] Step S430: Drawing an obstacle bounding box for the static obstacle on the single-frame basic canvas according to the pixel coordinate data of the static obstacle, and assigning different colors to different types of static obstacles;

[0165] Step S440: obtaining dynamic obstacle coordinate data from the dynamic obstacle information, and performing coordinate conversion and pixel conversion on the dynamic obstacle coordinate data in the vehicle coordinate system to obtain dynamic obstacle pixel coordinate data;

[0166] Step S450: Drawing an obstacle bounding box for the dynamic obstacle on the single-frame basic canvas according to the pixel coordinate data of the dynamic obstacle, and assigning different colors to different types of dynamic obstacles;

[0167] Step S460: Draw a preset mark for a dynamic obstacle associated with at least one of the vehicle lane change information, the vehicle cut-in information, and the preset label information, and assign a different mark color.

[0168] Specifically, first, based on the dynamic and static obstacle information, the dynamic obstacle information and static obstacle information of the current frame are obtained, where the dynamic obstacle information also includes vehicle lane change information, vehicle cut-in information, and preset label information. Preset label information refers to object label information that has been pre-identified by the driving data processing algorithm, where the driving data processing algorithm is selected and set by the relevant personnel and can be a deep learning algorithm or a machine learning algorithm. Preset label information can be label information generated based on the object type (such as pedestrians, vehicles, animals, street lights, etc.) and the object's dynamic behavior (acceleration, deceleration, braking, etc.).

[0169] Reference Figure 5 Because the outline of obstacles cannot be ignored, it is necessary to obtain the coordinate data of the static obstacle rectangle from the static obstacle information, and first convert the coordinate data of the static obstacle rectangle to the coordinate system of the vehicle, and then further perform pixel conversion to obtain the pixel coordinate data of the static obstacle rectangle.

[0170] According to the pixel coordinate data of the static obstacle rectangle, the obstacle boundary box is drawn on the single-frame basic canvas, and different colors are assigned to different types of static obstacles. For example, key obstacles are drawn with highlighted colors.

[0171] Similarly, the data visualization device needs to obtain the coordinate data of the dynamic obstacle rectangle from the dynamic obstacle information, and first convert the coordinate data of the dynamic obstacle rectangle to the coordinates in the vehicle coordinate system, and then further perform pixel conversion to obtain the pixel coordinate data of the dynamic obstacle rectangle.

[0172] According to the pixel coordinate data of the dynamic obstacle rectangle, the obstacle boundary box of the dynamic obstacle is drawn on the single-frame basic canvas, and different colors are assigned to different types of dynamic obstacles. For example, key obstacles are drawn with highlighted colors.

[0173] Then, for dynamic obstacles associated with at least one of the following information: lane change information, vehicle cut-in information, or preset tag information, a preset marker is drawn and assigned a different color depending on the associated information. Preset markers are markers pre-assigned by relevant personnel and associated with at least one of the following information: lane change information, vehicle cut-in information, or preset tag information.

[0174] In addition to representing the location information of dynamic and static obstacles, the visualization of identifiable ID, speed, driving direction, object length and width, and other information in dynamic and static obstacles is also very important. Data visualization equipment can also use buttons on the visualization interface to display various related object information of dynamic and static obstacles, allowing users to selectively display them in a single-frame basic canvas and vehicle driving visualization video, and use the color of the identifiable ID to indicate whether there is an abnormality in the object, whether the object has changed lanes, and other information.

[0175] Furthermore, this application also includes a visualization support display module for displaying intermediate information or final conclusions during the analysis of each frame of data. This display can be categorized into basic information, frame validity check information, map complexity information, lane change information, and more. This allows for the addition or removal of displayed information based on specific needs.

[0176] Among them, basic information may include: vehicle driving data file name, vehicle driving data version, data frame timestamp, data frame formatting time, map source, distance from the intersection, vehicle coordinates, vehicle speed, vehicle geometric dimensions, number of objects contained in the frame and other information.

[0177] The frame validity detection information includes information on whether the frame is abnormal or normal, and the reason for the frame abnormality.

[0178] Map complexity information refers to the complexity calculation results of the map dimension.

[0179] The vehicle lane change information includes the vehicle number of the vehicle changing lanes, the data frame sequence number of the vehicle lane change, and the vehicle lane binding information before and after the vehicle lane change.

[0180] Through the above scheme, this embodiment obtains dynamic obstacle information and static obstacle information of the current frame based on the dynamic and static obstacle information, and draws the dynamic obstacle information and static obstacle information in the current frame. This can fully and detailedly display the obstacle information in the vehicle intelligent driving data, so as to intuitively understand the relationship between various objects in the intelligent driving data.

[0181] This application also provides a data visualization device, please refer to Figure 6 , the data visualization device comprises:

[0182] An acquisition module 10 is configured to acquire vehicle driving data, wherein the vehicle driving data includes a plurality of single-frame driving data;

[0183] The data visualization module 20 is configured to perform data visualization on the plurality of single-frame driving data in the vehicle coordinate system based on preconfigured visualization parameters to obtain a plurality of single-frame visualization data;

[0184] The splicing module 30 is used to splice the plurality of single-frame visualization data to obtain a vehicle driving visualization video.

[0185] The data visualization device provided in this application utilizes the data visualization method described in the aforementioned embodiments, resolving the technical issues of existing methods, which lack flexibility and are unable to fully and comprehensively present multiple data points within intelligent driving data. Compared to the prior art, the data visualization device provided in this application offers the same beneficial effects as the data visualization method described in the aforementioned embodiments. Other technical features of the data visualization device are the same as those disclosed in the aforementioned embodiments and are not further elaborated here.

[0186] The present application provides a data visualization device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the data visualization method in the above-mentioned embodiment example.

[0187] Reference below Figure 7 , which shows a schematic diagram of the structure of a data visualization device suitable for implementing the embodiments of the present application. The data visualization device in the embodiments of the present application may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 7 The data visualization device shown is merely an example and should not limit the functions and scope of use of the embodiments of the present application.

[0188] like Figure 7As shown, the data visualization device may include a processing device 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes based on programs stored in a read-only memory (ROM) 1002 or programs loaded from a storage device 1003 into a random access memory (RAM) 1004. RAM 1004 also stores various programs and data required for the operation of the data visualization device. Processing device 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input devices 1007 including, for example, a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage device 1003 including, for example, a magnetic tape, hard disk, etc.; and communication devices 1009. Communication device 1009 can allow the data visualization device to communicate with other devices wirelessly or wired to exchange data. Although the figure shows a data visualization device with various systems, it should be understood that it is not required to implement or have all of the systems shown. More or fewer systems can be implemented or have alternatively.

[0189] In particular, according to the embodiments disclosed in the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program comprising program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0190] The data visualization device provided in this application utilizes the data visualization method described in the aforementioned embodiment, resolving the technical issues of existing methods, which lack flexibility and are unable to fully and comprehensively present multiple data points within intelligent driving data. Compared to the prior art, the data visualization device provided in this application offers the same beneficial effects as the data visualization method described in the aforementioned embodiment. Other technical features of this data visualization device are the same as those disclosed in the aforementioned embodiment and are not further elaborated upon here.

[0191] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0192] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0193] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer program) stored thereon, wherein the computer-readable program instructions are used to execute the data visualization method in the above-mentioned embodiment.

[0194] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0195] The computer-readable storage medium may be included in the data visualization device, or may exist independently without being assembled into the data visualization device.

[0196] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by a data visualization device, the data visualization device is enabled to: obtain vehicle driving data, wherein the vehicle driving data includes a plurality of single-frame driving data; perform data visualization on the plurality of single-frame driving data in a vehicle coordinate system based on preconfigured visualization parameters to obtain a plurality of single-frame visualization data; and splice the plurality of single-frame visualization data to obtain a vehicle driving visualization video.

[0197] Computer program code for performing the operations of the present application may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages such as Java, Smalltalk, C++, and conventional procedural programming languages such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on the remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0198] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, program segment or a part of code, and the module, program segment or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs the specified function or operation, or can be implemented by a combination of dedicated hardware and computer instructions.

[0199] The modules described in the embodiments of the present application may be implemented in software or hardware, wherein the name of a module does not necessarily limit the unit itself.

[0200] The computer-readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the aforementioned data visualization method. This computer-readable storage medium can address the technical issues of existing methods, which lack flexibility and are unable to fully and comprehensively present multiple data items in intelligent driving data. Compared to the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the data visualization method provided in the aforementioned embodiments, and are not further elaborated here.

[0201] An embodiment of the present application provides a computer program product, including a computer program, which implements the steps of the above-mentioned data visualization method when executed by a processor.

[0202] The computer program product provided in this application addresses the technical issues of existing methods, which lack flexibility and are unable to fully and comprehensively present multiple data points within intelligent driving data. Compared to the prior art, the beneficial effects of the computer program product provided in the embodiments of this application are similar to those of the data visualization methods provided in the aforementioned embodiments, and are not further elaborated here.

[0203] The above are only preferred embodiments of the present application and do not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent processing scope of the present application.

Claims

1. A data visualization method, characterized in that: The method comprises: Acquiring vehicle driving data, wherein the vehicle driving data includes a plurality of single-frame driving data; Based on preconfigured visualization parameters, the plurality of single-frame driving data are visualized in a vehicle coordinate system to obtain a plurality of single-frame visualization data; The plurality of single-frame visualization data are spliced together to obtain a vehicle driving visualization video.

2. The method according to claim 1, wherein The preconfigured visualization parameters include a data frame validity flag, image pixels, and drawing elements. The step of visualizing the plurality of single-frame driving data in a vehicle coordinate system based on the preconfigured visualization parameters to obtain the plurality of single-frame visualization data includes: Extracting vehicle information, map information, and dynamic and static obstacle information from the single-frame driving data; Drawing a single-frame basic canvas according to the data frame validity identifier and the image pixels; Determining a drawing target on the single-frame basic canvas according to the drawing element, wherein the drawing target is at least one of a target vehicle, a lane centerline, a lane boundary, a road boundary, a crosswalk, and a dynamic or static obstacle; Obtaining the vehicle positioning data according to the vehicle information, and drawing the target vehicle on the single-frame basic canvas; Drawing lane centerlines, lane boundaries, road boundaries, and crosswalks on the single-frame base canvas based on the map information and the vehicle positioning data; Drawing the dynamic and static obstacles on the single-frame basic canvas according to the dynamic and static obstacle information and the vehicle positioning data; The drawn target vehicle, lane centerline, lane boundary, road boundary, crosswalk, dynamic and static obstacles are integrated through the single-frame basic canvas to obtain single-frame visualization data.

3. The method according to claim 2, wherein The preconfigured visualization parameters include a historical frame number, a future frame number, and a zoom factor. The drawn target also includes a dynamic target trajectory. After the steps of obtaining the vehicle positioning data based on the vehicle information and drawing the target vehicle on the single-frame basic canvas, the method further includes: According to the historical frame number and the future frame number, obtaining historical multi-frame driving data and future multi-frame driving data of corresponding frame numbers; Extracting the dynamic target trajectory coordinates from the historical multi-frame driving data and forming a historical trajectory point sequence of several dynamic targets; Extracting the dynamic target trajectory coordinates from the future multi-frame driving data and forming a sequence of future trajectory points of multiple dynamic targets; According to the vehicle positioning data, the coordinates of each trajectory point in the historical trajectory point sequence and the future trajectory point sequence are converted into trajectory point coordinates in the vehicle coordinate system to obtain the historical trajectory point sequence and the future trajectory point sequence in the vehicle coordinate system; Based on the historical trajectory point sequence and the future trajectory point sequence in the vehicle coordinate system, the pixel coordinates of the historical trajectory point sequence and the future trajectory point sequence are calculated according to the scaling factor and the vehicle positioning data; According to the pixel coordinates of the historical trajectory point sequence and the future trajectory point sequence, the trajectory of the dynamic target is drawn on the single-frame basic canvas, and different types of trajectories of different dynamic targets are given different colors.

4. The method according to claim 3, wherein The preconfigured visualization parameters include a core area size and a secondary core area size, and the drawing target also includes a core area and a secondary core area. After the steps of obtaining the vehicle positioning data based on the vehicle information and drawing the target vehicle on the single-frame basic canvas, the method further includes: Calculating vertex coordinates of the core area rectangle and the sub-core area rectangle in the vehicle coordinate system based on the core area size, the sub-core area size, and the vehicle positioning data; Based on the vertex coordinates of the core rectangular area and the sub-core rectangular area in the vehicle coordinate system, the pixel coordinates of the core rectangular area and the sub-core rectangular area are calculated according to the scaling factor and the vehicle positioning data; According to the pixel coordinates of the core rectangular area and the sub-core rectangular area, the core rectangular area and the sub-core rectangular area are interpolated and drawn respectively on the single-frame basic canvas, and different colors are given to the core rectangular area and the sub-core rectangular area.

5. The method according to claim 2, wherein The step of drawing the lane centerline on the single-frame basic canvas according to the map information and the vehicle positioning data includes: According to the map information, obtaining lane information of the current frame, wherein the lane information includes lane centerline information, lane boundary information, and lane type; Obtaining a background area of the lane centerline according to the lane centerline information and the lane boundary information, and assigning a background color to the background area of the lane centerline for drawing; Obtaining lane centerline coordinate data from the lane centerline information, and performing coordinate conversion and pixel conversion on the lane centerline coordinate data in a vehicle coordinate system to obtain lane centerline pixel coordinate data; Extracting lane centerline segment points from the lane centerline pixel coordinate data based on a preset sampling interval; According to the lane centerline segment points, lane centerline segments are drawn on the single-frame basic canvas, and different types of lane centerlines are given different colors, as well as different starting point identifiers and end point identifiers.

6. The method according to claim 2, wherein The step of drawing the dynamic and static obstacles on the single-frame basic canvas according to the dynamic and static obstacle information and the vehicle positioning data includes: According to the dynamic and static obstacle information, dynamic obstacle information and static obstacle information of the current frame are obtained, wherein the dynamic obstacle information includes vehicle lane change information, vehicle cut-in information, and preset label information; Obtaining static obstacle coordinate data from the static obstacle information, and performing coordinate conversion and pixel conversion on the static obstacle coordinate data in a vehicle coordinate system to obtain static obstacle pixel coordinate data; Drawing an obstacle bounding box for the static obstacle on the single-frame basic canvas according to the pixel coordinate data of the static obstacle, and assigning different colors to different types of static obstacles; Obtaining dynamic obstacle coordinate data from the dynamic obstacle information, and performing coordinate conversion and pixel conversion on the dynamic obstacle coordinate data in a vehicle coordinate system to obtain dynamic obstacle pixel coordinate data; Drawing an obstacle bounding box for the dynamic obstacle on the single-frame basic canvas according to the pixel coordinate data of the dynamic obstacle, and assigning different colors to different types of dynamic obstacles; A preset logo is drawn for a dynamic obstacle associated with at least one of vehicle lane change information, vehicle cut-in information, and preset label information, and is assigned a different logo color.

7. The method according to any one of claims 2 to 6, characterized in that The preconfigured visualization parameters include a map adjustment factor, and the method further includes: Adjusting the image pixels according to the image adjustment factor to obtain adjusted image pixels; The single-frame basic canvas is adjusted based on the adjusted image pixels.

8. A data visualization device, characterized in that: The data visualization device comprises: An acquisition module, configured to acquire vehicle driving data, wherein the vehicle driving data includes a plurality of single-frame driving data; A data visualization module is used to visualize the plurality of single-frame driving data in a vehicle coordinate system based on preconfigured visualization parameters to obtain a plurality of single-frame visualization data; The splicing module is used to splice the plurality of single-frame visualization data to obtain a vehicle driving visualization video.

9. A data visualization device, characterized in that: The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the data visualization method according to any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the data visualization method according to any one of claims 1 to 7 are implemented.

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