Method for intelligent cabin atmosphere lamp to perceive extracabinevironment changes

Abstract

The application discloses a method for sensing changes of extracab environment by intelligent cab ambient light, which comprises the following steps: S1, collecting the video of the motor vehicle outside scene by the camera; S2, predicting the synchronous images of both sides of the motor vehicle according to the video; S3, dividing the synchronous images into a plurality of sub-images along the horizontal direction according to the number of lamp beads of the ambient light strip; S4, obtaining the color average value of each sub-image; S5, synchronously displaying the color average value of each sub-image on the lamp beads. The beneficial effects of the application are as follows: the algorithm in the host computer obtains the data of the camera, and then performs prediction or direct acquisition by comparing the data obtained by the camera in a period of time. Then, the color average value is obtained by segmentation, and then the lamp beads on the ambient light strip are dispatched to display the related color, so that the scenes on both sides are simulated to create a more immersive driving atmosphere.

Description

Technical Field

[0001] This invention relates to the field of intelligent vehicles, and more particularly to a method for intelligent cabin ambient lighting to sense changes in the external environment. Background Technology

[0002] With the booming development of electric vehicles, the configuration of large displays, high-performance main units, and ambient lighting in small passenger cars has gradually become commonplace. The integration of smart cockpits into electric vehicles represents a future trend. However, due to a lack of sensor and algorithm support, existing ambient lighting in smart cockpits can only offer basic lighting effects. The external environment of a smart cockpit typically changes with space and time, and visual cameras usually only capture the environment directly in front of the cockpit. Current methods cannot allow ambient lighting to predict and follow environmental changes, resulting in a rigid and unrealistic approach that fails to provide a good ambiance for users. Summary of the Invention

[0003] The purpose of this invention is to provide a method for intelligent cockpit ambient lighting to sense changes in the external environment, thus enhancing the driving experience.

[0004] To achieve the above objectives, the present invention provides the following technical solution:

[0005] A method for sensing changes in the external environment through ambient lighting in an intelligent cockpit, wherein the intelligent cockpit is installed inside a motor vehicle, and the intelligent cockpit is equipped with a main unit and several ambient light strips arranged on both sides of the intelligent cockpit. Each ambient light strip includes several LEDs and at least one camera for capturing images of the scene outside the intelligent cockpit. The camera and the ambient light strips are electrically and data connected to the main unit. The method includes the following steps:

[0006] S1. Capture video of the exterior of the vehicle using the camera;

[0007] S2. Predict synchronized images of both sides of the motor vehicle based on the video;

[0008] S3. Divide the synchronized image into several sub-images along the horizontal direction according to the number of LED beads in the ambient light strip;

[0009] S4. Calculate the average color value of each sub-image;

[0010] S5. The average color value of each of the sub-images is synchronously displayed on the LED beads.

[0011] Preferably, step S2 includes the following steps:

[0012] S2-1. Acquire several scene images from the video at preset time intervals;

[0013] S2-2. Obtain the outlines of objects in the scene image according to the edge detection algorithm, and then obtain each object in the scene image;

[0014] S2-3. Calculate the relative velocity of each object based on its position in different scene images;

[0015] S2-4. Determine the positions of all the objects in the synchronized image to obtain the predicted synchronized image.

[0016] Preferably, the edge detection algorithm is the Canny edge detection algorithm.

[0017] Preferably, step S4 includes the following steps:

[0018] S4-1. Select several sampling points evenly in each of the sub-images;

[0019] S4-2. Obtain the RGB value of the color of each sampling point;

[0020] S4-3. Accumulate the R, G, and B values ​​of all the sampling points and calculate the average value;

[0021] S4-4. Use the obtained average values ​​as R, G, and B values ​​respectively to obtain the color average.

[0022] Preferably, the camera is a wide-angle camera positioned directly in front of the vehicle.

[0023] This invention also provides another method for intelligent cockpit ambient lighting to sense changes in the external environment. The intelligent cockpit is installed inside a motor vehicle. The intelligent cockpit includes a main unit and several ambient light strips located on both sides of the intelligent cockpit. Each ambient light strip includes several LEDs and two cameras for capturing images of the scenery on the sides of the intelligent cockpit. The cameras and ambient light strips are electrically and data-connected to the main unit. The method is characterized by the following steps:

[0024] S11. Capture video of the scenery on the side of the vehicle using the camera;

[0025] S12. Periodically extract images from the video;

[0026] S13. Divide the image into several sub-images along the horizontal direction according to the number of LED beads in the ambient light strip;

[0027] S14. Calculate the average color value of each sub-image;

[0028] S15. The average color value of each of the sub-images is synchronously displayed on the LED beads.

[0029] The beneficial effects of this invention are as follows: The algorithm within the host unit predicts or directly acquires data from the camera by comparing the data obtained by the camera over a period of time. It then segments the data, calculates the average color value, and schedules the LEDs on the ambient lighting strip to display the relevant colors, thereby simulating the scenery on both sides and creating a more immersive driving atmosphere. Attached Figure Description

[0030] Figure 1 This is a schematic diagram illustrating the principle of the present invention;

[0031] Figure 2 This is a schematic diagram of an ambient light strip display, using streetlights as an example.

[0032] Figure 3 This is a schematic diagram of an ambient light strip display, using a street light as an example. Detailed Implementation

[0033] The technical solution of this patent will be further described in detail below with reference to specific embodiments.

[0034] In the description of this invention, it should be noted that the terms "inner", "outer", "upper", "lower", "horizontal", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0035] like Figures 1 to 3 As shown, this invention provides a method for intelligent cockpit ambient lighting to sense changes in the external environment. The intelligent cockpit is installed inside a vehicle, and includes a main unit and several ambient light strips positioned on both sides of the cockpit. Each ambient light strip includes several LEDs, and a wide-angle camera positioned at the front of the vehicle. The camera and ambient light strips are electrically and data-connected to the main unit.

[0036] This invention includes the following steps:

[0037] S1. Capture video of the exterior of a motor vehicle using a camera;

[0038] S2. Predict synchronized images of both sides of the vehicle based on the video;

[0039] S3. Divide the synchronized image into several sub-images along the horizontal direction according to the number of LED beads in the ambient light strip;

[0040] S4. Calculate the average color value of each sub-image;

[0041] S5. Display the average color value of each sub-image synchronously on the LED beads.

[0042] Step S2 includes the following steps:

[0043] S2-1. Acquire several scene images from the video at preset time intervals;

[0044] S2-2. Obtain the outlines of objects in the scene image based on the edge detection algorithm, and then obtain each object in the scene image;

[0045] S2-3. Calculate the relative velocity of each object based on its position in different scene images;

[0046] S2-4. Determine the positions of all objects in the synchronized image to obtain the predicted synchronized image.

[0047] Step S4 includes the following steps:

[0048] S4-1. Select several sampling points evenly in each sub-image;

[0049] S4-2. Obtain the RGB value of the color at each sampling point;

[0050] S4-3. Accumulate the R, G, and B values ​​of all sampling points and calculate the average value;

[0051] S4-4. Use the obtained average values ​​as R, G, and B values ​​respectively to obtain the color average.

[0052] The edge detection algorithm of this invention adopts the Canny edge detection algorithm (of course, other mature edge detection algorithms can also be used). The algorithm principle of the Canny edge detection algorithm is as follows:

[0053] 1. Gaussian blur, simply put, is when the color of each pixel in an image is the average of the colors of all other pixels within an n*n range centered on that pixel (n is a specified value; the larger n is, the higher the blur, but a larger range makes it easier to miss some weak contours). The purpose of this step is mainly to remove noise, because noise is more easily identified as contours, and it is necessary to reduce this false recognition.

[0054] 2. Calculate the gradient magnitude and direction, which simply means calculating the direction of the image contour.

[0055] 3. Non-maximum suppression: The contours obtained in the first two steps are usually more than one pixel wide, so some filtering is required. Essentially, this is also a kind of blurring algorithm.

[0056] 4. To remove edge noise, the Canny algorithm is implemented using two algorithms: double threshold and hysteresis boundary tracking.

[0057] 4.1) Dual thresholds:

[0058] Typical edge detection algorithms use a threshold to filter out small gradient values ​​caused by noise or color changes, while retaining large gradient values. That is, a high threshold and a low threshold are used to distinguish edge pixels. If the gradient value of an edge pixel is greater than the high threshold, it is considered a strong edge. If the gradient value is less than the high threshold but greater than the low threshold, it is marked as a weak edge. Pixels with gradient values ​​less than the low threshold are suppressed.

[0059] 4.2) Lag Boundary Tracking:

[0060] Strong edge points can be considered true edges. Weak edge points, on the other hand, may be true edges, or they may be caused by noise or color variations. For accurate results, weak edge points caused by the latter should be removed. It is generally assumed that weak edge points caused by true edges are connected to strong edge points, while weak edge points caused by noise are not. The so-called hysteresis boundary tracking algorithm examines the 8-connected neighborhood pixels of a weak edge point; if a strong edge point exists, the weak edge point is considered a true edge and retained.

[0061] by Figure 2 and Figure 3 Taking the streetlights shown as an example, the positions of the streetlights are different. Figure 2 This is a forecast graph for the previous time point. Figure 3 This is a forecast graph for the next time point. See also... Figure 1 We know that the camera can only capture objects in front of it and has blind spots. When the streetlight moves to the side of the vehicle, in order to synchronously project the streetlight image captured by the camera onto the LED, we first need to predict the position of the streetlight, which requires calculating the speed of the streetlight.

[0062] The video is composed of multiple images. We acquire these images at certain intervals and use edge detection algorithms to identify the outlines of streetlights and their emitted light. The outlines of the same object do not change much between consecutive images, so we can determine the position of the object in the consecutive images, that is, the displacement of the object within the sampling period. In this way, we can calculate its speed.

[0063] Once the speed of the streetlights relative to the vehicle is known, it is possible to predict when they will move into the blind spots on both sides, which means that the images of the vehicle on both sides at that time point can be "drawn" by prediction.

[0064] If the ambient light strip has 5 LEDs, the predicted image is divided into 5 equal parts along the horizontal direction, the average color of each part is calculated, and the average color is displayed on the corresponding LED.

[0065] The color average is calculated by uniformly selecting points on the sub-image and representing the color of each point in RGB format. R represents red, G represents green, and B represents blue; all are natural numbers less than 255. The sum of all R, G, and B values, divided by the number of selected points, yields the average of these values, which together form the RGB color average.

[0066] This invention also provides another method for intelligent cockpit ambient lighting to sense changes in the external environment. The intelligent cockpit is installed inside a motor vehicle. The intelligent cockpit includes a main unit and several ambient light strips positioned on both sides of the intelligent cockpit. Each ambient light strip includes several LEDs and two cameras for capturing images of the scenery on the sides of the intelligent cockpit. The cameras and ambient light strips are electrically and data-connected to the main unit. The method is characterized by the following steps:

[0067] S11. Capture video of the scenery on the side of the vehicle using a camera;

[0068] S12. Periodically extract images from the video;

[0069] S13. Divide the image into several sub-images along the horizontal direction according to the number of LED beads in the ambient light strip;

[0070] S14. Calculate the average color value of each sub-image;

[0071] S15. The average color value of each sub-image is synchronously displayed on the LED beads.

[0072] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for sensing changes in the external environment using ambient lighting in a smart cockpit, wherein the smart cockpit is installed inside a motor vehicle, a main unit is installed inside the smart cockpit, several ambient light strips are arranged on both sides of the smart cockpit, each ambient light strip includes several LEDs, at least one camera is used to capture the scenery outside the smart cockpit, and the camera and the ambient light strips are electrically and data connected to the main unit; characterized in that, The method includes the following steps: S1. The camera is a wide-angle camera located directly in front of the vehicle to capture video of the exterior of the vehicle. S2. Predict synchronized images of both sides of the motor vehicle based on the video; S3. Divide the synchronized image into several sub-images along the horizontal direction according to the number of LED beads in the ambient light strip; S4. Calculate the average color value of each sub-image; S5. The average color value of each of the sub-images is synchronously displayed on the LED beads; Step S2 includes the following steps: S2-1. Acquire several scene images from the video at preset time intervals; S2-2. Obtain the outlines of objects in the scene image according to the edge detection algorithm, and then obtain each object in the scene image; S2-3. Calculate the relative velocity of each object based on its position in different scene images; S2-4. Determine the positions of all objects in the synchronized image to obtain the predicted synchronized image.

2. The method for sensing changes in the external environment in an intelligent cockpit ambient light according to claim 1, characterized in that, The edge detection algorithm is the Canny edge detection algorithm.

3. The method for sensing changes in the external environment in an intelligent cockpit ambient light according to claim 1, characterized in that, Step S4 includes the following steps: S4-1. Select several sampling points evenly in each of the sub-images; S4-2. Obtain the RGB value of the color of each sampling point; S4-3. Accumulate the R, G, and B values ​​of all the sampling points and calculate the average value; S4-4. Use the obtained average values ​​as R, G, and B values ​​respectively to obtain the color average.

Images