A multi-channel control method for a chassis atmosphere lamp

Abstract

The application relates to the technical field of chassis atmosphere lamp control, and discloses a multi-channel control method of a chassis atmosphere lamp, which comprises the following steps: acquiring a vehicle state, starting a sensing module to acquire a vehicle external environment according to the vehicle state, generating an environment parameter, comparing the environment parameter with color sensing parameters in historical data in sequence, acquiring a vehicle parameter, acquiring an adjusting parameter, detecting an environment below a chassis by using a detection module, acquiring a chassis environment image, recognizing a ground environment and an interference factor below the chassis by using OpenCV according to the chassis environment image and the environment parameter, and acquiring a ground environment refractive index and an external environment refractive index. The adjusting parameter best matched with the current environment is acquired through extraction and fusion, so that the light emitted by the atmosphere lamp is not conspicuous, can be perfectly matched with the current environment, and can highlight the car light, so that the overall atmosphere is improved, and the aesthetic feeling is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of chassis ambient light control, in particular to a multi-channel control method of a chassis ambient light. BACKGROUND

[0002] As an important configuration for improving the atmosphere inside the car, the car ambient light has been continuously innovated in design and function in recent years. Its core functions include personalized decoration, support for single-color, multi-color, breathing rhythm and comfort enhancement, such as warm color system to enhance the sense of warmth, and cold color system to strengthen the sense of technology.

[0003] The existing chassis ambient light can only emit a relative light color according to the manually selected color. This light color is only a single selection by manual, without reference to the parking environment, vehicle color, parking light environment, etc. When various interference environmental factors are intertwined, the light effect of the chassis ambient light will be out of place, which cannot improve the aesthetics, and thus the chassis ambient light has no any use effect. SUMMARY

[0004] The present application provides a multi-channel control method of a chassis ambient light, which has the beneficial effect of good atmosphere effect, and solves the problem of the chassis ambient light emitting light effect that is out of place and cannot improve the aesthetics mentioned in the background.

[0005] The present application provides the following technical solution: a multi-channel control method of a chassis ambient light, comprising the following steps:

[0006] Obtain the vehicle state, start the perception module to obtain the vehicle external environment according to the vehicle state, and generate the environment parameter;

[0007] Compare the environment parameter with the color perception parameter in the historical data in sequence and obtain the vehicle parameter to generate the adjustment parameter;

[0008] Detect the environment below the chassis by using the detection module, obtain the chassis environment image, and identify the ground environment and the interference factors below the chassis by using OpenCV based on the chassis environment image and the environment parameter;

[0009] Obtain the refractive index of the ground environment and the refractive index of the external environment, and combine the current lamp body data to optimize the adjustment parameter;

[0010] Import the optimized adjustment parameter generation program into the lamp body MCU, and control the lamp body to execute the change of the light parameter according to the generation program by using the lamp body MCU.

[0011] As an optional solution of the multi-channel control method of the chassis ambient light, the interference factors below the chassis are identified and marked by coordinates.

[0012] The marker coordinates are combined to generate a lamp body flashing guide parameter, the flashing guide parameter is used to generate a guide program, and the guide program is imported into the lamp body MCU:

[0013] When the driver approaches the vehicle, the lamp body MCU executes the guide program to control the lamp body to issue a guide warning.

[0014] As an optional solution of the multi-channel control method of the chassis ambient light, the driving habits of the driver are obtained, and the exit turning radius of the driving vehicle is predicted and calculated in combination with the current road condition information.

[0015] The driving area is calculated according to the exit turning radius of the driving vehicle.

[0016] It is determined whether there is an interference factor in the driving area of the vehicle, and when there is an interference factor, the interference factor is detected, identified and marked by the detection module, so as to optimize the guide program.

[0017] As an optional solution of the multi-channel control method of the chassis ambient light, the ground refractive index and the vehicle external environment refractive index are identified and obtained according to the ground environment and the external environment.

[0018] The vehicle parameters are obtained.

[0019] The current lamp body data is optimized and adjusted according to the ground refractive index, the vehicle external environment refractive index and the vehicle parameters, and the adjustment parameters are adjusted again.

[0020] As an optional solution of the multi-channel control method of the chassis ambient light, the environmental parameters include vehicle parking scene parameters, color parameters of the vehicle parking scene, and position parameters of the vehicle parking scene.

[0021] The perception module includes a camera, a color sensor and a GPS.

[0022] The camera is used to shoot the vehicle chassis scene and the vehicle external scene, and shooting pictures are obtained, which are identified and analyzed by OpenCV.

[0023] The color sensor is used to perceive the vehicle chassis scene and the vehicle external scene, and the color parameters of the vehicle parking scene are generated by combining OpenCV identification and analysis data calibration.

[0024] The GPS is used to obtain the position parameters of the vehicle parking scene, and the color parameters of the vehicle parking scene are optimized by the position parameters of the vehicle parking scene.

[0025] As an optional solution of the multi-channel control method of the chassis ambient light, the method for obtaining the vehicle state comprises:

[0026] acquiring a vehicle reverse light on signal;

[0027] acquiring a vehicle reverse light on signal;

[0028] determining whether a destination is about to be reached in combination with vehicle navigation;

[0029] wherein when the vehicle speed is 0-10km / h, the vehicle reverse light is on, and the distance to the destination is less than 0-5m, the vehicle state is parking at the destination.

[0030] As an optional solution of the multi-channel control method of the chassis ambient light, the method further comprises establishing an environment model according to the environment parameter and vehicle information;

[0031] acquiring a color perception parameter matching the environment parameter in historical data;

[0032] extracting and fusing the color perception parameter by an algorithm to import the environment model, so as to determine a rendering environment model and generate a plurality of rendering parameters;

[0033] establishing a scoring table, and when the rendering parameter score is less than 60, re-computing the extracted color perception parameter.

[0034] As an optional solution of the multi-channel control method of the chassis ambient light, the method further comprises establishing an environment model according to the environment parameter and vehicle information;

[0035] realizing vehicle sensing of a driver based on radio frequency identification;

[0036] when the driver carries a mobile phone close to the vehicle, receiving a signal emitted by the mobile phone through a Bluetooth module;

[0037] the Bluetooth module transmits the received signal to a vehicle ECU for verification, and after verification, the vehicle ECU executes a generation program to control the light body to emit light.

[0038] The present application has the following advantages:

[0039] 1. The multi-channel control method of the chassis ambient light extracts and fuses an adjustment parameter best adapted to the current environment to make the light emitted by the ambient light not conspicuous, perfectly integrate the current environment, and highlight the vehicle light, so as to improve the overall atmosphere and enhance the aesthetic feeling.

[0040] 2. The multi-channel control method of the chassis ambient light, by marking the coordinate address, since the chassis ambient light surrounds a circle around the vehicle chassis, it is roughly divided into four directions of front, back, left and right, and the disturbance coordinate address is determined to belong to one of the four directions, and the light body flickering guide parameter is generated, that is, the ambient light starts to flicker from the driving position clockwise and counterclockwise to the marked coordinate address, so as to guide the driver to find the disturbance and eliminate it in time, reduce the risk of vehicle operation, and realize the warning and guiding function of the ambient light.

[0041] 3. The multi-channel control method of the chassis ambient light, by combining the image captured by the camera with the current road map, the turning radius driving area is marked and calculated, and the captured image is recognized by OpenCV, so as to determine the hidden risks in the turning radius driving area. When there is a risk in the turning radius driving area, the ambient light is used to guide the driver to eliminate the risk, so as to further improve the guiding and warning effect. BRIEF DESCRIPTION OF DRAWINGS

[0042] Figure 1 The schematic diagram of the overall system architecture of the present application.

[0043] Figure 2 The schematic diagram of the overall operation process of the present application.

[0044] Figure 3 The local operation code of color fusion of the present application.

[0045] Figure 4 The local operation code of color contrast selection of the present application. DETAILED DESCRIPTION

[0046] The technical solutions in the embodiments of the present application will be described clearly and completely in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0047] Embodiment 1

[0048] Please refer to Figures 1-4 , and discloses a multi-channel control method of a chassis ambient light, comprising the following steps:

[0049] Obtain the vehicle state, start the perception module to obtain the vehicle external environment according to the vehicle state, and generate the environment parameter;

[0050] Compare the environment parameter with the color perception parameter in the historical data in sequence and obtain the vehicle parameter to generate the adjustment parameter;

[0051] The detection module is used to detect the environment under the chassis, obtain the chassis environment image, and identify the ground environment and interference factors under the chassis through the chassis environment image and environmental parameters using OpenCV;

[0052] The refractive index of the ground environment and the refractive index of the external environment are obtained, and the current lamp body data is combined to optimize the adjustment parameters;

[0053] The optimized adjustment parameters are generated into the lamp body MCU, and the lamp body is controlled by the lamp body MCU to execute the change of the light parameters according to the generated program.

[0054] According to Figure 1 As shown in the figure, the vehicle speed is perceived by GPS, and the signal of whether the vehicle reverse light is on is obtained, and whether the destination is about to be reached is determined in combination with the vehicle navigation. When the vehicle speed is 0-10 km / h, the vehicle reverse light is on, and the distance to the destination is less than 0-5 m, that is, the vehicle state is about to reach the destination and stop, at this time the sensing module is powered on and starts to work, wherein the sensing module includes a camera, a color sensor and a GPS;

[0055] Two cameras are used to shoot the vehicle chassis scene and the vehicle external scene, and the shooting pictures are obtained, which are analyzed by OpenCV recognition, one of which is installed on the chassis atmosphere lamp, and the other is the camera for vehicle automatic driving. Among them, the chassis atmosphere lamp is electrically connected with the vehicle;

[0056] The color sensor is used to perceive the vehicle chassis scene and the vehicle external scene, and the color parameters of the vehicle parking scene are generated by combining OpenCV recognition and analysis data calibration;

[0057] The GPS is used to obtain the position parameters of the vehicle parking scene, and the color parameters of the vehicle parking scene are optimized through the position parameters of the vehicle parking scene;

[0058] The pictures taken are analyzed by OpenCV recognition to determine the situation near the vehicle and the road surface, that is, the vehicle road situation and the object situation existing in the vehicle surrounding environment. For example, the vehicle road situation is asphalt road, and nails, potholes, etc. appear in the parking road area, and the objects existing in the vehicle surrounding environment include street lamps, shop neon lights, flowerpots, etc. The taken pictures are recognized by OpenCV, and the GPS positioning coordinates are combined;

[0059] It is worth noting that OpenCV is an open-source, cross-platform computer vision library initiated and continuously maintained by Intel, widely used in image processing, video analysis, object detection, etc. Core functions include image processing, video analysis, and machine learning and deep learning. Among them, image processing includes filtering, edge detection, morphological operations, etc. Video analysis includes support for motion detection, object tracking, and real-time video capture. Machine learning and deep learning include the use of integrated traditional algorithms such as SVM, KNN, and deep learning frameworks such as TensorFlow, PyTorch;

[0060] According to the identified vehicle road conditions and the object conditions existing in the vehicle surroundings, the color sensor perceives the vehicle chassis scene and the vehicle external scene, and combines OpenCV to recognize and analyze the data to calibrate the color parameters of the vehicle parking scene. For example,

[0061] Color sensor readings are:

[0062] The first sensor, chassis environment: R=130, G=145, B=160, Lux=90, CCT=6200K;

[0063] The second sensor, external environment: R=135, G=150, B=165, Lux=100, CCT=6300K;

[0064] Chassis image: average RGB value is (128, 142, 157), main color is (130, 144, 159);

[0065] External environment image: average RGB value is (132, 148, 163), main color is (134, 150, 165);

[0066] Sensor data is given a weight, for example 0.7, and image data is given a weight, for example 0.3, and the fused color parameters are calculated as follows:

[0067] Fused RGB value = (chassis RGB * 0.7 + external RGB * 0.3);

[0068] Chassis: ;

[0069] Among them, the color temperature is more accurate due to the sensor, and the average value of the sensor readings is directly used as CCT fused =(6200+6300) / 2=6250K;

[0070] In summary, it can be known that RGB is (131, 146, 161), color temperature is 6250K, and illumination level is Bright. Based on the Lux value, this is the color parameter of the current environment parameter, which will be stored in the historical database, and based on the historical data, the most suitable ambient light color scheme for the current environment is generated through data fusion and intelligent selection strategy. Specifically, it includes:

[0071] The current calibrated color parameter is compared with the records in the historical database. The Euclidean distance algorithm is used to comprehensively compare the proximity of the RGB value and color temperature of the current scene with the historical records, and several similar scenes are selected as references. The ambient light parameters recommended in the matched historical records are weighted and fused, and the weight is dynamically adjusted according to the scene similarity. The higher the similarity of the scene, the higher the weight of the recommended light, and finally an integrated RGB value is output, and the style is judged according to the color characteristics, such as "cool tone, warm tone, etc. Form the final ambient light adjustment parameter;

[0072] Among them, the similar scene matching algorithm based on Euclidean distance includes:

[0073] Feature vector construction, the core color parameters of each scene record include:

[0074] RGB three-channel value, reflecting the overall color tone of the environment, such as blue and warm;

[0075] Color temperature (CCT, unit: K), indicating the cold and warm properties of the light source, such as 6500K for daylight white and 3000K for warm yellow;

[0076] To unify the dimension, the CCT value is divided by 100 for normalization to avoid the distance calculation imbalance caused by the value much larger than RGB.

[0077] Therefore, each scene can be represented as a four-dimensional feature vector:

[0078] ;

[0079] For example, the current scene: RGB=(131, 146, 161), CCT=6250K to vector (131, 146, 161, 62.5);

[0080] Historical record: RGB=(130, 144, 159), CCT=6200K to vector (130, 144, 159, 62.0);

[0081] For the current scene vector And each historical record vector Calculate its Euclidean distance:

[0082] ;

[0083] The smaller the distance d, the more similar the color environment of the two scenes;

[0084] Traverse the entire historical database, calculate the distance between the current scene and each record, and set a similarity threshold, for example, threshold = 120. All historical records with a distance less than the threshold are considered "similar scenes" and are sorted by distance from small to large to form a candidate set,

[0085] For example: distance < 50: highly similar, highest weight,

[0086] Distance 50~100: moderate similarity, normal reference,

[0087] Distance > 100: large difference, not included in the recommendation;

[0088] It also includes establishing an environment model according to the environmental parameters and vehicle information;

[0089] Obtain the color perception parameters of the matching environmental parameters in the historical data;

[0090] Extract and fuse the color perception parameters by algorithm and import them into the environment model to determine the rendering environment model and generate several rendering parameters;

[0091] Establish a scoring table, and when the rendering parameter score is < 60, recalculate the extracted color perception parameters.

[0092] In summary, the extracted and fused adjustment parameters that best fit the current environment are obtained, so that the light emitted by the atmosphere lamp is not conspicuous, can perfectly blend into the current environment, and highlights the car light, thereby improving the overall atmosphere and enhancing the aesthetic sense;

[0093] Further, obtain the vehicle parameters, which include the vehicle color, and import the vehicle color to optimize the adjustment parameters;

[0094] It should be particularly noted that since the environmental parameters change from time to time, for example, the color of the neon light changes, and the sunlight changes, therefore, the vehicle external environment is obtained in real time through the perception module, so as to adjust the atmosphere lamp in real time, and thereby the light emitted by the atmosphere lamp maintains the best state with the current environment.

[0095] It should be particularly noted that for historical data, the scene color parameters after each calibration and their corresponding atmosphere lamp recommendation scheme, such as RGB value and style type, are saved to the local historical database in a structured format, such as JSON format. Each record contains a timestamp, environmental color parameters, lighting conditions, geographical location, and recommended atmosphere lamp configuration.

[0096] Further, the ground environment refractive index and the refractive index of the external environment are obtained, and the current lamp body data is combined to optimize the adjustment parameter;

[0097] Specifically, the external environment refraction includes the reflection of surrounding vehicles, the reflection of flowerpot tiles, etc. Whether the ground is reflective, has water stains, etc. is recognized by OpenCV, and the refractive index is mapped in combination with the known material library, so as to further optimize and adjust the atmosphere lamp. The air refractive index and the ground refractive index are calculated, and then the reflectivity is calculated. According to the reflectivity result and the air refractive index and the ground refractive index, the atmosphere lamp is optimized. The air refractive index and the ground refractive index calculation include:

[0098] ;

[0099] The air refractive index (≈1.0);

[0100] The ground refractive index (such as 1.33);

[0101] The incident angle (lamp body irradiation angle);

[0102] The refraction angle (into the ground);

[0103] The reflectivity calculation formula is:

[0104] ;

[0105] For example, air to asphalt (1.0 to 1.55), R=0.05, 5% reflection, 95% absorption;

[0106] Air to water accumulation (1.0 to 1.33), R=0.02, 2% reflection, 98% absorption;

[0107] For example, the environment is that there is water accumulation on the ground, the refractive index n=1.33, the ambient light is dark (Lux=30), the current atmosphere lamp RGB=(100, 150, 200), the brightness=200, and the color temperature=6500K. The optimization process is to detect high reflection, such as water accumulation, low reflectivity but strong specular reflection, reduce the brightness to 150 to avoid the ground glare reflection, reduce the color temperature from 6500K to 5500K to avoid the cold light in the water to appear gloomy, and reduce the beam angle from 30° to 20° to form a focused light band and improve the line sense.

[0108] In summary, by referring to the refraction factor, the light effect of the atmosphere lamp is optimized again, so as to further improve the light atmosphere effect;

[0109] It needs to be particularly pointed out that its atmosphere lamp is generally composed of a light source and a light guide pipe. By installing a regulating device, such as a lead screw, in the atmosphere lamp, the light source and the light guide pipe are moved by the lead screw to change the light emitting position.

[0110] Embodiment 2

[0111] This embodiment is an improvement based on embodiment 1. For details, please refer to Figures 1-4 by acquiring and marking the coordinates of the interference factors under the chassis;

[0112] Generate the flashing guidance parameters of the lamp body combined with the marked coordinates, generate the guidance program from the flashing guidance parameters, and import it into the lamp body MCU;

[0113] Through sensing and early warning, the driver is notified when he approaches the vehicle. The lamp body MCU executes the guidance program to control the lamp body to issue a guidance warning.

[0114] Specifically, after the vehicle is parked, it is detected whether there is an abnormal object under the chassis, such as a stone with a height difference > 5 cm, a liquid reflective area, a moving organism, etc. Through OpenCV image analysis, it is confirmed that the object is a potential interference source. A two-dimensional coordinate system is established with the center of the vehicle or the driving position as the origin,

[0115] X-axis: front-to-back direction (+X for the front of the vehicle, -X for the rear of the vehicle);

[0116] Y-axis: left-to-right direction (+Y for the right side, -Y for the left side);

[0117] Calculate the centroid coordinates (x, y) of the interference material, for example: (-30, 15) indicates that it is located 30 cm to the right of the rear of the vehicle;

[0118] Set the guidance logic, starting from the light source corresponding to the driving position, flow clockwise or counterclockwise along the lamp strip until the target direction. For example, the interference object is in the right rear, and the driving position (left rear) is the starting point, and the light segment is counterclockwise to the right rear. The interference object is in the left front, and the driving position is the starting point, and the light segment is clockwise to the left front;

[0119] Compile the above parameters into a guidance program executable by the lamp strip, based on the PWM instruction sequence of the WS2812B protocol. Write the program into the lamp body MCU through CAN / LIN bus or I2C interface. The MCU stores the program and waits for the trigger condition. When the system senses that a legal driver approaches the vehicle through RFID, Bluetooth, or keyless entry, the preloaded guidance program is triggered, the chassis atmosphere lamp dynamically flashes according to the set path, forming a light flow guidance effect, and the driver's line of sight naturally follows the light flow direction, quickly locating the position of the interference object;

[0120] In summary, when the interference under the vehicle chassis is identified and obtained, it will affect the vehicle to drive out of the current parking place, and the coordinate address is marked. Since the chassis atmosphere lamp surrounds the vehicle chassis, it is roughly divided into four directions, front, back, left and right. One of the four directions is determined by the coordinate address of the interference, and the lamp body flashing guide parameter is generated, that is, the atmosphere lamp starts to flash from the driving position, and the atmosphere lamp starts to flash clockwise and counterclockwise to the marked coordinate address. In this way, the driver can find the interference and eliminate it in time, reduce the risk of vehicle operation, and realize the warning and guiding function of the atmosphere lamp.

[0121] Further, due to different environmental interference, not all red atmosphere lamps are eye-catching, and not all flashing is effective. The color of the warning light must form the maximum visual contrast with the background environment to quickly attract attention. Therefore, red or yellow conventional warning colors should not be used fixedly, but the best warning color should be dynamically optimized according to the actual environment color. Specifically,

[0122] Based on the color parameters of the current environment parameters obtained in example 1, in the standard warning color library, such as red, yellow, blue, green, white, magenta, cyan, the visual contrast of each color with the current environment background is calculated, and the color with the largest contrast is selected as the recommended warning color. The eye-catching color selected is fused with the flashing guide path parameter generated before to generate the final dynamic guide program, which is sent to the lamp body MCU for execution.

[0123] Among them, using HSV color space (hue H, saturation Saturation, brightness Value) is more in line with human visual perception. In particular, hue H determines the color type red, yellow, green, etc., which is suitable for complementary color judgment;

[0124] The hue difference method and brightness difference method are used for comprehensive evaluation. The closer the hue difference is to 180°, the greater the brightness difference, and the stronger the visual impact;

[0125] Among them, the warning color candidate library,

[0126] ;

[0127] Adaptive color selection strategy example:

[0128] ;

[0129] Further, the method for sensing and warning includes:

[0130] Realize vehicle sensing driver based on radio frequency identification;

[0131] When the driver carries a mobile phone close to the vehicle, the mobile phone signal is received through the Bluetooth module;

[0132] Bluetooth module will receive the signal transmission to the vehicle ECU verification, verification, vehicle ECU execution generation program control lamp body to emit light source.

[0133] Specifically including:

[0134] The first step, preliminary perception, RFID trigger;

[0135] When the driver carries RFID tag close to the vehicle, such as reaching out to touch the door handle, the vehicle RFID reader detects the label signal system is awakened, into the "to be verified" state, ready to start the next step of Bluetooth authentication;

[0136] The second step, identity verification, Bluetooth signal matching;

[0137] The vehicle Bluetooth module actively scans the surrounding equipment, search for the bound legal mobile phone MAC address or pre-shared key,

[0138] If the detection of paired mobile phone issued effective Bluetooth signal, such as continuous broadcast Beacon package, will signal data transmission to the vehicle ECU. ECU on the signal encryption verification, such as using TLS / PSK mechanism, confirm the legality of the device;

[0139] The third step, decision and execution, ECU control lamp body;

[0140] If RFID and Bluetooth double verification are passed, ECU determines the legal user, execute the preset generation program, atmosphere lamp gradually bright, personalized color;

[0141] If any verification fails, do not respond or trigger low level prompt, such as single short flash, prevent misoperation;

[0142] The fourth step, light source output, lamp body response;

[0143] Lamp body according to ECU instruction to emit light source, form including:

[0144] Static light, always bright indication;

[0145] Dynamic light, water, breathing, pulse and other animation;

[0146] Color light, according to user preference or scene mode adjustment RGB value.

[0147] Example 3

[0148] This embodiment is made on the basis of example 2, specific, please refer to Figures 1-4 , get the driving habits of the driver, combined with the current road traffic information, forecast calculation of driving vehicles out of the warehouse turning radius;

[0149] According to the exit turning radius of the driving vehicle, the driving area is calculated;

[0150] Determine whether there is an interference factor in the vehicle driving area, and when there is an interference factor, detect and mark through the detection module to optimize the guidance program.

[0151] Further, on the basis of embodiment 2, in order to better guide the driver to handle the starting risk in advance, the driving habit of the driver is obtained, the habit starting turning radius size of the driver is understood, the current road is determined through GPS, when it is a forward lane, the vehicle must turn right, when it is a reverse lane, the vehicle must turn left, therefore, combined with the starting turning radius size, the turning radius driving area of the vehicle starting to exit the warehouse is predicted, the image is captured by the camera combined with the current road map, so as to mark and calculate the turning radius driving area, and the image is recognized by OpenCV, so as to determine the hidden risk in the turning radius driving area. When there is a risk in the turning radius driving area, the control atmosphere lamp in embodiment 2 is used to guide the driver to eliminate the risk, so as to further improve the guiding warning effect.

[0152] Specifically, the driving behavior data of the driver, such as turning radius, acceleration, etc., is collected by the vehicle-mounted sensor and the driving recorder for a long time. These data are analyzed by using machine learning algorithm to understand the habit starting turning radius size of the driver. The current road information is determined by using GPS, the current position of the vehicle is obtained by using GPS module, and the current lane is determined to be forward or reverse by combining online map service, such as Google Maps API or Map API. The turning direction prediction predicts the turning direction (right turn / left turn) of the vehicle when starting according to the lane type, forward / reverse. The driving area is predicted by combining the starting turning radius size. Based on the average starting turning radius size obtained by analyzing the driving habit, the turning radius driving area that may be occupied when starting is estimated by combining the width and length of the vehicle. The driving area is marked on the map for subsequent risk assessment. The image is captured by the camera and the turning radius driving area is marked by combining the map information. The front road condition image is captured in real time by using the front camera. The image is preprocessed by using OpenCV, such as denoising, edge detection, etc., and then the turning radius driving area is marked in the image by combining the map information.

[0153] For example,

[0154] The vehicle width W=1.8m;

[0155] The driver habitually turns slowly when starting, and the habit turning radius Rh=4.8m, which is slightly larger than the minimum value and close to the real driving behavior;

[0156] The road condition is that the parking space is located on the right side of the road;

[0157] The current lane is a forward lane, and according to the logic, the vehicle must turn right after starting;

[0158] Therefore, the calculation formula of the turning radius driving area is:

[0159] The outer radius (the maximum trajectory radius on the left side) is:

[0160] ;

[0161] The inner radius (the minimum trajectory radius on the right side) is:

[0162] ;

[0163] Determine the sector opening angle:

[0164] The starting direction is that the vehicle faces the inside of the parking space (assuming the heading angle is 0°), the ending direction is that the vehicle drives along the road after completing the left turn (the heading angle is 90°), and the sector angle is ;

[0165] Therefore, the calculation formula of the driving area is:

[0166] ;

[0167] ;

[0168] In summary, the driving area under the outer radius and the inner radius on both sides of the vehicle is Therefore, the image of the cover area is accurately recognized by OpenCV to determine the hidden risks in the turning radius driving area.

[0169] The embodiments of the present specification also provide a computer readable storage medium, which stores instructions, when the instructions are executed on a computer or a processor, the computer or the processor executes a plurality of steps in the above embodiments. Each component module of the above electronic device, if realized in the form of a software function unit and sold or used as an independent product, can be stored in the computer readable storage medium.

[0170] The embodiments of the present specification also provide a computer program product, which includes a computer program, and the computer program is executed by a processor to realize a plurality of steps in the above embodiments.

[0171] In the case of no conflict, the technical features in the embodiments and the embodiments can be combined arbitrarily.

[0172] In the above embodiments, all or part of the embodiments can be implemented by software, hardware, firmware or any combination thereof. When implemented by software, all or part of the embodiments can be implemented in the form of a computer program product. The computer program product includes a plurality of computer instructions. When the computer instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present specification are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in or transmitted by a computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center through a wired (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (such as infrared, wireless, microwave, etc.) manner. The computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server, data center, etc. integrated with a plurality of available media. The available media can be a magnetic medium (for example, a floppy disk, a hard disk, a magnetic tape), an optical medium (for example, a digital versatile disc (DVD)), or a semiconductor medium (for example, a solid state disk (SSD)) and the like.

[0173] When implemented by hardware or firmware, the foregoing method processes are programmed into a hardware circuit to obtain a corresponding hardware circuit structure, and the corresponding functions are implemented. For example, a programmable logic device (PLD) (such as a field programmable gate array (FPGA)) is an integrated circuit whose logic function is determined by the user programming the device. A digital system is "integrated" on a PLD by the designer himself programming, without having to ask the chip manufacturer to design and manufacture a special integrated circuit chip. Moreover, instead of manually making integrated circuit chips, such programming is now mostly implemented using "logic compiler" software, which is similar to the software compiler used when developing programs, and the original code to be compiled must also be written in a specific programming language, which is called a hardware description language (HDL), and there are many HDLs. Those skilled in the art should also understand that only the method processes need to be logically programmed in the above-mentioned several hardware description languages and programmed into integrated circuits to easily obtain hardware circuits that implement the logical method processes.

[0174] It is to be noted that, in the present document, relational terms such as first and second and the like can be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises", "comprising", or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus.

[0175] The above description is only the preferred embodiment of the present application, it should be pointed out that, for those skilled in the art, without departing from the technical principles of the present application, a number of improvements and refinements can be made, these improvements and refinements should also be considered as the protection scope of the present application.

Claims

1. A multi-channel control method for chassis ambient lighting, characterized in that, include: The vehicle status is obtained, and the perception module is activated based on the vehicle status to obtain the external environment of the vehicle and generate environmental parameters. The environmental parameters are compared sequentially with the color perception parameters in historical data, and the vehicle parameters are obtained to generate adjustment parameters. The detection module is used to detect the environment under the chassis and obtain an image of the chassis environment. The ground environment and interference factors under the chassis are identified by OpenCV based on the chassis environment image and environmental parameters. Obtain the refractive index of the ground environment and the refractive index of the external environment, and optimize the adjustment parameters based on the current lamp body data; The optimized adjustment parameter generation program is imported into the lamp body MCU, and the lamp body MCU is used to control the lamp body to change the lighting parameters according to the generation program. By acquiring and identifying interference factors beneath the chassis, and marking their coordinates; Based on the marked coordinates, generate flashing guidance parameters for the lamp body, generate a boot program from the flashing guidance parameters, and import it into the lamp body MCU: The system senses and warns the driver. When the driver approaches the vehicle, the MCU of the lamp body executes a guidance program to control the lamp body to issue a guidance warning. By acquiring drivers' driving habits and combining them with current road conditions, the turning radius of the vehicle when exiting the parking space can be predicted and calculated. Calculate the driving area based on the vehicle's stated turning radius when exiting the parking space; The system determines whether there are interfering factors in the vehicle's driving area. When interfering factors are present, they are detected, identified, and marked by the detection module to optimize the guidance program.

2. The multi-channel control method for chassis ambient lighting according to claim 1, characterized in that: Based on the ground environment and the external environment, the ground refractive index and the vehicle external environment refractive index are identified and obtained. Obtain vehicle parameters; Based on the ground refractive index, the refractive index of the vehicle's external environment, and vehicle parameters, the current lamp body data is optimized and adjusted again, and the adjustment parameters are then adjusted.

3. The multi-channel control method for chassis ambient lighting according to claim 2, characterized in that: The environmental parameters include vehicle parking scene parameters, vehicle parking scene color parameters, and vehicle parking scene position parameters. The sensing module includes a camera, a color sensor, and a GPS; The camera is used to capture images of the vehicle chassis and exterior, and these images are then analyzed using OpenCV. The color sensor is used to perceive the vehicle chassis scene and the vehicle exterior scene, and the color parameters of the vehicle parking scene are calibrated and generated by combining the OpenCV recognition and analysis data. The GPS is used to obtain the vehicle parking scene location parameters, and the color parameters of the vehicle parking scene are optimized based on the parking scene location parameters.

4. The multi-channel control method for chassis ambient lighting according to claim 3, characterized in that, Methods for obtaining vehicle status include: Vehicle speed is sensed via the GPS; Receive the signal to turn on the vehicle's reverse lights; Use vehicle navigation to determine if you are about to reach your destination; Specifically, when the vehicle speed is between 0-10 km / h, the reversing lights are on, and the distance to the destination is less than 0-5 m, the vehicle is considered to be about to arrive at the destination and stop.

5. The multi-channel control method for chassis ambient lighting according to claim 1, characterized in that, It also includes establishing an environmental model based on the environmental parameters and vehicle information; Obtain color perception parameters that match the environmental parameters from historical data; The color perception parameters are extracted and imported into the environment model through the algorithm to determine the rendering environment model and generate several rendering parameters. A scoring table is established, and when the score of the rendering parameter is less than 60, the extracted color perception parameters are recalculated.

6. The multi-channel control method for chassis ambient lighting according to claim 1, characterized in that, The method for sensing and early warning includes: Vehicle driver detection based on radio frequency identification; When a driver approaches the vehicle with a mobile phone, the system receives signals from the phone via Bluetooth. The Bluetooth module transmits the received signal to the vehicle ECU for verification. Once the verification is successful, the vehicle ECU executes the generation program to control the lamp to emit light.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 2-6.

8. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 2-6.

Images