ADAS calibration auxiliary method, apparatus and device, and storage medium

Abstract

The embodiment of the invention discloses an ADAS calibration auxiliary method, device and equipment and a storage medium, which are used for improving the calibration efficiency and accuracy of an ADAS system in a non-ideal environment, reducing the dependence on manual operation and realizing the intelligent adjustment and control of a calibration process. The method comprises the steps of obtaining vehicle type information of a target vehicle through a diagnosis interface to extract calibration parameters corresponding to the vehicle type information; the calibration parameters at least comprise a target specification, a spatial distance threshold value and an ambient brightness threshold value; environment data of a calibration site are collected; the environment data comprises environment openness and an environment brightness value; generating an adjustment instruction set based on the vehicle type information, the calibration parameters and the environmental data; the adjustment instruction set at least comprises one of a vehicle position adjustment instruction, an ambient brightness adjustment instruction and a target adjustment instruction; after the instruction set is adjusted, an ADAS calibration program is triggered and started, and ADAS calibration is completed.

Description

Technical Field

[0001] This application relates to the field of Advanced Driver Assistance System (ADAS) calibration, and more particularly to ADAS calibration assistance methods, apparatus, devices, and storage media. Background Technology

[0002] With the development of intelligent driving technology, ADAS has been widely used in vehicle safety control. To ensure its proper functioning, the system needs to be precisely calibrated after leaving the factory or after maintenance. Traditional calibration methods usually rely on manual operation, where targets are manually set up in a standard area according to vehicle parameters, and the vehicle position and lighting environment are adjusted before calibration is performed.

[0003] However, the above-mentioned manual methods have the following drawbacks: First, environmental data relies on manual visual inspection or measurement with simple tools, and cannot quantify factors such as light intensity in real time; second, they cannot be flexibly adjusted and adapted according to vehicle type and camera configuration, especially in situations with limited space and uneven lighting, which often leads to calibration failure or substandard accuracy. Summary of the Invention

[0004] Based on the above problems, this application provides ADAS calibration assistance methods, devices, equipment and storage media, with the aim of realizing automatic perception and judgment of the calibration environment, reducing manual intervention, and ensuring the effective execution and success rate of ADAS calibration work, especially in non-standard or restricted environments.

[0005] In a first aspect, embodiments of this application provide an ADAS calibration assistance method, including:

[0006] The vehicle model information of the target vehicle is obtained through the diagnostic interface to extract the calibration parameters corresponding to the vehicle model information; the calibration parameters include at least the target size, spatial distance threshold and ambient brightness threshold.

[0007] Collect environmental data of the calibration site; the environmental data includes ambient openness and ambient brightness values.

[0008] Based on the vehicle information, the calibration parameters, and the environmental data, an adjustment instruction set is generated; the adjustment instruction set includes one or more of the following: vehicle position adjustment instruction, ambient brightness adjustment instruction, and target adjustment instruction.

[0009] After executing the set of adjustment instructions, the Advanced Driver Assistance System (ADAS) calibration procedure is initiated and completed.

[0010] In one embodiment, the process of generating the vehicle position adjustment command specifically includes:

[0011] The vehicle size is determined based on the vehicle model information, and it is also determined whether the target vehicle supports autonomous driving functions.

[0012] Based on the vehicle size, the spatial distance threshold, and the environmental openness, calculate the coordinates of the optimal calibration area;

[0013] If the target vehicle supports autonomous driving, a vehicle position adjustment command is generated based on the optimal calibration area coordinates to move the target vehicle to the optimal calibration area coordinates.

[0014] In one embodiment, the process of generating the ambient brightness adjustment command specifically includes:

[0015] If the ambient brightness value of the calibration site is lower than the ambient brightness threshold, an ambient brightness adjustment command is generated based on the ambient brightness value and the ambient brightness threshold to control the supplementary lighting device to enhance the ambient brightness value to the target ambient brightness value.

[0016] If the ambient brightness value of the calibration site is higher than the ambient brightness threshold, an ambient brightness adjustment command is generated based on the ambient brightness value and the ambient brightness threshold to control the shading device to reduce the ambient brightness value to the target ambient brightness value.

[0017] In one embodiment, the process of generating the target adjustment command specifically includes:

[0018] If the ambient openness of the calibration site does not meet the spatial distance threshold, the type of camera equipped on the target vehicle is determined based on the vehicle model information.

[0019] If the camera type is a monocular camera, the required target scaling ratio is calculated based on the available space in the current calibration site, and a target adjustment command for scaling a standard-sized target is generated.

[0020] If the camera type is a binocular camera, then a target adjustment instruction for the target graphic used for parallax compensation is generated based on the parallax parameters of the binocular camera.

[0021] In one embodiment, the method further includes:

[0022] Detect whether there are any interference sources in the current calibration site, and the interference sources include at least obstructions;

[0023] If an interference source is present, generate an alarm message that includes the type of interference source, its coordinates, and suggested handling methods.

[0024] In one embodiment, the method further includes:

[0025] Determine whether the vehicle status of the target vehicle needs to be adjusted based on the calibration parameters;

[0026] If it is necessary to adjust the vehicle status of the target vehicle and the target vehicle supports bus control, then control commands are sent through the vehicle bus system according to the required adjustment of the vehicle status of the target vehicle.

[0027] If the target vehicle does not support bus control, a prompt message will be output to complete the above vehicle status adjustment operation.

[0028] In one embodiment, the vehicle status includes engine start / stop and air conditioning switch, and the control commands include engine start command, engine stop command, air conditioning start command, and air conditioning stop command.

[0029] Secondly, embodiments of this application also provide an ADAS calibration auxiliary device, comprising:

[0030] The vehicle model recognition unit is used to obtain the vehicle model information of the target vehicle through a diagnostic interface, so as to extract the calibration parameters corresponding to the vehicle model information; the calibration parameters include at least the target size, spatial distance threshold and ambient brightness threshold.

[0031] An environmental acquisition unit is used to collect environmental data of the calibration site; the environmental data includes ambient openness and ambient brightness values.

[0032] The instruction generation unit is used to generate an adjustment instruction set based on the vehicle model information, the calibration parameters, and the environmental data; the adjustment instruction set includes at least one of the following: vehicle position adjustment instruction, ambient brightness adjustment instruction, and target adjustment instruction;

[0033] The calibration unit is used to execute the adjustment instruction set, trigger the start of the ADAS calibration program, and complete the ADAS calibration.

[0034] Thirdly, embodiments of this application also provide a computer device, including:

[0035] Central processing unit, memory, input / output interfaces;

[0036] The memory is either a short-term storage memory or a persistent storage memory;

[0037] The central processing unit is configured to communicate with the memory and execute instructions in the memory to perform any of the ADAS calibration assistance methods described above.

[0038] Fourthly, embodiments of this application also provide a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, it performs any of the ADAS calibration assistance methods described above.

[0039] As can be seen from the above technical solutions, the embodiments of this application have the following advantages:

[0040] Because this embodiment obtains the target vehicle's model information through a diagnostic interface and extracts matching calibration parameters (including target specifications, spatial distance thresholds, and ambient brightness thresholds), it ensures that the selected calibration configuration is highly compatible with the specific vehicle model, thereby improving calibration accuracy and adaptability. Furthermore, by collecting real-time data on the calibration site environment, including ambient openness and brightness values, it can accurately determine whether the current environment meets the various calibration requirements, avoiding subjective errors or judgment biases that may arise from relying on manual visual inspection or simple tools. Based on this, the system generates corresponding adjustment instruction sets according to the vehicle model information, calibration parameters, and environmental data, including key operations such as vehicle position adjustment, ambient brightness control, and target placement. This enables intelligent assistance or automated control of the vehicle calibration preparation process, reducing operational complexity and human intervention. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0042] Figure 1 This is a schematic diagram of an ADAS calibration assistance method provided in an embodiment of this application;

[0043] Figure 2 This is a schematic diagram of the structure of an ADAS calibration auxiliary device provided in an embodiment of this application;

[0044] Figure 3 This is a schematic diagram of a computer device structure provided in an embodiment of this application. Detailed Implementation

[0045] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0046] With the development of intelligent driving technology, ADAS, as an important component of modern vehicle safety and intelligence, is mainly used in functions such as lane keeping, automatic emergency braking, and forward collision warning. To ensure the accuracy and reliability of all functions of the ADAS system, the ADAS sensors (such as cameras and radars) must undergo precise calibration and adjustment after the vehicle leaves the factory or undergoes maintenance. The calibration process typically relies on specific physical environmental conditions and standard equipment configurations to correctly identify lanes, target objects, and obstacles, achieving high-precision perception and judgment.

[0047] In existing technologies, ADAS system calibration mostly employs manual setup and static procedures. Typically, this takes place in repair shops or similar locations. Vehicle model information is obtained using diagnostic equipment, and then, according to the manufacturer's calibration specifications, a suitable target is selected, the vehicle's position is adjusted, the lighting environment is set up, and the calibration process is executed. This process requires sufficient open space and lighting conditions at the calibration site, and the target placement must meet strict distance and angle requirements. While some vehicle diagnostic systems have the ability to match model calibration parameters, manual judgment and repeated adjustments are still necessary in complex or insufficient environments, making the entire process inefficient and prone to errors.

[0048] Therefore, existing ADAS calibration methods have the following key problems: First, they cannot be separated from human perception and judgment of the actual calibration environment, as well as the assessment of whether the site meets the calibration requirements; Second, under conditions such as limited space and uneven lighting, coupled with different camera configurations for different vehicle models, manual calibration in such a complex environment can easily lead to calibration failure or substandard accuracy.

[0049] Based on this, in various embodiments of this application, by acquiring the vehicle model information and corresponding calibration parameters of the target vehicle, and combining real-time acquisition and analysis of calibration site environmental data, an adjustment instruction set including vehicle position, target placement and ambient lighting adjustment is automatically generated, and the ADAS calibration program is automatically triggered after execution, thereby realizing intelligent assistance and automatic control of the entire calibration process, improving the calibration success rate and adaptability to different scenarios.

[0050] The various embodiments of this application will now be described in further detail with reference to the accompanying drawings.

[0051] This application provides an ADAS calibration assistance method, such as... Figure 1 As shown, the method includes steps S101-S104.

[0052] S101: Obtain the vehicle model information of the target vehicle through the diagnostic interface to extract the calibration parameters corresponding to the vehicle model information.

[0053] After obtaining the target vehicle's model information through the diagnostic interface, the system extracts the corresponding calibration parameters from a pre-set database based on this information. Here, the diagnostic interface can be a standard OBD-II interface or a manufacturer-specific diagnostic bus interface, used to communicate with the vehicle's internal ECU and read core parameters such as the Vehicle Identification Number (VIN), vehicle year, camera configuration, and sensor placement. Based on the identified model information, the system matches and extracts the corresponding calibration data from a local or remote server. Calibration parameters may include one or more of the following: target specifications, spatial distance threshold, and ambient brightness threshold. Target specifications include, but are not limited to, target size, pattern type, and installation height; the spatial distance threshold refers to the minimum open space distance in front and to the sides required for calibration; and the ambient brightness threshold is used to determine whether the current lighting conditions meet the camera's image acquisition requirements.

[0054] S102: Collect environmental data of the calibration site.

[0055] In addition to the calibration parameters determined based on vehicle model information, environmental data of the current calibration site also needs to be collected. This environmental data includes ambient openness and ambient brightness. Environmental data acquisition can be accomplished by an integrated laser ranging module, a visual image recognition unit, and a light sensor. Ambient openness is determined by using multi-point ranging and image / visual recognition technology to acquire the distribution of obstacles and available space around the vehicle, combined with preset spatial distance thresholds to determine whether the site meets the requirements for target placement and vehicle movement. Ambient brightness is measured by a light sensor to collect the current ambient brightness level and compare it with the required ambient brightness threshold for the vehicle. By acquiring accurate environmental parameters in real time, the system can dynamically adapt to different calibration site conditions, improving the environmental adaptability of the calibration process.

[0056] S103: Generates an adjustment instruction set based on vehicle model information, calibration parameters, and environmental data;

[0057] Based on the vehicle model information, calibration parameters, and environmental data obtained in the above steps, a comprehensive analysis is performed to generate an adjustment instruction set. This instruction set guides or automatically completes the calibration preparation operation and may include one or more of the following: vehicle position adjustment instruction, ambient brightness adjustment instruction, and target adjustment instruction. Specifically, the vehicle position adjustment instruction controls the vehicle with automatic movement capability to move to the optimal calibration position calculated based on the environmental data. The ambient brightness adjustment instruction controls the adjustable lighting equipment to increase or decrease the light intensity, or controls the adjustable shading equipment to increase or decrease the light intensity, so that the ambient brightness value meets a preset threshold. The target adjustment instruction generates a deployment plan based on the available space and camera type at the current calibration site, including target position, orientation adjustment, or pattern scaling.

[0058] S104: After executing the adjustment instruction set, the ADAS calibration procedure is triggered and the ADAS calibration is completed.

[0059] After executing the aforementioned adjustment instruction set and confirming that the vehicle position, target placement, and ambient lighting all meet the conditions corresponding to the calibration parameter settings, the ADAS calibration program is automatically triggered and the calibration operation is performed. This ADAS calibration program can be executed by the calibration module integrated into the diagnostic system or the vehicle ECU, calling the corresponding vehicle model's cameras, radar, and other sensors for parameter initialization and calibration. Executing the ADAS calibration program and completing the ADAS calibration is existing technology and will not be elaborated here. The primary technical objective of this application's embodiments is to generate an adjustment instruction set by identifying the environmental parameters required for ADAS calibration (such as vehicle position, target placement, ambient lighting, and spatial openness) to automatically control the vehicle to complete the calibration preparation operation. This reduces reliance on manual judgment, minimizes repetitive and ineffective placement and operation, and makes the ADAS calibration process more convenient. After the ADAS calibration is completed, calibration result information can also be output, and relevant environmental data and adjustment logs can be recorded for subsequent traceability and quality control.

[0060] Because this embodiment obtains the target vehicle's model information through a diagnostic interface and extracts matching calibration parameters (including target specifications, spatial distance thresholds, and ambient brightness thresholds), it ensures that the selected calibration configuration is highly compatible with the specific vehicle model, thereby improving calibration accuracy and adaptability. Furthermore, by collecting real-time data on the calibration site environment, including ambient openness and brightness values, it can accurately determine whether the current environment meets the various calibration requirements, avoiding subjective errors or judgment biases that may arise from relying on manual visual inspection or simple tools. Based on this, the system generates corresponding adjustment instruction sets according to the vehicle model information, calibration parameters, and environmental data, including key operations such as vehicle position adjustment, ambient brightness control, and target placement. This enables intelligent assistance or automated control of the vehicle calibration preparation process, reducing operational complexity and human intervention.

[0061] In existing technologies, ADAS system calibration typically relies on manually moving the vehicle to the required calibration location. This process has several problems: First, manual judgment of position can lead to significant errors, causing the vehicle to be outside the optimal recognition area of ​​the target, resulting in camera viewing angle deviation or target image distortion, thus affecting the accuracy of the calibration results. Second, in confined or complex environments, operators often struggle to accurately determine the vehicle's correct position and angle, requiring multiple adjustments and leading to low efficiency. Therefore, in one embodiment, to avoid ADAS calibration failure, image acquisition errors, or decreased calibration accuracy due to inaccurate vehicle parking, the vehicle position adjustment command generation process specifically includes: determining the vehicle size based on vehicle model information and determining whether the target vehicle supports autonomous driving functions; calculating the optimal calibration area coordinates based on vehicle size, spatial distance threshold, and environmental openness; if the target vehicle supports autonomous driving functions, generating a vehicle position adjustment command based on the optimal calibration area coordinates to move the target vehicle to the optimal calibration area coordinates; if the target vehicle does not support autonomous driving functions, generating a movement prompt message containing the optimal calibration area coordinates.

[0062] The process of generating vehicle position adjustment commands first determines the specific body dimensions of the target vehicle based on vehicle model information, and then further identifies whether the vehicle supports autonomous driving control functions. Vehicle model information can be obtained by reading the vehicle identification number from the database via a diagnostic interface, including but not limited to vehicle length, width, front and rear overhang distances, wheelbase, and camera mounting positions. The identification of whether autonomous driving functions are supported can be determined based on the automatic control function identifier field in the vehicle model database. For example, models like the Tesla Model 3 and Chevrolet M9 have vehicle motion control capabilities, while traditional models such as the BYD Qin and Nissan Sylphy do not support automatic vehicle displacement.

[0063] Subsequently, combining the acquired vehicle size information, preset spatial distance thresholds, and the ambient openness of the current calibration site, spatial simulation calculations are performed to determine the optimal area coordinates for vehicle calibration within the site. Specifically, the system compares the ambient openness data—the front-to-back and left-to-right spatial distance values ​​obtained by the calibration equipment through laser ranging or visual recognition—with the minimum spatial distance thresholds required for each vehicle type to construct a virtual two-dimensional or three-dimensional spatial model, simulating a stationary area for the vehicle that meets the calibration requirements. During the simulation, the required forward clear field of view distance, lateral obstacle avoidance distance, and optimal angle and distance matching relationship between the target and the camera are considered for different vehicle types. Ultimately, the most suitable stationary position coordinate range for the current vehicle type is determined as the optimal calibration area, which is then used as the area for vehicle target movement.

[0064] When the system detects that the target vehicle supports autonomous driving control, it will generate specific vehicle position adjustment instructions based on the calculated optimal calibration area coordinates. These instructions will then be sent to the vehicle via the vehicle bus system or a dedicated interface, causing the vehicle to automatically move to the target area. The vehicle position adjustment control process may include path planning, speed adjustment, and destination parking control. For example, for the autonomous driving-capable Wenjie M9 model, it can be controlled to move forward at a speed of 0.5 meters per second and automatically brake to complete positioning when 1.2 meters from an obstacle ahead.

[0065] For vehicles without automatic movement capabilities, such as the Nissan Sylphy, no control commands are generated. Instead, clear movement instructions are output through the interactive interface. These instructions include the relative position of the target calibration area to the current location, the vehicle's parking angle, and a movement path plan, guiding the operator to move the vehicle to the target position. The movement instructions can be presented through voice prompts, interface animations, or AR overlays, minimizing the user's understanding and operational barriers and preventing placement errors from affecting calibration accuracy.

[0066] This application achieves intelligent adjustment of the vehicle's stationary position by utilizing vehicle size information and an environmental space model. It can also flexibly adjust the specific execution method of vehicle position adjustment based on whether the vehicle possesses automatic control capabilities. For models with automatic movement capabilities, position adjustment can be completed directly, improving automation levels and calibration efficiency. For models that do not support automatic movement, easily understandable movement instructions improve operator accuracy, reduce trial-and-error and human judgment errors, and effectively ensure the calibration success rate and accuracy stability of the ADAS system in diverse environments.

[0067] In traditional ADAS calibration, the quality of camera image acquisition plays a decisive role in the accuracy of target recognition. If the ambient light is too low, the camera image will exhibit problems such as darkness, increased noise, and blurred edges, making it difficult to clearly capture the target pattern and potentially leading to recognition failure or incorrect positioning. Conversely, if the ambient light is too high, especially under strong sunlight or strong reflective interference, the image is prone to overexposure, light spots, or reduced contrast, which can prevent the image processing algorithm from correctly segmenting the target area, affecting calibration accuracy. Furthermore, in situations where the ambient light is unstable or deviates from the recommended range, even if the calibration process is forcibly executed, image errors may cause deviations in camera viewpoint estimation and position calibration, ultimately leading to safety hazards such as false warnings and recognition errors in actual vehicle operation. Therefore, in order to avoid image recognition abnormalities, target recognition failures, increased calibration errors, or calibration interruptions due to ambient brightness not meeting ADAS calibration requirements, in one embodiment, the generation process of the ambient brightness adjustment command proposed in this application specifically includes: if the ambient brightness value of the calibration site is lower than the ambient brightness threshold, an ambient brightness adjustment command is generated based on the ambient brightness value and the ambient brightness threshold to control the supplementary lighting device to enhance the ambient brightness value to the target ambient brightness value; if the ambient brightness value of the calibration site is higher than the ambient brightness threshold, an ambient brightness adjustment command is generated based on the ambient brightness value and the ambient brightness threshold to control the shading device to reduce the ambient brightness value to the target ambient brightness value.

[0068] In this embodiment, the ambient brightness adjustment command first collects the ambient brightness value of the calibration site in real time through a brightness sensor or image recognition module. This brightness value can be quantified in lux (Lux), and the overall illuminance level of the image acquisition area in front of the vehicle is evaluated through multi-point sampling. The ambient brightness value is compared and analyzed with a pre-set ambient brightness threshold. Here, the ambient brightness threshold is a minimum illuminance standard preset based on the light sensitivity of the camera used by the target vehicle and the requirements of the target recognition algorithm, to avoid target recognition failure due to excessively dark or overexposed images.

[0069] If the current ambient brightness value is detected to be lower than the ambient brightness threshold, the difference between the actual brightness value and the target brightness value will be further calculated. Based on the controllable brightness gain range of the supplementary lighting device, a corresponding ambient brightness adjustment command will be generated. In this case, the ambient brightness adjustment command is used to control the external light source, such as an adjustable LED light group, to adjust its power output according to a preset brightness enhancement curve, thereby increasing the ambient brightness value within the calibration area to the target ambient brightness value. For example, if the collected brightness value in an indoor venue is 80 Lux, and the preset threshold is 200 Lux, the system will output a control signal instructing the supplementary lighting to operate at 60% power until the illuminance reaches the target value, and will monitor the lighting effect in real time to ensure that the ambient brightness meets the ambient brightness threshold.

[0070] Conversely, if the system detects that the current ambient brightness value is higher than the ambient brightness threshold, it will also calculate the brightness deviation to obtain the target ambient brightness value. Based on the shading capability of the shading device (such as an electrically controlled roller blind, dimming glass, or neutral density curtain), it will generate an ambient brightness adjustment command to control the shading device to perform shading operations. This command reduces the effective illuminance entering the camera's field of view by adjusting the opening angle, shading ratio, or light transmittance of the shading device. For example, in a strong outdoor light scene, if the detected brightness value is 1600 Lux, while the optimal recognition range for the vehicle camera is 300-600 Lux, the system will automatically execute shading control operations, closing the sunroof roller blind by 70% or reducing the light transmittance of the adjustable glass to 30%, thus reducing the ambient brightness to the target ambient brightness value.

[0071] To ensure the accuracy and dynamic stability of ambient brightness control, a closed-loop feedback mechanism can be employed during the ambient brightness adjustment process. This involves continuously comparing the adjusted brightness changes with the target ambient brightness value in real time. If the deviation exceeds a set tolerance range, the output is continuously adjusted until the ambient brightness value stabilizes at the target level. Furthermore, differentiated brightness control settings can be configured for the camera sensitivity of different vehicle models to further improve image acquisition quality and calibration accuracy.

[0072] This application, by collecting and comparing brightness data and combining the automatic control capabilities of supplementary lighting and shading devices, can effectively address the problem of light changes in the calibration site under different times and environmental conditions. This ensures the stability and consistency of image acquisition quality during ADAS calibration, avoids image deviations caused by excessive brightness or darkness, improves the target recognition success rate, and further enhances the accuracy, robustness, and environmental adaptability of ADAS calibration operations.

[0073] During the calibration process of ADAS systems, the size, deployment distance, and viewing angle requirements of the targets are predefined by the optical structure and algorithm model of the cameras equipped in the vehicle. Typically, standard-sized targets need to be deployed within a specific spatial distance range to ensure that the cameras can capture a complete and undistorted target pattern from an accurate viewing angle. If the calibration site space is insufficient, forcibly deploying standard-sized targets may cause the cameras to capture images with proportional distortion due to excessively close distances or excessively large angles. This is especially true in binocular camera systems, where parallax errors are amplified, thus affecting the accuracy of 3D spatial reconstruction. For monocular cameras, failure to scale and adapt the targets will result in abnormal target sizes in the images, affecting the calculation of camera intrinsic and extrinsic parameters, ultimately leading to inaccurate calibration data. For binocular cameras, using standard graphics in a space-constrained environment will result in discrepancies in the parallax values ​​seen by the two cameras, leading to unstable or even invalid 3D calculation results.

[0074] Therefore, to avoid ADAS calibration failure or a severe decrease in accuracy due to insufficient space in the calibration site leading to incorrect target identification, camera image distortion, mismatched viewing angles, and incorrect parallax calculations, thus causing ADAS calibration failure or a severe decrease in accuracy, in one embodiment, the target adjustment instruction generation process provided in this application specifically includes: if the environmental openness of the calibration site does not meet the spatial distance threshold, then determining the camera type equipped on the target vehicle based on the vehicle model information; if the camera type is a monocular camera, then calculating the required target scaling ratio based on the available space of the current calibration site, and generating a target adjustment instruction for scaling a standard-sized target; if the camera type is a binocular camera, then generating a target adjustment instruction for a target graphic used for parallax compensation based on the parallax parameters of the binocular camera. Furthermore, if the environmental openness of the calibration site meets the spatial distance threshold, then a standard-sized target corresponding to the vehicle model information is used.

[0075] In this embodiment, the collected environmental openness data of the calibration site is compared and analyzed with a preset spatial distance threshold. Environmental openness is a quantitative parameter reflecting the available space in front of the vehicle, to the left and right sides, and between the vehicle and the target. The unit is typically meters (m), which can be obtained through laser ranging equipment, visual recognition systems, or ultrasonic sensors. The spatial distance threshold is the minimum requirement for target placement distance based on different vehicle models during ADAS calibration, including forward viewing distance and horizontal placement width. If the environmental openness is greater than or equal to the spatial distance threshold, the system will assume the site meets the standard placement conditions and directly place a standard-sized target according to the predetermined placement coordinates without requiring target adjustment.

[0076] If the environmental openness of the calibration site does not meet the spatial distance threshold, meaning that some space is insufficient to meet the standard target deployment requirements, the type of camera equipped on the target vehicle will be further identified based on the vehicle model information. Camera type information is typically contained in a vehicle model database and corresponds one-to-one with the VIN code, including monocular, binocular, tricular, or multi-camera configurations. Based on this camera type, a subsequent target adjustment strategy can be determined to ensure that calibration can be completed even in non-standard sites through image reconstruction or target transformation.

[0077] When the camera type is identified as a monocular camera, such as those found in conventional passenger vehicles like the BYD Qin and Nissan Sylphy, the system dynamically calculates the appropriate target scaling ratio based on the available space parameters of the current site. Specifically, the system calculates the required target scaling ratio using similar trigonometric relationships, based on the field of view formed by the standard target at a preset distance and the maximum achievable distance between the target vehicle and the target in the existing calibration site. For example, if the standard deployment distance is 3 meters, but the current maximum achievable deployment distance is only 2 meters, the system will scale the target size by a 2:3 ratio and control the projection equipment or electronic display equipment to generate the corresponding scaled graphic through target adjustment commands, forming a new target pattern for monocular vision recognition.

[0078] When using binocular cameras, such as in vehicles like the Volkswagen Passat or Subaru Forester, in addition to considering the distance between the vehicle and the target, it's also necessary to calculate the parallax and depth reconstruction error between the binocular cameras. Therefore, after analyzing the maximum deployment distance in the field, a special target graphic that meets the depth synthesis requirements is generated by combining the baseline distance (e.g., 120mm) and parallax parameters such as focal length of the two cameras. This target graphic includes left and right view offset patterns or stereo calibration codes to adapt to the matching algorithm of the vehicle's dual cameras, ensuring that the two cameras can accurately form a fused field of view and extract 3D information. Furthermore, target adjustment commands control the projection or electronic display device to generate corresponding target graphics for parallax compensation, forming a new target pattern for binocular visual recognition.

[0079] For vehicles with cameras identified as tri-lens cameras or higher-level multi-lens cameras, such as the Wenjie M9 or Yangwang U8, which are high-end intelligent vehicles, the requirements for target placement distance and environmental openness are higher. If the current calibration site cannot meet the spatial distance threshold, effective calibration cannot be achieved through scaling or parallax compensation. In this case, a prompt message can be generated to inform the operator that the current site does not meet the calibration conditions and that a calibration site that meets the spatial requirements must be found to perform the calibration operation.

[0080] This application's embodiments, by recognizing and judging the ambient space and camera type, enable the system to flexibly adjust the target placement strategy according to actual site conditions. No professional judgment experience is required from the operator, allowing for highly vehicle-adaptable and easy-to-operate calibration preparation. For monocular camera vehicles, space shortages are compensated by scaling the target; for binocular camera vehicles, parallax compensation achieves targeted optimization of the target image; and for high-level camera types, a site change prompt is issued to avoid operator errors or calibration failures, thus improving the ADAS system's adaptability and calibration success rate in various environments.

[0081] During ADAS system calibration, cameras or radar sensors typically need to acquire static target images (such as targets) in a specific environment to calculate internal or external parameters. This process has high requirements for image purity and background interference. If obstructions (such as maintenance equipment, personnel, pillars, etc.) in the calibration area block part of the target pattern, it will directly cause target recognition failure or incorrect recognition contour, resulting in parameter calculation deviation and ultimately invalid calibration. If highly reflective surfaces (such as glass walls, metal tools, etc.) are present, they may cause light interference, leading to image overexposure or artifacts, misleading the visual recognition module. In addition, if personnel or vehicles move during the calibration process, they may be incorrectly identified as targets, interfering with the calibration logic.

[0082] Therefore, in order to avoid image recognition errors, target loss, measurement data distortion, or calibration failure during ADAS calibration due to interference sources (such as obstructions, reflective objects, or moving objects) in the calibration site, in one embodiment, the method of this application further includes: detecting whether there are interference sources in the current calibration site, the interference sources including at least obstructions; if there are interference sources, generating alarm information including the type of interference source, the coordinate location of the interference source, and interference source handling suggestions.

[0083] Based on the completion of vehicle model information reading and environmental data collection, the area around the vehicle and the predetermined target area are scanned and analyzed using methods such as camera recognition, laser ranging, and structured light scanning, thereby realizing the detection of potential interference sources. In this embodiment, interference sources include at least obstructions, such as toolboxes, brackets, maintenance equipment, or personnel in front of the target, which may cause occlusion, misjudgment, or calibration errors in the ADAS system's image recognition.

[0084] When detecting interference sources, the system uses previously read vehicle model information, including target type, target location, and camera recognition area, combined with real-time image and distance data, to build a spatial model of the working area between the vehicle and the target. This model determines whether any object enters the field of view and obstructs the view. If an abnormal object is found, and its geometric dimensions and color characteristics do not match the preset clean background template, it is identified as an interference source. Furthermore, the system can calculate the position of the interference source in a three-dimensional coordinate system, generating specific coordinate values ​​or relative position parameters (e.g., the interference source is located 1.2 meters in front of the vehicle, 0.5 meters to the left, and 0.6 meters above it) to ensure the accuracy and operability of the alerts.

[0085] After identifying the interference source, the system can further generate alarm information containing interference source handling suggestions based on the type and location of the interference source, and provide prompts through the human-machine interface. Handling suggestions may include phrases such as "Please remove the toolbox in the front repair bay," "It is recommended to move the obstacle on the left to avoid camera recognition failure," and "A reflector has been detected, which may affect image accuracy; please cover it." These prompts can be presented in text, voice, or image annotations. For vehicle models with high requirements for specific camera types, such as those using binocular or tri-lens cameras, the system can assign different warning levels to the same interference source based on its dependence on image clarity and boundary information.

[0086] Furthermore, to improve the relevance and efficiency of the prompts, the system can construct a polygonal judgment area for the field of view obstruction based on the camera's field of view, installation height, and target deployment area, thereby determining whether the interference source truly affects the calibration process. If the interfering object is located outside the camera's field of view, no prompt will be generated; if it is located within the preset interference source identification path, a mandatory alarm will be triggered to prevent user error from causing calibration failure, and also to ensure the accuracy of the system prompts, avoiding false alarms and missed alarms.

[0087] During ADAS calibration, different vehicle models typically have specific requirements regarding vehicle conditions, depending on the installation location of their cameras or radars, power supply methods, and operating temperature requirements. For example, some models require the camera to be powered while the engine is running to ensure stable operating voltage; others require the engine to be off to avoid vibration affecting image clarity or interfering with sensor stability. Furthermore, some cameras are installed inside the vehicle; if the air conditioning is on (especially at high fan speed), it may cause airflow disturbances, temperature changes, or condensation, affecting image acquisition quality. If these vehicle-related factors are not assessed and adjusted, they can easily lead to reduced calibration accuracy or even calibration failure.

[0088] Therefore, to avoid sensor power supply abnormalities, image acquisition failures, vehicle control response errors, or overall calibration interruptions during the calibration process due to inconsistencies between the current operating state of the target vehicle and the state required for ADAS calibration, in one embodiment, the method of this application further includes: determining whether the vehicle state of the target vehicle needs to be adjusted based on calibration parameters; if the vehicle state of the target vehicle needs to be adjusted and the target vehicle supports bus control, then sending control commands through the vehicle bus system according to the required adjustment of the vehicle state of the target vehicle; if the target vehicle does not support bus control, then outputting prompt information to complete the above-mentioned vehicle state adjustment operation.

[0089] In this embodiment, to ensure that the ADAS system calibration process meets the required operating environment and vehicle working conditions, the system needs to determine the state of the target vehicle before performing calibration. Specifically, the vehicle model information can be read from the diagnostic interface, and calibration parameters corresponding to the model can be extracted. Calibration parameters include, but are not limited to, specific state requirements of the vehicle during the calibration process, such as whether it needs to be in parking mode, whether the turn signal system needs to be turned off, and whether the vehicle lighting system needs to be switched to a specific mode. By analyzing the current vehicle state against the preset ideal working state in the calibration parameters, it is confirmed whether there are any vehicle state items that need adjustment.

[0090] After identifying vehicle status items requiring adjustment, the system further identifies whether the target vehicle supports bus control capabilities. If the target vehicle has bus control functionality, meaning it supports status configuration via the vehicle control bus (such as the CAN bus), and based on the vehicle status items requiring adjustment, corresponding control commands are generated and sent to the corresponding control module via the vehicle bus system. For example, in the calibration process of certain vehicle models, to ensure sensor stability, it is required to temporarily disable the vehicle's automatic parking radar system during calibration. In this case, a command to disable the automatic parking radar system is automatically generated and sent to the control module via the bus communication protocol, thus completing the system function shutdown.

[0091] On the other hand, for vehicles that do not support bus control, the system cannot complete the above-mentioned status configuration through program control. In this case, a prompt message can be generated to guide the operator to complete the necessary status adjustments manually. This prompt message may include the status item name, current status, desired status, and operation suggestions. The specific content of the prompt message will be displayed in graphic and textual form through the diagnostic interface or the vehicle terminal display device. For example, if the vehicle is detected to be in a non-parking gear, but the calibration requirement is a parking state, the system will prompt "Please place the shift lever in the P position to continue calibration".

[0092] More specifically, in practical applications, some vehicle ADAS sensors (such as cameras and radars) are powered by the vehicle's main power supply or auxiliary power supply, and whether these power supplies are available often depends on whether the engine is running. For example, if the engine is not running and the vehicle is in low-power mode, some sensor modules will not be able to be activated normally, thus failing to complete image or signal acquisition, resulting in the inability to perform calibration tasks. Conversely, if the engine should not be running during the calibration process, engine vibration, noise, or cooling airflow may affect the stability of the camera, causing image blurring or calibration distortion.

[0093] Similarly, the operation of the air conditioning system can also affect the stability of the calibration environment. For example, turning on the air conditioner in an enclosed space may cause fogging or temperature difference interference on the camera lens surface, affecting image quality; or high-volume airflow disturbances may cause slight shaking in the camera image, affecting the clarity of image acquisition and the accuracy of subsequent image recognition and processing. Therefore, in order to avoid problems such as insufficient sensor power supply, unstable image acquisition, field of view interference, and abnormal environmental control caused by the engine or air conditioning operating status, which may lead to calibration failure, parameter errors, or system malfunctions, in one embodiment, based on the above embodiment of "determining whether the vehicle status of the target vehicle needs to be adjusted according to calibration parameters", the vehicle status specifically includes engine start / stop and air conditioning switch, and the control commands may include engine start command, engine stop command, air conditioning start command, and air conditioning stop command.

[0094] It's understandable that the engine's start-stop status directly affects the vehicle's power stability and the camera module's operating voltage, while the air conditioning system's operation can interfere with airflow and noise levels inside and outside the vehicle, indirectly affecting target recognition or image processing accuracy. Therefore, the extracted calibration parameters can be used to determine whether engine start-stop control is necessary. For example, some vehicle models require the camera module to operate normally under ignition voltage conditions, thus requiring the engine to be running during calibration; other models require the engine to be shut off to avoid image blurring or stability degradation due to engine vibration. The system reads the current engine operating status through the diagnostic interface and compares it with the preset calibration status requirements. If the vehicle is currently in a non-target state and has bus control capabilities, the system will generate an engine start command or engine stop command and send it to the vehicle control module via the vehicle bus (such as the CAN bus) to automatically switch the engine status. For example, when the system detects that the vehicle is in ACC mode (auxiliary power), but the calibration parameters require the engine to be in RUN mode, the system automatically sends an ignition signal to start the engine.

[0095] In addition, it can also determine whether the operating status of the air conditioning system meets the requirements of the calibration parameters. For some models where cameras are installed inside the vehicle (such as in the rearview mirror or windshield area), if the air conditioning system is operating at high fan speed, it can easily cause fogging of the camera window, airflow disturbance, or temperature changes, affecting the accuracy of image acquisition. To avoid such interference, the calibration parameters can be used to require the air conditioning system to be turned off during the calibration process. Specifically, the start / stop status of the air conditioning compressor and the fan speed can be read first. If the current status is found to be inconsistent with the requirements, and the vehicle supports bus control, an air conditioning shutdown command is generated and sent to the on-board air conditioning control unit for execution via the bus. Conversely, if the calibration parameters require the air conditioning to be on (such as maintaining stable cabin temperature in high-temperature environments), the system can also automatically generate an air conditioning start command to execute the corresponding operation.

[0096] If the target vehicle does not support bus control, meaning the engine and air conditioning status cannot be remotely controlled via diagnostic equipment, manual operation prompts can be output based on the above judgment results to guide the operator to complete the corresponding status adjustments. The prompts can specifically display the current status, desired status, and operation path, such as: "The air conditioning is currently on; please manually turn it off before continuing," or "The engine is not started; please start the vehicle before continuing calibration." This ensures that non-automatically controlled vehicles also meet the calibration prerequisites and avoids calibration interruptions or failures due to inconsistent statuses.

[0097] For example, this application also proposes a specific application embodiment of the ADAS calibration assistance method, applicable to scenarios such as repair shops and 4S stores that have basic diagnostic equipment and intelligent assistance equipment. This embodiment includes:

[0098] S201: Connects to the vehicle diagnostic interface via diagnostic software to obtain the vehicle model information, including the vehicle year and camera type (e.g., monocular, binocular, or tri-lens). Based on the vehicle model information, corresponding calibration parameters (e.g., target type, target installation location requirements, and requirements for ambient brightness and occlusion) can be obtained.

[0099] S202: Collect environmental data of the current calibration site through environmental sensing devices (such as laser rangefinders, image recognition cameras, and light sensors), including environmental openness (available space size) and environmental brightness value (unit: Lux).

[0100] S203: Analyze the collected environmental information and calibration parameters to determine whether the current calibration environment meets the conditions for ADAS calibration execution.

[0101] S204: Identify the spatial conditions around the vehicle, simulate the maximum forward, backward, left, and right movement range that the vehicle can achieve in the current site, and calculate the coordinates of the optimal calibration area by combining the spatial distance threshold.

[0102] S205: Determine whether the target vehicle supports automatic movement. If it does, send a movement command via the bus control module to automatically move the vehicle to the optimal calibration area; if it does not, output guidance information on the system interface to remind the user to manually move the vehicle to the designated location.

[0103] S206: Compare and judge the ambient brightness. If the current ambient brightness value is lower than the ambient brightness threshold, control the supplementary light source to issue an ambient brightness adjustment command to increase the illuminance to the calibration range. If the current ambient brightness value is higher than the ambient brightness threshold, control the shading device to block or reduce the light brightness.

[0104] S207: Determine whether the current ambient openness meets the spatial distance threshold. If it does, directly set the target position according to the standard deployment plan and then jump to step 209; if it does not meet the threshold, then jump to step S208 to perform compensation processing based on the camera type.

[0105] S208: If the camera type is a monocular camera, calculate the required target scaling ratio based on the available space in the current calibration site, and generate a target adjustment command for scaling a standard-sized target; if the camera type is a binocular camera, generate a target adjustment command for the target graphic for parallax compensation based on the parallax parameters of the binocular camera; if the camera is a tri- or multi-lens type and the deployment space is insufficient, output a prompt message to remind the user to move to a larger space for calibration.

[0106] S209: Determine whether the vehicle status of the target vehicle needs to be adjusted based on the calibration parameters; if the vehicle status of the target vehicle needs to be adjusted and the target vehicle supports bus control, then send control commands through the vehicle bus system according to the required adjustment of the vehicle status of the target vehicle; if the target vehicle does not support bus control, then output a prompt message to prompt the user to manually adjust the vehicle status.

[0107] S210: After completing the adjustments to vehicle position, lighting, target, and vehicle status, the system displays a standard-sized target graphic or a special graphic after scaling and parallax compensation through an electronic display screen or projection device based on the calculation results.

[0108] S211: After confirming that the vehicle is in place, the environment meets the requirements, and the graphic display is complete, the system automatically calls the ADAS calibration module that matches the vehicle model and executes the calibration procedure; after calibration is completed, it outputs a prompt message and records the calibration process data and results for later viewing or synchronization to the cloud platform.

[0109] In this embodiment, the specific process of comparison in steps S201 to S211 is the same as or similar to the corresponding method in the above embodiment. For parts not described in detail, please refer to the method embodiment, which will not be repeated here.

[0110] It is understood that the above is merely an example of a specific situation and should not constitute any limitation on the implementation process of the embodiments of this application. In practical applications, the specific execution order, the integration method between modules, the calling logic and the triggering mechanism, etc., can be flexibly adjusted according to application requirements, hardware architecture or performance optimization goals, and all equivalent substitutions or equivalent variations should fall within the protection scope of this application.

[0111] To implement the ADAS calibration assistance method of this application embodiment, this application embodiment also provides an ADAS calibration assistance device, such as... Figure 2 As shown, the device includes:

[0112] The vehicle model recognition unit 201 is used to obtain the vehicle model information of the target vehicle through the diagnostic interface, so as to extract the calibration parameters corresponding to the vehicle model information; the calibration parameters include at least the target size, spatial distance threshold and ambient brightness threshold.

[0113] The environmental acquisition unit 202 is used to acquire environmental data of the calibration site; the environmental data includes the ambient openness and ambient brightness values.

[0114] The instruction generation unit 203 is used to generate an adjustment instruction set based on the vehicle model information, the calibration parameters, and the environmental data; the adjustment instruction set includes one or more of the following: vehicle position adjustment instruction, ambient brightness adjustment instruction, and target adjustment instruction;

[0115] The calibration unit 204 is used to execute the adjustment instruction set, trigger the start of the ADAS calibration program, and complete the ADAS calibration.

[0116] In one embodiment, the process of generating the vehicle position adjustment command specifically includes:

[0117] The vehicle size is determined based on the vehicle model information, and it is also determined whether the target vehicle supports autonomous driving functions.

[0118] Based on the vehicle size, the spatial distance threshold, and the environmental openness, calculate the coordinates of the optimal calibration area;

[0119] If the target vehicle supports autonomous driving, a vehicle position adjustment command is generated based on the optimal calibration area coordinates to move the target vehicle to the optimal calibration area coordinates.

[0120] In one embodiment, the process of generating the ambient brightness adjustment command specifically includes:

[0121] If the ambient brightness value of the calibration site is lower than the ambient brightness threshold, an ambient brightness adjustment command is generated based on the ambient brightness value and the ambient brightness threshold to control the supplementary lighting device to enhance the ambient brightness value to the target ambient brightness value.

[0122] If the ambient brightness value of the calibration site is higher than the ambient brightness threshold, an ambient brightness adjustment command is generated based on the ambient brightness value and the ambient brightness threshold to control the shading device to reduce the ambient brightness value to the target ambient brightness value.

[0123] In one embodiment, the process of generating the target adjustment command specifically includes:

[0124] If the ambient openness of the calibration site does not meet the spatial distance threshold, the type of camera equipped on the target vehicle is determined based on the vehicle model information.

[0125] If the camera type is a monocular camera, the required target scaling ratio is calculated based on the available space in the current calibration site, and a target adjustment command for scaling a standard-sized target is generated.

[0126] If the camera type is a binocular camera, then a target adjustment instruction for the target graphic used for parallax compensation is generated based on the parallax parameters of the binocular camera.

[0127] In one embodiment, the method further includes:

[0128] Detect whether there are any interference sources in the current calibration site, and the interference sources include at least obstructions;

[0129] If an interference source is present, generate an alarm message that includes the type of interference source, its coordinates, and suggested handling methods.

[0130] In one embodiment, the method further includes:

[0131] Determine whether the vehicle status of the target vehicle needs to be adjusted based on the calibration parameters;

[0132] If it is necessary to adjust the vehicle status of the target vehicle and the target vehicle supports bus control, then control commands are sent through the vehicle bus system according to the required adjustment of the vehicle status of the target vehicle.

[0133] If the target vehicle does not support bus control, a prompt message will be output to complete the above vehicle status adjustment operation.

[0134] In one embodiment, the vehicle status includes engine start / stop and air conditioning switch, and the control commands include engine start command, engine stop command, air conditioning start command, and air conditioning stop command.

[0135] It should be noted that the ADAS calibration assistance device provided in the above embodiments is only illustrated by the division of the above-described program modules. In practical applications, the above processing can be assigned to different program modules as needed, that is, the internal structure of the device can be divided into different program modules to complete all or part of the processing described above. In addition, the ADAS calibration assistance device and the ADAS calibration assistance method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0136] Based on the hardware implementation of the above program modules, and in order to implement the ADAS calibration assistance method provided in this application embodiment, this application embodiment also provides a computer device, such as... Figure 3 As shown, computer device 300 includes:

[0137] Central processing unit 301, memory 302, and input / output interface 303;

[0138] The memory 302 is a short-term storage memory or a persistent storage memory;

[0139] The central processing unit 301 is configured to communicate with the memory 302 and execute instructions in the memory 302 to perform any of the above-described ADAS calibration assistance methods.

[0140] Of course, in practical applications, the various components in the computer device 300 are coupled together through a bus system 304. It is understood that the bus system 304 is used to realize communication between these components. In addition to a data bus, the bus system 304 also includes a power bus, a control bus, and a status signal bus. However, for clarity, in... Figure 3 The general designated all buses as Bus System 304.

[0141] The memory 302 in this embodiment is used to store various types of data to support the operation of the computer device 300. Examples of such data include any computer program used to operate on the computer device 300.

[0142] It is understood that when the processor in the computer device described above executes the computer program, it can also realize the functions of each unit in the corresponding device embodiments described above, which will not be repeated here. Exemplarily, the computer program can be divided into one or more modules / units, one or more modules / units are stored in memory and executed by the processor to complete the various embodiments of this application. One or more modules / units can be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the computer device. For example, the computer program can be divided into units in the aforementioned computer device, and each unit can implement the specific functions described in the corresponding computer device above.

[0143] Computer equipment can be desktop computers, laptops, handheld computers, and cloud servers, among other computing devices. Computer equipment may include, but is not limited to, processors and memory. Those skilled in the art will understand that processors and memory are merely examples of computer equipment and do not constitute a limitation on the computer equipment. It may include more or fewer components, or combinations of certain components, or different components. For example, computer equipment may also include input / output devices, network access devices, buses, etc.

[0144] A processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of a computer device, connecting all parts of the computer device through various interfaces and lines.

[0145] Memory can be used to store computer programs and / or modules. The processor performs various functions of the computer device by running or executing the computer programs and / or modules stored in the memory, and by accessing data stored in the memory. Memory can primarily include a program storage area and a data storage area. The program storage area can store the operating system, at least one application program required for a function, etc.; the data storage area can store data created based on terminal usage, etc. Furthermore, memory can include high-speed random access memory, and can also include non-volatile memory, such as hard disks, RAM, plug-in hard disks, smart media cards (SMC), secure digital cards (SD), flash cards, at least one disk storage device, flash memory device, or other volatile solid-state storage devices.

[0146] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, performs any of the ADAS calibration assistance methods described above.

[0147] This application also provides a computer program product storing a computer program / instruction, which, when executed by a processor, is used to implement the ADAS calibration assistance method described in the first aspect or any specific implementation of the first aspect of this application.

[0148] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0149] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between apparatuses or units through some interfaces, and may be electrical, mechanical, or other forms.

[0150] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0151] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0152] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

Claims

1. An ADAS calibration assistance method, characterized in that, include: The vehicle model information of the target vehicle is obtained through the diagnostic interface to extract the calibration parameters corresponding to the vehicle model information; the calibration parameters include at least the target size, spatial distance threshold and ambient brightness threshold. Collect environmental data of the calibration site; the environmental data includes ambient openness and ambient brightness values. Based on the vehicle information, the calibration parameters, and the environmental data, an adjustment instruction set is generated; the adjustment instruction set includes one or more of the following: vehicle position adjustment instruction, ambient brightness adjustment instruction, and target adjustment instruction. After executing the set of adjustment instructions, the Advanced Driver Assistance System (ADAS) calibration procedure is initiated and completed.

2. The method according to claim 1, characterized in that, The process of generating the vehicle position adjustment command specifically includes: The vehicle size is determined based on the vehicle model information, and it is also determined whether the target vehicle supports autonomous driving functions. Based on the vehicle size, the spatial distance threshold, and the environmental openness, calculate the coordinates of the optimal calibration area; If the target vehicle supports autonomous driving, a vehicle position adjustment command is generated based on the optimal calibration area coordinates to move the target vehicle to the optimal calibration area coordinates.

3. The method according to claim 1, characterized in that, The process of generating the ambient brightness adjustment command specifically includes: If the ambient brightness value of the calibration site is lower than the ambient brightness threshold, an ambient brightness adjustment command is generated based on the ambient brightness value and the ambient brightness threshold to control the supplementary lighting device to enhance the ambient brightness value to the target ambient brightness value. If the ambient brightness value of the calibration site is higher than the ambient brightness threshold, an ambient brightness adjustment command is generated based on the ambient brightness value and the ambient brightness threshold to control the shading device to reduce the ambient brightness value to the target ambient brightness value.

4. The method according to claim 1, characterized in that, The process of generating the target adjustment command specifically includes: If the ambient openness of the calibration site does not meet the spatial distance threshold, the type of camera equipped on the target vehicle is determined based on the vehicle model information. If the camera type is a monocular camera, the required target scaling ratio is calculated based on the available space in the current calibration site, and a target adjustment command for scaling a standard-sized target is generated. If the camera type is a binocular camera, then a target adjustment instruction for the target graphic used for parallax compensation is generated based on the parallax parameters of the binocular camera.

5. The method according to claim 1, characterized in that, The method further includes: Detect whether there are any interference sources in the current calibration site, and the interference sources include at least obstructions; If an interference source is present, generate an alarm message that includes the type of interference source, its coordinates, and suggested handling methods.

6. The method according to claim 1, characterized in that, The method further includes: Determine whether the vehicle status of the target vehicle needs to be adjusted based on the calibration parameters; If it is necessary to adjust the vehicle status of the target vehicle and the target vehicle supports bus control, then control commands are sent through the vehicle bus system according to the required adjustment of the vehicle status of the target vehicle. If the target vehicle does not support bus control, a prompt message will be output to complete the above vehicle status adjustment operation.

7. The method according to claim 6, characterized in that, The vehicle status includes engine start / stop and air conditioning switch, and the control commands include engine start command, engine stop command, air conditioning start command, and air conditioning stop command.

8. An ADAS calibration auxiliary device, characterized in that, include: The vehicle model recognition unit is used to obtain the vehicle model information of the target vehicle through a diagnostic interface, so as to extract the calibration parameters corresponding to the vehicle model information; the calibration parameters include at least the target size, spatial distance threshold and ambient brightness threshold. An environmental acquisition unit is used to collect environmental data of the calibration site; the environmental data includes ambient openness and ambient brightness values. The instruction generation unit is used to generate an adjustment instruction set based on the vehicle model information, the calibration parameters, and the environmental data; the adjustment instruction set includes at least one of the following: vehicle position adjustment instruction, ambient brightness adjustment instruction, and target adjustment instruction; The calibration unit is used to execute the adjustment instruction set, trigger the start of the ADAS calibration program, and complete the ADAS calibration.

9. A computer device, characterized in that, include: Central processing unit, memory, and input / output interfaces; The memory is either a short-term storage memory or a persistent storage memory; The central processing unit is configured to communicate with the memory and execute instructions in the memory to perform the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it performs the method as described in any one of claims 1 to 7.

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