Intelligent temperature control method and system for intelligent driving multi-camera

Abstract

The application discloses an intelligent temperature control method and system for a multi-view camera of intelligent driving, and relates to the technical field of intelligent driving; the application collects temperature data of preset sensing points in a multi-view camera, and collects environmental parameters outside a vehicle; a target temperature control standard is matched according to the environmental parameters and the working state of the multi-view camera; and a temperature intelligent control scheme is generated based on the target temperature control standard and the temperature data of the sensing points; the application can accurately trigger the corresponding level of temperature control strategy in a low-temperature environment, effectively prevents the normal work of the multi-view camera from being affected due to the mismatch of the temperature control standard, improves the scene adaptability and reliability of the temperature control strategy from the decision source, guarantees the stable work of the multi-view camera in the whole low-temperature range, maintains the imaging quality and sensing accuracy, and provides continuous and reliable visual input for the environmental sensing of intelligent driving.

Description

Technical Field

[0001] This invention belongs to the field of intelligent driving technology and relates to intelligent temperature control technology for multi-camera systems, specifically an intelligent temperature control method and system for intelligent driving multi-camera systems. Background Technology

[0002] As the core visual sensor for vehicle environmental perception, multi-view cameras for intelligent driving are prone to problems such as lens frost and ice formation, increased dark current in sensors, chip timing deviation, and optical axis calibration drift in low-temperature environments. These problems directly lead to decreased image quality and perception failure, affecting the safety and reliability of intelligent driving systems.

[0003] This invention provides an intelligent temperature control method and system for intelligent driving multi-camera systems to solve the above-mentioned technical problems. Summary of the Invention

[0004] The present invention aims to solve at least one of the technical problems existing in the prior art; to this end, the present invention proposes an intelligent temperature control method and system for intelligent driving multi-view cameras.

[0005] To achieve the above objectives, a first aspect of the present invention provides an intelligent temperature control method for a multi-view camera used in intelligent driving, comprising: Temperature data from preset sensing points in the multi-view camera system is collected, as well as environmental parameters from outside the vehicle. The sensing points are preset based on the impact of low-temperature environment on the multi-view camera system, and the environmental parameters include temperature and humidity data. The target temperature control standard is obtained by matching environmental parameters and the working status of the multi-view camera; the target temperature control standard includes the temperature control range, triggering conditions, and temperature control target; A temperature intelligent control scheme is generated based on the target temperature control standard and the temperature data of the sensing points. The zone heating unit is controlled based on the temperature intelligent control scheme to realize intelligent temperature control of the multi-view camera. The zone heating unit is set according to the heating target.

[0006] In one possible implementation, the target temperature control standard is obtained by matching environmental parameters and the operating status of the multi-view camera, including: Extract environmental parameters and the working status of the multi-view camera; the working status includes normal operation, lens frosting, or lens icing. The target temperature control standard is obtained by matching environmental parameters and temperature data from the temperature level control standard; the temperature level control standard is based on the preset critical failure temperature of each component in the multi-view camera.

[0007] In one possible implementation, a preset temperature control standard is included, including: The temperature control zone is divided into several temperature regulation ranges; where the temperature control zone is the temperature range corresponding to the low temperature environment; Based on the failure critical temperature of each component of the multi-camera system, several temperature control ranges are matched with temperature control targets and triggering conditions to generate several levels of temperature control standards; these several levels of temperature standards are then integrated into a temperature level control standard.

[0008] In one possible implementation, the target temperature control standard is modified, including: The presence of pre-set risks in each lens of a multi-view camera is determined based on environmental parameters and lens temperature. These pre-set risks include the risk of frost or ice formation, and the lens temperature is extracted from the collected temperature data. Yes, the target temperature control standard level is upgraded to obtain the updated target temperature control standard; No, when the multi-camera system is in the cold start phase, the target temperature control standard will be adjusted to the highest level; otherwise, the target temperature control standard will not be adjusted.

[0009] In one possible implementation, the partitioned heating unit includes a lens heating assembly, a sensor and chip heating assembly, and a lens holder and housing heating assembly.

[0010] In one possible implementation, the intelligent temperature control solution includes a lens intelligent control solution, a sensor and chip intelligent control solution, and a lens bracket and housing intelligent control solution; the generation of the lens intelligent control solution includes: The target temperature is determined based on the temperature control target in the target temperature control standard, and the operating conditions are determined based on the vehicle speed; the operating conditions include low-speed conditions, normal conditions and high-speed conditions. Based on operating conditions, target temperature, and environmental parameters, the optimal heating power and its duty cycle are matched from the heating power range corresponding to the target temperature control standard. Based on the heating power and its duty cycle, a lens intelligent control solution is generated.

[0011] In one possible implementation, when matching the optimal heating power and its duty cycle from the heating power range corresponding to the target temperature control standard, a corresponding heating power sub-range is determined from the heating power range based on the temperature sensitivity of each lens in the multi-camera system, and the optimal heating power and duty cycle of the lens are matched within the heating power sub-range.

[0012] In one possible implementation, matching the optimal heating power and its duty cycle includes: Invoke a pre-trained parameter matching model; where the parameter matching model is built based on an artificial intelligence model; After standardizing the operating conditions, target temperature, environmental parameters, and heating power range, the data is input into the parameter matching model, which outputs the optimal heating power and its duty cycle.

[0013] A second aspect of the present invention provides an intelligent temperature control system for a multi-view camera used in intelligent driving, comprising: Environmental perception module: used to collect temperature data from preset sensing points in the multi-view camera, as well as to collect environmental parameters outside the vehicle; Intelligent temperature control module: used to obtain the target temperature control standard by matching environmental parameters and the working status of the multi-view camera; and used to generate a temperature intelligent control scheme based on the target temperature control standard and the temperature data of the sensing points, and control the zone heating unit based on the temperature intelligent control scheme to realize intelligent temperature control of the multi-view camera.

[0014] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention collects temperature data from preset sensing points in a multi-view camera system, as well as environmental parameters from outside the vehicle. Based on the environmental parameters and the operating status of the multi-view camera, a target temperature control standard is obtained. A temperature intelligent control scheme is generated based on the target temperature control standard and the temperature data from the sensing points. This invention can accurately trigger corresponding temperature control strategies in low-temperature environments, effectively preventing the multi-view camera from operating normally due to temperature control standard mismatch. It improves the scenario adaptability and reliability of the temperature control strategy from the decision-making source, ensuring stable operation of the multi-view camera across the entire low-temperature range, maintaining imaging quality and sensing accuracy, and providing continuous and reliable visual input for intelligent driving environmental perception.

[0015] 2. This invention first determines the target temperature based on the target temperature control standard. Then, it matches the optimal heating power and duty cycle to the operating conditions, environmental parameters, and the temperature sensitivity of each lens. Based on the matched heating power and duty cycle, a differentiated and precise intelligent lens control scheme is formed. This scheme adapts the heating power to the temperature control requirements and temperature tolerance of different lenses, ensuring that the heating power is matched to the lens characteristics one by one, avoiding uneven heating or component damage caused by power mismatch. Simultaneously, using a unified target temperature as a benchmark, this invention maintains a high degree of temperature consistency among the lenses through precise power and duty cycle control, eliminating problems such as thermal expansion and contraction deformation, optical axis misalignment, and stereo matching failure caused by lens temperature differences. Therefore, this invention can ensure the consistency of temperature control among the lenses of a multi-view camera, guaranteeing the accuracy of multi-view visual fusion and the reliability of environmental perception, while achieving optimal matching between heating power and lens characteristics. This reduces energy consumption while ensuring effective temperature control, improving the low-temperature imaging quality and long-term operational stability of the intelligent driving multi-view camera. Attached Figure Description

[0016] 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 some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a schematic diagram of the method flow for the intelligent temperature control method for intelligent driving multi-view cameras in an embodiment of the present invention; Figure 2 This is a schematic diagram illustrating the principle of modifying the target temperature control standard in an embodiment of the present invention; Figure 3 This is a schematic diagram illustrating the generation principle of the lens intelligent control scheme in an embodiment of the present invention. Detailed Implementation

[0018] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Intelligent driving multi-view cameras are machine vision perception systems designed for autonomous driving environmental perception tasks. They consist of two or more optical imaging units with fixed spatial baselines, different field of view / focal lengths, or different spectral responses. Through multi-view image synchronous acquisition, stereo matching, feature fusion, and spatiotemporal alignment, they achieve structured perception of three-dimensional traffic scenes. They are core visual sensors in the vehicle environment perception layer and are often combined with millimeter-wave radar, lidar, and IMU to form a multi-source fusion perception architecture.

[0020] In low-temperature environments (usually below -10℃, with extreme temperatures ranging from -30℃ to -40℃), if multi-camera systems do not employ active temperature control strategies, a chain reaction of anomalies will occur across multiple levels, including optics, hardware and electrical systems, and imaging quality, directly leading to perception failure or even the discontinuation of intelligent driving functions.

[0021] At the optical level, under low temperature and high humidity conditions, the surface temperature of the lens is lower than the dew point or even the freezing point. Water vapor in the air condenses into frost or thin ice on the lens, causing visual data to become blurred, contrast to decrease, and high-frequency details to be lost. In severe cases, the image appears as a large area of ​​white fog. The lens bracket, housing, and lens adhesive layer of the multi-view camera will undergo slight displacement and stress deformation due to thermal expansion and contraction at low temperatures, resulting in optical axis offset, internal and external parameter drift, etc., which will damage the calibration accuracy of the multi-view camera.

[0022] At the hardware level, image sensors experience increased dark current, higher readout noise, and decreased photoelectric conversion efficiency at low temperatures, leading to a surge in image noise, compression of dynamic range, and a decline in low-light recognition capabilities. Furthermore, automotive chips experience increased internal resistance and timing characteristic shifts at low temperatures, which can easily result in frame delays, frame drops, increased synchronization errors, and decreased processing power. In severe cases, this can lead to startup anomalies, restarts, or data packet loss.

[0023] At the imaging level, frost and fog scatter light, causing AE / AWB algorithms to misjudge, resulting in overexposure, severe color cast, and grayscale distortion, which significantly reduces the accuracy of target recognition models based on color and texture. The degree of frost, light transmission attenuation, and distortion drift vary among lenses, causing the differences between left and right / multi-view images to exceed the algorithm's compensation range, directly leading to extremely low depth confidence and invalid perception output.

[0024] Please see Figure 1 The first aspect of this invention provides an intelligent temperature control method for a multi-camera system for intelligent driving, comprising: Temperature data from preset sensing points in a multi-camera system is collected, along with environmental parameters from outside the vehicle. The sensing points are preset based on the impact of low-temperature environments on the multi-camera system, and the environmental parameters include temperature and humidity data. A target temperature control standard is obtained by matching the environmental parameters with the operating status of the multi-camera system. This target temperature control standard includes a temperature control range, triggering conditions, and a temperature control target. A temperature intelligent control scheme is generated based on the target temperature control standard, and the zoned heating units are controlled based on this scheme to achieve intelligent temperature control of the multi-camera system. The zoned heating units are configured according to the heating target.

[0025] The environmental perception module is used to collect temperature data from preset sensing points in the multi-view camera, as well as environmental parameters outside the vehicle. The sensing points are preset based on the impact of low temperature environment on the multi-view camera, and the environmental parameters include temperature data and humidity data.

[0026] The environmental perception module is used to achieve multi-point, high-precision, and real-time data acquisition, providing a data foundation for intelligent temperature control decisions. The module employs a distributed temperature measurement scheme, deploying temperature sensors at pre-set sensing points within a multi-camera system to collect temperature data at these points. The module also collects external environmental parameters of the intelligent driving vehicle, including ambient temperature and humidity. These parameters can be acquired through onboard sensors or by independently deployed sensors.

[0027] Sensing points are set up based on the impact of low-temperature environments on multi-view cameras. For example, low temperatures can cause frost or thin ice to condense on the lenses, so the lens surface is used as a sensing point. Based on this principle, a temperature sensor is placed on the lens surface of each lens (e.g., wide-angle, medium-telephoto, telephoto), the image sensor (e.g., CMOS / CCD) package, the lens bracket, the ISP processing chip, and the camera housing to measure temperature data at each sensing point. High-precision PT1000 platinum resistance temperature sensors can be used, with a temperature range of -50℃ to 150℃ and an accuracy of ±0.1℃. If environmental parameters are collected through independently deployed sensors, an additional sensor is placed outside the multi-view camera to collect environmental parameters and assist in assessing the risk of frost / ice formation.

[0028] The collected temperature data and environmental parameters can be transmitted to the intelligent temperature control module at a set frequency or in real time. Specifically, the transmission can be carried out via the SPI bus, and the collection timestamp is recorded to ensure spatiotemporal synchronization with the imaging data from the multi-view camera.

[0029] When making intelligent temperature decisions, the intelligent temperature control module first collects environmental parameters and extracts the operating status of the multi-camera system through the environmental perception module. The environmental parameters include ambient temperature and humidity. Based on the environmental parameters and operating status, it matches the target temperature control standard from the temperature level control standard, and performs intelligent temperature control based on the target temperature control standard to ensure that the multi-camera system does not affect intelligent driving.

[0030] It should be noted that the working status type of the multi-view camera is used to match the triggering conditions in the temperature control standard. The working status includes normal operation, frosting, icing, etc., and the working status can be determined by the image data collected by the multi-view camera.

[0031] When making intelligent temperature control decisions, the intelligent temperature control module needs to retrieve a pre-built temperature level control standard. The temperature control range is divided into several temperature adjustment ranges. Simultaneously, the temperature control targets and trigger conditions for each temperature adjustment range are set based on the critical failure temperatures of various components in the multi-view camera at low temperatures, and these are integrated to obtain the temperature level control standard. The temperature level control standard is set as follows: 1) Level 1 temperature control standard: Used for general heat preservation, with a temperature control range of (-10℃, 0℃). The trigger condition is that the ambient temperature is ≤0℃, the camera is operating normally, and there are no signs of frost / ice. The temperature control goal is to maintain the temperature of each component above 0℃ to avoid condensation and ensure clear imaging. 2) Level 2 temperature control standard: used for light heating, with a temperature control range of (-20℃, -10℃). The triggering condition is that the ambient temperature is ≤-10℃, or the temperature of a certain lens element is ≤0℃, with no obvious frost. The temperature control target is to raise the lens temperature to [5℃, 10℃] and the image sensor temperature to above 0℃ to ensure image quality. 3) Three-level temperature control standard: used for medium heating, with a temperature control range of (-30℃, -20℃). The triggering conditions are ambient temperature ≤ -20℃, or slight frost on the lens, or abnormal noise in the image sensor. The temperature control target is to raise the lens temperature to [10℃, 15℃], and the image sensor and ISP chip temperature to above 5℃ to eliminate frost. 4) Level 4 temperature control standard: Used for extreme heating, with a temperature control range of (-∞℃, -30℃). The triggering conditions are ambient temperature ≤ -30℃, or lens icing, abnormal camera startup, or severely blurred imaging. The temperature control goal is to prioritize camera startup, raise the lens temperature to [15℃, 20℃], and raise the core component (image sensor, ISP chip) temperature to above 10℃ for rapid de-icing and defrosting.

[0032] Please see Figure 2 After matching the target temperature control standard based on environmental parameters and the operating status of the multi-view camera, the target temperature control standard can be modified according to the operating stage of the multi-view camera or preset risks. Preset risks include the risk of lens frost / icing.

[0033] When the multi-view camera is in the cold start phase (0-3 minutes after camera startup), the highest level of temperature control standard is selected for intelligent temperature control to ensure that the temperature of the core components of the multi-view camera rises rapidly and that it quickly enters working condition. When the multi-view camera is in the normal operation phase, intelligent temperature control is performed according to the temperature control standard that matches the location temperature and environmental parameters to ensure image quality.

[0034] When it is determined that there is a risk of frost or ice formation in each lens of the multi-lens camera based on environmental parameters and lens temperature, the matching target temperature control standard is upgraded, such as upgrading the level 2 temperature control standard to the level 3 temperature control standard. If the target temperature control standard is already the highest level temperature control standard, no upgrade or correction is made.

[0035] For example, suppose the target temperature control standard is a level 2 temperature control standard; if the multi-camera is in the cold start phase (0-3 minutes after the camera starts), the level 4 temperature control standard will be used as the target temperature control standard; if the ambient humidity is ≥60% and the lens temperature is ≤0℃, it is determined that there is a risk of frosting, and the temperature control standard level will be upgraded to level 3 temperature control standard as the target temperature control standard.

[0036] After determining the final target temperature control standard, a temperature intelligent control scheme can be generated based on the target temperature control standard. This scheme then controls the zoned heating units to achieve intelligent temperature control for the multi-camera system. In this embodiment, the temperature intelligent control scheme is mainly constructed based on the temperature control target and temperature data from the sensing points within the target temperature control standard. This, combined with temperature data feedback calibration, enables intelligent temperature control for the multi-camera system.

[0037] For example, the temperature control target in the target temperature control standard is extracted, the target temperature is determined according to the temperature control target, and the heating unit of the zone is controlled to heat the zone based on the difference between the target temperature and the actual collected temperature data until the temperature data is consistent with the target temperature. This completes the intelligent temperature control.

[0038] Due to the different temperature control requirements of multi-camera systems, when performing intelligent temperature control on multi-camera systems, it is necessary to heat the lens and other core components in separate zones to achieve precise temperature control.

[0039] 1) Lens heating assembly: A transparent electric heating film with a thickness of ≤0.1mm and a light transmittance of ≥95% is used to adhere to the outer lens surface of each lens to avoid obstructing the imaging field of view; the heating film adopts pulse width modulation (PWM) control mode, and the working power can be dynamically adjusted between 0W-20W. The pulse duty cycle is adjusted in real time according to the temperature control standard and the lens temperature to achieve precise heating, while preventing the heating film from overheating and damaging the lens coating.

[0040] 2) Sensor and chip heating components: Flexible heating tape is wrapped around the image sensor package and the ISP chip surface. A constant temperature heating mode is adopted, and the working power is controlled between 0W and 10W. The heating status is adjusted in real time through temperature feedback to ensure that the working temperature of the image sensor and ISP chip is stable within the target range, avoiding low temperature drift and computing power reduction.

[0041] 3) Lens bracket and housing heating assembly: Sheet-shaped heating elements are arranged on the inner wall of the camera bracket and housing, with the working power controlled between 0W and 5W. They are mainly used for heat preservation during the first stage of temperature control, as well as to prevent bracket deformation under extreme low temperatures and ensure the stability of multi-view baseline. The housing heating adopts an intermittent heating mode to avoid energy waste caused by continuous heating.

[0042] It should be noted that all of the above heating units have built-in overheat protection sensors. When the temperature of the heated part exceeds 25°C (lens) or 15°C (image sensor / ISP chip), the heating power supply is automatically cut off to prevent the components from overheating and being damaged. At the same time, reverse connection protection and short circuit protection circuits are set to improve the reliability of the temperature control system.

[0043] During operation, multi-camera systems used in intelligent driving typically maintain real-time temperature consistency with the target temperature through feedback adjustment. This method only adjusts when a temperature difference arises. However, significant temperature differences between individual lenses can affect the final image quality. Specifically, if the lens temperatures of the various lenses in a multi-camera system are inconsistent, it may cause stereo matching failure, thus impacting the final image quality. Furthermore, different heating methods are required for intelligent temperature control of multi-camera systems under different road conditions. Using a single heating method cannot simultaneously meet the needs of both high-speed and low-speed driving conditions in intelligent driving vehicles.

[0044] Please see Figure 3This invention determines the target temperature and heating power of each heating unit based on the temperature control target in the temperature level control standard, and identifies the operating conditions of the intelligent driving vehicle; it matches the heating power and duty cycle according to the target temperature, environmental parameters, operating conditions and heating power range, and integrates the target temperature of the lens and the matched heating power and duty cycle into a lens intelligent control scheme.

[0045] In one example, due to the different functions and temperature sensitivities of the lenses in a multi-camera system, it is necessary to match the optimal heating power and duty cycle for each lens within the heating power range, based on the operating conditions, target temperature, and environmental parameters. The lens identification, operating conditions, target temperature, environmental parameters, and heating power range are standardized (including normalization or one-thermal encoding) and used as model input data. This model input data is then fed into a pre-built parameter matching model to obtain the optimal heating power and duty cycle for each lens. By controlling the lens temperature according to this heating power and duty cycle, the real-time temperature can be kept consistent with the target temperature under the current operating conditions and environmental parameters.

[0046] In multi-camera systems, apart from the lens temperature which needs to be kept consistent, the temperatures of other core components do not need to be kept consistent. Therefore, a temperature intelligent control scheme can be generated based on the temperature control target in the target temperature control standard, such as a sensor and chip intelligent control scheme and a lens bracket and housing intelligent control scheme.

[0047] It should be noted that the operating conditions of intelligent driving vehicles include high-speed conditions, normal conditions, and low-speed conditions. The classification of operating conditions can be referred to as follows: when the vehicle speed is ≤30km / h, it is considered a low-speed condition; when the vehicle speed is ≥80km / h, it is considered a high-speed condition; and when the speed is 30km / h < vehicle speed <80km / h, it is considered a normal condition.

[0048] It is worth noting that the heating power and duty cycle are obtained by matching the target temperature of the lens, environmental parameters, and heating power range. These heating power and duty cycle are important parameters that determine the energy consumption of intelligent temperature control and the consistency of lens temperature, and need to be considered when constructing the matching model.

[0049] It's important to explain that the core function of a multi-view camera is to achieve high-precision stereo matching and environmental perception through multi-view and multi-focal length collaboration. Its perception accuracy depends on the stability of the multi-view baseline, i.e., the relative positions and optical axis angles of each lens. Inconsistent lens temperatures will affect the stability of the multi-view baseline. Specifically, temperature differences can cause varying degrees of micro-deformation in the lenses and lens holders due to thermal expansion and contraction, leading to optical axis shift, compromising multi-target positioning accuracy, and causing stereo matching failure. Inconsistent temperatures will also cause differences in the light transmittance and image clarity of each lens, resulting in inconsistent multi-view image features, hindering effective fusion of multi-view data, drastically reducing target recognition rates, and affecting the normal operation of intelligent driving functions. If the temperature difference is too large (e.g., exceeding 3°C), it will exacerbate lens baseline drift, potentially causing irreversible hardware deformation with long-term use, further reducing perception reliability. Apart from lens temperature, other core components of each lens do not require uniform temperature.

[0050] The control of multi-camera systems used in intelligent driving also faces the challenge of varying lens requirements. Multi-camera systems consist of lenses with different focal lengths, each with different sensitivities to heat and temperature control priorities. Telephoto lenses are responsible for long-distance detection and are the core of AEB and LCC systems, making them the most temperature-sensitive; medium-telephoto lenses are responsible for regular road detection; and wide-angle lenses are responsible for close-range field of view detection, offering the highest temperature tolerance.

[0051] To address the issue of varying lens requirements, this invention determines a corresponding sub-range of heating power from the heating power range based on the temperature sensitivity of each lens, and associates this sub-range with the corresponding lens. Based on the target temperature of each lens element, environmental parameters, and operating conditions, the optimal heating power and duty cycle are determined from the heating power sub-range, thereby generating a lens intelligent control solution.

[0052] Higher heating power enables rapid heating and stable temperature control. This quickly counteracts the effects of low temperatures on sensitive lenses, preventing frost / icing and component deformation. Furthermore, it maintains a stable target lens temperature, preventing imaging anomalies caused by temperature fluctuations and ensuring the sensitive lens remains within its optimal operating temperature range. Therefore, in determining the heating power sub-range, the more temperature-sensitive the lens, the higher its corresponding upper limit for the heating power sub-range.

[0053] In one example, assuming the lens heating assembly operates within a power range of [0W, 20W], and considering that telephoto lenses are more sensitive to heat, their heating power sub-range is determined to be [15W, 20W], medium telephoto lenses to be [8W, 15W], and wide-angle lenses to be more temperature-tolerant, their heating power sub-range is determined to be [0W, 10W]. The heating power sub-ranges of each lens can overlap.

[0054] When matching the optimal heating power and duty cycle, the heating power ranges for each lens are different: the model input data for the telephoto lens is [lens identifier, operating condition, target temperature, environmental parameters, heating power range one], the model input data for the medium telephoto lens is [lens identifier, operating condition, target temperature, environmental parameters, heating power range two], and the model input data for the wide-angle lens is [lens identifier, operating condition, target temperature, environmental parameters, heating power range three]. Referring to the previous example, the lens identifier and heating power range differ among the three model input data sets: heating power range one is [15W, 20W], heating power sub-range two is [8W, 15W], and heating power sub-range three is [0W, 10W].

[0055] In a preferred embodiment, the optimal heating power and its duty cycle are matched from the heating power range corresponding to the target temperature control standard through a parameter matching model.

[0056] The parameter matching model is built and trained based on an artificial intelligence model, including a BP neural network model or an RBF neural network model. Since it only needs to use the model to build a nonlinear mapping relationship between the input and output, the existing mature model architecture can be used without the need to improve the model architecture.

[0057] A training sample set is constructed by extracting sample data from historical data or laboratory simulation data. The sample data includes lens identification, operating conditions, target temperature, environmental parameters, heating power range, and the corresponding optimal heating power and its duty cycle. The process of training the artificial intelligence model using the training sample set can refer to existing solutions and will not be elaborated here.

[0058] It should be noted that the optimal heating power and its duty cycle refer to the minimum energy consumption required to heat the lens element corresponding to the lens marking to the target temperature under the corresponding operating conditions and environmental parameters. Thus, the heating power and duty cycle obtained through parameter matching modeling can ensure temperature control of the lens element with low energy consumption.

[0059] A second aspect of the present invention provides an intelligent temperature control system for a multi-view camera used in intelligent driving, comprising: Environmental perception module: used to collect temperature data from preset sensing points in the multi-view camera, as well as environmental parameters outside the vehicle; the sensing points are preset according to the impact of low temperature environment on multi-view camera, and the environmental parameters include temperature data and humidity data. There are multi-view cameras installed inside the vehicle. Intelligent temperature control module: used to obtain a target temperature control standard by matching the environmental parameters and the working status of the multi-view camera; and used to generate a temperature intelligent control scheme based on the target temperature control standard, and control the partition heating unit based on the temperature intelligent control scheme to realize intelligent temperature control of the multi-view camera.

[0060] The environmental sensing module and the intelligent temperature control module are connected. The intelligent temperature control system also includes a feedback calibration module, which monitors the temperature control effect and the working status of the multi-camera system in real time, transmitting feedback data to the intelligent temperature control module to achieve dynamic adjustment of temperature control parameters and form a closed-loop control system. The working function of the feedback calibration module is as follows: 1) Temperature feedback control: Real-time collection of temperature data from each sensing point. If the temperature data is inconsistent with the target temperature, it is fed back to the intelligent temperature control module. The intelligent temperature control module updates the intelligent control scheme to ensure that the deviation of the temperature data from the temperature value is within the allowable range.

[0061] 2) Parameter Calibration: The temperature sensor is calibrated every preset time (e.g., 10 minutes) to eliminate temperature measurement errors. Simultaneously, based on camera usage time and changes in environmental conditions, the temperature control standards are adaptively corrected, primarily adjusting the temperature control target and heating parameters to prevent temperature control failure due to hardware aging or environmental changes. Specifically, a pre-established temperature correction function is retrieved, and the camera usage time is input into the function to obtain the corrected value for the temperature control target. This corrected value is then used to update the temperature control standards.

[0062] It should be understood that the descriptions of technical features, technical solutions, beneficial effects, or similar language in this application do not imply that all features and advantages can be achieved in any single embodiment. Rather, it is understood that the description of a feature or beneficial effect means that a specific technical feature, technical solution, or beneficial effect is included in at least one embodiment. Therefore, the descriptions of technical features, technical solutions, or beneficial effects in this specification do not necessarily refer to the same embodiment. Furthermore, the technical features, technical solutions, and beneficial effects described in this embodiment can be combined in any suitable manner. Those skilled in the art will understand that embodiments can be implemented without one or more specific technical features, technical solutions, or beneficial effects of a particular embodiment. In other embodiments, additional technical features and beneficial effects may be identified in specific embodiments that do not embody all embodiments.

[0063] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any other combination thereof. When implemented using a software program, it can be implemented entirely or partially in the form of a computer program product. This computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device containing one or more servers, data centers, etc., that can be integrated with the medium. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state disks (SSDs)).

[0064] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. An intelligent temperature control method for multi-view cameras used in intelligent driving, characterized in that, include: Temperature data from preset sensing points in the multi-view camera system is collected, as well as environmental parameters from outside the vehicle. The sensing points are preset based on the impact of low-temperature environment on the multi-view camera system, and the environmental parameters include temperature and humidity data. The target temperature control standard is obtained by matching the environmental parameters and the working status of the multi-view camera; wherein, the target temperature control standard includes the temperature control range, triggering conditions and temperature control target; A temperature intelligent control scheme is generated based on the target temperature control standard and the temperature data of the sensing points. The zone heating unit is controlled based on the temperature intelligent control scheme to realize intelligent temperature control of the multi-camera system. The zone heating unit is set according to the heating target.

2. The intelligent temperature control method for multi-camera intelligent driving according to claim 1, characterized in that, The target temperature control standard is obtained by matching the environmental parameters and the working status of the multi-view camera, including: Extract the environmental parameters and the working status of the multi-view camera; wherein, the working status includes normal operation, lens frosting, or lens icing; The target temperature control standard is obtained by matching the environmental parameters and the temperature data from the temperature control standard; wherein, the temperature control standard is based on the preset critical failure temperature of each component in the multi-view camera.

3. The intelligent temperature control method for multi-camera intelligent driving according to claim 2, characterized in that, The preset temperature control standard includes: The temperature control zone is divided into several temperature regulation ranges; where the temperature control zone is the temperature range corresponding to the low temperature environment; Based on the failure critical temperature of each component of the multi-camera, several temperature control ranges are matched with temperature control targets and triggering conditions to generate several levels of temperature control standards; these several levels of temperature standards are then integrated into a temperature level control standard.

4. The intelligent temperature control method for multi-camera intelligent driving according to claim 1, characterized in that, The target temperature control standard is revised, including: The presence of pre-set risks in each lens of the multi-view camera is determined based on the environmental parameters and lens temperature. These pre-set risks include the risk of frost formation or the risk of icing. The lens temperature is extracted from the collected temperature data. Yes, the target temperature control standard level is upgraded to obtain the updated target temperature control standard; No, when the multi-camera system is in the cold start phase, the target temperature control standard will be adjusted to the highest level; otherwise, the target temperature control standard will not be adjusted.

5. The intelligent temperature control method for multi-camera intelligent driving according to claim 1, characterized in that, The partitioned heating unit includes a lens heating assembly, a sensor and chip heating assembly, and a lens bracket and housing heating assembly.

6. The intelligent temperature control method for multi-camera intelligent driving according to claim 1, characterized in that, The intelligent temperature control solution includes a lens intelligent control solution, a sensor and chip intelligent control solution, and a lens bracket and housing intelligent control solution; the generation of the lens intelligent control solution includes: The target temperature is determined based on the temperature control target in the target temperature control standard, and the operating conditions are determined based on the vehicle speed; the operating conditions include low-speed conditions, normal conditions and high-speed conditions. Based on operating conditions, target temperature, and environmental parameters, the optimal heating power and its duty cycle are matched from the heating power range corresponding to the target temperature control standard. Based on the heating power and its duty cycle, a lens intelligent control solution is generated.

7. The intelligent temperature control method for multi-camera intelligent driving according to claim 6, characterized in that, When matching the optimal heating power and its duty cycle from the heating power range corresponding to the target temperature control standard, the corresponding heating power sub-range is determined from the heating power range based on the temperature sensitivity of each lens in the multi-camera system, and the optimal heating power and duty cycle of the lens are matched within the heating power sub-range.

8. The intelligent temperature control method for multi-camera intelligent driving according to claim 7, characterized in that, Matching the optimal heating power and its duty cycle includes: Invoke a pre-trained parameter matching model; where the parameter matching model is built based on an artificial intelligence model; After standardizing the operating conditions, target temperature, environmental parameters, and heating power range, the data are input into the parameter matching model, which outputs the optimal heating power and its duty cycle.

9. An intelligent temperature control system for a multi-camera intelligent driving system, used to execute the intelligent temperature control method for a multi-camera intelligent driving system according to any one of claims 1 to 8, characterized in that, include: Environmental perception module: used to collect temperature data from preset sensing points in the multi-view camera, as well as to collect environmental parameters outside the vehicle; Intelligent temperature control module: used to obtain the target temperature control standard by matching the environmental parameters and the working status of the multi-view camera; and, This is used to generate a temperature intelligent control scheme based on the target temperature control standard and the temperature data of the sensing points, and to control the partition heating unit based on the temperature intelligent control scheme to realize intelligent temperature control of the multi-view camera.

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