Laser radar adjusting method and system for rainy and foggy weather, vehicle and medium

Abstract

The invention provides a laser radar adjusting method and system for rainy and foggy weather, a medium and a vehicle, and belongs to the technical field of intelligent driving, and the method comprises the steps: obtaining real-time weather state information which comprises at least one of rainfall information and foggy weather visibility information; based on the real-time weather state information, calculating an influence factor of a current weather condition on a laser radar atmospheric propagation path; based on the influence factor, determining a laser radar atmospheric attenuation coefficient under the current weather condition; and based on the atmospheric attenuation coefficient, adjusting working parameters of the laser radar to compensate echo signal intensity reduction caused by atmospheric attenuation. According to the invention, the accuracy of laser radar adjustment in rainy and foggy weather is improved, and the intelligent degree and safety of intelligent driving are improved.

Description

LiDAR adjustment methods, systems, vehicles, and media for rain and fog weather Technical Field

[0001] This invention relates to the field of intelligent driving technology, specifically to a lidar adjustment method, system, vehicle, and medium for rainy and foggy weather. Background Technology

[0002] As intelligent driving technology extends from high-speed scenarios to complex urban roads, the all-weather adaptability of environmental perception systems has become one of the key factors restricting system availability. LiDAR, with its active imaging and immunity to ambient light, is widely used in intelligent driving vehicles, forming the basic perception unit of multi-sensor fusion solutions. However, in weather conditions such as rain, fog, and snow, the detection performance of LiDAR also faces significant degradation. Raindrops and fog particles in the atmosphere scatter and absorb the laser beam, causing an exponential decrease in echo signal intensity, reduced point cloud density, and deteriorated signal-to-noise ratio, thus affecting the accuracy of target recognition and distance measurement.

[0003] Existing lidar systems typically operate with fixed transmit power and fixed receive gain, and their parameter settings are only based on nominal operating conditions under clear weather conditions, lacking the ability to respond to real-time weather conditions. Although some autonomous vehicles are equipped with rain sensors or have the ability to acquire connected weather information, this information is only used for peripheral functions such as wiper control and route planning, and has not yet been integrated with the lidar perception system, thus failing to compensate for the degradation of perception performance caused by weather factors.

[0004] Therefore, how to utilize the vehicle's existing environmental perception resources without increasing additional hardware costs, enabling LiDAR to dynamically adjust its operating parameters according to real-time weather conditions in order to maintain the echo signal-to-noise ratio and detection stability under adverse weather conditions, is a technical challenge that current intelligent driving perception systems urgently need to solve. Summary of the Invention

[0005] In view of this, it is necessary to provide a lidar adjustment method, system, vehicle, and storage medium for rainy and foggy weather, in order to solve the technical problems of weak vehicle environmental perception and insufficient radar adjustment caused by the lack of responsiveness to real-time weather conditions in the existing operating mode of fixed transmit power and fixed receive gain.

[0006] To address the aforementioned technical problems, in a first aspect, the present invention provides a lidar adjustment method for rainy and foggy weather, comprising: acquiring real-time weather state information, wherein the real-time weather state information includes at least one of rainfall information and fog visibility information; calculating, based on the real-time weather state information, an influence factor of the current weather conditions on the atmospheric propagation path of the lidar; determining, based on the influence factor, the atmospheric attenuation coefficient of the lidar under the current weather conditions; and adjusting the operating parameters of the lidar based on the atmospheric attenuation coefficient to compensate for the decrease in echo signal intensity caused by atmospheric attenuation.

[0007] In one possible implementation, the calculation of the influencing factor includes: acquiring the current rainfall value measured by a rain sensor, and / or the current fog visibility value acquired through a gateway; acquiring a pre-calibrated reference rainfall value and / or reference visibility value; and constructing an influencing factor to characterize the degree of atmospheric environmental deterioration based on the ratio of the current rainfall value to the reference rainfall value and the ratio of the current visibility value to the reference visibility value.

[0008] In one possible implementation, the influence factor is calculated using the following formula: in, As the impact factor, This is the current rainfall value. This represents the current visibility value in foggy weather. For reference rainfall values, For reference visibility values, This is a correction factor for rainy days. This is a correction factor for foggy weather. , This is a preset constant.

[0009] In one possible implementation, determining the lidar atmospheric attenuation coefficient under current weather conditions based on the influencing factor includes: obtaining a reference atmospheric attenuation coefficient; and calculating the current atmospheric attenuation coefficient by associating the reference atmospheric attenuation coefficient with the influencing factor.

[0010] In one possible implementation, the step of calculating the current atmospheric attenuation coefficient by associating the reference atmospheric attenuation coefficient with the influencing factor includes: calculating using the following formula: in, This represents the current atmospheric attenuation coefficient. The reference atmospheric attenuation coefficient, The influencing factor is denoted as .

[0011] In one possible implementation, obtaining the reference atmospheric attenuation coefficient includes: under good weather conditions, selecting a distance Known reflectivity The calibration target; acquiring lidar system parameters, including transmit power. System efficiency Effective cross-sectional area Angle of incidence Based on the lidar equation, the reference atmospheric attenuation coefficient is obtained by inversion using the following formula:

[0012] in, For reference to the atmospheric attenuation coefficient, To calibrate the distance between the target and the lidar, Pref( Under good weather conditions and at a distance The theoretical reflection intensity of the calibration target was detected at the location. To calibrate the reflectivity of the target, This represents the effective cross-sectional area of ​​the laser spot illuminating the calibration target.

[0013] In one possible implementation, adjusting the operating parameters of the lidar includes: adjusting the lidar's transmit power; and / or, adjusting the gain coefficient of the lidar receiver; and / or, adjusting the lidar's pulse transmission frequency.

[0014] On the other hand, the present invention also provides a lidar adjustment system for rainy and foggy weather, comprising: a weather information acquisition module for acquiring real-time weather status information, wherein the real-time weather status information includes at least one of rainfall information and fog visibility information; an influence factor calculation module for calculating the influence factor of the current weather conditions on the atmospheric propagation path of the lidar based on the real-time weather status information; an attenuation coefficient determination module for determining the atmospheric attenuation coefficient of the lidar under the current weather conditions based on the influence factor; and a lidar parameter adjustment module for adjusting the operating parameters of the lidar based on the atmospheric attenuation coefficient to compensate for the decrease in echo signal intensity caused by atmospheric attenuation.

[0015] Thirdly, the present invention also provides a vehicle including the aforementioned lidar adjustment system for rainy and foggy weather.

[0016] Fourthly, the present invention also provides a computer-readable storage medium for storing a computer-readable program or instructions, which, when executed by a processor, can implement the steps in the lidar adjustment method for rainy and foggy weather described in any of the above implementations.

[0017] The beneficial effects of this invention are as follows: The lidar adjustment method for rainy and foggy weather provided by this invention acquires real-time weather status information including rainfall and fog visibility, introducing a method to avoid the energy consumption and heat burden caused by maintaining high-power operation for extended periods to cope with extreme weather. It calculates influencing factors characterizing the current degree of atmospheric deterioration; then maps these influencing factors to the atmospheric attenuation coefficient of the lidar. The quantitative correlation between the influencing factors and the attenuation coefficient enables precise control of the compensation range, avoiding over-adjustment or under-compensation. Finally, based on this attenuation coefficient, it dynamically adjusts adjustable parameters such as the lidar's transmission power. The adaptive increase in transmission power directly compensates for the attenuation loss of the echo signal in the atmospheric path, improving the point cloud reflection intensity and signal-to-noise ratio in rainy and foggy weather without replacing hardware. This enables the lidar to actively adapt to weather changes, improving the perception reliability and radar adjustment accuracy of the lidar in adverse weather conditions without increasing hardware costs, ensuring the functional availability and safety of the intelligent driving system in all-weather scenarios. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying 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.

[0019] Figure 1 is a flowchart illustrating an embodiment of the lidar adjustment method for rainy and foggy weather provided by the present invention; Figure 2 is a flowchart illustrating an embodiment of S102 in Figure 1 of the present invention; Figure 3 is a flowchart illustrating an embodiment of S103 in Figure 1 of the present invention; Figure 4 shows a schematic diagram of an intelligent driving safety visual perception system provided by an embodiment of the present invention; Figure 5 is a structural schematic diagram of an embodiment of the lidar adjustment system for rainy and foggy weather provided by the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0021] In the description of the embodiments of the present invention, unless otherwise stated, "multiple" means two or more. "And / or" describes the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone.

[0022] The terms "first," "second," etc., used in the embodiments of this invention are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a technical feature defined with "first" or "second" may explicitly or implicitly include at least one of that feature.

[0023] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0024] This invention provides a method, system, vehicle, and storage medium for adjusting lidar in rainy and foggy weather. The technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0025] Figure 1 is a schematic flowchart of an embodiment of the lidar adjustment method for rainy and foggy weather provided by the present invention. As shown in Figure 1, the lidar adjustment method for rainy and foggy weather includes: S101, acquiring real-time weather status information, wherein the real-time weather status information includes at least one of rainfall information and fog visibility information.

[0026] Real-time weather information refers to meteorological parameters acquired by a vehicle during operation through onboard sensors (such as rain sensors and visibility sensors) or vehicle-to-cloud communication links (such as weather warning data received by a T-Box), which are matched with the vehicle's current geographical location and time. This information has an update frequency of seconds or minutes and can reflect instantaneous changes in environmental conditions.

[0027] When the intelligent driving function is activated, the system continuously collects real-time weather information. This information includes at least one of rainfall and fog visibility information, or both. Rainfall information is acquired through the vehicle's rain sensor, which is typically installed inside the windshield and outputs a voltage signal or digital quantity proportional to the rainfall intensity by utilizing the refraction of infrared light in water droplets. Fog visibility information is read through a gateway, and its sources include, but are not limited to: real-time weather service data issued by the vehicle networking platform, regional visibility levels broadcast by roadside units, or direct measurements from onboard visibility sensors (such as scattering visibility meters). The above information is periodically sent to the processing unit executing step S102 in the form of CAN signals, Ethernet frames, or service interfaces.

[0028] As an example, in a certain implementation scenario, the rain sensor outputs a current rainfall value of 12 mm / h, and the vehicle-to-everything (V2X) platform sends a current road visibility value of 150 m. The system temporarily stores these two values ​​in memory for later use. If the vehicle is only equipped with a rain sensor and not visibility perception capability, it will only collect rainfall information and set the fog visibility information to the default value (e.g., no fog).

[0029] S102. Based on real-time weather information, calculate the influence factor of current weather conditions on the atmospheric propagation path of lidar.

[0030] The influencing factor is an intermediate variable constructed in this embodiment to map discrete, nonlinear meteorological parameters into a continuous, linear index of atmospheric deterioration. Its physical meaning can be understood as a normalized proportionality factor of the current atmospheric transmittance relative to the transmittance under clear weather conditions. This factor satisfies the mapping relationship of "decreasing with increasing rainfall intensity, decreasing with decreasing visibility, and being 1 under clear weather conditions."

[0031] Raindrops and fog particles in the atmosphere cause Mie scattering of the laser beam, leading to beam energy attenuation. This step converts the raw meteorological data obtained in step S101 into a dimensionless scalar, which quantifies the degree of deterioration of the current weather compared to clear weather, denoted as the influencing factor. Specifically, the rainfall and visibility values ​​are compared with a pre-calibrated reference deterioration threshold, and their contributions are weighted and fused to output a normalized value ranging from (0, 1). The closer the value is to 0, the more severe the obstruction of laser propagation by rain and fog; a value of 1 indicates clear weather with no additional attenuation.

[0032] For example, when the system only collects rainfall information, it calculates a rainy day single-item impact factor based on the ratio of the current rainfall to the reference rainfall threshold; when it only collects visibility information, it calculates a foggy day single-item impact factor; when both types of information exist, the two factors are superimposed and normalized. This embodiment does not limit the specific function form, as long as the function satisfies the following: outputs 1 when there is no fog on a clear day, outputs tend to 0 during severe weather, and monotonically decreases as rainfall increases or visibility decreases.

[0033] S103. Based on the influencing factors, determine the atmospheric attenuation coefficient of the lidar under the current weather conditions.

[0034] The atmospheric attenuation coefficient is a key parameter in the lidar equation, usually denoted as β, with units of m. -1 Its physical definition is the relative rate at which the intensity of a light beam decreases due to scattering and absorption per unit distance traveled through the atmosphere. Under standard atmospheric conditions, the β value is approximately 0.01~0.1m. -1 The magnitude can reach up to 0.5 m in rainy or foggy weather. -1 Even higher.

[0035] The atmospheric attenuation coefficient is a physical quantity in the lidar equation that characterizes the degree of attenuation of the light beam per unit distance due to atmospheric absorption and scattering. Under clear weather conditions, this coefficient is a relatively small constant, which can be obtained through factory calibration or offline testing, and is denoted as the reference atmospheric attenuation coefficient.

[0036] This step correlates the influence factor obtained in step S102 with the reference attenuation coefficient: since the influence factor reflects the ratio of the current weather to the severity of clear weather, the current atmospheric attenuation coefficient can be expressed as the reference attenuation coefficient divided by the influence factor. This calculation logic conforms to physical laws: the smaller the influence factor, the more severe the weather, and the larger the current attenuation coefficient; when the influence factor is 1, the current attenuation coefficient is equal to the reference attenuation coefficient.

[0037] S104. Based on the atmospheric attenuation coefficient, adjust the operating parameters of the lidar to compensate for the decrease in echo signal intensity caused by atmospheric attenuation.

[0038] The operating parameters of a lidar system refer to the operational parameters that can be adjusted by software configuration or firmware. Transmit power is the most common adjustable parameter, achieved by changing the laser diode drive current or pulse width; receive gain is achieved by adjusting the bias voltage of the transimpedance amplifier or variable gain amplifier; and pulse repetition frequency is achieved by modifying the timer count value in the FPGA or DSP. All of these parameters are adjusted within factory safety thresholds and do not exceed the lidar's electrical specifications and eye safety levels.

[0039] Specifically, the echo signal strength of lidar is positively correlated with factors such as transmission power, atmospheric transmittance, target reflectivity, and the reciprocal of the square of the distance. When the atmospheric attenuation coefficient increases, the optical power returned to the same target at the same distance decreases exponentially.

[0040] This step compensates for the aforementioned signal attenuation by actively adjusting the controllable parameters of the lidar.

[0041] The operating parameters to be adjusted include, but are not limited to: transmit power (W), receiver gain (dB), pulse repetition frequency (Hz), and single pulse energy (μJ). The adjustment strategy is based on maintaining the echo signal-to-noise ratio under clear weather conditions. The required compensation is calculated according to the deviation between the current atmospheric attenuation coefficient and the reference attenuation coefficient, and the corresponding command is sent to the lidar controller for execution.

[0042] For example, in an implementation that only adjusts the transmit power, the system calculates the difference between the current attenuation coefficient and the reference attenuation coefficient, multiplies it by the target distance coefficient, and then takes the exponent to obtain the power compensation factor. Multiplying the reference transmit power by this factor gives the transmit power that should be set at the moment. When the target distance is a dynamically changing value, a typical distance (such as 50 m) can be selected as the compensation benchmark, or a weighted average can be performed based on the distance distribution of the region of interest.

[0043] By acquiring real-time weather information including rainfall and visibility in foggy weather, this approach avoids the energy consumption and heat burden caused by maintaining high-power operation for extended periods to cope with extreme weather. It calculates influencing factors characterizing the current degree of atmospheric deterioration and maps these factors to the atmospheric attenuation coefficient of the lidar. The quantitative correlation between the influencing factors and the attenuation coefficient enables precise control of the compensation range, avoiding over-adjustment or under-compensation. Finally, based on this attenuation coefficient, it dynamically adjusts adjustable parameters such as the lidar's transmission power. The adaptive increase in transmission power directly compensates for the attenuation loss of the echo signal in the atmospheric path, improving the point cloud reflection intensity and signal-to-noise ratio in rainy and foggy weather without replacing hardware. This enables the lidar to actively adapt to weather changes, improving the perception reliability and radar adjustment accuracy in adverse weather conditions without increasing hardware costs, thus ensuring the functional availability and safety of the intelligent driving system in all-weather scenarios.

[0044] In some embodiments of the present invention, as shown in FIG2, step S102, based on real-time weather status information, calculates the influence factor of current weather conditions on the atmospheric propagation path of lidar, including: S201, obtaining the current rainfall value measured by the rain sensor, and / or the current fog visibility value obtained through the gateway; S202, obtaining the pre-calibrated reference rainfall value and / or reference visibility value; S203, constructing an influence factor to characterize the degree of atmospheric environmental deterioration based on the ratio of the current rainfall value to the reference rainfall value and the ratio of the current visibility value to the reference visibility value.

[0045] Step S201 involves the acquisition of two types of weather state information, whose information sources and collection methods differ, and will be explained separately below.

[0046] Rainfall information is acquired through the vehicle's rain sensor. This sensor is typically installed on the inside of the windshield and operates on the principle of total internal reflection: when the glass is dry, the light emitted by the infrared emitter undergoes total internal reflection at the glass-air interface, and the receiver receives a high-energy signal; when raindrops are present on the glass surface, the light is scattered through the water droplets, and the energy received by the receiver decreases. The sensor control chip outputs an analog voltage signal or pulse-width modulation signal proportional to the rainfall intensity based on the degree of energy attenuation. This signal is converted into a quantized rainfall value on the CAN bus by the vehicle's body controller, typically in millimeters per hour (mm / h). In current mass-produced vehicles, the rain sensor output value range is generally 0~40 mm / h, where 0 indicates no rain and 40 mm / h corresponds to heavy rain intensity.

[0047] Fog visibility information is obtained through a gateway, and its sources include, but are not limited to, the following three implementation methods: First, real-time meteorological service data pushed by the vehicle-to-everything (V2X) platform based on geographical location, such as road visibility level warnings issued by the China Meteorological Administration; Second, regional visibility monitoring values ​​broadcast by roadside units (RSUs) through dedicated short-range communication; Third, vehicle-mounted visibility sensors, such as forward-scattering visibility meters, which invert visibility distance by measuring the intensity of infrared light scattering by atmospheric particulate matter.

[0048] The above information is transmitted to the intelligent driving domain controller in a standard format via Ethernet or CAN FD, typically in meters (m). When the vehicle lacks fog perception capabilities or network connectivity is unavailable, this information can be set to a default value, for example... = 0, representing a fog-free state.

[0049] In step S202, the reference rainfall and reference visibility values ​​are used as offline calibration parameters and stored in a non-volatile storage medium. The calibration test is conducted under controlled environmental conditions, and the specific method is as follows.

[0050] The calibration of the reference rainfall value is carried out in a controlled rainfall simulation laboratory. The vehicle to be calibrated is placed in a constant rainfall intensity environment, and a standard reflective target plate is placed at a fixed distance in front of the lidar. The curve of echo signal intensity attenuation as rainfall intensity increases is recorded. When the echo intensity attenuates to a preset threshold of the clear weather reference value (e.g., -6 dB, i.e., attenuated to 25%), this rainfall intensity value is calibrated as the reference rainfall value. This threshold characterizes the maximum attenuation level that the system can tolerate. Exceeding this threshold, power compensation alone cannot meet human eye safety constraints, requiring functional degradation or shutdown. In typical mass production calibration, the reference rainfall value is taken in the range of 30–50 mm / h.

[0051] The calibration of the reference visibility value is carried out in a low-visibility simulation chamber by adjusting the aerosol generator to produce fog droplets of different concentrations, using the echo intensity of a standard target panel as the observation object. When the visibility meter's measured value drops to the point where the echo intensity decays to a preset threshold, this visibility value is calibrated as the reference visibility value (lref). In typical calibration results, the reference visibility value ranges from 50 to 100 m, corresponding to the dense fog level.

[0052] Step S203 transforms weather state information from different physical dimensions and sources into a unified, dimensionless quantitative indicator, denoted as the influencing factor.

[0053] When only rainy day information exists, the rainy day influence factor Calculated by the following formula:

[0054] in, Current rainfall value, in millimeters per hour (mm / h), is collected in real time by a rain sensor, and the value range is typically 0~40 mm / h, where 0 indicates no rain; For reference rainfall values, the unit is millimeters per hour (mm / h), obtained through offline calibration, and defined as the rainfall intensity corresponding to the attenuation of the lidar echo intensity to a preset threshold; where, The rain correction coefficient is dimensionless and ranges from (0, 1). It is used to adjust the weight of rainfall in the total influence factor. It is determined through calibration experiments, and the preferred value is 0.8 to 1.0.

[0055] When only foggy weather information exists, the foggy weather impact factor Calculated by the following formula:

[0056] in, The fog correction factor is dimensionless and ranges from (0, 1). It is used to adjust the weight of visibility influence in the total influence factor. It is determined through calibration experiments, and the preferred value is 0.8~1.0.

[0057] In some embodiments of the present invention, when rainy and foggy weather information coexist, the effects of the two are superimposed and normalized, and the total influence factor is calculated using the following formula: in, As the impact factor, This is the current rainfall value. This represents the current visibility value in foggy weather. For reference rainfall values, For reference visibility values, This is a correction factor for rainy days. This is a correction factor for foggy weather. , This is a preset constant.

[0058] It should be noted that the physical significance of this construction method lies in the fact that when the weather is sunny with no rain or fog, =0, = 0, substituting into the formula yields =1 indicates that atmospheric propagation conditions are consistent with the nominal operating conditions, and no compensation is required; when rainfall increases or visibility decreases, the corresponding ratio term increases. A value decreasing towards 0 indicates a worsening of atmospheric deterioration. The coefficient "2" in the denominator is used to normalize the two contributions to the (0, 1) interval when rainy and foggy days coexist, preventing the summed values ​​from exceeding the limit.

[0059] In this embodiment, the rain sensor is an on-board sensor used to detect rainfall intensity, typically operating based on the principle of infrared optical total internal reflection. Its output signal can be analog voltage, PWM duty cycle, or digital quantity, which is parsed by the vehicle controller and converted into a rainfall intensity value in millimeters per hour. Some vehicle models also output a binary status signal indicating the presence or absence of raindrops.

[0060] In this embodiment, the gateway refers to the central communication hub in an intelligent connected vehicle, responsible for data routing and protocol conversion between different bus networks (such as CAN, LIN, and Ethernet), and also capable of remote communication with the cloud service platform. The gateway receives weather data from the vehicle network from the T-Box, converts the format, and distributes it to the functional domain controllers via the internal bus.

[0061] In this embodiment, offline calibration is a parameter determination process completed during the product development phase and embedded in the software parameter table. Calibration is typically performed in a controlled environment (such as an environmental wind tunnel or rain / fog simulation chamber) using standard measuring instruments as a reference to obtain the optimal parameter combination under specific operating conditions. Once the calibration result is determined, it remains unchanged throughout the lifecycle of the mass-produced vehicle.

[0062] In this embodiment, the correction coefficient is an adjustment factor used to adjust the deviation between the theoretical model and the actual physical process. The reasons for introducing the correction coefficient include: differences in raindrop size distribution under different rainfall intensities; the non-linear mapping relationship between fog droplet concentration and visibility; and differences in the sensitivity of different types of lidar to the same meteorological conditions. The value of the correction coefficient is obtained by minimizing the residual between the measured attenuation curve and the theoretical attenuation curve.

[0063] In this embodiment, normalization is a process of mapping parameters with different value ranges and different physical dimensions to the same numerical interval through mathematical transformation. In this embodiment, the superimposed rain and fog influence factors are normalized to the interval (0, 1), so that the output value has a clear physical boundary, which facilitates unified processing in subsequent calculation stages.

[0064] The impact factor calculation method constructed in this embodiment does not rely on dedicated meteorological instruments, but makes full use of the existing sensor resources of intelligent connected vehicles, releasing the weather information from the rain sensor and gateway from their respective independent traditional functional domains, and realizing the value-added utilization of cross-domain information.

[0065] In some embodiments of the present invention, as shown in FIG2, step S103, which determines the atmospheric attenuation coefficient of the lidar under the current weather conditions based on the influencing factor, includes: S301, obtaining the reference atmospheric attenuation coefficient; S302, calculating the reference atmospheric attenuation coefficient in association with the influencing factor to obtain the current atmospheric attenuation coefficient.

[0066] In step S301, the reference atmospheric attenuation coefficient is a physical constant characterizing the energy attenuation rate per unit distance of laser propagation in the atmosphere under clear weather conditions, with units of per meter (m). -1 This coefficient is not a universal constant derived from theory, but a system parameter that is strongly related to the hardware characteristics of the lidar, such as the operating wavelength, the collimation performance of the transmitting optical system, and the field of view of the receiving optical system. Therefore, it needs to be obtained in advance through offline calibration and stored in the controller's non-volatile memory.

[0067] In some embodiments of the present invention, obtaining the reference atmospheric attenuation coefficient includes: under good weather conditions, selecting a distance Known reflectivity The calibration target; acquiring lidar system parameters, including transmit power. System efficiency Effective cross-sectional area Angle of incidence Based on the lidar equation, the reference atmospheric attenuation coefficient is obtained by inversion using the following formula:

[0068] in, For reference to the atmospheric attenuation coefficient, To calibrate the distance between the target and the lidar, Pref( Under good weather conditions and at a distance The theoretical reflection intensity of the calibration target was detected at the location. To calibrate the reflectivity of the target, This represents the effective cross-sectional area of ​​the laser spot illuminating the calibration target.

[0069] In step S302, the reference atmospheric attenuation coefficient is correlated with the influencing factor to calculate the current atmospheric attenuation coefficient.

[0070] This step establishes a quantitative mapping relationship from weather influencing factors to laser propagation physical parameters. The influencing factors numerically characterize the ratio of atmospheric transmittance under current weather conditions to atmospheric transmittance under clear weather conditions. Since atmospheric transmittance has a negative exponential relationship with the atmospheric attenuation coefficient, at the distance element level, the transmittance proportionality factor is inversely related to the attenuation coefficient.

[0071] In some embodiments of the present invention, the current atmospheric attenuation coefficient is calculated by associating a reference atmospheric attenuation coefficient with an influencing factor, including by using the following formula: in, This represents the current atmospheric attenuation coefficient. For reference to the atmospheric attenuation coefficient, It is the impact factor.

[0072] Correlative calculation refers to the process of deriving unknown quantities from known quantities by performing operations on two or more physically related parameters through defined mathematical relationships. In this embodiment, correlative calculation specifically refers to division operations, which have a concise mathematical form, clear physical meaning, and minimal computational overhead, making them suitable for intelligent driving domain controller environments with stringent real-time requirements.

[0073] The Reference Atmospheric Attenuation Coefficient is a system parameter tied to a specific lidar hardware platform, characterizing the lidar's atmospheric transmission characteristics under factory acceptance testing conditions. This parameter comprehensively reflects the absorption loss of the laser wavelength within the current atmospheric window, Rayleigh scattering loss, and the stray light suppression capability of the optical system. It is the only variable in the lidar equation that is environmentally dependent but requires pre-calibration. Its physical unit is meters (m). -1 The reciprocal of has the dimension of length, and its physical meaning can be understood as the reciprocal of the average propagation distance of a photon before it is absorbed or scattered by the atmosphere.

[0074] The Lidar Equation is a mathematical model describing the quantitative relationship between the lidar echo signal power and the transmitted power, target characteristics, atmospheric transmission characteristics, and system optical efficiency. This equation forms the theoretical basis for lidar system design, performance analysis, and parameter calibration. This embodiment only references the mapping relationship between the attenuation coefficient and the exponential attenuation term, and does not involve the specific calculations of the entire equation.

[0075] The physical meaning of this calculation formula is: when the weather is fine, =1, then = The current attenuation coefficient is consistent with the reference value and no compensation is needed; however, in rainy or foggy weather... <1, then > ,and The smaller, The larger the value, the more severe the atmospheric attenuation effect. This relationship exhibits a strictly monotonically decreasing characteristic, which perfectly matches the logarithmic relationship between the attenuation coefficient and transmittance in the theory of atmospheric radiative transfer.

[0076] It should be noted that this calculation formula is not the only implementation method. Any function that satisfies the mapping relationship of "outputting a reference value under favorable conditions and increasing the output value proportionally to the degree of deterioration under adverse conditions" can achieve the purpose of this step. For example, in an implementation scheme with sufficient system storage resources, the corresponding values ​​for different influencing factors can also be pre-defined. The values ​​are then compiled into a two-dimensional lookup table, and the output is obtained through linear interpolation. In implementations with high nonlinearity correction requirements, a higher-order polynomial compensation stage can be connected in series with the division operation. All of these variations remain within the core concept of this step.

[0077] In some embodiments of the present invention, adjusting the operating parameters of the lidar includes: adjusting the lidar's transmission power; and / or, adjusting the gain coefficient of the lidar receiver; and / or, adjusting the lidar's pulse transmission frequency.

[0078] This embodiment further details the specific implementation of step S104, "adjusting the operating parameters of the lidar based on the atmospheric attenuation coefficient." The controllable operating parameters of the lidar include, but are not limited to, transmit power, receiver gain, and pulse transmission frequency. These parameters can be adjusted independently or in combination according to a preset strategy, all falling within the protection scope of this embodiment.

[0079] Transmit power is one of the direct determinants of lidar echo signal strength. In the lidar equation, echo power is linearly proportional to transmit power. When the atmospheric attenuation coefficient... When increasing the power leads to attenuation of the echo signal, increasing the transmission power is the adjustment method with the most direct physical meaning and the highest compensation efficiency.

[0080] This embodiment provides a closed-loop transmission power adjustment strategy based on atmospheric attenuation coefficient deviation. This strategy prioritizes meeting the human eye's safe irradiance limit while maintaining the echo intensity at a predetermined detection distance under clear weather conditions.

[0081] In some embodiments of the present invention, the compensated transmit power is calculated using the following formula:

[0082] in, For reference transmission power, the nominal transmission power corresponds to good weather conditions. This represents the current atmospheric attenuation coefficient. R is the target detection range, with reference to the atmospheric attenuation coefficient.

[0083] It should be noted that R is in meters (m) and represents the reference distance value to be compensated. The selection of this distance can be, but is not limited to, using the distance to the main target within the current area of ​​interest as a reference, using a fixed nominal distance (e.g., 50 m) as a reference, or using a weighted average distance across the entire range as a reference.

[0084] The physical derivation of this formula is based on the following constraint: at the same target and the same distance R, the compensated echo intensity should be equal to the echo intensity corresponding to the reference transmission power under clear weather conditions. As shown in the lidar equations, the echo intensity is directly proportional to the transmission power and related to atmospheric transmittance. Proportional. Let the echo intensity be equal before and after compensation, and then rearrange the terms and take the natural logarithm to obtain the above formula.

[0085] Figure 4 shows a schematic diagram of the intelligent driving safety visual perception system. The system uses an intelligent driving domain controller as its core processing unit. Its inputs are connected to a rain sensor and a gateway. The rain sensor collects the current rainfall value and sends it to the domain controller, while the gateway obtains real-time fog visibility information from the vehicle-to-everything (V2X) platform or roadside units and forwards it to the domain controller. The domain controller integrates an impact factor calculation module and an atmospheric attenuation coefficient update module. The former calculates the impact factor under the current weather conditions based on the received rainfall and fog information, while the latter correlates the impact factor with a pre-stored reference atmospheric attenuation coefficient and outputs the current atmospheric attenuation coefficient. The domain controller's output is connected to a lidar controller, sending it calculated lidar operating parameter adjustment commands. These commands include at least one of a transmit power adjustment command and a receive gain adjustment command. The lidar controller executes real-time updates of the corresponding parameters based on the received commands, forming a complete closed-loop control link from weather perception to underlying parameter adjustment.

[0086] In practical engineering applications, the adjustment of transmit power is limited by two boundary conditions. The first is the human eye safety boundary: the transmit power of the lidar must meet the Level 1 human eye safety limit specified in IEC 60825-1 standard, and the adjusted peak or average power must not exceed this limit. The second is the laser driving capability boundary: the maximum drive current and maximum duty cycle of the laser diode or fiber amplifier both have physical upper limits. Therefore, when the calculated transmit power P exceeds the system's maximum allowable transmit power, the adjustment will be affected. max At that time, the system will clamp the output value at P. max It also reports the power compensation saturation status to the upper-level functional software.

[0087] Receiver gain refers to the voltage or current amplification factor between the output of the photodetector and the input of the analog-to-digital converter in the lidar receiver link. It is typically achieved by cascading a transimpedance amplifier (TIA) and a variable gain amplifier (VGA). Adjusting the receiver gain changes the amplitude of the echo signal, which is equivalent to backcompensating for the echo intensity.

[0088] Compared to transmit power adjustment, receive gain adjustment has advantages such as fast response speed, no human-eye safety constraints, and no impact on laser lifespan. However, it cannot improve the signal-to-noise ratio (SNR) – the gain amplifies the signal while proportionally amplifying the noise. Therefore, it is suitable for scenarios where signal attenuation has not yet degraded the SNR to the point of being undetectable. In an optional implementation, receive gain adjustment serves as a supplement to or alternative to transmit power adjustment.

[0089] Pulse emission frequency, also known as pulse repetition frequency (PRF), is the number of laser pulses emitted per second by a lidar system, measured in Hertz (Hz). PRF directly affects point cloud density, maximum unambiguous range, and average emission power.

[0090] Under a fixed peak power constraint, increasing the PRF (Power Ratio Rendering) increases the number of transmitted pulses per unit time, thereby improving point cloud density, but reduces single-pulse energy (if the average power supply is limited) or shortens the maximum detection range (due to the need to wait for the preceding pulse echo). Conversely, decreasing the PRF increases single-pulse energy (if the energy storage capacitor charging time is extended), improving long-range detection capability, but sacrifices angular resolution.

[0091] As an optional implementation, this embodiment provides a PRF adjustment strategy based on the atmospheric attenuation coefficient: when When the increase is reached, the system should be appropriately reduced. This involves allocating a limited average power budget towards single-pulse energy to compensate for the echo intensity of distant targets; when When restored to the reference value, Restore to the nominal value.

[0092] The lidar operating parameter adjustment method disclosed in this embodiment transforms the weather influencing factors and atmospheric attenuation coefficients obtained in the preceding steps into specific execution commands, forming a complete closed loop from environmental perception to underlying control. This enables the point cloud reflection intensity fluctuation range to be controlled within expectations under rainy and foggy weather, thereby improving the perception and safety performance of intelligent driving.

[0093] To better implement the lidar adjustment method for rainy and foggy weather in this embodiment of the invention, based on the lidar adjustment method for rainy and foggy weather, as shown in Figure 5, this embodiment of the invention also provides a lidar adjustment system for rainy and foggy weather. The lidar adjustment system 500 for rainy and foggy weather includes: a weather information acquisition module 501, used to acquire real-time weather status information, which includes at least one of rainfall information and fog visibility information; an influence factor calculation module 502, used to calculate the influence factor of the current weather conditions on the atmospheric propagation path of the lidar based on the real-time weather status information; an attenuation coefficient determination module 503, used to determine the atmospheric attenuation coefficient of the lidar under the current weather conditions based on the influence factor; and a lidar parameter adjustment module 504, used to adjust the operating parameters of the lidar based on the atmospheric attenuation coefficient to compensate for the decrease in echo signal intensity caused by atmospheric attenuation.

[0094] The lidar adjustment system 500 for rainy and foggy weather provided in the above embodiments can realize the technical solutions described in the above embodiments of lidar adjustment method for rainy and foggy weather. The specific implementation principles of each module or unit can be found in the corresponding content in the above embodiments of lidar adjustment method for rainy and foggy weather, and will not be repeated here.

[0095] Accordingly, this application also provides a vehicle that uses the aforementioned lidar adjustment system for rainy and foggy weather.

[0096] Accordingly, this application also provides a computer-readable storage medium for storing computer-readable programs or instructions. When the programs or instructions are executed by a processor, they can implement the steps or functions of the lidar adjustment method for rainy and foggy weather provided in the above-described method embodiments.

[0097] Those skilled in the art will understand that all or part of the processes of the methods described in the above embodiments can be implemented by a computer program instructing related hardware (such as a processor, controller, etc.), and the computer program can be stored in a computer-readable storage medium. The computer-readable storage medium may be a disk, optical disk, read-only memory, or random access memory, etc.

[0098] The above provides a detailed description of the lidar adjustment method, system, vehicle, and storage medium for rainy and foggy weather provided by the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for adjusting lidar in rainy and foggy weather, characterized in that, include: Obtain real-time weather status information, which includes at least one of rainfall information and fog visibility information; Based on the real-time weather information, calculate the influence factor of the current weather conditions on the atmospheric propagation path of the lidar; based on the influence factor, determine the atmospheric attenuation coefficient of the lidar under the current weather conditions. Based on the atmospheric attenuation coefficient, the operating parameters of the lidar are adjusted to compensate for the decrease in echo signal intensity caused by atmospheric attenuation.

2. The method according to claim 1, characterized in that, The step of calculating the influence factor of current weather conditions on the atmospheric propagation path of lidar based on the real-time weather status information includes: obtaining the current rainfall value measured by the rain sensor, and / or the current fog visibility value obtained through the gateway; obtaining the pre-calibrated reference rainfall value and / or reference visibility value; and constructing an influence factor to characterize the degree of atmospheric environmental deterioration based on the ratio of the current rainfall value to the reference rainfall value and the ratio of the current visibility value to the reference visibility value.

3. The method according to claim 2, characterized in that, The impact factor is calculated using the following formula: in, As the impact factor, This is the current rainfall value. This represents the current visibility value in foggy weather. For reference rainfall values, For reference visibility values, This is a correction factor for rainy days. This is a correction factor for foggy weather. 、 This is a preset constant.

4. The method according to claim 1, characterized in that, The step of determining the atmospheric attenuation coefficient of the lidar under the current weather conditions based on the influencing factor includes: obtaining a reference atmospheric attenuation coefficient; and calculating the current atmospheric attenuation coefficient by associating the reference atmospheric attenuation coefficient with the influencing factor.

5. The method according to claim 4, characterized in that, The step of correlating the reference atmospheric attenuation coefficient with the influencing factor to obtain the current atmospheric attenuation coefficient includes calculation using the following formula: in, This represents the current atmospheric attenuation coefficient. The reference atmospheric attenuation coefficient, The influencing factor is denoted as .

6. The method according to claim 4, characterized in that, The process of obtaining the reference atmospheric attenuation coefficient includes: under good weather conditions, selecting a distance... Known reflectivity The calibration target; acquiring lidar system parameters, including transmit power. System efficiency Effective cross-sectional area Angle of incidence Based on the lidar equation, the reference atmospheric attenuation coefficient is obtained by inversion using the following formula: in, For reference to the atmospheric attenuation coefficient, To calibrate the distance between the target and the lidar, Pref( Under good weather conditions and at a distance The theoretical reflection intensity of the calibration target was detected at the location. To calibrate the reflectivity of the target, This represents the effective cross-sectional area of ​​the laser spot illuminating the calibration target.

7. The method according to claim 1, characterized in that, The adjustment of the operating parameters of the lidar includes: adjusting the lidar's transmission power; and / or, adjusting the gain coefficient of the lidar receiver; and / or, adjusting the lidar's pulse transmission frequency.

8. A lidar adjustment system for rainy and foggy weather, characterized in that, include: The weather information acquisition module is used to acquire real-time weather status information, which includes at least one of rainfall information and fog visibility information. The impact factor calculation module is used to calculate the impact factor of the current weather conditions on the atmospheric propagation path of the lidar based on the real-time weather status information. The attenuation coefficient determination module is used to determine the atmospheric attenuation coefficient of the lidar under the current weather conditions based on the influencing factors. The radar parameter adjustment module is used to adjust the operating parameters of the lidar based on the atmospheric attenuation coefficient to compensate for the decrease in echo signal intensity caused by atmospheric attenuation.

9. A vehicle, characterized in that, Including the lidar adjustment system for rainy and foggy weather as described in claim 8.

10. A computer-readable storage medium, characterized in that, Used to store computer-readable programs or instructions, which, when executed by a processor, can implement the steps in the lidar adjustment method for rainy and foggy weather as described in any one of claims 1 to 7.

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