Vehicle-mounted laser radar fog detection capability simulation method and system, terminal and medium

Abstract

The application discloses a vehicle-mounted laser radar fog detection capability simulation method and system, a terminal and a medium, a third-order polynomial error model is constructed and solved in advance, the third-order polynomial error model is used to express the error between the theoretical calculation value and the actual value of the attenuation function, when simulating the fog detection capability, first, the attenuation function value is calculated according to the general empirical formula of attenuation, then the attenuation function value error is calculated according to the third-order polynomial error model, the attenuation function value error is used to correct the attenuation function value, and finally, the maximum detection capability is solved according to the corrected attenuation function value. The vehicle-mounted laser radar detection capability simulation under the fog can be more accurate and real.

Description

Technical Field

[0001] This invention relates to the field of autonomous driving sensor simulation technology, and in particular to a method, system, terminal, and medium for simulating the fog detection capability of vehicle-mounted lidar. Background Technology

[0002] Autonomous driving simulation testing is considered an essential approach to achieving safe, controllable, efficient, and economical testing of autonomous driving systems. Sensor simulation is a core technology in autonomous driving simulation systems. High-confidence sensor simulation models are crucial for supporting the feasibility of autonomous driving simulation testing.

[0003] Current simulations of lidar detection capabilities primarily focus on energy attenuation caused by target material absorption, neglecting the attenuation effects during atmospheric transmission under varying weather conditions. However, lidar used in autonomous driving systems mainly utilizes laser wavelengths concentrated in the 905nm and 1550nm bands. Due to their shorter wavelengths, atmospheric aerosols strongly absorb and scatter their signals, making them susceptible to weather conditions. This leads to energy attenuation beyond the theoretically optimal detection range, impacting the actual detection distance. Furthermore, current simulation methods cannot accurately simulate lidar detection capabilities in foggy weather conditions. Summary of the Invention

[0004] The main objective of this invention is to provide a method, system, intelligent terminal, and computer-readable storage medium for simulating the fog detection capability of vehicle-mounted lidar, aiming to solve the problem of not being able to accurately simulate the lidar detection capability under foggy weather conditions.

[0005] To achieve the above objectives, a first aspect of the present invention provides a method for simulating the fog detection capability of a vehicle-mounted lidar, the method comprising:

[0006] A third-order polynomial error model is pre-established and the coefficients of each polynomial in the third-order polynomial error model are solved. The third-order polynomial error model is used to characterize the error between the theoretically calculated value and the actual value of the decay function.

[0007] Based on visibility, the attenuation function value is calculated according to the general empirical formula for attenuation.

[0008] Based on visibility, the attenuation function value error is calculated according to the third-order polynomial error model.

[0009] Based on the attenuation function value error and the attenuation function value, the corrected attenuation function value is obtained;

[0010] Based on the corrected attenuation function value, the maximum detection capability equation is solved to obtain the maximum detection capability.

[0011] Optionally, solving for the polynomial coefficients of the third-order polynomial error model includes:

[0012] On several foggy days, several reflection angles were used to collect several maximum detection distances of the lidar to be simulated. The visibility on the foggy days belonged to different visibility segments.

[0013] Based on the detection distance, the reflection angle, and the lidar system parameters in the pre-calibrated lidar detection capability equation, calculate the actual attenuation function value for each visibility segment;

[0014] Based on the general empirical formula for attenuation, the theoretical attenuation function values ​​for each visibility segment are calculated according to the visibility in the foggy weather.

[0015] By comparing the actual attenuation function value with the theoretical attenuation function value, the error of the attenuation function value for each visibility segment is obtained;

[0016] Based on the attenuation function error and the theoretical attenuation function value, solve for the polynomial coefficients of the third-order polynomial error model.

[0017] Optionally, the step of calculating the theoretical attenuation function value for each visibility segment based on the universal empirical formula for attenuation, according to the visibility in the foggy weather, includes:

[0018] Determine the wavelength correction factor corresponding to each visibility segment;

[0019] Based on the visibility on the foggy day and the wavelength correction factor corresponding to the visibility on the foggy day, the theoretical attenuation function value of the visibility segment to which the visibility on each foggy day belongs is calculated.

[0020] Optional, the lidar system parameters in the lidar detection capability equation are calibrated, including:

[0021] Under preset visibility conditions, the calibration detection range of the simulated lidar is collected using targets with two different reflectivities.

[0022] The parameters of the lidar system are determined based on the product data of the lidar to be simulated and the calibrated detection distance.

[0023] Optionally, the step of solving for the polynomial coefficients of the third-order polynomial error model based on the attenuation function value error and the theoretical attenuation function value includes:

[0024] The least squares method is used to fit and solve the coefficients of each polynomial in the third-order polynomial error model based on the error of the attenuation function value and the theoretical attenuation function value.

[0025] Optionally, the calculation of the attenuation function value based on visibility and according to a general empirical formula for attenuation includes:

[0026] Based on visibility, determine the wavelength correction factor corresponding to the visibility.

[0027] The attenuation function value is calculated based on the attenuation general empirical formula, according to the visibility and the wavelength correction factor.

[0028] A second aspect of the present invention provides a simulation system for vehicle-mounted lidar fog detection capabilities, comprising:

[0029] The third-order polynomial solving module is used to pre-establish a third-order polynomial error model and solve for the coefficients of each polynomial in the third-order polynomial error model. The third-order polynomial error model is used to characterize the error between the theoretical calculated value and the actual value of the decay function.

[0030] The attenuation function value module is used to calculate the attenuation function value based on visibility and according to a general empirical formula for attenuation.

[0031] The attenuation function value error module is used to calculate the attenuation function value error based on visibility and according to the third-order polynomial error model.

[0032] The correction module is used to obtain the corrected attenuation function value based on the attenuation function value error and the attenuation function value.

[0033] The maximum detection capability module is used to solve the maximum detection capability equation based on the corrected attenuation function value to obtain the maximum detection capability.

[0034] Optionally, the third-order polynomial solving module includes: an acquisition unit, an actual attenuation function value unit, a theoretical attenuation function value unit, an error unit, and a solving unit. The acquisition unit is used to acquire several detection distances of the simulated lidar at several reflection angles on several foggy days, where the visibility on the foggy days belongs to different visibility segments. The actual attenuation function value unit is used to calculate the actual attenuation function value for each visibility segment based on the detection distance, the reflection angle, and the lidar system parameters in the pre-calibrated lidar detection capability equation. The theoretical attenuation function value unit is used to calculate the theoretical attenuation function value for each visibility segment based on the general empirical formula for attenuation and the visibility on the foggy days. The error unit is used to compare the actual attenuation function value and the theoretical attenuation function value to obtain the attenuation function value error for each visibility segment. The solving unit is used to solve for the polynomial coefficients of the third-order polynomial error model based on the attenuation function value error and the theoretical attenuation function value.

[0035] A third aspect of the present invention provides a smart terminal, the smart terminal including a memory, a processor, and a vehicle-mounted lidar fog detection capability simulation program stored in the memory and executable on the processor, wherein the vehicle-mounted lidar fog detection capability simulation program, when executed by the processor, implements any one of the steps of the vehicle-mounted lidar fog detection capability simulation method.

[0036] A fourth aspect of the present invention provides a computer-readable storage medium storing a simulation program for the fog detection capability of an onboard LiDAR, wherein the simulation program, when executed by a processor, implements any of the steps of the above-described simulation method for the fog detection capability of an onboard LiDAR.

[0037] As can be seen from the above, this invention pre-constructs and solves a third-order polynomial error model, using this model to express the error between the theoretically calculated and actual values ​​of the attenuation function. During foggy weather detection capability simulation, the attenuation function value is first calculated using a general empirical formula for attenuation, then the error in the attenuation function value is calculated using the third-order polynomial error model. This error is then used to correct the attenuation function value, and finally, the maximum detection capability is calculated based on the corrected attenuation function value. This enables more accurate and realistic simulation of vehicle-mounted lidar detection capabilities in foggy weather. Attached Figure Description

[0038] To more clearly illustrate the technical solutions in the embodiments of the present invention, 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.

[0039] Figure 1 This is a schematic diagram of the simulation method for vehicle-mounted lidar fog detection capability provided in an embodiment of the present invention;

[0040] Figure 2 This is a flowchart illustrating the process of solving the coefficients of each polynomial in a third-order polynomial error model, as provided in an embodiment of the present invention.

[0041] Figure 3 A schematic flowchart illustrating the calibration of lidar system parameters provided in an embodiment of the present invention;

[0042] Figure 4 Functional block diagram of the vehicle-mounted lidar fog detection capability simulation system provided in this embodiment of the invention;

[0043] Figure 5 This is a block diagram illustrating the internal structure of a smart terminal provided in an embodiment of the present invention. Detailed Implementation

[0044] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of the invention. However, those skilled in the art will understand that the invention can be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods are omitted so as not to obscure the description of the invention with unnecessary detail.

[0045] It should be understood that, when used in this specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0046] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0047] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0048] As used in this specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrases "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0049] 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 the 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.

[0050] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0051] Modeling the actual detection capability of vehicle-mounted lidar in foggy conditions is a key technology in lidar physical modeling. However, current simulations of vehicle-mounted lidar detection capabilities do not consider the attenuation effects during atmospheric transmission under different weather conditions, making accurate simulations of lidar detection capabilities in foggy weather impossible. Therefore, a precise model of vehicle-mounted lidar detection capabilities in foggy conditions is lacking.

[0052] This invention obtains parameter values ​​related to the lidar system through a simple target calibration method, solving the problem of lidar design black box in the modeling process. It establishes a third-order polynomial error model to express the error between the theoretical and actual values ​​of the attenuation function, and takes into account the attenuation effect caused by the incident angle and reflection angle of the laser. Then, it uses a fitting method to calculate the polynomial coefficients, which can improve the calculation accuracy of the attenuation rate. Finally, it corrects the attenuation function value according to the third-order polynomial error model, solves the maximum detection capability in foggy weather, and realizes the simulation of the fog detection capability of vehicle-mounted lidar.

[0053] This invention provides a method for simulating the fog detection capability of a vehicle-mounted lidar, which runs on an electronic device to simulate the fog detection capability of a vehicle-mounted lidar. Figure 1 As shown, the specific steps include:

[0054] Step S100: Establish a third-order polynomial error model in advance and solve for the coefficients of each polynomial in the third-order polynomial error model. The third-order polynomial error model is used to characterize the error between the theoretical calculated value and the actual value of the decay function.

[0055] The third-order polynomial error model is used to characterize the error between the theoretically calculated value and the actual value of the decay function, expressed as: y i =C3x i 3 +C2x i 2 +C1x i 1 +C0. Where y i x represents the error between the theoretically calculated value and the actual value. i These are theoretical calculation values. C3, C2, C1, and C0 are polynomial coefficients. By solving for the coefficients of each polynomial in the third-order polynomial error model, the error of the theoretical attenuation function value can be calculated, thereby correcting the theoretical attenuation function value and accurately simulating the lidar detection capability under foggy weather conditions.

[0056] In this embodiment, as Figure 2 As shown, the specific steps for solving the polynomial coefficients of the third-order polynomial error model include:

[0057] Step S110: On several foggy days, using several reflection angles, collect several maximum detection distances of the lidar to be simulated. The visibility on the aforementioned foggy days belongs to different visibility segments.

[0058] Based on the Kim empirical model (an empirical model of fog attenuation), five visibility segments were designed: greater than 50km, less than or equal to 50km and greater than 6km, less than or equal to 6km and greater than 1km, less than or equal to 1km and greater than 0.5km, and less than or equal to 0.5km.

[0059] Based on real-time visibility data from the National Meteorological Center, different visibility segments V were selected. mt For several days (visibility segment numbers t = 0, 1, 2, 3, 4), a reflectivity plate with reflectivity ρ was used as a target, and different reflection angles β were observed. i The maximum detection range R of the simulated lidar is collected under the values ​​(e.g., 0, 0.66, 0.132, ..., 79.86). i In this article, the reflection angle number is uniformly represented by i, such as R i This indicates the maximum detection distance at each reflection angle.

[0060] Step S120: Calculate the actual attenuation function value for each visibility segment based on the detection distance, reflection angle, and the lidar system parameters in the pre-calibrated lidar detection capability equation;

[0061] The lidar system parameter K in the lidar detection capability equation is pre-calibrated. In this embodiment, the SICKLMS-511 model lidar is selected as the lidar to be simulated. For ease of description, the lidar system parameter K is denoted as K. LMS_511 .

[0062] Then the maximum detection range R i Reflection angle β i Parameters K of the lidar system LMS_511 Substituting into formula (1), calculate the actual attenuation function value μ for each visibility segment obtained from measured data at different reflection angles. it In this article, t is used to represent the visibility segment number.

[0063]

[0064] It should be noted that the laser wavelength λ = 905nm in formula (1) is only used to indicate a calculation method under one laser wavelength, and can also be replaced by another laser wavelength λ = 1550nm.

[0065] Step S130: Based on the universal empirical formula for attenuation, calculate the theoretical attenuation function value for each visibility segment according to the visibility in foggy weather;

[0066] In the troposphere near the ground, the atmospheric attenuation coefficient can theoretically represent the scattering and absorption caused by atmospheric molecules and aerosols. Major atmospheric molecules, such as nitrogen and oxygen molecules (which account for over 90% of the total), exhibit almost no absorption characteristics in the wavelength range of vehicle-mounted lidar. Other atmospheric molecules, such as helium, argon, xenon, and ozone, although possessing absorption characteristics in the near-infrared region, are present in very small amounts and their absorption characteristics are generally not considered. Furthermore, their molecular scattering is far less than the attenuation caused by aerosols. Therefore, for vehicle-mounted lidar, the main factor affecting the atmospheric attenuation coefficient is the absorption and scattering attenuation caused by atmospheric aerosols. Atmospheric aerosols are extremely complex. Assuming all particles are isotropic, uniform spherical particles, after obtaining the particle radius and distribution function, modeling can be performed based on Mie scattering theory. However, because spherical particles in foggy weather may have various sizes and distributions, it is difficult for particles to meet the modeling assumptions of Mie scattering theory, making it inconvenient to use in engineering implementation. To address the above problems, this invention expresses the attenuation caused by atmospheric aerosols in foggy weather as a general empirical formula for attenuation:

[0067]

[0068] Where q represents the wavelength correction factor, V m μ represents visibility, λ represents wavelength, and μ(λ,V) represents the wavelength. m ) represents the attenuation coefficient.

[0069] Using the general empirical formula for atmospheric attenuation of lidar as described above, and combined with the Kim model, the theoretical attenuation function values ​​for each visibility segment are calculated.

[0070] As mentioned earlier, based on visibility V m In this embodiment, based on Kim's empirical model, five visibility segments are designed: greater than 50km, less than or equal to 50km and greater than 6km, less than or equal to 6km and greater than 1km, less than or equal to 1km and greater than 0.5km, and less than or equal to 0.5km. The formula for the wavelength correction factor in each visibility segment is as follows:

[0071]

[0072] By consulting the formula for the wavelength correction factor mentioned above, the wavelength correction factor corresponding to each visibility segment can be determined. When calculating the theoretical attenuation function value, the visibility V on a specific foggy day is considered. m (Assuming a speed of 60km), based on visibility V in foggy weather m The segment to which it belongs (i.e., the first segment) determines the visibility V in foggy weather. m The corresponding wavelength correction factor (1.6) is then used to adjust the visibility V in foggy weather. m Substituting the corresponding wavelength correction factor into the general empirical formula for attenuation, we can obtain the visibility V in foggy weather. m The theoretical attenuation function value for the corresponding segment. Visibility V in foggy weather under different visibility segments. m Calculate the theoretical attenuation function value for each visibility segment.

[0073] Specifically, with V mt q represents the visibility in foggy weather under different visibility segments. mt This represents the wavelength correction factor for different visibility segments. Substituting the wavelength into the above general empirical formula for attenuation, the general empirical formula for attenuation can be expressed as:

[0074]

[0075] The theoretical attenuation function value for each visibility segment can be calculated using this formula. In this embodiment, the theoretical attenuation function value μ′ for each visibility segment at different incident angles (0, 0.66, 0.132, ..., 79.86) was calculated. it .

[0076] Step S140: Compare the actual attenuation function value with the theoretical attenuation function value to obtain the error of the attenuation function value for each visibility segment;

[0077] The actual attenuation function value μ it and the theoretical decay function value μ′ it Subtraction yields the attenuation function error Δμ for each visibility segment at different incident angles. it =μ it -μ′ it .

[0078] Step S150: Solve for the coefficients of each polynomial in the third-order polynomial error model based on the error of the attenuation function value and the theoretical attenuation function value.

[0079] A third-order polynomial error model is constructed, and the parameters are fitted based on the error of the decay function value and the theoretical decay function value to solve for the coefficients of each polynomial in the third-order polynomial error model.

[0080] Specifically, for each visibility segment, the theoretical attenuation function value μ′ is used.it For input x i The error in the attenuation function value Δμ it To output y i Construct a third-order polynomial function y i =C3x i 3 +C2x i 2 +C1x i 1 +C0, using the least squares method to fit the parameters, solve for the coefficients C3, C2, C1, C0 of each polynomial. Then, substitute the polynomial coefficients into the third-order polynomial function. The third-order polynomial error model and the detection capability equation together constitute the vehicle-mounted lidar detection capability model in foggy conditions.

[0081] As described above, by establishing a third-order polynomial error model to express the error between the theoretically calculated value and the actual value of the attenuation function, and considering the high-precision attenuation rate value under fog conditions, the maximum ranging capability value of the lidar under specific material subdivision and reflection angle constraints, it is possible to accurately measure the energy attenuation that occurs on the theoretically optimal detection distance benchmark and obtain more accurate simulation results.

[0082] Step S200: Based on visibility, calculate the attenuation function value according to the general empirical formula for attenuation;

[0083] First, determine the visibility. The visibility segment is determined, and then the wavelength correction factor corresponding to the visibility is obtained according to formula (3). Visibility and wavelength correction factor Input the general empirical formula for attenuation, i.e., the above formula (2), and calculate the theoretical attenuation function value μ′. it .

[0084] Step S300: Based on visibility, calculate the attenuation function value error according to the third-order polynomial error model;

[0085] Step S400: Obtain the corrected attenuation function value based on the attenuation function value error and the attenuation function value;

[0086] Regarding visibility Obtain the theoretical decay function value μ′ it Then, let x = μ′ i Substituting into the third-order polynomial error model y = C3x 3 +C2x 2 +C1x 1 +C0 is used to calculate y, which is the error Δμ of the attenuation function value. it Then, the error Δμ of the attenuation function value is... itand the decay function value μ′ it Summation is performed to obtain the corrected attenuation function value μ. it =μ′ it +△μ it

[0087] Step S500: Based on the corrected attenuation function value, solve the maximum detection capability equation to obtain the maximum detection capability.

[0088] Obtain the corrected attenuation function value μ it Then, with a material reflectivity of ρ and a reflection angle of β... i Under these conditions, the equation for maximum detection capability is: Solving this equation using Newton's method yields the value at the reflection angle β. i The maximum distance detection capability R = x under the material reflectivity ρ.

[0089] As described above, the theoretical atmospheric attenuation coefficient of the laser radar is calculated using the atmospheric attenuation theory equation and the Kim model. This atmospheric attenuation coefficient, combined with the laser radar detection capability equation, yields the theoretical detection range of the laser beam. Then, considering weather conditions, laser radar model, target reflectivity, and laser incident angle, the system parameters in the laser radar detection capability equation are calculated in reverse. Next, detection ranges of actual targets at different incident angles are collected under foggy conditions with varying visibility, forming a dataset. Finally, a third-order polynomial error model is fitted between the theoretically calculated data and the actual collected data using the least squares method, resulting in a more accurate detection capability model for vehicle-mounted laser radar in foggy conditions. This simulation of vehicle-mounted laser radar's foggy detection capability utilizes a hybrid modeling approach based on mechanism and data-driven methods. It considers both the theoretical attenuation calculation of close-range targets during atmospheric transmission in foggy conditions and leverages the advantages of data-driven nonlinear modeling, enabling a more accurate and realistic simulation of vehicle-mounted laser radar's foggy detection capability.

[0090] In one embodiment, such as Figure 3 As shown, the specific steps for calibrating the lidar system parameters in the lidar detection capability equation in step S120 above include:

[0091] Step S121: Under preset visibility conditions, use targets with two different reflectivities to collect the calibration detection range of the simulated lidar;

[0092] Step S122: Determine the lidar system parameters based on the product data and calibrated detection distance of the lidar to be simulated.

[0093] The lidar detection capability equation describes the lidar's detection capability from an energy perspective. In autonomous driving scenarios, the lidar beam divergence angle is very small. When detecting at close range on the ground (detection distance less than 200 meters), the area of ​​dynamic and static targets in the typical scenario is much larger than the area of ​​the lidar spot on the target. In this invention, all targets in the scenario are considered as extended Lambertian targets, meaning that the emissivity in all directions of diffuse reflection is equal. Therefore, the theoretical maximum detection range R of the lidar is... max Represented as:

[0094]

[0095]

[0096] Among them, P t Represents the transmit power of the lidar, measured in W; η sys It is the overall transmission efficiency of the lidar optical system; S r This represents the effective photosensitive area of ​​the laser receiver, measured in meters (m²). 2 ;P nep The equivalent power of the noise, measured in W; n min This means that if the power of the laser radar detection and reception signal is set to be at least n times the noise power... min Only when the signal is multiplied by a factor of 1 is it considered a valid target signal. Therefore, for a specific type of lidar, K is a system-dependent, fixed value.

[0097] η atm =exp(-2μ(λ,V) m )R max ), where η atm λ is the atmospheric attenuation coefficient, representing the one-way atmospheric transmittance calculated by the theoretical atmospheric attenuation model; λ is the lidar wavelength, typically 905nm or 1550nm for vehicle-mounted lidar; μ(λ,V) m () is the attenuation coefficient, in km. -1 μ is the laser radar wavelength λ and visibility V. m functions of R; max This is the theoretical maximum detection range of the lidar. ρ represents the orthoreflectivity of the target material in the scene, and β represents the angle between the laser reflection direction and the normal for a Lambertian target, in degrees (deg).

[0098] For any given lidar model, by obtaining the maximum detection range for low-reflectivity and high-reflectivity targets under the same visibility conditions, μ and the system parameter K can be solved. Let ρ1 represent the reflectivity value of the Lambertian calibration plate of the low-reflectivity standard lidar, and R1 represent the detection range of the lidar for the low-reflectivity target; let ρ2 represent the reflectivity value of the Lambertian calibration plate of the high-reflectivity standard lidar, and R2 represent the detection range of the lidar for the high-reflectivity target. Then we have:

[0099]

[0100]

[0101] Based on formulas (7) and (8), given that the weather conditions, lidar model, target reflectivity, and laser incident angle are determined, the lidar system parameter K in the lidar detection capability equation (5) can be calculated in reverse.

[0102] The lidar model in this embodiment is SICK LMS-511. According to the experimental data given in its user manual, its detection distances are R1 = 40m and R2 = 80m under low reflectivity ρ1 = 10% and high reflectivity ρ2 = 100%, respectively. For the maximum capability test, the reflection energy loss is minimal when the laser line reflection angle is 0°, and the maximum detection distance can be achieved. Therefore, β1 = β2 = 0. Substituting these values ​​into the formula (8) above, the lidar system parameter K can be calculated. LMS_511 .

[0103] As described above, the parameters of the lidar system can be calculated through a simple target calibration method. It is not necessary to build detailed decomposition models of each component inside the lidar or to understand a large number of component design parameters. This method can complete the physical detection capability model covering the influence of the lidar system, and only one calibration is required for the same model of product.

[0104] This invention also provides a simulation system for vehicle-mounted lidar fog detection capabilities, such as... Figure 4 As shown, the system includes:

[0105] The third-order polynomial solving module 600 is used to pre-establish a third-order polynomial error model and solve for each polynomial coefficient of the third-order polynomial error model. The third-order polynomial error model is used to characterize the error between the theoretical calculated value and the actual value of the decay function.

[0106] The attenuation function value module 610 is used to calculate the attenuation function value based on visibility and according to the general empirical formula for attenuation.

[0107] The attenuation function value error module 620 is used to calculate the attenuation function value error based on visibility and according to the third-order polynomial error model.

[0108] The correction module 630 is used to obtain the corrected attenuation function value based on the attenuation function value error and the attenuation function value.

[0109] The maximum detection capability module 640 is used to solve the maximum detection capability equation based on the corrected attenuation function value to obtain the maximum detection capability.

[0110] Optionally, the third-order polynomial solving module includes: an acquisition unit, an actual attenuation function value unit, a theoretical attenuation function value unit, an error unit, and a solving unit. The acquisition unit is used to acquire several detection distances of the simulated lidar at several reflection angles on several foggy days, where the visibility on the foggy days belongs to different visibility segments. The actual attenuation function value unit is used to calculate the actual attenuation function value for each visibility segment based on the detection distance, the reflection angle, and the lidar system parameters in the pre-calibrated lidar detection capability equation. The theoretical attenuation function value unit is used to calculate the theoretical attenuation function value for each visibility segment based on the general empirical formula for attenuation and the visibility on the foggy days. The error unit is used to compare the actual attenuation function value and the theoretical attenuation function value to obtain the attenuation function value error for each visibility segment. The solving unit is used to solve for the polynomial coefficients of the third-order polynomial error model based on the attenuation function value error and the theoretical attenuation function value.

[0111] Specifically, in this embodiment, the specific functions of the above-mentioned vehicle-mounted lidar fog detection capability simulation system can be referred to the corresponding description in the above-mentioned vehicle-mounted lidar fog detection capability simulation method, and will not be repeated here.

[0112] Based on the above embodiments, the present invention also provides a smart terminal, the principle block diagram of which can be as follows: Figure 5 As shown. The aforementioned intelligent terminal includes a processor, memory, network interface, and display screen connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and a simulation program for vehicle-mounted LiDAR fog detection capabilities. The internal memory provides an environment for the operation of the operating system and the vehicle-mounted LiDAR fog detection capability simulation program stored in the non-volatile storage medium. The network interface of the intelligent terminal is used for communication with external terminals via a network connection. When the vehicle-mounted LiDAR fog detection capability simulation program is executed by the processor, it implements the steps of any of the aforementioned vehicle-mounted LiDAR fog detection capability simulation methods. The display screen of the intelligent terminal can be an LCD screen or an e-ink screen.

[0113] Those skilled in the art will understand that Figure 5The block diagram shown is merely a partial structural diagram related to the present invention and does not constitute a limitation on the smart terminal to which the present invention is applied. A specific smart terminal may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0114] This invention also provides a computer-readable storage medium storing a simulation program for vehicle-mounted lidar fog detection capability. When the simulation program is executed by a processor, it implements the steps of any vehicle-mounted lidar fog detection capability simulation method provided in this invention.

[0115] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0116] Those skilled in the art will understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the functions described above can be assigned to different functional units and modules as needed, that is, the internal structure of the above device / system can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this invention. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0117] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0118] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0119] In the embodiments provided by this invention, it should be understood that the disclosed apparatus / system / terminal device and method can be implemented in other ways. For example, the apparatus / system / terminal device embodiments described above are merely illustrative. For instance, the division of the above modules or units is merely a logical functional division, and in actual implementation, it can be divided in other ways. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed.

[0120] If the integrated modules / units described above are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable storage medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction.

[0121] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not mean that the essence of the corresponding technical solutions deviates from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A simulation method for vehicle-mounted lidar fog detection capability, characterized in that, The method includes: A third-order polynomial error model is pre-established and the coefficients of each polynomial in the third-order polynomial error model are solved. The third-order polynomial error model is used to characterize the error between the theoretically calculated value and the actual value of the decay function. Based on visibility, the attenuation function value is calculated according to the general empirical formula for attenuation. Based on visibility, the attenuation function value error is calculated according to the third-order polynomial error model. Based on the attenuation function value error and the attenuation function value, the corrected attenuation function value is obtained; Based on the corrected attenuation function value, the maximum detection capability equation is solved to obtain the maximum detection capability. Solving the coefficients of each polynomial in the third-order polynomial error model includes: On several foggy days, several reflection angles were used to collect several maximum detection distances of the lidar to be simulated. The visibility on the foggy days belonged to different visibility segments. Based on the detection distance, the reflection angle, and the lidar system parameters in the pre-calibrated lidar detection capability equation, calculate the actual attenuation function value for each visibility segment; Based on the general empirical formula for attenuation, the theoretical attenuation function values ​​for each visibility segment are calculated according to the visibility in the foggy weather. By comparing the actual attenuation function value with the theoretical attenuation function value, the error of the attenuation function value for each visibility segment is obtained; Based on the attenuation function error and the theoretical attenuation function value, solve for the polynomial coefficients of the third-order polynomial error model.

2. The simulation method for vehicle-mounted lidar fog detection capability as described in claim 1, characterized in that, The calculation of theoretical attenuation function values ​​for each visibility segment based on the universal empirical formula for attenuation, according to the visibility in foggy weather, includes: Determine the wavelength correction factor corresponding to each visibility segment; Based on the visibility on the foggy day and the wavelength correction factor corresponding to the visibility on the foggy day, the theoretical attenuation function value of the visibility segment to which the visibility on each foggy day belongs is calculated.

3. The simulation method for vehicle-mounted lidar fog detection capability as described in claim 1, characterized in that, The lidar system parameters in the lidar detection capability equation are calibrated, including: Under preset visibility conditions, the calibration detection range of the simulated lidar is collected using targets with two different reflectivities. The parameters of the lidar system are determined based on the product data of the lidar to be simulated and the calibrated detection distance.

4. The simulation method for vehicle-mounted lidar fog detection capability as described in claim 1, characterized in that, The step of solving for the polynomial coefficients of the third-order polynomial error model based on the attenuation function value error and the theoretical attenuation function value includes: The least squares method is used to fit and solve the coefficients of each polynomial in the third-order polynomial error model based on the error of the attenuation function value and the theoretical attenuation function value.

5. The simulation method for vehicle-mounted lidar fog detection capability as described in claim 1, characterized in that, The calculation of the attenuation function value based on visibility and according to the general empirical formula for attenuation includes: Based on visibility, determine the wavelength correction factor corresponding to the visibility. The attenuation function value is calculated based on the attenuation general empirical formula, according to the visibility and the wavelength correction factor.

6. A simulation system for vehicle-mounted lidar fog detection capability, characterized in that, include: The third-order polynomial solving module is used to pre-establish a third-order polynomial error model and solve for the coefficients of each polynomial in the third-order polynomial error model. The third-order polynomial error model is used to characterize the error between the theoretical calculated value and the actual value of the decay function. The attenuation function value module is used to calculate the attenuation function value based on visibility and according to a general empirical formula for attenuation. The attenuation function value error module is used to calculate the attenuation function value error based on visibility and according to the third-order polynomial error model. The correction module is used to obtain the corrected attenuation function value based on the attenuation function value error and the attenuation function value. The maximum detection capability module is used to solve the maximum detection capability equation based on the corrected attenuation function value to obtain the maximum detection capability. The third-order polynomial solving module includes: an acquisition unit, an actual attenuation function value unit, a theoretical attenuation function value unit, an error unit, and a solving unit. The acquisition unit is used to acquire several detection distances of the simulated lidar at several reflection angles on several foggy days, where the visibility on each foggy day belongs to a different visibility segment. The actual attenuation function value unit is used to calculate the actual attenuation function value for each visibility segment based on the detection distance, the reflection angle, and the lidar system parameters in the pre-calibrated lidar detection capability equation. The theoretical attenuation function value unit is used to calculate the theoretical attenuation function value for each visibility segment based on a general empirical formula for attenuation, according to the visibility on the foggy days. The error unit is used to compare the actual attenuation function value with the theoretical attenuation function value to obtain the attenuation function value error for each visibility segment. The solving unit is used to solve for the polynomial coefficients of the third-order polynomial error model based on the attenuation function value error and the theoretical attenuation function value.

7. A smart terminal, characterized in that, The intelligent terminal includes a memory, a processor, and a vehicle-mounted lidar fog detection capability simulation program stored in the memory and executable on the processor. When the vehicle-mounted lidar fog detection capability simulation program is executed by the processor, it implements the steps of the vehicle-mounted lidar fog detection capability simulation method as described in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a simulation program for the fog detection capability of a vehicle-mounted lidar. When the simulation program is executed by a processor, it implements the steps of the vehicle-mounted lidar fog detection capability simulation method as described in any one of claims 1-5.

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