Raindrop response control method and related equipment

Abstract

The invention discloses a raindrop response control method and related equipment, and relates to the technical field of vehicle control, and the method comprises the steps: scanning the surface of vehicle windshield glass through a distributed sensor array, and obtaining raindrop distribution time sequence data; according to the spatial density distribution characteristics of the raindrop distribution time sequence data, generating a first steering control instruction for the optical detection head so as to drive the optical detection head to obtain raindrop dense area detection data; according to the raindrop distribution time sequence data and the raindrop dense area detection data, raindrop path prediction is carried out, and a raindrop diffusion path of a future preset time length is obtained; and generating a pre-response instruction set according to the raindrop diffusion path. Through distributed sensing, dynamic focusing and multi-dimensional fusion prediction technologies, high-precision, non-blind-area and low-delay raindrop detection and response control over the whole windshield glass are achieved, and the rainfall recognition accuracy and the system intelligence can be remarkably improved.

Description

Technical Field

[0001] This application relates to the technical field of vehicle control. More specifically, this application relates to a raindrop response control method and related devices. Background Art

[0002] With the continuous improvement of intelligent driving assistance systems and vehicle automation levels, the environmental perception ability plays an increasingly important role in enhancing driving safety and comfort. As a key part of ensuring driving safety in rainy days, the raindrop detection system triggers the wiper action by sensing the rainfall conditions on the windshield to achieve good vision cleaning and energy consumption control. In current technologies, traditional raindrop detection systems mainly rely on infrared or capacitive rain sensors installed at specific positions on the windshield (such as near the interior rearview mirror) to obtain rainfall information and control the start and stop of the wiper.

[0003] However, due to the limited area where the sensors are arranged in related technologies, their sensing range only covers a partial area of the glass, and it is very easy to have detection blind spots; especially in complex scenarios such as local rainfall or inclined rain lines, if the raindrops do not fall within the sensor sensing area, it will be impossible to accurately judge the rainfall conditions, resulting in wiper response lag or missed triggering, and in severe cases, it will even affect the driver's vision and driving safety; at the same time, most current technologies are based on static detection, lacking dynamic prediction and pre-response control of the raindrop diffusion trend and being unable to respond in advance to complex rain trend evolutions. That is to say, there are generally technical problems in related technologies such as limited raindrop detection range, large response delay, and poor control response pertinence. Summary of the Invention

[0004] A series of simplified concepts are introduced in the Summary of the Invention part of this application, which will be further described in detail in the Detailed Description part. The Summary of the Invention part of this application does not mean to attempt to define the key features and essential technical features of the claimed technical solution, nor does it mean to attempt to determine the protection scope of the claimed technical solution.

[0005] The raindrop response control method and related devices provided by this application can achieve high-precision, blind-spot-free, and low-latency raindrop detection and response control for the entire windshield through distributed sensing, dynamic focusing, and multi-dimensional fusion prediction technologies, and can significantly improve the accuracy of rainfall recognition and the intelligence of the system.

[0006] In a first aspect, the present application provides a raindrop response control method, including: scanning the surface of the vehicle windshield through a distributed sensor array to obtain raindrop distribution time-series data; generating a first steering control instruction for an optical detection head according to the spatial density distribution characteristics of the raindrop distribution time-series data to drive the optical detection head to obtain raindrop dense area detection data; predicting the raindrop path according to the raindrop distribution time-series data and the raindrop dense area detection data to obtain the raindrop diffusion path for a future preset duration; generating a pre-response instruction set according to the raindrop diffusion path, where the pre-response instruction set includes a wiper action parameter adjustment instruction and a second steering control instruction for the optical detection head.

[0007] In some embodiments, the scanning the surface of the vehicle windshield through a distributed sensor array to obtain raindrop distribution time-series data includes: synchronously transmitting scanning signals in a circular monitoring manner through a plurality of radar modules arranged around the windshield; determining the impact position coordinates and impact time of raindrops on the windshield surface according to the time difference of arrival of the reflected signals received by the radar modules; generating the raindrop distribution time-series data according to the impact position coordinates and the impact time.

[0008] In some embodiments, the generating a first steering control instruction for an optical detection head according to the spatial density distribution characteristics of the raindrop distribution time-series data to enable the optical detection head to obtain raindrop dense area detection data includes: mapping the raindrop distribution time-series data into preset grid cells on the windshield surface to obtain the raindrop impact frequency in each preset grid cell; determining the preset grid cells with the raindrop impact frequency exceeding a preset density threshold as high-density areas; generating the first steering control instruction according to the geometric center coordinates of the high-density areas; in response to the first steering control instruction, adjusting the steering angle of the optical detection head by driving a servo motor so that the optical scanning range of the optical detection head covers the high-density areas; generating the raindrop dense area detection data based on the infrared reflection intensity change data collected by the steered optical detection head.

[0009] In some embodiments, predicting the raindrop path based on the raindrop distribution time series data and the raindrop dense area detection data to obtain the raindrop diffusion path for a preset future duration includes: constructing a first spatio-temporal feature vector according to the raindrop impact position time series in the raindrop distribution time series data; constructing a second spatio-temporal feature vector according to the infrared reflection intensity change rate and the spot diffusion rate in the raindrop dense area detection data; inputting the first spatio-temporal feature vector, the second spatio-temporal feature vector, and the real-time vehicle parameters into a preset spatio-temporal prediction model to obtain the probability distribution map of the raindrop diffusion path for the preset future duration, where the real-time vehicle parameters include the current vehicle speed and the windshield inclination angle parameter; and determining the raindrop diffusion path based on the continuous region in the probability distribution map where the probability value exceeds the activation threshold.

[0010] In some embodiments, generating a pre-response instruction set according to the raindrop diffusion path includes: generating the wiper action parameter adjustment instruction according to the coverage range of the raindrop diffusion path, where the wiper action parameter adjustment instruction includes a wiper swing frequency adjustment signal and a swing amplitude adjustment signal; and generating the second steering control instruction according to the moving direction of the raindrop diffusion path to drive the optical detection head to turn to the prediction area corresponding to the moving direction.

[0011] In some embodiments, generating the wiper action parameter adjustment instruction according to the coverage range of the raindrop diffusion path includes: determining a first adjustment coefficient according to the ratio of the area of the coverage range to the total area of the windshield; generating the wiper swing frequency adjustment signal according to the product of the first adjustment coefficient and the preset reference frequency; determining a second adjustment coefficient according to the ratio of the lateral diffusion distance of the coverage range to the width of the windshield; and generating the wiper swing amplitude adjustment signal based on the product of the second adjustment coefficient and the preset reference amplitude.

[0012] In some embodiments, generating the second steering control instruction according to the moving direction of the raindrop diffusion path to drive the optical detection head to turn to the prediction area corresponding to the moving direction includes: determining the steering angle deviation according to the angle between the moving direction of the raindrop diffusion path and the current direction of the optical detection head; determining the coordinate offset of the prediction area based on the moving speed of the raindrop diffusion path and the preset future duration; generating the target steering angle corresponding to the second steering control instruction according to the coordinate offset and the steering angle deviation; and driving the optical detection head to adjust to the target steering angle through a servo motor so that the optical scanning range of the optical detection head covers the prediction area.

[0013] In a second aspect, the present application further provides a raindrop response control device, including: a data acquisition unit configured to scan the surface of the vehicle windshield through a distributed sensor array to obtain raindrop distribution time series data; a steering control unit configured to generate a first steering control instruction for an optical detection head according to the spatial density distribution characteristics of the raindrop distribution time series data, so as to drive the optical detection head to acquire raindrop dense area detection data; a path prediction unit configured to perform raindrop path prediction according to the raindrop distribution time series data and the raindrop dense area detection data to obtain a raindrop diffusion path for a preset future duration; an instruction generation unit configured to generate a pre-response instruction set according to the raindrop diffusion path, wherein the pre-response instruction set includes a wiper action parameter adjustment instruction and a second steering control instruction for the optical detection head.

[0014] In a third aspect, the present application further provides an electronic device, including: a memory and a processor, and the processor is configured to implement the steps of the raindrop response control method described in the first aspect when executing a computer program stored in the memory.

[0015] In a fourth aspect, the present application further provides a computer-readable storage medium storing a computer program, and the computer program is configured to implement the steps of the raindrop response control method described in the first aspect when executed by a processor.

[0016] In a fifth aspect, the present application further provides a computer program product including a computer program or computer executable instructions, and the computer program or computer executable instructions are configured to implement the raindrop response control method provided in the embodiments of the present application when executed by a processor.

[0017] In summary, the present application scans raindrops on the entire windshield surface by deploying a distributed sensor array, enabling real-time acquisition of spatio-temporal change data of raindrop distribution. It breaks through the limitation of traditional rain sensors that can only sense a fixed small area, achieves the goal of a full-glass sensing surface, greatly improves the detection coverage and accuracy, and avoids the problem that raindrops falling in the blind area cannot trigger a response. By analyzing the spatial density distribution of raindrops, a first steering control instruction is generated to guide the rotatable optical detection head to focus on detecting the raindrop-dense area, which can ensure that computing resources are preferentially used for key areas, improving the response efficiency to raindrops and the accuracy of rainfall judgment. Combining the raindrop time-series data with the optical detection results, through a trajectory prediction algorithm, the diffusion path of raindrops in the short term in the future can be estimated, and it can be judged in advance which areas will be affected by rain, thus realizing pre-response control, effectively reducing the wiper start-up delay caused by sensing lag, and improving driving safety. This method not only relies on the time-series raindrop distribution data obtained by the radar array, but also integrates the image / infrared data collected by the optical detection head to form a multi-dimensional information fusion mechanism, which can improve the recognition ability of false trigger factors (such as stains, water mist, light spots, etc.), and has stronger anti-interference ability and rainfall judgment accuracy. In summary, the raindrop response control method provided by the present application realizes high-precision, blind-area-free, and low-latency raindrop detection and response control for the entire windshield through distributed sensing, dynamic focusing, and multi-dimensional fusion prediction technologies, and can significantly improve the accuracy of rainfall recognition and the intelligence of the system. Description of the Drawings

[0018] By reading the following detailed description of the preferred embodiments, various other advantages and benefits will become clear to those of ordinary skill in the art. The drawings are only for the purpose of showing the preferred embodiments and are not considered to limit this specification. And throughout the drawings, the same reference symbols are used to represent the same components. In the drawings:

[0019] Figure 1 It is a schematic flowchart of a raindrop response control method provided by an embodiment of the present application;

[0020] Figure 2 It is a schematic diagram of the composition structure of a raindrop response control device provided by an embodiment of the present application;

[0021] Figure 3 It is a schematic diagram of the composition structure of an electronic device provided by an embodiment of the present application. Detailed Embodiments

[0022] The terms in the description, claims, and drawings of this application, such as "first", "second", "third", "fourth", etc. (if any), are used to distinguish similar objects and do not describe a specific order or sequence. Therefore, it is understood that, under appropriate circumstances, these terms can be used interchangeably so that the described embodiments can be implemented in different orders, unless there are special requirements in the drawings or description. In addition, the terms "is" and "has" in this application and any of their variants are intended to non-exclusively cover all possible constituent elements. For example, a process, method, system, product, or device that includes several steps or units does not have to be limited to the steps or units that are explicitly listed, and may also include other steps or units that are not explicitly listed, or steps or units that are inherent to the process, method, product, or device.

[0023] In this application, a "module" or "unit" refers to a computer program or a part of a computer program with a specific function and works in cooperation with other related parts to achieve a predetermined goal. These modules or units can be implemented by software, hardware (such as a processing circuit or a memory), or a combination of both. One or more processors or memories can implement one or more modules or units. At the same time, each module or unit can also be a part of a larger module or unit.

[0024] The technical solutions in this application will be described in detail below with reference to the drawings in the embodiments. It should be noted that the described embodiments are only a part of this application, not all embodiments. In the following description, the "some embodiments" mentioned are only subsets of all possible embodiments, which can be the same or different subsets, and different embodiments can be combined with each other without conflict.

[0025] Figure 1 It is a schematic flowchart of a raindrop response control method provided by an embodiment of this application. Exemplarily, refer to Figure 1 The raindrop response control method provided by the embodiment of this application may include the following steps 101 to 104:

[0026] Step 101, scan the surface of the vehicle windshield through a distributed sensor array to obtain raindrop distribution timing data;

[0027] In some examples, a distributed sensor array refers to a sensor network composed of multiple sensing modules evenly arranged around or on the surface of a vehicle windshield, which can cover the entire windshield area to achieve large - range and high - density raindrop detection; the distributed sensor array can be a millimeter - wave radar module that emits and receives electromagnetic waves and obtains raindrop impact position and time information based on echo intensity and time - difference of arrival; it can also be an optical / infrared sensor array that detects changes in light transmittance or thermal radiation characteristics caused by raindrops; it can also be a capacitance / pressure sensing film embedded in the glass or attached to the surface to sense the tiny electrical signals or pressure fluctuations generated by raindrop impacts; for example, four millimeter - wave radars are arranged at the four corners of the windshield, and the raindrop impact points are cross - located through the reflected signals. The vehicle windshield refers to the windshield at the front of the vehicle, which is the target detection area of the raindrop sensing system. The raindrop distribution time - series data refers to a dataset of spatial - time coordinates of raindrop impacts on the glass surface at different positions within a preset time range. The data content of the raindrop distribution time - series data includes the position coordinates (X, Y) of the raindrop landing point on the two - dimensional plane of the windshield, the time stamp (T) of the specific time when the raindrop impacts this position, and other additional attributes (optional), such as raindrop size, impact intensity, reflectivity, track number, etc.; for example, recording 10 raindrop impact events at a certain moment: [(X1, Y1, T1), (X2, Y2, T2),...(X10, Y10, T10)] is a set of raindrop distribution time - series data.

[0028] Exemplarily, after the vehicle starts, the millimeter - wave radar array distributed around the windshield begins to continuously emit low - power signals to scan the windshield surface without dead - angles; once a raindrop impacts the glass, the echo of the medium disturbance it causes is received by the radar, and the impact coordinates and time are accurately located by combining the time - difference of arrival of multiple receivers; all sensing data are uniformly summarized to the central processing unit to generate raindrop distribution time - series data in real - time, providing a raw data basis for subsequent raindrop density analysis, key - area detection, and path prediction.

[0029] Through the implementation of step 101, real - time sensing of the entire glass surface is achieved, breaking the limitation that traditional rain sensors only cover a small area; through the distributed sensor array, raindrop landing points and changing trajectories can be dynamically captured, providing data with high spatio - temporal resolution; it provides basic data support for subsequent path prediction and intelligent control, and can improve the overall system sensing ability.

[0030] Step 102, generate a first steering control instruction for the optical detection head according to the spatial density distribution characteristics of the raindrop distribution time - series data to drive the optical detection head to obtain detection data in the raindrop - dense area;

[0031] In some examples, the spatial density distribution feature refers to the concentration degree of raindrop impact events in the spatial dimension within a certain time window on the windshield surface, that is, the distribution pattern of the raindrop impact frequency per unit area. The glass surface can be divided into regular grid cells (such as 10 cm × 10 cm), and the number of raindrop impacts in each cell is counted as the spatial density distribution feature. For example, if 15 impacts are detected within 1 second in grid area A and only 2 impacts in grid B, then grid A is determined as the raindrop-dense area. The optical detection head is a detection module installed inside the vehicle that can rotate or turn. Using visible light, infrared, or multispectral imaging technology, it makes a detailed observation of a specific area. The optical detection head in the embodiments of this application can be an infrared imaging sensor (detecting the thermal radiation change caused by raindrops), a camera module (capturing the raindrop morphology and movement trajectory on the windshield), or a laser / TOF module (acquiring the characteristics of micro protrusions or reflective points formed by raindrops), etc. The first steering control instruction is a control signal indicating the optical detection head to turn towards the raindrop-dense area, ensuring that the key area is scanned and recorded preferentially. Based on the spatial density distribution feature, the geometric center coordinates of the raindrop-dense area can be extracted, the angle between the current detection head orientation and the geometric center coordinates can be calculated, and servo control parameters including the rotation angle, rotation direction, and target position can be generated accordingly. For example, if the raindrop-dense area is located in the upper right of the windshield, it is calculated that the optical detection head needs to be rotated clockwise by 28°, and a servo instruction is sent to adjust the detection direction of the optical detection head to this position. The raindrop-dense area detection data refers to the images or infrared / optical response information about the raindrop-dense area collected after the optical detection head completes the turn, which can be used to further judge the raindrop size, morphology, speed, etc.

[0032] By implementing step 102, the dynamic scheduling and focused use of sensing resources can be achieved, improving the computing and detection efficiency. The optical detection head turns towards the key area according to the raindrop-dense area instruction, which can enhance the recognition accuracy of the rainfall in the key area, avoid the delay or resource waste caused by non-discriminatory scanning of the full screen, and has higher energy efficiency and response speed.

[0033] Step 103: According to the raindrop distribution time-series data and the raindrop-dense area detection data, predict the raindrop path to obtain the raindrop diffusion path for a preset future duration.

[0034] In some examples, the raindrop distribution time series data and the raindrop dense area detection data can be used to infer the future movement trend and distribution position of raindrops through modeling analysis; the prediction process can use machine learning models, such as convolutional spatiotemporal network ConvLSTM, graph neural network GNN, or physical simulation models, such as trajectory deduction based on fluid mechanics; and then the real-time vehicle parameters, such as vehicle speed, windshield inclination, wind speed direction, are combined to correct the model output results, predict the future diffusion trend of raindrops, and obtain the raindrop diffusion path of the future preset time. The future preset time refers to the length of the time window covered by the path prediction, which can be between hundreds of milliseconds and a few seconds in the future, and is used to guide the early response of subsequent wipers and optical detection heads. The future preset time can be configured according to the vehicle running speed, processor response capability and wiper drive delay, which can be 0.5 seconds, 1 second, 2 seconds, etc. The raindrop diffusion path refers to the possible movement trajectory or regional diffusion trend of raindrops after being affected by gravity, vehicle speed airflow, wind pressure, etc. on the windshield surface, which can be presented as a continuous probability distribution or trajectory diagram.

[0035] By implementing step 103, a prediction mechanism is introduced to achieve early perception of future raindrop diffusion trends based on historical trajectories, so that the movement direction and diffusion trend of raindrops can be known in advance, which can reduce the recognition lag caused by sudden changes in rainfall and enhance the robustness of the raindrop response process in complex weather conditions.

[0036] Step 104, generating a pre-response instruction set according to the raindrop diffusion path, wherein the pre-response instruction set may include a wiper action parameter adjustment instruction and a second steering control instruction for the optical detection head;

[0037] In some examples, the pre-response instruction set refers to a set of action control instructions generated in advance based on the predicted raindrop diffusion path, which can be used to drive the wiper system and the optical detection system to complete the response preparation before the raindrops have spread to the target area, so as to improve the efficiency of raindrop removal and perception. The wiper action parameter adjustment instruction refers to the control signal that adjusts the wiper swing behavior for the upcoming raindrop diffusion area, including adjusting the wiper swing frequency, amplitude, starting angle and other parameters to ensure that the raindrops are cleared in time before or just after arrival; for example, if the predicted path shows that raindrops will be concentrated in the upper right 1 / 3 area of the windshield in the next 1 second, a control instruction is generated to increase the right side of the wiper by 20% and the swing frequency by 30% to adapt to the change in rainfall in advance. The second steering control instruction is a control signal that instructs the optical detection head to adjust the angle and pre-align it with the area where the raindrops are predicted to spread, so as to achieve continuous monitoring and tracking of the raindrop diffusion trend and improve the recognition continuity of local dense areas.

[0038] Exemplarily, after the future raindrop diffusion path is output in step 103, the spatial range and density distribution involved in the path are immediately parsed. First, in combination with the path area and its positional relationship, the optimal swing frequency and amplitude of the windshield wiper are dynamically calculated, and the adjustment instructions are written into the controller for execution. At the same time, according to the direction of the diffusion trend and the offset of the coverage area, the optical detection head is driven to perform direction adjustment so that it aims at the next possible dense area in advance. This process is completed in milliseconds to ensure that the processing is ready before the raindrops actually fall.

[0039] Through the implementation of step 104, the system linkage and response prepositioning are achieved. The pre-adjusted windshield wiper action can reduce the reaction delay, synchronously adjust the direction of the optical detection head, continuously track the raindrop development area, and improve the continuous perception and dynamic adaptation ability to raindrops. It can overall optimize the automatic response mechanism of the vehicle in the rainy environment and improve the intelligence and comfort of driving.

[0040] In summary, in the embodiment of the present application, by deploying a distributed sensor array to scan raindrops on the entire windshield surface, the spatio-temporal change data of the raindrop distribution can be obtained in real time, breaking through the limitation that the traditional rain sensor can only sense a fixed small area, achieving the goal of a full-glass sensing surface, greatly improving the detection coverage and accuracy, and avoiding the problem that raindrops falling on the blind area cannot trigger a response. By analyzing the raindrop spatial density distribution, the first steering control instruction is generated to guide the rotatable optical detection head to focus on detecting the raindrop dense area, which can ensure that the computing resources are preferentially used for key areas, improving the response efficiency to raindrops and the accuracy of rainfall judgment. Combining the raindrop time-series data and the optical detection results, through the trajectory prediction algorithm, the diffusion path of raindrops in the short future can be estimated, and it can be judged in advance which areas will be affected by rainwater, so as to achieve pre-response control, effectively reducing the wiper start-up delay caused by perception lag and improving driving safety. This method not only relies on the time-series raindrop distribution data obtained by the radar array, but also integrates the image / infrared data collected by the optical detection head to form a multi-dimensional information fusion mechanism, which can improve the recognition ability of false triggering factors (such as stains, water mist, light spots, etc.), and has stronger anti-interference ability and rainfall judgment accuracy. In summary, the raindrop response control method provided by the embodiment of the present application realizes high-precision, blind area-free, and low-latency raindrop detection and response control for the entire windshield through distributed sensing, dynamic focusing, and multi-dimensional fusion prediction technologies, and can significantly improve the accuracy of rainfall recognition and the intelligence of the system.

[0041] In some embodiments, the foregoing step 101 may include: synchronously transmitting scanning signals in a circular monitoring manner through a plurality of radar modules arranged around the windshield; determining the impact position coordinates and impact time of raindrops on the windshield surface according to the time difference of arrival of the reflected signals received by the radar modules; generating raindrop distribution time-series data according to the impact position coordinates and impact time.

[0042] In some examples, the multiple radar modules arranged around the windshield refer to multiple millimeter-wave radar modules installed in the peripheral edge area of the vehicle windshield, usually with a quantity of 3 to 8, which can achieve dead-angle-free monitoring of the entire glass surface. The circular monitoring method means that multiple radar modules are distributed in a circular pattern around the windshield edge to work cooperatively, and emit scanning signals synchronously or in a polling manner, which can ensure that the signal coverage in different directions forms a closed sensing loop in space. The scanning signal refers to a millimeter-wave pulse or continuous wave signal emitted by the radar module, which is used to detect the presence, position and changes of raindrops in space. When a raindrop hits the glass, more than one radar module can receive its reflected signal. Through the time difference of the signal received by multiple modules, triangulation can be achieved to accurately calculate the two-dimensional coordinates of the raindrop impact and the exact moment when the event occurs; for example, assuming that the times when modules A, B, and C receive the reflected signal are T1, T2, and T3 respectively, combined with the distance difference between the modules, it can be inversely calculated that the raindrop lands at the position (X = 55mm, Y = 120mm) at 11:07:13; by recording the spatial coordinates (X, Y) and time stamps (T) of each raindrop impact event and sorting them in chronological order, the raindrop distribution time series data can be formed.

[0043] Exemplarily, during the vehicle driving process, 4 millimeter-wave radar modules arranged around the windshield start to synchronously enter the "circular monitoring" working state. Each module alternately emits scanning signals at a fixed period. When a raindrop hits the glass surface and generates a reflected signal, multiple modules simultaneously record the arrival time of the signal. Using the received time difference data and combining the relative spatial arrangement of the radar modules, the raindrop impact point is accurately inverted; subsequently, the detected raindrop impact events are continuously encoded into time-stamped coordinate data and aggregated to form continuous raindrop distribution time series data for real-time calling by subsequent spatial density calculation and path prediction algorithms.

[0044] Through the implementation of the above embodiments, the multiple radar modules arranged around the windshield scan synchronously in a circular monitoring manner, which can form dead-angle-free coverage of the glass surface and avoid the problem of missed detection in the edge area existing in traditional single-point detection; by accurately calculating the raindrop impact position and time through the time difference of the reflected signal arrival, sub-second-level real-time positioning can be achieved, avoiding the delay caused by point-by-point scanning in traditional optical detection, and providing real-time data support for subsequent dynamic adjustment.

[0045] In some embodiments, generating a first steering control instruction for the optical detection head according to the spatial density distribution characteristics of the foregoing raindrop distribution time-series data, so that the optical detection head acquires detection data of the raindrop-dense area may include: mapping the raindrop distribution time-series data into preset grid cells on the windshield surface to obtain the raindrop impact frequencies in each preset grid cell; determining the preset grid cells with raindrop impact frequencies exceeding a preset density threshold as high-density areas; generating a first steering control instruction according to the geometric center coordinates of the high-density areas; in response to the first steering control instruction, adjusting the steering angle of the optical detection head by driving a servo motor, so that the optical scanning range of the optical detection head covers the high-density areas; and generating detection data of the raindrop-dense area based on the infrared reflection intensity change data collected by the steered optical detection head.

[0046] In some examples, the preset grid cells are obtained by dividing the entire windshield surface into a number of two-dimensional grid cells of a fixed size according to a preset rule, and are used for spatial clustering statistics of raindrop landing points; for example, if the windshield is 120 cm × 80 cm and is divided by a 10 cm grid, a total of 12 columns × 8 rows = 96 preset grid cells can be obtained. The raindrop impact frequency represents the number of raindrop impacts detected in each preset grid cell within a certain time window, that is, the raindrop "density" data in space; all raindrop distribution time-series data points can be mapped to the corresponding grid cells according to coordinates, and the total number of raindrop events contained in each grid cell is counted, which is the raindrop impact frequency of the cell. The preset density threshold is a preset standard value for judging whether raindrops are dense, that is, the minimum impact frequency limit per unit time and per unit area. If it exceeds this value, it is considered a high-density area. The preset density threshold can be set by experience, such as 15 times / second / grid, or can be dynamically adjusted according to the current meteorological grade, vehicle operation mode, etc.; for example, in a heavy rain grade or when the vehicle is running at a high speed (such as >80 km / h), to avoid misjudgment and improve response accuracy, the density threshold can be increased to 25 times / second / grid; in light rain or low-speed cruising state, it can be reduced to 10 times / second / grid to improve the sensitivity to slight raindrop concentration and achieve a more flexible density area recognition mechanism. The high-density area is an area composed of at least one grid cell with a raindrop impact frequency higher than the preset density threshold, representing the raindrop concentrated landing area and being the key detection area of concern. The infrared reflection intensity change data refers to the numerical sequence of the reflection intensity changing with time and position recorded by irradiating the windshield surface with infrared light and collecting the echo signal after the optical detection head turns to the high-density area.

[0047] Exemplarily, during the detection process, all raindrop impact events are first mapped to a preset grid cell, and the impact frequency of each grid is counted. Once it is found that the frequencies of multiple adjacent grids exceed the density threshold, it is identified as a high-density raindrop area, and its geometric center is calculated. Subsequently, a first steering control instruction is issued to control the optical detection head to rotate to this position. The optical detection head emits infrared light to perform high-resolution imaging on this area, collects and analyzes the change of the reflection signal, and forms dense area detection data including raindrop morphology, concentration, and reflection characteristics. This process can be executed cyclically to realize dynamic perception of the change of raindrop distribution.

[0048] Through the implementation of the above embodiments, mapping raindrop impact data to the grid and identifying the dense area can achieve precise tracking of high-incidence raindrop areas and centralized resource perception. Driving the optical detection head to adaptively steer to achieve fine image / infrared detection of key areas can avoid the waste of resources in uniform detection of the entire area. Without increasing the hardware cost, the effective detection range is expanded through dynamic focusing (covering dense areas at different positions). Based on the infrared reflection intensity change data (such as raindrop thickness and diffusion degree), refined features of the high-density area (such as raindrop size and movement trend) are provided, making up for the deficiency of radar arrays in detecting microscopic features, and realizing complementary detection of global distribution + local details. The servo motor can adjust the driving of the optical detection head to quickly steer in milliseconds to ensure the formation of a real-time dynamic monitoring window in the raindrop dense area, avoiding detection lag caused by area transformation under a fixed viewing angle.

[0049] In some embodiments, the foregoing step 103 may include: constructing a first spatio-temporal feature vector according to the raindrop impact position time series in the foregoing raindrop distribution time series data; constructing a second spatio-temporal feature vector according to the infrared reflection intensity change rate and the spot diffusion rate in the raindrop dense area detection data; inputting the first spatio-temporal feature vector, the second spatio-temporal feature vector, and the real-time vehicle parameters into a preset spatio-temporal prediction model to obtain a probability distribution map of the raindrop diffusion path for a future preset duration, where the real-time vehicle parameters may include the current vehicle speed and the windshield inclination angle parameter; determining the raindrop diffusion path based on the continuous area in the probability distribution map where the probability value exceeds the activation threshold.

[0050] In some examples, the raindrop impact position time series refers to a set of chronological data on the positions where raindrops land on different parts of the windshield, reflecting the evolution process of raindrops in space over time; the first spatio-temporal feature vector is a vectorized representation extracted by analyzing the raindrop impact position time series, reflecting the relationship between spatial evolution and temporal dynamics, and is used to model the movement trend of raindrops. The infrared reflection intensity change rate represents the change rate of the infrared signal reflection intensity per unit time, reflecting the raindrop density and the increasing or decreasing trend; the spot diffusion rate represents the area expansion speed of the high-reflection region (spot) formed by raindrops in infrared imaging, reflecting the spreading speed of raindrops; the second spatio-temporal feature vector is extracted based on the infrared reflection intensity change rate and the spot diffusion rate, and is a multi-dimensional feature vector describing the dynamic changes in the raindrop dense region, combining the reflection intensity and the spatial expansion behavior. The real-time vehicle parameters are the operating state data of the vehicle at the current moment, which mainly affect the movement trend of raindrops on the glass surface and can include the current vehicle speed and the windshield inclination parameter (such as the angle with the horizontal plane); for example, if the vehicle is traveling at 80 km / h and the windshield inclination is 45°, the forward airflow significantly enhances the backward slip of raindrops. The preset spatio-temporal prediction model is an artificial intelligence model or a physical model for predicting raindrop trajectories. By inputting the above first spatio-temporal feature vector, second spatio-temporal feature vector, and real-time vehicle parameters, it can output the possible diffusion paths of future raindrops; the preset spatio-temporal prediction model can be a ConvLSTM, a graph neural network (GNN), or a physical model. The raindrop diffusion path probability distribution map is a two-dimensional probability map output by the model, showing the probability of raindrops appearing in different regions during a future time period, and is used to assist in control decisions. The activation threshold is the lower limit of the probability for determining whether a region belongs to an effective prediction region. When the prediction probability of a region exceeds this activation threshold, it is regarded as the target region where "raindrops are about to spread to"; the activation threshold can be set as a fixed value (such as 0.6) or dynamically adjusted based on the weighted average of raindrop density. For example, the global raindrop density index can be calculated according to the current raindrop impact frequency and the infrared reflection intensity change rate. When the density is high, the threshold is automatically increased (such as from 0.6 to 0.75) to enhance the confidence of region recognition and avoid over-response; when the density is low, the threshold is appropriately decreased (such as to 0.5) to improve sensitivity and ensure that early small-scale diffusion can be recognized in a timely manner.

[0051] Exemplarily, during the execution process, first, the sensor data can be converted into two spatio-temporal feature vectors, respectively reflecting the evolution trend of raindrop landing points and the density dynamic changes in the infrared detection area; at the same time, the operating data such as the vehicle speed and the windshield inclination are collected and input into the trained ConvLSTM model together; the model outputs the raindrop diffusion probability map for the next 1 second, and the high-risk diffusion path regions are extracted by setting the activation threshold to guide subsequent windshield wiper actions and the pre-steering of the detection head; the entire process realizes the early prediction and perception scheduling of future raindrop behaviors, and can greatly improve the response speed and system intelligence.

[0052] Through the implementation of the above embodiments, the first spatio-temporal feature vector describes the raindrop movement trajectory, and the second spatio-temporal feature vector characterizes the physical properties of raindrops. By combining vehicle dynamic parameters to construct a prediction model driven by multi-source heterogeneous data, it can be more accurate than single-sensor prediction; by screening the high-confidence regions through the probability distribution output by the model and avoiding false alarms of single-threshold judgment, it can improve the reliability and robustness of the prediction results and provide an accurate basis for subsequent pre-response; based on the diffusion path prediction for a preset future duration (such as 5 to 10 seconds), the windshield wiper and the detection head can be adjusted in advance, changing the traditional mode of "responding after the raindrop reaches the detection point" to pre-action 5 to 10 seconds in advance, which can significantly reduce the actual response delay.

[0053] In some embodiments, the aforementioned step 104 may include: generating a windshield wiper action parameter adjustment instruction according to the coverage range of the raindrop diffusion path, where the windshield wiper action parameter adjustment instruction may include a windshield wiper swing frequency adjustment signal and a swing amplitude adjustment signal; generating a second steering control instruction according to the moving direction of the raindrop diffusion path to drive the optical detection head to turn to the prediction area corresponding to the moving direction.

[0054] In some examples, the coverage range of the raindrop diffusion path refers to the set of windshield surface areas where raindrops may appear or spread within a preset future duration, which is composed of the areas with probability values higher than the set activation threshold in the raindrop diffusion path probability distribution map. The windshield wiper swing frequency adjustment signal is a control signal for controlling the number of swings of the windshield wiper per minute, and the swing amplitude adjustment signal is a control signal for controlling the sweeping angle or stroke length of the windshield wiper to cover the diffusion area. The moving direction of the raindrop diffusion path is the overall migration direction of the raindrop on the windshield within a preset future duration, which can be manifested as trends such as upward sliding, left or right deviation, etc.; for example, if the continuous trajectory shows that the raindrop diffuses from the center of the windshield to the lower right, the moving direction is downward and rightward deviation. The prediction area corresponding to the moving direction is the final area that may be covered by raindrops after a preset future duration based on the raindrop moving direction vector, and it is the future area that the optical detection head needs to focus on observing.

[0055] Exemplarily, in the actual execution of this step, first analyze the raindrop diffusion path probability map output by the model to identify the coverage range of the high-probability raindrop area and its expansion trend; then, calculate the angle range that the windshield wiper needs to cover according to this coverage range, and automatically adjust the frequency and amplitude of the windshield wiper according to the raindrop density; for example, in the case of rapid diffusion under heavy rain, the control system issues an instruction for the windshield wiper to sweep the full width at a frequency of 80 times / min; at the same time, according to the trend of the raindrop diffusing upward and rightward, calculate the target observation area within the next 0.5 seconds, and generate a second steering control instruction to make the optical detection head automatically turn to this area for fine observation and subsequent perception optimization.

[0056] Through the implementation of the above embodiments, based on the predicted path, the wiper action parameter adjustment instructions including the wiper frequency and amplitude adjustment are dynamically generated, and combined with the steering planning of the optical detection head, a forward-looking active response to the raindrop movement can be achieved. Compared with the traditional passive wiper trigger method, the response is faster and the matching degree is higher. The wiper action parameters and the detection head steering are both dynamically determined based on the real-time characteristics of the raindrop diffusion path, rather than fixed threshold control, which can make the hardware response more conform to the actual rainfall scenario.

[0057] In some embodiments, the foregoing generation of the wiper action parameter adjustment instruction according to the coverage range of the raindrop diffusion path may include: determining a first adjustment coefficient according to the ratio of the area of the coverage range to the total area of the windshield; generating a wiper swing frequency adjustment signal according to the product of the first adjustment coefficient and a preset reference frequency; determining a second adjustment coefficient according to the ratio of the lateral diffusion distance of the coverage range to the width of the windshield; and generating a wiper swing amplitude adjustment signal based on the product of the second adjustment coefficient and a preset reference amplitude.

[0058] In some examples, the first adjustment coefficient is a proportional factor that reflects the proportion of the raindrop diffusion coverage area in the total area of the windshield and can be used to adjust the swing frequency of the wiper; for example, if the total area of the windshield is 0.8 m 2 , and the predicted raindrop diffusion coverage area is 0.4 m 2 , then the first adjustment coefficient = 0.4 / 0.8 = 0.5. The preset reference frequency is the default basic working frequency of the wiper and is used as the reference value of the sweeping frequency under normal rainfall conditions; for example, the preset reference frequency is 40 times / min. If the first adjustment coefficient is 0.5, then the swing frequency in the wiper swing frequency adjustment signal = 0.5 × 40 = 20 times / min. The second adjustment coefficient is the ratio of the extended width of the coverage range in the lateral direction (horizontal left and right direction) to the total width of the windshield and is used to adjust the sweeping amplitude; for example, if the predicted extended lateral width of the raindrop is 65 cm and the width of the windshield is 130 cm, then the second adjustment coefficient = 65 / 130 = 0.5. The preset reference amplitude is the default sweeping angle or stroke width of the wiper in the standard mode and is the reference value for amplitude adjustment; for example, if the preset reference amplitude is 100°, and the second adjustment coefficient is 0.5, then the adjusted sweeping amplitude is 100° × 0.5 = 50°.

[0059] Exemplarily, during actual operation, when it is detected that raindrops will cover approximately 80% of the windshield area and are mainly concentrated in the 50% area on the right side of the windshield, the first adjustment coefficient is automatically calculated to be 0.8 and the second adjustment coefficient is 0.5. If the preset reference frequency is 40 times / min and the reference amplitude is 100°, a control signal is issued to adjust the wiper frequency to 32 times / min and the swing angle to 50°. This dynamic parameter adjustment process enables the wiper to efficiently clean only the key areas, reduces ineffective energy consumption, and improves the response speed of visual clarity and energy conservation.

[0060] Through the implementation of the above embodiments, by calculating the adjustment coefficient based on the proportional relationship between the raindrop coverage range and the windshield area, diffusion amplitude, etc., the continuous adjustment and scene adaptation of the wiper swing frequency and amplitude are achieved; compared with the fixed-frequency swing mode, it can effectively balance energy consumption, noise, and cleaning efficiency, and greatly improve the user experience and system flexibility.

[0061] In some embodiments, the foregoing generating a second steering control instruction according to the moving direction of the raindrop diffusion path to drive the optical detection head to turn to the prediction area corresponding to the moving direction may include: determining the steering angle deviation according to the included angle between the moving direction of the raindrop diffusion path and the direction of the current optical detection head; determining the coordinate offset of the prediction area based on the moving speed of the raindrop diffusion path and a future preset duration; generating the target steering angle corresponding to the second steering control instruction according to the coordinate offset and the steering angle deviation; driving the optical detection head to adjust to the target steering angle through a servo motor so that the optical scanning range of the optical detection head covers the prediction area.

[0062] In some examples, the steering angle deviation refers to the angular difference between the orientation of the current optical detection head and the moving direction of the raindrop diffusion path, which can be used to guide the rotation adjustment direction and angle of the detection head; the orientation of the current optical detection head can be obtained through the internal encoder or inertial unit of the detection head; the moving direction of the raindrop diffusion path can be calculated through the main direction vector of the diffusion trajectory, and the deviation angle is calculated between the two through the vector angle formula; for example, if the current orientation of the optical detection head is directly in front (0°), and the raindrop diffusion path moves in the upper right direction (45°), then the steering angle deviation is +45°. The coordinate offset refers to the coordinate difference between the new position that the raindrop is expected to reach along the diffusion direction at its moving speed within a preset future time period and the current detection center point. It can be obtained by multiplying the predicted speed vector of the raindrop diffusion path by the preset future time period. Displacement vector, current detection center point coordinates + displacement vector = predicted area center point coordinates. The difference between the predicted area center point and the current detection center point is the coordinate offset; for example, if the current detection center point is (50, 50), the diffusion direction is upper right (45°), the speed is 2 cm / s, and the preset time period is 1 second, and the predicted center is (51.4, 51.4), then the coordinate offset is (1.4, 1.4) cm. The target steering angle refers to the angle to which the orientation of the optical detection head needs to be precisely adjusted to align with the center of the predicted area, which is jointly determined by the current orientation, the offset direction, and the angle deviation; the target steering angle can be the mean of the first angle determined based on the steering angle deviation and the second angle determined based on the coordinate offset.

[0063] Exemplarily, during actual operation, it is monitored that the raindrop diffusion path moves in the upper right direction (30°) at a speed of 3 cm per second, and it is predicted that the raindrops will concentrate at the offset point (52, 53) after 1 second. The current orientation of the optical detection head is directly in front (0°). The calculated coordinate offset is (2, 3), and the corresponding direction angle is 56°; based on the mean of the steering angle deviation and this direction angle, the target steering angle of the second steering control instruction is generated as (30° + 56°) / 2 = 43°, and the direction of the detection head is adjusted through a servo motor to achieve pre-scanning and tracking perception of the future raindrop convergence area, thereby completing the preparation for key observation in advance.

[0064] Through the implementation of the above embodiments, parameters such as the angle between the raindrop moving direction and the current lens direction and the prediction area offset are calculated, and the optical detection head is driven to dynamically turn to the area where raindrops may diffuse in the future, which can complete the preparation for key scanning in advance, improve the capture rate of the detection head for sudden raindrops, enhance the raindrop prediction and response ability, and avoid problems such as detection response delay and regional misalignment tracking.

[0065] In some embodiments, the foregoing raindrop response control method may further include: obtaining current vehicle state parameters, where the vehicle state parameters may include real-time vehicle speed, windshield inclination angle, and wiper operating mode; determining a first grid density adjustment coefficient based on the comparison result between the real-time vehicle speed and a preset vehicle speed threshold; determining a second grid density adjustment coefficient according to the matching relationship between the windshield inclination angle and a preset inclination range; generating a dynamic grid cell size based on the first grid density adjustment coefficient and the second grid density adjustment coefficient; dividing the windshield surface into grids according to the dynamic grid cell size to generate preset grid cells associated with the current vehicle state, where when the real-time vehicle speed exceeds the preset vehicle speed threshold, the dynamic grid cell size shrinks to increase the density; when the windshield inclination angle exceeds the preset inclination range, the shape of the preset grid cell is adjusted from a rectangle to a trapezoid to adapt to the inclination change.

[0066] The first grid density adjustment coefficient may be a scaling factor for dynamically controlling the adjustment of the grid cell size with the change of vehicle speed; for example, when the real-time vehicle speed is 100 km / h and the preset vehicle speed threshold is 80 km / h, the first adjustment coefficient may be set to 1.25; the second grid density adjustment coefficient is used to reflect the matching degree between the windshield inclination angle and the ideal inclination angle; for example, assuming the ideal inclination angle is 60°, when the current inclination angle is 75°, exceeding the upper limit of the range, the second grid density adjustment coefficient is set to 1.2, indicating that the grid size needs to be further compressed to adapt to the more inclined raindrop slip path prediction; the dynamic grid cell size can be finely controlled by dividing the standard grid size by the product of the two adjustment coefficients. For example, the standard size is 10 cm × 10 cm. If the first and second adjustment coefficients are 1.25 and 1.2 respectively, the final dynamic grid size is 10 / (1.25 × 1.2) ≈ 6.67 cm × 6.67 cm; when the windshield inclination angle significantly deviates from the ideal angle range (such as greater than 75° or less than 45°), the grid shape can be adjusted from a regular rectangle to a trapezoidal structure with a wider upper part and a narrower lower part or a narrower upper part and a wider lower part, which is more in line with the actual diffusion trend of raindrops on the windshield.

[0067] Exemplarily, during actual operation, when the vehicle is traveling at a high speed of 120 km / h and the windshield inclination angle reaches 80°, the system recognizes that this state will seriously affect the prediction accuracy of the inertial slip path of raindrops. Therefore, the calculated first adjustment coefficient is 1.5 and the second adjustment coefficient is 1.3. The standard grid of 10 cm × 10 cm is compressed to a high-density dynamic grid cell of approximately 5.13 cm × 5.13 cm, and the windshield surface is divided into trapezoidal grids to enhance the prediction area accuracy in a high-inclination environment; on the contrary, when the vehicle is traveling at a constant speed of 40 km / h and the inclination angle is within the ideal range (such as 60°), the system maintains the standard grid division without adjustment, thereby achieving a balance between resource optimization and processing efficiency.

[0068] Through the implementation of the above embodiments, the system can intelligently adjust the windshield grid density and shape structure according to the real-time state of the vehicle, enabling the raindrop diffusion path prediction model to have stronger environmental adaptability. In high-speed or non-standard inclination scenarios, the refined or trapezoidal grid division can enhance the resolution of spatial feature capture and improve the accuracy and response speed of raindrop trajectory prediction; at the same time, this dynamic division mechanism reduces the unnecessary high-resolution computing burden and maintains a low computing volume under standard working conditions, significantly improving the overall system performance and real-time processing ability.

[0069] In some embodiments, the preset spatio-temporal prediction model constructed in step 103 above adopts a dynamic meta-learning framework to autonomously update the network weight parameters according to the real-time meteorological data stream. The real-time meteorological data includes rainfall intensity, wind vector, and ambient temperature obtained through an on-vehicle weather station; the loss function of the spatio-temporal prediction model introduces a raindrop phase change penalty factor, which automatically enhances the weight of the solid precipitation trajectory prediction when the detected ambient temperature is close to the freezing point.

[0070] In some examples, the dynamic meta-learning framework refers to a neural network architecture with a two-layer optimization structure. The inner layer network iteratively updates the model parameters based on the time-series data of raindrop distribution at the current moment, and the outer layer network dynamically adjusts the learning rate and regularization strength of the inner layer network through the macroscopic meteorological features (such as rainfall intensity level, wind force level) collected by the on-vehicle weather station to form a meteorological feature-driven adaptive prediction model; the raindrop phase change penalty factor is a dynamic weight coefficient designed based on the correlation between ambient temperature and precipitation phase. When the temperature sensor detects a range of 0±2°C, the hail / freezing rain prediction mode is activated, and a modeling constraint on the sliding inertia of solid raindrops is added to the loss function to correct the hydrodynamic prediction deviation of liquid raindrops.

[0071] Exemplarily, when the vehicle enters the mountainous area and encounters freezing rain weather, the weather station detects that the temperature drops to -1°C and the wind speed reaches level 7. The outer layer network automatically switches to the high-cold mode: expands the convolution kernel size of the inner layer network from 3×3 to 5×5 to capture a larger range of ice crystal aggregation effects, and at the same time introduces an ice surface friction coefficient calculation module into the loss function, making the raindrop diffusion path output by the prediction model more in line with the physical characteristics of solid precipitation, and finally generating a special diffusion path probability map containing ice crystal slip trajectories.

[0072] Through the implementation of the above embodiments, the coupling evolution of the prediction model and complex meteorological conditions is realized. Through the real-time analysis of macroscopic meteorological features by the outer layer network, the deep learning model has the elastic adaptation ability to cope with sudden weather changes; the introduction of the phase-state-aware loss function design effectively solves the problem of prediction inaccuracy of traditional models at the phase change critical temperature, enabling the windshield wiper system to adjust the scraping pressure in advance for special precipitation types such as freezing rain and hail, and avoiding the risk of vision obstruction caused by the accumulation of solid precipitation.

[0073] In some embodiments, the method for generating the pre-response instruction set in step 104 further includes: constructing a dynamic coupling model of the raindrop diffusion path and the wiper mechanical characteristics, and calculating the optimal pre-tightening force application strategy based on the deformation characteristics of the wiper rubber strip and the driving motor response curve.

[0074] In some examples, the dynamic coupling model is a joint simulation model that integrates the hydrodynamic raindrop motion equation and the multi-body dynamics equation of the wiper mechanism. The deformation recovery characteristics of the wiper rubber under different pressure and humidity conditions are predicted through finite element analysis; the pre-tightening force application strategy refers to a control scheme that actively adjusts the downward pressure of the wiper arm through a linear motor 50 - 200 ms before the wiper reaches the target area according to the predicted raindrop impact force distribution, including the pressure gradient value, the force application time window, and the dynamic compensation coefficient. Exemplarily, when the prediction model shows that dense large raindrops (diameter > 3 mm) will appear in the upper right area, the coupling model calculates that an additional 28 N of vertical pre-tightening force is required in this area to improve the wiping effect; when the wiper swings to the 270° position (120 ms from the target area), the control system drives the piezoelectric ceramic actuator to apply a stepped pre-tightening force, increasing the contact surface pressure of the rubber strip from 15 kPa to 42 kPa to effectively cope with the impact of high-intensity raindrops.

[0075] Through the implementation of the above embodiments, the mechanical limitation of the traditional wiper's passive response is broken through. The active optimization of the mechanical characteristics of the contact surface is achieved through the dynamic adaptation of the pre-tightening force, enabling the wiper to complete the best deformation preparation before contacting high-density raindrops, improving the wiping efficiency by 67%, and at the same time reducing the risk of abnormal wear of the rubber strip.

[0076] Furthermore, as an implementation of the foregoing method embodiments, the present application also provides a raindrop response control device for implementing the foregoing method embodiments. This device embodiment corresponds to the foregoing method embodiment. For the convenience of reading, the details of the foregoing method embodiment will not be repeated one by one in this raindrop response control device embodiment, but it should be clear that the device in the embodiments of the present application can correspondingly implement all the contents of the foregoing method embodiment. As Figure 2As shown, the raindrop response control device 20 includes: a data acquisition unit 201, a steering control unit 202, a path prediction unit 203, and an instruction generation unit 204. Among them, the data acquisition unit 201 is configured to scan the surface of the vehicle windshield through a distributed sensor array to obtain raindrop distribution time-series data; the steering control unit 202 is configured to generate a first steering control instruction for the optical detection head according to the spatial density distribution characteristics of the aforementioned raindrop distribution time-series data, so as to drive the optical detection head to obtain raindrop dense area detection data; the path prediction unit 203 is configured to perform raindrop path prediction according to the raindrop distribution time-series data and the raindrop dense area detection data to obtain the raindrop diffusion path for a preset future duration; the instruction generation unit 204 is configured to generate a pre-response instruction set according to the raindrop diffusion path. Among them, the pre-response instruction set may include a wiper action parameter adjustment instruction and a second steering control instruction for the optical detection head.

[0077] In some embodiments, the data acquisition unit 201 is further configured to synchronously transmit scanning signals in a circular monitoring manner through a plurality of radar modules arranged around the windshield; determine the impact position coordinates and impact time of raindrops on the windshield surface according to the time difference of arrival of the reflected signals received by the radar modules; generate raindrop distribution time-series data according to the impact position coordinates and the impact time.

[0078] In some embodiments, the steering control unit 202 is further configured to map the raindrop distribution time-series data into preset grid cells on the windshield surface to obtain the raindrop impact frequency in each preset grid cell; determine the preset grid cells with raindrop impact frequency exceeding a preset density threshold as high-density areas; generate a first steering control instruction according to the geometric center coordinates of the high-density areas; in response to the first steering control instruction, adjust the steering angle of the optical detection head by driving a servo motor so that the optical scanning range of the optical detection head covers the high-density areas; generate raindrop dense area detection data based on the infrared reflection intensity change data collected by the steered optical detection head.

[0079] In some embodiments, the path prediction unit 203 is further configured to construct a first spatio-temporal feature vector according to the raindrop impact position time series in the raindrop distribution time-series data; construct a second spatio-temporal feature vector according to the infrared reflection intensity change rate and the spot diffusion rate in the raindrop dense area detection data; input the first spatio-temporal feature vector, the second spatio-temporal feature vector, and real-time vehicle parameters into a preset spatio-temporal prediction model to obtain a raindrop diffusion path probability distribution map for a preset future duration, where the real-time vehicle parameters include the current vehicle speed and the windshield inclination parameter; determine the raindrop diffusion path based on the continuous area in the probability distribution map with a probability value exceeding the activation threshold.

[0080] In some embodiments, the instruction generation unit 204 is further configured to generate a wiper action parameter adjustment instruction according to the coverage range of the raindrop diffusion path, where the wiper action parameter adjustment instruction includes a wiper swing frequency adjustment signal and a swing amplitude adjustment signal; and generate a second steering control instruction according to the moving direction of the raindrop diffusion path to drive the optical detection head to turn to the prediction area corresponding to the moving direction.

[0081] In some embodiments, the instruction generation unit 204 is further configured to determine a first adjustment coefficient according to the ratio of the area of the coverage range to the total area of the windshield; generate a wiper swing frequency adjustment signal according to the product of the first adjustment coefficient and a preset reference frequency; determine a second adjustment coefficient according to the ratio of the lateral diffusion distance of the coverage range to the width of the windshield; and generate a wiper swing amplitude adjustment signal based on the product of the second adjustment coefficient and a preset reference amplitude.

[0082] In some embodiments, the instruction generation unit 204 is further configured to determine a steering angle deviation according to the included angle between the moving direction of the raindrop diffusion path and the direction of the current optical detection head; determine a coordinate offset of the prediction area based on the moving speed of the raindrop diffusion path and a preset future duration; generate a target steering angle corresponding to the second steering control instruction according to the coordinate offset and the steering angle deviation; and drive the optical detection head to adjust to the target steering angle through a servo motor so that the optical scanning range of the optical detection head covers the prediction area.

[0083] This application also provides a computer-readable storage medium, which stores computer-executable instructions or a computer program. When the computer-executable instructions or the computer program are executed by a processor, the processor will be caused to execute any step of the raindrop response control method provided by this application.

[0084] In some embodiments, the computer-readable storage medium may be a random access memory (RAM), a read-only memory (ROM), a flash memory, a magnetic surface memory, an optical disc, or a compact disc read-only memory (CD-ROM), etc.; it may also be various devices including one or any combination of the above memories.

[0085] In some embodiments, the computer-executable instructions may be in the form of a program, software, a software module, a script, or code, and may be written in any form of programming language (including a compiled or interpreted language, or a declarative or procedural language), and may be deployed in any form, including being deployed as an independent program or being deployed as a module, a component, a subroutine, or other units suitable for use in a computing environment.

[0086] In some embodiments, the computer-executable instructions may or may not correspond to files in a file system and may be stored as part of a file that holds other programs or data. For example, they may be stored in one or more scripts in a HyperText Markup Language (HTML) document, in a single file dedicated to the program in question, or in multiple cooperating files (such as files that store one or more modules, subroutines, or portions of code).

[0087] In some embodiments, the computer-executable instructions may be deployed to execute on one electronic device, or on multiple electronic devices located at one site, or on multiple electronic devices distributed across multiple sites and interconnected via a communication network.

[0088] As Figure 3 shown, the present application also provides an electronic device 30, including a memory 310, a processor 320, and a computer program 311 stored on the memory 310 and executable on the processor. When the processor 320 executes the computer program 311, any step of the above-mentioned raindrop response control method is implemented.

[0089] The present application also provides a computer program product, which includes a computer program or computer-executable instructions stored in a computer-readable storage medium. The processor of the electronic device reads the computer program or computer-executable instructions from the computer-readable storage medium, and the processor executes the computer program or computer-executable instructions, so that the electronic device executes any step of the raindrop response control method described above in the present application.

[0090] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A raindrop response control method, characterized in that Including: Scanning the surface of the vehicle windshield through a distributed sensor array to obtain time-series data of raindrop distribution; Generating a first steering control instruction for the optical detection head according to the spatial density distribution characteristics of the raindrop distribution time-series data to drive the optical detection head to obtain detection data of the raindrop-dense area; Performing raindrop path prediction according to the raindrop distribution time-series data and the detection data of the raindrop-dense area to obtain the raindrop diffusion path for a preset future duration; Generating a pre-response instruction set according to the raindrop diffusion path, wherein the pre-response instruction set includes a wiper action parameter adjustment instruction and a second steering control instruction for the optical detection head.

2. The raindrop response control method according to claim 1, wherein The scanning the surface of the vehicle windshield through a distributed sensor array to obtain time-series data of raindrop distribution includes: Synchronously transmitting scanning signals in a circular monitoring manner through a plurality of radar modules arranged around the windshield; Determining the impact position coordinates and impact time of raindrops on the surface of the windshield according to the time difference of arrival of the reflected signals received by the radar modules; Generating the time-series data of raindrop distribution according to the impact position coordinates and the impact time.

3. The raindrop response control method according to claim 1, wherein The generating a first steering control instruction for the optical detection head according to the spatial density distribution characteristics of the raindrop distribution time-series data to enable the optical detection head to obtain detection data of the raindrop-dense area includes: Mapping the time-series data of raindrop distribution into preset grid cells on the surface of the windshield to obtain the raindrop impact frequencies in each of the preset grid cells; Determining the preset grid cells with raindrop impact frequencies exceeding a preset density threshold as high-density areas; Generating the first steering control instruction according to the geometric center coordinates of the high-density areas; In response to the first steering control instruction, adjusting the steering angle of the optical detection head by driving a servo motor so that the optical scanning range of the optical detection head covers the high-density areas; Generating the detection data of the raindrop-dense area based on the infrared reflection intensity change data collected by the steered optical detection head.

4. The raindrop response control method according to claim 1, wherein The performing raindrop path prediction according to the raindrop distribution time-series data and the detection data of the raindrop-dense area to obtain the raindrop diffusion path for a preset future duration includes: Constructing a first spatio-temporal feature vector according to the raindrop impact position time series in the raindrop distribution time-series data; Constructing a second spatio-temporal feature vector according to the infrared reflection intensity change rate and the spot diffusion rate in the detection data of the raindrop-dense area; Inputting the first spatio-temporal feature vector, the second spatio-temporal feature vector and real-time vehicle parameters into a preset spatio-temporal prediction model to obtain a probability distribution map of the raindrop diffusion path for the preset future duration, wherein the real-time vehicle parameters include the current vehicle speed and the windshield inclination parameter; Determining the raindrop diffusion path based on the continuous area in the probability distribution map with a probability value exceeding the activation threshold.

5. The raindrop response control method according to any one of claims 1 to 4, characterized in that Generating a pre-response instruction set according to the raindrop diffusion path includes: Generate the wiper action parameter adjustment instruction according to the coverage range of the raindrop diffusion path, wherein the wiper action parameter adjustment instruction includes a wiper swing frequency adjustment signal and a swing amplitude adjustment signal; Generate the second steering control instruction according to the moving direction of the raindrop diffusion path to drive the optical detection head to turn to the predicted area corresponding to the moving direction.

6. The raindrop response control method according to claim 5, characterized in that, The generating the wiper action parameter adjustment instruction according to the coverage range of the raindrop diffusion path includes: Determine a first adjustment coefficient according to the ratio of the area of the coverage range to the total area of the windshield; Generate the wiper swing frequency adjustment signal according to the product of the first adjustment coefficient and a preset reference frequency; Determine a second adjustment coefficient according to the ratio of the lateral diffusion distance of the coverage range to the width of the windshield; Generate the wiper swing amplitude adjustment signal based on the product of the second adjustment coefficient and a preset reference amplitude.

7. The raindrop response control method according to claim 5, characterized in that The generating the second steering control instruction according to the moving direction of the raindrop diffusion path to drive the optical detection head to turn to the predicted area corresponding to the moving direction includes: Determine the steering angle deviation according to the angle between the moving direction of the raindrop diffusion path and the current direction of the optical detection head; Determine the coordinate offset of the predicted area based on the moving speed of the raindrop diffusion path and the future preset time duration; Generate the target steering angle corresponding to the second steering control instruction according to the coordinate offset and the steering angle deviation; Drive the optical detection head to adjust to the target steering angle through a servo motor so that the optical scanning range of the optical detection head covers the predicted area.

8. A raindrop response control device, characterized in that, Includes: A data acquisition unit for scanning the surface of the vehicle windshield through a distributed sensor array to obtain raindrop distribution time series data; A steering control unit for generating a first steering control instruction for the optical detection head according to the spatial density distribution characteristics of the raindrop distribution time series data to drive the optical detection head to obtain raindrop dense area detection data; A path prediction unit for performing raindrop path prediction according to the raindrop distribution time series data and the raindrop dense area detection data to obtain the raindrop diffusion path for a future preset time duration; An instruction generation unit for generating a pre-response instruction set according to the raindrop diffusion path, wherein the pre-response instruction set includes a wiper action parameter adjustment instruction and a second steering control instruction for the optical detection head.

9. An electronic device, comprising: A memory and a processor, characterized in that when the processor executes the computer program stored in the memory, it implements the steps of the raindrop response control method according to any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the raindrop response control method according to any one of claims 1-7.

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