Method and device for determining auxiliary driving strategy and product

Abstract

The invention provides an auxiliary driving strategy determination method and device, electronic equipment, a storage medium and a computer program product, relates to the technical field of computers, in particular to the technical fields of intelligent driving, map navigation and the like, and can be applied to scenes such as map navigation and the like. The specific implementation scheme is as follows: in a navigation process, combining actual wind condition data of a subsequent navigation path and detection wind condition data of a current position of a vehicle to determine fused wind condition data of the subsequent navigation path; determining the influence degree of wind on the driving safety of the vehicle according to the fused wind condition data and the pose data of the vehicle in the subsequent navigation path; and determining an auxiliary driving strategy of the risk road section in the subsequent navigation path according to the influence degree. According to the method, the wind condition is converted into the navigation reference variable from an uncontrollable factor so as to be fused into the navigation process, the risk road section and the auxiliary driving strategy for the risk road section are determined in real time, the problem of crosswind interference in the driving process is solved, and the driving safety is improved.

Description

Technical Field

[0001] The present disclosure relates to the field of computer technology, specifically to technical fields such as intelligent driving and map navigation, and in particular to a method, device, electronic device, storage medium, and computer program product for determining an assisted driving strategy, which can be applied to scenarios such as map navigation. Background Art

[0002] With the development of navigation technology, positioning systems combined with real-time traffic data have become widely used in navigation systems. However, crosswinds are frequent in high-altitude areas (such as mountainous areas and plateaus), often causing steering wheel vibration, vehicle deviation, and even traffic accidents. Summary of the Invention

[0003] The present disclosure provides a method, device, electronic device, storage medium, and computer program product for determining an assisted driving strategy.

[0004] According to a first aspect, a method for determining an assisted driving strategy is provided, comprising: during a navigation process, determining fused wind condition data for a subsequent navigation path by combining the actual wind condition data for the subsequent navigation path and the detected wind condition data for the vehicle's current position; determining the degree of influence of the wind on the vehicle's driving safety based on the fused wind condition data and the vehicle's position data in the subsequent navigation path; and determining an assisted driving strategy for risky sections in the subsequent navigation path based on the degree of influence.

[0005] According to the second aspect, a device for determining an assisted driving strategy is provided, including: a wind condition determination unit, configured to determine, during the navigation process, fused wind condition data of the subsequent navigation path by combining the actual wind condition data of the subsequent navigation path and the detected wind condition data of the vehicle's current position; a degree determination unit, configured to determine the degree of influence of the wind on the driving safety of the vehicle based on the fused wind condition data and the vehicle's posture data in the subsequent navigation path; and a strategy determination unit, configured to determine the assisted driving strategy for the risky section in the subsequent navigation path based on the degree of influence.

[0006] According to a third aspect, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so as to enable the at least one processor to execute the method described in any implementation manner of the first aspect.

[0007] According to a fourth aspect, a non-transitory computer-readable storage medium storing computer instructions is provided, where the computer instructions are used to cause a computer to execute the method as described in any implementation of the first aspect.

[0008] According to a fifth aspect, a computer program product is provided, comprising: a computer program, which implements the method described in any implementation manner of the first aspect when executed by a processor.

[0009] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The accompanying drawings are used to better understand the present invention and do not constitute a limitation of the present invention. Figure 1 is an exemplary system architecture diagram in which an embodiment of the present disclosure may be applied; Figure 2 is a flowchart of an embodiment of a method for determining an assisted driving strategy according to the present disclosure; Figure 3 A schematic diagram of an application scenario of the method for determining an assisted driving strategy according to this embodiment; Figure 4 is a flowchart of another embodiment of a method for determining an assisted driving strategy according to the present disclosure; Figure 5 is a structural diagram of an embodiment of a device for determining an assisted driving strategy according to the present disclosure; Figure 6 It is a schematic diagram of the structure of a computer system suitable for implementing the embodiments of the present disclosure. DETAILED DESCRIPTION

[0011] The following description of exemplary embodiments of the present disclosure is made in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding. These details should be considered as merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0012] In the technical solutions disclosed herein, the collection, storage, use, processing, transmission, provision and disclosure of user personal information involved comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0013] Figure 1 An exemplary architecture 100 is shown to which the method and apparatus for determining an assisted driving strategy of the present disclosure may be applied.

[0014] like Figure 1As shown, system architecture 100 may include terminal devices 101, 102, and 103, a network 104, and a server 105. The communication connections between terminal devices 101, 102, and 103 constitute a topological network, and network 104 is used to provide a medium for communication links between terminal devices 101, 102, and 103 and server 105. Network 104 may include various connection types, such as wired or wireless communication links or fiber optic cables.

[0015] The terminal devices 101, 102, and 103 can be hardware devices or software that support network connection for data interaction and data processing. When the terminal devices 101, 102, and 103 are hardware, they can be various electronic devices that support network connection, information acquisition, interaction, display, processing and other functions, including but not limited to vehicle-mounted terminal devices such as vehicle-mounted computers and sensors, as well as computer terminal devices such as smart phones, tablet computers, e-book readers, laptop portable computers and desktop computers. When the terminal devices 101, 102, and 103 are software, they can be installed in the electronic devices listed above. They can be implemented as multiple software or software modules for providing distributed services, for example, or they can be implemented as a single software or software module. No specific limitation is made here.

[0016] Server 105 may be a server that provides various services, such as generating navigation routes based on navigation requests from terminal devices 101, 102, and 103, and a backend processing server that references wind data during navigation to determine risky road sections and implements assisted driving strategies for those risky road sections. For example, server 105 may be a cloud server.

[0017] It should be noted that a server can be either hardware or software. When a server is hardware, it can be implemented as a distributed server cluster consisting of multiple servers, or as a single server. When a server is software, it can be implemented as multiple software programs or software modules (for example, software or software modules used to provide distributed services), or as a single software program or software module. This is not specifically limited here.

[0018] It should also be noted that the methods for determining assisted driving strategies provided in the embodiments of the present disclosure are generally executed by a server, but this does not preclude the possibility of execution by a terminal device, or of a server and terminal device cooperating with each other. Accordingly, the various components (e.g., various units) included in the apparatus for determining assisted driving strategies may be entirely located in the server, entirely located in the terminal device, or separately located in the server and the terminal device.

[0019] It should be understood that Figure 1The number of terminal devices, networks, and servers described is merely illustrative. Any number of terminal devices, networks, and servers may be provided based on implementation needs. When the electronic device on which the method for determining the assisted driving strategy is running does not need to transmit data with other electronic devices, the system architecture may include only the electronic device (e.g., terminal device or server) on which the method for determining the assisted driving strategy is running.

[0020] Please refer to Figure 2 , Figure 2 A data processing flow diagram of a method for determining an assisted driving strategy provided by an embodiment of the present disclosure. In process 200, the following steps are included: Step 201 : During navigation, the real-time wind condition data of the subsequent navigation path and the detected wind condition data of the current position of the vehicle are combined to determine the fused wind condition data of the subsequent navigation path.

[0021] In this embodiment, the execution subject of the method for determining the assisted driving strategy (for example, Figure 1 The server in the vehicle) can obtain the real-time wind condition data and the detected wind condition data remotely or locally through a wired network connection or a wireless network connection, and during the navigation process, combine the real-time wind condition data of the subsequent navigation path with the detected wind condition data of the current position of the vehicle to determine the fused wind condition data of the subsequent navigation path.

[0022] Live wind data is the actual wind conditions in the area covered by the navigation route, including wind speed and direction. This data can be obtained from weather services. Examples of such services include online weather services such as the Meteorological Bureau and weather forecast applications. For example, live wind data can be collected from weather forecast applications using the weather service API (Application Programming Interface).

[0023] Detected wind data is wind data obtained by detecting the wind conditions at the vehicle's current location. This data can be obtained using a wind sensor. Examples of wind sensors include mechanical wind vane sensors, photoelectric wind direction sensors, and mechanical wind speed sensors, thermal wind speed sensors, and laser Doppler anemometers.

[0024] During navigation, live wind data and detected wind data can be acquired in real time or periodically. For example, wind data may be collected and detected every preset time interval (e.g., 5 minutes) to update the live wind data for the subsequent navigation path and the detected wind data for the vehicle's current location.

[0025] For live wind data, the aforementioned execution entity connects to the weather service API via a 4G / 5G network every five minutes to obtain wind data for the subsequent navigation route. This data is received in JSON (JavaScript Object Notation) format, containing latitude and longitude, wind speed (in meters per second), and wind direction (0-360°, with true north as 0°). The JSON data is decoded into a two-dimensional array, with array indices corresponding to grid coordinates. For example, the array index [i, j] represents the grid cell at row i and column j.

[0026] As an example, based on the principles of fluid mechanics, a wind propagation model for different terrains and environments is constructed, taking into account factors such as terrain undulations and building obstruction. Real-time wind data is used as the initial field and boundary conditions for the propagation model, and combined with geographic information on the navigation path, wind conditions are deduced in time and space. The detected wind data at the current location is used as a real-time correction factor for the propagation model to correct the wind conditions for subsequent navigation paths derived from the propagation model. The fused wind data for the subsequent navigation path is determined by combining the results of the physical model deduction with the real-time correction data.

[0027] By using physical models to model wind propagation patterns and integrating live wind data from weather services with wind sensor data, the accuracy and physical rationality of wind forecasts are improved. In areas with complex terrain and limited meteorological data resolution, wind conditions for subsequent navigation paths can be more accurately inferred based on physical laws, reducing forecast bias caused by sparse or inaccurate data and providing vehicles with more reliable wind warnings.

[0028] Specifically, we first constructed an intelligent terrain-wind field coupling model. Based on the principles of CFD (Computational Fluid Dynamics), we built an intelligent and adaptive wind field propagation model. This model not only considers basic fluid dynamics equations but also incorporates the coupled effects of terrain and environmental factors. Deep learning algorithms are used to train the wind field propagation model, enabling it to automatically identify and adapt to the impact of different terrain and environmental characteristics on the wind field. For example, in mountainous areas, the wind field propagation model can learn the blocking and guiding effects of mountains on wind; in urban areas, it can identify the obstruction and wind channeling effects of buildings. The model training data includes historical meteorological data, geographic information data, and historical wind sensor detection data, using a data-driven approach to improve the model's accuracy and adaptability.

[0029] Then, spatiotemporal deduction and dynamic correction are performed based on the actual wind data and the detected wind data. The actual wind data is used as the initial field and boundary conditions of the model, and combined with the geographic information on the navigation path, high-precision spatiotemporal deduction is performed to predict the wind conditions on the subsequent navigation path. Wind data detected by the wind sensor is received in real time and used as a real-time correction factor for the model. A dynamic weight adjustment algorithm automatically adjusts the weights of the two during the fusion process based on the difference between the wind sensor's detected wind data and the model's predicted data. For example, when the wind sensor data deviates significantly from the model's predicted data, the weight of the detected wind data is increased to quickly correct the model's prediction results. A correction mechanism based on the Particle Swarm Optimization (PSO) algorithm is introduced to optimize the wind propagation model parameters in real time, ensuring that the wind propagation model can quickly adapt to wind field changes and improving the accuracy and reliability of the prediction.

[0030] As another example, live wind data along the subsequent navigation route is first collected, including wind speed and direction at different times and locations, to form a weather service dataset. Furthermore, wind data from the vehicle's current location is collected over a continuous period of time and organized into a time series. Then, a data assimilation algorithm is used to fuse the wind sensor data with the live wind data from the weather service. Based on Bayesian estimation theory, this algorithm aims to minimize the difference between the two and identify the most likely true wind conditions. During the assimilation process, the weighting of the two data is dynamically adjusted based on the uncertainty and correlation of the data. Detected wind data with high sensor accuracy and good temporal and spatial alignment with the weather service data is given a higher weight; conversely, data with greater uncertainty is given a lower weight. Finally, after data assimilation, fused wind data for the subsequent navigation route is obtained, including more accurate wind speed, direction, and their changing trends.

[0031] It should be noted that in order to improve the processing efficiency of fused wind data and reduce data processing pressure, the current position of the vehicle can be used as the starting point to determine the fused wind data of a section of a preset length (for example, 2000 meters) in the subsequent navigation path.

[0032] In some optional implementations of this embodiment, the execution entity may perform step 201 as follows: The first step is to adjust the original spatial resolution of the live wind data to the target spatial resolution required by the navigation process to obtain refined wind data.

[0033] Generally, the original spatial resolution of live wind data is lower than the target spatial resolution required for navigation. For example, the original spatial resolution has a grid cell with a side length of 100 meters, while the target spatial resolution has a grid cell length of 10 meters. This means that navigation requires wind data for every 10-meter grid cell along the subsequent navigation path.

[0034] As an example, a wind model suitable for small scales (10 meters, corresponding to the target spatial resolution) is pre-built based on fluid mechanics principles. This model considers the flow characteristics of wind over different terrain and surface features, such as the blocking effect of buildings and the wind attenuation caused by surface roughness. A physical equation is established for each 10-meter x 10-meter grid cell, describing how wind speed and direction vary with spatial position. A large amount of historical meteorological data and corresponding high-precision wind observation data (such as from meteorological observatories, lidar, and other data sources with 10-meter resolution) are prepared. This data is used to train a machine learning model. The model inputs the original 100-meter grid wind data, along with terrain and feature information such as altitude, building density, and vegetation cover. The model outputs refined wind data for a 10-meter grid. Using the convolutional neural network (CNN) architecture from deep learning, this model automatically extracts spatial features and patterns from the data.

[0035] The second step is to combine the refined wind condition data and the detected wind condition data to determine the fused wind condition data.

[0036] In this implementation method, the above-mentioned execution entity can refer to the above-mentioned method of "combining the actual wind condition data of the subsequent navigation path and the detected wind condition data of the vehicle's current position to determine the fused wind condition data of the subsequent navigation path", combine the refined wind condition data and the detected wind condition data, and determine the fused wind condition data, which will not be repeated here.

[0037] In this implementation, the original spatial resolution of the actual wind condition data is refined to the target resolution and fused with the detected wind condition data to obtain more accurate and detailed fused wind condition data, which helps to improve the accuracy of predicting risky sections based on the fused wind condition data.

[0038] In some optional implementations of this embodiment, the above-mentioned execution entity can perform the above-mentioned first step in the following manner: for the refined unit area in the original unit area, determine the refined wind condition data of the refined unit area based on the position of the refined unit area in the original unit area, the actual wind condition data of the original unit area and the actual wind condition data of the adjacent unit areas of the original unit area.

[0039] The original unit area is the unit area corresponding to the original spatial resolution, and the refined unit area is the unit area corresponding to the target spatial resolution.

[0040] As an example, the execution entity may input the position of the refined unit area within the original unit area, the positional relationship between the original unit area and the adjacent unit area, the actual wind condition data of the original unit area, and the actual wind condition data of the adjacent unit area into a pre-trained wind condition calculation model, and the wind condition calculation model outputs the refined wind condition data of the refined unit area. The wind condition calculation model is used to characterize the position of the refined unit area within the original unit area, the positional relationship between the original unit area and the adjacent unit area, the actual wind condition data of the original unit area, the actual wind condition data of the adjacent unit area, and the corresponding relationship between the refined wind condition data of the refined unit area.

[0041] The wind condition calculation model can be obtained by training using neural network models such as recurrent neural networks and long short-term memory networks. First, a training sample set is obtained. The training samples include the sample position of the refined unit area in the original unit area, the sample position relationship between the original unit area and the adjacent unit area, the sample actual wind condition data of the original unit area, the sample actual wind condition data of the adjacent unit area, and the refined wind condition data label of the refined unit area. Then, a machine learning algorithm is used, with the sample position of the refined unit area in the original unit area, the sample position relationship between the original unit area and the adjacent unit area, the sample actual wind condition data of the original unit area, and the sample actual wind condition data of the adjacent unit area as input, and the refined wind condition data label corresponding to the input data as the expected output, to train and obtain the wind condition calculation model.

[0042] In this embodiment, the actual wind condition data of the original unit area and the actual wind condition data of the adjacent unit areas of the original unit area are combined to determine the refined wind condition data of the refined unit area, thereby improving the spatial accuracy and making the distribution of wind condition data on the navigation path more detailed.

[0043] In some optional implementations of this embodiment, the above-mentioned execution entity can determine the refined wind condition data of the refined unit area in the following manner: first, based on the position of the refined unit area in the original unit area, determine the weights corresponding to the actual wind condition data of the original unit area and the actual wind condition data of the adjacent unit area; then, based on the weights, combine the actual wind condition data of the original unit area and the actual wind condition data of the adjacent unit area to determine the refined wind condition data of the refined unit area.

[0044] Continuing with the example of the original unit area being a large-scale grid with a side length of 100 meters and the refined unit area being a small-scale grid with a side length of 10 meters, for the large-scale grid ( , ), the four surrounding large-scale grids (adjacent unit areas) are ( , )、( , )、( , )and( , ), the wind speeds of the four large-scale grids are 、 、 、 , the wind directions are 、 、 、 .

[0045] Target point( ) is a point in the small-scale grid, and the normalized distance operation is performed on it using the following formula to obtain the normalized distance parameter:

[0046] in, 、 For large-scale grids ( , )'s lower-left corner coordinates.

[0047] The weights corresponding to the actual wind data of the original unit area and the actual wind data of the adjacent unit area are determined according to the normalized distance parameter. , )、( , )、( , )and( , ) The weights of the actual wind condition data in each grid are 、 、 、 .

[0048] The wind speed at the target point is calculated using the following formula: :

[0049] The wind direction at the target point is calculated as follows : First, convert the wind directions of the four large-scale grids into vector form: 、 、 、 ; Then, the vector is calculated using the following formula ( ):

[0050] Finally, the following formula is used to convert the vector to an angle, with an angle range of 0-360°:

[0051] In this implementation, a specific implementation method for adjusting the spatial resolution of real-time wind condition data is provided. By utilizing the real-time wind condition data of four adjacent grids, the wind speed and direction of the target point are calculated, thereby refining the wind condition data from low spatial resolution (original spatial resolution) to high spatial resolution (target spatial resolution), further improving the accuracy of the refined wind condition data and helping to provide more accurate wind condition information for vehicles.

[0052] In some optional implementations of this embodiment, the above-mentioned execution entity can perform the above-mentioned second step in the following manner to obtain fused wind condition data: using a weighted Kalman filter, combining the refined wind condition data and the detected wind condition data, to determine the fused wind condition data.

[0053] As an example, first, the wind speed and wind direction and its trigonometric function components as the state vector , in order to fully describe the wind conditions and facilitate subsequent calculations.

[0054] Then, the cosine and sine values ​​of wind speed and direction are obtained from the weather service API to form the real-time wind vector , and set its noise covariance matrix 0.1 , to reflect the uncertainty of the actual wind data.

[0055] The vehicle's wind condition sensor is used to measure the cosine and sine values ​​of wind speed and direction to obtain the detected wind condition vector , set its noise covariance matrix 0.05 , which represents the error characteristics of the detected wind condition data.

[0056] Then, assuming the wind remains steady in the short term, the predicted state vector Directly take the state vector of the previous moment ,Right now = , prepare for subsequent updates.

[0057] Then, calculate the Kalman gain ,in, is the forecast covariance matrix, which is used to measure the uncertainty of the forecast state. is the measurement noise covariance matrix.

[0058] Then, the weighted measurements are calculated ,in, 、 Represent the weight of the actual wind data and the weight of the detected wind data, respectively. For example =0.3, =0.7.

[0059] Then, update the state vector , the actual wind data and the detected wind data are integrated to obtain a more accurate wind condition estimation.

[0060] Finally, from the updated state vector Extract wind speed and wind direction , forming a wind condition vector representing the fused wind condition data ( ), providing high-precision wind information for subsequent modules.

[0061] In this implementation, a wind data fusion method based on a weighted Kalman filter is provided to balance the stability of the actual wind data and the real-time performance of the detected wind data, thereby improving the accuracy of the fused wind data.

[0062] In some optional implementations of this embodiment, the execution entity may perform the second step in the following manner: First, the errors between the timestamps of the refined wind condition data, the timestamps of the detected wind condition data, and the timestamps corresponding to the navigation process are determined.

[0063] As an example, a first error between a timestamp of the refined wind condition data and a timestamp corresponding to the navigation process, and a second error between a timestamp of the detected wind condition data and a timestamp corresponding to the navigation process are determined. The timestamp of the refined wind condition data is the timestamp of the live wind condition data.

[0064] The timestamp corresponding to the navigation process is the current timestamp of the navigation system. When the first error and the second error are large, it indicates that the refined wind condition data and the detected wind condition data have not been updated within a certain period of time from the current time represented by the timestamp of the navigation system.

[0065] Then, in response to the error being within a preset error range, a weighted Kalman filter is used to combine the refined wind condition data and the detected wind condition data to determine fused wind condition data.

[0066] In response to the fact that both the first error and the second error are within the preset error range, indicating that the time interval between the update time of the refined wind condition data and the detected wind condition data and the current time is small, a weighted Kalman filter is used to combine the refined wind condition data and the detected wind condition data to determine the fused wind condition data.

[0067] In response to the first error exceeding the preset error range, it is necessary to re-collect the actual wind condition data to obtain refined wind condition data based on the actual wind condition data; in response to the second error exceeding the preset error range, it is necessary to re-determine the detected wind condition data.

[0068] To improve data collection flexibility and meet the collection characteristics of different data, different preset error ranges can be set for refined wind data and detected wind data. For example, if live wind data has higher stability, its preset error range is larger than the preset error range for detected wind data.

[0069] In this implementation method, it is necessary to determine the update timing of the actual wind data and the detected wind data based on the timestamp corresponding to the navigation process and the preset error range, so that the actual wind data and the detected wind data are adapted to the current time, so as to improve the real-time and accuracy of the fused wind data.

[0070] In some optional implementations of this embodiment, the execution entity may further perform the following operations before executing step 201: First, the propagation time difference of the ultrasonic wave between the two ultrasonic transducers in each ultrasonic transducer pair is determined by using two ultrasonic transducer pairs set on the vehicle, wherein the two ultrasonic transducer pairs are arranged in a cross shape on the vehicle; then, the detected wind condition data is determined based on the propagation time difference.

[0071] An ultrasonic anemometer (Gill WindSonic, for example, with a measurement range of 0-60 m / s, an accuracy of ±0.1 m / s, and a directional accuracy of ±2°) is installed in the center of the vehicle roof. The ultrasonic anemometer consists of two ultrasonic transducer pairs. Transducers A and B in one pair are oriented along the north-south axis, while transducers C and D in the other pair are oriented along the east-west axis. The distance between the two transducers in each pair is 0.2 meters. The two transducer pairs are located in the center of the vehicle roof to avoid areas of airflow interference. Power is supplied by the vehicle's 12V power supply, and signals are transmitted to the onboard processor via an RS-485 interface.

[0072] Then, the wind speed and direction are calculated using the propagation time difference of the ultrasonic wave in the wind. Specifically, ultrasonic transducer A transmits ultrasonic waves to ultrasonic transducer B and records the propagation time. Ultrasonic transducer B transmits ultrasonic waves to ultrasonic transducer A and records the propagation time Similarly, ultrasonic transducer C transmits ultrasonic waves to ultrasonic transducer D and records the propagation time. Ultrasonic transducer D transmits ultrasonic waves to ultrasonic transducer C and records the propagation time .

[0073] Calculate the north-south wind speed component :

[0074] Similarly, calculate the east-west wind speed component :

[0075] According to the above wind speed components and , calculate the wind speed in the detected wind data :

[0076] According to the above wind speed components and , calculate the wind direction in the detected wind data :

[0077] wind direction The angle range is 0-360°, where due north is 0°.

[0078] In this implementation, each pair of ultrasonic transducers emits 10 ultrasonic pulses per second (period 100ms), generating 10 groups ( ) data. Then 10 groups ( ) take the average value and calculate based on the average value and .

[0079] In this implementation, the detected wind condition data can also be zero-calibrated and outlier removed. In the zero-point calibration operation, when the vehicle is stationary, the data collected within a specified time (for example, 5 seconds) is continuously collected and the average value is obtained. and , the above obtained minus Get the calibrated wind speed data and convert the above minus Get the calibrated wind direction data.

[0080] Because the ultrasonic anemometer measures the relative wind speed relative to the surrounding air. When the vehicle is stationary, the ultrasonic anemometer is fixed on the roof and there is no relative wind speed caused by the movement of the vehicle. Therefore, in theory, the wind speed measured by the ultrasonic anemometer at rest is It should be 0, but it is found that the mean value of the measured value in the static state is not 0, indicating that there may be some errors in the hardware. Based on this, using the above non-static state and Subtract offset and To correct the error.

[0081] In the abnormal rejection operation, if a single measurement is obtained and , the deviation from the mean of the previous multiple times (for example, 10 times) is greater than the standard deviation of the preset speed (for example, 3 times), indicating that the data collected this time is abnormal data, which is caused by gusts of wind or sensor jitter, and the data collection is abnormal, and the data needs to be discarded.

[0082] Finally, based on the ultrasonic transducer pair, a wind condition data is generated every second ( , ), with accuracies of 0.1 m / s and 1° respectively.

[0083] In this implementation, a method for obtaining detected wind condition data based on an ultrasonic transducer pair is provided, and the accuracy and real-time performance of the detected wind condition data are improved by combining two ultrasonic transducer pairs arranged in a cross pattern.

[0084] Step 202 : Determine the degree of influence of wind on the driving safety of the vehicle based on the fused wind condition data and the position data of the vehicle in the subsequent navigation path.

[0085] In this embodiment, the execution entity may determine the degree of influence of wind on the driving safety of the vehicle based on the fusion of wind condition data and the position data of the vehicle in the subsequent navigation path.

[0086] As an example, first, based on the subsequent navigation path, the vehicle's posture data at each location along the subsequent navigation path, such as driving direction and pitch angle, is determined. Then, the fused wind data and the vehicle's posture data along the subsequent navigation path are input into an impact determination model to determine the degree of wind impact on the vehicle's driving safety. The impact determination model can be obtained by training a neural network model (e.g., a residual network or a long short-term memory network) using a machine learning algorithm.

[0087] Specifically, first, a training sample set is obtained. The training sample set includes sample wind condition data, sample position data, and impact degree labels. Then, a machine learning algorithm is used to train the initial impact degree determination model using the sample wind condition data and sample position data as inputs and the impact degree labels as the expected outputs of the initial impact degree determination model. This yields the impact degree determination model.

[0088] As another example, first, based on the principles of vehicle dynamics, risk assessment indicators are designed, including vehicle lateral displacement, lateral acceleration, and vehicle stability. These indicators directly reflect the dynamic performance of the vehicle under the influence of wind. The vehicle's lateral displacement trend is calculated by combining the angle between the vehicle's direction of travel and the wind direction with the wind speed. The lateral acceleration caused by the wind on the vehicle is calculated based on the wind speed and vehicle speed. Vehicle stability is assessed using parameters such as vehicle acceleration and attitude changes.

[0089] Then, while the vehicle is in motion, the aforementioned risk assessment indicators are calculated in real time. These multiple indicators are normalized to a value between [0, 1]. Using a weighted comprehensive risk assessment formula, these three indicators are combined into a single numerical value representing the degree of wind impact on vehicle safety.

[0090] In some optional implementations of this embodiment, the execution entity may perform step 202 as follows: The first step is to determine the lateral wind speed to which the vehicle is subjected based on the fusion of wind condition data and posture data.

[0091] The lateral wind speed represents the component of the wind speed in the fused wind condition data in the direction perpendicular to the driving direction of the vehicle.

[0092] As an example, for each location point of the vehicle in the candidate navigation path, perform the following operations: First, determine the fused wind condition vector obtained in step 201 ( ), and the direction of vehicle travel Vehicle driving direction Determined by: Continuously sampling position through GPS (Global Positioning System) module ( )and( ), the time interval is 1 second. Calculate the vehicle's direction of travel , which ranges from 0-360°.

[0093] Then, the side wind speed is calculated according to the following formula:

[0094] in, is the sine of the angle between the wind direction and the vehicle's travel direction, is the lateral wind speed (in meters per second). If | |>180°, adjust to = ± 360°, take and The minimum angle between them is calculated to ensure the sine value is correct; the final calculated Keep 2 decimal places (such as = 8.34 m / s).

[0095] The second step is to determine the degree of impact based on the lateral wind speed.

[0096] As an example, the principle that the lateral wind speed is positively correlated with the impact degree is adopted, and the impact degree is determined based on the lateral wind speed.

[0097] In this implementation, a method for determining the degree of influence based on the lateral wind speed is provided, thereby improving the accuracy of the degree of influence.

[0098] In some optional implementations of this embodiment, the execution entity may perform the second step to determine the degree of influence in the following manner: determining the degree of influence based on the lateral wind speed and the inertial dynamic parameters of the vehicle.

[0099] A vehicle's inertial dynamic parameters describe its inertial properties against changes in motion, as well as a set of parameters related to external dynamic excitations (such as wind loads). For example, these parameters include the vehicle's wind sensitivity coefficient and mass. The above-mentioned execution entity can obtain the vehicle's mass M (kg) and crosswind sensitivity coefficient k (unitless, defaulting to 1.0 for sedans and 1.5 for trucks) through user input or the OBD (On-Board Diagnostics) interface.

[0100] As an example, the impact can be calculated using the following formula:

[0101] The unit of the impact value is Newton (N). For example, = 10 m / s, = 1500 kg, =1.0, then = 1.0 × 10 × 1500 = 15000 N.

[0102] In this implementation, when determining the degree of influence, in addition to referring to the lateral wind speed, the inertial dynamic parameters of the vehicle are also referred to, so that the degree of influence of different vehicles can be determined in a targeted manner, thereby improving the accuracy of the degree of influence.

[0103] Step 203: Determine the assisted driving strategy for the risky section in the subsequent navigation path according to the degree of impact.

[0104] In this implementation, the execution entity may determine risky sections in the subsequent navigation path and assisted driving strategies for the risky sections based on the degree of impact.

[0105] For example, based on the comparison of the impact level and the risk judgment threshold, each location in the subsequent navigation path is determined to be a risk point. The road section represented by the adjacent risk points is then identified as a risk section. The assisted driving strategy for the risk section is determined based on factors such as the length and terrain of the risk section.

[0106] Specifically, the risky road section is first divided into sections based on its length, taking into account terrain changes (such as continuous curves on mountain roads and the starting and ending locations of bridges). Then, a risk index model is developed based on the length of the section and the terrain characteristics. For example, long mountain roads have a higher risk index due to the continuous curves and slope changes; while short bridge sections, mainly considering the impact of crosswinds, have a relatively lower risk index.

[0107] The assisted driving strategy is then determined based on the risk index. For example, the vehicle speed is dynamically adjusted based on the risk index. On long, high-risk sections, the vehicle speed is gradually reduced to a safe range and maintained at a stable speed. On short, low-risk sections, the speed is moderately reduced to ensure safe passage. The vehicle stability control system is activated, and control parameters are adjusted according to the terrain and wind conditions. Stability support is increased on curves on mountainous roads, and resistance to crosswinds is enhanced on bridge sections. Risk warnings and assisted driving strategies are provided to the driver through the in-vehicle infotainment system. On high-risk sections, it is recommended to maintain concentration and avoid dangerous maneuvers such as sudden lane changes. On long, risky sections, the driver is reminded to rest and avoid fatigued driving.

[0108] As another example, the impact level value is input into the assisted driving strategy generation model, which then generates an assisted driving strategy. The assisted driving strategy generation model is used to characterize the correspondence between the impact level value and the assisted driving strategy. For example, for a higher impact level, the assisted driving strategy may include stopping for safe waiting or replanning the subsequent navigation route; for a lower impact level, the assisted driving strategy may include providing a safe speed reminder.

[0109] In some optional implementations of this embodiment, the execution entity may perform step 203 as follows: The first step is to determine the risk level of the risk section based on the degree of impact.

[0110] In this implementation, corresponding numerical ranges may be set for different risk levels, thereby determining the risk level according to the numerical range in which the impact degree lies.

[0111] For example, R < 5000 N indicates low risk; 5000 ≤ R ≤ 15000 N indicates medium risk; and R > 15000 N indicates high risk.

[0112] In this implementation, a preset calculation frequency, such as once per second, may be used to determine the impact degree; and the average of the impact degrees within the current specified time period is used as the current impact degree.

[0113] The second step is to determine the assisted driving strategy for risky sections based on the risk level.

[0114] As an example, the assisted driving strategy corresponding to each risk level is determined in advance, so that after the risk level corresponding to the impact degree is determined, the assisted driving strategy corresponding to the risk level is determined.

[0115] For each risky section in the subsequent navigation path, in response to the vehicle being close to the section, for example, the distance between the vehicle and the risky section is less than a preset distance threshold, assisted driving suggestions for the risky section are displayed to the vehicle driver through voice, text, etc.

[0116] For risky road sections with different risk levels, the corresponding preset distance thresholds are different. For example, the preset distance threshold is positively correlated with the risk level, that is, the higher the risk level, the larger the preset distance threshold.

[0117] In this implementation, the assisted driving strategy for the risky road section is determined based on the risk level of the risky road section. Through accurate risk assessment, the vehicle can take appropriate preventive measures in advance, which helps to reduce the probability of accidents and improve driving safety.

[0118] In some optional implementations of this embodiment, the execution entity may further perform the following operations: First, the lateral force on the vehicle is determined based on the lateral wind speed and the aerodynamic geometry parameters of the vehicle.

[0119] The lateral wind speed represents the component of the wind speed in the fused wind data that is perpendicular to the vehicle's direction of travel. Aerodynamic geometry represents the geometric properties and efficiency parameters of the interaction between the vehicle's shape and airflow, including parameters such as lateral area and lateral drag coefficient.

[0120] As an example, the following formula is used to calculate the lateral force:

[0121] Where ρ represents the air density. The standard air density is 1.225 kg / m³. The air density at high altitudes can be adjusted according to the air pressure. For example, the air density at an altitude of 5000 meters is ρ≈0.736 kg / m³. Indicates the lateral drag coefficient of the vehicle, which can be adjusted by the vehicle model database, such as SUV (Sport Utility Vehicle, sports utility vehicle) model =0.45; A represents the side area of ​​the vehicle, which is determined by user input or vehicle model matching. For example, the side area of ​​a sedan is A≈2.5 m²; Indicates the side wind speed.

[0122] Then, the degree of directional deviation of the vehicle under the influence of the lateral force is determined based on the lateral force and the stiffness parameters of the vehicle.

[0123] As an example, the degree of directional deviation of a vehicle under the influence of a lateral force can be determined by the following formula: It indicates the degree of directional deviation, with the unit being degree; K is the vehicle suspension stiffness, which can be determined according to the vehicle model. For example, K of a truck can be set to 15000, with the unit being N / degree.

[0124] The degree of directional deviation is calculated using a preset calculation frequency, such as updating δ once per second, which is the same as the calculation frequency of the impact degree.

[0125] For example, = 61.25 N, K = 10000 N / degree, δ = (61.25 / 10000) ≈ 0.35.

[0126] In this implementation, the execution subject may execute the assisted driving strategy determination process in the following manner: determining the assisted driving strategy for the risky road section according to the risk level and the degree of direction deviation.

[0127] According to the combination of risk level and direction deviation degree, formulate corresponding assisted driving strategy. For example: When the risk level is low and the degree of directional deviation is small, only slight directional adjustment suggestions may be provided to assist the driver in staying within the lane.

[0128] When the risk level is low but the degree of directional deviation is large, in addition to providing directional adjustment suggestions, the sensitivity of the vehicle stability control system (ESC) can also be moderately increased to prevent the vehicle from becoming unstable due to directional deviation.

[0129] If the risk level is medium and the degree of direction deviation is small, slight direction correction assistance will be automatically activated to remind the driver to maintain the correct driving direction.

[0130] If the risk level is medium and the degree of direction deviation is large, the driver will be reminded of the risk and advised to slow down. At the same time, the direction correction assistance will be enhanced, and the maximum speed limit of the vehicle may be appropriately lowered.

[0131] When the risk level is high and the degree of directional deviation is small, the system automatically makes appropriate directional corrections and reminds the driver to stay focused, while activating functions such as forward collision warning to deal with possible emergencies.

[0132] When the risk level is high and the direction deviation is large, an emergency warning is triggered, advising the driver to slow down immediately and avoid high-risk operations such as changing lanes. At the same time, obvious direction correction assistance is provided and the emergency braking assist function can be automatically enabled to reduce the risk of accidents.

[0133] For example, if the impact value R > 15000 N and δ > 1°, the driver will be informed by voice: "The crosswind is X meters ahead. m / s, hold the steering wheel tightly and adjust δ° to the left, where X is the distance between the starting point of the risk area and the vehicle.

[0134] Before a vehicle enters a risky road section, the system automatically executes or reminds the driver to implement the appropriate assisted driving strategy based on the pre-assessed risk level and the real-time calculated degree of directional deviation. During driving, the system continuously monitors changes in vehicle status, road conditions, and directional deviation, dynamically adjusting the assisted driving strategy based on the latest data to ensure its real-time effectiveness. After the driving session, the system collects driver feedback and actual driving data to evaluate and analyze the effectiveness of the assisted driving strategy for optimization and improvement.

[0135] In this implementation, the risk level and the degree of directional deviation are combined to determine the assisted driving strategy for the risky road section, which further improves the accuracy of the assisted driving strategy and its matching degree with the risk scenario.

[0136] In some optional implementations of this embodiment, the execution subject may further perform the following operations: during the navigation process, a preset display method is used to display risky sections and risk levels of the risky sections on the navigation interface.

[0137] Among them, the preset display method is, for example, to display the risk section and the risk level of the risk section through text through a floating layer, a floating window, etc.

[0138] In some implementations, a semi-transparent polygon is overlaid on the navigation path in the navigation map using OpenGL (Open Graphics Library). The border is connected by the waypoints of the risky path and the width is set to the lane width (default 3.5 meters). The polygon color varies depending on the wind direction level. For example, linear interpolation can be used to smooth the border transition, creating a color gradient effect between risky road sections with different risk levels.

[0139] In this implementation, a preset display method is adopted to display risk sections and their risk levels on the navigation interface, so that users can intuitively understand risk sections and their risk levels, thereby improving user experience and information acquisition efficiency.

[0140] In some optional implementations of this embodiment, the execution subject may further perform the following operations: during the navigation process, a preset icon is used to display the degree of influence on the navigation interface.

[0141] A variety of preset icons can be set, and the icon style to be displayed to the user is determined based on the user's selection operation. In the preset icons, different levels of influence can be displayed through different symbols.

[0142] As an example, a wind turbine icon is added to the navigation interface of the navigation map. The icon color is determined by the level of impact R: green for R < 5000 N, yellow for 5000-15000 N, and red for R > 15000 N. Specifically, an RGB (Red, Green, Blue) gradient function is used to represent the degree of wind impact on driving safety: = 0 in, Indicates the mean of the influence degree R.

[0143] In this implementation, a preset icon is used to display the degree of impact on the navigation interface, so that the user can intuitively understand the degree of impact, further improving the user experience and information acquisition efficiency.

[0144] Continue to see Figure 3 , Figure 3This is a schematic diagram 300 of an application scenario of the method for determining an assisted driving strategy according to this embodiment. User 301 requests a navigation route from a starting point to a destination from server 303 via a navigation application in a terminal device 302, and drives the vehicle according to the navigation route. During the navigation process, server 302 determines the fused wind data of the subsequent navigation route by combining the real-time wind data of the subsequent navigation route obtained from a meteorological application and the detected wind data of the vehicle's current position collected from an on-board wind sensor; determines the degree of influence of the wind on the driving safety of the vehicle based on the fused wind data and the position data of the vehicle in the subsequent navigation route; and determines the risk section 304 in the subsequent navigation route and the assisted driving strategy 305 for the risk section based on the degree of influence, such as "crosswind 18 m / s 50 meters ahead, hold the steering wheel tightly, and adjust 2° to the left."

[0145] In this embodiment, a method for determining an assisted driving strategy is provided. During the navigation process, the actual wind condition data of the subsequent navigation path and the detected wind condition data of the vehicle's current position are combined to determine the fused wind condition data of the subsequent navigation path; based on the fused wind condition data and the vehicle's posture data in the subsequent navigation path, the degree of influence of the wind on the driving safety of the vehicle is determined; based on the degree of influence, the risk sections in the subsequent navigation path and the assisted driving strategy for the risk sections are determined, thereby converting the wind condition from an uncontrollable factor into a navigation reference variable for integration into the navigation process, and determining the risk sections and the assisted driving strategy for the risk sections in real time, thereby solving the problem of crosswind interference during driving and helping to improve driving safety.

[0146] Continue to refer Figure 4 , shows a schematic process 400 of another embodiment of a method for determining an assisted driving strategy according to the present disclosure. In process 400, the following steps are included: Step 401 : During the navigation process, the original spatial resolution of the live wind condition data of the subsequent navigation path is adjusted to the target spatial resolution required by the navigation process to obtain refined wind condition data.

[0147] Step 402 : Determine the errors between the timestamp of the refined wind condition data, the timestamp of the detected wind condition data at the current position of the vehicle, and the timestamp corresponding to the navigation process.

[0148] In step 403 , in response to the error being within a preset error range, a weighted Kalman filter is used to combine the refined wind condition data and the detected wind condition data to determine fused wind condition data.

[0149] Step 404 : Determine the lateral wind speed to which the vehicle is subjected based on the fused wind condition data and the position data of the vehicle in the subsequent navigation path.

[0150] The lateral wind speed represents the component of the wind speed in the fused wind condition data in the direction perpendicular to the driving direction of the vehicle.

[0151] Step 405 : Determine the degree of influence of wind on the driving safety of the vehicle based on the lateral wind speed and the inertial dynamic parameters of the vehicle.

[0152] Step 406: Determine the risk level of the risky section in the subsequent navigation path according to the impact degree.

[0153] Step 407 : Determine the lateral force on the vehicle based on the lateral wind speed and the aerodynamic geometry parameters of the vehicle.

[0154] Step 408 : Determine the degree of directional deviation of the vehicle under the influence of the lateral force based on the lateral force and the stiffness parameters of the vehicle.

[0155] Step 409 : Determine an assisted driving strategy for the risky road section based on the risk level and the degree of direction deviation.

[0156] Compared with the above-mentioned process 200, the process 400 of the method for determining the assisted driving strategy in this embodiment specifically describes the process of determining the integrated wind condition data, the process of determining the degree of influence, and the process of determining the assisted driving strategy. It converts the wind condition from an uncontrollable factor into a navigation reference variable to be integrated into the navigation process, determines the risk sections and the assisted driving strategy for the risk sections in real time, solves the problem of crosswind interference during driving, and helps to improve driving safety.

[0157] Continue to refer Figure 5 As an implementation of the methods shown in the above figures, the present disclosure provides an embodiment of a device for determining an assisted driving strategy. Figure 2 Corresponding to the method embodiment shown, the system can be specifically applied to various electronic devices.

[0158] like Figure 5 As shown, the device 500 for determining the assisted driving strategy includes: a wind condition determination unit 501, which is configured to determine the fused wind condition data of the subsequent navigation path in combination with the actual wind condition data of the subsequent navigation path and the detected wind condition data of the current position of the vehicle during the navigation process; a degree determination unit 502, which is configured to determine the degree of influence of the wind on the driving safety of the vehicle based on the fused wind condition data and the posture data of the vehicle in the subsequent navigation path; and a strategy determination unit 503, which is configured to determine the assisted driving strategy for the risky section in the subsequent navigation path based on the degree of influence.

[0159] In some optional implementations of this embodiment, the wind condition determination unit 501 is further configured to: adjust the original spatial resolution of the actual wind condition data to the target spatial resolution required for the navigation process to obtain refined wind condition data; combine the refined wind condition data and the detected wind condition data to determine the fused wind condition data, and the original spatial resolution is less than the target spatial resolution.

[0160] In some optional implementations of this embodiment, the wind condition determination unit 501 is further configured to: for the refined unit area in the original unit area, determine the refined wind condition data of the refined unit area based on the position of the refined unit area in the original unit area, the actual wind condition data of the original unit area and the actual wind condition data of the adjacent unit areas of the original unit area, wherein the original unit area is the unit area corresponding to the original spatial resolution, and the refined unit area is the unit area corresponding to the target spatial resolution.

[0161] In some optional implementations of this embodiment, the wind condition determination unit 501 is further configured to: determine the weights corresponding to the actual wind condition data of the original unit area and the actual wind condition data of the adjacent unit area based on the position of the refined unit area in the original unit area; and determine the refined wind condition data of the refined unit area based on the weights, combining the actual wind condition data of the original unit area and the actual wind condition data of the adjacent unit area.

[0162] In some optional implementations of this embodiment, the wind condition determination unit 501 is further configured to: adopt a weighted Kalman filter to combine the refined wind condition data and the detected wind condition data to determine the fused wind condition data.

[0163] In some optional implementations of this embodiment, the wind condition determination unit 501 is further configured to: determine the errors between the timestamps of the refined wind condition data, the timestamps of the detected wind condition data, and the timestamps corresponding to the navigation process; in response to the error being within a preset error range, use a weighted Kalman filter to combine the refined wind condition data and the detected wind condition data to determine the fused wind condition data.

[0164] In some optional implementations of this embodiment, the degree determination unit 502 is further configured to: determine the lateral wind speed that the vehicle is subjected to based on the fused wind condition data and posture data, wherein the lateral wind speed represents the component of the wind speed in the fused wind condition data in the direction perpendicular to the vehicle's driving direction; and determine the degree of impact based on the lateral wind speed.

[0165] In some optional implementations of this embodiment, the degree determination unit 502 is further configured to determine the degree of influence according to the lateral wind speed and the inertial dynamic parameters of the vehicle.

[0166] In some optional implementations of this embodiment, the strategy determination unit 503 is further configured to: determine the risk level of the risky road section according to the degree of impact; and determine the assisted driving strategy for the risky road section according to the risk level.

[0167] In some optional implementations of this embodiment, the degree determination unit 502 is further configured to: determine the lateral force borne by the vehicle based on the lateral wind speed and the aerodynamic geometric parameters of the vehicle, wherein the lateral wind speed represents the component of the wind speed in the fused wind condition data in the direction perpendicular to the vehicle's driving direction; determine the degree of directional deviation of the vehicle under the influence of the lateral force based on the lateral force and the stiffness parameters of the vehicle; and the strategy determination unit 503 is further configured to: determine the assisted driving strategy for the risky road section based on the risk level and the degree of directional deviation.

[0168] In some optional implementations of this embodiment, the above-mentioned device also includes a detection unit (not shown in the figure), which is configured to: determine the propagation time difference of the ultrasonic wave between the two ultrasonic transducers in each ultrasonic transducer pair through two ultrasonic transducer pairs set on the vehicle, wherein the two ultrasonic transducer pairs are arranged in a cross shape on the vehicle; and determine the detected wind condition data based on the propagation time difference.

[0169] In some optional implementations of this embodiment, the above-mentioned device also includes: a display unit (not shown in the figure), which is configured to use a preset display method to display risky sections and risk levels of risky sections on the navigation interface during the navigation process.

[0170] In some optional implementations of this embodiment, the display unit (not shown in the figure) is further configured to display the degree of influence on the navigation interface using a preset icon during the navigation process.

[0171] In this embodiment, a device for determining an assisted driving strategy is provided. During the navigation process, the fused wind condition data of the subsequent navigation path is combined with the detected wind condition data of the vehicle's current position to determine the fused wind condition data of the subsequent navigation path; the degree of influence of the wind on the driving safety of the vehicle is determined based on the fused wind condition data and the vehicle's posture data in the subsequent navigation path; and the risk sections in the subsequent navigation path and the assisted driving strategy for the risk sections are determined based on the degree of influence, thereby converting the wind condition from an uncontrollable factor into a navigation reference variable for integration into the navigation process, determining the risk sections and the assisted driving strategy for the risk sections in real time, solving the problem of crosswind interference during driving, and helping to improve driving safety.

[0172] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can implement the method for determining the assisted driving strategy described in any of the above embodiments when executing.

[0173] According to an embodiment of the present disclosure, the present disclosure further provides a readable storage medium, which stores computer instructions, and the computer instructions are used to enable a computer to implement the method for determining the assisted driving strategy described in any of the above embodiments when executed.

[0174] An embodiment of the present disclosure provides a computer program product, which, when executed by a processor, can implement the method for determining the assisted driving strategy described in any of the above embodiments.

[0175] Figure 6 A schematic block diagram of an example electronic device 600 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are provided as examples only and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0176] like Figure 6 As shown, device 600 includes a computing unit 601, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 602 or a computer program loaded from a storage unit 608 into a random access memory (RAM) 603. RAM 603 may also store various programs and data required for the operation of device 600. Computing unit 601, ROM 602, and RAM 603 are interconnected via a bus 604. An input / output (I / O) interface 605 is also connected to bus 604.

[0177] Various components in device 600 are connected to I / O interface 605, including an input unit 606, such as a keyboard, mouse, etc.; an output unit 607, such as various types of displays, speakers, etc.; a storage unit 608, such as a magnetic disk, optical disk, etc.; and a communication unit 609, such as a network card, modem, wireless communication transceiver, etc. The communication unit 609 allows device 600 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0178] The computing unit 601 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 601 performs the various methods and processes described above, such as the method for determining the assisted driving strategy. For example, in some embodiments, the method for determining the assisted driving strategy can be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 608. In some embodiments, part or all of the computer program can be loaded and / or installed onto the device 600 via the ROM 602 and / or the communication unit 609. When the computer program is loaded into the RAM 603 and executed by the computing unit 601, one or more steps of the method for determining the assisted driving strategy described above can be performed. Alternatively, in other embodiments, the computing unit 601 can be configured to perform the method for determining the assisted driving strategy through any other suitable means (e.g., via firmware).

[0179] Various embodiments of the systems and techniques described above can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0180] The program code used to implement the methods of the present disclosure can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable device for determining driver assistance strategies, so that when executed by the processor or controller, the program code implements the functions / operations specified in the flowcharts and / or block diagrams. The program code can be executed entirely on the machine, partially on the machine, as a standalone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0181] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of machine-readable storage media may include an electrical connection based on one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), optical fibers, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0182] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0183] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0184] A computer system may include a client and a server. The client and server are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, establishing a client-server relationship. The server can be a cloud server, also known as a cloud computing server or cloud host. This server is a host product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosts and virtual private server (VPS) services. It can also be a server in a distributed system or a server integrated with blockchain.

[0185] According to the technical solution of the embodiment of the present disclosure, a method and device for determining an assisted driving strategy are provided. During the navigation process, the fused wind condition data of the subsequent navigation path is combined with the detected wind condition data of the vehicle's current position to determine the fused wind condition data of the subsequent navigation path; the degree of influence of the wind on the driving safety of the vehicle is determined based on the fused wind condition data and the vehicle's posture data in the subsequent navigation path; and the risk sections in the subsequent navigation path and the assisted driving strategy for the risk sections are determined based on the degree of influence, thereby converting the wind condition from an uncontrollable factor into a navigation reference variable for integration into the navigation process, and determining the risk sections and the assisted driving strategy for the risk sections in real time, thereby solving the problem of crosswind interference during driving and helping to improve driving safety.

[0186] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions provided by this disclosure can be achieved. This is not a limitation herein.

[0187] The above specific embodiments do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure shall be included within the scope of protection of this disclosure.

Claims

1. A method for determining an assisted driving strategy, comprising: During navigation, combining the live wind condition data of the subsequent navigation path with the detected wind condition data of the vehicle's current position to determine fused wind condition data for the subsequent navigation path; determining, based on the fused wind condition data and the position data of the vehicle in the subsequent navigation path, the degree of influence of wind on the driving safety of the vehicle; An assisted driving strategy for the risky section in the subsequent navigation path is determined according to the degree of impact.

2. The method according to claim 1, wherein The combining of the live wind condition data of the subsequent navigation path and the detected wind condition data of the current position of the vehicle to determine the fused wind condition data of the subsequent navigation path includes: Adjusting the original spatial resolution of the live wind condition data to the target spatial resolution required by the navigation process to obtain refined wind condition data, wherein the original spatial resolution is smaller than the target spatial resolution; The refined wind condition data and the detected wind condition data are combined to determine the fused wind condition data.

3. The method according to claim 2, wherein: The adjusting the original spatial resolution of the live wind condition data to the target spatial resolution required by the navigation process to obtain refined wind condition data includes: For a refined unit area in an original unit area, the refined wind condition data of the refined unit area is determined according to the position of the refined unit area in the original unit area, the actual wind condition data of the original unit area and the actual wind condition data of the unit areas adjacent to the original unit area, wherein the original unit area is the unit area corresponding to the original spatial resolution, and the refined unit area is the unit area corresponding to the target spatial resolution.

4. The method according to claim 3, wherein: The determining, according to a position of the refined unit area in the original unit area, the live wind condition data of the original unit area, and the live wind condition data of unit areas adjacent to the original unit area, the refined wind condition data of the refined unit area includes: Determining, according to a position of the refined unit area in the original unit area, respective weights corresponding to the live wind condition data of the original unit area and the live wind condition data of the adjacent unit area; According to the weight, the actual wind condition data of the original unit area and the actual wind condition data of the adjacent unit area are combined to determine the refined wind condition data of the refined unit area.

5. The method according to claim 2, wherein: The combining the refined wind condition data and the detected wind condition data to determine the fused wind condition data includes: A weighted Kalman filter is used to combine the refined wind condition data and the detected wind condition data to determine the fused wind condition data.

6. The method according to claim 5, wherein: The method of using a weighted Kalman filter to combine the refined wind condition data and the detected wind condition data to determine the fused wind condition data includes: Determining errors between the timestamp of the refined wind condition data, the timestamp of the detected wind condition data, and the timestamp corresponding to the navigation process; In response to the error being within a preset error range, a weighted Kalman filter is used to combine the refined wind condition data and the detected wind condition data to determine the fused wind condition data.

7. The method according to claim 1, wherein The determining, based on the fused wind condition data and the position data of the vehicle in the subsequent navigation path, the degree of influence of wind on the driving safety of the vehicle includes: Determining a lateral wind speed experienced by the vehicle based on the fused wind condition data and the posture data, wherein the lateral wind speed represents a component of the wind speed in the fused wind condition data in a direction perpendicular to a traveling direction of the vehicle; The degree of influence is determined according to the lateral wind speed.

8. The method according to claim 7, wherein: Determining the impact degree according to the lateral wind speed includes: The degree of influence is determined according to the lateral wind speed and the inertial dynamic parameters of the vehicle.

9. The method according to claim 1, wherein The determining, based on the impact degree, a driving assistance strategy for a risky section in the subsequent navigation path includes: Determining the risk level of the risk section according to the impact degree; According to the risk level, an assisted driving strategy for the risky road section is determined.

10. The method according to claim 9, wherein: Among them, also include: determining a lateral force borne by the vehicle based on a lateral wind speed and aerodynamic geometric parameters of the vehicle, wherein the lateral wind speed represents a component of the wind speed in the fused wind condition data in a direction perpendicular to the traveling direction of the vehicle; determining a degree of directional deviation of the vehicle under the influence of the lateral force based on the lateral force and a stiffness parameter of the vehicle; and The step of determining an assisted driving strategy for the risky road section according to the risk level includes: An assisted driving strategy for the risky road section is determined according to the risk level and the degree of direction deviation.

11. The method according to claim 1, wherein In the navigation process, before determining the fused wind condition data of the subsequent navigation path by combining the live wind condition data of the subsequent navigation path and the detected wind condition data of the current position of the vehicle, the method further includes: Determining a propagation time difference of an ultrasonic wave between two ultrasonic transducers in each ultrasonic transducer pair by using two ultrasonic transducer pairs provided on the vehicle, wherein the two ultrasonic transducer pairs are arranged in a cross pattern on the vehicle; The detected wind condition data is determined according to the propagation time difference.

12. The method according to any one of claims 1 to 11, wherein Also includes: During the navigation process, a preset display method is used to display the risk section and the risk level of the risk section on the navigation interface.

13. The method according to any one of claims 1 to 11, wherein Also includes: During the navigation process, a preset icon is used to display the impact degree on the navigation interface.

14. A device for determining an assisted driving strategy, comprising: a wind condition determination unit configured to, during navigation, combine the live wind condition data of the subsequent navigation path and the detected wind condition data of the current position of the vehicle to determine fused wind condition data of the subsequent navigation path; a degree determination unit configured to determine a degree of influence of wind on driving safety of the vehicle based on the fused wind condition data and the position data of the vehicle in the subsequent navigation path; The strategy determination unit is configured to determine the assisted driving strategy for the risky section in the subsequent navigation path according to the impact degree.

15. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 13.

16. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to cause the computer to execute the method according to any one of claims 1 to 13.

17. A computer program product comprising: A computer program which, when executed by a processor, implements the method according to any one of claims 1 to 13.

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