Vehicle control method, apparatus, device, storage medium, and product

Abstract

This application discloses a vehicle control method, device, equipment, storage medium, and product, belonging to the field of autonomous driving technology. It includes: collecting data on the current road environment of the first vehicle using multiple target sensors installed on the first vehicle to obtain multiple first environmental information; preprocessing the multiple first environmental information to obtain multiple second environmental information; extracting key information from the multiple second environmental information using an information extraction model based on weather information to obtain multiple third environmental information, wherein the third environmental information obtained by extracting key information from the second environmental information is valid under the weather conditions indicated by the weather information; fusing the multiple third environmental information based on the characteristic information of the multiple target sensors to obtain first target environmental information; determining key features of the road environment using an environmental perception model based on the first target environmental information; and determining target control decision information for the first vehicle based on the key features of the road environment.

Description

Technical Field

[0001] This application relates to the field of autonomous driving technology, and in particular to a vehicle control method, device, equipment, storage medium, and product. Background Technology

[0002] In autonomous driving technology, cameras or lidar are used to perceive the environment, and then the vehicle makes corresponding decisions based on the perceived environmental information, such as adjusting speed, changing lanes, or avoiding obstacles. Environmental perception is crucial for the safe and stable driving of the vehicle. However, cameras or lidar are affected by weather conditions, leading to inaccurate environmental information. For example, cameras may be blurred by water droplets or fog, and lidar may be affected by scattering from raindrops or fog, resulting in reduced detection range and accuracy. Therefore, improving the accuracy of sensor-based environmental perception in adverse weather conditions, and subsequently making decisions based on the perceived environmental information, is a key research focus in the industry. Summary of the Invention

[0003] This application provides a vehicle control method, apparatus, device, storage medium, and product. The technical solution is as follows:

[0004] On the one hand, a vehicle control method is provided, the method comprising:

[0005] Determine the current weather information for the first vehicle, the weather information being used to indicate the current weather conditions;

[0006] The first vehicle collects data on the current road environment by using multiple target sensors installed on the first vehicle, and obtains multiple first environmental information. Different target sensors are used to collect environmental information in different dimensions.

[0007] The plurality of first environmental information is preprocessed to obtain a plurality of second environmental information, wherein the preprocessing includes at least one of filtering, noise reduction and data synchronization.

[0008] Based on the weather information, key information is extracted from the multiple second environmental information using an information extraction model to obtain multiple third environmental information. For any second environmental information, the third environmental information obtained by extracting key information from the second environmental information is valid under the weather conditions indicated by the weather information.

[0009] Based on the characteristic information of the multiple target sensors, the multiple third environmental information are fused by a fusion algorithm to obtain the first target environment information of the current road environment of the first vehicle;

[0010] Based on the first target environment information, the key features of the road environment are determined through an environmental perception model;

[0011] Based on the key features of the road environment, target control decision information for the first vehicle is determined, and the target control decision information is used to control the first vehicle.

[0012] In one possible implementation, the step of fusing the multiple third environmental information sources based on the characteristic information of the multiple target sensors using a fusion algorithm to obtain the first target environment information of the current road environment of the first vehicle includes:

[0013] Based on the characteristic information of the multiple target sensors, a portion of the third environmental information is deleted from the multiple third environmental information to obtain multiple second target environmental information. The deleted third environmental information includes erroneous environmental information and / or redundant environmental information. Based on the weights of the target sensors to which the second target environmental information belongs, a weighted average is performed on the multiple second target environmental information to obtain the first target environmental information of the current road environment of the first vehicle; or...

[0014] The multiple third environmental information items are input into an information fusion model, which outputs the first target environmental information of the current road environment of the first vehicle. The information fusion model is used to fuse the multiple third environmental information items based on the characteristic information of the multiple target sensors; or...

[0015] Based on the characteristic information of the multiple target sensors, a state-space model of the multiple target sensors is established. The state-space model includes dynamic equations and observation equations. The dynamic equations are used to predict the environmental information at the next moment, and the observation equations are used to determine the sensor gain information. The multiple third environmental information is used as prior information. Based on the prior information, the fourth environmental information at the next moment is predicted through the dynamic equations. Based on the fourth environmental information and the multiple third environmental information, the prediction error of the dynamic equation is determined. If the prediction error is less than a preset error, the fourth environmental information is determined as the first target environmental information. If the prediction error is not less than the preset error, the gain information of the multiple target sensors is determined based on the prediction error and the observation equations. The fourth environmental information is corrected based on the gain information to obtain fifth environmental information. The fifth environmental information is used as the prior information. Then, the step of predicting the multiple fourth environmental information at the next moment through the dynamic equations based on the prior information is executed until the first target environmental information is determined.

[0016] In another possible implementation, the process of filtering the first environmental information to obtain the second environmental information includes:

[0017] If the first environmental information is a first environmental image and the second environmental information is a second environmental image, then the first environmental image is dehazed to obtain the second environmental image, and the contrast of the second environmental image is higher than that of the first environmental image; or...

[0018] If the first environmental information is first point cloud data and the second environmental information is second point cloud data, then by performing point cloud hierarchical filtering on the first point cloud data to remove raindrop noise in the first point cloud data, the second point cloud data is obtained.

[0019] In another possible implementation, determining the target control decision information for the first vehicle based on the key features of the road environment includes:

[0020] Based on the key features of the road environment, target detection is performed to obtain the target objects included in the current road environment of the first vehicle.

[0021] The target object is tracked to obtain its operational information;

[0022] The target object is classified to obtain its category information and attribute information.

[0023] Based on the target object's operational information, category information, and attribute information, the target control decision information for the first vehicle is determined.

[0024] In another possible implementation, the method further includes:

[0025] Based on the historical driving records of multiple second vehicles, optimization information is determined. The optimization information includes at least one of the following: parameter optimization information of multiple target sensors, first model optimization information of the information extraction model, algorithm optimization information of the fusion algorithm, and second model optimization information of the environmental perception model.

[0026] Based on the parameter optimization information, the parameters of the plurality of target sensors are optimized; and / or,

[0027] Based on the optimization information from the first model, the information extraction model is optimized; and / or,

[0028] Based on the algorithm optimization information, the fusion algorithm is optimized; and / or,

[0029] Based on the optimization information from the second model, the environmental perception model is optimized.

[0030] In another possible implementation, the method further includes:

[0031] Based on the weather information, multiple target sensors that match the weather conditions indicated by the weather information are determined from multiple sensors installed on the first vehicle. The multiple target sensors include at least two types of sensors selected from meteorological sensors, optical sensors, and microwave sensors.

[0032] On the other hand, a vehicle control device is provided, the device comprising:

[0033] The first determining module is used to determine the current weather information of the first vehicle, and the weather information is used to indicate the current weather conditions.

[0034] The data acquisition module is used to collect data on the current road environment of the first vehicle through multiple target sensors installed on the first vehicle, and obtain multiple first environmental information. Different target sensors are used to collect environmental information in different dimensions.

[0035] The preprocessing module is used to preprocess the plurality of first environmental information to obtain a plurality of second environmental information. The preprocessing includes at least one of filtering, noise reduction and data synchronization.

[0036] The extraction module is used to extract key information from the multiple second environmental information based on the weather information using an information extraction model to obtain multiple third environmental information. For any second environmental information, the third environmental information obtained by extracting key information from the second environmental information is valid under the weather conditions indicated by the weather information.

[0037] The fusion module is used to fuse the multiple third environmental information based on the characteristic information of the multiple target sensors through a fusion algorithm to obtain the first target environment information of the current road environment of the first vehicle;

[0038] The second determining module is used to determine the key features of the road environment based on the first target environment information and through an environment perception model.

[0039] The third determining module is used to determine the target control decision information of the first vehicle based on the key features of the road environment, and the target control decision information is used to control the first vehicle.

[0040] In one possible implementation, the fusion module is configured to, based on the characteristic information of the plurality of target sensors, delete a portion of the third environmental information from the plurality of third environmental information to obtain a plurality of second target environmental information, wherein the deleted third environmental information includes erroneous environmental information and / or redundant environmental information; and, based on the weights of the target sensors to which the second target environmental information belongs, perform a weighted average of the plurality of second target environmental information to obtain the first target environmental information of the current road environment of the first vehicle; or...

[0041] The fusion module is used to input the multiple third environmental information into an information fusion model and output the first target environmental information of the current road environment of the first vehicle. The information fusion model is used to fuse the multiple third environmental information based on the characteristic information of the multiple target sensors; or...

[0042] The fusion module is used to establish a state-space model of the multiple target sensors based on their characteristic information. The state-space model includes dynamic equations and observation equations. The dynamic equations are used to predict environmental information at the next moment, and the observation equations are used to determine the sensor gain information. Multiple third environmental information pieces are used as prior information. Based on the prior information, a fourth environmental information at the next moment is predicted using the dynamic equations. Based on the fourth environmental information and the multiple third environmental information pieces, the prediction error of the dynamic equations is determined. If the prediction error is less than a preset error, the fourth environmental information is determined as the first target environmental information. If the prediction error is not less than the preset error, the gain information of the multiple target sensors is determined based on the prediction error and the observation equations. The fourth environmental information is corrected based on the gain information to obtain a fifth environmental information. This fifth environmental information is used as the prior information. Then, the step of predicting multiple fourth environmental information pieces at the next moment using the dynamic equations based on the prior information is executed until the first target environmental information is determined.

[0043] In another possible implementation, the preprocessing module is used to perform dehazing on the first environmental image when the first environmental information is a first environmental image and the second environmental information is a second environmental image, to obtain the second environmental image, wherein the contrast of the second environmental image is higher than the contrast of the first environmental image; or...

[0044] The preprocessing module is used to process the first point cloud data (where the first environmental information is first point cloud data) and the second environmental information is second point cloud data (where the second environmental information is second point cloud data), by performing point cloud hierarchical filtering on the first point cloud data to remove raindrop noise and obtain the second point cloud data.

[0045] In another possible implementation, the third determining module is used to perform target detection based on key features of the road environment to obtain target objects included in the current road environment of the first vehicle; track the target objects to obtain the operation information of the target objects; classify the target objects to obtain the category information and attribute information of the target objects; and determine the target control decision information of the first vehicle based on the operation information, category information and attribute information of the target objects.

[0046] In another possible implementation, the device further includes:

[0047] The fourth determining module is used to determine optimization information based on the historical driving records of multiple second vehicles. The optimization information includes at least one of the following: parameter optimization information of multiple target sensors, first model optimization information of the information extraction model, algorithm optimization information of the fusion algorithm, and second model optimization information of the environmental perception model.

[0048] An optimization module is used to optimize the parameters of the plurality of target sensors based on the parameter optimization information; and / or to optimize the information extraction model based on the first model optimization information; and / or to optimize the fusion algorithm based on the algorithm optimization information; and / or to optimize the environmental perception model based on the second model optimization information.

[0049] In another possible implementation, the device further includes:

[0050] The fifth determining module is used to determine, based on the weather information, multiple target sensors from multiple sensors installed on the first vehicle that match the weather conditions indicated by the weather information, wherein the multiple target sensors include at least two types of sensors selected from meteorological sensors, optical sensors, and microwave sensors.

[0051] On the other hand, a vehicle controller is provided, which includes a main control module, a processor and a memory, wherein the memory stores at least one piece of program code, which is loaded and executed by the processor to implement the above-mentioned vehicle control method.

[0052] On the other hand, a computer-readable storage medium is provided, wherein at least one piece of program code is stored in the storage medium, the at least one piece of program code being loaded and executed by a processor to implement the above-described vehicle control method.

[0053] On the other hand, a computer program product is provided, the product storing at least one piece of program code, the at least one piece of program code being executed by a processor to implement the above-described vehicle control method.

[0054] In this embodiment of the application, since rain and fog often reduce visibility, the traditional single sensor is greatly limited in acquiring environmental information. Therefore, multiple target sensors are used to perceive the environment, thereby utilizing the complementarity of multiple target sensors to improve the accuracy and reliability of environmental perception. This enables early warning of potential dangers to the first vehicle, helping the first vehicle to make safe avoidance and emergency handling, thereby significantly improving driving safety.

[0055] It should be understood that the above general description and the following detailed description are merely exemplary and do not limit this disclosure. Attached Figure Description

[0056] Figure 1 This is a schematic diagram illustrating the implementation environment of a vehicle control method according to an exemplary embodiment of this application;

[0057] Figure 2 This is a flowchart illustrating a vehicle control method in an exemplary embodiment of this application;

[0058] Figure 3 This is a block diagram illustrating a vehicle control device in an exemplary embodiment of this application;

[0059] Figure 4 This is a block diagram illustrating a vehicle controller in an exemplary embodiment of this application. Detailed Implementation

[0060] To make the technical solution and advantages of this application clearer, the embodiments of this application will be described in further detail below.

[0061] The terms "first," "second," "third," and "fourth," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0062] It should be noted that all information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, stored data, displayed data, etc.), and signals involved in this application have been authorized by the user or fully authorized by all parties, and the collection, use, and processing of related data must comply with the relevant laws, regulations, and standards of the relevant countries and regions. For example, the reference longitudinal control curves involved in this application were obtained with full authorization.

[0063] Please refer to Figure 1 This illustration shows a schematic diagram of an implementation environment for a vehicle control method according to an exemplary embodiment of this application. The implementation environment includes a first vehicle, in which a vehicle controller 101 is configured, and a first terminal is equipped with multiple target sensors 102. The vehicle controller 101 communicates with the multiple target sensors 102 via a CAN (Controller Area Network) bus. In this embodiment, the multiple target sensors 102 are used to sense the environment and transmit multiple first environmental information obtained from the sensed information to the vehicle controller 101 via the CAN bus. The vehicle controller 101 controls the first vehicle based on the multiple first environmental information.

[0064] In some embodiments, the first vehicle is a new energy vehicle, such as a pure electric vehicle, a plug-in hybrid electric vehicle, or a fuel cell electric vehicle. The plurality of target sensors 102 include at least two types of sensors selected from meteorological sensors, optical sensors, and microwave sensors. The meteorological sensors include at least one of temperature and humidity sensors, barometric pressure sensors, and wind direction and speed sensors. The optical sensors include at least one of visible light cameras, infrared cameras, and lidar. The microwave sensors include at least one of millimeter-wave radar and microwave hygrometers.

[0065] Please refer to Figure 2 The diagram illustrates a flowchart of a vehicle control method according to an exemplary embodiment of this application. (Reference) Figure 2 The method includes:

[0066] Step 201: The vehicle controller determines the current weather information of the first vehicle. The weather information is used to indicate the current weather conditions.

[0067] Weather conditions can include rain, fog, snow, sunshine, and wind. In one possible implementation, a weather sensor is installed in the first vehicle, and the vehicle controller determines the current weather information of the first vehicle through the weather sensor. In another possible implementation, a weather application is installed in the onboard terminal of the first vehicle, and the vehicle controller determines the current weather information of the first vehicle through the weather application.

[0068] In one possible implementation, regardless of the weather conditions, the vehicle controller uses multiple sensors installed on the first vehicle to collect data; correspondingly, the vehicle controller identifies multiple sensors as multiple target sensors, thereby using a sufficient number of sensors to collect data and improving the comprehensiveness of data collection.

[0069] In another possible implementation, different weather information corresponds to different sensors; accordingly, after the vehicle controller determines the weather information, it identifies multiple target sensors from among the multiple sensors installed on the first vehicle that match the weather conditions indicated by the weather information. These multiple target sensors include at least two types of sensors selected from meteorological sensors, optical sensors, and microwave sensors.

[0070] The meteorological sensor includes at least one of a temperature and humidity sensor, a barometric pressure sensor, and a wind direction and speed sensor. The temperature and humidity sensor is used to collect the current ambient temperature and humidity of the first vehicle; the barometric pressure sensor is used to collect the current ambient barometric pressure of the first vehicle; and the wind direction and speed sensor is used to collect the current ambient wind direction and speed of the first vehicle.

[0071] Optical sensors include at least one of visible light cameras (high-definition cameras), infrared cameras, and lidar. Visible light cameras capture images within the visible light range, infrared cameras capture images within the infrared light range, and lidar determines point cloud data by emitting laser pulses. Point cloud data includes spatial coordinate information, reflection intensity information, and other auxiliary information. Spatial coordinate information includes the X-axis, Y-axis, and Z-axis coordinates of each point, representing its position in three-dimensional space. Analyzing these coordinates allows for the acquisition of the three-dimensional shape of objects such as pedestrians, vehicles, and obstacles. Reflection intensity information refers to the intensity of light reflected back from the object by the laser pulse emitted by the lidar. Other auxiliary information includes the number of echoes, category, RGB color information, GPS time, scanning angle, and scanning direction.

[0072] Microwave sensors include at least one of millimeter-wave radar and microwave hygrometers. Millimeter-wave radar uses electromagnetic waves in the millimeter-wave band for detection. Microwave hygrometers are used to detect global atmospheric humidity profiles and heavy rainfall.

[0073] For example, lidar is used to detect obstacles and terrain, providing relatively accurate data even in rainy or foggy weather. Millimeter-wave radar has strong penetrating power through rain and fog, providing stable detection results in adverse weather conditions. High-definition cameras provide rich visual information, helping the system identify road signs, vehicles, and pedestrians.

[0074] In this embodiment of the application, by selecting sensors based on weather information, multiple target sensors adapted to the current weather can be selected for environmental perception, thereby improving the accuracy of environmental perception based on multiple identified targets.

[0075] Step 202: The vehicle controller collects data on the current road environment of the first vehicle through multiple target sensors installed on the first vehicle, and obtains multiple first environmental information. Different target sensors are used to collect environmental information in different dimensions.

[0076] In this embodiment, multiple target sensors can operate effectively in adverse weather conditions such as rain and fog, and can capture key environmental information. Furthermore, multiple target sensors can be selected according to specific application scenarios and requirements.

[0077] Furthermore, rainy and foggy weather often reduces visibility, significantly limiting the ability of traditional single sensors, such as cameras or lidar, to acquire environmental information. Cameras may become blurred by water droplets or fog, while lidar may be affected by scattering from raindrops or fog, leading to a decrease in detection range and accuracy. Therefore, it is necessary to utilize multiple sensors to compensate for these shortcomings and improve the accuracy and reliability of environmental perception.

[0078] Step 203: The vehicle controller preprocesses multiple first environmental information to obtain multiple second environmental information. The preprocessing includes at least one of filtering, noise reduction, and data synchronization.

[0079] In one possible implementation, preprocessing includes filtering; this step could be: the vehicle controller filters out interference signals from multiple first environmental information items to obtain multiple second environmental information items. For example, the first environmental information is a first environmental image, and the second environmental information is a second environmental image. The vehicle controller performs dehazing on the first environmental image to obtain the second environmental image. The contrast of the second environmental image is higher than that of the first environmental image, thus dehazing the first environmental image improves its contrast and eliminates the visual impact of fog. The vehicle controller can use an image dehazing algorithm to dehaze the first environmental image to obtain the second environmental image; the image dehazing algorithm can be a dark channel prior dehazing algorithm or a deep learning-based dehazing algorithm.

[0080] For example, if the first environmental information is the first point cloud data and the second environmental information is the second point cloud data, then by performing point cloud hierarchical filtering on the first point cloud data to remove raindrop noise, the second point cloud data can be obtained, thereby removing raindrop noise and ensuring the accuracy of the point cloud data.

[0081] In another possible implementation, preprocessing includes noise reduction; in this case, the vehicle controller can remove noise signals from multiple first environmental information items to obtain multiple second environmental information items. In yet another possible implementation, preprocessing includes data synchronization; in this case, the vehicle controller can synchronize and spatially align multiple first environmental information items to obtain multiple second environmental information items, thereby ensuring the consistency of the multiple first environmental information items.

[0082] In this embodiment, the vehicle controller preprocesses multiple first environmental information to eliminate noise and interference in the multiple first environmental information, thereby improving data quality and ensuring the accuracy and consistency of the multiple first environmental information.

[0083] Step 204: Based on weather information, the vehicle controller extracts key information from multiple second environmental information through an information extraction model to obtain multiple third environmental information. For any second environmental information, the third environmental information obtained by extracting key information from the second environmental information is valid under the weather conditions indicated by the weather information.

[0084] The information extraction model can learn how to extract useful information from multiple secondary environmental information in rainy and foggy weather, and then perform subsequent fusion based on the extracted multiple third environmental information.

[0085] Step 205: Based on the characteristic information of multiple target sensors, the vehicle controller fuses multiple third-environment information through a fusion algorithm to obtain the first target environment information of the current road environment of the first vehicle.

[0086] The first method: The vehicle controller, based on the characteristic information of multiple target sensors, deletes some third environmental information from multiple third environmental information to obtain multiple second target environmental information. The deleted third environmental information includes erroneous environmental information and / or redundant environmental information. Based on the weight of the target sensor to which the second target environmental information belongs, the multiple second target environmental information are weighted and averaged to obtain the first target environmental information of the current road environment of the first vehicle.

[0087] The characteristic information of the target sensor is used to indicate the characteristics of the target sensor, specifically its advantages. Correspondingly, the vehicle controller, based on the characteristic information of multiple target sensors, deletes some third environmental information from multiple third environmental information to obtain multiple second target environmental information. This can be achieved as follows: For multiple target sensors, if the same type of environmental information exists among them, for ease of description, two sensors that collect the same type of environmental information are referred to as the first sensor and the second sensor. Based on the characteristics of the first and second sensors, the vehicle controller determines the sensor with higher accuracy under the weather conditions indicated by the weather information. It retains the third environmental information collected by the sensor with higher accuracy and deletes the third environmental information collected by the sensor with lower accuracy. Alternatively, for each target sensor, the vehicle controller verifies the currently collected third environmental information based on historical environmental information collected by that target sensor before the current time to determine if the third environmental information is correct. If the third environmental information is correct, it is retained; if the third environmental information is incorrect, it is deleted.

[0088] The second implementation method is as follows: The vehicle controller inputs multiple third-environment information into the information fusion model and outputs the first target environment information of the current road environment of the first vehicle. The information fusion model is used to fuse multiple third-environment information based on the characteristic information of multiple target sensors.

[0089] In this embodiment, the vehicle controller uses an information fusion model to fuse environmental information, thereby improving the accuracy of information fusion.

[0090] The third implementation method: The vehicle controller establishes a state-space model of multiple target sensors based on the characteristic information of multiple target sensors. The state-space model includes dynamic equations and observation equations. The dynamic equations are used to predict the environmental information at the next moment, and the observation equations are used to determine the sensor gain information. Multiple third environmental information is used as prior information. Based on the prior information, the fourth environmental information at the next moment is predicted through the dynamic equations. Based on the fourth environmental information and multiple third environmental information, the prediction error of the dynamic equation is determined. If the prediction error is less than a preset error, the fourth environmental information is determined as the first target environmental information. If the prediction error is not less than the preset error, the gain information of multiple target sensors is determined based on the prediction error and the observation equations. The fourth environmental information is corrected based on the gain information to obtain the fifth environmental information. The fifth environmental information is used as prior information. Then, the step of predicting multiple fourth environmental information at the next moment through the dynamic equations based on the prior information is executed until the first target environmental information is determined.

[0091] It should be noted that the vehicle controller can use any of the three methods mentioned above for information fusion. Alternatively, the vehicle controller can use all three methods simultaneously for information fusion, and then fuse the three first target environment information values ​​again to obtain a single first target environment information value. Alternatively, the vehicle controller can select a fusion method from the three methods that best suits the weather conditions indicated by the weather information, and then perform information fusion based on that fusion method.

[0092] In this embodiment, the vehicle controller fully considers the complementarity and redundancy of various target sensors. For example, lidar and millimeter-wave radar have advantages in obstacle detection, while cameras can provide rich visual information; therefore, by fusing the first environmental information from multiple target sensors, a comprehensive perception of the environment can be achieved, resulting in a more comprehensive and accurate environmental perception result.

[0093] Step 206: Based on the first target environment information, the vehicle controller determines the key features of the road environment through the environment perception model.

[0094] Key features include object characteristics such as the shape and size of the target object; when the target object is a moving object such as a pedestrian or vehicle, key features also include object characteristics such as the target object's speed and direction of movement. Additionally, since multiple target sensors include weather sensors, key features also include environmental characteristics such as temperature, humidity, and visibility.

[0095] Step 207: The vehicle controller determines the target control decision information of the first vehicle based on the key features of the road environment. The target control decision information is used to control the first vehicle.

[0096] This step can be achieved through the following steps 2071-2074, including:

[0097] Step 2071: The vehicle controller performs target detection based on the key features of the road environment to obtain the target objects included in the current road environment of the first vehicle.

[0098] The vehicle controller uses a target detection algorithm based on key features of the road environment to detect target objects, such as vehicles, pedestrians, or obstacles. The target detection algorithm can be YOLO (You Only Look Once) or SSD (Single Shot MultiBox Detector). In this embodiment, an advanced target detection algorithm can accurately identify vehicles, pedestrians, and other targets in the scene.

[0099] Step 2072: The vehicle controller tracks the target object and obtains the target object's operating information.

[0100] Operational information includes motion trajectory and speed. The vehicle controller continuously tracks the target object using a tracking algorithm to acquire its motion information.

[0101] Step 2073: The vehicle controller classifies the target object to obtain the category information and attribute information of the target object.

[0102] For example, if the target object is a vehicle, then the category information of the target object is "vehicle," and the attribute information includes information such as the vehicle's color and size. Similarly, if the target object is a pedestrian, then the category of the target object is "person," and the attribute information includes information such as gender and age range.

[0103] In this step, the vehicle controller uses a classification algorithm to classify the target object and obtain its category and attribute information. This classification algorithm can be a classification model trained based on a convolutional neural network.

[0104] Step 2074: The vehicle controller determines the target control decision information for the first vehicle based on the target object's operating information, category information, and attribute information.

[0105] The vehicle controller, based on the motion, category, and attribute information of the target object, provides a decision information acquisition model to determine the target control decision information for the first vehicle. This target control decision information is used to control the first vehicle, enabling it to make corresponding decisions, such as adjusting speed, changing lanes, or avoiding obstacles. In one possible implementation, if the first vehicle is an autonomous or intelligent driving vehicle, the vehicle controller outputs the target control decision information to the actuators of the first vehicle. The actuators then execute the corresponding decisions based on this information. The actuators can be, for example, a braking system or a steering system.

[0106] In another possible implementation, if the first vehicle is a non-autonomous or non-intelligent driving vehicle, the vehicle controller outputs target control decision information, and the driver of the first vehicle executes the corresponding decision based on this information. For example, if the vehicle controller prompts the driver to slow down, the driver will then perform the deceleration operation.

[0107] In this embodiment, under rainy or foggy weather conditions, the vehicle controller can adjust the vehicle's driving status and control strategy in real time to adapt to different road conditions and traffic environments, ensuring stable vehicle operation. Furthermore, the method provided in this embodiment is not only applicable to autonomous vehicles but can also be applied to other vehicles or systems that require environmental perception in rainy or foggy weather, demonstrating broad application prospects.

[0108] In this embodiment, to continuously improve the system's perception performance and robustness, the vehicle controller needs to periodically optimize and evaluate the system. Optimization includes target sensor parameters, optimizing the fusion algorithm, updating the information extraction model, updating the environmental perception model, and updating the target detection algorithm. Simultaneously, the system's performance is evaluated through actual testing and simulation experiments to ensure its stable and reliable operation under various rainy and foggy weather conditions.

[0109] The process of periodically optimizing the system by the vehicle controller can be achieved through the following steps (1) to (5):

[0110] (1) The vehicle controller determines optimization information based on the historical driving records of multiple second vehicles. The optimization information includes at least one of the following: parameter optimization information of multiple target sensors, first model optimization information of information extraction model, algorithm optimization information of fusion algorithm, and second model optimization information of environmental perception model.

[0111] In one possible implementation, the historical driving record includes multiple first environmental information items and control responses. The control responses are used to indicate the response to the target control decision information; for example, the control response indicates that the decision corresponding to the target control decision information is accurate; or, conversely, the control response indicates that the decision corresponding to the target control decision information is incorrect. Accordingly, the step of the vehicle controller determining the parameter optimization information of multiple target sensors based on the historical driving records of multiple second vehicles can be as follows: the vehicle controller determines the parameter optimization information of multiple target sensors through a first optimization model based on the multiple first environmental information items and control responses. The parameter optimization information of the target sensors can be information such as the attitude of the target sensors.

[0112] In another possible implementation, the historical driving records include multiple second environmental information and control responses. Accordingly, the step of the vehicle controller determining the first model optimization information of the information extraction model based on the historical driving records of multiple second vehicles can be: the vehicle controller updates the information extraction model based on the multiple second environmental information and control responses, and determines the updated model parameters as the first model optimization information.

[0113] In another possible implementation, historical driving records include multiple third-party environmental information and control responses. Accordingly, the step of the vehicle controller determining the first model optimization information of the information extraction model based on the historical driving records of multiple second vehicles can be: the vehicle controller determines algorithm optimization information through a second optimization model based on the multiple third-party environmental information and control responses. The algorithm optimization information is used to optimize the fusion algorithm; for example, the algorithm optimization information can be the updated weights of multiple target sensors; or, for example, the algorithm optimization information can be the updated fusion algorithm, etc.

[0114] In another possible implementation, historical driving records include key features of the road environment and control responses. Accordingly, the step of the vehicle controller determining the second model optimization information of the environmental perception model based on the historical driving records of multiple second vehicles can be as follows: the vehicle controller updates the environmental perception model based on the key information of the road environment and the control responses, and determines the updated model parameters as the second model optimization information.

[0115] (2) The vehicle controller optimizes the parameters of multiple target sensors based on parameter optimization information.

[0116] (3) The vehicle controller optimizes the information extraction model based on the first model optimization information.

[0117] The vehicle controller modifies the model parameters of the information extraction model to the model parameters included in the first model optimization information, thereby optimizing the information extraction model.

[0118] (4) The vehicle controller optimizes the fusion algorithm based on the algorithm optimization information.

[0119] For example, if the algorithm optimization information is the updated weights of multiple target sensors, then the vehicle controller will modify the weights of the multiple target sensors in the fusion algorithm to the updated weights. As another example, if the algorithm optimization information is the updated fusion algorithm, then the vehicle controller will update the fusion algorithm to the updated fusion algorithm.

[0120] (5) The vehicle controller optimizes the environmental perception model based on the second model optimization information.

[0121] The vehicle controller modifies the model parameters of the environmental perception model to the model parameters included in the second model optimization information, thereby optimizing the environmental perception model.

[0122] It should be noted that after the vehicle controller completes step (1), it executes at least one of steps (2)-(5).

[0123] In this embodiment of the application, since rain and fog often reduce visibility, the traditional single sensor is greatly limited in acquiring environmental information. Therefore, multiple target sensors are used to perceive the environment, thereby utilizing the complementarity of multiple target sensors to improve the accuracy and reliability of environmental perception. This enables early warning of potential dangers to the first vehicle, helping the first vehicle to make safe avoidance and emergency handling, thereby significantly improving driving safety.

[0124] Please refer to Figure 3 This illustration shows a block diagram of a vehicle control device according to an exemplary embodiment of this application. The device includes:

[0125] The first determining module 301 is used to determine the current weather information of the first vehicle, and the weather information is used to indicate the current weather conditions.

[0126] The acquisition module 302 is used to acquire data on the current road environment of the first vehicle through multiple target sensors installed on the first vehicle, and obtain multiple first environmental information. Different target sensors are used to acquire environmental information in different dimensions.

[0127] Preprocessing module 303 is used to preprocess the plurality of first environmental information to obtain a plurality of second environmental information. The preprocessing includes at least one of filtering, noise reduction and data synchronization.

[0128] Extraction module 304 is used to extract key information from the plurality of second environmental information based on the weather information through an information extraction model to obtain a plurality of third environmental information. For any second environmental information, the third environmental information obtained by extracting key information from the second environmental information is valid under the weather conditions indicated by the weather information.

[0129] The fusion module 305 is used to fuse the multiple third environmental information based on the characteristic information of the multiple target sensors through a fusion algorithm to obtain the first target environment information of the current road environment of the first vehicle;

[0130] The second determining module 306 is used to determine the key features of the road environment based on the first target environment information and through an environment perception model.

[0131] The third determining module 307 is used to determine the target control decision information of the first vehicle based on the key features of the road environment, and the target control decision information is used to control the first vehicle.

[0132] In one possible implementation, the fusion module 305 is configured to, based on the characteristic information of the plurality of target sensors, delete a portion of the third environmental information from the plurality of third environmental information to obtain a plurality of second target environmental information, wherein the deleted third environmental information includes erroneous environmental information and / or redundant environmental information; and, based on the weights of the target sensors to which the second target environmental information belongs, perform a weighted average of the plurality of second target environmental information to obtain the first target environmental information of the current road environment of the first vehicle; or...

[0133] The fusion module 305 is used to input the plurality of third environmental information into an information fusion model and output the first target environmental information of the current road environment of the first vehicle. The information fusion model is used to fuse the plurality of third environmental information based on the characteristic information of the plurality of target sensors; or...

[0134] The fusion module 305 is used to establish a state-space model of the multiple target sensors based on the characteristic information of the multiple target sensors. The state-space model includes dynamic equations and observation equations. The dynamic equations are used to predict the environmental information at the next moment, and the observation equations are used to determine the sensor gain information. The multiple third environmental information is used as prior information. Based on the prior information, the fourth environmental information at the next moment is predicted through the dynamic equations. Based on the fourth environmental information and the multiple third environmental information, the prediction error of the dynamic equation is determined. If the prediction error is less than a preset error, the fourth environmental information is determined as the first target environmental information. If the prediction error is not less than the preset error, the gain information of the multiple target sensors is determined based on the prediction error and the observation equations. The fourth environmental information is corrected based on the gain information to obtain fifth environmental information. The fifth environmental information is used as the prior information. Then, the step of predicting the multiple fourth environmental information at the next moment through the dynamic equations based on the prior information is executed until the first target environmental information is determined.

[0135] In another possible implementation, the preprocessing module 303 is configured to perform dehazing on the first environmental image if the first environmental information is a first environmental image and the second environmental information is a second environmental image, to obtain the second environmental image, wherein the contrast of the second environmental image is higher than that of the first environmental image; or...

[0136] The preprocessing module 303 is used to obtain the second point cloud data by performing point cloud hierarchical filtering on the first point cloud data when the first environmental information is first point cloud data and the second environmental information is second point cloud data.

[0137] In another possible implementation, the third determining module 307 is used to perform target detection based on the key features of the road environment to obtain the target objects included in the current road environment of the first vehicle; track the target objects to obtain the operation information of the target objects; classify the target objects to obtain the category information and attribute information of the target objects; and determine the target control decision information of the first vehicle based on the operation information, category information and attribute information of the target objects.

[0138] In another possible implementation, the device further includes:

[0139] The fourth determining module is used to determine optimization information based on the historical driving records of multiple second vehicles. The optimization information includes at least one of the following: parameter optimization information of multiple target sensors, first model optimization information of the information extraction model, algorithm optimization information of the fusion algorithm, and second model optimization information of the environmental perception model.

[0140] An optimization module is used to optimize the parameters of the plurality of target sensors based on the parameter optimization information; and / or to optimize the information extraction model based on the first model optimization information; and / or to optimize the fusion algorithm based on the algorithm optimization information; and / or to optimize the environmental perception model based on the second model optimization information.

[0141] In another possible implementation, the device further includes:

[0142] The fifth determining module is used to determine, based on the weather information, multiple target sensors from multiple sensors installed on the first vehicle that match the weather conditions indicated by the weather information, wherein the multiple target sensors include at least two types of sensors selected from meteorological sensors, optical sensors, and microwave sensors.

[0143] In this embodiment of the application, since rain and fog often reduce visibility, the traditional single sensor is greatly limited in acquiring environmental information. Therefore, multiple target sensors are used to perceive the environment, thereby utilizing the complementarity of multiple target sensors to improve the accuracy and reliability of environmental perception. This enables early warning of potential dangers to the first vehicle, helping the first vehicle to make safe avoidance and emergency handling, thereby significantly improving driving safety.

[0144] It should be noted that the vehicle control device provided in the above embodiments is only illustrated by the division of the above functional modules when performing vehicle control. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the vehicle controller can be divided into different functional modules to complete all or part of the functions described above. In addition, the vehicle control device and the vehicle control method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0145] Figure 4 This is a structural block diagram of a vehicle controller provided in an embodiment of this application. Typically, the vehicle controller 400 includes: a main control module 401, a CAN interface 402, a hard-wired input interface 403, and a hard-wired output interface 404. The main control module 401 is connected to the CAN interface 402, the hard-wired input interface 403, and the hard-wired output interface 404, respectively.

[0146] The main control module 401 typically includes a processor and memory. The processor may include one or more processing cores, such as a quad-core processor. The processor can be implemented using at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), or PLA (Programmable Logic Array). The processor may also include a main processor and a coprocessor. The main processor, also known as a CPU (Central Processing Unit), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state. In some embodiments, the processor may integrate a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content required to be displayed on the vehicle's screen. In some embodiments, the processor may also include an AI (Artificial Intelligence) processor, which is used to handle computational operations related to machine learning. The memory may include one or more computer-readable storage media, which may be non-transitory. The memory may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory devices. In some embodiments, a non-transitory computer-readable storage medium in the memory is used to store at least one computer program, which is executed by a processor to implement the vehicle control method provided in the method embodiments of this application.

[0147] The CAN interface 402 may include a powertrain CAN interface, a motor CAN interface, and a diagnostic CAN interface. The powertrain CAN interface is used to communicate with the vehicle's powertrain module, the motor CAN interface is used to communicate with the vehicle's motor controller, and the diagnostic CAN interface is used to communicate with diagnostic equipment.

[0148] The hard-wired input interface 403 is used to receive hard-wired control signals. The hard-wired output interface 404 is used to send control commands to the vehicle's electronic control components, causing the vehicle's electronic control components to perform corresponding actions. The vehicle's electronic control components include a power management system, a motor controller, an on-board charger, and a body control system.

[0149] The main control module 401 can communicate with the vehicle's powertrain module, motor controller and diagnostic equipment through the CAN interface 402, and generate control commands based on the hard-wired control signals received by the hard-wired input interface 403, so as to send the control commands to the vehicle's electronic control components through the hard-wired output interface 404.

[0150] Those skilled in the art will understand that Figure 4 The structure shown does not constitute a limitation on the vehicle controller 400, and may include more or fewer components than shown, or combine certain components, or use different component arrangements.

[0151] This application also provides a computer-readable storage medium storing at least one piece of program code, which is loaded and executed by a processor to implement the vehicle control method described in any of the above implementations. Optionally, the storage medium may be a non-transitory computer-readable storage medium, such as ROM (Read-Only Memory), RAM (Random Access Memory), CD-ROM (Compact Disc Read-Only Memory), magnetic tape, floppy disk, and optical data storage device.

[0152] This application also provides a computer program product that stores at least one piece of program code, which is loaded and executed by a processor to implement the vehicle control method shown in the above embodiments.

[0153] In some embodiments, the computer program product involved in the present application can be deployed and executed on a vehicle controller, or on multiple vehicle controllers located in one location, or on multiple vehicle controllers distributed in multiple locations and interconnected through a communication network. Multiple vehicle controllers distributed in multiple locations and interconnected through a communication network can form a blockchain system.

[0154] Those skilled in the art will understand that all or part of the steps of the above embodiments can be implemented by hardware or by a program instructing related hardware. The program can be stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk.

[0155] The above description is only for the purpose of enabling those skilled in the art to understand the technical solution of this application, and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A vehicle control method, characterized in that, The method includes: Determine the current weather information for the first vehicle, the weather information being used to indicate the current weather conditions; The first vehicle collects data on the current road environment by using multiple target sensors installed on the first vehicle, and obtains multiple first environmental information. Different target sensors are used to collect environmental information in different dimensions. The plurality of first environmental information is preprocessed to obtain a plurality of second environmental information, wherein the preprocessing includes at least one of filtering, noise reduction and data synchronization. Based on the weather information, key information is extracted from the multiple second environmental information using an information extraction model to obtain multiple third environmental information. For any second environmental information, the third environmental information obtained by extracting key information from the second environmental information is valid under the weather conditions indicated by the weather information. Based on the characteristic information of the multiple target sensors, the multiple third environmental information are fused by a fusion algorithm to obtain the first target environment information of the current road environment of the first vehicle; Based on the first target environment information, the key features of the road environment are determined through an environmental perception model; Based on the key features of the road environment, target control decision information for the first vehicle is determined, and the target control decision information is used to control the first vehicle. The method further includes: Based on the historical driving records of multiple second vehicles, optimization information is determined. The optimization information includes at least one of the following: parameter optimization information of multiple target sensors, first model optimization information of the information extraction model, algorithm optimization information of the fusion algorithm, and second model optimization information of the environmental perception model. Based on the parameter optimization information, the parameters of the plurality of target sensors are optimized; and / or, Based on the optimization information from the first model, the information extraction model is optimized; and / or, Based on the algorithm optimization information, the fusion algorithm is optimized; and / or, Based on the optimization information of the second model, the environmental perception model is optimized; The historical driving records include multiple first environmental information and control responses, wherein the control responses are used to indicate the response of the target control decision information, and the control responses are used to indicate whether the target control decision information is correct or incorrect; the step of determining the parameter optimization information of the multiple target sensors based on the historical driving records of the multiple second vehicles includes: Based on the multiple first environmental information and the control response, the parameter optimization information of the multiple target sensors is determined by the first optimization model, and the parameter optimization information of the target sensors is the attitude information of the target sensors; The historical driving records include multiple second environmental information and control responses. The step of determining the first model optimization information of the information extraction model based on the historical driving records of the multiple second vehicles includes: Based on the multiple second environmental information and the control response, the information extraction model is updated, and the updated model parameters are determined as the first model optimization information. The historical driving records include multiple third-party environmental information and control responses. The step of determining the algorithm optimization information for the fusion algorithm based on the historical driving records of the multiple second vehicles includes: Based on the multiple third environmental information and the control response, the algorithm optimization information is determined by the second optimization model. The algorithm optimization information is the updated weights of the multiple target sensors; or, the algorithm optimization information is the updated fusion algorithm. The historical driving records include key features of the road environment and control responses; the step of determining the second model optimization information of the environmental perception model based on the historical driving records of the multiple second vehicles includes: Based on the key features of the road environment and the control response, the environmental perception model is updated, and the updated model parameters are determined as the second model optimization information.

2. The method according to claim 1, characterized in that, The first target environment information, based on the characteristic information of the multiple target sensors, is obtained by fusing the multiple third environmental information through a fusion algorithm to obtain the current road environment of the first vehicle, including: Based on the characteristic information of the multiple target sensors, a portion of the third environmental information is deleted from the multiple third environmental information to obtain multiple second target environmental information. The deleted third environmental information includes erroneous environmental information and / or redundant environmental information. Based on the weights of the target sensors to which the second target environmental information belongs, a weighted average is performed on the multiple second target environmental information to obtain the first target environmental information of the current road environment of the first vehicle; or... The multiple third environmental information items are input into an information fusion model, which outputs the first target environmental information of the current road environment of the first vehicle. The information fusion model is used to fuse the multiple third environmental information items based on the characteristic information of the multiple target sensors; or... Based on the characteristic information of the multiple target sensors, a state-space model of the multiple target sensors is established. The state-space model includes dynamic equations and observation equations. The dynamic equations are used to predict the environmental information at the next moment, and the observation equations are used to determine the sensor gain information. The multiple third environmental information is used as prior information. Based on the prior information, the fourth environmental information at the next moment is predicted through the dynamic equations. Based on the fourth environmental information and the multiple third environmental information, the prediction error of the dynamic equation is determined. If the prediction error is less than a preset error, the fourth environmental information is determined as the first target environmental information. If the prediction error is not less than the preset error, the gain information of the multiple target sensors is determined based on the prediction error and the observation equations. The fourth environmental information is corrected based on the gain information to obtain fifth environmental information. The fifth environmental information is used as the prior information. Then, the step of predicting the multiple fourth environmental information at the next moment through the dynamic equations based on the prior information is executed until the first target environmental information is determined.

3. The method according to claim 1, characterized in that, The process of filtering the first environmental information to obtain the second environmental information includes: If the first environmental information is a first environmental image and the second environmental information is a second environmental image, then the first environmental image is dehazed to obtain the second environmental image, and the contrast of the second environmental image is higher than that of the first environmental image; or... If the first environmental information is first point cloud data and the second environmental information is second point cloud data, then by performing point cloud hierarchical filtering on the first point cloud data to remove raindrop noise in the first point cloud data, the second point cloud data is obtained.

4. The method according to claim 1, characterized in that, The determination of the target control decision information for the first vehicle based on the key features of the road environment includes: Based on the key features of the road environment, target detection is performed to obtain the target objects included in the current road environment of the first vehicle. The target object is tracked to obtain its operational information; The target object is classified to obtain its category information and attribute information. Based on the target object's operational information, category information, and attribute information, the target control decision information for the first vehicle is determined.

5. The method according to claim 1, characterized in that, The method further includes: Based on the weather information, multiple target sensors that match the weather conditions indicated by the weather information are determined from multiple sensors installed on the first vehicle. The multiple target sensors include at least two types of sensors selected from meteorological sensors, optical sensors, and microwave sensors.

6. A vehicle control device, characterized in that, The device includes: The first determining module is used to determine the current weather information of the first vehicle, and the weather information is used to indicate the current weather conditions. The data acquisition module is used to collect data on the current road environment of the first vehicle through multiple target sensors installed on the first vehicle, and obtain multiple first environmental information. Different target sensors are used to collect environmental information in different dimensions. The preprocessing module is used to preprocess the plurality of first environmental information to obtain a plurality of second environmental information. The preprocessing includes at least one of filtering, noise reduction and data synchronization. The extraction module is used to extract key information from the multiple second environmental information based on the weather information using an information extraction model to obtain multiple third environmental information. For any second environmental information, the third environmental information obtained by extracting key information from the second environmental information is valid under the weather conditions indicated by the weather information. The fusion module is used to fuse the multiple third environmental information based on the characteristic information of the multiple target sensors through a fusion algorithm to obtain the first target environment information of the current road environment of the first vehicle; The second determining module is used to determine the key features of the road environment based on the first target environment information and through an environment perception model. The third determining module is used to determine the target control decision information of the first vehicle based on the key features of the road environment, and the target control decision information is used to control the first vehicle. The fourth determining module is used to determine optimization information based on the historical driving records of multiple second vehicles. The optimization information includes at least one of the following: parameter optimization information of multiple target sensors, first model optimization information of the information extraction model, algorithm optimization information of the fusion algorithm, and second model optimization information of the environmental perception model. An optimization model is used to optimize the parameters of the plurality of target sensors based on the parameter optimization information; and / or to optimize the information extraction model based on the first model optimization information; and / or to optimize the fusion algorithm based on the algorithm optimization information; and / or to optimize the environmental perception model based on the second model optimization information. The historical driving record includes multiple first environmental information and control responses. The control responses are used to indicate the response of the target control decision information and to indicate whether the target control decision information is correct or incorrect. The fourth determining module is used to determine the parameter optimization information of the multiple target sensors based on the multiple first environmental information and the control responses through a first optimization model. The parameter optimization information of the target sensors is the attitude information of the target sensors. The historical driving record includes multiple second environmental information and control response. The fourth determining module is used to update the information extraction model based on the multiple second environmental information and the control response, and determine the updated model parameters as the first model optimization information. The historical driving record includes multiple third-environment information and control responses. The fourth determining module is used to determine the algorithm optimization information based on the multiple third-environment information and the control responses through a second optimization model. The algorithm optimization information is the updated weights of the multiple target sensors; or, the algorithm optimization information is the updated fusion algorithm. The historical driving record includes key features of the road environment and control response. The fourth determining module is used to update the environment perception model based on the key features of the road environment and the control response, and determine the updated model parameters as the second model optimization information.

7. A vehicle controller, characterized in that, The vehicle controller includes a main control module, which includes a processor and a memory. The memory stores at least one piece of program code, which is loaded and executed by the processor to implement the vehicle control method as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The storage medium stores at least one piece of program code, which is loaded and executed by a processor to implement the vehicle control method as described in any one of claims 1 to 5.

9. A computer program product, characterized in that, The product stores at least one piece of program code, which is executed by a processor to implement the vehicle control method as described in any one of claims 1 to 5.

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