Vehicle and road cloud communication, control, storage and calculation integrated system and control method thereof

Abstract

According to the vehicle-road cloud communication, control, storage and calculation integrated system and the control method thereof provided by the invention, the design of integrated communication and control of the Internet of Vehicles is realized by integrating the external interface, the calculation module, the efficient data aggregation gateway and the Internet module, the external interface ensures the connection between the system and various sensors, diversified data acquisition is realized, and the communication and control efficiency of the Internet of Vehicles is improved. The computing module realizes efficient processing and real-time control of data, the data aggregation gateway ensures smooth information transmission among the modules, and the Internet module provides real-time data exchange capability with a cloud server. According to the design, the computing power and the data processing efficiency of the system are greatly improved, the real-time performance and the reliability of the system are enhanced, and the intelligent driving requirements of modern intelligent network connection automobiles and cloud control systems can be better met.

Description

Technical Field

[0001] The present application relates to the field of communication control technology, and in particular to a vehicle-road-cloud communication control storage and computing integrated system and a control method thereof. Background Art

[0002] With the rapid development of intelligent connected vehicle technology, the automotive industry is moving towards a new era of intelligence and interconnection. Intelligent connected vehicles are not only equipped with advanced sensors, controllers, actuators and other devices, but also integrate cutting-edge technologies such as modern communications, networks and artificial intelligence, realizing intelligent information exchange and sharing between vehicles, roads, people, and clouds. It has core functions such as complex environment perception, intelligent decision-making and collaborative control, enabling vehicles to achieve safe, efficient, comfortable and energy-saving driving, and is expected to eventually replace human driving.

[0003] In order to meet the needs of intelligent connected vehicles, a new cyber-physical system, the cloud control system, has been formed. As the core hub of the cloud control system, the cloud control platform consists of a cloud control basic platform and a cloud control application platform, and has become an important infrastructure for intelligent connected vehicles. With the support of the cloud control basic platform, vehicle-road-cloud collaborative technology and cloud optimization technology for mixed traffic have been developed, which has promoted the application and development of the cloud control platform. However, with the continuous improvement of vehicle intelligence, the number of vehicle sensors and the amount of data are also growing rapidly, which puts higher requirements on chip computing power, storage capacity, communication bandwidth and software functions.

[0004] The traditional in-vehicle computing platform adopts a distributed flat electronic engineering (EE) architecture, but it has problems such as waste of hardware resources, complex wiring harness layout, and difficulty in standardizing basic software, and can no longer meet the needs of modern intelligent connected vehicles. Summary of the invention

[0005] In view of this, the purpose of this application is to propose an integrated vehicle-road-cloud communication, control, storage and computing system and its control method to solve the above-mentioned technical problems.

[0006] Based on the above objectives, the first aspect of the present application provides a vehicle-road-cloud communication control storage and computing integrated system, including:

[0007] External interface, connected with external sensors of intelligent connected vehicles;

[0008] a calculation module, configured to receive the vehicle driving state data collected by the external sensor and transmitted by the external interface, determine the position of the target object and the position of the intelligent networked vehicle according to the vehicle driving state data, and process the position of the target object and the position of the intelligent networked vehicle through a safe vehicle distance control algorithm to obtain a traffic control parameter of the intelligent networked vehicle;

[0009] A data aggregation gateway is configured to receive the traffic control parameters transmitted by the calculation module, and transmit the traffic control parameters to the intelligent networked vehicle through the external interface, so that the intelligent networked vehicle adjusts the driving parameters of the intelligent networked vehicle according to the traffic control parameters;

[0010] The Internet module is configured to receive the vehicle driving status data transmitted by the computing module, and upload the vehicle driving status data to a cloud server, so that the cloud server can use the vehicle driving status data to perform a road condition analysis, obtain a road condition analysis result, determine a target driving route based on the road condition analysis result, and transmit the target driving route to the intelligent connected vehicle.

[0011] Optionally, the computing module includes: a centralized computing submodule and a distributed computing submodule;

[0012] The centralized computing submodule is configured to receive the vehicle driving state data collected by the external sensor, clean the vehicle driving state data using a denoising algorithm to obtain cleaned vehicle driving state data, and perform feature extraction on the cleaned vehicle driving state data to obtain feature data;

[0013] The distributed computing submodule is configured to read feature data from the centralized computing submodule, extract the target object position and the position of the intelligent connected vehicle from the feature data, and process the target object position and the position of the intelligent connected vehicle through a safe vehicle distance control algorithm to obtain the traffic control parameters of the intelligent connected vehicle.

[0014] Optionally, the centralized computing submodule includes: a data preprocessing unit, a feature extraction unit and a data storage unit;

[0015] The data preprocessing unit is configured to receive the vehicle driving state data collected by the external sensor, and clean the vehicle driving state data using a denoising algorithm to obtain cleaned vehicle driving state data;

[0016] The feature extraction unit is configured to receive the cleaned vehicle driving state data transmitted by the data preprocessing unit, perform feature extraction on the cleaned vehicle driving state data, and obtain feature data;

[0017] The data storage unit is configured to receive the vehicle driving status data collected by the external sensor and store the vehicle driving status data, and to receive the feature data transmitted by the feature extraction unit and store the feature data so that the distributed computing sub-module can read the feature data from the data storage unit in real time.

[0018] Optionally, the data storage unit includes: a first storage subunit and a second storage subunit;

[0019] The first storage subunit is configured to receive the vehicle driving state data collected by the external sensor and store the vehicle driving state data, so that when the cleaned vehicle driving state data is abnormal, the vehicle driving state data can be re-acquired from the first storage subunit by using the data preprocessing unit;

[0020] The second storage subunit is configured to receive the feature data transmitted by the feature extraction unit, and store the feature data so that the distributed computing submodule can read the feature data from the second storage subunit in real time.

[0021] Optionally, the distributed computing submodule includes: a control decision unit, a cloud perception unit and a fusion unit;

[0022] The control decision unit is configured to read the characteristic data from the data storage unit, extract the target object position and the position of the intelligent networked vehicle from the characteristic data, and process the target object position and the position of the intelligent networked vehicle through a safe vehicle distance control algorithm to obtain a traffic control parameter of the intelligent networked vehicle, and transmit the traffic control parameter to the data aggregation gateway;

[0023] The cloud sensing unit is configured to read the characteristic data from the data storage unit and obtain the road condition analysis result from the cloud server using the Internet module;

[0024] The fusion unit is configured to receive the feature data and the road condition analysis results transmitted by the cloud perception unit, fuse the feature data and the road condition analysis results to obtain fused data, and transmit the fused data to the intelligent networked vehicle through the transmission, so that the intelligent networked vehicle can adjust the driving parameters of the intelligent networked vehicle based on the fused data.

[0025] Optionally, the control decision unit includes: a vehicle control subunit and an intelligent decision subunit;

[0026] The vehicle control subunit is configured to read the characteristic data from the data storage unit, and extract the position of the target object and the position of the intelligent networked vehicle from the characteristic data;

[0027] The intelligent decision-making subunit is configured to receive the target object position and the position of the intelligent connected vehicle transmitted by the vehicle control subunit, process the target object position and the position of the intelligent connected vehicle through a safe vehicle distance control algorithm, obtain the traffic control parameters of the intelligent connected vehicle, and transmit the traffic control parameters to the data aggregation gateway.

[0028] Optionally, the cloud sensing unit includes: a vehicle-side sensing subunit and a cloud-side sensing subunit;

[0029] The vehicle-side sensing subunit is configured to read feature data from the data storage unit and transmit the feature data to the fusion unit;

[0030] The cloud sensing subunit is configured to obtain a traffic analysis result from a cloud server using the Internet module, and transmit the traffic analysis result to the fusion unit.

[0031] Optionally, the external interface includes at least one of the following:

[0032] Universal serial bus interface, Ethernet interface, controller area network bus interface, camera interface, ultrasonic interface and lidar interface.

[0033] Optionally, the external sensor includes at least one of the following:

[0034] LiDAR sensors, camera sensors, and ultrasonic sensors.

[0035] Based on the same inventive concept, the second aspect of the present application proposes a control method applied to the vehicle-road-cloud communication-control-storage-computing integrated system described in the first aspect, the control method comprising:

[0036] Using a calculation module to receive vehicle driving state data transmitted by an external interface, determining the position of a target object and the position of the intelligent networked vehicle according to the vehicle driving state data, and processing the target object position and the position of the intelligent networked vehicle through a safe vehicle distance control algorithm to obtain a traffic control parameter of the intelligent networked vehicle, wherein the vehicle driving state data is collected and obtained by an external sensor of the intelligent networked vehicle connected to the external interface;

[0037] Receiving the traffic control parameters transmitted by the calculation module through the data convergence gateway, and transmitting the traffic control parameters to the intelligent networked vehicle through the external interface, so that the intelligent networked vehicle adjusts the driving parameters of the intelligent networked vehicle according to the traffic control parameters;

[0038] The vehicle driving status data transmitted by the computing module is received by an Internet module, and the vehicle driving status data is uploaded to a cloud server, so that the cloud server can use the vehicle driving status data to perform a road condition analysis, obtain a road condition analysis result, and determine a target driving route based on the road condition analysis result, and transmit the target driving route to the intelligent connected vehicle.

[0039] From the above, it can be seen that the vehicle-road-cloud communication control storage and computing integrated system and its control method provided by this application realizes the design of integrated vehicle network communication control by integrating external interfaces, computing modules, efficient data aggregation gateways and Internet modules. The external interfaces ensure the connection between the system and various sensors to realize diversified data collection. The computing modules realize efficient data processing and real-time control. The data aggregation gateway ensures the smooth transmission of information between various modules, and the Internet module provides real-time data exchange capabilities with cloud servers. This design not only greatly improves the computing power and data processing efficiency of the system, but also enhances the real-time and reliability of the system, and can better meet the intelligent driving needs of modern intelligent networked vehicles and cloud control systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the present application or related technologies, the drawings required for use in the embodiments or related technical descriptions are briefly introduced below. Obviously, the drawings described below are only embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0041] Figure 1A This is a first structural schematic diagram of the vehicle-road-cloud communication control storage and computing integrated system of an embodiment of the present application;

[0042] Figure 1B This is a second structural schematic diagram of the vehicle-road-cloud communication control storage and computing integrated system of an embodiment of the present application;

[0043] Figure 2 This is a flow chart of the control method of the vehicle-road-cloud communication, control, storage and computing integrated system of an embodiment of the present application.

[0044] Among them, in the figure:

[0045] 1. External interface; 2. Computing module; 2-1. Centralized computing submodule; 2-11. Data preprocessing unit; 2-12. Feature extraction unit; 2-13. Data storage unit; 2-131. First storage subunit; 2-132. Second storage subunit; 2-2. Distributed computing submodule; 2-21. Control decision unit; 2-211. Vehicle control subunit; 2-212. Intelligent decision subunit; 2-22. Cloud perception unit; 2-221. Vehicle-side perception subunit; 2-222. Cloud perception subunit; 2-23. Fusion unit; 3. Data aggregation gateway; 4. Internet module. DETAILED DESCRIPTION

[0046] In order to make the objectives, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below in combination with specific embodiments and with reference to the accompanying drawings.

[0047] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present application should be the usual meanings understood by people with ordinary skills in the field to which the present application belongs. The "first", "second" and similar words used in the embodiments of the present application do not represent any order, quantity or importance, but are only used to distinguish different components. "Including" or "comprising" and similar words mean that the elements or objects appearing in front of the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0048] It is understandable that before using the technical solutions of each embodiment of the present application, the type, scope of use, usage scenarios, etc. of the personal information involved will be informed to the user in an appropriate manner, and the user's authorization will be obtained.

[0049] For example, in response to receiving an active request from a user, a prompt message is sent to the user to clearly remind the user that the operation requested to be performed will require obtaining and using the user's personal information. Thus, the user can independently choose whether to provide personal information to the electronic device, application, server, storage medium or other software or hardware that performs the operation of the technical solution of the present application according to the prompt message.

[0050] As an optional but non-limiting implementation, in response to receiving the user's active request, the prompt information may be sent to the user in the form of a pop-up window, in which the prompt information may be presented in text form. In addition, the pop-up window may also carry a selection control for the user to choose "agree" or "disagree" to provide personal information to the electronic device.

[0051] It is understandable that the above notification and the process of obtaining user authorization are merely illustrative and do not constitute a limitation on the implementation method of this application. Other methods that meet relevant laws and regulations may also be applied to the implementation method of this application.

[0052] Figure 1A The vehicle-road-cloud communication control storage and computing integrated system of the present application is shown, including:

[0053] External interface 1, connected to the external sensor of the intelligent connected vehicle;

[0054] The calculation module 2 is configured to receive the vehicle driving state data collected by the external sensor and transmitted by the external interface 1, determine the target object position and the position of the intelligent networked vehicle according to the vehicle driving state data, and process the target object position and the position of the intelligent networked vehicle through a safe vehicle distance control algorithm to obtain the traffic control parameters of the intelligent networked vehicle;

[0055] The data aggregation gateway 3 is configured to receive the traffic control parameters transmitted by the calculation module 2, and transmit the traffic control parameters to the intelligent networked vehicle through the external interface 1, so that the intelligent networked vehicle adjusts the driving parameters of the intelligent networked vehicle according to the traffic control parameters;

[0056] The Internet module 4 is configured to receive the vehicle driving status data transmitted by the computing module 2, and upload the vehicle driving status data to the cloud server, so that the cloud server can use the vehicle driving status data to perform road condition analysis, obtain road condition analysis results, and determine the target driving route based on the road condition analysis results, and transmit the target driving route to the intelligent connected vehicle.

[0057] In the above scheme, the external interface 1 is responsible for connecting with the external sensors of the intelligent connected vehicle. The external sensors may include radar, LiDAR, camera, Global Positioning System (GPS) receiver, etc., which are used to collect vehicle driving status data. The vehicle driving status data includes: environmental data around the vehicle (such as obstacle location, traffic signs, road information, etc.) and the vehicle's own driving status data (such as speed, acceleration, direction, etc.).

[0058] The computing module 2 receives the vehicle driving status data transmitted from the external interface 1, and uses the data to determine the position of the target object (such as other vehicles, pedestrians, obstacles, etc.) and the position of the intelligent connected vehicle itself.

[0059] The calculation module 2 then processes these data using a safe distance control algorithm (such as adaptive cruise control, collision warning system, etc.) to calculate the traffic control parameters of the intelligent connected vehicle. These parameters may include speed adjustment, braking instructions, steering instructions, etc., to ensure safe and efficient driving of the vehicle.

[0060] The data aggregation gateway 3 is responsible for receiving the traffic control parameters transmitted by the calculation module and transmitting these parameters back to the intelligent networked vehicle through the external interface 1. The intelligent networked vehicle will then adjust its own driving parameters (such as acceleration, deceleration, steering, etc.) according to these parameters to achieve a safe and comfortable driving experience.

[0061] The Internet module (5G module) 4 is responsible for uploading the vehicle driving status data to the cloud server. These data are crucial for road condition analysis because they reflect information such as traffic conditions and vehicle behavior on the actual road.

[0062] The cloud server uses the received vehicle driving status data to conduct in-depth road condition analysis and generate road condition analysis results. These results may include traffic congestion, accident warnings, optimal driving routes, etc.

[0063] Based on the road condition analysis results, the cloud server will determine the target driving route and transmit this information back to the intelligent connected vehicle. The intelligent connected vehicle can adjust the driving route based on this information to avoid congested sections, accident areas, etc., to improve driving efficiency and safety.

[0064] The vehicle-road-cloud communication, control, storage and computing integrated system integrates multiple parts such as external interface 1, computing module 2, data aggregation gateway 3 and Internet module 4, realizing efficient interaction and intelligent decision-making between intelligent connected vehicles and the external environment, providing vehicles with a safer, more efficient and more comfortable driving experience.

[0065] In a specific embodiment, Figure 1B As shown, the computing module 2 includes: a centralized computing submodule 2-1 and a distributed computing submodule 2-2;

[0066] The centralized computing submodule 2-1 is configured to receive the vehicle driving state data collected by the external sensor, clean the vehicle driving state data using a denoising algorithm to obtain cleaned vehicle driving state data, and perform feature extraction on the cleaned vehicle driving state data to obtain feature data;

[0067] The distributed computing submodule 2-2 is configured to read feature data from the centralized computing submodule 2-1, extract the target object position and the position of the intelligent connected vehicle from the feature data, and process the target object position and the position of the intelligent connected vehicle through a safe vehicle distance control algorithm to obtain the traffic control parameters of the intelligent connected vehicle.

[0068] In the above scheme, the centralized computing submodule 2-1 is responsible for receiving vehicle driving status data collected from external sensors (such as radar, camera, lidar, etc.). These data contain various information generated by the intelligent connected vehicle during driving, such as speed, acceleration, direction, surrounding environment perception data, etc.

[0069] Since sensor data may contain noise or redundant information, such as random noise in the LiDAR point cloud, the system uses voxel filtering or other denoising algorithms to remove noise and simplify the data structure for point cloud data; for camera image data, the system uses image denoising algorithms to improve image quality and remove noise caused by lighting changes or motion blur. The data quality after cleaning is higher, ensuring the accuracy and reliability of subsequent processing.

[0070] The cleaned data is further processed to extract features that are useful for vehicle driving decisions. These features may include changes in vehicle speed, acceleration, changes in direction, the distance and speed of surrounding objects, etc. The purpose of feature extraction is to convert raw data into more meaningful information that can be used for subsequent analysis and decision-making. For image data, the system uses deep learning models (such as convolutional neural networks) to detect targets such as pedestrians, vehicles, and traffic signs, and determine their position, size, and category. For lidar point cloud data, the system uses target detection algorithms (such as VoxelNet) to extract three-dimensional features of surrounding obstacles, including the shape, size, and motion state of objects. This process generates rich feature information, such as the position of pedestrians in front of the vehicle, the speed of the vehicle in front, and the three-dimensional coordinates of surrounding objects.

[0071] The distributed computing submodule 2-2 reads the extracted feature data from the centralized computing submodule 2-1. These feature data provide a basis for subsequent decision-making processing.

[0072] In the feature data, the distributed computing submodule 2-2 pays special attention to extracting the position of the target object (such as other vehicles, pedestrians, obstacles, etc.) and the position of the intelligent connected vehicle itself. These position information are important bases for evaluating the driving safety of the vehicle and formulating traffic strategies.

[0073] Based on the extracted target position and the position of the intelligent networked vehicle, the distributed computing submodule 2-2 uses the safe vehicle distance control algorithm for processing. This algorithm will take into account factors such as the vehicle's driving speed, acceleration, braking performance, and the complexity of the surrounding environment, and calculate the safe vehicle distance and other traffic control parameters (such as acceleration, deceleration, etc.) that the intelligent networked vehicle should maintain. These parameters will guide the driving behavior of the intelligent networked vehicle to ensure its safety and efficiency during driving.

[0074] Through the coordinated work of the centralized computing submodule 2-1 and the distributed computing submodule 2-2, effective processing of the driving status data of the intelligent networked vehicle and safe passage control are achieved. This processing method not only ensures the accuracy and efficiency of data processing, but also improves the safety and intelligence level of the driving of the intelligent networked vehicle.

[0075] In a specific embodiment, Figure 1B As shown, the centralized computing submodule 2-1 includes: a data preprocessing unit 2-11, a feature extraction unit 2-12 and a data storage unit 2-13;

[0076] The data preprocessing unit 2-11 is configured to receive the vehicle driving state data collected by the external sensor, and clean the vehicle driving state data using a denoising algorithm to obtain cleaned vehicle driving state data;

[0077] The feature extraction unit 2-12 is configured to receive the cleaned vehicle driving state data transmitted by the data preprocessing unit 2-11, perform feature extraction on the cleaned vehicle driving state data, and obtain feature data;

[0078] The data storage unit 2-13 is configured to receive the vehicle driving status data collected by the external sensor and store the vehicle driving status data, and receive the feature data transmitted by the feature extraction unit 2-12 and store the feature data so that the distributed computing sub-module 2-2 can read the feature data from the data storage unit 2-13 in real time.

[0079] In the above scheme, the data preprocessing unit 2-11 receives the original vehicle driving state data collected from external sensors (such as speed sensors, acceleration sensors, GPS, etc.). These data may contain noise, redundant information, erroneous or inconsistent information, and are not suitable for direct use in subsequent analysis or processing.

[0080] The system first cleans the received raw data using denoising algorithms (such as filtering algorithms, smoothing algorithms, etc.). For example, for random noise in the lidar point cloud, the system uses voxel filtering or other denoising algorithms to remove noise and simplify the data structure. For camera image data, the system uses image denoising algorithms to improve image quality and remove noise caused by lighting changes or motion blur. The data quality after cleaning is higher, ensuring the accuracy and reliability of subsequent processing. The result of this process is cleaned vehicle driving status data, which is more suitable for subsequent analysis and processing.

[0081] The feature extraction unit 2-12 receives the cleaned vehicle driving state data from the data preprocessing unit 2-11, and extracts features to obtain feature data, which are key features of the vehicle driving state, such as speed, acceleration, driving direction, driving trajectory, etc. These feature data are the basis for subsequent analysis and model training. For image data, the system uses deep learning models (such as convolutional neural networks) to detect targets such as pedestrians, vehicles, traffic signs, etc., and determine their positions, sizes, and categories. For lidar point cloud data, the system uses target detection algorithms (such as VoxelNet) to extract three-dimensional features of surrounding obstacles, including the shape, size, and motion state of the objects. This process generates rich feature information, such as the position of pedestrians in front of the vehicle, the speed of the vehicle in front, and the three-dimensional coordinates of surrounding objects. It aims to extract information or features from the raw data that are useful for subsequent analysis.

[0082] The data storage unit 2-13 is responsible for storing the vehicle driving state data directly received from the external sensor and the feature data received from the feature extraction unit 2-12. It plays the role of a data warehouse and provides data support and access interface for the entire system.

[0083] The data storage unit 2-13 may use a database, a file system or other data storage technologies to store data, which ensures the persistent storage of data so that it can be accessed at any time when needed.

[0084] In addition, the data storage unit 2-13 also supports the real-time reading of feature data by the distributed computing submodule. This means that when the distributed computing submodule 2-2 needs to perform real-time analysis or processing, it can quickly obtain the latest feature data from the data storage unit 2-13, thereby achieving efficient real-time data processing and analysis.

[0085] The centralized computing submodule realizes the effective processing and management of vehicle driving status data through the coordinated work of the data preprocessing unit 2-11, the feature extraction unit 2-12 and the data storage unit 2-13, providing a solid foundation for subsequent data analysis.

[0086] In a specific embodiment, Figure 1B As shown, the data storage unit 2-13 includes: a first storage subunit 2-131 and a second storage subunit 2-132;

[0087] The first storage subunit 2-131 is configured to receive the vehicle driving state data collected by the external sensor and store the vehicle driving state data, so that when the cleaned vehicle driving state data is abnormal, the vehicle driving state data can be re-acquired from the first storage subunit 2-131 by using the data preprocessing unit 2-11;

[0088] The second storage subunit 2-132 is configured to receive the feature data transmitted by the feature extraction unit 2-12, and store the feature data so that the distributed computing submodule 2-2 can read the feature data from the second storage subunit 2-132 in real time.

[0089] In the above solution, the first storage subunit 2-131 receives vehicle driving state data from external sensors, which may include but are not limited to speed sensors, acceleration sensors, gyroscopes, GPS locators, etc., which capture various driving state information of the vehicle in real time.

[0090] The received data will be stored in the first storage subunit 2-131 for subsequent processing and analysis. This storage is temporary or permanent, depending on the design and requirements of the system.

[0091] When the vehicle driving state data after cleaning (i.e., removing noise, outliers and other preprocessing steps) is abnormal or needs further verification, the data preprocessing unit 2-11 can reacquire the original vehicle driving state data from the first storage subunit 2-131. This helps to ensure the accuracy and traceability of data processing, ensure the integrity and accessibility of the original data, and provide a reliable data source for data preprocessing and subsequent analysis.

[0092] The second storage subunit 2-132 receives the feature data transmitted from the feature extraction unit 2-12. These feature data are key information extracted from the original vehicle driving state data after a series of processing (such as filtering, transformation, dimensionality reduction, etc.).

[0093] The characteristic data are stored in the second storage subunit 2-132 so that the distributed computing submodule 2-2 can read and process the data in real time, providing an efficient data access interface for the distributed computing submodule 2-2 and supporting parallel processing and large-scale data analysis.

[0094] The design of the data storage unit 2-13 realizes classified management and efficient use of data by separating the original data (first storage subunit 2-131) and the characteristic data (second storage subunit 2-132). The first storage subunit 2-131 ensures the integrity and traceability of the original data, while the second storage subunit 2-132 supports fast data processing and analysis by optimizing the storage structure and providing an efficient data access interface. This design is of great significance for improving the efficiency and accuracy of vehicle driving status data processing.

[0095] In a specific embodiment, Figure 1B As shown, the distributed computing submodule 2-2 includes: a control decision unit 2-21, a cloud perception unit 2-22 and a fusion unit 2-23;

[0096] The control decision unit 2-21 is configured to read the feature data from the data storage unit 2-13, extract the target object position and the position of the intelligent networked vehicle from the feature data, and process the target object position and the position of the intelligent networked vehicle through a safe vehicle distance control algorithm to obtain a traffic control parameter of the intelligent networked vehicle, and transmit the traffic control parameter to the data aggregation gateway 3;

[0097] The cloud sensing unit 2-22 is configured to read feature data from the data storage unit 2-13 and obtain a road condition analysis result from a cloud server using the Internet module 4;

[0098] The fusion unit 2-23 is configured to receive the feature data and the road condition analysis results transmitted by the cloud perception unit 2-22, fuse the feature data and the road condition analysis results to obtain fused data, and transmit the fused data to the intelligent networked vehicle through the transmission, so that the intelligent networked vehicle can adjust the driving parameters of the intelligent networked vehicle based on the fused data.

[0099] In the above scheme, the control decision unit 2-21 first reads feature data from the data storage unit 2-13 (which may be a local or remote database). These feature data may include information about the vehicle's surrounding environment collected by vehicle sensors (such as radar, camera, laser radar, etc.).

[0100] The positions of target objects (such as pedestrians, other vehicles, obstacles, etc.) and the position of the intelligent connected vehicle itself are extracted from the feature data.

[0101] Based on the extracted position information, the safe vehicle distance control algorithm (such as adaptive cruise control, collision warning system, etc.) is used to calculate the traffic control parameters that the intelligent connected vehicle should adopt under the current road conditions. These parameters may include vehicle speed, acceleration, braking intensity, etc. to ensure the safety and efficiency of vehicle driving. The calculated traffic control parameters are transmitted to the data aggregation gateway for further transmission to the vehicle control unit to achieve direct control of vehicle driving.

[0102] The cloud perception unit reads real-time or recent feature data from the data storage unit 2-13, which reflects the current state of the vehicle and changes in the surrounding environment. Then, the Internet module is used to establish a connection with the cloud server to obtain the road condition analysis results from the cloud server. These analysis results may be based on big data analysis, machine learning and other technologies, and are obtained by comprehensive analysis of real-time traffic flow, accident warning, road construction and other information.

[0103] The fusion unit 2-23 receives the characteristic data and the road condition analysis results transmitted by the cloud sensing unit 2-22, and fuses the two types of data. The fusion process may involve pre-processing steps such as data cleaning, deduplication, and normalization, as well as data fusion technology based on specific algorithms (such as Kalman filtering, particle filtering, etc.) to obtain a more comprehensive and accurate description of the vehicle driving environment.

[0104] The fused data (i.e., fused data) is then transmitted to the intelligent connected vehicle through appropriate communication protocols and transmission methods (such as controller area network bus, Ethernet, etc.). These fused data provide the vehicle with richer environmental perception information, helping the vehicle to make more intelligent and safe driving decisions.

[0105] Based on the received fusion data, intelligent connected vehicles can automatically adjust their driving parameters (such as vehicle speed, steering angle, braking intensity, etc.) to adapt to the current road conditions and driving environment, and improve driving safety and comfort.

[0106] The distributed computing submodule 2-2 provides intelligent connected vehicles with powerful environmental perception and intelligent decision-making capabilities by integrating control decision-making, cloud perception and data fusion functions, which helps to improve the overall performance and safety of the vehicle.

[0107] In a specific embodiment, Figure 1B As shown, the control decision unit 2-21 includes: a vehicle control subunit 2-211 and an intelligent decision subunit 2-212;

[0108] The vehicle control subunit 2-211 is configured to read feature data from the data storage unit 2-13, and extract the target object position and the position of the intelligent connected vehicle from the feature data;

[0109] The intelligent decision-making subunit 2-212 is configured to receive the target object position and the position of the networked vehicle transmitted by the vehicle control subunit 2-211, and process the target object position and the position of the intelligent networked vehicle through a safe vehicle distance control algorithm to obtain the traffic control parameters of the intelligent networked vehicle, and transmit the traffic control parameters to the data aggregation gateway 3.

[0110] In the above scheme, the vehicle control subunit 2-211 first accesses the data storage unit 2-13, which may contain real-time data collected by vehicle sensors (such as radar, camera, lidar, etc.), as well as information received from other vehicles or infrastructure through vehicle networking technology.

[0111] After reading a large amount of data, the vehicle control subunit 2-211 will process the data and extract feature data useful for decision-making, especially the location of the target object (such as other vehicles, pedestrians, obstacles, etc.) and the location of the intelligent networked vehicle itself. These data are the basis for the vehicle to make intelligent decisions and autonomous control.

[0112] The extracted location information is then transmitted to the intelligent decision subunit 2-212 for further decision processing.

[0113] The intelligent decision subunit 2-212 receives the position information of the target object and the intelligent networked vehicle from the vehicle control subunit 2-211, processes the information using the safe vehicle distance control algorithm, and analyzes and processes the received position information. This algorithm takes into account various factors, such as vehicle speed, road conditions, traffic rules, and surrounding traffic conditions, to calculate the optimal traffic control parameters. To calculate the optimal traffic control parameters for the intelligent networked vehicle. These parameters may include speed adjustment, lane change suggestions, braking instructions, etc., to ensure the safety and efficiency of vehicle driving.

[0114] Finally, these control parameters are transmitted to the data aggregation gateway 3, and then may be sent to the vehicle's control system through the Internet of Vehicles technology to achieve autonomous control and decision execution of the vehicle.

[0115] The entire control decision unit 2-21 realizes real-time data collection, processing, decision-making and control during the driving of the intelligent connected vehicle through the coordinated work of the vehicle control subunit 2-211 and the intelligent decision subunit 2-212, thereby improving the safety and intelligence level of vehicle driving.

[0116] In a specific embodiment, Figure 1BAs shown, the cloud sensing unit 2-22 includes: a vehicle-side sensing subunit 2-221 and a cloud-side sensing subunit 2-222;

[0117] The vehicle-side sensing subunit 2-221 is configured to read feature data from the data storage unit 2-13 and transmit the feature data to the fusion unit 2-23;

[0118] The cloud sensing subunit 2-222 is configured to obtain the traffic analysis result from the cloud server using the Internet module 4, and transmit the traffic analysis result to the fusion unit 2-23.

[0119] In the above scheme, the vehicle-side sensing subunit 2-222 reads feature data from the data storage unit 2-13 or other related sensors (such as radar, camera, laser radar, etc.). These feature data usually include the vehicle's own state information (such as speed, acceleration, direction), information about the surrounding environment (such as the distance, type, and location of obstacles), etc.

[0120] The collected feature data is then transmitted to the fusion unit 2-23 for integration with information from other sources (such as the cloud). It provides the vehicle with real-time, local environmental perception capabilities, which is the basis for the vehicle's autonomous navigation and obstacle avoidance.

[0121] The cloud sensing subunit communicates with the cloud server through the Internet module 4 and obtains the road condition analysis results from the cloud server. These road condition analysis results may include real-time traffic flow, accident information, road construction conditions, weather conditions, etc. This information is crucial for the vehicle to plan the optimal driving path and adjust the driving strategy.

[0122] The obtained road condition analysis results are also transmitted to the fusion unit 2-23 for fusion analysis with the data provided by the vehicle-side perception subunit 2-221. The cloud-based perception subunit 2-222 expands the vehicle's perception range, enabling it to obtain a wide range of information beyond the capabilities of the vehicle's own sensors, thereby making more comprehensive and accurate decisions.

[0123] The fusion unit 2-23 is a component responsible for integrating, analyzing and processing information from the vehicle-side perception subunit 2-221 and the cloud-side perception subunit 2-222. It provides a more comprehensive and accurate environmental model for the vehicle control system through comprehensive evaluation of multi-source information to support intelligent decision-making and autonomous navigation of the vehicle.

[0124] The cloud-based sensing unit 2-22 realizes the vehicle's comprehensive perception of the local environment and remote road conditions through its two sub-units, namely the vehicle-side sensing sub-unit 2-221 and the cloud-based sensing sub-unit 2-222, and provides powerful information support for the vehicle's intelligent system.

[0125] In a specific embodiment, the external interface includes at least one of the following:

[0126] Universal serial bus interface, Ethernet interface, controller area network bus interface, camera interface, ultrasonic interface and lidar interface.

[0127] In the above scheme, the Universal Serial Bus (USB) has the advantages of plug-and-play, fast transmission speed, and hot-swap support, and is convenient for communicating with external sensors.

[0128] The Ethernet interface is an interface used for Local Area Network (LAN) communication, allowing devices to be connected through network cables and communicate with each other.

[0129] The Controller Area Network (CAN) interface can reliably transmit data in high electromagnetic interference environments and support distributed real-time control systems.

[0130] Camera interfaces are used to connect camera devices in order to capture images or video signals. In modern devices, digital interfaces are becoming more and more popular due to their high transmission efficiency and strong anti-interference ability. Camera interfaces are crucial in video surveillance, robot vision, autonomous driving and other fields.

[0131] The ultrasonic interface is used to connect ultrasonic sensors, which measure distance or detect the presence of objects by emitting and receiving ultrasonic waves.

[0132] The LiDAR interface is used to connect a LiDAR device, which calculates the distance by emitting laser pulses and measuring the time it takes for them to reflect back, thereby constructing a three-dimensional (3D) model of the surrounding environment.

[0133] The existence of these interfaces enables the device to exchange and communicate data efficiently and reliably with external sensors of intelligent connected vehicles.

[0134] In a specific embodiment, the external sensor includes at least one of the following:

[0135] LiDAR sensors, camera sensors, and ultrasonic sensors.

[0136] In the above solution, the LiDAR (Light Detection and Ranging) sensor can construct a 3D image or point cloud map of the surrounding environment by collecting data in multiple directions.

[0137] In driverless cars and robots, lidar is used to detect obstacles, pedestrians, other vehicles, and road markings on the road, enabling high-precision environmental perception and obstacle avoidance.

[0138] It is insensitive to changes in lighting conditions and can work normally at night or in bad weather conditions. It is an indispensable part of autonomous driving technology.

[0139] Camera sensors provide visual information by capturing images or videos of the surrounding environment.

[0140] In driverless vehicles, camera sensors are used to identify traffic signals, road signs, pedestrians, vehicles, etc., and the accuracy and efficiency of recognition are continuously improved through machine learning algorithms.

[0141] Cameras can also be used in conjunction with other sensors such as lidar to achieve more comprehensive environmental perception and safer autonomous driving.

[0142] Ultrasonic sensors measure distance by emitting high-frequency sound waves and detecting how long it takes for them to reflect off a surface. The speed at which these sound waves travel varies very little in air, so ultrasonic sensors are able to provide relatively accurate distance measurements.

[0143] LiDAR sensors, camera sensors and ultrasonic sensors each have their own unique advantages and applicable scenarios, and they play an irreplaceable role in external perception and environmental monitoring. The combination of these three sensors can greatly improve the performance and safety of the vehicle-road-cloud communication control storage and computing integrated system.

[0144] Based on the same inventive concept, Figure 2 As shown, an embodiment of the present application proposes a control method for a vehicle-road-cloud communication-control-storage-computing integrated system applied to any of the above embodiments, characterized in that the control method includes:

[0145] Step 201, using a computing module to receive vehicle driving status data transmitted by an external interface, determining a target object position and a position of the intelligent connected vehicle according to the vehicle driving status data, and processing the target object position and the position of the intelligent connected vehicle through a safe vehicle distance control algorithm to obtain a traffic control parameter of the intelligent connected vehicle, wherein the vehicle driving status data is collected by an external sensor of the intelligent connected vehicle connected to the external interface.

[0146] Step 202: receiving the traffic control parameters transmitted by the calculation module through the data convergence gateway, and transmitting the traffic control parameters to the intelligent connected vehicle through the external interface, so that the intelligent connected vehicle can adjust the driving parameters of the intelligent connected vehicle according to the traffic control parameters.

[0147] Step 203, using the Internet module to receive the vehicle driving status data transmitted by the calculation module, and uploading the vehicle driving status data to the cloud server, so that the cloud server can use the vehicle driving status data to perform road condition analysis, obtain road condition analysis results, and determine the target driving route based on the road condition analysis results, and transmit the target driving route to the intelligent connected vehicle.

[0148] Through the above solution, by integrating external interfaces, computing modules, efficient data aggregation gateways and Internet modules, the integrated design of vehicle networking communication control is realized. The external interfaces ensure the connection between the system and various sensors to achieve diversified data collection. The computing modules realize efficient data processing and real-time control. The data aggregation gateway ensures the smooth transmission of information between modules, and the Internet module provides real-time data exchange capabilities with cloud servers. This design not only greatly improves the system's computing power and data processing efficiency, but also enhances the system's real-time and reliability, and can better meet the intelligent driving needs of modern intelligent networked vehicles and cloud control systems.

[0149] It should be noted that the method of the embodiment of the present application can be performed by a single device, such as a computer or server. The method of this embodiment can also be applied to a distributed scenario and completed by multiple devices cooperating with each other. In the case of such a distributed scenario, one of the multiple devices can only perform one or more steps in the method of the embodiment of the present application, and the multiple devices will interact with each other to complete the described method.

[0150] It should be noted that the above describes some embodiments of the present application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the above embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0151] To simplify the description and discussion, and in order not to make the embodiments of the present application difficult to understand, the known power supply / ground connection with the integrated circuit (IC) chip and other components may or may not be shown in the provided drawings. In addition, the device can be shown in the form of a block diagram to avoid making the embodiments of the present application difficult to understand, and this also takes into account the following fact, that is, the details of the implementation of these block diagram devices are highly dependent on the platform to be implemented in the embodiments of the present application (that is, these details should be fully within the scope of understanding of those skilled in the art). In the case of elaborating specific details (e.g., circuit) to describe exemplary embodiments of the present application, it is obvious to those skilled in the art that the embodiments of the present application can be implemented without these specific details or when these specific details are changed. Therefore, these descriptions should be considered to be illustrative rather than restrictive.

[0152] Although the present application has been described in conjunction with specific embodiments of the present application, many replacements, modifications and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may use the embodiments discussed.

[0153] Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present application (including the claims) is limited to these examples. In line with the concept of the present application, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of the different aspects of the present application as described above, which are not provided in detail for the sake of simplicity.

[0154] The embodiments of the present application are intended to cover all such substitutions, modifications and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present application should be included in the scope of protection of the present application.

Claims

1. A vehicle-road-cloud communication control storage and computing integrated system, characterized in that: include: External interface, connected to the external sensors of the intelligent connected vehicle; a calculation module, configured to receive the vehicle driving state data collected by the external sensor and transmitted by the external interface, determine the position of the target object and the position of the intelligent networked vehicle according to the vehicle driving state data, and process the position of the target object and the position of the intelligent networked vehicle through a safe vehicle distance control algorithm to obtain a traffic control parameter of the intelligent networked vehicle; A data aggregation gateway is configured to receive the traffic control parameters transmitted by the calculation module, and transmit the traffic control parameters to the intelligent networked vehicle through the external interface, so that the intelligent networked vehicle adjusts the driving parameters of the intelligent networked vehicle according to the traffic control parameters; The Internet module is configured to receive the vehicle driving status data transmitted by the computing module, and upload the vehicle driving status data to a cloud server, so that the cloud server can use the vehicle driving status data to perform a road condition analysis, obtain a road condition analysis result, determine a target driving route based on the road condition analysis result, and transmit the target driving route to the intelligent connected vehicle.

2. The vehicle-road-cloud communication control storage and computing integrated system according to claim 1 is characterized in that: The computing module includes: a centralized computing submodule and a distributed computing submodule; The centralized computing submodule is configured to receive the vehicle driving state data collected by the external sensor, clean the vehicle driving state data using a denoising algorithm to obtain cleaned vehicle driving state data, and perform feature extraction on the cleaned vehicle driving state data to obtain feature data; The distributed computing submodule is configured to read feature data from the centralized computing submodule, extract the target object position and the position of the intelligent connected vehicle from the feature data, and process the target object position and the position of the intelligent connected vehicle through a safe vehicle distance control algorithm to obtain the traffic control parameters of the intelligent connected vehicle.

3. The vehicle-road-cloud communication control storage and computing integrated system according to claim 2 is characterized in that: The centralized computing submodule includes: a data preprocessing unit, a feature extraction unit and a data storage unit; The data preprocessing unit is configured to receive the vehicle driving state data collected by the external sensor, and clean the vehicle driving state data using a denoising algorithm to obtain cleaned vehicle driving state data; The feature extraction unit is configured to receive the cleaned vehicle driving state data transmitted by the data preprocessing unit, perform feature extraction on the cleaned vehicle driving state data, and obtain feature data; The data storage unit is configured to receive the vehicle driving status data collected by the external sensor and store the vehicle driving status data, and to receive the feature data transmitted by the feature extraction unit and store the feature data so that the distributed computing sub-module can read the feature data from the data storage unit in real time.

4. The vehicle-road-cloud communication control storage and computing integrated system according to claim 3 is characterized in that: The data storage unit comprises: a first storage subunit and a second storage subunit; The first storage subunit is configured to receive the vehicle driving state data collected by the external sensor and store the vehicle driving state data, so that when the cleaned vehicle driving state data is abnormal, the vehicle driving state data can be re-acquired from the first storage subunit by using the data preprocessing unit; The second storage subunit is configured to receive the feature data transmitted by the feature extraction unit, and store the feature data so that the distributed computing submodule can read the feature data from the second storage subunit in real time.

5. The vehicle-road-cloud communication control storage and computing integrated system according to claim 3 is characterized in that: The distributed computing submodule includes: a control decision unit, a cloud perception unit and a fusion unit; The control decision unit is configured to read the characteristic data from the data storage unit, extract the target object position and the position of the intelligent networked vehicle from the characteristic data, and process the target object position and the position of the intelligent networked vehicle through a safe vehicle distance control algorithm to obtain a traffic control parameter of the intelligent networked vehicle, and transmit the traffic control parameter to the data aggregation gateway; The cloud sensing unit is configured to read the characteristic data from the data storage unit and obtain the road condition analysis result from the cloud server using the Internet module; The fusion unit is configured to receive the feature data and the road condition analysis results transmitted by the cloud perception unit, fuse the feature data and the road condition analysis results to obtain fused data, and transmit the fused data to the intelligent networked vehicle through the transmission, so that the intelligent networked vehicle can adjust the driving parameters of the intelligent networked vehicle based on the fused data.

6. The vehicle-road-cloud communication control storage and computing integrated system according to claim 5 is characterized in that: The control decision unit includes: a vehicle control subunit and an intelligent decision subunit; The vehicle control subunit is configured to read the characteristic data from the data storage unit, and extract the position of the target object and the position of the intelligent networked vehicle from the characteristic data; The intelligent decision-making subunit is configured to receive the target object position and the position of the intelligent connected vehicle transmitted by the vehicle control subunit, process the target object position and the position of the intelligent connected vehicle through a safe vehicle distance control algorithm, obtain the traffic control parameters of the intelligent connected vehicle, and transmit the traffic control parameters to the data aggregation gateway.

7. The vehicle-road-cloud communication control storage and computing integrated system according to claim 5 is characterized in that: The cloud sensing unit includes: a vehicle-side sensing subunit and a cloud-side sensing subunit; The vehicle-side sensing subunit is configured to read feature data from the data storage unit and transmit the feature data to the fusion unit; The cloud sensing subunit is configured to obtain a traffic analysis result from a cloud server using the Internet module, and transmit the traffic analysis result to the fusion unit.

8. The vehicle-road-cloud communication control storage and computing integrated system according to claim 1 is characterized in that: The external interface includes at least one of the following: Universal serial bus interface, Ethernet interface, controller area network bus interface, camera interface, ultrasonic interface and lidar interface.

9. The vehicle-road-cloud communication control storage and computing integrated system according to claim 1 is characterized in that: The external sensor includes at least one of the following: LiDAR sensors, camera sensors, and ultrasonic sensors.

10. A control method applied to the vehicle-road-cloud communication-control-storage-computing integrated system according to any one of claims 1 to 9, characterized in that: The control method comprises: Using a calculation module to receive vehicle driving state data transmitted by an external interface, determining the position of a target object and the position of the intelligent networked vehicle according to the vehicle driving state data, and processing the target object position and the position of the intelligent networked vehicle through a safe vehicle distance control algorithm to obtain a traffic control parameter of the intelligent networked vehicle, wherein the vehicle driving state data is collected and obtained by an external sensor of the intelligent networked vehicle connected to the external interface; Receiving the traffic control parameters transmitted by the calculation module through the data convergence gateway, and transmitting the traffic control parameters to the intelligent networked vehicle through the external interface, so that the intelligent networked vehicle adjusts the driving parameters of the intelligent networked vehicle according to the traffic control parameters; The vehicle driving status data transmitted by the computing module is received by an Internet module, and the vehicle driving status data is uploaded to a cloud server, so that the cloud server can use the vehicle driving status data to perform a road condition analysis, obtain a road condition analysis result, and determine a target driving route based on the road condition analysis result, and transmit the target driving route to the intelligent connected vehicle.

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