A cloud control platform-based collaborative perception operation method

Abstract

The application relates to the technical field of big data, and discloses a collaborative perception operation method based on a cloud control platform, which comprises the following steps: S1, a road test perception platform transmits vehicle data to a cloud control platform, and the cloud control platform fuses the received vehicle data; S2, the cloud control platform disassembles the received transmission data, decomposes original data structures into a plurality of effective data, and according to a certain attribute of the vehicle, the effective data are grouped and then issued to a next layer program; S3, the cloud control platform perceives various scenes by using a scene perception algorithm on the effective data obtained in S2; by adopting a big data streaming processing framework to group data, the data can be more efficiently processed; by using distributed storage database technology, the networked vehicles in a specific range are divided, the effect of accurate perception positioning is achieved, and relevant information is issued according to a message processing framework, so that information processing is faster.

Description

Technical Field

[0001] This invention relates to the field of big data technology, and more specifically to a collaborative sensing operation method based on a cloud control platform. Background Technology

[0002] Intelligent connected vehicles are cars capable of sensing their surroundings and autonomously cruising without or with minimal driver intervention. In the future, smart transportation and smart cities will increasingly utilize connected and autonomous vehicles. The larger and more accurate the sensing range of intelligent connected vehicles, the greater their role in traffic guidance. The onboard computing platform connects to the onboard terminal platform via onboard Ethernet to acquire beyond-line-of-sight perception data, such as map data and environmental data, integrates and calculates these data to formulate vehicle driving plans, and then uploads the results to the cloud platform via the onboard terminal.

[0003] Existing technical solutions cannot perform specific processing on perceived data, such as decomposition, fusion, and grouping, thus failing to improve data processing efficiency. They also cannot meet the needs of complex algorithm scenarios, such as notifying vehicles within a 200-meter radius behind the event vehicle. Furthermore, they do not provide a clear and specific solution for disseminating perceived results, leaving existing connected vehicles without a concrete implementation plan available. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, the present invention provides a collaborative sensing operation method based on a cloud control platform. The technical problem to be solved by the present invention is that the existing solutions cannot decompose and group the relevant sensing data.

[0005] A collaborative sensing operation method based on a cloud control platform includes the following steps:

[0006] S1. The roadside perception platform transmits vehicle data to the cloud control platform, which then merges the received vehicle data.

[0007] S2. The cloud control platform will break down the received transmission data, decompose the original data structure into several valid data, and group the valid data according to a certain attribute of the vehicle before sending it to the next layer program.

[0008] S3, the cloud control platform uses scene perception algorithms to perceive various scenes based on the effective data obtained from S2, divides vehicles within a specific range through distributed storage database technology, and sends the relevant information to the next program.

[0009] S4, the cloud control platform sends the information sent by S3 to the vehicle through the network transmission protocol and sends various perception commands calculated by the perception algorithm. The connected vehicle receives the commands through the vehicle-side device to achieve the cloud control effect.

[0010] A real-time big data processing framework is used to perform specific processing on the perceived data, such as decomposition, fusion, and grouping, which improves the efficiency of data processing. Distributed storage database technology is used to store relevant perceived data and then locate vehicles within a specific range, improving positioning time and increasing data processing speed. By using a big data streaming processing framework to group data, data can be processed more efficiently. Then, using distributed storage database technology, connected vehicles within a specific range are divided to achieve accurate perception and positioning. Finally, according to the message processing framework, relevant information is sent to the next layer of the program, making information processing faster.

[0011] In a preferred embodiment, the valid data generated by the cloud control platform is grouped using a big data streaming processing framework, and the data within the cloud control platform needs to be processed in real time. The relevant information within the cloud control platform is then sent to the next-level program using a message processing framework. By using a big data streaming processing framework to group the data, the data can be processed more efficiently. The relevant information is then sent to the next-level program according to the message processing framework, thus making information processing faster.

[0012] In a preferred embodiment, the cloud control platform includes a user module and a management module. The user module creates accounts and binds them to users' vehicles based on their individual characteristics. The management module can manage users and partition the user module. The cloud control platform has both user and management modules, which facilitates user management by administrators and increases the operating speed of the cloud control platform. The user partitioning by the management module is mainly based on the type of user's vehicle, which facilitates management.

[0013] In a preferred embodiment, the roadside sensing platform includes roadside sensing devices, environmental acquisition devices, and a computer system. The effective range for data collection by the roadside sensing devices is 150m-200m. The environmental acquisition devices need to collect information such as temperature, light, visibility, and road smoothness. The computer system preprocesses the data collected by the roadside sensing devices and the environmental acquisition devices and reports the preprocessed data to the cloud control platform. When collecting vehicle data through the roadside sensing devices, environmental factors are included in the collected data, and the collected data is preprocessed to increase the accuracy of the final data processing results and the calculated instructions.

[0014] In a preferred embodiment, the environmental acquisition device is connected to the scene perception algorithm. The data collected by the environmental acquisition device is preprocessed and then used by the scene perception algorithm to perceive various scenes. The vehicle data and the perceived scenes are then processed together. By comprehensively considering the vehicle information and the perceived scenes, and processing them in a partitioned yet integrated manner, the processing efficiency is improved.

[0015] In a preferred embodiment, the raw data from the road test perception platform is fused, grouped using a big data streaming processing framework, and then divided into connected vehicles within a specific range using distributed storage database technology. Since the raw data cannot be processed directly, grouping it allows for more efficient data processing, and the use of distributed storage database technology achieves accurate perception and positioning.

[0016] In a preferred embodiment, the data processed by the perception algorithm of the road test perception platform is data within a specific range defined by the distributed storage database technology. The perception algorithm also needs to be range-defined, and the range defined must be consistent with the range defined by the distributed storage database. By aligning the range of the perception algorithm of the road test perception platform with the range defined by the distributed storage database technology, the processing time is improved, and the sensing commands issued by the platform are more accurate.

[0017] The technical effects and advantages of this invention are as follows:

[0018] 1. This invention employs a big data real-time processing framework to perform specific processing on the perceived data, such as decomposition, fusion, and grouping, thereby improving the efficiency of data processing. It also uses distributed storage database technology to store relevant perceived data and then locate vehicles within a specific range, improving the positioning time and increasing the data processing speed.

[0019] 2. This invention uses a big data streaming processing framework to group data, enabling more efficient data processing. It then utilizes distributed storage database technology to segment connected vehicles within a specific range, achieving precise perception and positioning. Finally, it uses a message processing framework to send relevant information to the next layer of the program, thus making information processing faster. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the overall structure of the present invention. Detailed Implementation

[0021] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. In addition, the forms of the various structures described in the following embodiments are merely illustrative. The collaborative perception operation method based on the cloud control platform involved in the present invention is not limited to the structures described in the following embodiments. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] Example 1: This invention provides a collaborative sensing operation method based on a cloud control platform, comprising the following steps:

[0023] A1. The road test data reporting process is as follows:

[0024] First, data is collected through roadside testing equipment. Environmental data, including temperature, light, and visibility data for the day, is entered into the computer system. Roadside sensing equipment collects information about vehicles in motion and the driver's driving status. In addition to sensing the main vehicle, the roadside sensing equipment also collects data on vehicles within a 200-meter radius in front of and behind the main vehicle. All collected data is then input into the computer system. Meaningless information is removed from the data, and the pre-processed data is reported to the cloud control platform.

[0025] A2. The data processing procedure of the cloud control platform is as follows:

[0026] The cloud control platform receives the pre-processed raw information, merges all the data information, and uses a big data streaming processing framework to break it down into several groups of effective data. It also generates a separate group of on-site environmental data, groups the data according to the vehicle length attribute, and then sends the grouped data out.

[0027] A3. The process of issuing sensing commands is as follows:

[0028] Data grouped according to vehicle length attributes is used in the scene perception algorithm to perceive various scenes. Distributed storage database technology is used to divide vehicles within a specific range, and the information is sent to the perception algorithm through network transmission protocol to calculate the sensing command.

[0029] A4. The process of receiving and executing sensor commands is as follows:

[0030] Connected vehicles receive sensing commands from the cloud control platform through on-vehicle receiving devices and take actions based on the sensing commands. All sensing commands issued by the cloud control platform are backed up, and the management module processes the sensing commands to determine whether the issued sensing commands are correct.

[0031] Example 2:

[0032] The process begins with data being reported from the roadside platform to the cloud control platform. The cloud control platform then performs data fusion, followed by real-time processing. This involves data decomposition, breaking down a single data point into several data points and distributing them to the next layer of the program. The next layer group and distributes the data based on the vehicle's height attribute, ensuring continuous and complete processing of data from different vehicles. This generates corresponding algorithmic perception results, which are then triggered by the cloud control platform's real-time processing. Next, distributed storage database technology is used to define a specific range of vehicles. Once defined, the relevant information is sent to the next layer of the program via a message processing framework, and then transmitted to the vehicle-side via a network protocol. The vehicle-side then controls the vehicle through its onboard intelligent devices.

[0033] Example 3

[0034] The process begins with data being reported from the roadside platform to the cloud control platform. The cloud control platform then performs data fusion, followed by real-time processing. This involves data decomposition, breaking down a single data point into several data points and distributing them to the next layer of the program. This next layer group and distributes the data based on vehicle speed attributes, ensuring continuous and complete processing of data from different vehicles. This generates corresponding algorithmic perception results, which are then triggered by the cloud control platform's real-time processing. Next, distributed storage database technology is used to define a specific range of vehicles. Once defined, the relevant information is sent to the next layer of the program via a message processing framework, and then transmitted to the vehicle-side via a network protocol. The vehicle-side then controls the vehicle through its onboard intelligent devices.

[0035] Example 4:

[0036] The process begins with data being reported from the roadside platform to the cloud control platform. The cloud control platform then performs data fusion, followed by real-time processing. This involves data decomposition, breaking down a single data point into several data points and distributing them to the next layer of the program. The next layer then groups and distributes the data based on the vehicle's acceleration attributes, ensuring continuous and complete processing of data from different vehicles to generate corresponding algorithmic perception results. The cloud control platform's real-time processing triggers relevant algorithms. Next, distributed storage database technology is used to define a specific range of vehicles. Once defined, the relevant information is sent to the next layer of the program via a message processing framework, and then transmitted to the vehicle-side via a network protocol. The vehicle-side then controls the vehicle through onboard intelligent devices.

[0037] When grouping by vehicle length, the issued sensor commands are more accurate in avoiding obstacles while the vehicle is in motion, and can be used to arrange for vehicles to travel on height-restricted sections. When grouping by vehicle length, the issued sensor commands are more reasonable in the arrangement of vehicles in front and behind, and can prevent vehicles from driving on narrow roads and avoid complex turning situations due to excessive vehicle length. When grouping by vehicle speed, the issued sensor commands can more accurately estimate the vehicle's position while it is in motion, and can know the position that the vehicle can reach within a certain driving time. When grouping by vehicle acceleration, the issued sensor commands can pre-plan unexpected situations that may occur while the vehicle is in motion, understand the vehicle's acceleration, and can determine the vehicle's subsequent speed and speed changes when the vehicle brakes, which can reduce the probability of accidents.

[0038] Finally, the following points should be noted: First, in the description of this application, it should be noted that, unless otherwise specified and limited, the terms "installation", "connection", and "linkage" should be interpreted broadly, and can be mechanical or electrical connections, or internal connections between two components, or direct connections. "Up", "down", "left", "right", etc. are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may change.

[0039] Secondly: The accompanying drawings of the embodiments disclosed in this invention only involve the structures involved in the embodiments disclosed in this invention. Other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of this invention can be combined with each other.

[0040] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1.A cloud control platform based cooperative perception operation method, characterized in that, The method comprises the following steps: S1, the roadside sensing platform transmits vehicle data to the cloud control platform, and the cloud control platform fuses the received vehicle data; S2, the cloud control platform disassembles the received transmission data, decomposes the original data structure into a plurality of effective data, and according to a certain attribute of the vehicle, groups the effective data and then issues them to the next step; wherein the attributes of the vehicle include vehicle length, vehicle speed or vehicle acceleration; the effective data generated by the cloud control platform is grouped by using a big data streaming processing framework, and the data in the cloud control platform needs to be processed in real time, and the related information in the cloud control platform is issued to the next step through a message processing framework; S3, the cloud control platform perceives various scenes by using a scene perception algorithm on the effective data obtained in S2, divides the vehicles in a specific range by using a distributed storage database technology, and sends the related information to the next step; wherein the data processed by the scene perception algorithm is the data in a specific range divided by the distributed storage database technology, and the scene perception algorithm also needs to be divided in range, and the divided range needs to be consistent with the range divided by the distributed storage database; S4, the cloud control platform issues the sensing instructions calculated in the scene perception algorithm to the vehicles through a network transmission protocol, and issues various sensing instructions calculated by the scene perception algorithm to the vehicles, so that the vehicles receive the instructions through the vehicle terminal equipment to achieve the cloud control effect; Wherein, the cloud control platform comprises a user module and a management module, the user module establishes an account and binds it with its own vehicle according to different users, and the management module can manage the users and partition the user module; the roadside sensing platform comprises a roadside sensing device, an environment collection device and a computer system, the effective range of data collected by the roadside sensing device is 150-200m, the environment collection device needs to collect temperature, light, visibility and road flatness information, and the computer system pre-processes the data collected by the roadside sensing device and the environment collection device, and reports the pre-processed data to the cloud control platform.

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