CN116841292A - Vehicle control method and system based on cloud computing - Google Patents
Vehicle control method and system based on cloud computing Download PDFInfo
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Abstract
A vehicle control method and system based on cloud computing, the method includes obtaining a high definition map and real-time traffic data related to a vehicle-end driving path, constructing a road environment model, wherein the real-time traffic data at least comprises one of road environment data and intelligent traffic road platform data collected by a vehicle; acquiring vehicle running data uploaded by a vehicle and constructing a vehicle running model; and outputting real-time running control data of the control vehicle by using an automatic driving control model according to the parameters input by the road environment model and the vehicle running model. According to the invention, the vehicle-end automatic driving control model is moved up to the cloud end, the vehicle-end reserves basic automatic driving calculation capability, and the acquired data is processed, so that the cloud end calculation is facilitated, and the redundant configuration of automatic driving is realized. The vehicle end data are acquired through the data mining technology, the vehicle instructions are issued to the vehicle end through cloud simulation calculation to control the real-time running of the vehicle in real time, the calculation force of the vehicle end is liberated, and the cost reduction of a bicycle is realized.
Description
Technical Field
The invention relates to the field of automatic driving, in particular to a vehicle control method and system based on cloud computing.
Background
The existing automatic driving technology is only realized on a bicycle, road information is obtained through sensing equipment and a high-precision map of the bicycle, and the bicycle is automatically driven through an intelligent driving calculation controller, so that an intelligent chip with high calculation power is needed. And the high-precision map adopted by the automatic driving technology has poor timeliness and long updating period, and cannot feed back real-time road conditions of roads.
At present, most automobile factories and merchants apply a vehicle data acquisition technology on a bicycle, real-time data of the vehicle is uploaded to a cloud end through the technology to conduct remote diagnosis and the like on the vehicle, but the data is not applied to cloud end automatic driving.
Disclosure of Invention
Aiming at the technical problems, the invention provides a vehicle control method and a vehicle control system based on cloud computing, which can relieve the calculation force of a vehicle end and realize the cost reduction of a bicycle.
In a first aspect of the present invention, a vehicle control method based on cloud computing is provided, applied to a cloud, including:
acquiring a high-definition map and real-time traffic data related to a vehicle-end driving path, and constructing a road environment model, wherein the real-time traffic data at least comprises one of road environment data and intelligent traffic road platform data acquired by a vehicle;
acquiring vehicle running data uploaded by a vehicle and constructing a vehicle running model;
and outputting real-time running control data of the control vehicle by using an automatic driving control model according to the parameters input by the road environment model and the vehicle running model.
In an alternative embodiment, when the vehicle end and the cloud end lose network connection; the vehicle end obtains real-time road information through the sensing system and transmits data to the whole vehicle domain controller, and the whole vehicle domain controller issues a vehicle control instruction to the whole vehicle controller for execution.
In an alternative embodiment, the road environment model outputs lane line information, obstacle information, traffic indication information at least to the automatic driving control model.
In an alternative embodiment, the vehicle driving model outputs at least vehicle speed, acceleration, steering angle, braking state, longitude and latitude to the automatic driving control model.
In an optional embodiment, the vehicle control method based on cloud computing further includes obtaining road environment data collected by a vehicle, and updating the high-definition map by using the road environment data.
In a second aspect of the present invention, a vehicle control method based on cloud computing is provided, applied to a vehicle end, including:
sensing system road environment data and uploading the road environment data to the cloud;
the whole vehicle domain control unit acquires vehicle running data of a self vehicle from the whole vehicle controller and transmits the vehicle running data to the intelligent domain control unit;
the intelligent domain control unit uploads the vehicle running data to the cloud;
the intelligent domain control unit receives the vehicle control instruction fed back by the cloud and transmits the vehicle control instruction to the whole vehicle domain control unit, and the whole vehicle domain control unit issues the vehicle control instruction to the whole vehicle controller for execution.
In an alternative embodiment, when the vehicle end and the cloud end lose network connection; the vehicle end obtains real-time road information through the sensing system and transmits data to the whole-vehicle-domain control unit, and the whole-vehicle-domain control unit issues a vehicle control instruction to the whole vehicle controller for execution.
In a third aspect of the present invention, there is provided a vehicle control system based on cloud computing, including:
the whole vehicle domain controller at the vehicle end acquires real-time data of vehicle speed, acceleration, steering angle, vehicle braking and longitude and latitude from the whole vehicle controller, and uploads the real-time data to the cloud end through the intelligent domain controller to serve as input of an automatic driving control model;
the method comprises the steps that a perception system at a vehicle end acquires road environment data, processes the road environment data into image feature data which can be fused with a high-definition map, and uploads the image feature data to a cloud end;
and the cloud acquires a high-definition satellite map, fuses the high-definition satellite map with the image characteristic data, inputs the high-definition satellite map into an automatic driving control model, and outputs strategy functions related to the vehicle speed, the acceleration, the steering angle, the vehicle braking and the longitude and latitude at the next control moment according to the real-time data of the vehicle speed, the acceleration, the steering angle, the vehicle braking and the longitude and latitude at the current control moment.
In a fourth aspect of the present invention, there is provided an electronic apparatus comprising:
at least one processor; and at least one memory communicatively coupled to the processor, wherein: the memory stores program instructions executable by the processor, the processor invoking the program instructions to perform the methods according to the first and second aspects of the embodiments of the invention.
In a fifth aspect of the present invention, there is provided a computer readable storage medium having stored thereon a computer program which, when executed by a computer, performs the method according to the first and second aspects of the embodiments of the present invention.
According to the cloud computing method, the vehicle-end automatic driving control model is moved up to the cloud, the vehicle-end reserves basic automatic driving computing capability, and the acquired data are processed, so that cloud computing is facilitated; the invention realizes redundant configuration of automatic driving. The vehicle end data are acquired through the data mining technology, the vehicle instructions are issued to the vehicle end through cloud simulation calculation to control the real-time running of the vehicle in real time, the calculation force of the vehicle end is liberated, and the cost reduction of a bicycle is realized.
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Fig. 1 is a schematic diagram of a technical implementation of a vehicle control system based on cloud computing in an embodiment of the present invention.
Fig. 2 is a flow chart of a vehicle control method based on cloud computing in an embodiment of the invention.
Fig. 3 is a flowchart of another vehicle control method based on cloud computing according to an embodiment of the invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The vehicle end refers to a vehicle configured to be in an autonomous driving mode in which the vehicle navigates with little or no driver intervention. The vehicle end includes a perception system having one or more sensors configured to detect information about a driving environment of the vehicle therein. The vehicle end and its associated controller(s) use the detected information to navigate the drive. The vehicle end may complete autonomous driving or assisted driving in a manual mode, a full autonomous mode, or a partial autonomous mode.
Ethernet and LVDS (Low-Voltage Differential Signaling, low voltage differential signaling technology) are rapidly developed at the vehicle end, the transmission efficiency of real-time data of the vehicle is improved, meanwhile, 4G/5G communication is rapidly developed, the interaction capability of cloud and vehicle end data is improved, and a guarantee is provided for the cloud automatic driving technology. Therefore, the cloud terminal can realize that data are applied to the cloud terminal to realize automatic driving, the cloud terminal can realize one-to-many vehicle control, and the automatic driving calculation service is placed in the cloud terminal, so that on one hand, the calculation force pressure of a vehicle terminal can be liberated, and on the other hand, the released calculation force of the vehicle terminal can better serve other functions of the vehicle, and better service is provided for users.
Referring to fig. 1, the present invention provides a vehicle control system based on cloud computing, which includes a cloud end and a vehicle end. The vehicle end keeps the basic calculation force of the automatic driving of the vehicle, the real-time data acquired by the vehicle sensing system is directly uploaded to the whole vehicle domain control unit (Computation Electronic Control Unit, ECUC), and then the real-time data is uploaded to the cloud through the intelligent domain control unit (Antenna Electronic Control Unit, ECUA). The whole-vehicle-domain control unit issues instructions to the whole-vehicle controller for controlling the whole-vehicle controller (Vehicle Control Unit, VCU) to transmit real-time driving data of the vehicle to the whole-vehicle-domain control unit, and the whole-vehicle-domain controller is uploaded to the cloud through the intelligent-linked-domain control unit.
The vehicle perception system includes a computer vision system or functionality of a computer vision system to process and analyze images captured by one or more cameras to identify objects or features in the environment of the host vehicle. The objects may include traffic signals, road boundaries, other vehicles, pedestrians, and/or other obstacles, etc. Computer vision systems may use object recognition algorithms, video tracking, and other computer vision techniques. In some embodiments, the computer vision system may map the environment, track the object, and estimate the speed of the object, etc. The vehicle perception system may implement a predicted trajectory of an obstacle that predicts a path of the moving obstacle in an area related to the current travel path. The predicted trajectory may be generated based on a current state of the moving obstacle (e.g., a speed, a position, a heading, an acceleration, or a type of the moving obstacle), map data, and traffic rules.
The automatic driving control system may recognize the obstacle as a vehicle sensed to travel in the driving lane according to the advancing direction and position of the obstacle, and complete driving control such as overtaking, lane changing, idle running, and the like.
Specifically, the whole-vehicle-domain controller at the vehicle end acquires real-time data of vehicle speed, acceleration, steering angle, vehicle braking, longitude and latitude (positioning) from the whole-vehicle controller through Ethernet communication. The real-time data of the vehicle speed, acceleration, steering angle, vehicle braking and longitude and latitude (positioning) can be used as the data of the vehicle running, and then uploaded to the cloud end through the intelligent domain controller, and the vehicle speed, acceleration, steering angle, vehicle braking and longitude and latitude are used as the input of an automatic driving control model (reinforcement learning model).
The intelligent domain controller comprises a TBOX (Telematics BOX) system, and can realize data interaction between a vehicle end and a cloud end through a vehicle networking system. The automatic driving control model is an artificial intelligent model for controlling automatic driving, which is obtained by training the automatic driving control model by utilizing a machine learning algorithm model, such as a Convolutional Neural Network (CNN), a strong Reinforcement (RL), a clustering algorithm and the like. The automatic driving control model can refer to an automatic driving model adopted by a vehicle end in the prior art, and the method is different from the prior art in that the method transfers the automatic driving calculation of the vehicle end to a cloud end. The cloud end can realize one-to-many vehicle control, can liberate the vehicle end calculation power, reduces the cost of vehicle end use high calculation power treater.
As described above, the sensing system at the vehicle end acquires the road environment data, processes the road environment data into the image feature data that can be fused with the high-definition map, and uploads the image feature data to the cloud. The road environment data acquired by the sensing system comprise lane lines, pedestrians, vehicles, street lamp signals, construction signs and the like, effective information data in an image can be specifically identified by utilizing an image identification technology and are usually presented in a point cloud mode, and the point cloud data can be mapped with a high-definition map, for example, a coordinate system of the sensing system is mapped into a coordinate system of the high-definition map. The high-definition map is used for guiding the vehicle end to run, and the road environment information is contained in the high-definition map, so that the vehicle end can be better guided to run.
Therefore, the cloud can acquire a high-definition satellite map and fuse with the image feature data, and the travel cloud road environment model outputs lane lines, pedestrians (obstacles), traffic signs and the like.
The cloud acquires real-time data of vehicle speed, acceleration, steering angle, vehicle braking, longitude and latitude (positioning), and a cloud real-time vehicle model can be constructed based on the data. The cloud real-time vehicle model simulates the running state of the vehicle and is consistent with the real running state of the vehicle end. The cloud real-time vehicle model inputs the running state data of the vehicle to the automatic driving control model. The automatic driving model outputs strategy functions related to the vehicle speed, the acceleration, the steering angle, the vehicle braking and the longitude and latitude at the next control moment according to the real-time data of the vehicle speed, the acceleration, the steering angle, the vehicle braking and the longitude and latitude at the current control moment, and outputs vehicle control instructions according to the strategy functions. The function strategy is used for adjusting the speed, acceleration, steering angle and vehicle braking of the vehicle, so that the vehicle running can be controlled rapidly.
The intelligent domain control unit at the vehicle end receives the vehicle control instruction fed back by the cloud end and transmits the vehicle control instruction to the whole vehicle domain control unit, and the whole vehicle domain control unit issues the vehicle control instruction to the whole vehicle controller for execution.
According to the invention, the vehicle-end automatic driving model is moved up to the cloud end, the automatic driving computing capacity of the vehicle-end foundation is reserved, and the automatic driving redundancy configuration is realized. And acquiring vehicle end data through a data mining technology, and issuing a vehicle instruction to a vehicle end through cloud simulation calculation to control the real-time running of the vehicle in real time.
Referring to fig. 2, the invention further provides a vehicle control method based on cloud computing, which is applied to the cloud and comprises the following steps:
step 210: and acquiring a high-definition map and real-time traffic data related to a vehicle-end driving path, and constructing a road environment model, wherein the real-time traffic data at least comprises one of road environment data and intelligent traffic road platform data acquired by a vehicle.
The intelligent traffic road platform can provide real-time traffic signal lamp data service to the cloud, wherein the data service comprises traffic lights, green wave bands and the like, such as traffic light time, road driving direction and the like; road condition information such as lane construction, lane blocking and the like can also be provided. The cloud end can be combined with the high-definition map to conduct path planning.
The road environment data collected by the vehicle can be used for fusing with a high-definition map, and the data of the lane and the surrounding environment of the road are used for automatic driving control decision. Likewise, intelligent traffic road platform data is also used as an autopilot control decision; if the traffic light is a red light, the vehicle is automatically stopped, and if the traffic light is a green light, the vehicle is automatically started to run.
Step 220: and obtaining vehicle running data uploaded by the vehicle and constructing a vehicle running model.
Uploading real-time data of vehicle speed, acceleration, steering angle, vehicle braking, longitude and latitude (positioning) by a vehicle end, and constructing a vehicle model with the same running state as the vehicle end by using the information by a cloud end; control of the vehicle model is control of the vehicle end.
Step 230: and outputting real-time running control data of the control vehicle by using an automatic driving control model according to the parameters input by the road environment model and the vehicle running model.
In this step, the road environment model outputs at least lane line information, obstacle information, and traffic indication information to the automatic driving control model. Compared with the automatic driving control model which controls the vehicle to automatically run according to the road environment obtained by a high-definition map and a perception system by using the automatic driving control model at the vehicle end, the automatic driving control model can obtain more road information, better utilize the high-definition map to serve more vehicles and release the calculation pressure of a bicycle.
Further, when the vehicle end and the cloud end are connected through a good network, the cloud end controls the vehicle end to automatically drive. If the connection between the vehicle end and the cloud network does not meet the automatic driving control condition, the vehicle end executes the automatic driving control. Namely, when the vehicle end and the cloud end lose network connection; the vehicle end obtains real-time road information through the sensing system and transmits data to the whole vehicle domain controller, and the whole vehicle domain controller issues a vehicle control instruction to the whole vehicle controller for execution. When the network connection is poor, the vehicle end realizes automatic driving by utilizing the self basic calculation force, and the vehicle end carries an automatic driving control model and can still realize an automatic driving function.
Furthermore, the vehicle control method based on cloud computing further comprises the steps of acquiring road environment data acquired by a vehicle and updating the high-definition map by utilizing the road environment data.
For example, the image recognition technology at the vehicle end can be utilized to recognize effective information data in the acquired image, including lane lines, obstacles, traffic signals and the like; the lane lines are usually presented in a point cloud manner, the point cloud data can be mapped with a high-definition map, for example, a coordinate system of the sensing system is mapped into a coordinate system of the high-definition map, and the obstacle can be displayed at a corresponding position according to the coordinate system of the high-definition map. In the prior art, the update period of the high-definition map is longer, and the timeliness is poorer; the high-definition map can be updated by utilizing the information data, such as construction of a certain lane position, road change and the like, and the updated high-definition map is used for guiding the vehicle end to travel, and the road environment information in the high-definition map can better guide the vehicle end to travel.
As shown in fig. 3, the present invention further provides a vehicle control method based on cloud computing, which is applied to a vehicle end and includes:
step 310: and sensing system road environment data and uploading the road environment data to the cloud.
The road environment data acquired by the sensing system comprise lane lines, pedestrians, vehicles, street lamp signals, construction signs and the like, and effective information data in the image can be specifically identified by utilizing an image identification technology.
Step 320: the whole vehicle domain control unit acquires vehicle running data of the own vehicle from the whole vehicle controller and transmits the vehicle running data to the intelligent domain control unit.
The whole vehicle domain controller acquires real-time data of vehicle speed, acceleration, steering angle, vehicle braking and longitude and latitude (positioning) from the whole vehicle controller through Ethernet communication. The real-time data of the vehicle speed, the acceleration, the steering angle, the vehicle braking and the longitude and latitude can be used as the data of the vehicle running, and then uploaded to the cloud end through the intelligent domain controller, and the vehicle speed, the acceleration, the steering angle, the vehicle braking and the longitude and latitude are used as the input of an automatic driving control model.
Step 330: and uploading the vehicle driving data to the cloud by the intelligent domain control unit.
The intelligent domain controller includes a generic TBOX.
Step 340: the intelligent domain control unit receives the vehicle control instruction fed back by the cloud and transmits the vehicle control instruction to the whole vehicle domain control unit, and the whole vehicle domain control unit issues the vehicle control instruction to the whole vehicle controller for execution.
The vehicle control command is used to control vehicle speed, acceleration, steering angle, vehicle braking, etc. of the vehicle.
Further, when the vehicle end and the cloud end lose network connection; the vehicle end obtains real-time road information through the sensing system and transmits data to the whole-vehicle-domain control unit, and the whole-vehicle-domain control unit issues a vehicle control instruction to the whole vehicle controller for execution.
When the vehicle end and the cloud end lose network connection; the vehicle end obtains real-time road information through the sensing system and transmits data to the whole vehicle domain controller, and the whole vehicle domain controller issues a vehicle control instruction to the whole vehicle controller for execution. When the network connection is poor, the vehicle end realizes automatic driving by utilizing the self basic calculation force, and the vehicle end carries an automatic driving control model and can still realize an automatic driving function.
The present invention also provides an electronic device including:
at least one processor; and at least one memory communicatively coupled to the processor, wherein: the memory stores program instructions executable by the processor, and the processor invokes the program instructions to execute the vehicle control method based on cloud computing.
The invention also provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program realizes the vehicle control method based on cloud computing when being executed by a processor.
It is understood that the computer-readable storage medium may include: any entity or device capable of carrying a computer program, a recording medium, a USB flash disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a software distribution medium, and so forth. The computer program comprises computer program code. The computer program code may be in the form of source code, object code, executable files, or in some intermediate form, among others. The computer readable storage medium may include: any entity or device capable of carrying computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a software distribution medium, and so forth.
In some embodiments of the present invention, a controller may be included, where the controller is a single-chip microcomputer chip, integrated with a processor, memory, communication module, etc. The processor may refer to a processor comprised by the controller. The processor may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and additional implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present invention.
Those of ordinary skill in the art will appreciate that the elements and algorithm steps of the examples described in connection with the embodiments disclosed herein may be embodied in electronic hardware, in computer software, or in a combination of the two, and that the elements and steps of the examples have been generally described in terms of function in the foregoing description to clearly illustrate the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. The vehicle control method based on cloud computing is applied to the cloud and is characterized by comprising the following steps of:
acquiring a high-definition map and real-time traffic data related to a vehicle-end driving path, and constructing a road environment model, wherein the real-time traffic data at least comprises one of road environment data and intelligent traffic road platform data acquired by a vehicle;
acquiring vehicle running data uploaded by a vehicle and constructing a vehicle running model;
and outputting real-time running control data of the control vehicle by using an automatic driving control model according to the parameters input by the road environment model and the vehicle running model.
2. The cloud computing-based vehicle control method according to claim 1, wherein when a vehicle end loses network connection with a cloud; the vehicle end obtains real-time road information through the sensing system and transmits data to the whole vehicle domain controller, and the whole vehicle domain controller issues a vehicle control instruction to the whole vehicle controller for execution.
3. The cloud computing-based vehicle control method according to claim 1, wherein the road environment model outputs lane line information, obstacle information, traffic indication information at least to the automatic driving control model.
4. The cloud computing-based vehicle control method according to claim 1, wherein the vehicle running model outputs at least a vehicle speed, an acceleration, a steering angle, a braking state, a longitude and latitude to the automatic driving control model.
5. The cloud computing-based vehicle control method of claim 1, further comprising obtaining road environment data collected by a vehicle, and updating the high-definition map with the road environment data.
6. The vehicle control method based on cloud computing is applied to a vehicle end and is characterized by comprising the following steps of:
sensing system road environment data and uploading the road environment data to the cloud;
the whole vehicle domain control unit acquires vehicle running data of a self vehicle from the whole vehicle controller and transmits the vehicle running data to the intelligent domain control unit;
the intelligent domain control unit uploads the vehicle running data to the cloud;
the intelligent domain control unit receives the vehicle control instruction fed back by the cloud and transmits the vehicle control instruction to the whole vehicle domain control unit, and the whole vehicle domain control unit issues the vehicle control instruction to the whole vehicle controller for execution.
7. The cloud computing-based vehicle control method according to claim 6, wherein when the vehicle end and the cloud end lose network connection; the vehicle end obtains real-time road information through the sensing system and transmits data to the whole-vehicle-domain control unit, and the whole-vehicle-domain control unit issues a vehicle control instruction to the whole vehicle controller for execution.
8. A vehicle control system based on cloud computing, comprising:
the whole vehicle domain controller at the vehicle end acquires real-time data of vehicle speed, acceleration, steering angle, vehicle braking and longitude and latitude from the whole vehicle controller, and uploads the real-time data to the cloud end through the intelligent domain controller to serve as input of an automatic driving control model;
the method comprises the steps that a perception system at a vehicle end acquires road environment data, processes the road environment data into image feature data which can be fused with a high-definition map, and uploads the image feature data to a cloud end;
and the cloud acquires a high-definition satellite map, fuses the high-definition satellite map with the image characteristic data, inputs the high-definition satellite map into an automatic driving control model, and outputs strategy functions related to the vehicle speed, the acceleration, the steering angle, the vehicle braking and the longitude and latitude at the next control moment according to the real-time data of the vehicle speed, the acceleration, the steering angle, the vehicle braking and the longitude and latitude at the current control moment.
9. An electronic device, comprising:
at least one processor; and at least one memory communicatively coupled to the processor, wherein: the memory stores program instructions executable by the processor, the processor invoking the program instructions capable of performing the method of any of claims 1-5 or claims 6, 7.
10. A computer-readable storage medium, on which a computer program is stored, which, when being run by a computer, performs the method of any one of claims 1 to 5 or 6, 7.
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