CN109729164B - Grading distribution method for vehicle end and cloud end operation of intelligent networked vehicle computing platform - Google Patents
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Abstract
The invention discloses a grading distribution method for vehicle end and cloud end operation of an intelligent networked vehicle computing platform, which is characterized in that a vehicle end executes data computation with high real-time requirement, small computation amount and strong pertinence to vehicle characteristics, and the cloud end executes data computation with large computation amount, strong sharing property and low real-time requirement as a criterion, a scene information grade function F (t) related to time is constructed, a function threshold value is set, a computing task is executed at the cloud end when the value of F (t) is more than or equal to the threshold value, and the computing task is executed at the vehicle end when the value of F (t) is less than the threshold value. According to the invention, through resource sharing and labor division cooperation of the vehicle end and the cloud end, the computing platform of the intelligent networked vehicle under the intelligent and networked background can be designed according to, and the problems of huge computing system and heavy computing burden of the intelligent vehicle are solved.
Description
Technical Field
The invention relates to intelligent networked automobile data calculation, in particular to a grading distribution method for automobile end and cloud end operation of an intelligent networked automobile calculation platform.
Background
With the application of technologies such as internet, artificial intelligence, cloud computing and big data, the degree of intellectualization and networking of the automobile is higher and higher, and the development of the intelligent automobile automatic driving technology, the popularization of efficient vehicle-mounted networks and countless communication interfaces provide brand-new challenges for the automobile. For example, the amount of information interaction between the smart networked automobile and the outside world is greatly increased, the data processing load is increased, and the vehicle architecture becomes complicated and huge, which is not desirable for smart automobile manufacturing.
With the sharing of big data and the application of cloud platforms, automobile developers and manufacturers want to utilize more resources, realize resource sharing, reduce the operation burden of vehicles, simplify the system architecture, and realize the light weight of vehicles as much as possible. However, the current cloud platform (hereinafter referred to as cloud) communication speed limitation and information security consideration are prevented, the resources are not effectively shared, the cloud is only used as a data source of a vehicle-mounted computing platform (hereinafter referred to as vehicle end), and the computing work of the intelligent driving vehicle is mainly concentrated on the vehicle-end computing platform. However, with the development of communication technology in the future (for example, the development of 5G and 6G high-speed networks), the progress of information security technology and the soundness of laws and regulations, the phenomenon is not suitable for all the time, the computing burden of the smart vehicle will be increased, and how to allocate computing tasks of the cloud and the vehicle end and how to cooperate with each other, so that the optimal path and action are planned for the vehicle, which is a huge problem.
At present, in the field of intelligent driving, no clear division of labor is available for how to distribute computing tasks to a cloud end and a vehicle end, so that a reasonable division manner is researched, the standardization of an intelligent vehicle data computing module is strived to be constructed, the intelligent vehicle data computing module is in butt joint with a big data cloud platform, a vehicle system is simplified, the cost of a vehicle-mounted computing platform is reduced, and the method is a subject worthy of research.
Disclosure of Invention
In order to realize resource sharing, division of labor cooperation and development by utilizing the internet technology, the invention aims to solve the problems of huge operation system and heavy operation burden of an intelligent automobile in the prior art, and provides a grading distribution method for vehicle-end and cloud operation of an intelligent networked automobile computing platform.
The technical scheme adopted by the invention is as follows: a vehicle end and cloud end operation grade distribution method of an intelligent networked vehicle computing platform is characterized in that a scene information grade function F (t) related to time is constructed by taking data computation with high real-time requirement, small computation amount and strong pertinence to vehicle characteristics, and data computation with large cloud end execution computation amount, strong sharing property and low real-time requirement as a criterion; and setting a function threshold for the scene information level function F (t), executing a calculation task at a cloud end when the value of F (t) is greater than or equal to the threshold, and executing the calculation task at a vehicle end when the value of F (t) is less than the threshold.
Further, the constructed scene information level function f (t) is as follows:
F(t)＝[N+V+C]*η1*η2*η3*η4*η5*η6
wherein, F (t) is a scene information grade function value at the time t; n is the number parameter of intelligent terminals around the vehicle in the scene, and V is the speed parameter of the vehicle; c is road network condition parameters; eta1Is a visibility influence coefficient, η2For historical accident rate influence coefficient, eta, of a road section3Is a vehicle congestion influence coefficient, eta4For non-motor vehicles or pedestrians to walk through at will, η5Is the signal lamp influence coefficient, eta6Is the highway section influence coefficient.
Further, a function threshold is defined for each model.
Further, a value range of 1-N is set for the number parameter N of the intelligent terminals in the scene, and the number of the peripheral terminals is larger and larger according to the number value of the intelligent terminals around the vehicle.
Further, a value range of the speed parameter V of the self-vehicle is set to be 0-n, according to the value of the current vehicle speed of the self-vehicle, when the vehicle speed is less than or equal to 20km/h, the value of V is 0, and the value is larger when the vehicle speed is higher.
Further, for the road network condition parameter C, a value range is set to be 0-n, and a value is taken according to a proportion of the current bandwidth of a certain road section to the maximum bandwidth of the road network, wherein the larger the proportion of the current bandwidth to the maximum bandwidth of the road network is, the larger the value is.
Further, the value upper limit n of each parameter is a natural number and is set according to the vehicle type.
Further:
influence coefficient eta on visibility1The higher the visibility is, the smaller the value is;
influence coefficient eta of historical accident rate on road section2The lower the historical accident rate of the road section is, the smaller the value is;
influence coefficient eta on vehicle congestion3The lighter the congestion condition is, the smaller the value is;
influence coefficient eta for arbitrary passing of non-motor vehicles or pedestrians4The more serious the non-motor vehicle or pedestrian interference is, the smaller the value is;
coefficient of influence eta on signal lamp5The value of the road section with the traffic light is relatively large, and the value of the road section without the traffic light is relatively small;
influence coefficient eta for high speed road section6In the high-speed road section, the value is relatively large, and in the non-high-speed road section, the value is relatively small.
Furthermore, the value of each influence coefficient is between 0 and 1.
Compared with the prior art, the invention has the following remarkable beneficial effects:
1. according to the vehicle-mounted computing platform, the vehicle end and the cloud end are used for cooperative computing, after a part of computing tasks are distributed by the cloud end, the computing force requirement of the vehicle-mounted computing platform can be greatly reduced, and therefore the cost of the vehicle-mounted computing platform is reduced.
2. The working boundaries of the cloud and the vehicle-mounted computing platform can be clearly divided, a clear division range is determined for future research of vehicle-side and cloud computing, and computing redundancy or deficiency is reduced.
3. According to the national conditions, the weather, the road conditions of pedestrians and vehicles passing through at will and the road conditions of vehicle congestion are considered, and the applicable scenes of the cloud pipe end system are enriched.
4. All calculations can be uploaded to the cloud storage in real time, and data sharing is provided for other vehicles.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the invention.
Detailed Description
The present invention is described in detail with reference to the following examples, but it should be understood by those skilled in the art that the following examples are not intended to limit the technical scope of the present invention, and any equivalent changes or modifications made within the spirit of the technical scope of the present invention should be considered as falling within the scope of the present invention.
The invention combines the basic architecture of the current intelligent vehicle and the technical characteristics of a cloud platform and the Internet, and adopts the basic strategy according to the computing power and the working emphasis of a vehicle end and a cloud end: the vehicle end is mainly used for realizing data calculation which is high in real-time requirement, small in calculation amount and only specific to the individuality of the vehicle, and the cloud end is mainly used for realizing data calculation which is huge in calculation amount, strong in sharing performance and relatively low in real-time requirement. Since the calculation capability and specific algorithm of the vehicle-end calculation platform are different and the unified standard cannot be used for grading, the invention adopts the method corresponding to different road conditions and scenes to carry out grading processing on the scenes where the vehicles are located, so that the division calculation of the vehicle-end and the cloud end is used as a reference to obtain unified measurement.
According to the invention, a scene information grade function related to time is constructed from multiple dimensions of information processing amount, information processing real-time performance, current network condition and the like of a scene, and in a specific implementation benefit, the grade function is constructed as follows:
F(t)＝[N+V+C]*η1*η2*η3*η4*η5*η6
if the grade function value is larger, the cloud computing task is heavier, and if the grade function value is smaller, the computing task executed at the vehicle end is heavier.
Wherein, F (t) is a scene information level function value at the time t; n is number parameter of intelligent terminals around the self-vehicle in sceneThe number (for example, a vehicle, roadside equipment, a base station and the like capable of performing end-to-end communication) is determined by the number of intelligent terminals around the vehicle at the time t; v is a speed parameter of the vehicle and is determined by the current speed of the vehicle; c is road network condition parameter, which is determined by the proportion of the current bandwidth of the road section to the maximum bandwidth of the network; eta1～η6Correcting the coefficients for various influences, wherein the visibility influence coefficient is set to eta1The influence coefficient of the historical accident rate of the road section is eta2The vehicle congestion influence coefficient is eta3The influence coefficient of the arbitrary passing of the non-motor vehicle or the pedestrian is eta4Signal lamp influence coefficient is eta5The influence coefficient of a highway section is eta6Other impact coefficients may be accumulated.
All the parameters are represented by numerical quantification, and finally, a numerical evaluation function is formed.
The above parameters and correction coefficients are set based on the vehicle type, and these parameters have a uniform setting standard for each vehicle type.
The self-vehicle can carry out information interaction between the terminal and the terminal with surrounding intelligent terminals, and the more the intelligent terminals are, the more the communication between the terminal and the terminal is facilitated. And aiming at each type of vehicle, the influence parameter N of the number of the peripheral intelligent terminals in the driving process can be set according to the characteristics of the vehicle. The parameter setting principle is that the number of the terminals around is more, and the parameter value is larger, wherein n is a natural number.
For example, when the number of surrounding terminals is small, the parameter level may be set to be slightly lower, and each time one terminal, or two terminals, or one cell is added, the parameter level is increased by one, and when the number is large, the parameter level is classified as the highest parameter level. As shown in table 1, the number of peripheral intelligent terminals is within 2, the influence parameter is set to 1, 2 when reaching 3, 3 and … when reaching 4, and 10 after reaching 10.
Table 1:
| number of surrounding vehicles | 2 | 3 | 4 | … | 10 | … |
| Parameter N | 1 | 2 | 3 | … | 10 | 10 |
Of course, other access rules may be set, for example, every time 2 vehicles are added as a parameter level, the values are 1 for 1-2 vehicles, 2 for 3-4 vehicles, …, and 6 for 10- ….
The speed parameter V of the vehicle is mainly related to the current vehicle speed of the vehicle, and the higher the vehicle speed is, the larger the amount of information to be processed in unit time is, the stronger the calculation capability of the calculation end is required. The higher the vehicle speed is, the higher the parameter level is, the larger the value is, for example, the parameter value range is set to be 0-n, the V value is 0 when the vehicle speed is less than or equal to 20km/h, n grades are divided at equal intervals when the vehicle speed is more than 20km/h, and the highest value n is set after the vehicle speed exceeds 120 km/h. As shown in table 2, set nmax to 10.
Table 2:
the road network condition parameter C of a certain road section is determined by the proportion of the current bandwidth of the road section to the maximum bandwidth of the network under the existing conditions, if the proportion of the current bandwidth to the maximum bandwidth of the network is larger, the network transmission capability is high, the cloud end can quickly transmit the calculation result, and the calculation burden of the vehicle end is reduced. Similarly, the range of the network condition parameter C is set to 1-n, and the limit occupation ratio is 100% of the current bandwidth occupying the maximum bandwidth of the network under the current condition, so the network condition parameter C can be set to the maximum value, for example, the value 4 in table 3. For the intermediate ratio, the values can be segmented, for example, the ratio 0-0.4 in table 3 is 1, and the value 0.4-0.6 is 2. The value can be increased by an equivalent value, for example, the value is increased by an order of magnitude every time the ratio is increased by 0.1 until the ratio is 1.
Table 3:
besides the above main factors, weather conditions, road section accidents, vehicle congestion, non-motor vehicles or pedestrians randomly walk, signal lamps, high-speed road sections and other conditions all affect the calculation capability of the above factors, so that some influence coefficients can be set for scene information correction. Setting the visibility influence coefficient to eta1The influence coefficient of the historical accident rate of the road section is eta2The vehicle congestion influence coefficient is eta3The influence coefficient of the arbitrary passing of the non-motor vehicle or the pedestrian is eta4Signal lamp influence coefficient is eta5The influence coefficient of a highway section is eta6And may accumulate if other factors of influence are present.
(1) In severe weather or under the condition of low visibility, the information acquired by the vehicle-mounted sensor of the self is limited, and large-scale communication between the cloud and other vehicles and roads is needed for decision making, so that the driving route and operation of the self are planned, and therefore, the vehicle-end computing capacity is higher in the case of high visibilityThe stronger the visibility influence coefficient eta1The smaller.
(2) In a traffic accident frequent road section, the cloud end can utilize traffic accident data analysis before the road section to plan a safe and reasonable passing route and operation for a self-vehicle, the cloud end can make the safe route planning of the road section in advance when the self-vehicle approaches the accident frequent road section, and after the self-vehicle safely passes through the road section, a path can also be used as path data storage when a follow-up vehicle passes through, so that the historical accident influence rate coefficient eta of the road section in case of no accident2And minimum.
(3) In a congested road section of a vehicle, the vehicle running speed is low, the real-time requirement on data processing is low, vehicles around the congested road section are more, and the data volume to be calculated is huge, so that a cloud is selected to plan a quick passing route for a self vehicle, the cloud can be combined with the passing routes of the vehicles around, signal lamps and other environmental conditions to plan a quick and safe passing mode for each vehicle in the congested road section, and a vehicle end only takes over when receiving danger early warning of collision, so that the influence coefficient eta is more and less in the congested condition3The smaller.
(4) On the road section where pedestrians or motor vehicles pass randomly, the calculation end needs to quickly respond to the irregular movement of surrounding pedestrians or non-motor vehicles, so that the requirement on the real-time performance of calculation is high, the vehicle end sensor can sense surrounding pedestrians or non-motor vehicles in real time and quickly send out an instruction to command the vehicles to safely and reasonably avoid, and therefore, under the condition that the non-motor vehicles or the pedestrians are interfered, the influence coefficient eta is4The smaller the value, the more serious the interference, the smaller the value.
(5) In a road section near the signal lamp intersection, the vehicle path is relatively regular, and the cloud end can plan a safe and economic route for the self vehicle according to the state of the signal lamp in front and the state of the vehicle in front. For example, when the vehicle is away from the red light by a certain distance and no vehicle follows behind the red light, the cloud end commands the vehicle to start sliding at a proper position, and the signal light is enabled to be green when the vehicle reaches the position near the signal light; and the road section without signal lamps usually needs the vehicle end to carry out emergency treatment, so the real-time requirement is high. Therefore, the signal lamp influence coefficient eta is in the standard road section with signal lamp and the like5The larger the value is, the influence coefficient eta is on the road section without signal lamp5The smaller the value.
(6) In the highway section, the vehicle environment is relatively simple, the real-time requirement on the calculation end is low, and the cloud end can reasonably plan the vehicle route. For example, when the vehicle is planned to the right lane in front of the exit of the expressway and is planned to be imported to the entrance of the expressway, the vehicle which is imported to the right lane and the vehicle which is already on the lane are planned, collision is avoided, and meanwhile, the driving of other vehicles is not influenced. Thus, in high speed road sections, the influence coefficient η6The value is relatively large; in non-highway sections, the influence coefficient eta6The value is relatively small.
The values of the coefficients are all between 0 and 1.
If other factors of influence can accumulate, the scene information rating function can be extended to:
F(t)＝[N+V+C]*η1*η2*η3*η4*η5*η6*η7*…*ηn
according to the design principle of the visible scene information level function F (t), the scene information processing level is judged according to the function value, the larger the function value is, the larger the calculation task at the cloud end is, the smaller the function value is, and the larger the calculation task at the vehicle end is. A function threshold value can be defined by each type of vehicle in a self-defined mode, computing tasks are executed on the cloud side when the function threshold value is larger than or equal to the threshold value, and computing tasks are executed on the vehicle side when the function threshold value is smaller than the threshold value. The invention fully utilizes the advantages of the vehicle end and the cloud end, cooperatively executes the calculation task, and can realize the purposes of efficiently analyzing the automatic driving condition and reasonably planning the driving path.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention.
Claims (4)
1. A vehicle end and cloud end operation grade distribution method of an intelligent networked vehicle computing platform is characterized in that a scene information grade function F (t) related to time is constructed by taking data computation with high real-time requirement, small computation amount and strong pertinence to vehicle characteristics executed by a vehicle end and data computation with large computation amount, strong sharing property and low real-time requirement executed by a cloud end as a criterion;
setting a function threshold for the scene information level function F (t), executing a calculation task at a cloud end when the value of F (t) is greater than or equal to the threshold, and executing the calculation task at a vehicle end when the value of F (t) is less than the threshold;
F(t)＝[N+V+C]*η1*η2*η3*η4*η5*η6
wherein, F (t) is a scene information grade function value at the time t; n is the number parameter of the surrounding intelligent terminals in the scene; v is the speed parameter of the vehicle; c is road network condition parameters; eta1Is a visibility influence coefficient, η2For historical accident rate influence coefficient, eta, of a road section3Is a vehicle congestion influence coefficient, eta4For non-motor vehicles or pedestrians to walk through at will, η5Is the signal lamp influence coefficient, eta6The influence coefficient of the highway section is;
setting a value range of 1-N for N, and taking values according to the number of intelligent terminals around the vehicle, wherein the larger the number of the surrounding terminals is, the larger the value is;
setting a value range of 0-n for V, and taking a value according to the current real-time speed of the vehicle, wherein when the speed is less than 20km/h, the value is 0, and the value is larger when the speed is higher;
setting the value range of C to be 0-n, and taking the value according to the proportion of the current bandwidth of a certain road section to the maximum bandwidth of the road network, wherein the larger the proportion of the current bandwidth to the maximum bandwidth of the road network is, the larger the value is;
influence coefficient eta on visibility1The higher the visibility is, the smaller the value is;
influence coefficient eta of historical accident rate on road section2The lower the historical accident rate of the road section is, the smaller the value is;
influence coefficient eta on vehicle congestion3The lighter the congestion condition is, the smaller the value is;
influence coefficient eta for arbitrary passing of non-motor vehicles or pedestrians4The more serious the non-motor vehicle or pedestrian interference is, the smaller the value is;
coefficient of influence eta on signal lamp5The value of the road section with the signal lamp is relatively large, and the value of the road section without the signal lamp is relatively small;
influence coefficient eta for high speed road section6In the high-speed road section, the value is relatively large, and in the non-high-speed road section, the value is relatively small.
2. The intelligent networked automobile computing platform vehicle-end and cloud computing grading distribution method according to claim 1, wherein a function threshold is defined for each type of vehicle.
3. The intelligent networked automobile computing platform vehicle-end and cloud computing grading distribution method according to claim 1, wherein a value upper limit n in each parameter is a natural number and is set according to a vehicle type.
4. The intelligent networked automobile computing platform vehicle-end and cloud computing grade distribution method according to claim 1, wherein each influence coefficient value is between 0 and 1.
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| CN109729164A (en) | 2019-05-07 |
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