Car networking knowledge base representation method, device and system

A knowledge representation and Internet of Vehicles technology, applied in the field of Internet of Vehicles, can solve problems such as difficulties and shortages

Active Publication Date: 2019-01-11
HUAWEI TECH CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Due to the lack of general and basic means applicable to the entire IOV f...

Method used

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  • Car networking knowledge base representation method, device and system
  • Car networking knowledge base representation method, device and system
  • Car networking knowledge base representation method, device and system

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0290] Example 1: SC-based IOV knowledge representation

[0291] ○ Use SC as the carrier of knowledge on the IOV multi-dimensional continuous space to perform discrete sampling and continuous deduction.

[0292] Use a k-dimensional SC with a k’-dimensional function value to represent an IOV knowledge f:R k →R k’ (that is, a function with k input variables and k' output values: 1 ,...,x k >→1 ,...,y k’ >):

[0293] ■ Any point in SC represents an input point of f;

[0294] ■Any point in SC has a k-dimensional coordinate, that is, the value of k input variables at this point;

[0295] ■Any point in SC has a k'-dimensional function value, that is, k' output values ​​of f at this point.

[0296] ●Discrete sampling, continuous deduction:

[0297] ■Discrete sampling: use the vertices of SC as sampling points, only record coordinates and function values ​​at these points, and do not record at other points (non-sampling points).

[0298] ■Continuous deduction: Use SC units as ...

Embodiment 2

[0301] Embodiment 2: Import method of existing knowledge of IOV based on SC

[0302] ○Designed a general method of importing knowledge into SC (that is, the initialization method of knowledge base): first select a suitable set of SC vertices (discrete sampling points), and calculate the function value on each vertex, and then establish SC units to seamlessly Connect the vertices.

[0303] ○Specifically, the existing knowledge also includes the knowledge from the rule-based IOV system, so as to realize the reexpression and import of the existing rules (technical problem 1).

Embodiment 3

[0304] Embodiment 3: SC-based method for quickly locating IOV knowledge points

[0305] ○Designed a method to speed up the search by attaching additional clues: the problem of directly searching for the target unit where the given target point is located is transformed into an indirect search process: first determine a starting unit and starting point for the search, and then start from the Starting from the starting unit, along the ray from the starting point to the target point, linearly search for the target unit. In this way, the originally vast search space (the entire SC) can be reduced to a series of cells penetrated by rays, and an exponential search speedup is obtained, thereby making up for the biggest shortcoming of topological SC (technical problem 6).

[0306] ○The acquisition method of additional clues is designed: the entire SC space is structured and partitioned, and according to the continuity of multiple positioning, the agent unit of the area where the targe...

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Abstract

The embodiment of the invention provides a knowledge representation method of a vehicle networking IOV based on a simplex SC. The method comprises the following steps: k-dimensional SC with k-dimensional function value is used to represent k-dimensional knowledge on k-dimensional continuous space, wherein the coordinates of the vertices of the SC are the values (x1,..., xk) of k input variables, and the function values on the vertices are k 'output values (y1,..., yk') of the function, and the relationship between the two is (y1,..., yk ') = f (x1,..., xk'), where f is a correspondence function based on IOV knowledge; K and k 'are natural numbers; the boundary of SC is used to represent the security boundary of IOV knowledge, where the IOV knowledge includes the steering wheel angle of theautonomous vehicle, The relationship between the road curvature and the speed of the autonomous vehicle and the parameters is in accordance with the objective law of vehicle dynamics, wherein the steering wheel angle of the autonomous vehicle, the road curvature and the speed of the autonomous vehicle are obtained by sensors on the autonomous vehicle. The steering wheel angle of the autonomous vehicle, the road curvature and the speed of the autonomous vehicle are obtained by sensors on the autonomous vehicle.

Description

technical field [0001] The present invention relates to the field of the Internet of Vehicles, in particular to a representation method, device and system for a knowledge base of the Internet of Vehicles. Background technique [0002] Recently, an obvious development trend of the Internet of Vehicles is the transition from a rule-based approach to a learning-based approach. Transition methods can be divided into two categories: split transition and fusion transition. In the fragmented transition, the new learning-based IOV system completely follows the learning method, learns from scratch, from form to content, and completely abandons the old rule-based system. In other words, a large number of rules and experience (or knowledge) that were summarized and accumulated under the rule-based framework before were also discarded and wasted. In a blended transition, the knowledge of the rule-based system is applied to the learning-based framework to maximize the former's energy. ...

Claims

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Application Information

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IPC IPC(8): G06F16/22
CPCG06N5/022G06N20/00G06N3/08G06N3/042B60W2552/30B60W30/08B62D15/021G06N5/025
Inventor 殷晓田李剑陶永祥
Owner HUAWEI TECH CO LTD
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