Simplicial Complex IOV Knowledge Representation for Safe Boundaries
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current learning-based Internet of Vehicles (IOV) systems primarily follow a transition in split mode, abandoning existing rule-based knowledge, whereas a transition in integrated mode is underdeveloped, lacking a general means to effectively utilize and integrate knowledge across the IOV field.
Innovation Solution
The application proposes a method and system for representing an IOV knowledge base using a simplicial complex (SC), enabling the representation of k′-dimensional knowledge in a k-dimensional continuous space. This involves using a k-dimensional SC with k′-dimensional function values to map input variables to output values, and utilizing the boundary of the SC to represent a safe boundary of IOV knowledge.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If transition in split mode is used to build learning-based IOV systems, then the systems can be constructed with clear methodology, but existing rule-based knowledge is abandoned and wasted
Solution Approach 1:
The patent merges rule-based knowledge representation with learning-based systems by integrating symbolic rules into neural network architectures. This allows the system to incorporate existing rule-based knowledge while enabling learning capabilities, thus avoiding the waste of accumulated knowledge during transition to learning-based systems.
Solution Approach 2:
The patent creates a universal knowledge representation framework that can accommodate both rule-based and learning-based approaches. The system design allows it to perform multiple functions: storing rule-based knowledge, enabling learning processes, and facilitating transition between different operational modes, thereby preserving existing knowledge while building new capabilities.
2Loss of substance
If transition in integrated mode is used to utilize rule-based knowledge in learning-based framework, then knowledge is preserved, but lack of general basic means makes implementation difficult
Solution Approach 1:
The patent segments the complex task of integrating rule-based knowledge into learning-based systems into manageable components. It divides the knowledge representation into distinct layers and modules, allowing systematic implementation while preserving existing knowledge. This segmentation reduces implementation complexity by breaking down the integration process into structured steps.
Solution Approach 2:
The patent introduces intermediary structures that facilitate the integration between rule-based knowledge and learning-based frameworks. These intermediary representations serve as bridges, translating and adapting rule-based knowledge into formats compatible with learning systems, thereby reducing implementation complexity while preserving knowledge integrity.
3Adaptability or versatility
If AGI mode learning is implemented in IOV field, then online learning and updating can be performed continuously, but lack of general basic means creates great difficulty
Solution Approach 1:
The patent prepares the system in advance by establishing a structured knowledge representation framework before implementing continuous learning. This preliminary structuring of knowledge spaces, boundaries, and representation methods creates a foundation that enables online learning and updating while reducing the complexity of implementing AGI-mode learning in the IOV field.
Data Source
AI summary
An embodiment of the application provides a method for representing internet of vehicles (IOV) knowledge based on a simplicial complex (SC). The method includes: representing k′-dimensional knowledge in a k-dimensional continuous space by using a k-dimensional SC with k′-dimensional function values; and representing a safe boundary of the IOV knowledge by using a boundary of the SC, where the IOV knowledge includes a steering wheel angle of an ego-vehicle, a road curvature, a speed of the ego-vehicle, and an inter-parameter relationship compliant with an objective law of vehicle dynamics, where the steering wheel angle of the ego-vehicle, the road curvature, and the speed of the ego-vehicle are obtained by using a sensor on the ego-vehicle.