Distributed Knowledge Base for Vehicle Localization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current semi-automated vehicle systems are unreliable due to complexity and uncertainty in operating environments, lacking accurate navigation and obstacle avoidance, especially in environments with heavy foliage or obstructions that interfere with GPS signals.
Innovation Solution
A distributed knowledge base system combining a fixed and learned knowledge base, which includes a priori and online information, is used to control vehicle operations, integrating sensor data from various sources for accurate navigation and obstacle avoidance, allowing for both manned and autonomous vehicle modes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If GPS-based navigation is used for vehicle localization, then navigation capability is provided, but reliability deteriorates in environments with heavy foliage or obstructions that interfere with GPS signals
Solution Approach 1:
The patent introduces an intermediary system consisting of a distributed network of fixed and mobile reference vehicles that relay position information. Instead of relying directly on GPS signals that are blocked by foliage, the system uses intermediate vehicles equipped with reference identifiers to mediate the localization process, allowing vehicles to determine positions through cooperative communication rather than direct satellite signal reception.
Solution Approach 2:
The patent replaces the GPS satellite-based electromagnetic signal system with a vehicle-to-vehicle communication system using reference identifiers and distributed knowledge bases. This substitution eliminates dependence on external satellite signals that are vulnerable to environmental interference, using instead a self-contained network of vehicles that share localization information through direct communication.
2Extent of automation
If completely automated vehicle systems are implemented, then operator control is eliminated, but reliability deteriorates due to system complexity and uncertainty in operating environments
Solution Approach 1:
The patent implements a dynamic system where the vehicle operates in different modes (fully autonomous, semi-autonomous, or manual control) depending on environmental conditions and task requirements. The system can transition between automation levels, allowing the operator to take control when uncertainty is high while maintaining high automation in favorable conditions, thus adapting the extent of automation rather than fixing it at one level.
Solution Approach 2:
The system incorporates continuous feedback loops where sensor data, localization accuracy, and environmental conditions are monitored and used to adjust the level of automation and trigger operator intervention when necessary. The distributed knowledge base provides feedback on position uncertainty, enabling the system to recognize when automated operation becomes unreliable and switch to semi-autonomous or manual modes.
3Measurement precision
If distributed knowledge base system is implemented for vehicle control, then navigation accuracy improves, but device complexity increases
Solution Approach 1:
The patent divides the knowledge base system into distributed segments across multiple vehicles rather than using a centralized system. Each vehicle maintains its own knowledge base with local information and exchanges data with neighboring vehicles, segmenting the overall system into independent but cooperative units. This segmentation reduces the complexity burden on any single vehicle while maintaining collective localization accuracy.
Data Source
AI summary
The illustrative embodiments provide a method for controlling a vehicle. In an illustrative embodiment, a dynamic condition is identified and the vehicle is controlled using a knowledge base comprising a fixed knowledge base and a learned knowledge base.