Distributed Knowledge Base for Vehicle Navigation Accuracy
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Solution Overview
Problem
Current automated and semi-automated vehicle systems face reliability issues due to complexity and uncertainty in operating environments, particularly with obstacle detection and navigation, leading to inaccuracies in location sensing and navigation.
Innovation Solution
A distributed knowledge base system within vehicles that combines a fixed and learned knowledge base to control vehicle operations, utilizing sensor data and dynamic condition recognition for navigation and obstacle avoidance, allowing for both autonomous and operator-assisted modes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated or semi-automated vehicle systems are implemented, then productivity and operational efficiency are improved, but reliability deteriorates due to system complexity and environmental uncertainty
Solution Approach 1:
The vehicle control system is divided into multiple independent modules including sensor modules, processing modules, and actuator modules. Each module performs a specific function (e.g., obstacle detection, path planning, navigation) and can operate semi-independently, allowing the system to maintain productivity while improving reliability through modular fault isolation and redundancy.
2Measurement precision
If complex sensor systems and automated control are used, then navigation accuracy and obstacle detection capability are improved, but device complexity increases
Solution Approach 1:
The vehicle system employs multi-functional sensors and processing units that can perform multiple tasks. For example, sensors can detect both obstacles and map the environment, and processing modules can handle both navigation control and work operation management. This reduces overall system complexity while maintaining high measurement precision through shared resources.
Solution Approach 2:
A knowledge base acts as an intermediary between raw sensor data and control decisions. It stores pre-processed information about the environment, obstacles, and optimal paths, allowing the system to make accurate navigation decisions without requiring complex real-time processing of all sensor inputs, thus reducing device complexity while maintaining accuracy.
3Ease of operation
If fully autonomous operation is implemented, then ease of operation is improved, but reliability worsens due to inability to handle uncertain situations
Solution Approach 1:
The vehicle system dynamically adjusts its level of autonomy based on environmental conditions and task requirements. In familiar or low-risk environments, it operates fully autonomously for ease of operation. In uncertain or complex situations, it transitions to semi-autonomous mode, allowing human operator intervention to maintain reliability. This dynamic adaptability resolves the contradiction between ease of operation and reliability.
Data Source
AI summary
The illustrative embodiments provide a computer program product for controlling a vehicle. In an illustrative embodiment, a computer program product is comprised of a computer recordable media having computer usable program code for identifying a dynamic condition. When the dynamic condition is identified, computer usable program code using a knowledge base controls the vehicle.