Context-Aware Autonomous Agent Decisions for Explainable Navigation
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Solution Overview
Problem
Conventional autonomous vehicle decision-making systems face limitations in explainability and adaptability, particularly in complex environments, leading to safety concerns and inefficiencies in data requirements for route navigation.
Innovation Solution
A context-aware decision-making system for autonomous agents that combines deep learning and rule-based processes, utilizing a hybrid architecture with a small operational design domain (ODD) to enable efficient, explainable, and safe navigation by decomposing models into micro-modules with context-specific learning modules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If machine learning based approaches are used for decision making in autonomous vehicles, then the system can handle complex environments, but the explainability is reduced causing safety concerns
Solution Approach 1:
The patent segments the decision-making process into distinct modules: a classical decision engine that provides explainable rules-based decisions, and a machine learning module that handles complex pattern recognition. Each module operates independently with defined interfaces, allowing the system to leverage both classical explainability and ML adaptability for different aspects of autonomous vehicle decision-making.
2Loss of information
If classical programming approaches are used for decision making, then the system is explainable, but it becomes extremely involved and most likely impossible to handle various environments
Solution Approach 1:
The patent divides the autonomous vehicle system into multiple specialized modules: classical decision engines for explainable rule-based control, machine learning modules for complex environment perception, and context-aware components for situation-specific decision-making. This segmentation allows each module to excel at its specific function while working together to handle diverse environments.
Solution Approach 2:
The patent introduces context-aware components as intermediaries between classical decision engines and machine learning modules. These intermediaries translate between the rule-based language of classical systems and the probabilistic outputs of ML models, enabling seamless integration while preserving explainability through context tracking and decision rationale generation.
3Adaptability or versatility
If a large operational design domain is attempted, then the system can drive in various environments, but the data requirements increase significantly
Solution Approach 1:
The patent implements context-aware decision-making where the system adapts its behavior and data requirements based on the specific operational context. Instead of requiring comprehensive data for all possible scenarios, the system uses context identification to activate only the necessary decision-making modules and parameters for the current situation, significantly reducing data requirements while maintaining broad operational capability.
Data Source
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AI summary
A system for context-aware decision making of an autonomous agent includes a computing system having a context selector and a map. A method for context-aware decision making of an autonomous agent includes receiving a set of inputs, determining a context associated with an autonomous agent based on the set of inputs, and optionally any or all of: labeling a map; selecting a learning module (context-specific learning module) based on the context; defining an action space based on the learning module; selecting an action from the action space; planning a trajectory based on the action S260; and/or any other suitable processes.