Autonomous Agent Decision Architecture for ODD Domain Adaptation
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Solution Overview
Problem
Autonomous vehicles face challenges in expanding their operational design domain due to poor generalization of decision-making systems, leading to performance issues and safety risks, largely because of the high costs and time required for data annotation and limited domain scaling.
Innovation Solution
A system and method that utilize transfer learning and domain adaptation to adapt a decision-making architecture from a source domain to a target domain, leveraging a compact latent space representation and modular models to efficiently expand the operational design domain, reducing the need for extensive data and annotation, and minimizing negative transfer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional decision-making systems are deployed in autonomous vehicles, then the vehicle can operate in basic scenarios, but the system generalizes poorly when scaling to new domains, leading to performance degradation and safety risks
Solution Approach 1:
The decision-making system is divided into multiple domain-specific models (source domain model and target domain model) that can be independently trained and adapted. This segmentation allows the system to specialize in different operational domains while maintaining overall reliability through selective model application.
Solution Approach 2:
The system changes the parameter of domain adaptability by introducing domain adaptation techniques that transform the decision-making system from being domain-specific to domain-agnostic. This allows the same core architecture to serve multiple domains with appropriate adaptation, improving both versatility and reliability.
2Reliability
If large amounts of labeled data are collected and annotated to improve decision-making performance in new domains, then model accuracy improves, but annotation costs and time-to-market increase significantly
Solution Approach 1:
The system performs preliminary action by pre-training domain-specific models on source domain data before deployment to target domains. This advance preparation reduces the need for extensive annotation in new domains, as the pre-trained models can be adapted with fewer labeled examples, thereby reducing both time and cost.
Solution Approach 2:
The system creates copies of the decision-making architecture for different domains (source domain copy and target domain copy). These copied models can be independently trained and refined, allowing knowledge transfer between domains without requiring complete re-annotation, thus reducing time-to-market while maintaining accuracy.
3Adaptability or versatility
If domain-specific models are trained separately for each operational domain, then model specialization improves, but the complexity of managing and deploying multiple models increases
Solution Approach 1:
The system implements a universal decision-making architecture that can function across multiple domains. The same core model structure serves as the foundation for all domain-specific models, allowing the system to maintain specialization benefits while reducing management complexity through a unified framework.
Solution Approach 2:
The system introduces domain adaptation mechanisms as intermediaries between the source domain model and target domain applications. These intermediaries facilitate knowledge transfer and model adaptation without requiring direct management of all domain-specific details, thereby reducing system management complexity while maintaining specialization.
Data Source
AI summary
A system for expanding the operational design domain (ODD) of an autonomous agent includes a decision-making platform (equivalently referred to herein as a decision-making architecture). A method for expanding the operational design domain (ODD) includes determining a decision-making architecture for a first domain and adapting the decision-making architecture to a second domain. Additionally or alternatively, the method 200 can include implementing the decision-making architecture S300 and/or any other processes.