Autonomous Agent Decision Architecture for Cross-Domain ODD Expansion
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Solution Overview
Problem
Current autonomous vehicle systems face challenges in expanding their operational design domain due to poor generalization of decision-making systems, leading to performance issues and safety risks, particularly due to the high costs and time required for data annotation and limited domain scaling.
Innovation Solution
A system and method that utilize a decision-making architecture with modular and sequential models, leveraging transfer learning and domain adaptation to adapt knowledge from previous tasks, and employing a compact latent space representation to efficiently map tasks between domains, thereby reducing the need for extensive data and time in training new models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional decision-making systems are used for autonomous vehicles, then the system can operate in limited domains, 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 modular components including perception modules, prediction modules, and decision modules. Each module can be independently trained and adapted to specific domains, allowing the overall system to scale to new domains while maintaining reliable performance through modular composition rather than requiring complete retraining of a monolithic system
Solution Approach 2:
The patent develops domain-adaptive models that can function across multiple domains (e.g., different geographic regions, weather conditions, road types). The system uses transfer learning to create universal decision-making capabilities that adapt to new domains while maintaining reliability through shared architectural components and standardized interfaces
2Reliability
If large amounts of labeled data are collected and retrained for new domains, then model performance can be maintained, but annotation costs increase and time-to-market increases
Solution Approach 1:
The system performs preliminary domain adaptation by pre-processing and augmenting source domain data to better represent target domain characteristics before full training. This includes synthetic data generation, domain-specific data augmentation, and pre-training on diverse datasets to prepare the model for rapid adaptation to new domains with minimal additional training time
Solution Approach 2:
The patent uses transfer learning to copy knowledge from source domain models to target domain models. Instead of collecting and annotating large amounts of new data for each domain, the system copies pre-trained weights and architectural configurations from source domains, then fine-tunes them on smaller target domain datasets, dramatically reducing both annotation costs and training time while maintaining performance
3Adaptability or versatility
If domain adaptation is performed without proper task mapping, then knowledge transfer may occur, but negative transfer can occur degrading performance
Solution Approach 1:
The patent introduces task mapping as an intermediary layer between source domain tasks and target domain tasks. This mapping mechanism analyzes the semantic relationships between tasks in different domains and selectively transfers only compatible knowledge, using task similarity metrics and domain adaptation techniques to filter out potentially harmful transfers while facilitating beneficial knowledge reuse
Solution Approach 2:
The system dynamically adjusts training parameters, learning rates, and adaptation strategies based on the measured similarity between source and target domains. When domain differences are detected, the system modifies transfer learning parameters to prevent negative transfer, such as reducing the weight of source domain gradients or adjusting optimization hyperparameters to better suit the target domain characteristics
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.