Hybrid Driving Architecture with Alignment Modules for Task Coordination
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Solution Overview
Problem
Conventional autonomous driving systems face challenges in optimizing the full stack of processing tasks due to modular architectures that lack global optimization and alignment of modules, leading to deficient task coordination, accuracy loss, high compute costs, and complex updating, while fully end-to-end systems suffer from lack of human interpretable interfaces and complex branching.
Innovation Solution
A hybrid modular end-to-end architecture that combines human-defined and AI-defined interfaces within processing tasks, allowing for joint optimization and improved safety, interpretability, and seamless expansion, using a combination of human-defined and AI-defined interfaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a modular architecture is used for autonomous driving systems, then the system can be divided into independent modules for easier development and maintenance, but the modules lack global optimization and alignment leading to deficient task coordination and accuracy loss
Solution Approach 1:
The patent introduces alignment modules as intermediary components between independent processing modules. These alignment modules receive outputs from multiple modules, perform global optimization and coordination, and generate aligned outputs that maintain system-wide consistency. This mediator structure enables modular development while ensuring reliable task coordination through centralized alignment mechanisms.
2Ease of manufacture
If a modular architecture is used for autonomous driving systems, then the system structure is simplified for development, but the compute costs increase and updating becomes complex
Solution Approach 1:
The patent segments the autonomous driving system into independent processing modules that can be developed, tested, and updated separately. Each module handles specific tasks (e.g., perception, prediction, planning) independently, reducing overall system complexity while enabling modular development. The alignment modules provide minimal coordination overhead, maintaining simplicity despite the segmented structure.
3Reliability
If a fully end-to-end system is used, then global optimization is achieved, but the system lacks human interpretable interfaces and becomes complex with branching
Solution Approach 1:
The patent segments the end-to-end processing into modular units with clearly defined interfaces. Each module performs specific functions with human-interpretable inputs and outputs, maintaining simplicity while enabling global optimization through the alignment mechanisms that coordinate between segments.
4Manufacturing precision
If a fully end-to-end system is used, then end-to-end optimization is achieved, but human interpretable interfaces are lost and branching becomes complex
Solution Approach 1:
The alignment modules serve as interpretable intermediaries between AI processing stages. They provide human-understandable alignment criteria and decision-making logic that maintains interpretability while achieving end-to-end optimization across the full system stack.
Data Source
AI summary
An apparatus comprises one or more memories, and one or more processors in communication with the one or more memories. The one or processors are configured to execute an automated driving system having a modular hybrid architecture. The modular hybrid architecture includes a plurality of task units, and the modular hybrid architecture includes one or more human-defined interfaces, and one or more AI-defined interfaces. The one or more processors are further configured to receive input data from one or more sensors process the input data using the automated driving system having the modular hybrid architecture, and control at least one operation of a vehicle according to an output of the automated driving system.