Autonomous Vehicle Decision Explanation via Semantic State Segmentation
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Solution Overview
Problem
Autonomous vehicles lack the ability to provide human-understandable explanations for their decision-making processes, making it difficult for users, developers, and regulators to trust and validate their operations, especially in complex scenarios.
Innovation Solution
An autonomous vehicle operational management system that identifies distinct operational scenarios, instantiates decision components to model these scenarios, selects control actions, and generates explanations using semantic state factors, allowing for transparent and understandable decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If autonomous vehicles use complex decision-making components to handle diverse operational scenarios, then the vehicle's ability to traverse complex transportation networks is improved, but the system becomes unable to provide human-understandable explanations for its decisions
Solution Approach 1:
The patent divides the autonomous vehicle's decision-making system into multiple distinct decision components, where each component is responsible for a specific operational scenario (e.g., lane changing, intersection navigation, pedestrian interaction). This segmentation allows the complex system to be broken down into manageable, explainable units while maintaining the ability to handle diverse scenarios through composition of these specialized components
Solution Approach 2:
The patent introduces an explanation generation component that acts as an intermediary between the decision components and the user. This intermediary translates the internal state and decision logic of the autonomous vehicle into human-understandable explanations, bridging the gap between complex automated decision-making and human comprehension without altering the core decision-making functionality
2Loss of information
If the autonomous vehicle system maintains detailed semantic states for explainability, then explanation quality is improved, but computational complexity and processing requirements increase
Solution Approach 1:
The patent applies local quality by maintaining detailed semantic state information only where necessary for explanation generation, rather than uniformly across all system components. Each decision component maintains state details relevant to its specific operational scenario, allowing high-quality explanations to be generated for critical decisions while avoiding unnecessary computational overhead in other areas of the system
Data Source
AI summary
A processor is configured to execute instructions stored in a memory to identify distinct vehicle operational scenarios; instantiate decision components, where each of the decision components is an instance of a respective decision problem, and where the each of the decision components maintains a respective state describing the respective vehicle operational scenario; receive respective candidate vehicle control actions from the decision components; select an action from the respective candidate vehicle control actions, where the action is from a selected decision component of the decision components, and where the action is used to control the AV to traverse a portion of the vehicle transportation network; and generate an explanation as to why the action was selected, where the explanation includes respective descriptors of the action, the selected decision component, and a state factor of the respective state of the selected decision component.