Neural Network Reasoning Statements for Transparent Roadway Decisions
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Solution Overview
Problem
Public acceptance of autonomous agents is limited due to a lack of transparency in their decision-making processes, particularly in complex urban scenarios, leading to mistrust among users.
Innovation Solution
A system and method for training a neural network to generate interpretable reasoning statements by creating a training dataset with ranking classifications based on temporal and relational annotations of objects, using annotator reasoning statements to explain the importance of objects in the environment, thereby enhancing situational awareness and user trust.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If autonomous agents operate without providing reasoning explanations, then decision-making speed and operational efficiency are improved, but public acceptance and user trust deteriorate
Solution Approach 1:
The patent introduces an intermediary reasoning statement generation module that translates the autonomous agent's internal decision-making process into human-understandable explanations. This mediator component bridges the gap between fast automated decision-making and user comprehension, allowing the system to maintain operational speed while providing interpretability that builds user trust.
2Loss of information
If the system provides detailed reasoning statements for all detected objects, then interpretability and user understanding are improved, but computational complexity and processing time increase
Solution Approach 1:
The patent applies local quality by providing reasoning statements selectively rather than uniformly for all objects. The system identifies key objects that significantly impact the decision-making process and generates reasoning statements primarily for those objects, while omitting or simplifying explanations for less critical objects. This localized approach maintains interpretability for important decisions while reducing overall computational complexity.
Solution Approach 2:
The system dynamically adjusts the level of detail in reasoning statements based on object importance, distance, and relevance to the current task. By changing the parameter of explanation depth rather than maintaining a fixed level for all objects, the system optimizes the balance between interpretability and processing efficiency.
3Measurement precision
If multiple annotators provide ranking classifications for objects, then data accuracy and reliability are improved, but annotation time and resource requirements increase
Solution Approach 1:
The patent implements partial action by having annotators rank only a subset of detected objects rather than all objects in every scene. The system identifies the most relevant objects based on predefined criteria and requests annotations only for those, achieving sufficient data accuracy while significantly reducing the time and resources required for the annotation process.
Data Source
AI summary
Systems and methods for training a neural network for generating a reasoning statement are provided. In one embodiment, a method includes receiving sensor data from a perspective of an ego agent. The method includes identifying a plurality of captured objects in the at least one roadway environment. The method includes receiving a set of ranking classifications for a captured object of the plurality of captured objects. The annotator reasoning statement is a natural language explanation for the applied attribute. The method includes generating a training dataset for the object type including the annotator reasoning statements of the set of ranking classifications that include the applied attribute from the plurality of importance attributes in the importance category. The method includes training the neural network to generate a generated reasoning statement based on the training dataset in response to a training agent detecting a detected object of the object type.