Autonomous Vehicle Management System Confidence Scoring
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Solution Overview
Problem
Current autonomous driving technologies lack effective safety measures, particularly in ensuring functional safety during operations, due to unpredictable errors in AI model predictions caused by differences between inference and training datasets.
Innovation Solution
An autonomous vehicle management system that generates a model confidence score to assess the similarity of inference data to the training dataset, using techniques like Mahalanobis distance or Cosine Similarity, and dynamically controls sensor data and AI model usage to ensure safe operations by overriding predictions with low confidence scores and updating the training data with dissimilar points.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If AI models are used for autonomous driving decisions, then the productivity and automation of vehicle operations are improved, but the reliability and safety deteriorate due to unpredictable errors from dataset differences
Solution Approach 1:
The system implements a feedback mechanism by computing a model confidence score that compares inference data characteristics against training data characteristics. This feedback loop allows the system to monitor AI model performance reliability and trigger appropriate responses (override or update) based on the computed confidence level, thereby maintaining safety while enabling autonomous operations.
Solution Approach 2:
The patent introduces an intermediary component (the model confidence score computation mechanism) that acts as a mediator between the AI model predictions and the autonomous driving control system. This intermediary assesses the reliability of AI predictions by comparing data distribution characteristics and determines whether to trust or override AI decisions, resolving the contradiction between automation and safety.
2Reliability
If sensor data processing and AI model usage are strictly controlled for safety, then the reliability is improved, but the productivity and response time deteriorate
Solution Approach 1:
The system applies partial control by not always overriding AI predictions. Instead, it computes a confidence score and only overrides predictions when the score indicates low reliability (below threshold). This partial action approach maintains high productivity when AI predictions are reliable while ensuring safety when they are not, avoiding the need for constant human intervention or system overrides.
Solution Approach 2:
The system dynamically changes the parameter of AI model trustworthiness based on the computed confidence score. When the score is high, the system operates in autonomous mode with full AI control; when the score is low, it switches to a safer mode. This parameter change allows the system to adapt its safety level and response time based on real-time conditions rather than maintaining a fixed conservative approach.
3Adaptability or versatility
If the training dataset is continuously updated with dissimilar inference points, then the adaptability and model accuracy are improved, but the loss of time and computational resources worsen
Solution Approach 1:
The system performs preliminary assessment by computing the model confidence score before actually updating the training dataset. By pre-evaluating whether inference data points are sufficiently dissimilar from training data characteristics, the system can selectively update only when necessary and meaningful, avoiding unnecessary retraining operations that would waste time and computational resources while still improving adaptability when needed.
Data Source
AI summary
An infrastructure is provided for improving the safety of autonomous systems. An autonomous vehicle management system (AVMS) controls one or more autonomous functions or operations performed by a vehicle or machine such that the autonomous operations are performed in a safe manner. The AVMS uses various artificial intelligence (AI) based techniques (e.g., neural networks, reinforcement learning (RL) techniques, etc.) and models as part of its processing. For an inferring data point, for which a prediction is made by AVMS using an AI model, the AVMS checks how statistically similar (or dissimilar) the inferring data point is to the distribution of the training dataset. A score (confidence score) is generated indicative of how similar or dissimilar the inferring data point is to the training dataset. The AVMS uses this confidence score to decide how the prediction made by the AI model is to be used.