Natural Language Safety Case Discovery for Driving Edge Cases
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Solution Overview
Problem
Existing machine learning models for driving systems struggle to detect and categorize uncommon driving scenarios, leading to inefficiencies and potential liabilities, and are overburdened by excessive training data which can result in slow response times and false classifications.
Innovation Solution
A natural language-based safety case discovery system that receives sensor data, identifies features, generates multidimensional representations, and queries a search engine index to rank records by relevance, deduplicates results, and updates safety protocols to train machine learning models for optimizing driving systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning models are trained with extensive driving data to improve detection capability, then the model's ability to detect common scenarios improves, but the response time slows down and false positives increase
Solution Approach 1:
The patent segments the training data into common driving scenarios and edge case scenarios. The machine learning model is trained separately on these segmented datasets, allowing it to efficiently handle common scenarios while being specifically tuned for rare edge cases, thus improving response time without sacrificing detection capability.
Solution Approach 2:
The patent applies partial action by focusing training efforts only on the most critical edge cases rather than exhaustively training on all possible scenarios. The system identifies and prioritizes specific edge cases that pose the greatest safety risks, training the model selectively on these partial datasets to achieve optimal performance without the computational burden of complete dataset training.
2Reliability
If machine learning models are trained with extensive driving data to improve detection capability, then the model's ability to detect rare scenarios improves, but false positives and false negatives increase
Solution Approach 1:
The patent applies local quality by creating specialized processing paths for different types of driving scenarios. Common scenarios are handled by the general machine learning model, while edge cases are identified and processed through specialized detection mechanisms with tailored classification criteria, improving overall classification accuracy by matching the right quality of analysis to the right scenario type.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously monitors detection outcomes and uses this information to refine edge case identification and classification. The feedback loop allows the system to learn from false positives and negatives, adjusting its detection thresholds and classification parameters to improve measurement precision over time.
3Adaptability or versatility
If machine learning models are trained with extensive driving data to improve detection capability, then the model covers more scenarios, but the system complexity and computational burden increase
Solution Approach 1:
The patent applies preliminary action by pre-processing and categorizing driving data into common scenarios and edge cases before training the machine learning model. This preliminary segmentation allows the system to achieve comprehensive scenario coverage while maintaining manageable complexity, as the model is prepared in advance to handle different scenario types through dedicated processing paths rather than requiring complex real-time decision-making architecture.
Data Source
AI summary
A safety case discovery system includes a scenario framework and safety protocols for edge cases. The safety case discovery system receives sensor data generated by at least one sensor during operation of a vehicle and stores the sensor data in a data warehouse. The data warehouse can be queried based on a predefined scenario description to produce a subset of records which are ranked based on a relevancy of the records to the predefined scenario description. The safety case discovery system deduplicates the ranked results to produce edge cases and updates the safety protocol. The safety case discovery system can train a machine learning model vehicle based on the edge cases, to produce a trained machine learning model for optimizing the driving system.