AI Training Data Conditioning for Autonomous Vehicle Safety
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Solution Overview
Problem
Existing AI systems for autonomous vehicles face challenges in generating safe behavior due to biased training data, leading to high complexity and computing costs in optimizing data sets, making it difficult to achieve a safely behaving AI without prior optimization.
Innovation Solution
A computer program product that receives raw data from machines, conditions it by grouping into a hierarchical tree-like structure, detects and balances imbalances, and outputs optimized data for AI training, reducing complexity and computing costs while ensuring safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If raw data is used directly for AI training, then the training process is simple, but the AI safety and reliability deteriorate due to biased data
Solution Approach 1:
The patent applies preliminary action by performing data conditioning and balancing before AI training. The system pre-processes raw data to identify and correct biases, ensuring balanced representation of different driving scenarios, weather conditions, and road types. This preliminary optimization of training data quality prevents safety issues from arising during AI deployment.
2Reliability
If comprehensive data optimization is performed to ensure AI safety, then AI reliability improves, but computing costs and processing time increase
Solution Approach 1:
The patent segments the data optimization process into distinct modular steps: data collection, conditioning (structuring and categorizing), imbalance detection, and balancing. Each module handles a specific aspect of data processing independently, allowing for efficient computation and enabling parallel processing of different data subsets, thereby reducing overall processing time while maintaining comprehensive safety optimization.
3Productivity
If data is structured and conditioned before training, then AI training efficiency improves, but the preprocessing complexity increases
Solution Approach 1:
The patent implements a universal data conditioning framework that handles multiple data types (sensor data, trajectory data, environmental data) through a single standardized process. The conditioning module structures diverse data formats into a unified representation that can be used across different AI training scenarios, eliminating the need for separate preprocessing pipelines for each data type and reducing overall system complexity.
Data Source
AI summary
A computer program product comprising computer-readable instructions that, when executed in a computer system including one or more computers, cause the computer system to receive a data set of a machine driven by a human or robot driver; condition said data set by grouping the data based on predefined or definable machine-control parameters; search the conditioned data for determining data imbalances within groups or sub-groups of the conditioned data; balance the data for which an imbalance was determined; and output the balanced data.