Machine Learning Driving Data Analysis for Real-Time Safety Outputs
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Solution Overview
Problem
Identifying safe driving datasets in real-time and efficiently updating autonomous vehicle instructions is challenging due to insufficient data, lack of uniformity in understanding safe driving behaviors, and the need for rapid adaptation.
Innovation Solution
A system utilizing machine learning datasets generated from diverse data sources, including vehicle sensors and external data, to evaluate driving behaviors and generate safety outputs for autonomous vehicles, enabling real-time instruction updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional data collection methods are used to identify safe driving datasets, then data sufficiency is improved, but real-time identification capability deteriorates
Solution Approach 1:
The system performs preliminary actions by continuously collecting and pre-processing driving data in advance, building a comprehensive dataset repository before real-time safety assessment is needed. This allows the machine learning model to quickly evaluate safety without waiting for data collection during critical moments
Solution Approach 2:
The patent replaces traditional mechanical data collection and analysis methods with machine learning-based automated assessment. The ML model automatically evaluates driving behavior safety without manual intervention, enabling real-time processing while maintaining data sufficiency through continuous background data accumulation
2Measurement precision
If comprehensive driving data is collected from multiple sources, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system implements a universal data collection architecture that can ingest multiple data types (sensor data, map data, weather data) through a single integrated platform. This multi-functional design allows comprehensive data collection without proportionally increasing system complexity, as the same infrastructure handles diverse data sources
Solution Approach 2:
The patent introduces intermediary components such as data normalization layers and standardized communication protocols that mediate between diverse data sources and the core machine learning model. These intermediaries simplify integration of multiple data sources while maintaining measurement precision through consistent data processing
3Reliability
If machine learning models are updated frequently to improve safety output accuracy, then reliability is improved, but productivity decreases
Solution Approach 1:
The system applies partial updates to the machine learning model, updating only specific components or parameters that require improvement rather than performing complete model retraining. This selective update approach maintains safety output accuracy while significantly reducing computation time and preserving productivity
Solution Approach 2:
The patent implements continuous learning mechanisms where the model undergoes incremental updates based on incoming data streams. This continuous action allows the system to maintain high reliability through ongoing improvement while keeping update intervals short enough to preserve productivity and response speed
Data Source
AI summary
Systems and apparatuses for using machine learning to generate a safety output are provided. In some examples, data may be received from a plurality of sources, may be analyzed and one or more machine learning datasets may be generated based on the analyzed data. In some arrangements, data may be received from one or more vehicles. The vehicles may be autonomous, semi-autonomous, or non-autonomous, and/or configured to operate in one or more of those modes. The data may be evaluated based on the one or more machine learning datasets to determine a safety output associated with the data. The safety output may then be used to classify the data and/or to generate one or more instructions for operation of an autonomous vehicle. The instruction(s) may be transmitted to the autonomous vehicle and may modify operation of the vehicle (e.g., to improve safety associated with the vehicle).