Vehicle Event Correlation for Contextualized Driving Determination
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Solution Overview
Problem
Existing technologies in the automotive analysis field lack the capability to effectively determine contextualized vehicle operation by correlating exterior and interior driving events in real-time, which is crucial for autonomous vehicle control, maintenance analysis, and driver behavior monitoring.
Innovation Solution
A method and system for contextualized vehicle operation determination that involves sampling sensor data, extracting relevant data streams, determining exterior and interior events, and correlating these events to generate combined event data. This method can optionally include training an event-based model and implementing it for autonomous vehicle control and other driving-related analyses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor data is sampled and processed in real-time to determine vehicle operation context, then the capability to correlate exterior and interior driving events is improved, but the computational complexity and data processing requirements increase
Solution Approach 1:
The system segments the complex task of contextualized vehicle operation determination into distinct modules: exterior event determination (sensor sampling, object detection), interior event determination (driver behavior analysis), and correlation processing. This segmentation allows each module to handle specific aspects of data processing independently, reducing overall system complexity while maintaining measurement precision.
Solution Approach 2:
The patent introduces an intermediary correlation process that bridges exterior and interior event data streams. This intermediary layer processes and aligns the two separate event determination systems, enabling contextualized analysis without requiring direct integration of all sensor systems, thus managing computational complexity while achieving precise vehicle operation determination.
2Loss of information
If multiple sensor streams are processed simultaneously to extract exterior and interior events, then the comprehensiveness of driving event data is improved, but the data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing sensor streams and establishing event detection algorithms before actual driving events occur. Exterior and interior event determination frameworks are prepared in advance, allowing the system to quickly correlate events when they occur without extensive real-time computation, thus maintaining data completeness while reducing processing time.
Solution Approach 2:
The patent implements periodic sampling of sensor data streams at optimized intervals rather than continuous processing. This periodic action maintains comprehensive event detection capability while significantly reducing computational load and processing time, as the system only processes data at predetermined intervals rather than continuously analyzing all sensor inputs.
3Reliability
If event-based models are trained using combined exterior and interior event data, then the accuracy of autonomous vehicle control models is improved, but the training data requirements and processing complexity increase
Solution Approach 1:
The patent merges exterior event data and interior event data into a unified combined event dataset for model training. This merging consolidates multiple data sources into a single comprehensive training resource, improving model accuracy through integrated contextual information while managing data quantity through systematic consolidation rather than separate processing of numerous independent datasets.
Solution Approach 2:
The combined event data structure is designed with universal applicability for multiple autonomous vehicle functions including control, maintenance analysis, and driver behavior monitoring. This multi-functional design allows the same training dataset to serve various purposes, reducing the need for separate specialized datasets for each application and thereby managing training data requirements while maintaining high model accuracy across different functions.
Data Source
AI summary
A method for determining event data including: sampling a first data stream within a first time window at a first sensor of an onboard vehicle system coupled to a vehicle, extracting interior activity data from the first data stream; determining an interior event based on the interior activity data; sampling a second data stream within a second time window at a second sensor of the onboard vehicle system; extracting exterior activity data from the second image stream; determining an exterior event based on the exterior activity data; correlating the exterior event and the interior event to generate combined event data; automatically classifying the combined event data to generate an event label; and automatically labeling the first time window of the first data stream and the second time window of the second data stream with the combined event label to generate labeled event data.