Vehicle Sensor Event Labeling Using Trigger-Based Data Extraction
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Solution Overview
Problem
Manual analysis of vast amounts of sensor data from vehicles is expensive and time-consuming, hindering the efficient development and validation of advanced driver assistance systems.
Innovation Solution
A data analysis system that automatically identifies and labels events of interest in vehicular sensor data using trigger conditions, synchronizes relevant data, and allows user input for labeling, facilitating quick and resource-efficient scenario analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis of sensor data is performed, then accuracy of event identification is improved, but time consumption and cost increase significantly
Solution Approach 1:
The system performs preliminary automated processing of sensor data using trigger conditions to identify potential events of interest before human review. This preliminary action filters and prepares data, so that when analysts do review events, they are examining pre-validated candidates rather than raw data, thus maintaining accuracy while reducing time consumption.
Solution Approach 2:
The patent introduces an automated trigger condition system as an intermediary between raw sensor data and human analysts. This intermediary automatically evaluates sensor data against predefined criteria, identifies potential events, and presents them for human verification. The intermediary handles the time-consuming initial filtering, allowing human experts to focus on validation and complex judgment cases.
2Measurement precision
If manual analysis of sensor data is performed, then quality of labeling is improved, but resource requirements increase
Solution Approach 1:
The system automatically performs preliminary data processing, event detection, and initial labeling using trigger conditions before human reviewers examine the data. This preliminary automated labeling handles routine cases, reducing the number of events requiring human labeling effort while maintaining quality through subsequent human verification of the automated results.
Solution Approach 2:
The system enables self-service automated event identification and initial labeling through trigger conditions that automatically evaluate sensor data and identify events of interest. This self-service capability handles the bulk of labeling work automatically, requiring human resources only for validation and edge cases, thus reducing overall resource requirements while maintaining labeling quality.
3Productivity
If automated trigger conditions are used to identify events, then processing speed is improved, but complexity of the system increases
Solution Approach 1:
The patent segments the complex analysis task into distinct components: trigger condition evaluation, event identification, data synchronization, and user verification. Each component handles a specific aspect of the process independently, making the overall system more manageable despite the automation. The segmentation allows parallel processing of different sensor data streams and trigger conditions, maintaining high processing speed while organizing complexity into modular units.
4Measurement precision
If comprehensive sensor data is processed, then completeness of event detection is improved, but data processing time increases
Solution Approach 1:
The system extracts and processes only the specific sensor data and time windows relevant to identified events of interest, rather than processing all sensor data comprehensively. When a trigger condition is satisfied, the system extracts the pertinent portion of sensor data for further analysis and user review, leaving the rest of the data unprocessed. This extraction approach maintains completeness of event detection while significantly reducing overall data processing time.
Data Source
AI summary
A method for labeling events of interest in vehicular sensor data includes accessing sensor data captured by a plurality of sensors disposed at a vehicle, and providing a trigger condition including a plurality of threshold values. The trigger condition is satisfied when values representative of the sensor data satisfy each threshold value. An event of interest is identified when the trigger condition is satisfied at a point in time of the recording of sensor data. A visual indication of the event of interest is displayed on a graphical user interface. Visual elements derived from a portion of the sensor data representative of the event of interest are displayed on the graphical user interface. A label for the event of interest is received from a user. The label and the sensor data representative of the point in time are stored at a database time.