Vehicle Sensor Data Labeling Using GPS-Based Pre-Labels
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Solution Overview
Problem
Manual labeling of vehicular sensor data is time-consuming, expensive, and prone to errors due to human intervention, especially in large data collection for advanced driver assistance systems (ADAS), making it challenging to accurately identify events of interest.
Innovation Solution
A system that utilizes GPS data and databases to automatically apply location-based pre-labels to sensor data, correlating geographic positions and timestamps with map and weather information to enrich and categorize data without manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual labeling of vehicular sensor data is used, then human intervention can provide detailed analysis, but the process becomes time-consuming and expensive
Solution Approach 1:
The system enables self-service labeling by automatically generating location-based pre-labels using GPS data and databases, eliminating the need for manual human intervention in the labeling process. The system serves itself by autonomously processing sensor data, extracting geographic position information, and applying relevant labels without human input.
Solution Approach 2:
The system performs preliminary action by pre-labeling sensor data with location-based information before manual analysis is needed. By extracting geographic position information and obtaining location-based information from databases in advance, the system prepares labeled data ready for downstream processing, eliminating the need for time-consuming manual labeling.
2Measurement precision
If manual labeling of vehicular sensor data is used, then detailed analysis can be performed, but the process becomes expensive
Solution Approach 1:
The system enables self-service labeling by automatically generating location-based pre-labels using GPS data and databases, eliminating the need for manual human intervention in the labeling process. The system serves itself by autonomously processing sensor data, extracting geographic position information, and applying relevant labels without human input.
Solution Approach 2:
The system replaces the mechanical human labeling process with an automated computational system. Instead of manual human analysis, the system uses computer-based processes including GPS data extraction, database queries, and automated label application, thereby eliminating labor costs while maintaining labeling functionality.
3Measurement precision
If manual labeling is used for large data collection, then human analysis can be applied, but errors increase due to human intervention
Solution Approach 1:
The system enables self-service labeling by automatically generating location-based pre-labels using GPS data and databases, eliminating the need for manual human intervention in the labeling process. The system serves itself by autonomously processing sensor data, extracting geographic position information, and applying relevant labels without human input.
Solution Approach 2:
The system replaces the mechanical human labeling process with an automated computational system. Instead of manual human analysis, the system uses computer-based processes including GPS data extraction, database queries, and automated label application, thereby eliminating labor costs while maintaining labeling functionality.
4Productivity
If automated labeling using GPS and databases is used, then time and human resources are saved, but system complexity increases
Solution Approach 1:
The system achieves multi-functionality by integrating multiple capabilities into a single unified system: GPS data extraction, geographic position information processing, database queries for location-based information, and automated label application. This multi-functional approach consolidates what would otherwise require separate systems into one cohesive labeling solution.
Solution Approach 2:
The system uses an intermediary processing layer that coordinates between GPS data extraction, database queries, and label application. This intermediary component manages the complexity by providing a structured workflow that connects different system elements, making the overall system more manageable despite its integrated nature.
Data Source
AI summary
A method for labeling vehicular sensor data includes accessing sensor data captured by a plurality of sensors disposed at a vehicle. The method also includes accessing global positioning system (GPS) sensor data of the vehicle. For each time step of a plurality of time steps associated with the sensor data, vehicle position information is extracted from the GPS sensor data that represents a respective geographic location of the vehicle at the respective time step. For each time step of the plurality of time steps of the recording of sensor data, respective location-based information associated with the respective geographic location of the vehicle at the respective time step is obtained from a database. For each time step of the plurality of time steps of the recording of sensor data, the recording of sensor data at the respective time step is labeled using the respective obtained location-based information.