Vehicle Sensor Time Alignment for GPS and Camera Event Sync
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Solution Overview
Problem
Existing vehicle systems fail to accurately synchronize sensor data from multiple sensors, leading to inaccurate determination and analysis of events encountered during travel, which can be costly for navigation and environmental detection.
Innovation Solution
A method and system for synchronizing sensor data by determining a correct time stamp through comparing time stamps from different sensors and adjusting data based on an area under the curve (AUC) difference, using machine learning algorithms to align data sets and correct time delays.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If sensor data is collected from multiple sensors without synchronization, then data collection coverage is improved, but data accuracy deteriorates due to time misalignment
Solution Approach 1:
The system performs preliminary time alignment by comparing timestamps from multiple sensors before processing the data. By determining which timestamp is correct in advance and adjusting the data accordingly, the system ensures accurate temporal synchronization without requiring complex real-time coordination between sensors.
Solution Approach 2:
The system uses feedback mechanisms to compare timestamps from different sensors and adjust the data synchronization accordingly. By continuously monitoring timestamp discrepancies and applying corrections based on area under the curve differences, the system maintains high data accuracy while collecting from multiple sensors.
2Measurement precision
If time alignment processing is added to synchronize sensor data, then data accuracy is improved, but processing complexity increases
Solution Approach 1:
The system creates a simplified representation of the time alignment process by using timestamp comparisons and area under the curve calculations as stand-ins for complex real-time synchronization algorithms. This copying approach maintains accuracy while reducing the computational burden during actual data processing.
Solution Approach 2:
The system changes the parameters used for time alignment from complex continuous time adjustments to discrete timestamp comparisons and area under the curve differences. This parameter transformation simplifies the processing while maintaining the essential accuracy of synchronized data.
3Productivity
If incorrect timestamps are used for sensor data, then data processing speed is maintained, but location accuracy deteriorates
Solution Approach 1:
The system extracts and separates the time alignment function from the main data processing stream. By handling timestamp corrections as a preliminary step that can be pre-computed, the system maintains fast data processing speeds while ensuring location accuracy through separate, optimized time synchronization operations.
Data Source
AI summary
Systems and methods are provided for synchronizing sensor data from a plurality of sensors of a vehicle. The systems and methods may receive a first data relating to an event from a first sensor of a vehicle. The first data may include a first time stamp. The systems and methods may receive a second data relating to the event from a second sensor of the vehicle. The second data may include a second time stamp. The systems and methods may determine the first time stamp is not identical to the second time stamp. The systems and methods may determine which of the first time stamp and the second time stamp is a correct time stamp for the event. The systems and methods may synchronize the first and second data according to the correct time stamp and an area under the curve (AUC) difference between the first and second data.