Test Vehicle–Drone Temporal Synchronization Using Motion Signals
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Solution Overview
Problem
Existing methods for obtaining high-precision training data for neural networks in autonomous vehicle systems using drones face challenges in accurately aligning and synchronizing the coordinate systems of the drone and the test vehicle, particularly due to rotational shifts and time delays, which affect the accuracy of object position recognition by on-board sensors.
Innovation Solution
A method for temporal synchronization of data detection using a test vehicle and a drone that utilizes features and signals from the vehicle's operation, such as speeds, yaw rates, or emitted light patterns, directly from video data without additional hardware, to align the coordinate systems and synchronize temporal data without the need for additional synchronization data recording.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional synchronization methods (markers, additional sensors) are used to achieve precise temporal alignment between drone and vehicle data, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The system uses the vehicle's own operational signals (acceleration, braking, steering) as recorded by its existing sensors to create synchronization markers. These self-generated signals eliminate the need for external synchronization infrastructure while achieving precise temporal alignment between drone and vehicle data streams
Solution Approach 2:
The patent introduces synchronization markers as intermediary elements that bridge the temporal gap between drone camera footage and vehicle sensor data. These markers, derived from vehicle operational signals, serve as reference points for aligning the two independent data streams without requiring direct communication between systems
2Measurement precision
If additional synchronization hardware and markers are deployed to achieve accurate coordinate system alignment, then measurement precision is improved, but manufacturing precision and ease of manufacture deteriorate
Solution Approach 1:
The system leverages the vehicle's existing operational signals and sensor data to generate synchronization markers, eliminating the need for manual marker installation and reducing manufacturing complexity. The vehicle essentially synchronizes itself using its own operational characteristics
Solution Approach 2:
The patent extracts synchronization information from the vehicle's existing operational signals (acceleration, braking, steering) rather than adding separate synchronization hardware. This extraction approach simplifies implementation while maintaining precision
3Reliability
If precise temporal synchronization is achieved through complex synchronization systems, then reliability of training data is improved, but loss of time and resources for setup increases
Solution Approach 1:
The system performs preliminary extraction and storage of vehicle operational signals during normal vehicle operation, so that synchronization markers are already available when needed for data alignment. This preliminary action eliminates time-consuming synchronization setup procedures
Data Source
AI summary
A method for temporal synchronization of data detection using a test vehicle and data detection using a drone which accompanies the test vehicle. The method includes the following steps: a) recognizing signals and/or features of the operation of the test vehicle in video data recorded using a camera of the accompanying drone; b) performing the temporal synchronization using the signals and/or features recognized in step a).