Object-Based Sensor Fusion for Temporal Alignment of Fast-Moving Targets
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Different sensors in autonomous vehicles often capture data at different times, leading to temporal misalignment that can cause processing errors and inaccuracies in algorithms sensitive to time differences, especially for high-speed objects.
Innovation Solution
Systems and techniques for sensor fusion that temporally align data from multiple sensors by projecting objects to a common reference time based on predicted trajectories, optimizing fusion and tracking efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data from multiple sensors is processed without temporal alignment, then processing speed is maintained, but processing accuracy deteriorates due to temporal misalignment
Solution Approach 1:
The system performs temporal alignment of sensor data before processing by projecting sensor readings to a common reference time using predicted trajectories. This preliminary action ensures that all sensor data is temporally consistent before being fed into processing algorithms, thereby improving measurement precision without requiring slower processing of misaligned data
Solution Approach 2:
The system introduces an intermediary temporal alignment module that acts as a mediator between raw sensor data and processing algorithms. This module uses trajectory prediction to interpolate or extrapolate sensor readings to a common reference time, serving as a buffer that reconciles temporal differences between sensors while maintaining processing efficiency
2Measurement precision
If sensor data is temporally aligned to a common reference time, then processing accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments the sensor fusion process into distinct modules: trajectory prediction, temporal alignment, and data processing. By separating the temporal alignment function from the main processing pipeline, the system reduces overall complexity while maintaining accuracy. Each module handles a specific aspect of the problem independently, making the system more manageable and easier to implement
Solution Approach 2:
The trajectory prediction component uses historical sensor data to automatically generate predicted trajectories without requiring manual intervention or complex external systems. This self-service approach allows the system to perform temporal alignment using its own accumulated data, reducing the need for additional external resources or complex coordination mechanisms
Data Source
AI summary
Systems and techniques are provided for fusing sensor data from multiple sensors. An example method can include obtaining a first set of sensor data from a first sensor and a second set of sensor data from a second sensor; detecting an object in the first set of sensor data and the object in the second set of sensor data; aligning the object in the first set of sensor data and the object in the second set of sensor data to a common time; and based on the aligned object from the first set of sensor data and the aligned object from the second set of sensor data, fusing the aligned object from the first set of sensor data and the aligned object from the second set of sensor data.