Multimodal Sensor Time Alignment Using Neural Temporal Correction
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Solution Overview
Problem
Advanced Driver Assistance Systems (ADAS) and Autonomous Driving (AD) systems face challenges in accurately synchronizing data from non-synchronous sensors, leading to errors in sensor fusion and increased costs for synchronization measures.
Innovation Solution
A method using a neural network to identify and correct the chronological offset between measurement timepoints of different sensors, allowing data sets to be synchronized to a common reference timepoint without requiring precise timestamp knowledge, thereby reducing the need for additional synchronization measures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional synchronization measures (software timestamps, peer-to-peer synchronization) are used to align sensor data, then data synchronization quality is improved, but system complexity and costs increase
Solution Approach 1:
The patent replaces traditional mechanical/electronic synchronization mechanisms (hardware timestamps, peer-to-peer synchronization protocols) with a neural network-based temporal correction system. The neural network learns chronological relationships from multimodal data and automatically computes correction parameters, eliminating the need for complex synchronization hardware and software infrastructure while achieving high synchronization precision.
Solution Approach 2:
The system enables self-synchronization by using the neural network to automatically detect temporal offsets and compute correction parameters without external intervention. The network processes multimodal sensor data, identifies chronological relationships, and generates correction parameters that align data from different sensors, making the system self-regulating and eliminating the need for manual synchronization configuration.
2Measurement precision
If precise hardware timestamps and peer-to-peer synchronization are implemented, then measurement timepoint accuracy is improved, but device complexity and costs increase
Solution Approach 1:
The patent replaces precise hardware timestamps and peer-to-peer synchronization infrastructure with a neural network that processes data at any timing. The network learns temporal relationships from the data itself and computes correction parameters, eliminating the need for expensive synchronization hardware while achieving accurate temporal alignment through software-based neural processing.
3Productivity
If sensor data is fused without chronological correction, then processing speed is maintained, but measurement precision deteriorates due to chronological errors
Solution Approach 1:
The neural network performs preliminary learning of temporal relationships and correction parameter computation before the actual sensor fusion process. By pre-training on chronological relationships and pre-computing correction parameters, the system can quickly apply corrections during real-time processing without significantly impacting processing speed, thus maintaining both productivity and measurement precision.
Data Source
AI summary
A method for the chronological correction of multimodal data includes:receiving a first data set from a reference sensor with measurements at different measurement timepoints,receiving a second data set of a second sensor with measurements at different measurement timepoints, each not exactly matching those of the reference sensor,reading the first and the second data sets by a neural network and identifying a respective plurality of feature vectors for the first and second data set at the respective measurement timepoints,merging and comparing the respective feature vectors, which refer to corresponding, not exactly matching measurement timepoints, by the neural network so that parameters of a chronological correction are identified, andidentifying a chronological offset between the respective measurement timepoints of the reference sensor and the second sensor, and/or a corrected data set from the second sensor based on the measurement timepoints of the reference sensor.