Transformer Fusion for Multi-Modal Object Detection
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Solution Overview
Problem
Combining data from different sensor modalities, such as cameras, radar, and lidar, in real-time for object detection in autonomous driving is challenging due to the need for alignment and the complexity of processing multiple data types simultaneously.
Innovation Solution
A transformer-based machine learning model is used to combine data from different sensor modalities by employing an attention mechanism that allows for cross-attention across different sensor data, eliminating the need for explicit alignment and enabling concurrent processing of data from multiple sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data from multiple sensor modalities is combined for processing, then object detection accuracy is improved, but processing complexity increases
Solution Approach 1:
The patent combines data from multiple sensor modalities (camera, radar, lidar) into a unified processing pipeline using a transformer-based neural network. The model integrates multi-modal sensor inputs to perform joint object detection, improving accuracy by leveraging complementary information from different sensors while managing processing complexity through efficient architecture design.
2Measurement precision
If explicit alignment is performed between different sensor data, then processing accuracy is improved, but processing time increases
Solution Approach 1:
The patent introduces a transformer-based neural network as an intermediary that processes multi-modal sensor data without requiring explicit pre-alignment. The attention mechanism in the transformer model automatically handles the integration and correlation of data from different sensor modalities, eliminating the need for separate alignment steps while maintaining processing accuracy.
3Productivity
If traditional processing methods are used for multiple sensor types, then processing speed is maintained, but detection accuracy deteriorates
Solution Approach 1:
The patent replaces traditional mechanical or algorithmic alignment and processing methods with a transformer-based neural network that uses attention mechanisms. This substitution enables the system to process multiple sensor modalities concurrently with improved detection accuracy, as the neural network learns optimal integration strategies during training without requiring explicit alignment rules.
Data Source
AI summary
Disclosed are techniques for combining data using transformer-based machine learning models. In some examples, a first transformer is used to combine a first dataset with a second dataset. The results are then combined with a third dataset, using a second transformer. Each dataset can represent data from a different sensor modality. The transformers compute scores based on queries and apply the scores to values. The first dataset can be used to generate queries for the first transformer, and the values for the first transformer can be derived from the second dataset. Similarly, the third dataset can be used to generate queries for the second transformer, and the values for the second transformer can be derived from the output of the first transformer. The output of the second transformer is therefore a combination of all three datasets and can be used for object detection, for example, determining three-dimensional boundaries of objects.