Asynchronous Multimodal Feature Fusion for Vehicle Sensors
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Solution Overview
Problem
Existing vehicle systems struggle with effectively fusing asynchronous data from different sensor modalities to enhance situational awareness and prevent collisions, particularly due to differences in data sampling rates and temporal information.
Innovation Solution
The proposed solution involves embedding spatial features from image and ranging data with temporal information and using a transformer-based architecture with a self-attention mechanism to capture spatial and temporal relationships, thereby improving the fusion of sensor data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If asynchronous data from different sensor modalities is fused without temporal information, then the fusion process is simpler, but the accuracy of object detection and tracking deteriorates
Solution Approach 1:
The patent embeds temporal information into spatial features before the fusion process. Each spatial feature is associated with a timestamp indicating when it was captured, allowing the transformer network to process temporal relationships during the fusion operation. This preliminary enrichment of data enables accurate fusion of asynchronous sensor data without requiring complex temporal alignment procedures.
2Adaptability or versatility
If sensor data is sampled at different rates, then each sensor can operate independently, but the synchronization of data becomes more difficult
Solution Approach 1:
The patent employs a transformer-based architecture with self-attention mechanisms that dynamically handle asynchronous data from sensors operating at different rates. The self-attention mechanism automatically learns temporal attention weights, allowing the system to adapt to varying sampling rates without requiring fixed synchronization protocols. Each spatial feature includes temporal information (timestamps) that enables the network to dynamically align and process features from different temporal moments.
3Reliability
If temporal information is embedded in spatial features, then the capture of temporal dependencies is improved, but the data processing complexity increases
Solution Approach 1:
The patent uses a transformer network with self-attention mechanisms as an intermediary to process the enriched spatial features. The self-attention mechanism serves as a mediator that processes temporal relationships by computing attention weights based on temporal information embedded in the features. This intermediary architecture efficiently captures temporal dependencies through matrix operations rather than requiring complex explicit temporal processing logic.
Data Source
AI summary
This disclosure provides systems, methods, and devices for vehicle driving assistance systems that support image processing. In a first aspect, a method of image processing includes receiving image data from an image sensor; receiving ranging data from a ranging sensor; embedding first spatial features of the image data with first temporal information associated with the image data; embedding second spatial features of the ranging data with second temporal information associated with the ranging data; determining first bird's-eye-view (BEV) features based on the first spatial features embedded with first temporal information; determining second BEV features based on the second spatial features embedded with second temporal information; and determining, based on the first and second BEV features, a feature set for processing by a transformer network. The feature set includes at least a portion of both the first and second BEV features. Other aspects and features are also claimed and described.