Autonomous Vehicle Sensor Fusion Using Multimodal Series Transformation with Neural Upsampling and Error Resilience
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Solution Overview
Problem
Autonomous vehicles face challenges in efficiently compressing and transmitting multimodal sensor data due to bandwidth limitations, lack of efficient compression techniques, insufficient error resilience, and degradation of sensor data quality from lossy compression, which compromises safety-critical object detection and tracking.
Innovation Solution
A collaborative autonomous vehicle sensor fusion system applies priority-based compression and neural upsampling techniques to share multimodal sensor data between vehicles, leveraging cross-modal correlations for enhanced perception and robust error resilience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If lossy compression is applied to reduce data transmission bandwidth, then transmission efficiency is improved, but sensor data quality degrades compromising safety-critical object detection
Solution Approach 1:
The patent transforms sensor data from spatial domain to frequency domain using 3D FFT, changing the representation parameters to enable more efficient compression. By operating in frequency domain, the system can apply compression thresholds that preserve critical spatial-temporal patterns while removing redundant information, achieving better compression ratios without proportionally degrading detection quality
Solution Approach 2:
The patent introduces an intermediary reconstruction process using trained neural networks that predict and restore compressed sensor data. This intermediary step between compression and detection allows the system to tolerate compression artifacts by intelligently reconstructing missing or degraded features, thereby maintaining detection accuracy despite lossy compression
2Quantity of substance
If conventional compression algorithms are used to reduce data size, then transmission bandwidth is reduced, but spatial and temporal correlations essential for object detection are not preserved
Solution Approach 1:
The patent applies 3D FFT transformation to convert sensor data into frequency domain representation, fundamentally changing the data parameters from spatial coordinates to frequency components. This parameter transformation reveals the underlying spatial and temporal correlations in a form that is more amenable to compression while preserving the essential structure needed for reliable object detection and tracking
Solution Approach 2:
The patent segments the compressed sensor data transmission into priority-based portions, where critical spatial-temporal features identified through frequency domain analysis are preserved at higher fidelity while less critical components are compressed more aggressively. This segmentation allows selective preservation of detection-relevant information
3Loss of energy
If sensor data is compressed for V2V transmission, then bandwidth requirements are reduced, but error resilience in mobile vehicular environments is insufficient
Solution Approach 1:
The patent applies different compression quality levels to different portions of sensor data based on their importance to safety-critical detection. Critical regions containing potential objects or anomalies are compressed with higher fidelity and greater error protection, while background regions use more aggressive compression. This local quality differentiation maintains transmission reliability for important information while reducing overall bandwidth consumption
Data Source
AI summary
A collaborative autonomous vehicle sensor fusion system enables multiple vehicles to share multimodal sensor data for enhanced perception capabilities beyond individual vehicle limitations. Each autonomous vehicle captures multimodal sensor data, identifies safety-critical objects, applies priority-based compression based on safety criticality, and shares compressed data via vehicle-to-vehicle communication. An enhanced multi-vehicle AI deblocking network receives the compressed sensor data and enhances perception data for each vehicle using sensor data from multiple vehicles in the collaborative network. The system prioritizes reconstruction quality for safety-critical objects over non-safety-critical objects and enables detection of safety-critical objects occluded from individual vehicles through collaborative sensor fusion. The network fuses multimodal sensor data by identifying cross-modal correlations between different sensor types and uses these correlations to reconstruct sensor information that is degraded or occluded in individual vehicles, providing improved situational awareness for autonomous vehicle operation.