Temporal Voxel Occupancy Estimation Using Multi-Frame Aggregation
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Solution Overview
Problem
Existing perception systems in autonomous vehicles face challenges in accurately determining voxel occupancy due to noise and occlusions in single-frame image analysis, which can lead to inefficient and unsafe decision-making.
Innovation Solution
The method employs temporal multi-frame occupancy estimation using multiple camera frames to aggregate voxel features across time, incorporating a trained occupancy classifier and noise reduction techniques to enhance voxel occupancy estimation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If single-frame image analysis is used for voxel occupancy determination, then processing speed is maintained, but accuracy deteriorates due to noise and occlusions
Solution Approach 1:
The system performs preliminary occupancy estimation on historical frames before the current frame, creating a temporal baseline. This preliminary action allows the system to compare current occupancy estimates against historical data, filtering out noise and occlusions that would otherwise degrade measurement precision in single-frame analysis.
Solution Approach 2:
The system maintains continuous occupancy estimation across multiple frames rather than processing isolated frames. By continuously tracking voxel occupancy over time and aggregating features across frames, the system preserves useful temporal information while maintaining processing efficiency, thus improving accuracy without proportionally increasing complexity.
2Measurement precision
If multiple camera frames are used for temporal multi-frame occupancy estimation, then accuracy improves, but processing time increases
Solution Approach 1:
The system segments the processing task by identifying and focusing on changed voxels between frames rather than reprocessing all voxels. This segmentation allows the system to leverage temporal information from multiple frames for improved accuracy while minimizing redundant computations, thus reducing the time penalty associated with multi-frame processing.
Solution Approach 2:
The system performs partial processing by aggregating voxel features only for changed regions across frames rather than processing the entire scene. This partial action approach maintains the accuracy benefits of temporal multi-frame analysis while significantly reducing the computational burden and processing time compared to exhaustive multi-frame processing.
3Reliability
If voxel features are aggregated across multiple frames, then noise and occlusion effects are reduced, but computational complexity increases
Solution Approach 1:
The system performs preliminary identification of changed voxels between frames before aggregating features. This preliminary action creates a focused set of voxels that require aggregation, reducing the computational complexity of feature aggregation while maintaining the reliability benefits of temporal aggregation for noise and occlusion reduction.
Solution Approach 2:
The system applies different processing quality to different voxels based on their change status. Changed voxels receive full temporal aggregation processing for maximum reliability, while unchanged voxels are handled more efficiently. This local quality approach optimizes computational complexity by applying complex aggregation only where necessary while maintaining high reliability for critical occupancy determinations.
Data Source
AI summary
Examples described herein provide a method that includes receiving a first image captured by a camera of a vehicle at a first time t and receiving a second image captured by the camera of the vehicle at a second time t-1. The method further includes projecting each of a plurality of world voxels to the camera at the first time t and the second time t-1. The method further includes aggregating voxel features for the plurality of world voxels for the first image and the second image. The method further includes training an occupancy classifier using the aggregated voxel features.