Bird's-Eye View Feature Maps for Accurate 3D Bounding Boxes
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Solution Overview
Problem
Autonomous vehicles face challenges in accurately drawing 3D bounding boxes for objects in real-time driving environments due to insufficient semantic data from individual cameras and computationally intensive cross-relating of feature maps, making it difficult to identify and navigate around objects effectively.
Innovation Solution
The approach involves grouping grid cells of feature maps and using windows or subsets of images/feature maps for self-attention, reducing processing demands and enhancing the accuracy of object identification by enriching feature maps and generating bird's-eye view feature maps to improve bounding box determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If individual cameras are used to generate bounding boxes, then the system is simple and easy to implement, but the semantic data is insufficient leading to poor object identification accuracy
Solution Approach 1:
The patent merges multiple camera views into a unified bird's-eye view feature map by projecting and fusing features from multiple individual camera images. This combining approach enriches the semantic data available for object identification while maintaining a unified processing framework that manages complexity through systematic integration rather than treating each camera independently
Solution Approach 2:
The patent transforms 2D camera images into a synthesized 2.5D bird's-eye view representation by projecting features onto a common reference plane. This dimensional transformation consolidates information from multiple camera perspectives into a single enriched feature map that provides comprehensive spatial context for accurate object detection
2Measurement precision
If feature maps from multiple cameras are cross-related to improve accuracy, then object identification improves, but computational demand increases significantly
Solution Approach 1:
The patent performs preliminary feature extraction and enrichment on individual camera images before the final synthesis step. By pre-processing and enriching features from each camera independently using self-attention mechanisms, the system reduces the computational burden during the final merging phase, as the heavy lifting is done in parallel on smaller individual feature sets rather than processing the complete multi-camera dataset simultaneously
Solution Approach 2:
The patent segments the feature map generation process into independent per-camera processing units that operate in parallel. Each camera's features are processed separately through enrichment operations, and only the enriched features are combined in the final synthesis step. This segmentation enables distributed computation and significantly reduces peak computational demand compared to processing all camera features together
Data Source
AI summary
A perception system may be used to generate bounding boxes for objects in a vehicle scene. The perception system may receive images and generate feature maps corresponding to the received images. The perception system may generate object queries based on the feature maps and enrich the object queries. The perception system may use the enriched object queries to generate one or more bounding boxes for objects in the vehicle scene.