Multi-Window Feature Map Fusion 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 the computational intensity of cross-relating grid cells across feature maps.
Innovation Solution
The autonomous vehicle groups grid cells of feature maps and combines their features within the group, using regions or windows that traverse multiple feature maps to enrich and cross-correlate features, thereby reducing processing demands and improving processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If individual camera feature maps are used for object detection, then device complexity is reduced, but measurement precision of bounding boxes deteriorates due to insufficient semantic data
Solution Approach 1:
The patent combines feature maps from multiple cameras into a unified feature representation. The system integrates semantic data across different camera views by merging their feature maps, enabling more accurate bounding box detection without requiring each individual camera to be overly complex. This merging approach allows the system to leverage complementary information from multiple sensors while maintaining manageable device complexity.
Solution Approach 2:
The patent transitions from 2D feature maps to 3D bounding box representations by incorporating depth information and spatial relationships across multiple camera views. This dimensional transformation enables the system to generate accurate 3D bounding boxes by synthesizing information from multiple 2D camera perspectives, thereby improving measurement precision without proportionally increasing device complexity.
2Measurement precision
If grid cells across multiple feature maps are cross-related to enrich semantic data, then measurement precision improves, but use of energy increases due to computational intensity
Solution Approach 1:
The patent segments the feature map processing into distinct regions or windows rather than computing relationships across entire feature maps. By dividing the computational domain into smaller segments, the system reduces the overall computational burden and energy consumption while still capturing essential semantic relationships. This segmentation allows selective processing of relevant feature regions, improving efficiency without sacrificing measurement precision.
Solution Approach 2:
The patent applies different processing strategies to different regions of the feature maps based on their importance. Rather than uniformly processing all grid cells, the system focuses computational resources on locally significant regions that contribute most to object identification accuracy. This local quality approach optimizes energy usage by avoiding unnecessary computations in less critical areas while maintaining high measurement precision where needed.
3Productivity
If regions or windows are used to traverse multiple feature maps, then productivity improves by reducing processing demands, but device complexity increases due to multiple pluralities of windows
Solution Approach 1:
The patent designs the window structure to serve multiple functions simultaneously: it traverses multiple feature maps, extracts relevant features, and facilitates cross-correlation operations. This multi-functional window design improves productivity by consolidating several processing tasks into a single unified structure, thereby avoiding the need for separate complex mechanisms for each function and actually reducing overall device complexity.
Solution Approach 2:
The patent employs dynamic window configurations that can adapt their size, position, and number based on the specific processing requirements. Rather than using a fixed static window structure, the system dynamically adjusts window parameters to optimize processing efficiency for different scenarios. This dynamic approach improves productivity by allowing the system to allocate computational resources flexibly while keeping the window management mechanism relatively simple through rule-based adaptation.
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 feature maps corresponding to the received images. The perception system may generate multiple pluralities of windows and use the multiple pluralities of windows to enrich semantic data of the feature maps. The perception system may use the enriched semantic to generate one or more bounding boxes for objects in the vehicle scene.