3D Bounding Box Fusion Using Camera, Radar, and Lidar Queries
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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 and local information from individual cameras, and difficulties in capturing depth and velocity using cameras, while integrating radar and lidar data with vision data is also challenging.
Innovation Solution
Cross-correlating object queries with feature maps from various sensor modalities, including camera, radar, and lidar, using a vision transformer to enhance object detection by merging image data and applying self-attention and cross-attention processes to generate more accurate bounding boxes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If individual cameras are used for object detection, then the system is simple, but the detection accuracy and depth perception are insufficient
Solution Approach 1:
The patent combines data from multiple sensor modalities (camera, radar, lidar) into a unified feature map representation. The vision transformer processes multi-modal inputs by merging their features through cross-attention mechanisms, enabling accurate 3D bounding box detection while managing system complexity through integrated processing.
Solution Approach 2:
The vision transformer architecture serves multiple functions: it processes visual data from cameras, integrates radar point cloud information, fuses lidar depth data, and generates 3D bounding boxes. This multi-functional approach allows a single system to handle diverse sensor inputs and produce comprehensive object detection results.
2Measurement precision
If multi-modal sensor data is integrated, then object detection accuracy improves, but processing complexity increases
Solution Approach 1:
The patent introduces feature maps as an intermediary representation that bridges different sensor modalities. Instead of directly processing raw data from camera, radar, and lidar, the system converts all inputs into unified feature maps that can be processed by the vision transformer, simplifying the integration process while maintaining detection accuracy.
Solution Approach 2:
The system transforms multi-modal sensor data into a common parameter space through feature extraction. By converting diverse inputs (images, point clouds, radar returns) into standardized feature representations with consistent dimensions and formats, the vision transformer can efficiently process all modalities without being overwhelmed by their original complexity.
3Speed
If real-time processing is required, then response speed is fast, but detection accuracy may be compromised
Solution Approach 1:
The system performs preliminary feature extraction and organization before the main detection process. By pre-processing sensor data into structured feature maps and organizing spatial relationships in advance, the vision transformer can focus computational resources on the critical 3D bounding box prediction, achieving both speed and accuracy in real-time operation.
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 of various modalities and feature maps corresponding to the received images. The perception system may generate object queries. The perception system may use the generated object queries to generate one or more bounding boxes for objects in the vehicle scene.