Multi-Modal Bounding Box Tracking for Complex Vehicle Scenes
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Autonomous vehicles face challenges in accurately tracking 3D bounding boxes for objects due to movement, erroneous detection, and incomplete capture by individual sensors, leading to difficulties in identifying and navigating through complex scenes.
Innovation Solution
The system uses object queries enriched by cross-correlating data from multiple sensor modalities, including cameras and lidar, and employs a vision transformer with self-attention and cross-attention to improve detection and tracking accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple sensor modalities are used for object detection and tracking, then detection accuracy and reliability improve, but device complexity increases
Solution Approach 1:
The patent combines data from multiple sensor modalities (cameras, lidar, radar) into a unified detection framework. The system merges heterogeneous sensor data representations and processes them jointly through shared neural network components, achieving improved reliability while managing complexity through integration rather than separate processing chains.
Solution Approach 2:
The patent implements a multi-modal sensor system where a single detection framework processes multiple types of sensor data (visual, depth, point cloud). The system uses universal feature extraction and bounding box generation mechanisms that work across different sensor modalities, allowing one system to perform multiple detection functions simultaneously.
2Measurement precision
If 3D bounding boxes are generated for all detected objects, then spatial awareness improves, but computational load and processing time increase
Solution Approach 1:
The patent performs preliminary 2D bounding box detection in the image plane before projecting to 3D space. The system pre-processes sensor data to identify object candidates and their approximate locations, then generates refined 3D bounding boxes only for detected objects rather than processing the entire scene, reducing computational load while maintaining precision.
Solution Approach 2:
The patent divides the detection process into separate stages: 2D detection in image space, projection to 3D space, and refinement of bounding boxes. This segmentation allows the system to handle computational tasks in manageable steps, processing only relevant portions of sensor data at each stage rather than computing all 3D bounding boxes simultaneously.
3Loss of information
If individual sensors capture complete object information, then detection completeness improves, but measurement precision deteriorates due to sensor limitations
Solution Approach 1:
The patent merges data from multiple sensor modalities (camera images, lidar point clouds, radar data) to compensate for individual sensor limitations. By combining complementary information from different sensors, the system achieves both complete object feature representation and high measurement precision that individual sensors cannot provide alone.
Solution Approach 2:
The patent creates a composite sensing system where data from heterogeneous sensors (optical, electromagnetic, depth) are fused together. The system processes composite sensor inputs through unified neural network architectures, leveraging the strengths of each sensor type to achieve accurate and complete object detection beyond what any single sensor modality can achieve.
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 correlate object queries from previous time steps with object queries from the current time step.