Selective Radar Data Processing for Efficient Sensor Fusion
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Solution Overview
Problem
Processing raw radar data from autonomous vehicles is computationally expensive, and existing methods that extract radar point clouds for fusion with camera and lidar data often lose valuable information.
Innovation Solution
A neural network-based approach that selectively processes specific portions of raw radar data identified by an attention module as containing missing information from camera and lidar data, using a heatmap to focus processing on these regions while leaving others unprocessed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all raw radar data is processed, then object detection accuracy is improved, but computational cost increases
Solution Approach 1:
The patent divides the radar data processing into two segments: (1) processing only selected portions of radar data that are likely to contain objects of interest, and (2) leaving other portions unprocessed. This segmentation allows the system to maintain high detection accuracy for critical regions while reducing overall computational cost by avoiding processing of irrelevant data regions.
Solution Approach 2:
The patent applies different processing quality levels to different regions of radar data. High-quality processing is applied to selected portions identified as containing objects of interest, while other portions receive no processing. This local quality approach ensures that computational resources are concentrated where they provide the most value for detection accuracy.
2Productivity
If radar point cloud is extracted for fusion, then processing speed is improved, but information loss occurs
Solution Approach 1:
The patent extracts only the essential and relevant portions of radar data that are needed for object detection, rather than extracting the entire radar point cloud. By selectively extracting only the portions containing objects of interest, the system maintains processing speed while minimizing information loss of valuable radar data.
3Productivity
If selected portions of radar data are processed, then computational efficiency is improved, but detection coverage may be reduced
Solution Approach 1:
The patent performs preliminary identification of selected portions of radar data that are likely to contain objects of interest before conducting detailed processing. This preliminary action allows the system to focus computational resources on high-probability regions while maintaining adequate detection coverage through strategic selection of processing regions.
Solution Approach 2:
The system uses feedback from preliminary analysis and object detection results to iteratively refine which portions of radar data should be processed. This feedback mechanism ensures that as the system learns from detected objects, it improves its selection of regions to process, thereby maintaining detection coverage while optimizing computational efficiency.
Data Source
AI summary
Systems and methods for automatic removal for selected training data from a dataset are provided. Systems and methods are provided for identifying portions of radar data that contain information that is not present in camera and/or lidar data. The identified portions of the radar data can be processed by a neural network to provide the additional information. The identified parts of the radar data can then be processed while the remaining parts of the radar data remain unprocessed. In various examples, features can be extracted from identified regions of the radar data using a neural network and fused with camera and/or lidar features. In some examples, the fused features can be used for object detection.