Radar Scene Flow Synchronization for Camera-LiDAR-Radar Tracking
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Solution Overview
Problem
Existing vehicle collision prevention systems face challenges in accurately detecting and tracking objects, especially in low-light conditions and visually occluded situations, due to limitations in sensor fusion techniques.
Innovation Solution
The implementation of radar scene flow estimation to synchronize and fuse sensor data from multiple sensors, including radar, camera, and other sensors, to enhance object detection and tracking by leveraging the strengths of each sensor type.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor fusion techniques are used to improve object detection and tracking, then detection accuracy is improved, but reliability deteriorates in low-light conditions and visually occluded situations
Solution Approach 1:
The patent introduces radar scene flow as an intermediary to mediate between radar and camera data. The radar scene flow provides motion information that acts as a bridge to align and fuse camera features with radar detections, enabling reliable object detection and tracking even in low-light or occluded conditions where traditional sensor fusion fails.
2Reliability
If multiple sensors are fused to enhance object detection, then detection robustness is improved, but device complexity increases
Solution Approach 1:
The patent extracts and utilizes only the essential motion information from radar scene flow, rather than fusing all radar data with camera data. By taking out just the scene flow parameters needed for alignment and feature matching, the system achieves robust multi-sensor object detection while avoiding the complexity of complete sensor fusion.
Data Source
AI summary
This disclosure provides systems, methods, and devices for vehicle driving assistance systems that support enhanced sensor fusion techniques. In a first aspect, a method of includes receiving point cloud data for two or more frames from a radar device and generating scene flow parameter data based on the point cloud data. The method also includes generating voxel position adjustment data based on the scene flow parameter data, and generating feature concatenation information associated with two or more sensors based on the voxel position adjustment data and feature information associated with the two or more sensors. The method further includes performing feature detection and tracking based on the feature concatenation information to generate tracking information for one or more objects, and outputting the tracking information. Other aspects and features are also claimed and described.