Radar Point Cloud Split-Path Processing for Low-Latency Detection
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Solution Overview
Problem
Existing object detection methods in radar systems face challenges in maintaining accuracy and reducing latency, particularly when processing radar point clouds from both static and dynamic objects, as they often require multiple measurement cycles, leading to information loss and increased detection latency.
Innovation Solution
The method involves separately processing static and dynamic radar point clouds in distinct network paths, using different preprocessing techniques for each, followed by fusion in a deeper network level to enhance detection performance while minimizing latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple measurement cycles are used to aggregate radar data, then detection accuracy is improved, but detection latency increases
Solution Approach 1:
The patent segments the radar point cloud into static and dynamic objects based on velocity thresholds, processing each subset through separate network paths. This segmentation allows independent optimization of processing parameters for each object type, enabling accurate detection without requiring multiple measurement cycles for all objects uniformly, thus reducing overall detection latency while maintaining accuracy.
Solution Approach 2:
The patent implements dynamic processing strategies where static objects use one processing path optimized for accuracy with temporal aggregation, while dynamic objects use another path optimized for speed with minimal aggregation. This dynamic approach allows the system to adapt processing intensity based on object characteristics, resolving the contradiction between accuracy and latency.
2Reliability
If radar point cloud is processed as a whole, then spatial context knowledge is utilized, but processing complexity increases
Solution Approach 1:
The patent divides the radar point cloud into separate subsets based on velocity characteristics (static vs. dynamic objects) and processes them through distinct network paths. This segmentation reduces processing complexity by allowing specialized processing for each subset while maintaining spatial context knowledge through the fusion of processed results at the detection stage.
Solution Approach 2:
Instead of processing the entire point cloud uniformly, the patent applies partial processing strategies where different processing intensities are applied to different subsets. Static objects receive processing optimized for spatial context, while dynamic objects receive processing optimized for speed, reducing overall processing complexity while maintaining necessary spatial understanding.
3Productivity
If velocity threshold is used to separate static and dynamic objects, then processing efficiency is improved, but classification accuracy may be affected
Solution Approach 1:
The patent applies different velocity thresholds and processing parameters to different spatial regions and object types. Instead of a single global threshold, the system adapts classification criteria locally based on the specific characteristics of detected objects and their positions, improving both processing efficiency and classification accuracy by tailoring the approach to local conditions.
Solution Approach 2:
The patent dynamically adjusts velocity threshold parameters based on driving context, weather conditions, and detected object characteristics. This parameter adaptation allows the system to optimize the balance between processing efficiency and classification accuracy for different operational scenarios, preventing fixed thresholds from compromising either objective.
Data Source
AI summary
A system, a device, and a method for detecting objects by separately processing point clouds including information about the surroundings of the system and/or the device. The method includes: separating a point cloud into a plurality of point clouds according to one or more features of a plurality of features of each point of the point cloud; preprocessing the plurality of point clouds in a plurality of input paths of a network corresponding to the respective point cloud; fusing the output data of the plurality of input paths of the network; and further processing the fused output data in the network to detect the objects.