Radar Point Cloud Motion Detection Guided by Stationary Image Regions
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Solution Overview
Problem
Current automated driving technologies struggle to accurately determine whether objects in an image are moving or stationary, relying solely on image data is insufficient for this distinction.
Innovation Solution
A method utilizing point cloud data from radar to identify stationary objects and determine velocity ranges, allowing for the differentiation between moving and stationary objects by comparing velocities within these ranges.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image data alone is used to detect objects, then the detection process is simple, but the ability to determine whether objects are moving or stationary is insufficient
Solution Approach 1:
The patent combines image data from cameras with point cloud data from radars to create a fused detection system. The image processing unit detects object regions in images, while the radar processing unit extracts velocity information from point cloud data. By merging these two data sources, the system achieves accurate motion state detection (distinguishing moving from stationary objects) without relying solely on complex image analysis or standalone radar processing.
2Measurement precision
If point cloud data from radar is used to detect moving objects, then the velocity information is available, but the accuracy of detecting moving objects is still insufficient without proper differentiation from stationary objects
Solution Approach 1:
The patent segments the detection process into distinct functional units: an image processing unit that detects object regions and determines stationary status, and a radar processing unit that extracts velocity information. By segmenting the point cloud data processing based on object regions identified in images, the system can separately analyze moving and stationary objects, significantly improving detection accuracy while making the classification process more manageable through modular processing.
Solution Approach 2:
The patent uses image-based object region detection as an intermediary to guide the radar point cloud processing. The image processing unit acts as a mediator that identifies which regions in the radar point cloud correspond to stationary objects (detected in images). This intermediary step enables accurate separation of moving and stationary objects by providing spatial correspondence between image and radar data, resolving the classification difficulty.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Improves the accuracy of detecting moving objects by leveraging point cloud data to differentiate between stationary and moving objects, enhancing the reliability of autonomous driving systems.
Implementation Method 1
obtains a point cloud data set of a target scene and an image of the target scene; wherein the point cloud data set is obtained by a radar
Data Source
AI summary
A method of detecting moving objects is provided. The method obtains point cloud data set of a target scene and an image of the target scene. The method detects one or more stationary object areas from the image. The method records point cloud data, which corresponds to the stationary object areas in the point cloud data set, to be first point cloud data. The method determines a velocity range of the first point cloud data according to the first point cloud data. The method further determines whether one or more moving objects are present in the target scene according to second point cloud data and the velocity range of the first point cloud data. The second point cloud data is the point cloud data of the point cloud data set excluding the first point cloud data. A related electronic device and a non-transitory storage medium are provided.