Coherent Doppler LiDAR Scene Perception
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional LiDAR systems do not provide Doppler information and struggle to distinguish static and dynamic targets, leading to excessive processing of point cloud data.
Innovation Solution
A coherent Doppler LiDAR system that classifies targets as static or dynamic by comparing measured Doppler values with theoretical values, filtering out either static or dynamic targets for separate processing, thereby reducing the amount of data to be processed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional LiDAR systems process all return points to generate point cloud data, then complete scene coverage is achieved, but processing time and computational resources increase significantly
Solution Approach 1:
The patent segments the point cloud data processing by dividing targets into static and dynamic categories based on Doppler information. This segmentation allows the system to apply different processing strategies to different data subsets, reducing overall processing time while maintaining scene perception accuracy.
Solution Approach 2:
The patent extracts and utilizes Doppler information from the LiDAR return points to identify and separate dynamic targets from static ones. By extracting this specific feature, the system can filter out static points early in the processing pipeline, reducing the volume of data requiring intensive processing.
2Reliability
If traditional LiDAR systems process all return points including both static and dynamic targets, then comprehensive target detection is achieved, but computational complexity increases
Solution Approach 1:
The patent applies segmentation by separating static and dynamic targets using Doppler-based classification. This division simplifies the overall processing complexity by allowing specialized handling of each target type rather than applying complex algorithms uniformly to all points.
Solution Approach 2:
The patent enables the point cloud data to self-classify into static and dynamic categories through Doppler information analysis. This self-service mechanism reduces the need for complex external processing algorithms, as the data structure itself provides the differentiation needed for efficient processing.
3Productivity
If coherent Doppler LiDAR systems classify and filter targets, then processing volume is reduced, but system complexity increases
Solution Approach 1:
The patent implements a universal classification mechanism that handles both static and dynamic targets through a single Doppler-based filtering approach. This multi-functional system can process different target types using the same fundamental methodology, reducing the need for multiple specialized processing pipelines.
Solution Approach 2:
The patent utilizes Doppler velocity as an additional parameter to differentiate between static and dynamic targets. By introducing this parameter change to the processing framework, the system achieves efficient classification without requiring fundamentally new processing architectures.
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
This approach significantly improves processing capabilities by reducing the volume of Doppler point cloud data, allowing for more efficient scene perception and target classification without the need for complex algorithms.
Implementation Method 1
coherent Doppler LiDAR systems transmit laser light to targets and obtain Doppler information by measuring the frequency shift of the reflected laser light
Implementation Method 2
the range is measured based on the Time-of-Flight (TOF) of the emitted laser light
Data Source
AI summary
Embodiments of the present disclosure are directed to providing scene perception display requiring reduced processing capabilities. Sensor data indicative of one or more targets from an imaging and ranging subsystem, location data from a positioning subsystem defining a geographical location of the imaging and ranging subsystem and orientation data from an orientation subsystem defining an orientation of the imaging and ranging subsystem are received. Doppler point cloud data is generated based on Doppler information and point cloud data from the sensor data, the location data and the orientation data. The targets are classified as either a static or dynamic target. Afterwards, the Doppler point cloud data is filtered by removing either the dynamic or the static targets from the Doppler point cloud data. The Doppler point cloud data of either the dynamic or the static targets are further processed and the further processed Doppler point cloud data is rendered.