LiDAR Bush Detection Using Echo Pulse Width for Object Association
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Solution Overview
Problem
Current methods for determining whether objects are moving or stationary using LiDAR point data often misrecognize objects like flower beds, bushes, and street trees, leading to association errors in vehicle sensor fusion systems.
Innovation Solution
A bush detection method and system that uses echo pulse width (EPW) values from LiDAR point data to identify candidate bush objects, generates a grid map, and assigns flags to determine if an object is a bush, improving object tracking accuracy by distinguishing bushes from other objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If object classification is based on LiDAR point data shape and classification information, then object detection is performed, but misrecognition of bushes and flower beds occurs leading to association errors
Solution Approach 1:
The patent introduces echo pulse width (EPW) as a new parameter for object classification. By analyzing the EPW values of LiDAR point data, the system can distinguish bushes from other objects based on their characteristic EPW ranges, thereby improving classification accuracy and reducing association errors in sensor fusion systems
Solution Approach 2:
The patent introduces a grid map as an intermediary data structure to represent bush locations. The grid map stores EPW information and spatial distribution of candidate bush objects, serving as a mediator between raw LiDAR data and the object association system, enabling more reliable tracking by providing structured bush location information
2Productivity
If sensor fusion system performs association for object tracking based on LiDAR point data, then object tracking is achieved, but association errors occur due to false detection of objects like street trees and vehicles
Solution Approach 1:
The patent performs preliminary identification of candidate bush objects using EPW analysis before the association process. By pre-classifying objects with characteristic EPW values as bushes and representing them in the grid map, the system prevents these objects from causing association errors during the tracking phase, improving overall association accuracy
Solution Approach 2:
The patent segments the object detection process into distinct stages: initial object detection, EPW-based bush identification, grid map generation, and association processing. This segmentation allows the system to apply specialized processing for bush detection before the general association algorithm, reducing false detections and improving tracking reliability
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
The method effectively reduces misrecognition of moving objects, enhancing the accuracy of autonomous driving by correctly identifying bushes and improving association logic in vehicle tracking systems.
Implementation Method 1
a typical method of determining whether an object is in a moving and/or stationary state based on light detection and ranging (LiDAR) point data
Implementation Method 2
determining whether an object is bushes using an echo pulse width (EPW) of LiDAR point data of the object
Data Source
AI summary
A bushes detection method includes: based on an echo pulse width (EPW) value of point data of each object, determining an object having feature point data corresponding to a bush feature as a candidate bush object; generating a grid map based on point data of the candidate bush object; and based on point data that matches a cell including point data of the grid map, of point data of an object of interest (OOI) determined for an association, determining the OOI as bushes and outputting related information.