LiDAR Falling Object Detection via Free Fall Law
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Solution Overview
Problem
Current methods for identifying high-altitude falling objects are ineffective, particularly in harsh conditions like night, rain, or fog, and lack real-time detection capabilities, posing safety hazards to public and vehicular safety.
Innovation Solution
A LiDAR system-based method that obtains point cloud data sets at different moments, identifies dynamic point cloud clusters, and determines if they follow a law of free fall by tracking their position in the direction of gravity over a set time period, enabling real-time detection of falling objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional detection methods are used, then device complexity is reduced, but detection reliability deteriorates in harsh conditions like night, rain, or fog
Solution Approach 1:
The patent replaces traditional mechanical/optical detection systems (cameras, infrared sensors) with a LiDAR-based detection system. LiDAR uses laser ranging technology to actively illuminate the target and measure distance, enabling reliable detection in harsh conditions where passive optical systems fail. The system substitutes complex multi-sensor fusion with a specialized active sensing system that operates independently of ambient light conditions.
2Loss of time
If real-time detection is implemented, then safety response time is improved, but measurement precision requirements increase
Solution Approach 1:
The system performs preliminary actions by continuously scanning and building point cloud data before a falling object actually threatens safety. The LiDAR system operates in real-time, continuously updating the spatial environment model, so when a falling object appears, the system already has established baseline data for comparison. This preliminary continuous monitoring reduces the effective response time while maintaining precision through cumulative data processing.
Solution Approach 2:
The patent implements feedback mechanisms by continuously comparing point cloud data across multiple time points. The system detects changes in the point cloud structure over time, using feedback from previous frames to identify and track falling objects. This temporal feedback loop enables real-time detection by comparing current state against historical state, maintaining measurement precision through differential analysis.
3Productivity
If point cloud data processing is performed in real-time, then detection speed is improved, but computational complexity increases
Solution Approach 1:
The patent extracts and processes only the critical dynamic elements from the complete point cloud data. Instead of analyzing all points in the scene, the system identifies and focuses on moving objects by comparing point cloud differences between time points. This extraction of relevant information from the massive point cloud dataset reduces computational complexity while maintaining real-time detection speed by processing only the changed portions of the scene.
Solution Approach 2:
The system segments the point cloud processing task into distinct stages: static background modeling, dynamic object extraction, and falling object classification. By dividing the computational task into segmented processing steps, the system achieves real-time performance through parallel processing of different data aspects, reducing overall computational complexity while maintaining detection speed.
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 method provides accurate and continuous detection of falling objects, enhancing public and vehicular safety by enabling early warning and prevention systems, even in adverse conditions.
Implementation Method 1
obtaining a point cloud data set of a LiDAR system
Implementation Method 2
determining whether the data representing the position of the dynamic point cloud cluster in the direction of gravity within a set time period meets a law of free fall
Data Source
AI summary
The present disclosure provides a method and an apparatus for identifying a falling object based on a LiDAR system. The method includes obtaining a point cloud data set of a LiDAR system; identifying a dynamic point cloud cluster set based on the point cloud data at a first moment and a second moment, where the dynamic point cloud cluster set includes at least one dynamic point cloud cluster; and enabling a tracking and determination process in response to identifying the dynamic point cloud cluster set where, for each dynamic point cloud cluster, a data set of a dynamic point cloud cluster at each current moment following the second moment is updated in real time, and it is determined that an object represented by the dynamic point cloud cluster is a falling object in response to determining that the data meets a law of free fall.