LiDAR False Positive Filtering via Camera Verification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
LiDAR systems often inaccurately detect targets in their field of view, leading to false positive conditions due to factors like lighting and atmospheric changes, which can result in unwarranted and potentially hazardous system actions.
Innovation Solution
A method and apparatus that utilize a criteria-based learning circuit combining information from a LiDAR system with external sensors, such as cameras, to classify detected targets as true or false positives by evaluating additional information from the surrounding area, thereby filtering out erroneous conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If LiDAR system performs target detection in varying environmental conditions, then detection coverage is improved, but false positive rate increases
Solution Approach 1:
The patent introduces an external sensor (camera) as an intermediary to verify LiDAR detections. The camera captures images of detected targets, and the learning circuit compares visual information with LiDAR range data to confirm true targets, thereby reducing false positives while maintaining detection coverage
Solution Approach 2:
The system implements feedback through the learning circuit that continuously learns from paired LiDAR and camera data. The circuit uses feedback loops to adjust detection criteria based on environmental conditions, improving reliability by adapting to varying lighting and atmospheric conditions
2Measurement precision
If external sensor is integrated to verify target detection, then detection accuracy is improved, but device complexity increases
Solution Approach 1:
The learning circuit serves multiple functions: it processes LiDAR range data, analyzes camera images, correlates information from both sensors, and classifies targets as true or false positives. This multi-functionality reduces the need for separate dedicated circuits for each processing task, thereby managing complexity while improving accuracy
3Reliability
If criteria based learning circuit processes additional sensor information, then false positive reduction is improved, but processing time increases
Solution Approach 1:
The system applies partial verification by only processing external sensor information for targets that meet certain LiDAR detection criteria. Not all detected targets undergo full external sensor verification, which reduces processing time while still effectively reducing false positives by focusing verification resources on ambiguous detections
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 effectively reduces the occurrence of false positives and improves the accuracy of target detection, ensuring safer and more reliable system responses by validating target information through enhanced scanning and sensor integration.
Implementation Method 1
range information (e.g., distance, etc.) associated with a target is determined by irradiating the target with electromagnetic radiation in the form of light and then detecting timing and/or waveform characteristics of reflected light received back from the target
Implementation Method 2
An external sensor is initialized to sense additional information associated with the potential target. The external sensor may take the form of a camera
Data Source
AI summary
Method and apparatus for evaluating targets detected by an active light detection and ranging (LiDAR) system. A potential target and associated range information are obtained during an initial scan. An external sensor is initialized to sense additional information associated with the potential target. A criteria based learning circuit combines the external information from the external sensor with information from a subsequent scan to classify the potential target as a true detection condition in which a physical element is present down range from the LiDAR system, or a false positive condition where a physical element is not present down range from the LiDAR system as described by the detected range information. The external sensor may take the form of a camera. The external sensor may scan a larger surrounding area adjacent the detected potential target. Only some targets identified by the LiDAR system may be selected for evaluation using predetermined criteria.