Lidar Point Cloud Classification for Autonomous Vehicle False Positives
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Solution Overview
Problem
Autonomous vehicles face inefficiencies due to false positives in lidar point cloud data, which inaccurately indicate objects, leading to unnecessary navigation maneuvers and reduced operational efficiency.
Innovation Solution
A method involving lidar sensor systems that combine point cloud data with pulse feature data to identify and classify potential false positives as drivable or non-drivable, using machine learning techniques and sensor fusion to differentiate between actual objects and reflective surfaces or precipitation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the autonomous vehicle uses lidar to detect objects in the environment, then the safety and object detection capability is improved, but false positive detections occur leading to unnecessary navigation maneuvers and reduced operational efficiency
Solution Approach 1:
The patent introduces an intermediary classification system that processes lidar point cloud data before final object detection. This intermediary layer analyzes point cloud characteristics, density, and spatial distribution to filter out false positives caused by environmental factors like precipitation and reflective surfaces, thereby maintaining high detection accuracy while reducing false alarms that would compromise navigation efficiency
Solution Approach 2:
The patent dynamically adjusts detection parameters based on environmental conditions. By monitoring point cloud density, spatial distribution patterns, and return intensity characteristics, the system adapts detection thresholds and filtering parameters to distinguish between actual objects and false positives, resolving the contradiction between maintaining high reliability and avoiding unnecessary navigation maneuvers
2Reliability
If the autonomous vehicle navigates around perceived objects to ensure safety, then collision avoidance is improved, but the efficiency and speed of navigation is reduced due to unnecessary avoidance maneuvers
Solution Approach 1:
The patent performs preliminary classification and validation of detected objects before triggering navigation avoidance maneuvers. By pre-analyzing point cloud data characteristics, cross-validating with multiple sensor inputs, and assessing false positive probability, the system ensures that only genuine obstacles trigger avoidance actions, thereby maintaining collision avoidance reliability while eliminating unnecessary maneuvers that would waste navigation time
3Measurement precision
If the lidar sensor system processes all point cloud data as potential objects, then detection sensitivity is improved, but false positive identification increases leading to incorrect drivability classifications
Solution Approach 1:
The patent applies different analysis methods and classification criteria to different regions and types of point cloud data. By analyzing local point density, spatial distribution patterns, and geometric characteristics specific to each detected region, the system maintains high detection sensitivity for actual objects while applying targeted filtering to eliminate false positives from precipitation, reflective surfaces, and other environmental factors
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
Enables autonomous vehicles to navigate more efficiently by distinguishing between actual objects and false positives, allowing safe passage over reflective surfaces and through precipitation, thereby improving data processing and reducing unnecessary avoidance maneuvers.
Implementation Method 1
lidar sensor system that combine point cloud data with pulse feature data to identify and classify potential false positives
Data Source
AI summary
According to one aspect, an autonomous vehicle that includes a lidar unit collects lidar point cloud data that includes false returns or false positives, and characterizes the data associated with the false returns or false positives as drivable or not drivable. The false returns or false positives may be phantom points that are not associated with actual objects which may pose collision risks. Analyzing lidar point cloud data to characterize false returns or false positives as either drivable or not drivable enables an autonomous vehicle to operate efficiently by not having to avoid non-existent collision risks. False positives may be indicated when a wet or icy road surface acts as a mirror which reflects objects, and when precipitation such as raindrops appear as objects. Characterizing such false positives as drivable facilitates the efficient operation of an autonomous vehicle as the autonomous vehicle may drive over a mirror and/or through precipitation.