Lidar Ghost Detection Suppression via Channel Comparison
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Solution Overview
Problem
LIDAR systems face issues with false detections due to cross-channel noise from highly reflective objects, leading to inaccurate distance measurements and potential safety hazards in applications like autonomous vehicles.
Innovation Solution
The LIDAR system identifies false returns by determining simultaneous activity of adjacent channels, similar depth measurements, and disparate intensities, and suppresses these false detections by adjusting detection thresholds or discarding the signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple LIDAR channels are activated simultaneously to increase point cloud density, then measurement coverage and density are improved, but cross-channel noise and false detections increase
Solution Approach 1:
The patent segments the detection process by channel, analyzing returns from each channel independently and comparing them to identify false detections. By dividing the multi-channel data into separate analyzable units and applying channel-specific validation rules, the system maintains high detection accuracy while processing simultaneous multi-channel returns.
Solution Approach 2:
The patent introduces an intermediary validation process that compares returns across channels to identify and eliminate false detections. This intermediary step acts as a filter between raw sensor data and final detections, using cross-channel comparison to distinguish true objects from noise without reducing point cloud density.
2Measurement precision
If detection threshold is lowered to capture weak returns, then sensitivity is improved, but false positives from cross-channel noise increase
Solution Approach 1:
The patent merges information from multiple channels by comparing returns that occur at similar ranges across different channels. By combining data from multiple sources and looking for consistent patterns, the system can detect weak true returns while filtering out random noise that doesn't appear consistently across channels.
Solution Approach 2:
The patent implements a feedback mechanism where the detection system continuously validates returns by checking for consistency with returns from adjacent channels. This feedback loop allows the system to maintain low detection thresholds for sensitivity while using cross-channel validation to eliminate false positives that don't meet consistency criteria.
3Reliability
If adjacent channels are separated by larger azimuth to reduce cross-channel noise, then false detections are reduced, but point cloud density and coverage decrease
Solution Approach 1:
The patent changes the parameter being controlled from physical channel separation (azimuth) to signal processing parameters (detection thresholds, validation rules). By maintaining tight channel spacing for density while applying software-based false detection filtering, the system achieves both high point cloud density and low false detection rates without requiring physical channel separation.
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 reduces false positives, enhancing the accuracy and safety of LIDAR-generated data by distinguishing true and false returns, thereby improving the reliability of distance measurements.
Implementation Method 1
When the emitted light is incident on a surface, a portion of the light is reflected and received by the light sensor
Implementation Method 2
the light sensor, which converts light intensity to a corresponding electrical signal
Implementation Method 3
the system may measure the propagation time of a light signal as it travels from the laser emitter, to the surface, and back to the light sensor. A distance is then calculated based on the flight time and the known speed of light
Implementation Method 4
highly reflective objects, such as retroreflectors which are commonly integrated into traffic signs, license plates, etc., may reflect much of the emitted light
Data Source
AI summary
A LIDAR system that identifies, from a channel output, a false positive return and/or suppressing a corresponding false positive detection caused, in some cases, a strong reflection by a highly reflective surface that caused light to leak from a first channel to a second channel. The LIDAR system described herein may identify, as a false return, a return detected in the second channel that has an intensity that is much less than a return in the first channel and indicates a distance that is the same or very close to a distance indicated the return in the first channel. Based at least in part on identifying a return as a false return, the LIDAR system may suppress a false detection associated with the false return by modifying a detection threshold.