Differential Lidar Channel Analysis for Long-Range Visibility Estimation
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Solution Overview
Problem
Current lidar systems primarily estimate visibility in short ranges, leading to localized and inaccurate visibility estimates due to reliance on lidar return intensity and density, which do not account for atmospheric scattering media like fog, rain, and snow, limiting effective range and accuracy.
Innovation Solution
A method involving differential analysis of lidar channels, dividing them into threshold and non-threshold groups to detect atmospheric scattering media by analyzing statistical distributions and differentials in return metrics, enabling medium to long-range visibility estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If lidar return intensity and density are used to estimate visibility, then the estimation process is simple, but the visibility estimate is localized and inaccurate for medium to long range
Solution Approach 1:
The patent divides the lidar system into multiple channels with different threshold settings. Some channels use high thresholds to detect only strong returns (penetrating through scattering media), while others use low thresholds to detect all returns including scattered light. This segmentation allows simultaneous measurement of different atmospheric conditions using the same lidar hardware, resolving the contradiction between measurement precision and device complexity.
Solution Approach 2:
The patent changes the detection parameter (threshold level) across different channels to create differential sensitivity. By analyzing the statistical distribution differences of returns across channels with varying thresholds, the system can detect atmospheric scattering media and estimate visibility at medium to long ranges, improving measurement precision without requiring additional hardware complexity.
2Reliability
If current lidar techniques are used, then the system operation is straightforward, but atmospheric scattering media like fog, rain, and snow cannot be detected
Solution Approach 1:
The patent applies different threshold qualities to different channels, creating local quality variations in the detection system. Channels with low thresholds are optimized for detecting scattered light from atmospheric media, while channels with high thresholds penetrate through scattering media to detect background returns. This local quality differentiation enables reliable detection of atmospheric scattering media that would be invisible to uniform threshold systems.
Solution Approach 2:
The patent uses the statistical distribution of returns across multiple channels as an intermediary to detect atmospheric scattering media. By comparing the statistical properties (mean, variance, skewness) of returns from channels with different thresholds, the system indirectly detects the presence of scattering media without requiring direct measurement, thus reducing detection difficulty while improving reliability.
3Length of stationary object
If short-range visibility estimation is used, then the measurement process is simple, but the effective range is limited
Solution Approach 1:
The patent adds a new dimension to visibility estimation by introducing multiple threshold levels as an additional measurement dimension. Instead of relying solely on return intensity (one dimension), the system now measures return statistics across multiple threshold dimensions, enabling medium to long range visibility estimation while maintaining or improving accuracy through the additional measurement dimension.
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
Enhances visibility estimation accuracy and range by identifying atmospheric scattering media, improving operational driving conditions and spatially resolving their presence, thus enhancing the reliability of autonomous and semi-autonomous vehicles.
Implementation Method 1
Based on time differences between light emissions and receiving the reflected light, the lidar device can generate data that can be used to generate three-dimensional (3D) point cloud data
Implementation Method 2
Atmospheric scattering media that affect visibility in the environment, such as rain, fog, and snow, can also be determined by the representation
Data Source
AI summary
Example embodiments relate to differential methods for determining environment estimation, lidar impairment detection, and filtering. An example embodiment includes dividing a plurality of lidar device channels into a first group and a second group and interleaving the channels. The embodiment includes applying a threshold to the first group. The embodiment further includes emitting light pulses from a lidar device into an environment surrounding the lidar device, and detecting return light pulses. The return light pulses in the first group of channels are sampled from the signals that exceed the threshold. The embodiment may further include determining a differential in a statistical distribution between the return light pulses in the first group and the return light pulses in the second group. Based on the differential, the method can include detecting an atmospheric scattering medium in the environment surrounding the lidar device.