Backscatter Histogram Analysis for Daytime LIDAR Detection
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Solution Overview
Problem
Existing LIDAR systems face challenges in accurately determining environmental conditions and detection range due to high ambient light and diffuse backscatter, which can limit their effectiveness in autonomous vehicles.
Innovation Solution
A method for analyzing backscatter histogram data using time-correlated histogram data accumulation and correlation techniques to enhance signal-to-noise ratio, allowing for precise determination of environmental conditions and object detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Duration of action of moving object
If LIDAR systems operate in high ambient light conditions, then the system can function during daytime driving, but the signal-to-noise ratio decreases and detection range is limited
Solution Approach 1:
The patent segments the detected light signals into different categories using histogram analysis, separating object backscatter signals from ambient light and diffuse backscatter. This segmentation allows the system to identify and process only the relevant signal components, improving signal-to-noise ratio in high ambient light conditions
Solution Approach 2:
The patent introduces histogram analysis as an intermediary processing step between light detection and object identification. The histogram data serves as a mediator that characterizes the distribution of detected light signals, enabling the system to distinguish true object signals from ambient light noise before making detection decisions
2Loss of information
If LIDAR systems detect diffuse backscatter from particles in air, then environmental information can be obtained, but object detection precision is reduced
Solution Approach 1:
The patent segments the total detected light signal into distinct components: object backscatter, diffuse backscatter from air particles, and ambient light. By analyzing the histogram distribution characteristics of each component, the system can separate environmental information from object detection data, maintaining precision in object identification while preserving environmental condition awareness
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
Improves safety and reliability of autonomous vehicles by enhancing the detection of environmental conditions and solid objects, and optimizing detection range through improved analysis of backscatter histogram data.
Implementation Method 1
optical pulse runtime methods (e.g., optical runtime measurement), which can be based upon the so-called time-of-flight principle, in which the runtime of an emitted light signal reflected by an object is measured
Implementation Method 2
sensors in the motor vehicle environment based upon the so-called LIDAR (light detection and ranging) principle, in which the environment is scanned by periodically emitting pulses and detecting the reflected pulses
Implementation Method 3
the type of detected light signals can differ in LIDAR applications, e.g., depending on whether the emitted light signal is reflected by a solid object (object backscatter) or scattered back by particles in the air (diffuse backscatter), for example in fog or exhaust gases
Data Source
AI summary
A method for analyzing backscatter histogram data in an optical pulse runtime method, including the steps of receiving backscatter histogram data; and analyzing the received backscatter histogram data.