Lidar Receiver Calibration Using Triggered Noise Offset
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Solution Overview
Problem
Lidar systems face challenges in accurately calibrating receivers due to variations in readings caused by time, ambient temperature, and noise levels, particularly from ambient light and electrical noise, which can result in false-positive detections and corrupted measurements.
Innovation Solution
A method for calibrating lidar systems by detecting a triggering event, such as the vehicle stopping, to measure noise levels during a calibration period and adjust subsequent readings using a noise level metric, ensuring accurate energy level measurements of return light pulses by offsetting them with the calculated noise levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the lidar system continuously emits light to maintain operational readiness, then the system remains responsive to targets, but noise levels from ambient light and electrical noise increase causing false-positive detections
Solution Approach 1:
The patent implements periodic calibration cycles where the light source is temporarily deactivated to measure noise levels. This periodic action alternates between active detection mode and calibration mode, allowing the system to maintain operational readiness while periodically reducing noise accumulation. The controller schedules these calibration periods to offset the accumulating noise effects.
Solution Approach 2:
The patent converts the harmful effect of ambient light and electrical noise into a beneficial calibration mechanism. By measuring the noise levels during calibration periods and using these measurements to adjust detection thresholds and offset subsequent readings, the system transforms the previously harmful noise into useful calibration data that improves detection accuracy.
2Measurement precision
If the lidar system performs frequent calibration to maintain accuracy, then measurement precision improves, but system productivity decreases due to calibration overhead
Solution Approach 1:
The patent applies partial calibration action by performing calibration only when triggering events occur (such as vehicle stops or significant environmental changes) rather than continuously. This partial action approach maintains sufficient measurement precision by calibrating at critical moments while avoiding excessive calibration that would reduce system productivity.
Solution Approach 2:
The system performs self-calibration autonomously by detecting triggering events and automatically initiating calibration sequences without external intervention. The controller monitors system operation, detects appropriate calibration moments, executes the calibration process, and applies corrections to subsequent readings, enabling the system to maintain accuracy while minimizing productivity impact.
3Measurement precision
If the lidar system uses sensitive detectors to improve detection capability, then the system can detect weaker return signals, but electrical noise in the circuitry increases affecting measurement quality
Solution Approach 1:
The patent implements feedback by measuring noise levels during calibration periods and using these measurements to adjust detection parameters and offset subsequent readings. The controller uses the measured noise characteristics to dynamically adjust thresholds and apply corrections, creating a closed-loop system that compensates for electrical noise while maintaining sensitive detection capability.
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 improves the accuracy of lidar system detection by reducing false alarms and enhancing signal-to-noise ratio, leading to more reliable distance measurements and improved operational efficiency.
Implementation Method 1
a receiver configured to detect the light pulses scattered by one or more targets
Implementation Method 2
the system may determine the distance to the target based on the time of flight of a returned light pulse
Data Source
AI summary
A method for calibrating lidar systems operating in vehicles includes detecting a triggering event, causing the lidar system to not emit light during a calibration period, determining an amount of noise measured by the lidar system during the calibration period, generating a noise level metric based on the amount of noise detected during the calibration period, and adjusting subsequent readings of the lidar system using the noise level metric. The adjusting includes measuring energy levels of return light pulses emitted from the lidar system and scattered by targets and offsetting the measured energy levels by the noise level metric.