Optical Sensor Auto-Calibration Using Lane Lines for AV Navigation
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Solution Overview
Problem
Autonomous vehicles require detailed high-definition maps and an optical network for safe operation, but the condition of the optical network can degrade over time, leading to unsafe operation if not properly calibrated.
Innovation Solution
A navigation system with an automatic optical calibration mechanism that captures sensor data streams, extracts lane lines, optimizes intrinsic and extrinsic parameters, and compiles optimized parameters to alert users when maintenance is required, ensuring the optical sensors remain functional and safe for autonomous operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If optical sensors are used for autonomous vehicle navigation, then the vehicle can operate autonomously with high-definition map verification, but the optical network condition degrades over time leading to unsafe operation
Solution Approach 1:
The system performs preliminary calibration actions by capturing sensor data streams and extracting lane lines before degradation becomes critical. The automatic calibration mechanism proactively optimizes intrinsic and extrinsic parameters to prevent optical network failure, ensuring safety thresholds are maintained throughout the sensor service life.
Solution Approach 2:
The system implements continuous feedback by monitoring optical sensor performance through sensor data streams, comparing extracted lane lines against expected patterns, and automatically adjusting calibration parameters. This closed-loop feedback ensures reliability is maintained despite aging effects over the optical network's service life.
2Measurement precision
If manual calibration of optical sensors is performed, then measurement precision can be maintained, but the system complexity and operational burden increase
Solution Approach 1:
The system performs self-calibration by automatically capturing sensor data streams, extracting lane lines, and optimizing intrinsic and extrinsic parameters without human intervention. This self-service approach maintains measurement precision while eliminating the operational burden of manual calibration procedures.
Solution Approach 2:
The system automatically adjusts calibration parameters (intrinsic and extrinsic) based on extracted lane line data, dynamically changing parameters to maintain precision. This automated parameter optimization reduces system complexity by replacing manual adjustment procedures with algorithm-driven parameter changes.
3Measurement precision
If frequent optical sensor calibration is performed, then identification of roadway features improves, but loss of time and operational efficiency decrease
Solution Approach 1:
The system performs continuous calibration by capturing sensor data streams in real-time during normal operation, extracting lane lines continuously, and progressively optimizing parameters. This continuous useful action maintains roadway feature identification accuracy without requiring discrete calibration events that would cause operational interruptions and time loss.
Data Source
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AI summary
A navigation system (100) includes: a control circuit (512) configured to: a control circuit (512) configured to: capture a sensor data stream (111) provided by optical sensors (108), extract lane lines (402) from the sensor data stream (111), optimize an extrinsic parameter (418) and an intrinsic parameter (420) based on the extract of the lane lines (402), and compile optimized parameters (120) including the extrinsic parameter (418) and the intrinsic parameter (420); and a communication circuit (516), coupled to the control circuit (512), configured to receive an optical sensor alert (416) for displaying on a device (102).