Optical Sensor Self-Calibration for Lane-Based Vehicle Navigation
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Solution Overview
Problem
Autonomous vehicles require high-definition maps and optical networks for safe operation, but the condition of the optical network can degrade over time, necessitating a solution for monitoring and calibration to maintain accurate identification of roadway features.
Innovation Solution
A navigation system with an automatic optical calibration mechanism that captures sensor data streams from optical sensors, extracts lane lines, optimizes intrinsic and extrinsic parameters, and provides alerts for display on a device, utilizing a control circuit and communication circuit to manage the optical network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If optical sensors are used for autonomous vehicle navigation, then roadway feature identification capability is improved, but sensor degradation over time reduces reliability
Solution Approach 1:
The system performs preliminary calibration actions by capturing sensor data streams and extracting lane lines to optimize intrinsic and extrinsic parameters before degradation significantly impacts performance. This proactive calibration maintains measurement precision by addressing parameter drift before it causes reliability issues.
Solution Approach 2:
The system implements continuous feedback through optical sensor alerts that monitor sensor condition and trigger calibration processes when degradation is detected. This closed-loop feedback mechanism maintains reliability by dynamically adjusting calibration based on actual sensor performance and environmental conditions.
2Measurement precision
If manual calibration of optical sensors is performed, then parameter accuracy is improved, but operational complexity and time consumption increase
Solution Approach 1:
The system performs self-calibration by automatically capturing sensor data streams, extracting lane lines, and optimizing parameters without human intervention. This autonomous calibration process maintains parameter accuracy while eliminating the time loss and operational complexity associated with manual calibration procedures.
Solution Approach 2:
The system replaces manual mechanical calibration processes with automated computational methods. By using algorithmic parameter optimization based on extracted lane lines and sensor data, the system achieves accurate calibration without the time-consuming manual adjustments previously required.
3Measurement precision
If frequent optical sensor calibration is performed, then parameter optimization is improved, but system complexity and processing requirements increase
Solution Approach 1:
The calibration system is integrated into the existing autonomous vehicle navigation infrastructure, using the same optical sensors and processing pipelines already in place for roadway feature identification. This multi-functional approach maintains parameter optimization without adding significant system complexity, as the calibration processes reuse existing hardware and software components.
Data Source
AI summary
A navigation system includes: a control circuit configured to: a control circuit configured to: capture a sensor data stream provided by optical sensors, extract lane lines from the sensor data stream, optimize an extrinsic parameter and an intrinsic parameter based on the extract of the lane lines, and compile optimized parameters including the extrinsic parameter and the intrinsic parameter; and a communication circuit, coupled to the control circuit, configured to receive an optical sensor alert for displaying on a device.