Autonomous Vehicle Sensor Calibration Using Roadside Reference Points
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Solution Overview
Problem
Current calibration methods for autonomous vehicle sensors are complex, inefficient, and often fail to maintain accuracy during actual driving missions, requiring specialized facilities and procedures.
Innovation Solution
The system employs run-time calibration by identifying reference points in actual driving environments using data from multiple sensors, optimizing sensor parameters to minimize errors in coordinate determination across different sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional calibration methods are used, then sensor calibration can be performed, but the process becomes complex and requires specialized facilities
Solution Approach 1:
The system performs self-calibration by using its own sensors to identify reference points in the natural environment and compute calibration parameters automatically, eliminating the need for external specialized facilities and complex manual procedures
Solution Approach 2:
The calibration system uses the same sensors that perform driving functions (cameras, lidars, radars) for both operation and calibration, allowing the system to serve multiple purposes without requiring separate calibration equipment
2Measurement precision
If traditional calibration methods are used, then sensor calibration can be performed, but it is inefficient and time-consuming
Solution Approach 1:
The system continuously identifies reference points and performs calibration computations in advance during normal operation, so that calibration is already completed before it is needed, eliminating dedicated calibration time
Solution Approach 2:
The calibration process operates continuously during normal driving missions rather than requiring separate calibration sessions, maintaining sensor accuracy throughout operation without interrupting productivity
3Measurement precision
If sensors are calibrated in static environments, then calibration can be performed, but accuracy deteriorates during dynamic driving conditions
Solution Approach 1:
The system performs calibration dynamically during actual driving missions by tracking moving reference points and compensating for vehicle motion, allowing calibration to adapt to changing driving conditions rather than requiring static environments
Solution Approach 2:
The system continuously monitors sensor data during operation and adjusts calibration parameters based on real-time observations of reference points, maintaining accuracy by feedback from actual driving conditions
Data Source
AI summary
The described aspects and implementations enable efficient calibration of a sensing system of an autonomous vehicle (AV). In one implementation, disclosed is a method and a system to perform the method, the system including the sensing system configured to collect sensing data and a data processing system, operatively coupled to the sensing system. The data processing system is configured to identify reference point(s) in an environment of the AV, determine multiple estimated locations of the reference point(s), and adjust parameters of the sensing system based on a loss function representative of differences of the estimated locations.