Autonomous Vehicle Sensor Alignment Validation Using Data Deviation
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Solution Overview
Problem
Existing autonomous vehicles face challenges in maintaining accurate sensor position and orientation due to unintentional movements or vibrations, leading to irregularities and potential failures in data collection, without requiring additional sensors or physical appliances.
Innovation Solution
A validation module compares consecutive sensor data to validate the position or orientation of sensors using deviation analysis, pattern recognition, and cross-calibration techniques, utilizing existing sensors to detect and correct misalignments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If sensors are calibrated at specific points in time, then calibration is simple and quick, but sensor position accuracy deteriorates over time due to unintentional movements or vibrations
Solution Approach 1:
The system continuously monitors sensor data and compares it against stored calibration data to detect deviations in sensor position or orientation. This feedback mechanism allows the system to identify when sensors have moved from their calibrated positions, enabling timely recalibration or correction while maintaining both calibration simplicity and position accuracy.
Solution Approach 2:
The system performs preliminary validation by continuously comparing current sensor data with pre-stored calibration data before autonomous driving tasks are affected. This preliminary detection of sensor misalignment allows for proactive correction, preventing accuracy deterioration from impacting system performance.
2Measurement precision
If additional sensors or physical appliances are installed to monitor sensor position, then sensor position validation accuracy is improved, but device complexity and cost increase
Solution Approach 1:
The system uses the autonomous vehicle's existing sensors to validate their own positions and orientations. By comparing sensor data against stored calibration information and detecting deviations, the system performs self-validation without requiring external monitoring devices, thereby maintaining high measurement precision while avoiding increased device complexity.
Solution Approach 2:
The validation module serves multiple functions: it monitors sensor position, detects deviations, validates calibration status, and triggers alerts or recalibration. This multi-functionality is achieved using the existing sensor infrastructure, eliminating the need for dedicated monitoring hardware and reducing overall system complexity.
3Reliability
If sensor position is continuously monitored using existing sensors, then sensor alignment reliability is improved, but data processing requirements and computational load increase
Solution Approach 1:
The system performs partial validation by comparing sensor data against stored calibration data at key intervals or when specific conditions are met, rather than continuously processing all sensor data at full resolution. This approach maintains sensor alignment reliability while reducing computational energy consumption by processing only the necessary portions of sensor data.
Data Source
AI summary
An apparatus for validating a position or an orientation of one or more sensors of an autonomous vehicle is provided. The one or more sensors provide consecutive sensor data of surroundings of the vehicle. The apparatus includes a validation module, which is configured to compare the consecutive sensor data and to validate a position or an orientation of at least one sensor of the one or more sensors, based on a deviation in the consecutive sensor data.