Motion Sensor Calibration Using Occupancy Maps
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Solution Overview
Problem
Existing motion sensors in vehicles require calibration to compensate for manufacturing tolerances and environmental dependencies, such as temperature and tire pressure, to provide accurate ego motion information for advanced driver assistance systems (ADAS) and autonomous driving.
Innovation Solution
A computer-implemented method for calibrating motion sensors using environment perception sensors to monitor the vehicle's vicinity, determine occupancy maps based on motion models, and select calibration parameters based on the quality of these maps, allowing for real-time, onboard calibration without the need for a test stand.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If motion sensor calibration is performed using traditional test stand methods, then calibration accuracy can be ensured, but the calibration process requires removing the vehicle from service and cannot be performed in real-time operating conditions
Solution Approach 1:
The motion sensor performs self-calibration using environment perception sensor data and occupancy maps generated during normal vehicle operation. The system automatically compares expected occupancy patterns with actual sensor readings to compute calibration parameters without external intervention or removal from service.
Solution Approach 2:
The system continuously generates and stores occupancy maps during normal operation before calibration is needed. When calibration is performed, these pre-generated occupancy maps are immediately available for comparison, enabling rapid calibration without requiring test stand procedures or vehicle removal.
2Measurement precision
If motion sensor calibration is performed offline on a test stand, then comprehensive calibration can be achieved, but the calibration cannot account for real-time environmental conditions such as temperature and tire pressure variations
Solution Approach 1:
The calibration parameters are dynamically adjusted based on real-time environmental conditions detected during vehicle operation. The system continuously monitors temperature, tire pressure, and other environmental factors, and adapts calibration parameters accordingly to maintain accuracy across varying conditions.
Solution Approach 2:
The system uses feedback from environment perception sensors and occupancy map quality metrics to continuously refine calibration parameters. By comparing expected occupancy patterns with actual sensor readings in real-time, the system identifies calibration deviations and adjusts parameters to compensate for environmental variations.
3Measurement precision
If multiple calibration parameters are tested to find the optimal calibration, then calibration accuracy improves, but the computational complexity and time required for calibration increases
Solution Approach 1:
The patent replaces complex manual calibration procedures with an automated computational system that uses environment perception sensor data and occupancy map analysis. The system automatically evaluates multiple calibration parameters and selects the optimal one based on occupancy map quality metrics, eliminating the need for manual test stand procedures.
Solution Approach 2:
The system systematically varies calibration parameters and evaluates their impact on occupancy map quality. By computing processed motion measurement values with different calibration parameters and comparing the resulting occupancy maps, the system identifies optimal parameters without requiring complex manual intervention or extensive test procedures.
4Device complexity
If motion sensor calibration is performed without occupancy maps, then the calibration process is simpler, but the accuracy of ego motion information deteriorates due to manufacturing tolerances and environmental dependencies
Solution Approach 1:
The patent introduces occupancy maps as an intermediary element that bridges the motion sensor and environment perception sensors. The occupancy maps serve as a reference framework for comparing sensor readings and determining calibration accuracy, enabling precise calibration without requiring complex direct comparison methods.
Data Source
AI summary
A computer implemented method for calibrating a motion sensor arranged at a vehicle and configured to output a raw motion measurement value indicative of a current motion condition of the vehicle comprises: computing processed motion measurement values by modifying the outputted raw motion measurement value based on a set of different calibration parameters, monitoring the vicinity of the vehicle by means of an environment perception sensor, determining a set of occupancy maps by means of mapping data acquired by the environment perception sensor in accordance with a motion model for the vehicle, wherein each of the processed motion measurement values is used to establish the motion model for one of the occupancy maps, and selecting, based on a comparison of the determined occupancy maps, one of the different calibration parameters for modifying subsequently outputted raw motion measurement values.