Yaw Rate Sensor Calibration Using Camera Lane Detection
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Solution Overview
Problem
Existing methods for determining yaw rate in motor vehicles are hindered by sensor malfunctions due to temperature dependency and require accurate calibration during operation, especially for accurate lane prediction and driver assistance systems.
Innovation Solution
A method for calibrating yaw rate measurements using a combination of a yaw rate sensor, steering angle sensor, and camera system, which performs initial calibration at standstill and subsequent online calibration during vehicle movement, utilizing image data to estimate yaw rate and correct output values based on vehicle speed and lane curvature.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a yaw rate sensor is used for determining yaw rate, then the measurement can be obtained during vehicle operation, but the output signal drifts due to temperature dependency and sensor malfunction
Solution Approach 1:
The system continuously compares the yaw rate measured by the sensor with the yaw rate estimated from camera image data during vehicle operation. This feedback mechanism allows real-time detection of measurement deviations caused by temperature drift or sensor malfunction, enabling continuous calibration to maintain both reliability and precision of the yaw rate measurement.
2Measurement precision
If calibration is performed during vehicle operation, then accurate yaw rate determination is achieved, but the calibration process becomes more complex requiring multiple sensors and processing steps
Solution Approach 1:
The camera system originally designed for lane detection is made multi-functional by also using it for yaw rate calibration during vehicle operation. This allows the same hardware component to serve dual purposes: environmental perception and sensor calibration, thereby improving measurement precision without proportionally increasing device complexity.
Solution Approach 2:
The system performs self-calibration by using its own camera data to correct the yaw rate sensor measurements. The calibration process is autonomous, automatically detecting when calibration is needed and executing the correction without external intervention, which simplifies the overall system architecture while maintaining high calibration accuracy.
3Measurement precision
If image data from camera system is used for calibration during movement, then accurate lane prediction is achieved, but the calibration process requires additional processing time and computational resources
Solution Approach 1:
The system continuously processes camera image data to estimate lane position and vehicle orientation in advance, preparing this information for potential calibration needs. By maintaining ready-to-use lane and orientation estimates from the camera system, the calibration process can quickly compare these pre-processed data with sensor measurements without requiring intensive real-time computation, thus reducing calibration processing time while maintaining lane prediction accuracy.
Data Source
AI summary
In a method for the calibration of a yaw rate measurement in a motor vehicle, the motor vehicle features at least one device for determining the yaw rate, a camera system which is oriented in the forward direction, and, if applicable, a steering angle sensor or a lateral acceleration sensor. At least one initial calibration is carried out at a standstill and at least one second calibration is carried out when the motor vehicle is moving. The second calibration uses image data of the camera system, which detects the environment in front of the motor vehicle, to predict the course of the roadway lane on which the vehicle is driving, from which the yaw rate is estimated.