Yaw Rate Sensor Bias Estimation Using GNSS Straight-Line Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Yaw rate sensors in autonomous vehicles suffer from inherent bias due to manufacturing tolerances and environmental conditions, which change over time, leading to inaccurate yaw rate measurements, especially during non-stationary conditions, posing a challenge for safe blind stops.
Innovation Solution
Utilize Global Navigation Satellite System (GNSS) data to determine straight-line vehicle movement or stationary status, combining it with yaw rate sensor data to accurately estimate and compensate for sensor bias, ensuring reliable yaw rate measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If yaw rate sensor measurements are used directly, then the system is simple and responsive, but measurement precision deteriorates due to sensor bias
Solution Approach 1:
The patent introduces GNSS position data as an intermediary to indirectly determine yaw rate bias. Instead of directly measuring bias with another sensor, the system uses GNSS position changes over time to infer vehicle orientation and compare it with yaw rate sensor readings, thereby estimating bias through a mediating measurement chain.
Solution Approach 2:
The patent replaces direct mechanical sensing of yaw rate bias with a computational approach using GNSS position data. Instead of relying solely on mechanical inertial sensors to detect bias, the system substitutes part of the measurement function with satellite-based position tracking and mathematical processing to derive orientation information.
2Measurement precision
If bias estimation is performed continuously, then measurement precision improves, but use of energy increases due to constant processing
Solution Approach 1:
The patent implements periodic bias estimation by checking specific conditions (straight-line motion detection via GNSS, stationary status) before updating the bias estimate. Rather than continuous processing, the system periodically updates bias only when reliable measurement conditions are met, reducing energy consumption while maintaining accuracy.
Solution Approach 2:
The patent performs preliminary condition checking using GNSS data to determine if the vehicle is in straight-line motion or stationary before initiating bias estimation processing. This preliminary action filters out inappropriate processing scenarios, ensuring energy is only consumed when conditions warrant accurate bias measurement.
3Reliability
If multiple sensors are integrated for bias estimation, then reliability improves, but device complexity increases
Solution Approach 1:
The patent makes the GNSS receiver perform multiple functions: it provides both position navigation data and serves as an indirect source for yaw rate bias estimation. By making the existing GNSS receiver multi-functional, the system improves reliability through sensor integration without adding dedicated hardware for bias measurement, thus limiting complexity increase.
Solution Approach 2:
The patent merges the bias estimation function with the existing GNSS positioning system. Instead of creating a separate bias measurement subsystem, the system combines orientation inference from GNSS position data with yaw rate sensor processing in a unified algorithmic framework, reducing overall system complexity while improving reliability.
Data Source
AI summary
An example yaw rate related method includes: obtaining a plurality of measurements of yaw rate of a vehicle from a yaw rate sensor; and determining a yaw rate sensor bias estimate for the yaw rate sensor based on at least one of the plurality of measurements of yaw rate in response to: a plurality of indications of location of the vehicle over time based on global navigation satellite system signals being indicative of straight-line movement of the vehicle; or a determination that the vehicle is stationary.