Inertial Navigation Using Zero-Velocity Assumptions for Autonomous Driving
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Solution Overview
Problem
Current inertial navigation systems for autonomous driving rely heavily on external sensors like GPS and LIDAR, which are prone to failures in adverse conditions, leading to inaccurate position estimation and control issues when external sensor information deteriorates.
Innovation Solution
An inertial navigation device that uses an inertial measurement unit, vehicle velocity sensor, and steering angle sensor to estimate roll, pitch, and yaw angles, and subsequently the vehicle's position, even when external sensor data is unavailable, by assuming zero vertical and lateral velocities and employing a nonlinear Kalman filter for accurate side slipping estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If external sensors (GPS, LIDAR) are used for position estimation, then accuracy is improved under normal conditions, but reliability deteriorates when external environment deteriorates (rain, fog, nighttime)
Solution Approach 1:
The patent creates an inert navigation environment by using inertial sensors (accelerometers and gyroscopes) that are不受外部环境影响. The inertial measurement unit measures acceleration and angular velocity internally, providing reliable position and attitude estimation even when external sensors fail due to rain, fog, or nighttime conditions.
Solution Approach 2:
The patent introduces white line recognition as an intermediary reference system. By detecting white lines on the road and calculating their curvature, the system provides a mediating reference that helps correct drift in inertial navigation and maintains accuracy without relying on external sensors like GPS that may fail in adverse conditions.
2Measurement precision
If multiple sensors and information sources are used for position estimation, then accuracy is improved, but device complexity increases
Solution Approach 1:
The patent merges inertial navigation with white line recognition by integrating the curvature information from detected white lines into the inertial navigation calculations. This combination allows the system to maintain high accuracy using a unified processing framework rather than separate independent systems.
Solution Approach 2:
The white line detection system serves multiple functions: it provides lateral position reference, enables curvature estimation for drift correction, and offers a backup reference when GPS is unavailable. This multi-functionality reduces the need for separate dedicated systems for each function.
3Reliability
If inertial navigation is used without external sensor fusion, then reliability is improved during external sensor failure, but measurement precision deteriorates due to accumulation errors
Solution Approach 1:
The patent implements feedback by continuously monitoring the curvature of detected white lines and using this information to correct drift in inertial navigation. The system compares the expected vehicle path based on inertial data with the actual path inferred from white line curvature, and applies corrections to maintain accuracy over time.
Solution Approach 2:
The system performs preliminary white line detection and curvature calculation before they are needed for correction. By continuously tracking white line positions and computing curvature in advance, the system is ready to provide immediate drift correction when needed, reducing accumulation errors proactively rather than reactively.
Data Source
AI summary
An inertial navigation device that includes a processor that is configured to: assume a vertical velocity and a lateral velocity of a vehicle to be 0, and estimate a roll angle, a pitch angle and an azimuth angle, which are attitude angles of the vehicle, based on angular velocities and accelerations detected by the inertial measurement device, a longitudinal velocity detected by a vehicle velocity sensor, and an initial value of the azimuth angle of the vehicle, and assume the vertical velocity of the vehicle to be 0, and estimate a current position of the vehicle based on the estimated attitude angles, the angular velocities and the accelerations detected by the inertial measurement device, the longitudinal velocity detected by the vehicle velocity sensor, a steering angle detected by a steering angle sensor, and an initial value of a position of the vehicle.