Vehicle Positioning Fusion Algorithm Velocity Adaptation
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Solution Overview
Problem
Existing vehicle positioning methods using GNSS signals are susceptible to poor ambient conditions and provide low precision during high vehicle velocities, while traditional methods offer reliable but reduced precision in such conditions.
Innovation Solution
A method that switches between two calculation methods for determining vehicle position based on vehicle velocity, using different input variables for a fusion algorithm, where low velocity uses absolute position from GNSS signals and external corrections, and high velocity uses vehicle velocity and dynamic information from sensors, with a Kalman filter for error minimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GNSS signals are used for vehicle positioning, then position information is obtained, but precision deteriorates during high vehicle velocities and poor ambient conditions
Solution Approach 1:
The system dynamically switches between different calculation methods based on vehicle velocity. A first calculation method is used when velocity is below a threshold value, and a second calculation method is used when velocity exceeds the threshold. This dynamic adaptation resolves the contradiction by optimizing the positioning approach for different operational conditions.
Solution Approach 2:
The system changes the input variables for the fusion algorithm based on vehicle velocity. At low velocities, absolute position from GNSS signals is used as input. At high velocities, vehicle velocity and dynamic information from sensors are used instead. This parameter change allows the system to maintain precision across different velocity ranges.
2Measurement precision
If absolute position from GNSS signals is used as input variable, then positioning accuracy is improved at low velocity, but robustness deteriorates at high velocity
Solution Approach 1:
The fusion algorithm dynamically adapts its input variables based on vehicle velocity. When the vehicle is stationary or moving slowly, absolute position from GNSS is used. When the vehicle moves at high velocity, the system switches to using vehicle velocity and dynamic sensor information. This dynamic adaptation maintains both accuracy and robustness across different operating conditions.
Solution Approach 2:
The system changes the set of input variables for the fusion algorithm according to the vehicle's movement state. This parameter change strategy allows the system to optimize positioning accuracy when the vehicle is stationary while maintaining robustness during high-velocity movement, thereby resolving the contradiction between accuracy and adaptability.
3Device complexity
If a single fusion algorithm is used for all velocity conditions, then system complexity is reduced, but positioning accuracy deteriorates under varying velocity conditions
Solution Approach 1:
The positioning system is segmented into different calculation methods based on velocity thresholds. The first calculation method handles low-velocity scenarios using absolute position input, while the second calculation method handles high-velocity scenarios using velocity and dynamic information. This segmentation allows each method to be optimized for its specific operating range, maintaining high accuracy without excessive overall complexity.
Data Source
AI summary
A method for determining the position of a vehicle is disclosed. GNSS signals from a global satellite navigation system are received by a receiving device. A vehicle velocity is detected; and a check is carried out as to whether the detected vehicle velocity falls below or exceeds a threshold value. After the vehicle velocity falls below the threshold value, the position of the vehicle is determined on the basis of a first calculation method. After the vehicle velocity exceeds the threshold value, the position of the vehicle is determined on the basis of a second calculation method. Both calculation methods include filtering the GNSS signals by a fusion algorithm. The calculation methods differing by input variables of the fusion algorithm.