Positioning Precision Estimation Using Virtual Sensor Models
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Solution Overview
Problem
Current technologies lack an effective method for assessing precision error in high-precision positioning, which is crucial for autonomous driving, collision avoidance, and path planning.
Innovation Solution
A method involving a machine learning model trained on driving information and intermediate variables to estimate positioning precision error, utilizing a system architecture that includes data capture, training, and execution devices to assess precision error in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sensors are used for positioning, then positioning precision is improved, but device complexity and power consumption increase
Solution Approach 1:
The patent replaces physical sensor hardware with a virtual sensor system that uses software algorithms to estimate positioning precision. Instead of adding more physical sensors, the system uses existing sensor data combined with machine learning models to predict positioning accuracy, thereby avoiding increased device complexity while maintaining precision improvement
Solution Approach 2:
The patent creates a virtual copy of sensor functionality through software-based precision estimation. The virtual sensor system replicates the information-gathering capability of physical sensors by processing existing data through algorithms, eliminating the need for additional physical sensing components
2Measurement precision
If multiple sensors are used for positioning, then positioning precision is improved, but power consumption increases
Solution Approach 1:
The patent substitutes energy-intensive physical sensor operations with computationally-efficient software-based precision estimation. The virtual sensor system processes existing sensor data using algorithms that consume less power than activating and processing data from multiple additional physical sensors would require
Solution Approach 2:
The patent applies partial action by using only the necessary computational resources to estimate precision rather than fully activating multiple sensor systems. The machine learning models process data selectively to provide precision estimates without the full energy cost of operating multiple sensors at maximum capacity
3Reliability
If positioning precision estimation is performed, then positioning reliability is improved, but computing resources are consumed
Solution Approach 1:
The patent performs preliminary precision estimation using pre-trained machine learning models that have already learned from extensive data. This allows the system to quickly assess positioning reliability without performing computationally-intensive real-time analysis, consuming minimal computing resources while maintaining high reliability
Solution Approach 2:
The patent changes the computational parameters by using efficient machine learning models with optimized complexity. The system adjusts model parameters and selection based on available computing resources, choosing lighter models when resources are constrained while still providing reliable precision estimation
Data Source
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AI summary
A method for estimating positioning precision error and apparatus, a device and a storage medium, applied in the fields of maps, navigation, automatic driving, Internet of Vehicles, intelligent transportation, cloud computing, etc. The method for estimating positioning precision error comprises: acquiring first driving information of a vehicle collected by a sensor(510); according to the first driving information, obtaining first position information of the vehicle, and a first intermediate variable used in the process of determining the first position information(520); according to the first driving information and the first intermediate variable, determining a target precision estimation model among pre-trained precision estimation models (530); inputting the first position information and the first intermediate variable into the target precision estimation model to obtain second position information of the vehicle, and a precision error of the first position information relative to the second position information(540). The precision error of high-precision positioning can be effectively evaluated.