GNSS Quality Clustering for Vehicle Driving State Detection
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Solution Overview
Problem
Existing methods for vehicle navigation using GNSS signals struggle to reliably compensate for unpredictable interfering influences, leading to poor position estimation due to the complexity of data and variety of input signals, making precise determination of vehicle state challenging.
Innovation Solution
A method that involves receiving GNSS signals, determining quality parameters, comparing them to reference value clusters representing known driving states, and using a k-means algorithm to identify the current driving state, enabling precise and efficient control assistance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GNSS signals are used for high-accuracy vehicle locating with multiple quality parameters, then position estimation accuracy is improved, but the complexity of data processing and difficulty of detecting driving state increase
Solution Approach 1:
The patent segments the complex driving state detection problem into multiple quality parameters (signal strength, signal-to-noise ratio, number of visible satellites) that can be independently measured and then compared to reference value clusters. This segmentation allows the system to handle complexity by breaking it down into manageable parameter comparisons rather than processing raw GNSS data directly.
Solution Approach 2:
The patent applies preliminary action by pre-establishing reference value clusters for different driving states (tunnel, bridge, open sky, urban canyon) during system calibration. These reference clusters are stored and ready for comparison, eliminating the need for complex real-time analysis of GNSS signal characteristics. The system only needs to compare current quality parameters against the pre-computed reference clusters to identify the current driving state.
2Reliability
If multiple quality parameters of GNSS signals are analyzed to detect unpredictable interfering influences, then reliability of position estimation is improved, but computing time and processing effort increase
Solution Approach 1:
The patent uses copying by creating reference value clusters that represent typical quality parameter patterns for each driving state. Instead of performing complex real-time analysis of GNSS signal characteristics, the system copies the essential characteristics of each driving state into pre-computed reference clusters. During operation, it simply compares current measurements against these copied reference patterns, dramatically reducing computing time while maintaining reliability.
3Measurement precision
If reference value clusters with multiple quality parameters are used to identify driving state, then precision of driving state identification is improved, but memory requirements and data storage increase
Solution Approach 1:
The patent applies local quality by creating distinct reference value clusters for different driving states, where each cluster contains quality parameters specific to that local condition (tunnel, bridge, open sky, urban canyon). Rather than storing one large comprehensive dataset, the system stores multiple specialized clusters, each optimized for a specific driving state. This allows precise identification by matching current parameters to the appropriate local reference cluster.
Data Source
AI summary
A method for control assistance of a vehicle. The method includes: receiving GNSS signals from at least one navigation satellite; ascertaining quality parameters of the GNSS signals, the quality parameters describing a reception quality of the received GNSS signals; and ascertaining a driving state of the vehicle based on the quality parameters of the GNSS signals by comparing values of the quality parameters of the received GNSS signals to previously known reference value clusters, the reference value clusters including a plurality of reference values for the particular quality parameters of the GNSS signals, each reference value cluster representing a previously known driving state, and each previously known driving state describing a state of the vehicle influencing a signal transmission of the GNSS signals; and providing a control assistance function based on the ascertained driving state.