Vehicle Localization Algorithm Selection by Driving State
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing vehicle localization methods do not effectively adapt to different driving situations, leading to suboptimal position determination and increased error rates due to the independent optimization of sensors without considering their advantages in specific conditions.
Innovation Solution
A method that detects the current driving condition and selects the most suitable localization algorithm from a predetermined set, combining satellite navigation and vehicle dynamics sensors to switch between algorithms like Single Point Positioning (SPP) and Precision Point Positioning (PPP), and tire models, optimizing computing power and accuracy based on driving states such as standstill, normal driving, or controlled constant speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If localization algorithms are optimized independently for each sensor type, then each sensor can perform its basic function, but the overall localization precision is suboptimal because the advantages of respective sensors in different driving situations are not utilized
Solution Approach 1:
The patent implements dynamic selection of localization algorithms based on detected driving conditions. The system switches between different algorithms (e.g., dead reckoning, satellite navigation, fusion algorithms) according to the current driving state, making the localization system adaptive rather than static. This resolves the contradiction by enabling the system to leverage the advantages of different sensors and algorithms in different driving situations, thereby improving overall localization precision while maintaining adaptability.
Solution Approach 2:
The patent changes the operational parameters of the localization system by selecting different algorithms based on driving conditions. When GPS signal quality is poor, the system increases reliance on dead reckoning; when vehicle dynamics data is noisy, it reduces their weight. This dynamic parameter adjustment allows the system to optimize localization precision for each specific driving condition, resolving the contradiction between measurement precision and adaptability.
2Measurement precision
If complex localization algorithms are used continuously, then high precision can be achieved, but computing power is wasted during conditions where simple algorithms would suffice
Solution Approach 1:
The patent applies partial action by using complex localization algorithms only when necessary (i.e., when driving conditions require high precision and warrant the computational cost). During normal or predictable driving conditions, simpler algorithms are sufficient. This selective application of computational resources resolves the contradiction by avoiding excessive computing power consumption while maintaining high precision when actually needed.
Solution Approach 2:
The system periodically evaluates driving conditions and switches between algorithm complexity accordingly. During steady-state driving, simpler algorithms are used; during transient or critical conditions, more complex algorithms are activated. This periodic assessment and switching pattern optimizes the balance between localization precision and computing power consumption.
3Device complexity
If a single localization algorithm is used for all conditions, then the system is simple to implement, but localization accuracy decreases because different algorithms have advantages in different driving situations
Solution Approach 1:
The patent creates a universal localization system that can perform multiple functions by selecting from different algorithms based on driving conditions. The system maintains a library of localization algorithms and selects the most appropriate one for each situation, making the system multi-functional rather than single-purpose. This resolves the contradiction by enabling the system to achieve high localization precision across diverse driving conditions while managing complexity through a structured selection framework.
Solution Approach 2:
The patent introduces a driving condition detection module as an intermediary that mediates between the sensor inputs and the localization algorithm selection. This intermediary analyzes the current driving state and determines which algorithm should be used, thereby managing the complexity of having multiple algorithms while ensuring optimal localization precision for each condition.
4Measurement precision
If sensor data is processed continuously with high computational load, then localization accuracy is maximized, but the system becomes less reliable during conditions where computational resources are constrained
Solution Approach 1:
The patent implements dynamic adjustment of computational load based on driving conditions and available resources. During conditions where high precision is critical and resources are available, complex algorithms are used. During conditions where resources are constrained or reliability is more critical, the system switches to simpler, more reliable algorithms. This dynamic adaptation resolves the contradiction by ensuring the system remains reliable across varying operational conditions while maintaining precision when possible.
Data Source
Figure 1
AI summary
The invention relates to a method for selecting locating algorithms in a vehicle, wherein the locating algorithms, in particular for satellite navigation or vehicle dynamics sensors, are selected on the basis of driving states.