Dynamic Landmark Data Selection for Vehicle Attitude Determination
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Solution Overview
Problem
Current methods for determining a vehicle's attitude in partially automated driving require excessive computing power and data processing due to the use of a large number of landmarks, leading to inefficient resource utilization and unnecessary data generation, especially in scenarios where lower accuracy is sufficient.
Innovation Solution
The method dynamically adjusts the quantity of landmark data based on the localization scenario, reducing the number of landmarks processed and stored only as needed, using a two-dimensional map subdivided into regions and employing triangulation methods to maintain accurate vehicle guidance with minimal computational expenditure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large number of landmarks are processed to determine vehicle attitude, then measurement precision of vehicle position and orientation is improved, but computing power requirements and data processing load increase excessively
Solution Approach 1:
The patent extracts and processes only the necessary subset of landmark data required for accurate vehicle attitude determination, rather than processing all available landmark data. This selective extraction reduces computing power requirements while maintaining measurement precision by focusing computational resources on the most relevant landmarks for the current localization scenario.
Solution Approach 2:
The patent segments the environment into different localization scenarios, each requiring a specific quantity and type of landmark data. By dividing the overall localization task into scenario-specific segments, the system processes only the necessary amount of data for each scenario, reducing overall computational complexity while maintaining accuracy where needed.
2Reliability
If comprehensive landmark data is collected for all localization scenarios, then reliability of vehicle guidance is improved, but quantity of data generated and stored increases unnecessarily
Solution Approach 1:
The patent dynamically adjusts the quantity of landmark data processed and stored based on the current localization scenario. Rather than maintaining a fixed comprehensive dataset, the system adapts data processing requirements in real-time, collecting and storing only the necessary amount of landmark data for the active scenario, thereby reducing overall data quantity while maintaining guidance reliability.
Solution Approach 2:
The patent applies different data processing quality levels to different localization scenarios. Each scenario receives the appropriate amount and type of landmark data needed for reliable guidance in that specific context, rather than applying uniform comprehensive processing to all scenarios. This local optimization reduces total data quantity while preserving reliability where it matters most.
3Adaptability or versatility
If data processing is performed for all possible localization scenarios, then adaptability of the vehicle control system is improved, but productivity and resource utilization efficiency deteriorate
Solution Approach 1:
The patent performs preliminary classification of localization scenarios and pre-determines the appropriate data processing requirements for each scenario type. By preparing scenario categories and their corresponding data requirements in advance, the system can quickly adapt to new scenarios without performing exhaustive processing, thereby improving both adaptability and resource utilization efficiency.
Solution Approach 2:
The patent changes processing parameters such as the quantity and detail level of landmark data based on the specific localization scenario. By adjusting these parameters dynamically rather than maintaining fixed high-level processing for all scenarios, the system achieves versatile scenario coverage while optimizing resource utilization efficiency for each specific case.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach reduces the amount of data processed and transmitted, allowing for efficient use of resources while maintaining accurate vehicle attitude determination, even in scenarios with lower localization accuracy requirements, by selectively using the necessary landmarks and error correction techniques.
Implementation Method 1
employing triangulation methods to maintain accurate vehicle guidance with minimal computational expenditure
Data Source
AI summary
A method for determining is described, with the aid of landmarks, an attitude of a vehicle moving in an environment in an at least partially automated manner; where the vehicle is moved in the environment, and through which a sequence of localization scenarios is generated; and landmark data being digitally processed by at least one vehicle control system, in order to determine the attitude of the vehicle. The quantity of landmark data is increased or decreased as needed, as a function of the localization scenarios.