High Precision Map Generation Excluding Mobile Features
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Solution Overview
Problem
Current map generation methods for vehicles, especially automated vehicles, face challenges in achieving high precision and accuracy due to the inclusion of both static and mobile features, leading to increased memory usage and reduced operational safety.
Innovation Solution
A method and device that receive surrounding-area data values and movement data values to generate a highly precise map by excluding mobile features based on movement data, using static features for navigation, thereby improving map quality and reducing memory usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If both static and mobile surrounding-area features are included in the map data, then the map comprehensively represents the surrounding area, but the memory usage increases and the precision for navigation decreases
Solution Approach 1:
The patent extracts and separates mobile surrounding-area features from static surrounding-area features in the map data. By identifying features that move relative to the vehicle (such as other vehicles, pedestrians, or movable objects) and excluding them from the highly precise map, the system eliminates sources of positional uncertainty. This extraction principle directly resolves the contradiction by removing mobile features that degrade position precision while maintaining comprehensive representation through separate handling of mobile objects.
2Quantity of substance
If mobile surrounding-area features are included in the map, then the map data is more complete, but the memory requirements increase
Solution Approach 1:
The patent applies the extraction principle by separating mobile surrounding-area features from the static map data structure. Mobile features are identified through movement detection and extracted into a separate data category, allowing the highly precise map to contain only static features. This reduces the overall memory footprint of the map data while maintaining completeness through separate tracking of mobile objects, directly addressing the memory usage contradiction.
Solution Approach 2:
The patent segments map data into distinct categories: static surrounding-area features stored in the highly precise map, and mobile surrounding-area features tracked separately. This segmentation allows the system to optimize memory usage by storing only essential static features in the high-precision map structure, while mobile features are handled through separate, more compact data structures that reflect their transient nature.
3Measurement precision
If mobile surrounding-area features are excluded from the map, then the position precision improves, but the map generation process becomes more complex
Solution Approach 1:
The patent applies preliminary action by performing movement detection and feature classification during the map generation process itself. Rather than adding a separate post-processing step to identify and exclude mobile features, the system proactively detects movement characteristics and classifies features as static or mobile during data acquisition and processing. This preliminary classification simplifies the overall process by integrating the exclusion logic into the existing map generation workflow.
Solution Approach 2:
The patent employs feedback mechanisms where movement data from sensors is continuously fed back into the map generation process. This feedback loop allows the system to dynamically identify mobile features based on detected movement patterns and automatically adjust which features are included in the highly precise map. The feedback-driven approach automates the complexity of distinguishing static from mobile features, reducing manual intervention while maintaining high position precision.
Data Source
AI summary
A method and device for generating a highly precise map, including a step of receiving surrounding-area data values, which represent a surrounding area of a vehicle, the surrounding area encompassing at least one static surrounding-area feature and at least one mobile surrounding-area feature; including a step of receiving movement data values, which represent a movement of the at least one mobile surrounding-area feature in the surrounding area of the vehicle; including a step of generating a highly precise map on the basis of the surrounding-area data values, using the at least one static surrounding-area feature, and excluding the at least one mobile surrounding-area feature, the exclusion occurring as a function of the movement; and including a step of providing the highly precise map.