HD Map Localization for Precise Autonomous Vehicle Positioning
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Solution Overview
Problem
Existing autonomous vehicles face challenges in accurately localizing their position and navigating with high precision due to varying environmental conditions and the need for up-to-date road information, which can lead to unsafe driving scenarios.
Innovation Solution
The implementation of high-definition (HD) maps that include precise spatial geometric information and an HD map system that uses localization techniques tailored to specific contexts, combined with a localization index to efficiently select the best localization variant for accurate vehicle positioning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional localization methods are used, then device complexity is reduced, but measurement precision deteriorates leading to inaccurate vehicle positioning
Solution Approach 1:
The localization system is segmented into multiple specialized modules: HD map generation module, localization index creation module, and context-based localization module. Each module handles specific aspects of the localization process, allowing high precision through specialized processing while managing complexity through modular architecture.
Solution Approach 2:
HD maps and localization indices are pre-generated and stored before actual vehicle operation. The system pre-processes spatial geometric information and creates context-specific localization data structures in advance, enabling rapid high-precision positioning during actual driving without real-time computation overhead.
2Measurement precision
If HD maps with precise spatial information are implemented, then navigation accuracy is improved, but loss of information increases due to large data requirements
Solution Approach 1:
The system extracts only the essential spatial geometric information needed for localization from comprehensive HD maps. The localization index contains selectively extracted key features and parameters rather than complete map data, reducing information loss while maintaining navigation accuracy.
Solution Approach 2:
The localization system uses context-specific localization indices tailored to different driving scenarios and environments. Each localization index contains high-precision spatial information optimized for its specific context, ensuring accurate localization while minimizing overall data requirements through specialized rather than universal data structures.
3Reliability
If context-specific localization techniques are used, then reliability of localization is improved, but device complexity increases due to multiple localization variants
Solution Approach 1:
The system dynamically selects and switches between different localization variants based on current driving context, environmental conditions, and vehicle state. The localization index is structured to enable runtime selection of appropriate localization techniques, maintaining high reliability across varying conditions while managing complexity through adaptive rather than static architecture.
Solution Approach 2:
The system changes parameters of the localization process based on context, such as selecting different sensor fusion weights, localization algorithms, or map resolution levels depending on environmental conditions. This parameter-based adaptation allows reliable localization across diverse scenarios while avoiding the need for completely separate systems for each condition.
Data Source
AI summary
Embodiments of the present disclosure relate to a machine performing one or more planning, navigation, or control operations based at least on one or more outputs of one or more neural networks in which the one or more outputs are computed based at least on the one or more neural networks processing sensor data generated using a plurality of perception sensors of the machine.