Navigation Feature Alignment for Context-Grounded Vehicle Perception
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Solution Overview
Problem
Existing navigation systems often fail to effectively align perception features with navigation tasks, leading to inefficiencies and inaccuracies due to disconnected processing paths between perception and navigation processes, which limits flexibility and adaptability in real-world driving scenarios.
Innovation Solution
Implementing contextual grounding to align perception features with navigation features using machine learning models, such as transformers, to prioritize relevant scene elements for navigation, enabling improved feature extraction and alignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If perception processing is disconnected from navigation processing, then system modularity is improved, but feature extraction accuracy and navigation accuracy deteriorate
Solution Approach 1:
The patent introduces an intermediary alignment mechanism that connects the perception pipeline and navigation pipeline. This intermediary component extracts features from both pipelines and aligns them through matching operations, allowing the disconnected modular systems to exchange and coordinate information without compromising their independence or accuracy
Solution Approach 2:
The system segments the feature extraction process into distinct perception and navigation pipelines while introducing a separate alignment module. This segmentation maintains modularity while the alignment module ensures that features from both segments are properly coordinated and matched for accurate navigation decisions
2Loss of information
If all features are processed equally, then completeness of information is improved, but processing time and computational power usage increase
Solution Approach 1:
The alignment mechanism changes the parameter of feature prioritization by computing alignment scores that rank features based on their relevance to navigation tasks. Features with higher alignment scores are processed first or given greater weight, allowing the system to maintain information completeness while reducing processing time for less critical features
Solution Approach 2:
The system performs partial processing by focusing computational resources on aligning and processing only the most relevant features identified through the alignment mechanism, rather than treating all features with equal computational effort, thus reducing overall processing time while maintaining essential information
3Device complexity
If alignment between perception features and navigation features is not performed, then device complexity is reduced, but navigation accuracy and situational awareness deteriorate
Solution Approach 1:
A dedicated alignment intermediary is introduced between the perception and navigation pipelines. This intermediary component performs the feature matching and alignment operations, isolating the complexity of the alignment process to a specific module while maintaining the simplicity and accuracy of the overall navigation system
Data Source
AI summary
This disclosure provides systems, methods, and devices for processing and aligning sensor data features for navigation. In a first aspect, a method is provided that includes determining, based on received sensor data, a first set of features for an area surrounding the vehicle. A second set of features for the area surrounding the vehicle may be determined based on an occupancy map for the area surrounding the vehicle. A third set of features may be determined that align the first set of features with the second set of features. The third set of features may align each of at least a subset of the second set of features with at least one corresponding feature from the first set of features. Other aspects and features are also claimed and described.