Vehicle Localization via Dynamic Hyperspectral Range Adjustment
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Solution Overview
Problem
Autonomous vehicles face challenges in localizing and navigating on underdeveloped and unmarked roads due to the lack of distinct road surfaces and lane markings, as many roads are being reverted to unpaved conditions, necessitating new systems and methods for effective vehicle localization and navigation.
Innovation Solution
A method utilizing non-hyperspectral image sensors and audio data to classify the current scene and adjust the spectral range of hyperspectral image sensors to match stored scene models, allowing for accurate identification of traversable paths and optimal navigation on unmarked roads.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If hyperspectral image sensors capture full spectral range for all scenes, then measurement precision is improved, but processing time increases and productivity decreases
Solution Approach 1:
The system dynamically adjusts the spectral range of hyperspectral image sensors based on the classified scene type. Different spectral ranges are activated depending on whether the scene is classified as vegetation, water, urban, or other environments, allowing the system to capture only the necessary spectral information for each scene type rather than continuously capturing the full spectral range.
Solution Approach 2:
The system changes the operational parameters of hyperspectral sensors by adjusting the spectral range based on scene classification results. The control module modifies sensor parameters to match the specific requirements of different environmental conditions, optimizing both measurement precision and processing efficiency for each scene type.
2Measurement precision
If hyperspectral image sensors operate continuously at full spectral range, then measurement precision is improved, but energy consumption increases
Solution Approach 1:
The system implements periodic scene classification using non-hyperspectral sensors and audio data, then activates hyperspectral sensors only when needed and with adjusted spectral ranges. This periodic operation pattern reduces continuous energy consumption while maintaining measurement precision when hyperspectral imaging is actually required for navigation decisions.
Solution Approach 2:
The control module adjusts the spectral range parameters of hyperspectral sensors based on classified scene types, reducing energy consumption by activating only the necessary spectral bands for each environment rather than operating at full spectral range continuously.
3Reliability
If the system processes all spectral data for navigation decisions, then reliability is improved, but device complexity increases
Solution Approach 1:
The system segments the navigation decision-making process into distinct stages: initial scene classification using non-hyperspectral sensors and audio data, followed by selective hyperspectral imaging only when and where needed. This segmentation reduces overall system complexity while maintaining navigation reliability through multi-stage processing.
Solution Approach 2:
The system introduces non-hyperspectral image sensors and audio sensors as intermediary components that perform preliminary scene classification before activating the more complex hyperspectral sensors. This intermediary processing layer simplifies the overall system architecture by filtering when full hyperspectral processing is necessary.
Data Source
AI summary
A method for localizing and navigating a vehicle on underdeveloped or unmarked roads. The method includes: gathering image data with non-hyperspectral image sensors and audio data of a current scene; classifying the current scene based on the gathered image data and audio data to identify a stored scene model that most closely corresponds to the current scene; and setting spectral range of hyperspectral image sensors based on a spectral range used to capture a stored scene model that most closely corresponds to the current scene.