Retroreflective Feature Mapping for Low-Compute Vehicle Localization
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Solution Overview
Problem
Accurate localization of autonomous vehicles is challenging, particularly in urban environments with multiple interference sources, and current methods are often expensive and require significant computational resources.
Innovation Solution
Utilizing 3D and/or vector/semantic map data formats, combined with monitoring and vectorizing retroreflective features like billboards and roadway signage, to generate digital maps that allow for efficient localization and reduce manual maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional localization methods are used, then localization accuracy may be maintained, but computational resources and cost increase significantly
Solution Approach 1:
The patent extracts and utilizes retroreflective features from the environment as localized natural markers. By detecting these pre-existing retroreflective elements (billboards, road signs, reflectors) rather than deploying active transmitters or complex sensor arrays, the system achieves accurate localization without requiring significant computational resources for processing synthetic or artificial features.
Solution Approach 2:
The system leverages environmental retroreflective features that already exist in the urban landscape to perform localization. These features serve themselves as localization markers without requiring additional power, computation, or active engagement from the autonomous vehicle's systems beyond passive detection and mapping.
2Loss of information
If raster maps are used for mapping, then comprehensive spatial information is captured, but efficiency for localization is reduced
Solution Approach 1:
The patent transitions from traditional 2D raster map representations to incorporating 3D spatial coordinates and semantic vector data for retroreflective features. This dimensional enhancement allows the system to maintain comprehensive spatial information while enabling more efficient localization through structured, queryable feature data that can be rapidly matched against sensor inputs.
3Measurement precision
If manual maintenance of map data is increased, then map accuracy is improved, but maintenance costs and time increase
Solution Approach 1:
The system enables autonomous vehicles to automatically detect, map, and update retroreflective features in their environment during normal operation. This self-mapping capability eliminates the need for manual surveying and map maintenance, allowing the digital map to automatically reflect changes in the physical environment as vehicles traverse them.
Solution Approach 2:
The system implements a feedback loop where autonomous vehicles continuously detect retroreflective features, compare detected features against existing map data, and automatically update the digital map when discrepancies or new features are identified. This continuous feedback mechanism maintains map accuracy without requiring manual intervention.
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
Enables accurate and efficient localization of autonomous vehicles with reduced computational requirements and lower maintenance costs, enhancing their decision-making capabilities.
Implementation Method 1
Detection and mapping of generalized retroreflective surfaces
Data Source
AI summary
A method comprises monitoring, by a processor, using a sensor of a first vehicle, data associated with a retroreflective feature near a road being driven by the first vehicle; vectorizing, by the processor, the data associated with the retroreflective feature; generating, by the processor, a digital map including vectorized data associated with the retroreflective feature and a location associated with the retroreflective feature; receiving, by the processor, data associated with the retroreflective feature from a second vehicle; and executing, by the processor, a localization protocol to identify a location of the second vehicle using the digital map.