Retroreflective Feature Maps for Efficient 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 retroreflective surfaces as features in 3D and/or vector/semantic map data, allowing for efficient localization through vectorization and analysis by a processor, which generates and updates digital maps to enhance localization accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If raster maps are used as the source of mapping information, then the map provides comprehensive coverage, but the localization efficiency is reduced due to dense two-dimensional structure
Solution Approach 1:
The patent creates a simplified vector-based copy of the map environment that preserves essential localization features while eliminating the computational burden of raster processing. Vector maps serve as an efficient representation that maintains geometric accuracy for localization purposes without requiring dense pixel-based data
Solution Approach 2:
The patent transforms the map data structure from raster format (dense 2D pixel grid) to vector format (geometric primitives with mathematical definitions). This parameter change in data representation enables faster processing and more efficient localization algorithms while maintaining the necessary spatial information
2Reliability
If conventional localization methods are used, then localization can be achieved, but computational resources and cost are significantly increased
Solution Approach 1:
The patent extracts and utilizes retroreflective surfaces as specific localization features from the environment. By focusing on these distinctive, easily detectable features rather than processing entire map images or relying on expensive sensor suites, the system achieves accurate localization with reduced computational requirements
Solution Approach 2:
The patent employs computationally inexpensive vector map representations and retroreflective feature detection instead of expensive computational approaches. The vector-based system provides a cost-effective alternative to resource-intensive raster processing while maintaining localization accuracy
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, using retroreflective features to improve decision-making in complex environments.
Implementation Method 1
monitoring, by a processor, using a sensor of a first vehicle, data associated with a retroreflective feature
Data Source
AI summary
A method comprises receiving a first set of data associated with a plurality of retroreflective features from a vehicle; retrieving a digital map comprising vectorized data associated with the plurality of retroreflective features and a location associated with the plurality of retroreflective features, the map previously generated based on monitoring a second set of data retrieved from a sensor of a second vehicle where the second set of data is associated with the plurality of retroreflective features near a road being driven by the second vehicle; generating a score for each retroreflective feature indicating a match between each retroreflective feature as indicated within the digital map and a location of each retroreflective feature as identified within the first set of data; and localizing the vehicle.