Spatial Occupancy Mapping for Compact Navigation Data
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Solution Overview
Problem
Current navigation systems face challenges in efficiently modeling and storing the geometry of road furniture, such as guardrails and street lamps, for highly automated driving, which is crucial for precise vehicle positioning and obstacle detection.
Innovation Solution
A navigation data source with an object data set that indicates spatial vacancy or occupancy of sub-regions using a linear order, allowing for efficient spatial queries and compact storage, utilizing space filling curves like Z-order curves to reference sub-regions in a one-dimensional linear data space, and incorporating interval information to optimize data representation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If detailed 3D modeling of road furniture is implemented for precise vehicle positioning, then measurement precision is improved, but data size increases
Solution Approach 1:
The spatial region is divided into discrete sub-regions or voxels that can be independently modeled and stored. This segmentation allows the system to represent complex 3D environments using a grid of manageable units, where each voxel indicates occupancy status, thereby reducing overall data complexity while maintaining positioning precision.
Solution Approach 2:
The patent transforms 3D spatial data into a 1D linear representation by mapping three-dimensional voxel coordinates to one-dimensional array indices using space-filling curves. This dimensionality reduction enables compact storage and efficient processing of spatial occupancy information without losing the underlying 3D spatial relationships needed for accurate vehicle positioning.
2Reliability
If comprehensive spatial modeling of road furniture is performed, then reliability of obstacle detection is improved, but device complexity increases
Solution Approach 1:
The environment is segmented into discrete voxels with simple occupancy states, transforming complex continuous spatial modeling into discrete unit-based representation. This segmentation simplifies the data structure while maintaining comprehensive coverage of the spatial region, enabling reliable obstacle detection through straightforward voxel state queries.
Solution Approach 2:
The patent changes the representation parameters from detailed geometric models to simplified occupancy grids with discrete states. By parameterizing spatial information in terms of voxel occupancy rather than complex surface geometries, the system reduces computational complexity while preserving the essential information needed for reliable obstacle detection and vehicle positioning.
3Measurement precision
If high-resolution spatial data is stored for efficient positioning, then measurement precision is improved, but loss of time in data processing increases
Solution Approach 1:
The patent applies space-filling curves to map 3D voxel coordinates to 1D linear indices, enabling efficient storage and retrieval of spatial data. This transformation allows the system to maintain high-resolution spatial information for accurate positioning while reducing the computational overhead of 3D coordinate transformations during real-time processing, thereby decreasing data processing time.
4Manufacturing precision
If detailed geometry of road furniture is modeled, then manufacturing precision of the environmental model is improved, but ease of operation decreases
Solution Approach 1:
The patent transforms multi-dimensional spatial queries into one-dimensional array operations by mapping voxel coordinates to linear indices using space-filling curves. This transformation maintains the precision of spatial relationships while dramatically simplifying query operations, as range queries and spatial searches can be performed using efficient 1D array operations rather than complex 3D geometric computations.
Data Source
AI summary
A navigation data source is provided that includes an object data set including object data indicating a spatial vacancy and/or occupancy of sub-regions of a spatial region by one or more structural objects in the spatial region. The object data references the sub-regions based on a linear order of the sub-regions in the spatial region. The object data may include interval information about an at least partially occupied interval, such as a lower interval border and/or an upper interval border. The at least partially occupied interval indicates a group of, according to the linear order, one or more successional sub-regions, at least a part of which are spatially occupied. The object data includes an occupancy sequence indicating a spatial vacancy and/or occupancy of the successional sub-regions of the at least partially occupied interval.