Negative Road Feature Observation via Spatial Data
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Solution Overview
Problem
Current computer vision systems for autonomous driving primarily focus on detecting road features like traffic signs and road dividers, but they lack effective methods for capturing negative observations, where no signs are detected, which is crucial for validating the presence or absence of these features.
Innovation Solution
A method involving a spatial data structure that queries location points from vehicle traces to determine negative observations of road features by mapping traces to road links and identifying points where no feature was observed, thereby providing complementary data for improved mapping and navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If computer vision systems focus on detecting road features, then positive detection accuracy is improved, but the ability to capture negative observations (absence of features) deteriorates
Solution Approach 1:
The patent inverts the traditional detection approach by not only detecting the presence of road features but also systematically capturing their absence. Instead of only reporting when features are detected, the system generates negative observations by querying spatial data structures for location points where no features were observed, thereby capturing the complementary information about feature absence that traditional systems miss.
Solution Approach 2:
The patent introduces a spatial data structure as an intermediary between raw sensor data and feature detection results. This spatial data structure stores and organizes location points from vehicle traces, enabling efficient querying to determine whether features should have been observed but were not. The intermediary structure facilitates the generation of negative observations by mediating between the detection system and the spatial information.
2Loss of information
If the system queries spatial data structures for negative observations, then completeness of road feature data is improved, but computational complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-building and maintaining spatial data structures that organize location points from vehicle traces before they are needed for negative observation generation. The spatial data structure is constructed in advance with efficient indexing, enabling rapid querying when negative observations are needed without performing complex computations at query time.
Solution Approach 2:
The patent replaces complex computational analysis with efficient spatial data structure operations. Instead of performing heavy computational analysis to determine feature absence, the system uses pre-organized spatial data structures with efficient indexing and querying mechanisms, substituting mechanical computation with optimized data structure operations.
3Measurement precision
If location traces are map-matched to road links, then accuracy of negative observation determination is improved, but processing time increases
Solution Approach 1:
The patent performs map-matching of location traces to road links in advance, storing the matched paths in the spatial data structure before they are needed for negative observation determination. This preliminary processing ensures that when negative observations are queried, the map-matching work has already been completed, reducing real-time processing requirements.
Solution Approach 2:
The spatial data structure is designed to be self-sufficient by pre-storing map-matched location information and road link associations. When querying for negative observations, the system does not need to perform map-matching operations again; the spatial data structure already contains the pre-computed relationships, enabling rapid determination of whether features should have been observed.
Data Source
AI summary
An approach is provided for determining a negative observation of a road feature (e.g., traffic sign). The approach involves, for example, querying a spatial data structure for a set of location points based on a spatial radius around a location of the road feature. The spatial data structure stores a plurality of location points from a plurality of location traces that did not have a road feature observation. The approach also involves map matching each location trace in the set of location points to a matched path of road links. The approach further involves determining that said each location trace is a negative observation of the road feature based on determining that the location of the road feature falls between two map-matched location points of said each location trace that are on the matched path of road links.