Map Traffic Object Association with Supporting Structures
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Solution Overview
Problem
Autonomous vehicles face navigation challenges due to incomplete or inaccurate HD maps, which fail to represent associations and connections between traffic objects and their supporting structures, such as traffic poles.
Innovation Solution
Systems and methods that associate traffic objects (e.g., traffic signals, traffic signs) with traffic poles or other structures within maps by using threshold distances and iterative processes to determine poses and connections, and then generate structures to represent these associations within the maps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If HD maps are used for autonomous navigation, then navigation accuracy is improved, but the maps may be incomplete or inaccurate in representing associations between traffic objects and supporting structures
Solution Approach 1:
The system segments the representation of traffic infrastructure into distinct components: supporting structures (poles) and traffic objects (signals, signs). By creating separate but associated data structures for each component, the system captures detailed spatial relationships without losing information about connections between elements.
Solution Approach 2:
The patent introduces an intermediary data structure that represents the association between traffic objects and supporting structures. This intermediary layer captures the spatial and structural relationships (such as attachment points, orientations, and connections) that bridge the gap between individual traffic objects and their supporting infrastructure.
2Reliability
If detailed associations between traffic objects and supporting structures are added to maps, then map accuracy and visual realism are improved, but system complexity increases
Solution Approach 1:
The patent implements a universal data structure framework that can represent multiple types of traffic objects and supporting structures using common association rules and spatial relationships. This multi-functional approach allows the system to handle diverse infrastructure elements (poles, signals, signs, banners) through a unified model, reducing overall system complexity.
Solution Approach 2:
The system uses parameter-based representations of spatial relationships (distances, orientations, attachment points) that can be adjusted and optimized. By changing parameters rather than restructuring the entire data model, the system can improve accuracy while maintaining manageable complexity through configurable spatial parameters.
Data Source
AI summary
In various examples, associating traffic objects with traffic poles or other supporting structures in maps for autonomous systems and applications is described herein. Systems and methods are disclosed that associate traffic objects (e.g., traffic signals, traffic signs, etc.) with traffic poles within maps and/or generate structures that represent the associations within the maps. For instance, a map may indicate poses of one or more traffic objects and/or a traffic pole within an environment. As such, the poses may be used to associate the traffic object(s) with the traffic pole, such as by using one or more threshold distances. Next, the poses, the association(s), and/or general information associated with traffic poles may be used to generate a structure that represents the traffic object(s) connected to the traffic pole.