Drivable Area Boundary Segmentation for Road User Trajectory Prediction
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Solution Overview
Problem
Autonomous vehicles struggle to accurately predict the trajectories of uncertain road users, such as pedestrians and cyclists, due to the lack of meaningful information about smaller discrete target locations within large drivable areas, leading to potential collisions and hazardous situations.
Innovation Solution
The system segments drivable area boundaries into logical segments that represent potential goals for actors, using a threshold-based concavity analysis to identify edges and form segments that reflect pedestrian destinations, enabling accurate trajectory prediction and collision avoidance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If drivable areas are represented as large segments, then the representation is simplified and easier to process, but meaningful information about smaller discrete target locations cannot be inferred
Solution Approach 1:
The drivable area boundary is segmented into multiple logical segments based on concavity analysis. Each logical segment represents a potential target location for uncertain road users. This segmentation divides the large drivable area into meaningful smaller units while maintaining the overall simplified representation structure.
2Measurement precision
If drivable area boundaries are analyzed in detail to identify discrete target locations, then trajectory prediction accuracy is improved, but processing complexity and computational load increase
Solution Approach 1:
The system changes the parameter used for boundary analysis from general geometric features to specific concavity measurements. By calculating concavity values and comparing them against thresholds, the system efficiently identifies logical segments without requiring complex detailed analysis of the entire boundary geometry.
3Productivity
If traditional drivable area representation is used, then processing is faster and simpler, but collision avoidance with uncertain road users becomes less reliable
Solution Approach 1:
The system performs preliminary segmentation of the drivable area boundary into logical segments before trajectory prediction. This pre-processing step creates a structured representation that enables more reliable collision avoidance by identifying potential target locations in advance, while keeping the overall process efficient through threshold-based classification.
Data Source
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AI summary
Methods and systems for controlling navigation of an autonomous vehicle for traversing a drivable area are disclosed. The methods include receiving information relating to a drivable area that includes a plurality of polygons, identifying a plurality of logical edges that form a boundary of the drivable area, sequentially and repeatedly analyzing concavities of each the plurality of logical edges until identification of a first logical edge that has a concavity greater than a threshold, creating a first logical segment of the boundary of the drivable area. This segmentation may be repeated until each of the plurality of logical edges has been classified. The method may include creating and adding (to a map) a data representation of the drivable area that comprises an indication of the plurality of logical segments, and adding the data representation to a road network map comprising the drivable area.