Vectorized Trajectory Prediction for Spatially Rich Scene Encoding
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Solution Overview
Problem
Existing systems for predicting the future trajectory of agents in environments, such as autonomous vehicles, face challenges due to the lossy rendering and computationally intensive encoding required when using rendered images. This limits their ability to consider spatial locality and high-order interactions among scene components effectively.
Innovation Solution
The system employs vectorized representations of scenes, approximating geographic entities and agent dynamics using polylines represented as sequences of vectors. This approach avoids the need for lossy rendering and reduces computational intensity, allowing for more efficient trajectory prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If rendered images are used to represent the scene, then the system can capture visual information, but it requires lossy rendering and computationally intensive encoding
Solution Approach 1:
The patent changes the representation parameters from rasterized pixel data to vectorized geometric primitives (polylines, polygons, curves). This parameter transformation eliminates lossy rendering by representing scenes mathematically rather than as discrete pixels, and reduces computational complexity by enabling more efficient spatial operations on vector data structures.
Solution Approach 2:
The patent replaces the mechanical rendering process (rasterization that converts vector graphics to pixel grids with inherent information loss) with a direct vectorized processing approach. This substitution eliminates the need for lossy conversion while maintaining computational efficiency through native vector operations.
2Reliability
If rendered images are used for trajectory prediction, then visual scene data is available, but spatial locality and high-order interactions among scene components cannot be considered effectively
Solution Approach 1:
The patent segments the scene into distinct geometric primitives (polylines for trajectories, polygons for regions, curves for road boundaries). This segmentation preserves the structural relationships and spatial locality of scene components, enabling the prediction model to effectively process and reason about interactions among individual elements rather than treating the scene as a uniform pixel grid.
Solution Approach 2:
The patent transitions from two-dimensional raster pixel arrays to a multi-dimensional vector representation that explicitly encodes spatial relationships, geometric properties, and topological connections. This dimensional transformation enables more effective modeling of high-order interactions by preserving hierarchical and relational structures inherent in the scene geometry.
3Productivity
If conventional rendering systems are used, then scene visualization is achieved, but model parameters and computational requirements are significantly higher
Solution Approach 1:
The patent uses simplified geometric copies (vectorized polylines and polygons) to represent complex real-world scene elements. These vectorized copies retain the essential spatial and structural properties needed for trajectory prediction while requiring far fewer parameters to represent than full-resolution rendered images, thereby improving computational efficiency without sacrificing predictive accuracy.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for agent trajectory prediction using vectorized inputs.