Traffic Scene Behavior Planning With Semantic Tile Relationships
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Solution Overview
Problem
Current deep learning-based methods for predicting and planning in traffic scenes, such as those using CNNs, fail to adequately capture semantic relationships between tiles in a grid-based scene representation, leading to diluted information mapping to latent features, which is insufficient for accurate prediction and behavior planning, especially in complex urban scenarios.
Innovation Solution
The proposed method distributes scene-specific information across multiple semantic levels and subdivides tiles into sub-tiles based on these levels, using a vision transformer to model semantic relationships between sub-tiles, ensuring that relevant information is accurately reflected in latent features for prediction and planning tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If CNN-based deep learning methods are used for processing grid-based scene representation, then the system can process traffic scenes, but semantic relationships between tiles are not adequately captured leading to diluted information mapping
Solution Approach 1:
The patent divides the grid-based scene representation into multiple tiles and further segments each tile into sub-tiles based on semantic levels (e.g., foreground objects, background elements, infrastructure). This segmentation allows the model to process and preserve semantic relationships at different granularities, preventing information dilution while maintaining computational feasibility through hierarchical processing.
2Device complexity
If the grid-based scene representation is divided into multiple tiles, then processing complexity is reduced, but semantic relationships between tiles are lost or diluted
Solution Approach 1:
The patent implements a nested structure where tiles are divided into sub-tiles, and each level of segmentation maintains semantic relationships through hierarchical processing. The sub-tiles within each tile preserve local semantic information, while the tile level preserves broader spatial relationships, creating a nested representation that reduces complexity at each level while cumulative preservation of semantic information.
3Quantity of substance
If all semantic levels of tiles are processed equally, then comprehensive scene information is captured, but relevant information is diluted across multiple semantic levels
Solution Approach 1:
The patent applies different processing qualities to different semantic levels and sub-tiles based on their relevance to the prediction task. Foreground objects and dynamically relevant elements receive higher processing priority and more detailed analysis, while static background elements are processed with lower complexity. This local quality differentiation concentrates computational resources on relevant information while maintaining comprehensive scene awareness.
Data Source
AI summary
A computer-implemented method is for behavior planning of a participant in a traffic scene. The method includes generating a grid-based scene representation based on aggregated scene-specific information and dividing the grid-based scene representation into multiple tiles each representing a partial area of the traffic scene. The method further includes distributing the scene-specific information over at least two semantic levels of the scene representation. The distribution is retained during the division into tiles in order to form at least one type of sub-tile based on the tiles and the semantic levels. The method further includes mapping the grid-based scene representation to latent features based on the tiles or sub-tiles. The semantic relationships between the tiles or sub-tiles of at least one type are taken into account. The latent features thus generated are used as input for at least one downstream deep learning module for predicting a development of the traffic scene.