Multi-Interaction Spatio-Temporal Graph Network for Pedestrian Trajectory Prediction
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Solution Overview
Problem
Existing pedestrian trajectory prediction methods fail to accurately account for interactions between pedestrians and their environment, leading to low accuracy in complex scenes, as they primarily focus on pedestrian interactions without considering environmental context and have limitations in modeling these interactions effectively.
Innovation Solution
A multi-interaction spatio-temporal graph network is employed, which extracts global and local context features using scene segmentation and gridding, combines them through an attention mechanism, and utilizes Graph Convolutional Networks (GCN) and Transformers to model spatial and temporal dependencies, optimizing interaction weights and enhancing information transfer between pedestrians and their environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If only pedestrian-pedestrian interactions are considered in trajectory prediction, then the model complexity remains manageable, but the prediction accuracy deteriorates in complex scenes with environmental obstacles
Solution Approach 1:
The model segments environmental context into two distinct components: global context features representing the entire scene, and local context features representing immediate surroundings of each pedestrian. This segmentation allows comprehensive environmental modeling while managing complexity through modular feature extraction and processing
Solution Approach 2:
The patent introduces context features as intermediary representations that mediate between environmental observations and trajectory predictions. These context features serve as intermediate variables that capture environmental interactions without requiring direct complex modeling of all environmental factors
2Measurement precision
If traditional Transformer self-attention mechanism is used for spatial interaction modeling, then the implementation is straightforward, but the accuracy of simulating pedestrian interactions deteriorates
Solution Approach 1:
The patent introduces graph networks as an intermediary layer between pedestrian representations and interaction modeling. The graph network constructs a spatial relationship structure that mediates how pedestrians influence each other, providing more accurate interaction simulation than direct Transformer attention while maintaining computational feasibility through structured graph operations
Solution Approach 2:
The model creates a composite architecture combining Transformer mechanisms with graph network structures. This composite approach leverages the global attention capability of Transformers and the structured spatial reasoning of graph networks to achieve superior interaction modeling accuracy
3Measurement precision
If environmental context features are incorporated into pedestrian trajectory prediction, then the prediction accuracy in complex scenes improves, but the computational complexity increases
Solution Approach 1:
The patent extracts only the most relevant environmental context features needed for trajectory prediction, rather than processing all environmental data. The scene segmentation and context extraction mechanisms selectively identify and process only those environmental elements that influence pedestrian motion, reducing unnecessary computational overhead
Data Source
AI summary
The present invention discloses a pedestrian trajectory prediction method and system based on a multi-interaction spatio-temporal graph network, which belong to the field of pedestrian trajectory prediction. The method includes: extracting multi-interaction features of pedestrians in video frames; for each frame in a video sequence, abstracting each pedestrian in the frame as a vertex, connecting the pedestrian(s) with other pedestrians to form an edge(s), where the vertex attribute is the multi-interaction feature of the pedestrian so as to obtain a multi-interaction spatio-temporal graph network; for each multi-interaction spatio-temporal graph, obtaining spatial dependencies of each pedestrian with other pedestrians in the spatio-temporal graph, and optimizing the attribute of each vertex through the spatial dependencies between pedestrians; the vertices of adjacent time points of the pedestrians are connected to obtain time dependencies of the pedestrians, and then infer a trajectory of a future time point(s).