Scene-Graph Embedding for Explainable AV Collision Prediction
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Solution Overview
Problem
Current autonomous vehicle perception systems struggle to capture complex inter-object relationships and higher-level scene understanding, leading to poor performance in risky driving scenarios, and lack explainability and robustness in collision prediction, requiring significant resource investment and large datasets for training.
Innovation Solution
The roadscene2vec tool enables systematic extraction and embedding of road scene-graphs using a flexible framework that converts camera data into scene-graphs, incorporating spatial and temporal information, and utilizes multi-relational graph convolutional networks for risk assessment and collision prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If deep learning techniques (CNNs and MLPs) are used for AV perception, then typical use cases are handled successfully, but the ability to capture inter-object relationships and higher-level scene understanding is limited
Solution Approach 1:
The patent introduces scene-graphs as an intermediary representation that bridges raw sensor data and high-level scene understanding. Scene-graphs explicitly model inter-object relationships and semantic structures, allowing the system to capture information that traditional deep learning methods lose while maintaining effectiveness in typical perception tasks
2Loss of information
If scene-graph based approaches are used to capture inter-object relationships, then scene understanding is improved, but significant time and resource investment is required for development
Solution Approach 1:
The patent develops a universal scene-graph construction framework that can be applied across multiple autonomous vehicle perception tasks. This multi-functional framework eliminates the need for each research group to build scene-graph systems from scratch, significantly reducing development time and resource investment while maintaining the ability to capture inter-object relationships
3Device complexity
If traditional perception methods are used, then system complexity is lower, but explainability and robustness in collision prediction are insufficient
Solution Approach 1:
The patent segments the perception system into distinct components: scene-graph construction, relationship extraction, and prediction modules. This segmentation provides clear interpretability of how inter-object relationships influence collision predictions, enhancing explainability and robustness while maintaining manageable system complexity through modular architecture
Data Source
AI summary
The present invention is directed to a Spatiotemporal scene-graph embedding methodology that models scene-graphs and resolves safety-focused tasks for autonomous vehicles. The present invention features a computing system comprising instructions for accepting the one or more images, extracting one or more objects from each image, computing an inverse-perspective mapping transformation of the image to generate a bird's-eye view (BEV) representation of each image, calculating relations between each object for each image, and generating a scene-graph for each image based on the aforementioned calculations. The system may further comprise instructions for calculating a confidence value for whether or not a collision will occur through the generation of a spatio-temporal graph embedding based on a spatial graph embedding and a temporal model.