Search Results for author:
Found 7 papers, 5 papers with code
RS2G: Data-Driven Scene-Graph Extraction and Embedding for Robust Autonomous Perception and Scenario Understanding
Effectively capturing intricate interactions among road users is of critical importance to achieving safe navigation for autonomous vehicles.
Spatio-Temporal Scene-Graph Embedding for Autonomous Vehicle Collision Prediction
Finally, we demonstrate that sg2vec performs inference 9. 3x faster with an 88. 0% smaller model, 32. 4% less power, and 92. 8% less energy than the state-of-the-art method on the industry-standard Nvidia DRIVE PX 2 platform, making it more suitable for implementation on the edge.
Graph Learning for Cognitive Digital Twins in Manufacturing Systems
In this paper, we detail the cognitive digital twin as the next stage of advancement of a digital twin that will help realize the vision of Industry 4. 0.
roadscene2vec: A Tool for Extracting and Embedding Road Scene-Graphs
The goal of roadscene2vec is to enable research into the applications and capabilities of road scene-graphs by providing tools for generating scene-graphs, graph learning models to generate spatio-temporal scene-graph embeddings, and tools for visualizing and analyzing scene-graph-based methodologies.
HW2VEC: A Graph Learning Tool for Automating Hardware Security
To attract more attention, we propose HW2VEC, an open-source graph learning tool that lowers the threshold for newcomers to research hardware security applications with graphs.
GNN4IP: Graph Neural Network for Hardware Intellectual Property Piracy Detection
In this work, we propose a novel methodology, GNN4IP, to assess similarities between circuits and detect IP piracy.
Scene-Graph Augmented Data-Driven Risk Assessment of Autonomous Vehicle Decisions
Finally, we demonstrate that the use of spatial and temporal attention layers improves our model's performance by 2. 7% and 0. 7% respectively, and increases its explainability.
