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Found 9 papers, 0 papers with code
PreGSU-A Generalized Traffic Scene Understanding Model for Autonomous Driving based on Pre-trained Graph Attention Network
In this study, we propose PreGSU, a generalized pre-trained scene understanding model based on graph attention network to learn the universal interaction and reasoning of traffic scenes to support various downstream tasks.
Integrating Wearable Sensor Data and Self-reported Diaries for Personalized Affect Forecasting
Emotional states, as indicators of affect, are pivotal to overall health, making their accurate prediction before onset crucial.
Easy attention: A simple self-attention mechanism for transformer-based time-series reconstruction and prediction
To improve the robustness of transformer neural networks used for temporal-dynamics prediction of chaotic systems, we propose a novel attention mechanism called easy attention which we demonstrate in time-series reconstruction and prediction.
A Deep-Learning Method Using Auto-encoder and Generative Adversarial Network for Anomaly Detection on Ancient Stone Stele Surfaces
Accurate detection of natural deterioration and man-made damage on the surfaces of ancient stele in the first instance is essential for their preventive conservation.
A Survey on Datasets for Decision-making of Autonomous Vehicle
Autonomous vehicles (AV) are expected to reshape future transportation systems, and decision-making is one of the critical modules toward high-level automated driving.
Enhancing Mapless Trajectory Prediction through Knowledge Distillation
Scene information plays a crucial role in trajectory forecasting systems for autonomous driving by providing semantic clues and constraints on potential future paths of traffic agents.
$β$-Variational autoencoders and transformers for reduced-order modelling of fluid flows
Variational autoencoder (VAE) architectures have the potential to develop reduced-order models (ROMs) for chaotic fluid flows.
FEND: A Future Enhanced Distribution-Aware Contrastive Learning Framework for Long-tail Trajectory Prediction
In this paper, we focus on dealing with the long-tail phenomenon in trajectory prediction.
Physics-informed deep-learning applications to experimental fluid mechanics
High-resolution reconstruction of flow-field data from low-resolution and noisy measurements is of interest due to the prevalence of such problems in experimental fluid mechanics, where the measurement data are in general sparse, incomplete and noisy.
