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Found 11 papers, 8 papers with code
Integrating Large Language Models for Severity Classification in Traffic Incident Management: A Machine Learning Approach
Incorporating features from language models with those directly obtained from incident reports has shown to improve, or at least match, the performance of machine learning techniques in assigning severity levels to incidents, particularly when employing Random Forests and Extreme Gradient Boosting methods.
HOOD: Hierarchical Graphs for Generalized Modelling of Clothing Dynamics
We propose a method that leverages graph neural networks, multi-level message passing, and unsupervised training to enable real-time prediction of realistic clothing dynamics.
Traffic Accident Risk Forecasting using Contextual Vision Transformers
This problem is commonly tackled in the literature by using data-driven approaches that model the spatial and temporal incident impact, since they were shown to be crucial for the traffic accident risk forecasting problem.
Traffic incident duration prediction via a deep learning framework for text description encoding
Predicting the traffic incident duration is a hard problem to solve due to the stochastic nature of incident occurrence in space and time, a lack of information at the beginning of a reported traffic disruption, and lack of advanced methods in transport engineering to derive insights from past accidents.
Incident duration prediction using a bi-level machine learning framework with outlier removal and intra-extra joint optimisation
Predicting the duration of traffic incidents is a challenging task due to the stochastic nature of events.
How will electric vehicles affect traffic congestion and energy consumption: an integrated modelling approach
This paper explores the impact of electric vehicles (EVs) on traffic congestion and energy consumption by proposing an integrated bi-level framework comprising of: a) a dynamic micro-scale traffic simulation suitable for modelling current and hypothetical traffic and charging demand scenarios and b) a queue model for capturing the impact of fast charging station use, informed by traffic flows, travel distances, availability of charging infrastructure and estimated vehicle battery state of charge.
Point-Based Modeling of Human Clothing
We propose a new approach to human clothing modeling based on point clouds.
StylePeople: A Generative Model of Fullbody Human Avatars
We show that with the help of neural textures, such avatars can successfully model clothing and hair, which usually poses a problem for mesh-based approaches.
Neural Head Reenactment with Latent Pose Descriptors
We propose a neural head reenactment system, which is driven by a latent pose representation and is capable of predicting the foreground segmentation alongside the RGB image.
Coordinate-Based Texture Inpainting for Pose-Guided Human Image Generation
Since the input photograph always observes only a part of the surface, we suggest a new inpainting method that completes the texture of the human body.
Coordinate-based Texture Inpainting for Pose-Guided Image Generation
Since the input photograph always observes only a part of the surface, we suggest a new inpainting method that completes the texture of the human body.
