Chi Zhang

A study of deep learning-based multi-horizon building energy forecasting

Analyzing Factors Influencing Pedestrian Behavior in Urban Traffic Scenarios using Deep Learning

2023

The use of AI in AV human-factors research and human-factors requirements in AI-based AV design : Deliverable 2.4 in the EC ITN project SHAPE-IT

Cross or Wait? Predicting Pedestrian Interaction Outcomes at Unsignalized Crossings

2023

Perceived safety and predictability of human/AV interactions : Deliverable 1.2 in the EC ITN project SHAPE-IT

2023

Behavioural models explaining and predicting transparent negotiations between AVs and human road users : Deliverable 2.2 in the EC ITN project SHAPE-IT

Pedestrian Behavior Prediction Using Deep Learning Methods for Urban Scenarios: A Review

Spatial-Temporal-Spectral LSTM: A Transferable Model for Pedestrian Trajectory Prediction

2023

Data and data collection methodologies for the development of computational models of AV/VRU interaction and their integration into virtual simulation testing of AV : Deliverable 2.3 in the EC ITN project SHAPE-IT

Leveraging Deep Learning and Digital Twins to Improve Energy Performance of Buildings

Learning the Pedestrian-Vehicle Interaction for Pedestrian Trajectory Prediction

2022

Predicting Pedestrian Behavior in Urban Traffic Scenarios Using Deep Learning Methods

Social-IWSTCNN: A social interaction-weighted spatio- temporal convolutional neural network for pedestrian trajectory prediction in urban traffic scenarios

2021

Methodological Framework for Modelling and Empirical Approaches (Deliverable D1.1 in the H2020 MSCA ITN project SHAPE-IT)