Supporting the interaction of Humans and Automated vehicles: Preparing for the Environment of Tomorrow (Shape-IT)

Forskningsprojekt, 2019 – 2024

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Publikationer

Evaluation of comfort zone boundary based automated emergencybraking algorithms for car-to-powered-two-wheeler crashes inChina

Understanding the interaction between cyclists and motorized vehicles at unsignalized intersections: Results from a cycling simulator study

Active sampling: A machine-learning-assisted framework for finite population inference with optimal subsamples

Continuous Experimentation and Human Factors An Exploratory Study

Design guidelines for acceptable, transparent, and safe AVs in urban environments : Deliverable 2.6 in the EC ITN project SHAPE-IT

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

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

An extension of the human-factors methodological toolbox for human-AV interaction design research : Deliverable 1.4 in the EC ITN project SHAPE-IT

Computational interaction models for automated vehicles and cyclists

Long-term behavioural adaptation and learning curve of humans interacting with AVs : Deliverable 1.3 in the EC ITN project SHAPE-IT

Managing Human Factors in Automated Vehicle Development: Towards Challenges and Practices

How do expert and non-expert drivers interact with cyclists at unsignalized intersections? Results from naturalistic data

Cross or Wait? Predicting Pedestrian Interaction Outcomes at Unsignalized Crossings

Safety Evaluation Using Counterfactual Simulations: The use of computational driver behavior models in crash avoidance systems and virtual simulations with optimal subsampling

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

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

How do cyclists interact with motorized vehicles at unsignalized intersections? Modeling cyclists’ yielding behavior using naturalistic data

Design strategies and prototype HMI designs for pedestrians, cyclists, and non-automated cars : Deliverable D2.5 in the EC ITN project SHAPE-IT

Human factors in developing automated vehicles: A requirements engineering perspective

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

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

Optimal Subsampling Designs Under Measurement Constraints

Understanding the interaction between cyclists and automated vehicles: Results from a cycling simulator study

Managing Human Factors and Requirements in Agile Development of Automated Vehicles: An Exploration

Defining Requirements Strategies in Agile: A Design Science Research Study

Learning the Pedestrian-Vehicle Interaction for Pedestrian Trajectory Prediction

Predicting Pedestrian Behavior in Urban Traffic Scenarios Using Deep Learning Methods

Methods and Guidelines for Incorporating Human Factors Requirements in Automated Vehicles Development.

An Overview of Interfaces for Automated Vehicles (inside/outside) (Deliverable D2.1 in the H2020 MSCA ITN project SHAPE-IT)

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

Requirement engineering challenges for ai-intense systems development.

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

Mer information

Projektets webbplats

https://www.shape-it.eu/

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