Detection of saliency in crowdsourced gazes data.
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Updated
Jan 3, 2022 - Python
Detection of saliency in crowdsourced gazes data.
Crowdsourced experiment on the effects of automated vehicle characteristics on cyclist decision-making.
Crowdsourced experiment 2 on the color of external Human-Machine Interfaces (eHMIs) for a crossing scenario.
Crowdsourced experiment 1 on the color of external Human-Machine Interfaces (eHMIs) for a crossing scenario.
A framework for the analysis of un(certainty) in traffic, using a crowdsourcing approach.
A framework for the analysis of trust in the interaction between pedestrians and vehicle (manual and automated), from the perspective of the driver of a manual or an automated vehicle, using a crowdsourcing approach.
Crowdsourced experiment on bio-inpired interfaces for automated vehicles.
Crowdsourced experiment on the use of lateral position for communication between an automated vehicle and a pedestrian.
Framework for the analysis of crossing behaviour in the interaction between multiple pedestrians and an automated vehicle, from the perspective of one of the pedestrians using a crowdsourcing approach.
An REU (Research Experiences for Undergraduates) project on human behavior and automated driving features
Additional results for "Using Drones as Reference Sensors for Neural-Networks-Based Modeling of Automotive Perception Errors"
Crowdsourced experiment on eye contract between automated vehicles and pedestrians.
This project defines a framework for the analysis of crossing behaviour in the interaction between a pedestrian and an automated vehicle with a textual eHMI using a crowdsourcing approach.
Frontend of crowdsourced experiment on acceptance the AI-based lane changes of an automated car.
Term 3, Project 1 of Udacity Self Driving Car Nanodegree -- Path Planning
Examining eHMIs in critical driver-pedestrian encounters in a coupled simulator
This is the final project in the Course 1 - Introduction to Self Driving Cars of the Self-Driving Car Specialization offered by Coursera
A framework for the analysis of perceived risk in the interaction between pedestrian and vehicle, from the perspective of the driver using a crowdsourcing approach.
A converter from OpenDRIVE to NetworkX and GeoPandas
Building trust towards self driving, one step at a time.
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