About me

My name is Quentin Meteier and I’m currently a PhD student in Computer Science at the University of Fribourg. The main topic of my PhD is to build a physiological model of the driver state in vehicles, and more specifically for drivers of conditionally automated vehicles. I am also a scientific collaborator at the HumanTech Institute which is part of the University of Applied Sciences and Arts of Western Switzerland (Fribourg). In 2017, I graduated from ESTIA (Bidart, France) where I obtained a master in engineering. In the same year, I also graduated from the University of Salford (Manchester, United Kingdom) where I got a Master of Science in Robotics and Embedded Systems.

My PhD thesis

I mainly work on the AdVitam project, standing for Adaptive Driver-Vehicule InTerAction to Make future driving safer. It is a 4 years (2018-2022) research project co-funded by the Hasler Fundation. This project involves three PhD students. The goal is to build an adaptive model for shared collaboration between the driver and the car.

In this project, my work aims at investigating how we can use several physiological signals to build a model that assesses the driver state in real-time. The state of the sriver can be used by a higher model to adapt the interaction level in the car and maintain driver’s attention. The topic of my PhD thesis is to assess the driver’s physiological state with regard to 4 main components related to driving abilities: fatigue, stress, cognitive load and situation awareness.

3 experiments were conducted on a fixed-base driving simulator. Physiological signals suchs as electrodermal activity (EDA), electrocardiogram (ECG) and respiration are collected while participants drive in conditional automation and performed various Non-Driving-Related Tasks (NDRTs). The collected data are filtered and processed. Features are created from physiological indicators and a model is trained using machine learning techniques to classify the driver’s state. The ultimate goal is to evaluate the driver’s state in real-time using embedded sensors in the driving simulator.