Here are the different posters developed during the project:

The usefulness of psychophysiological data as indicator for situation awareness in semi-autonomous driving

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Authors: Quentin Meteier, Andreas Sonderegger, Omar Abou Khaled, Elena Mugellini

Abstract: Car manufacturers are developing semi-autonomous cars in order to increase road safety and driver strain. In the near future, drivers in automated cars will be able to perform a secondary task while the car is driving autonomously, without requiring them to monitor the vehicle environment. In critical situations however, the driver may have to take over control of the vehicle. In order to propose an optimal support to the driver when a takeover is required, knowledge about their level of situation awareness might be useful. This piece of research addresses the question whether it is possible to use physiological indicators of drivers in order to evaluate their level of situation awareness in different takeover situations. Ninety participants took part in a semi-autonomous driving session in a fixed-base driving simulator. Half of them performed backward counting in order to manipulate cognitive workload. In addition to driving behavior, subjective data and physiological measures such as electrodermal activity and electrocardiogram have been recorded. Data analysis indicates that physiological data might be an interesting indicator for situation awareness in automated driving.

Can we predict driver distraction without driver psychophysiological state ? A feasibility study on noninvasive distraction detection in manual driving

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Authors: Emmanuel de Salis, Dan Yan Baumgartner, Stefano Carrino

Abstract: Driver distraction is a major issue in manual driving, causing more than 30’000 fatal crashes on US roadways in 2015 only [11]. As such, it is widely studied in order to increase driving safety. Many studies show how to detect driver distraction using Machine Learning algorithms and driver psychophysiological data. In this study, we investigate the trade-off between efficiency and privacy while predicting driver distraction. Specifically, we want to assess the impact on the estimation of the driver state without access to his/her psychophysiological data. Different Machine Learning models (Convolutional Neural Networks, K-NN and Random forest) are implemented to evaluate the validity of the distraction detection with and without access to psychophysiological data. The results show that a Convolutional Neural Network model is still able to detect driver distraction without access to psychophysiological features, with an f1-score of 97.11%, losing only 1.37% in the process.

Secondary task and situation awareness, a mobile application for conditionally automated vehicles

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Authors: Marine Capallera, Emmanuel de Salis, Quentin Meteier, Leonardo Angelini, Stefano Carrino, Omar Abou Khaled, Elena Mugellini

Abstract: Autonomous vehicles are developing rapidly and will lead to a significant change in the driver’s role: s/he will have to move from the role of actor to the role of supervisor. Indeed, s/he will soon be able to perform a secondary task but s/he must be able to take over control when a critical situation is not managed by the driving system. The role of new interfaces and interactions within the vehicle is important to take into account. This article describes the design of an application that provides the driver with information about the environment perceived by the vehicle. This application is displayed as split screen on a tablet by which a secondary task can be performed. The results of initial experiment showed that the participants correctly identified all the factors limiting the proper functioning of the driving system while performing a secondary task on the tablet.

Convey situation awareness in conditionally automated driving with a haptic seat

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Authors: Marine Capallera, Peïo Barbé-Labarthe, Leonardo Angelini, Omar Abou Khaled, Elena Mugellini

Abstract: Conditionally automated driving is rapidly evolving and one of its major issues is the reduction of the driver’s attention to her/his environment. After a brief study of interactions increasing situation awareness, and more specifically haptic interactions, this paper proposes the use of vibrations in the seat. Vibrations, with the variation of their location, frequency and amplitude, allow to transmit to the driver various information such as the position of obstacles around her/his vehicle and the state of deterioration of track markings. The results of a first exploratory test are promising on the use of haptic interactions and they pave the way for future experiments.

Inside the cockpit of the semi-autonomous cars of tomorrow

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Authors: Quentin Meteier, Marine Capallera, Emmanuel de Salis, Leonardo Angelini, Stefano Carrino, Omar Abou Khaled, Elena Mugellini

Abstract: Simulations play a crucial role to investigate hazardous situations that are impossible to test in real-life con-ditions without endangering the user’s safety. This paper presents a simulator of conditionally automated cars aiming at enhancing the driver safety and driving comfort. In addition, thanks to the simulator’s highly repeatability, integrated sensors and controlled conditions we collected valuable scientific data, which is otherwise very difficult to gather.