This term refers to driving a vehicle manually without any vehicle automation technology. It will serve as a baseline concerning behaviors, representations and neural results associated with unassisted automobile driving.

This term refers to driving with warning technology which upon activation sounds an a warning when the vehicle is too close to the edge of the road (off-road warning , Navarro, Mars, & Hoc, 2007; Suzuki & Jansson, 2003) or too close to the vehicle in front of it (anti-collision warning; Lee, McGehee, Brown, & Reyes, 2002).

This term refers to shared tactical control between the driver and the automated assistive technology, both working simultaneously on the physical trajectory of the vehicle, laterally (Griffiths & Gillespie, 2005; Mulder, Abbink, & Boer, 2012) as well as longitudinal (Adell, Várhelyi, & Hjälmdahl, 2008).

This term refers to a situation where the lateral and longitudinal control of the driving are delegated to the automated assistive technology. It consists of a level of automatisation that today is possible to put into application and which often is referred to by the name "Highly Automated Driving" (Navarro, 2018). In this case, the driver is no longer the one who physically ensures the lateral and longitudinal control of the vehicle, but instead supervises the actions of the automated assistive technology.

From no automation of the vehicle to full automation through warnings, shared control and partly automated automation levels.

Functional magnetic resonance imaging measures brain activity by detecting changes associated with blood flow This technique relies on the fact that cerebral blood flow and neuronal activation are coupled. When an area of the brain is in use, blood flow to that region also increases. The primary form of fMRI uses the blood-oxygen-level dependent (BOLD) contrast in response to an experimental condition (Ogawa, Lee, Kay, & Tank, 1990), allowing researchers to track changes in oxygen consumption on the brain, and therefore brain activity. BOLD effect is computed by assessing the different relaxation times (T1 and T2) in the brain, as T1 and T2 are different in function of regional cerebral blood flows.

The outcome will be a change between mean BOLD effect oven session 1 versus session 2 versus session 3.

Participants will be asked to drive through a variety of driving situations in a driving simulator simulated environment equipped with an eye-tracker. The measure collected will be drivers' behaviors on the steering wheel and pedals as well as their visual behaviors.

The outcome will be a change in the number of actions on the steering wheel (a), pedals (b) and gaze dispersion (c) on the visual scene across the time points.