Jordan Navarro

Vehicle automation progressively shifts the driver’s role from active operator to passive supervisor, requiring behavioural adaptation. Although a timely issue, little is known about the influence of previous manual driving experience and the duration of automation use on those adaptations. Gaze behaviour during a car-following task were investigated under four automation conditions: Non-Automated (NA), Automated Steering (AS), Highly Automated Driving (HAD), and Fully Automated driving (FA). The sequences of visual explorations across the different areas of interest of the driving scene (i.e., scan paths) were analysed over seven weeks of automation use and for both unlicenced and experienced drivers. The findings reveal that automation alters the distribution of scan paths compared to manual driving. Specifically, automation promotes scenery and supervision scan paths at the expense of driving-control ones, which are crucial for monitoring the road and vehicle speed control. This shift became noticeable once the driver completely disengaged from the physical control of the vehicle, this function being supported by automation. Additionally, driving experience significantly influences gaze behaviour for each automation solution. Novice drivers focus more on speed-monitoring scan paths, whereas experienced drivers display more balanced scan paths. No significant changes in scan paths were observed over time, suggesting that gaze strategies are highly context-dependent. Altogether, this study provides a better understanding of the effects of automation on the dynamics of drivers’ gaze behaviour.

The ability to navigate spatially in the physical world is a fundamental cognitive skill. This study examines the anatomical correlates of map‐assisted wayfinding in an unfamiliar virtual environment using structural magnetic resonance magining (MRI). Thirty‐three participants were required to reach up to seven different locations represented on a navigational map in a simulated environment, while their gazing behavior was recorded, and, in close temporal proximity, the anatomical MRI of their brain was acquired. Significant predictors of wayfinding performance were the volumes of the right hippocampus, left retrosplenial cortex, and posterior cingulate cortex—left inferior frontal gyrus, right superior frontal gyrus, and right cerebellar lobule VIIB. Detailed analyses revealed a dissociation between two clusters of gray matter density in the right hippocampus. Compared with the poorest wayfinders, the best wayfinders exhibited more gray matter density in a cluster located in the right posterior hippocampus but less gray matter density in a cluster located in the anterior section of the hippocampus. In addition, top performers spent more time gazing at the map, highlighting the benefit of using external aids during navigation tasks. Altogether, these results underscore how structural adaptations are associated with spatial navigation performance.

Automated Vehicles (AVs) have the capability to manage all driving tasks, ranging from navigation decisions to vehicle control under various conditions. They are central to numerous discussions and are perceived as key factors in enhancing traffic flow, safety, and mobility. However, drivers of Conventional Vehicles (CVs) may adjust their behaviors in response to the introduction of AVs, such as by reducing their safety margins [15]. This study aims to prospectively explore the reasons behind potential behavioral changes in CV drivers using the theoretical framework of Social Representations (SR). One of the functions of SR is to guide behaviors and practices by providing meaning to our actions [1]. Additionally, SRs have been utilized to foster greater engagement [2]. This paper presents preliminary results derived from two social psychology methods: free and hierarchical associations, and a Test of Context Independence (TCI) to identify the central core of SR. The findings raise questions about the similarities between the SRs associated with AVs and CVs. Although this research is ongoing and will incorporate additional complementary methods, the initial results already offer valuable insights for adapting road safety measures to make them more specific and engaging.

Background: Car driving is more and more automated, to such an extent that driving without active steering control is becoming a reality. Although active driving requires the use of visual information to guide actions (i.e., steering the vehicle), passive driving only requires looking at the driving scene without any need to act (i.e., the human is passively driven).Materials & Methods: After a careful search of the scientific literature, 11 different studies, providing 17 contrasts, were used to run a comprehensive meta‐analysis contrasting active driving with passive driving.Results: Two brain regions were recruited more consistently for active driving compared to passive driving, the left precentral gyrus (BA3 and BA4) and the left postcentral gyrus (BA4 and BA3/40), whereas a set of brain regions was recruited more consistently in passive driving compared to active driving: the left middle frontal gyrus (BA6), the right anterior lobe and the left posterior lobe of the cerebellum, the right sub‐lobar thalamus, the right anterior prefrontal cortex (BA10), the right inferior occipital gyrus (BA17/18/19), the right inferior temporal gyrus (BA37), and the left cuneus (BA17).Discussion: From a theoretical perspective, these findings support the idea that the output requirement of the visual scanning process engaged for the same activity can trigger different cerebral pathways, associated with different cognitive processes. A dorsal stream dominance was found during active driving, whereas a ventral stream dominance was obtained during passive driving. From a practical perspective, and contrary to the dominant position in the Human Factors community, our findings support the idea that a transition from passive to active driving would remain challenging as passive and active driving engage distinct neural networks.

Forward collision warning systems (FCWSs) monitor the road ahead and warn drivers when the time to collision reaches a certain threshold. Using a driving simulator, this study compared the effects of FCWSs between novice drivers (unlicensed drivers) and experienced drivers (holding a driving license for at least four years) on near-collision events, as well as visual and driving behaviors. The experimental drives lasted about six hours spread over six consecutive weeks. Visual behaviors (e.g., mean number of fixations) and driving behaviors (e.g., braking reaction times) were collected during unprovoked near-collision events occurring during a car-following task, with (FCWS group) or without FCWS (No Automation group). FCWS presence reduced the number of near-collision events drastically and enhanced visual behaviors during those events. Unexpectedly, brake reaction times were observed to be significantly longer with FCWS, suggesting a cognitive cost associated with the warning process. Still, the FCWS showed a slight safety benefit for novice drivers attributed to the assistance provided for the situation analysis. Outside the warning events, FCWS presence also impacted car-following behaviors. Drivers took an extra safety margin, possibly to prevent incidental triggering of warnings. The data enlighten the nature of the cognitive processes associated with FCWSs. Altogether, the findings support the general efficiency of FCWSs observed through a massive reduction in the number of near-collision events and point toward the need for further investigations.