Research shows the dominant contribution of human factors to incidents and accidents in air (Wiegmann and Shappell, 2001), road (Petridou and Moustaki, 2000), rail (Baysari et al., 2009), and maritime traffic participation (Hetherington et al., 2006), as well as in (air) traffic control (Isaac and Ruitenberg, 2016). Operator errors are in majority associated with a non-optimal mental state, such as fatigue, drowsiness, stress, elevated mental workload, distraction from the main task, limited vigilance, and failing situation awareness (Borghini et al., 2014). In turn, most of these functional limitations can be expressed as an aberration of arousal (Collet and Musicant; Lohani et al.) and difficulties maintaining relevant information in working memory (Wu et al., 2017). In an attempt to further reduce traffic casualties, there is an increasing interest in the potential of monitoring the mental state of both professionals and non-professional users. The current Research Topic deals with the question about how mental states can be optimally tracked in simulated as well as naturalistic contexts; now and when technology progresses further toward autonomous driving. Assessment of fitness to drive by psychometric tools (e.g., self-report such as NASA-TLX; Hart and Staveland, 1988) has serious limitations. Construct validity, sensitivity, and reliability are limited because questionnaires rely on introspection and require a subjective judgment. More importantly, these techniques are not capable of capturing real-time changes, as they are typically not administered during action. Limited gain in traffic safety can be expected from identifying risk only after the fact. In contrast, dynamic measures have great added value in monitoring the operator's tendencies in real-time during simulated or naturalistic traffic participation. Parameters like steering variability and route compliance (e.g., Getzmann et al.) are directly relevant for operation safety. Similarly, subtle bodily motions can provide clues about the operator's behavioral and muscular tendencies as related to safety. Beggiato et al. showed an increase in backward pressure on the driver's seat when autonomous navigation led to the proximity of a truck. Ihme et al. classified video recordings of facial expressions and were able to identify muscular indicators of frustration, a predictor of less responsible driving. Previous studies have shown the value of tracking head tilt and yawning as indices of drowsiness or fatigue (Reyes-Muñoz et al., 2016). In contrast with yawning or tightening muscles, which can be perceived as byproducts of mental state, ocular behavior is a functional characteristic that may predict performance ...
Language: en
LA - en SN - 1662-5161 UR - http://dx.doi.org/10.3389/fnhum.2019.00410 ID - ref1 ER -