Driver brake vs. steer response to sudden forward collision scenario in manual and automated driving modes

Assessment selection in human-automation interaction studies: The Failure-GAM 2 E and review of assessment methods for highly automated driving

“…Interestingly, observation of foot and hand movements, as an indication of risk awareness or trust, was not found in any of the reviewed papers (Part A). Takeover situations was studied using measures such as response time and response type in several papers (Blommer et al, 2017;Gold et al, 2013;Merat et al, 2014;Payre et al, 2016; van den Beukel and van der Voort, 2017), and trust was measured by means of unnecessary takeovers (Beller et al, 2013) but no paper was found that used preparation for takeover as a measure. Clearly, the review of assessment methods (Part A) would not have led to the same assessment selections as Failure-GAM 2 E (Fig.…”

“…Take-over situations were studied in five papers. Blommer et al (2017); Gold et al (2013); Merat et al (2014); Payre et al (2016); and van den Beukel and van der Voort (2017) measured the response type (steering or braking), response time, range to the vehicle in front at take-over and/or collision occurrence.…”

Applicability of risky decision-making theory to understand drivers' behaviour during transitions of control in vehicle automation

“…In a driving task, many actions involve a decision-making process. Some examples include deciding: a comfortable car-following distance (Boer, 1999), what gaps we will accept when changing lanes (Gipps, 1986), how we respond to a potential forward collision (Blommer et al, 2017), and whether to disengage from automation (see Markkula et al, 2018, for more examples).…”

“…This prioritised information will most likely bias the decision towards a particular choice, depending on the information sampled, and not only on individual preferences. This kind of model is especially relevant for the transition of control in vehicle automation, as it is assumed that drivers in such situations can be overloaded with large volumes of spatially dispersed visual information, and may not be able to process all the information they would need Examples for such overload can be found in Gold et al (2013) and Blommer et al (2017), who show that drivers change their decisions about when to resume control from automation, based on the amount of time they have to react before the automated system reaches its limit. Although, it is worth considering that those authors haveonly considered visual information, so other factors might also have affected the observed results.…”

“…The authors believe that barriers imposed by the scenario, such as time pressure and a bounded rationality, forces the human decision-maker to deal with uncertainty, by making assumptions about certain conditions about the environment, based on their expectations, and, thus, adopting a risky decision. As examples relating to resumption of control from vehicle automation, Blommer et al (2017) and Gold et al (2013) showed that drivers have an increased probability to "just brake", instead of both braking and steering, whenever they had limited time to respond to the scenario. The authors noted that the scenario exceeded drivers' abilities to cope with the situation and to perform the ideal action.…”