Automation presumably frees cognitive resources because drivers do not have to control the vehicle. Those resources may be reallocated to processing visual information relevant to driving, such as optic flow, which is relevant for judgments of time-to-collision (TTC). On the other hand, drivers may not use cognitive resources freed during automation to process information relevant to the driving task and improve performance. Drivers may choose to allocate cognitive resources freed during automation to non-driving, secondary tasks (Merat, Jamson, Lai, & Carsten, 2012; Rudin-Brown & Parker, 2004). Therefore, automated driving may lead to performance decrements, particularly when drivers need to resume manual control of the vehicle (Strand, Nilsson, Karlsson, & Nilsson, 2014). The current study compared TTC judgments between automated and manual driving, using a prediction-motion (PM) task which presumably relies on cognitive resources (Tresilian, 1995). We included a braking task to determine whether we could replicate prior reports that drivers brake later during automated driving compared to manual driving (Rudin-Brown & Parker, 2004; de Winter, Happee, Martens & Stanton, 2014). Including PM and braking tasks let us determine whether automation affected only responses (i.e., brake reaction time) or also affected visual perception (i.e., TTC estimation). We hypothesized that automation would affect perceptual judgments rather than solely responses. We expected TTC judgments to be more accurate during automated driving compared to manual driving. We also expected that adding a secondary task that demands cognitive resources would be more detrimental to TTC judgments during automation because the driver would place more cognitive resources on the secondary task during automation than when manually controlling the vehicle. With a driving simulator, participants completed eight drives using manual or automated driving. During half of the drives, participants completed a secondary task, the twenty questions task (TQT), in addition to driving. The TQT is presumably similar to a cell phone conversation because it uses a “question and answer” format (Horrey, Lesch, & Garabet, 2009; Merat et al., 2012, p. 765). At the end of each drive, a critical incident occurred. A vehicle directly in front of the participant’s vehicle decelerated at a rate faster than the automation was capable of braking. Therefore, the automation did not respond to this vehicle’s deceleration. In the braking task, participants used the brake pedal to avoid collision with the lead vehicle. In the PM task, the lead vehicle decelerated for between 0.24 and 3.04 s and then the screen went black. Participants pressed a button to indicate when they thought their vehicle would have hit the lead vehicle if the vehicles’ motions continued in the same manner after the screen went black. Results suggest that automation can affect perceptual judgments in addition to driving responses (e.g., braking). TTC judgments were more accurate, and brake reaction time was faster, during automated driving than manual driving. This occurred even while performing a cognitively-demanding secondary task, suggesting that participants used resources freed by automation to process visual information relevant to TTC judgments rather than complete non-driving tasks. To realize this safety benefit, it is important to design automated systems so that freed cognitive resources are assigned to information relevant to the driving task and not to non-driving tasks.
Physiological Characteristics and Nonparametric Test for Master-Slave Driving Task’s Mental Workload Evaluation in Mountain Area Highway at Night
