Automation Expectation Mismatch: Incorrect Prediction Despite Eyes on Threat and Hands on Wheel

Abstract

Objective:The aim of this study was to understand how to secure driver supervision engagement and conflict intervention performance while using highly reliable (but not perfect) automation.Background:Securing driver engagement—by mitigating irony of automation (i.e., the better the automation, the less attention drivers will pay to traffic and the system, and the less capable they will be to resume control) and by communicating system limitations to avoid mental model misconceptions—is a major challenge in the human factors literature.Method:One hundred six drivers participated in three test-track experiments in which we studied driver intervention response to conflicts after driving highly reliable but supervised automation. After 30 min, a conflict occurred wherein the lead vehicle cut out of lane to reveal a conflict object in the form of either a stationary car or a garbage bag.Results:Supervision reminders effectively maintained drivers’ eyes on path and hands on wheel. However, neither these reminders nor explicit instructions on system limitations and supervision responsibilities prevented 28% (21/76) of drivers from crashing with their eyes on the conflict object (car or bag).Conclusion:The results uncover the important role of expectation mismatches, showing that a key component of driver engagement is cognitive (understanding the need for action), rather than purely visual (looking at the threat), or having hands on wheel.Application:Automation needs to be designed either so that it does not rely on the driver or so that the driver unmistakably understands that it is an assistance system that needs an active driver to lead and share control.

Beyond mere take-over requests: The effects of monitoring requests on driver attention, take-over performance, and acceptance

“…An additional aim of this study was to examine whether drivers' exhibited overreliance on the TORs. An on-road study by Victor et al, (2018) suggests that drivers may fail to act despite being alerted and having their eyes on the road. Thus, there is a certain risk that drivers may not act in a critical situation when the system fails to provide a TOR, despite the fact that an MR is presented beforehand.…”

“…From the 38 participants, three crashed into the pedestrians in the last scenario. Participants' eyes were on the road and hands on the wheel during all three crashes, but participants did not intervene (see also Victor et al, 2018). In a post-experiment interview, all three participants reported their expectation and reliance on the TOR.…”

Enhancing Trust in Autonomous Vehicles through Intelligent User Interfaces That Mimic Human Behavior

“…Drivers often engage in seemingly simple situations such as overtaking and crossing a high-speed road that are beyond their visual or perceptual capabilities [13]. Even when drivers of an AV have their eyes on a conflict object, they are unlikely to respond by taking action [14]. Fatigue and distractions are inherently human and play a pivotal role in the risk of crashing.…”

Peer Review #2 of "Adaptive automation: automatically (dis)engaging automation during visually distracted driving (v0.1)"

“…Cognitive distraction in driving (Strayer et al, 2013) has been discussed in different guises, including daydreaming (Galéra et al, 2012), mind wandering (Yanko & Spalek, 2013), looked-but-failed-to-see errors (Sabey & Staughton, 1975;Staughton & Storie, 1977;Labbett & Langham, 2006), cognitive tunneling (Reimer, 2009), attention focusing (Chapman & Underwood, 1998), loss of covert/peripheral attention via diminished functional field of view (Crundall et al, 1999), and highway hypnosis (Wertheim, 1978). We reiterate here that our Backup and Forced concepts cannot detect all forms of driver aberration: In reality, drivers may drive in an unsafe manner or crash into objects even when their eyes are on the road (Victor et al, 2018), and one should therefore not expect that the present Backup automation is a remedy to all types of driver distraction. However, given the predominant importance of visual information for driving (Sivak, 1996), the generally presumed eye-mind hypothesis where gaze direction is a strong correlate of cognitive activity (Just & Carpenter, 1980), and a substantial history of driving visual occlusion research (e.g., Senders et al, 1967;Van der Horst, 2004), adaptive automation based on visual attention alone could reasonably be expected to offer a beneficial contribution.…”