Redesigning Today's Driving Automation Toward Adaptive Backup Control With Context-Based and Invisible Interfaces.

Objective: We investigated a driver monitoring system (DMS) designed to adaptively back up distracted drivers with automated driving.

Background: Humans are likely inadequate for supervising today's on-road driving automation. Conversely, backup concepts can use eye-tracker DMS to retain the human as the primary driver and use computerized control only if needed. A distraction DMS where perceived false alarms are minimized and the status of the backup is unannounced might reduce problems of distrust and overreliance, respectively. Experimental research is needed to assess the viability of such designs.

Methods: In a driving simulator, 91 participants either supervised driving automation (), drove with different forms of DMS-induced backup control (; ), or drove without any automation. All participants performed a visual N-back task throughout.

Results: Supervised driving automation increased visual distraction and hazard non-responses compared to backup and conventional driving. improved response generation compared to . Across entire driving trials, the backup improved lateral performance compared to conventional driving. Without negatively impacting safety, the DMS reduced unnecessary automated control compared to the DMS conditions. produced low satisfaction ratings, whereas satisfaction was on par with automated driving. There were no appreciable negative consequences attributable to the driving automation.

Conclusions: We have demonstrated preliminary feasibility of DMS designs that incorporate driving context information for distraction assessment and suppress their status indication.

Application: An appropriately designed DMS can enable benefits for automated driving as a backup.