Measuring the influence of automation on situation awareness in highly automated vehicles

Abstract

Higher levels of automation, such as adaptive cruise control (ACC) and automated lane keeping (ALK), are becoming more and more common in vehicles. With the inclusion of these automated features, the role of the driver is shifting from an active, operator role to a passive, supervisory role. As drivers enter this transition, it is critical they understand how the automation is performing and remain aware of the roadway environment. Situation awareness (SA) is the understanding of what is going on around you. Previous research has shown how a driver’s SA is impacted by many factors including: age, driving experience, distraction, and secondary task engagement. Little work has explored the direct influence of level of automation on SA or how best to measure SA in an automated vehicle. To address these issues, this study examined how SA changes as a function of level of automation in the driving domain using three measures of SA. Participants completed two twenty-minute simulated drives with two levels of automation: low automation (ALK only); and high automation (ALK and ACC). The order of the drives were counterbalanced. Throughout the drives, the Situation Present Assessment Method (SPAM) and secondary task engagement were used to measure SA. SPAM is a query-based measure in which questions about the situation are periodically presented; the situation remains present and the participant continues to perform the task. Secondary task engagement was measured by the total time voluntarily spent playing a game of Tetris, a visuospatial task. After each drive, participants completed the SART questionnaire to subjectively measure their perceived SA. Additionally, the NASA-TLX and a Trust in Automation Scale were administered after each drive to measure subjective workload and trust. Results showed between the three administered measures of SA, query-based measures (SPAM) and subjective measures (SART) were more sensitive compared to performance measures (secondary task engagement). Further, there was evidence to suggest a combination of query-based and subjective measures is best to assess SA in the automated driving context. Concerning the impact of automation level on SA, high automation systems supported higher SA compared to low automation systems. The results also indicated the patterns of SA were different in the low and high automation drives. There were no significant changes in the pattern of SA during the low automation drive. However, the results suggested a quadratic trend best described the pattern of SA in the high automation drive. These insights will provide guidance to develop better standardized measures of SA for future research. In addition, these findings can inform the design of interventions to support driver SA, especially in low automated vehicles.