Name: Zoe M. Becerra 

Master’s Thesis Defense Meeting
Date: Friday, April 17, 2020
Time: 2:30 pm
Location: https://bluejeans.com/405756067
 
Advisor:
Bruce Walker, Ph.D. (Georgia Tech)
 
Thesis Committee Members:
Bruce Walker, Ph.D. (Georgia Tech)
Jamie Gorman, Ph.D. (Georgia Tech)
Richard Catrambone, Ph.D. (Georgia Tech)
 
Title: 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 examines how SA changes as a function of level of automation in a simulated driving task, using multiple measures of SA, workload, and trust. Participants completed two twenty-minute simulated drives with two levels of automation: low automation (ALK only); and high automation (ALK and ACC). Throughout the drives, the Situation Present Assessment Method (SPAM) and secondary task engagement were used to measure SA. After each drive, SART, NASA-TLX, and a Trust in Automation scale were administered to evaluate subjective SA, 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, SA was higher in the high automation drive compared to the low automation drive. 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 can 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.