Promote or inhibit: An inverted U-shaped effect of workload on driver takeover performance

Abstract

Objective In conditional automated driving (SAE Level 3), drivers are required to take over their vehicles when the automated systems fail. Non-driving related tasks (NDRTs) can positively or negatively affect takeover safety, but the underlying reasons for this inconsistency remain unclear. This study aims to investigate how various workload levels generated by NDRTs may influence the takeover performance of drivers and the lead time they require. Method Fifty drivers were randomly distributed into five groups, which corresponded to five workload levels (1-4 levels generated by Tetris game; control level generated by monitoring). Each driver completed vehicle takeover tasks upon receiving takeover requests with various lead times (3, 5, 7, 9, and 11 s) while engaging in NDRTs. The drivers’ takeover performance and subjective opinions were recorded. Results Drivers in the moderate workload condition (i.e., level 3) had significantly shorter takeover times and better takeover quality than those in the lower (i.e., level 1 and level 2) or higher (i.e., level 4) workload conditions. They also subjectively required less lead time in the moderate condition. Moreover, the drivers rated 7 s as the most appropriate lead time despite the improvement in their overall takeover performances with increased lead time. Conclusions This study found an inverted U-shaped relationship between the drivers’ workload generated by NDRTs and takeover performance. The moderate workload level (rather than the lower or higher workload level) led to a faster and better takeover performance, and it seemed to require minimal lead time for drivers. These findings help understand the relationship of drivers’ workload during the automation and takeover performance in conditional automated driving. An important recommendation emerging from this work is to investigate what should be the most efficient method to detect the drivers’ workload state real-time and give feedback to them when it comes to overload or underload during the automated driving.