Using pupillometry and gaze-based metrics for understanding drivers' mental workload during automated driving

This Horizon2020-funded driving simulator-based study on automated driving investigated the effect of different car-following scenarios, and takeover situations, on drivers' mental workload, as measured by eye tracking-based metrics of pupil diameter and self-reported workload ratings. This study incorporated a mixed design format, with 16 drivers recruited for the SAE Level 2 (L2; SAE International, 2021) automation group, who were asked to monitor the driving and road environment during automation, and 16 drivers in the Level 3 (L3) automation group, who engaged in a non-driving related task (NDRT; Arrows task) during automation. Drivers in each group undertook two experimental drives, lasting about 18 min each. To manipulate perceived workload, difficulty of the driving task was controlled by incorporating a lead vehicle which maintained either a Short (0.5 s) or Long (1.5 s) Time Headway (THW) condition during automated car-following (ACF). Each ACF session was followed by a subsequent request to takeover, which happened either in the presence or absence of a lead vehicle. Results from standard deviation of pupil diameter values indicated that drivers' mental workload levels fluctuated significantly more when monitoring the drive during L2 ACF, compared to manual car-following (MCF). Additionally, we found that drivers' mental workload, as indicated by their mean pupil diameter, increased steeply around takeovers, and was further exacerbated by the presence of a lead vehicle during the takeovers, especially in the Short THW condition, for both groups. Pupil diameter was found to be sensitive to subtle variations in mental workload, and closely resembled the trend seen in self-reported workload ratings. Further research is warranted to assess the feasibility of using eye-tracking-based metrics along with other physiological sensors, especially in real-world settings, to understand whether they can be used as real-time indicators of drivers' mental workload, in future driver state monitoring systems.

Automated summary

This Horizon2020-funded study investigated the effect of different car-following scenarios and takeover situations on drivers' mental workload using eye-tracking-based metrics and self-reported workload ratings. The results showed that drivers' mental workload fluctuated more during Level 2 automated car-following compared to manual car-following, and increased significantly around takeovers, especially when a lead vehicle was present. Pupil diameter was found to be sensitive and closely related to self-reported workload ratings. Further research is needed to explore the feasibility of using eye-tracking-based metrics in real-world settings as indicators of drivers' mental workload.