Andrea Valerio, M.Sc.

@inproceedings{10.1145/3743049.3748583,

title = {Assessment and Prediction of Remote Operators’ Mental Workload Through AoI Data in AV Scenarios},

author = {A Valerio and HP Nguyen and K Ihme and F Walocha},

url = {https://doi.org/10.1145/3743049.3748583},

doi = {10.1145/3743049.3748583},

isbn = {9798400715822},

year  = {2025},

date = {2025-08-30},

urldate = {2025-08-30},

booktitle = {Proceedings of the Mensch Und Computer 2025},

pages = {482–487},

publisher = {Association for Computing Machinery},

address = {New York, NY, USA},

series = {MuC '25},

abstract = {With the advancement of autonomous vehicles (AV), remote operation is pivotal in ensuring safe and efficient transportation, especially in partially automated systems. This research explores how mental workload influences ocular behavior in remote operators using a fleet management interface. Eye-tracking data from a user study manipulating task difficulty and frequency are analyzed using the Area-of-Interest (AoI) technique. Workload significantly influences AoI metrics, including fixation duration, fixation frequency, time-to-first fixation, visit frequency, dwell time, and stationary entropy. Difficulty and frequency independently affect visual strategies, with difficulty exhibiting broader influences across metrics. Predictive models obtained higher f1-scores than the majority class baseline. Binary workload (easy-slow vs. hard-fast) and frequency (slow vs. fast) states prediction achieved both above 80%, while difficulty (easy vs. hard) reached up to 72%. A four-class classification yielded 48% f1-score. These findings support the use of AoI metrics for workload assessment and contribute to the design of adaptive interfaces responsive to operator mental states.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1007/978-3-031-94153-5_10,

title = {Understanding Effects and Physiological Correlates of Operator Workload Across Remote Assistance Scenarios for Automated Vehicles - Results from a User Study},

author = {F Walocha and A Valerio and P Nguyen and Klas Ihme},

editor = {C Stephanidis and M Antona and S Ntoa and G Salvendy},

url = {https://doi.org/10.1007/978-3-031-94153-5_10},

doi = {10.1007/978-3-031-94153-5_10},

isbn = {978-3-031-94153-5},

year  = {2025},

date = {2025-05-30},

urldate = {2025-05-30},

booktitle = {HCI International 2025 Posters},

volume = {2523},

pages = {102–111},

publisher = {Springer Nature Switzerland},

abstract = {Remote assistance for highly automated vehicles involves a remote operator as a fallback whenever automated fleet vehicles require coordination and maneuver-based support. Similar to other control center professions, we expect that operator workload affects their attention and thereby their performance. To validate this, we conducted a simulator study to analyze operator workload under various task load conditions using a remote assistance dashboard mock-up. We used a within-participants design varying task frequency and task complexity for a set of scenarios generated from a previously defined remote operation scenario catalog. Our findings indicate that high workload through high task frequency and high task complexity results in longer task completion times and more errors when resolving a task. Additionally, low task frequency leads to longer task initiation times after a break. Combining high complexity and low frequency task load leads to the worst performance across multiple metrics, indicating an out-of-the-loop performance problem when low task frequency does not provide a sufficient base-load for complex tasks. Finally, we collected and analyzed physiological data from an electrocardiogram and an electro-dermal activity sensor to find stable indicators of workload changes across study participants. We find that heart rate and heart rate variability show significant differences when task frequency is modulated. In addition, the number of skin conductance response (SCR) peaks and the SCR half-recovery time vary significantly with task complexity. These results provide evidence that we are able to identify workload changes using physiological indicators in time-critical scenarios as basis to provide state-based support to operators when needed.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}