The interaction with Advanced Driver Assistance Systems has several positive implications for road safety, but also some potential downsides such as mental workload and automation complacency. Malleable attentional resources allocation theory describes two possible processes that can generate workload in interaction with advanced assisting devices. The purpose of the present study is to determine if specific analysis of the different modalities of autonomic control of nervous system can be used to discriminate different potential workload processes generated during assisted-driving tasks and automation complacency situations. Thirty-five drivers were tested in a virtual scenario while using head-up advanced warning assistance system. Repeated MANOVA were used to examine changes in autonomic activity across a combination of different user interactions generated by the advanced assistance system: (1) expected take-over request without anticipatory warning; (2) expected take-over request with two-second anticipatory warning; (3) unexpected take-over request with misleading warning; (4) unexpected take-over request without warning. Results shows that analysis of autonomic modulations can discriminate two different resources allocation processes, related to different behavioral performances. The user's interaction that required divided attention under expected situations produced performance enhancement and reciprocally-coupled parasympathetic inhibition with sympathetic activity. At the same time, supervising interactions that generated automation complacency were described specifically by uncoupled sympathetic activation. Safety implications for automated assistance systems developments are considered.
- Advanced Driver Assistance Systems
- Autonomic nervous system
- Cognitive workload
- Human Factors and Ergonomics
- Public Health, Environmental and Occupational Health
- Reaction times
- Safety, Risk, Reliability and Quality