Takeover and non-driving related task performance in conditional automated driving: EEG and behavior Parameters...

Area of Science:

• Human-Computer Interaction
• Cognitive Neuroscience
• Automotive Safety

Background:

• Augmented reality (AR) and virtual reality (VR) are increasingly used in automotive research.
• Understanding driver behavior and cognitive load in automated driving systems (ADS) is crucial for safety.
• L3 automation requires drivers to retake control, making takeover performance a key safety metric.

Purpose of the Study:

• To investigate the impact of augmented reality (AR) presented non-driving-related tasks (NDRTs) on driver task and takeover performance in a simulated L3 automated driving scenario.
• To explore the neural mechanisms underlying these effects using electroencephalography (EEG).

Main Methods:

• Simulated a conditional automated driving scenario using virtual reality (VR).
• 64 participants performed tasks while AR displays presented non-driving-related tasks (NDRTs).
• Electroencephalography (EEG) recorded brain activity to analyze neural correlates.

Main Results:

• Non-driving-related tasks (NDRTs) demanding high internal attention led to decreased task and takeover performance.
• Alpha power decline in the parietotemporal (PT) region correlated positively with takeover time.
• Greater alpha power decline in the right centroparietal (CP) hemisphere was linked to poorer NDRT memory quality.

Conclusions:

• High-attention NDRTs negatively affect driver performance and safety in L3 ADS.
• Neural activity in the ventral attention network (VAN) and right parietal cortex may explain impaired working memory during NDRTs.
• Findings offer insights for designing safer AR interfaces for L3 automated driving systems.