Takeover scenario semantic salience: a cognitive framework for linking scenario difficulty to driver performance and...

Area of Science:

• Human-Computer Interaction
• Automotive Safety
• Cognitive Psychology

Background:

• Driver takeover performance (TOP) in conditionally automated vehicles is a critical safety concern.
• Existing frameworks for assessing takeover difficulty lack comprehensive semantic dimensions.
• Understanding scenario difficulty is key to improving human-machine interaction in autonomous driving.

Purpose of the Study:

• To introduce and examine Takeover Scenario Semantic Salience (TSSS) as a framework for takeover scenario difficulty.
• To investigate the impact of execution clarity and information availability on driver takeover performance.
• To explore neurophysiological correlates of takeover difficulty using eye-tracking and EEG.

Main Methods:

• A driving simulator study with 41 participants using four simplified scenarios.
• Development of the Comprehensive Takeover Performance Index (CTOPI) for objective performance measurement.
• Utilized eye-tracking and electroencephalography (EEG) to assess cognitive and physiological states.

Main Results:

• Reduced execution clarity and information availability were linked to poorer driver takeover performance.
• Different scenario contrasts yielded varying performance patterns, indicating nuanced difficulty effects.
• Lower TSSS scenarios correlated with increased pupil diameter, suggesting higher cognitive demand.

Conclusions:

• The TSSS framework shows preliminary promise for describing takeover difficulty in controlled driving simulations.
• Scenario design emphasizing execution clarity and information availability is vital for safe automated driving transitions.
• Further validation of TSSS under real-world Level 3 driving conditions is necessary.