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Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

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Highly automated driving, secondary task performance, and driver state.

Natasha Merat¹, A Hamish Jamson, Frank C H Lai

¹Institute for Transport Studies, University of Leeds, Leeds LS2 9JT, UK. n.merat@its.leeds.ac.uk

|November 20, 2012

Summary

Highly automated driving performance is comparable to manual driving unless drivers are distracted by secondary tasks. Driver workload impacts blink patterns, with higher workload suppressing blinking in both driving modes.

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Area of Science:

Human-computer interaction
Automotive engineering
Cognitive psychology

Background:

The increasing integration of advanced driver assistance systems (ADAS) is shifting the driver's role from operator to supervisor.
Understanding the impact of vehicle automation on driver behavior and road safety is crucial.

Purpose of the Study:

To compare the effects of workload changes on performance in manual versus highly automated driving.
To investigate driver state variations through blink pattern analysis during different driving conditions.

Main Methods:

Fifty participants operated a driving simulator in both manual and highly automated modes.
Workload was manipulated through driving-specific tasks and a secondary "Twenty Questions Task."
Driver performance and blink patterns were recorded and analyzed.

Main Results:

Driver response to critical incidents was similar in manual and automated driving without secondary tasks.
Performance degraded significantly when drivers had to regain control in automated mode while distracted.
Blink frequency was more consistent in manual driving and suppressed under high workload.

Conclusions:

Highly automated driving does not inherently impair driver performance when attention is maintained.
Driver engagement and situation awareness remain critical factors in automated driving systems.
Further research is needed to understand the long-term implications of automation on driver engagement.