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Driving with a Congestion Assistant; mental workload and acceptance
Karel A Brookhuis1, Cornelie J G van Driel, Tineke Hof
1University of Groningen, 9712 TS Groningen/Delft University of Technology, Delft, Netherlands. k.a.brookhuis@rug.nl
Applied Ergonomics
|October 1, 2008
Summary
New driver support systems like the Congestion Assistant can reduce mental workload during traffic jams. However, the system may increase workload just before congestion begins, with generally high user acceptance reported.
Area of Science:
- Human-Computer Interaction
- Automotive Engineering
- Traffic Psychology
Background:
- Driver support systems are rapidly evolving.
- Congestion Assistant combines warning systems and Stop & Go features for traffic jams.
Purpose of the Study:
- To evaluate the impact of a Congestion Assistant on driver mental workload.
- To assess driver acceptance of the Congestion Assistant system.
Main Methods:
- Utilized an advanced driving simulator for controlled testing.
- Measured mental workload via physiological data (heart rate), a secondary task, and subjective scaling.
- Assessed system acceptance using a dedicated acceptance scale.
Main Results:
- Driving in congestion with the Congestion Assistant potentially decreases driver mental workload.
- The system may increase driver workload in the moments preceding congestion onset.
- Overall system acceptance was high post-experience, with some reservations.
Conclusions:
- Congestion Assistant shows promise in reducing driver workload during traffic congestion.
- Further research may be needed to optimize system performance during congestion transitions.
- Driver acceptance is a key factor for the successful integration of advanced driver-assistance systems.
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