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Analyzing the Influencing Factors and Workload Variation of Takeover Behavior in Semi-Autonomous Vehicles.
Hui Zhang1,2, Yijun Zhang1,2, Yiying Xiao1,2
1Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan 430063, China.
International Journal of Environmental Research and Public Health
|February 15, 2022
Summary
Driver workload in autonomous vehicles is affected by tasks and collision warnings. Objective and subjective measures reveal that complex non-driving tasks and obstacle avoidance increase workload, impacting safety.
Area of Science:
- Human-computer interaction
- Automotive engineering
- Cognitive psychology
Background:
- Autonomous driving systems necessitate understanding driver workload for safe operation.
- Driver workload is influenced by various factors during semi-autonomous driving.
- Assessing workload is crucial for developing effective takeover safety systems.
Purpose of the Study:
- To investigate the correlation between different factors and driver workload during autonomous driving.
- To examine workload variations from both subjective and objective perspectives.
- To provide insights for takeover safety prediction modeling.
Main Methods:
- Thirty-seven drivers participated in semi-autonomous driving experiments involving Non-Driving-Related Tasks (NDRTs).
- Subjective workload was measured using the NASA-TLX scale.
- Objective workload was assessed via pupil diameter data using eye-tracking during varying Time Budgets (TB) before takeover requests.
Main Results:
- Subjective workload was higher in obstacle-avoidance scenarios compared to lane-keeping, and for mistake finding > chatting > texting > monitoring tasks.
- Objective workload, indicated by pupil diameter, was higher in obstacle-avoidance scenarios and with a 7s TB compared to a 3s TB.
- Objective workload was greater for monitoring tasks (chatting, monitoring) than texting tasks (mistake finding, texting).
Conclusions:
- Driver workload is significantly influenced by the driving scenario and the nature of non-driving tasks.
- Objective and subjective workload measures provide complementary insights into driver state.
- Findings can inform the development of more accurate driver workload assessment and takeover safety prediction models.
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