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A Study on the Entire Take-Over Process-Based Emergency Obstacle Avoidance Behavior.

Yi Li¹, Zhaoze Xuan¹, Xianyu Li^2,3

¹Logistics Research Center, Shanghai Maritime University, Shanghai 201306, China.

International Journal of Environmental Research and Public Health

|February 25, 2023

Summary

This summary is machine-generated.

Driver takeover behavior in automated vehicles is influenced by traffic density and available time. Increased urgency shortens reaction times and alters control and recovery phases, impacting overall takeover duration and steering patterns.

Keywords:

automated driving scenario urgency take-over behavior take-over process

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Area of Science:

Human-computer interaction
Automotive engineering
Cognitive psychology

Background:

Conditional automated driving systems require driver intervention during emergencies or system limitations.
Understanding driver takeover behavior is crucial for safe human-machine interaction in autonomous vehicles.

Purpose of the Study:

To investigate how traffic density and takeover budget time affect driver takeover behavior in emergency obstacle avoidance scenarios.
To analyze the temporal and behavioral changes during the driver takeover process.

Main Methods:

A driving simulator study with a 2x2 factorial design (high/low traffic density, 3s/5s takeover time).
40 participants completed four simulation experiments, with the takeover process divided into reaction, control, and recovery phases.
Collected time, dynamics, and operation parameters for each phase.

Main Results:

Reaction time decreased with increased scenario urgency.
Control and recovery phases showed significant differences in speed, acceleration, braking, and steering based on urgency.
Overall takeover time increased with urgency; lateral behavior shifted from aggressive to defensive, while longitudinal behavior remained defensive.

Conclusions:

Traffic density and takeover time significantly influence driver takeover behavior and performance.
Findings support the development of improved takeover assistance systems and optimized human-machine interaction for emergency scenarios in automated vehicles.