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Post Take-Over Performance Varies in Drivers of Automated and Connected Vehicle Technology in Near-Miss Scenarios. | LitMetric

AI Article Synopsis

Article Abstract

Objective: This study examined the impact of monitoring instructions when using an automated driving system (ADS) and road obstructions on post take-over performance in near-miss scenarios.

Background: Past research indicates partial ADS reduces the driver's situation awareness and degrades post take-over performance. Connected vehicle technology may alert drivers to impending hazards in time to safely avoid near-miss events.

Method: Forty-eight licensed drivers using ADS were randomly assigned to either the active driving or passive driving condition. Participants navigated eight scenarios with or without a visual obstruction in a distributed driving simulator. The experimenter drove the other simulated vehicle to manually cause near-miss events. Participants' mean longitudinal velocity, standard deviation of longitudinal velocity, and mean longitudinal acceleration were measured.

Results: Participants in passive ADS group showed greater, and more variable, deceleration rates than those in the active ADS group. Despite a reliable audiovisual warning, participants failed to slow down in the red-light running scenario when the conflict vehicle was occluded. Participant's trust in the automated driving system did not vary between the beginning and end of the experiment.

Conclusion: Drivers interacting with ADS in a passive manner may continue to show increased and more variable deceleration rates in near-miss scenarios even with reliable connected vehicle technology. Future research may focus on interactive effects of automated and connected driving technologies on drivers' ability to anticipate and safely navigate near-miss scenarios.

Application: Designers of automated and connected vehicle technologies may consider different timing and types of cues to inform the drivers of imminent hazard in high-risk scenarios for near-miss events.

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Source
http://dx.doi.org/10.1177/00187208231219184DOI Listing

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