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Trust is crucial for ensuring the safety, security, and widespread adoption of automated vehicles (AVs), and if trust is lacking, drivers and the general public may hesitate to embrace this technology. This research seeks to investigate contextualized trust profiles in order to create personalized experiences for drivers in AVs with varying levels of reliability. A driving simulator experiment involving 70 participants revealed three distinct contextualized trust profiles (i.e., confident copilots, myopic pragmatists, and reluctant automators) identified through K-means clustering, and analyzed in relation to drivers' dynamic trust, dispositional trust, initial learned trust, personality traits, and emotions. The experiment encompassed eight scenarios where participants were requested to take over control from the AV in three conditions: a control condition, a false alarm condition, and a miss condition. To validate the models, a multinomial logistic regression model was constructed using the shapley additive explanations explainer to determine the most influential features in predicting contextualized trust profiles, achieving an F1-score of 0.90 and an accuracy of 0.89. In addition, an examination of how individual factors impact contextualized trust profiles provided valuable insights into trust dynamics from a user-centric perspective. The outcomes of this research hold significant implications for the development of personalized in-vehicle trust monitoring and calibration systems to modulate drivers' trust levels, thereby enhancing safety and user experience in automated driving.
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Post Take-Over Performance Varies in Drivers of Automated and Connected Vehicle Technology in Near-Miss Scenarios
ObjectiveThis 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. BackgroundPast 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. MethodForty-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. ResultsParticipants 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. ConclusionDrivers 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. ApplicationDesigners 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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- Award ID(s):
- 1949760
- PAR ID:
- 10478177
- Publisher / Repository:
- SAGE Publications
- Date Published:
- Journal Name:
- Human Factors: The Journal of the Human Factors and Ergonomics Society
- Volume:
- 66
- Issue:
- 11
- ISSN:
- 0018-7208
- Format(s):
- Medium: X Size: p. 2503-2517
- Size(s):
- p. 2503-2517
- Sponsoring Org:
- National Science Foundation
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