@article{ref1,
title="Driving aggressively or conservatively? Investigating the effects of automated vehicle interaction type and road event on drivers' trust and preferred driving style",
journal="Human factors",
year="2023",
author="Lee, Yuni and Dong, Miaomiao and Krishnamoorthy, Vidya and Akash, Kumar and Misu, Teruhisa and Zheng, Zhaobo and Huang, Gaojian",
volume="ePub",
number="ePub",
pages="ePub-ePub",
abstract="OBJECTIVE: This study aimed to investigate the impact of automated vehicle (AV) interaction mode on drivers' trust and preferred driving styles in response to pedestrian- and traffic-related road events. <br><br>BACKGROUND: The rising popularity of AVs highlights the need for a deeper understanding of the factors that influence trust in AV. Trust is a crucial element, particularly because current AVs are only partially automated and may require manual takeover; miscalibrated trust could have an adverse effect on safe driver-vehicle interaction. However, before attempting to calibrate trust, it is vital to comprehend the factors that contribute to trust in automation. <br><br>METHODS: Thirty-six individuals participated in the experiment. Driving scenarios incorporated adaptive SAE Level 2 AV algorithms, driven by participants' event-based trust in AVs and preferences for AV driving styles. The study measured participants' trust, preferences, and the number of takeover behaviors. <br><br>RESULTS: Higher levels of trust and preference for more aggressive AV driving styles were found in response to pedestrian-related events compared to traffic-related events. Furthermore, drivers preferred the trust-based adaptive mode and had fewer takeover behaviors than the preference-based adaptive and fixed modes. Lastly, participants with higher trust in AVs favored more aggressive driving styles and made fewer takeover attempts. <br><br>CONCLUSION: Adaptive AV interaction modes that depend on real-time event-based trust and event types may represent a promising approach to human-automation interaction in vehicles.   APPLICATION: Findings from this study can support future driver- and situation-aware AVs that can adapt their behavior for improved driver-vehicle interaction.<p /> <p>Language: en</p>",
language="en",
issn="0018-7208",
doi="10.1177/00187208231181199",
url="http://dx.doi.org/10.1177/00187208231181199"
}