Search results

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. 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. 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. 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. 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.