Need for Trust Calibration in Takeover request Performance in Level 3 Automated vehicles

Trust is a critical human factor influencing driver interaction with autonomous vehicles (AVs), particularly during takeover requests (TORs). Despite growing interest in trust calibration and TOR performance, no comprehensive review exists that synthesizes findings across these domains. This paper aims to address this gap by systematically reviewing the relationship between trust and TORs in Level 3 AVs. We examine how factors such as TOR timing, warning modalities, environmental conditions, driver traits, and system malfunctions impact trust dynamics and takeover performance. Additionally, we explore the role of trust calibration, its formation, miscalibration (overtrust or undertrust), and recovery mechanisms, in shaping effective human-automation interaction. By integrating insights from existing studies, we identify research gaps, including the need for adaptive TOR strategies based on real-time trust monitoring and individual differences. This review provides actionable recommendations for designing AV systems that optimize trust calibration, enhance safety, and improve user acceptance of automated driving technologies.