Toward Personalized Merging Behaviors: Enhancing Automated Vehicle Trust by Adapting to the Driving Style of Surrounding Vehicles

For successful real-world implementation of auto- mated vehicles, earning trust from other road users is crucial especially in scenarios that require interaction and cooperation such as merging under congestion. While previous research has developed a communication model to improve explicit and implicit intent communication for merging vehicles, this study focuses on the execution phase of the merging maneuver, where the merging vehicle interprets the host vehicle’s intent and must decide whether and if so when to merge. As different drivers perceive risk differently, we discovered that risk-averse drivers, classified by their characteristics of following preceding vehicles, prefer merging over longer distances, whereas risk-tolerant drivers expect to merge over shorter distances, as demonstrated through participant experiments. The correlation between the driving style of the host vehicle and the expectations toward merging vehicles indicates that trust toward the merging vehicle can be maximized by tailoring the merging vehicle’s decision- making threshold according to the host vehicle’s driving style. This study enhances our understanding of the differences in human drivers’ expectations and trust within interactive driving scenarios, offering new insights for improving cooperative behavior in automated driving systems.