As autonomous vehicle (AV) technology progresses, the necessity for a comprehensive understanding of interactions between AVs and human-driven vehicles (HVs) becomes paramount, particularly in critical manoeuvres such as merging. Mastering merging interactions is essential for enhancing road safety. Existing research in this field focuses on how the AV performs the merging manoeuvre but often fails to assess how they influence these interactions. By drawing inspiration from Human-Robot Interaction and Human Aware Navigation, this study aims to bridge this gap by examining how these interactions influence driver workload, measured through fixations duration, perceived safety and drivers’ subjective perception during merging scenarios. We employed a Virtual Reality environment to simulate realistic driving conditions and measure driver responses. We conducted an experiment where participants engaged in merging manoeuvres with each other and, subsequently and without being informed, with the AV described in “Planning for cars that coordinate with people” [1]. This approach allowed for an unbiased assessment of natural driver reactions to AV behaviours. Our findings reveal significant increases in driver workload and decreases in perceived safety during HV-AV interactions, compared to HV-HV interactions. These results suggest that current AV algorithms may not fully account for the complexity of human-AV interactions, highlighting a need for interaction evaluation in the AV development. Participants’ subjective feedback indicates a recognition of and negative reaction to AV driving behaviours, emphasizing the importance of designing AVs that are both efficient and intuitive for human drivers. The study’s implications suggest improving AV controllers’ evaluations by including their interactions with human drivers. By integrating interaction evaluation, AV technologies can achieve smoother and more successful integration into existing road systems, enhancing predictability and driver acceptance. This study marks a step towards understanding the interactions between AVs and HVs, offering insights that could steer future research and development in autonomous driving technologies.