Intelligent Information Design Based on Human-Machine Collaboration in Lane Change Overtaking Scenarios

In the realm of human-machine interaction, when vehicles possess the intelligence to autonomously perceive and make decisions, to a certain extent, they can be considered as collaborators with humans. As members of a human-machine intelligent collaboration team, team members need to attain mutual predictability. The absence of predictability may induce negative automation surprises in drivers, leading to discomfort, anxiety, or loss of trust. To facilitate collaboration in human-machine interaction, interface design should be grounded in human-machine cooperation methodologies to ensure that drivers maintain cognitive presence within the environment. In this context, we propose compensating for the lack of predictability in human-machine interaction by providing Human-Machine Interface (HMI) to enhance trust between agents. Departing from the perspective of Artificial Situational Awareness (ASA), we investigate the impact of HMI with and without perceptual decision information semantics on human-machine trust. Our findings suggest that delivering HMI with perceptual decision information can elevate the level of mutual predictability between humans and machines. Specifically, expressing perceptual information in the first phase of Situational Awareness (SA) proves to be more effective in preventing the erosion of driver trust and improving overall driver predictive capabilities.