Florian Mathis

Human-machine interaction is becoming increasingly relevant in traffic, where autonomous vehicles (AVs) and manual vehicles (MVs) share the same roads. When encountering AVs, drivers must interpret a machine agent’s behavior rather than a human’s. Does this impact human drivers’ behavior? Prior research suggests that people engage in mentalizing processes toward robots and autonomous agents, resulting in adjusted social behavior. Whether such processes similarly influence how human drivers respond to AVs remains unclear. We tested whether the perceived autonomy of vehicles impacts their perceived predictability and how this influences driving behavior of other drivers in a within-subjects virtual reality (VR) lab study (N = 690,094 observations from 61 participants) and in a field experiment (N = 27,198 observations from 1,414 vehicles). Across both studies, perceived autonomy of the vehicle was associated with greater following distance, though the evidence varied in strength. In the VR study, drivers maintained a significantly larger following distance (9%) to AVs relative to MVs. In the field experiment, manipulating perceived autonomy produced no overall difference in following distance to the same AV presented under different autonomy framings; however, an exploratory growth curve model revealed that following distance developed differently over time depending on perceived autonomy. Overall, the data offers indications that perceived autonomy might impact driving behavior, as the mental model could adjust differently for AVs during the first encounters. We quantify following distances in mixed-traffic, with implications for traffic planning regarding lane capacity and traffic flow at merges and bottlenecks when adopting AVs.

Mixed reality (MR) provides users with novel affordances that allow them to overlay and experience reality in various visual manifestations. However, existing works mainly focused on using MR to augment a user’s present, which does not exhaust the full potential of contextual MR. In this paper, we empirically explore MR-Driven Near-Future Realities, a future multimodal MR experience that can overlay semantically-related augmentations within a user’s everyday life to allow them to preview, manipulate, and reflect on a near-future reality. We investigated this concept during a VR-based empirical study to understand users’ perceptions and opinions about MR-Driven Near-Future Realities. The results showed that users were positive about the concept of MR-Driven Near-Future Realities and were capable of eliciting near-future realities in various simulated real-world manifestations, but more demanding scenarios negatively affected their experience and task completion performance. Our goal is to spark meaningful and critical discussions about the use of MR-Driven Near-Future Realities to preview possible near-future realities in everyday life.