Ryan P. McMahan

Autonomous vehicles (AVs) have the potential to improve road safety and influence travel behavior, but their lack of conventional driver–pedestrian communication cues has motivated research on external Human–Machine Interfaces (eHMIs) to communicate AV intentions to pedestrians, particularly at unsignalized crossings. However, most prior studies have evaluated eHMIs under limited road and traffic conditions, often focusing on a single road configuration. To address this limitation, the current study investigated pedestrian crossing behavior in front of AVs across diverse environments. Using a within-subjects design in immersive virtual reality(VR), participants performed a road-crossing task across combinations of three road configurations and three traffic conditions, in which AVs either yielded or did not yield. When yielding, AVs decelerated gradually and displayed either a Light Band or Pedestrian Icon eHMI. Results showed that pedestrians made similar crossing decisions based on timing of entry when encountering AVs with the Light Band and Pedestrian Icon eHMIs across the examined conditions. Moreover, pedestrians typically initiated crossing only after the yielding AV had fully stopped despite receiving eHMI information early during vehicle deceleration, suggesting that vehicle movement cues continued to influence crossing decisions. The effects of the eHMI depended on the road and traffic context, such that pedestrians generally waited less for AVs displaying an eHMI than for AVs without an eHMI in scenarios where these vehicles decelerated and stopped at stop-controlled intersections. These findings provide insights into pedestrian–AV interactions in scalable traffic environments and inform the design of eHMIs for safe pedestrian crossing.

Quantum information science (QIS) is a critical interdisciplinary field, deeply rooted in physics, that requires a well-educated workforce in the near future. Numerous researchers and educators have been actively investigating how to best educate and prepare such a workforce. An open issue has been the lack of a tool with a strong validation argument to assess QIS understanding without requiring college-level math. In this paper, we present the systematic development of and content validation argument for a new assessment instrument called the Quantum Information Science Concept Introductory Test (QISCIT). With feedback from 11 QIS experts, we have developed a 31-item version of QISCIT that covers concepts like quantum states, quantum measurement, qubits, entanglement, coherence and decoherence, quantum gates and computing, and quantum teleportation.

Interaction cues, which inform users about potential actions to take, are common to many types of extended reality applications. While numerous studies have compared individual interaction cues, few studies have investigated combinations of interaction cues for complex tasks, particularly ones involving high counts of similar actions. We present a within-subject study ( n = 48) investigating the effects of interaction cue combinations for guiding users through in-cockpit procedures for a UH-60 Black Hawk helicopter. The results of our study indicate significant effects for different interaction cue combinations for perceived mental effort and discomfort. Furthermore, the completion time results in our study contradict previous results pertaining to the effectiveness of specific interaction cues, which we believe are due to the context and complexity of the real-world application underlying our study. These results imply that Look Arrow and Pick Arrow are the optimal interaction cue combination for scenarios that involve dense operating environments.