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cs.HC updates on arXiv.org

Quantitative Movement Testing: Measuring Patient Movements from a Single Smartphone Video Vision-Language Models Suppress Female Representations Under Ambiguous Input The New Social Image: How AI Competency and AI Proactivity Influence Self- and Peer-Perceptions in the Workplace TUX: Measuring Human--AI Tacit Understanding LLUMI: Improving LLM Writing Assistance for Mental Health Support with Online Community Feedback VideoFDB: Evaluating Full-Duplex Vision-Speech Capabilities in Conversational Agents Label Over Logic? How Source Cues Bias Human Fallacy Judgments More Than LLMs Inform, Coach, Relate, Listen: Auditing LLM Caregiving Support Roles How Coding Agents Fail Their Users: A Large-Scale Analysis of Developer-Agent Misalignment in 20,574 Real-World Sessions MetaRanker: Human-in-the-loop Active Ranking for Metalens Image Quality Analyzing Persona Effects in Generated Explanations from Multimodal LLM Agents in Urban Perception First head-to-head comparison of agentic AI applied to the analysis of simulated data of the Einstein Telescope Granuscore: A Reference-Free Measure of Granularity for Text Analysis and Question Answering The Timing Dependencies of Trust: Speed, Accuracy, and cBCI Neuro-Decoupling in Human-AI Teams Bayesian Distributional Models of Executive Functioning Visual Matters: Connecting Aesthetic Appeal and Production Quality of Photos, Infographics and Data Visualizations to Credibility of Social Media Posts Data-driven Head Motion Generation through Natural Gaze-Head Coordination Agreement Metrics for LLM-as-Judge Evaluation: What to Report and Why Perceptually Lossless Tactile Texture Synthesis with Compact Spectral Envelope Models MambaGaze: Bidirectional Mamba with Explicit Missing Data Modeling for Cognitive Load Assessment from Eye-Gaze Tracking Data CogAdapt: Transferring Clinical ECG Foundation Models to Wearable Cognitive Load Assessment via Lead Adaptation Augmented Analytics and Decision Quality: The Role of Trust among Non-Technical BI Users Faster Completion, Less Learning: Generative AI Reduced Study Time on Math Problems and the Knowledge They Build PaintCopilot: Modeling Painting as Autonomous Artistic Continuation Personality Engineering with AI Agents: A New Methodology for Negotiation Research PULSE: Agentic Investigation with Passive Sensing for Proactive Intervention in Cancer Survivorship Access Timing as Scaffolding: A Reinforcement Learning Approach to GenAI in Education Conversations in Space: Structuring Non-Linear LLM Interactions on a Canvas MAPLE: Self-Supervised Learning-Enhanced Nonlinear Dimensionality Reduction for Visual Analysis nASR: An End-to-End Trainable Neural Layer for Channel-Level EEG Artifact Subspace Reconstruction in Real-Time BCI
Augmented Reality Warnings in Roadway Work Zones: Evaluat...
Sepehr Sabeti, Fatemeh Banani Ardecani, Omidreza Shoghli · 2024-03-23 · via cs.HC updates on arXiv.org

Given the aging highway infrastructure requiring extensive rebuilding and enhancements, and the consequent rise in the number of work zones, there is an urgent need to develop advanced safety systems to protect workers. While Augmented Reality (AR) holds significant potential for delivering warnings to workers, its integration into roadway work zones remains relatively unexplored. The primary objective of this study is to improve safety measures within roadway work zones by conducting an extensive analysis of how different combinations of multimodal AR warnings influence the reaction times of workers. This paper addresses this gap through a series of experiments that aim to replicate the distinctive conditions of roadway work zones, both in real-world and virtual reality environments. Our approach comprises three key components: an advanced AR system prototype, a VR simulation of AR functionality within the work zone environment, and the Wizard of Oz technique to synchronize user experiences across experiments. To assess reaction times, we leverage both the simple reaction time (SRT) technique and an innovative vision-based metric that utilizes real-time pose estimation. By conducting five experiments in controlled outdoor work zones and indoor VR settings, our study provides valuable information on how various multimodal AR warnings impact workers reaction times. Furthermore, our findings reveal the disparities in reaction times between VR simulations and real-world scenarios, thereby gauging VR's capability to mirror the dynamics of roadway work zones. Furthermore, our results substantiate the potential and reliability of vision-based reaction time measurements. These insights resonate well with those derived using the SRT technique, underscoring the viability of this approach for tangible real-world uses.