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cs.HC updates on arXiv.org

A Framework for Measuring Appropriate Reliance on Set-Valued AI Advice DeliChess: A Multi-party Dialogue Dataset for Deliberation in Chess Puzzle Solving From Explanation to Diagnosis: Next Generation Interactive Video Coach with Misstep Awareness SocialCoach: Personalized Social Skill Learning with RL-based Agentic Tutoring and Practice Formalizing all indexed mathematics as a benchmark for general reasoning, with the example of implementing dilatations of categories Face versus Body Tracking for Human-Robot Interaction: An Egocentric Dataset From Prompt to Service: An SLM-Based Agent Orchestration Gateway for AI-Driven Virtual Worlds What LLMs Must Forget to Teach Effectively: A DIY Approach to Premodern Japanese Language Pedagogy 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 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
Virtual Windshields: Merging Reality and Digital Content ...
Michelle Krüger Silvéria · 2014-05-05 · via cs.HC updates on arXiv.org

In recent years, the use of the automobile as the primary mode of transportation has been increasing and driving has become an important part of daily life. Driving is a multi-sensory experience as drivers rely on their senses to provide them with important information. In a vehicular context human senses are all too often limited and obstructed. Today, road accidents constitute the eighth leading cause of death. The escalation of technology has propelled new ways in which driver's senses may be augmented. The enclosed aspect of a car, allied with the configuration of the controls and displays directed towards the driver, offer significant advantages for augmented reality (AR) systems when considering the amount of immersion it can provide to the user. In addition, the inherent mobility and virtually unlimited power autonomy transform cars into perfect mobile computing platforms. However, automobiles currently present limited network connectivity and thus the created augmented objects are merely providing information captured by in-vehicle sensors, cameras and other databases. By combining the new paradigm of Vehicular Ad Hoc Networking (VANET) with AR human machine interfaces, we show that it is possible to design novel cooperative Advanced Driver Assistance Systems (ADAS), that base the creation of AR content on the information collected from neighbouring vehicles or roadside infrastructures. As such we implement prototypes of both visual and acoustic AR systems, which can significantly improve the driving experience. We believe our results contribute to the formulation of a vision where the vehicle is perceived as an extension of the body which permeates the human senses to the world outside the vessel, where the car is used as a better, multi-sensory immersive version of a mobile phone that integrates touch, vision and sound enhancements, leveraging unique properties of VANET.