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cs.RO updates on arXiv.org

FineCog-Nav: Integrating Fine-grained Cognitive Modules for Zero-shot Multimodal UAV Navigation DENALI: A Dataset Enabling Non-Line-of-Sight Spatial Reasoning with Low-Cost LiDARs SENSE: Stereo OpEN Vocabulary SEmantic Segmentation Continual Hand-Eye Calibration for Open-world Robotic Manipulation PLAF: Pixel-wise Language-Aligned Feature Extraction for Efficient 3D Scene Understanding GaussianFlow SLAM: Monocular Gaussian Splatting SLAM Guided by GaussianFlow GIST: Multimodal Knowledge Extraction and Spatial Grounding via Intelligent Semantic Topology $π_{0.7}$: a Steerable Generalist Robotic Foundation Model with Emergent Capabilities R3D: Revisiting 3D Policy Learning Vision-Based Safe Human-Robot Collaboration with Uncertainty Guarantees Benchmarking Classical Coverage Path Planning Heuristics on Irregular Hexagonal Grids for Maritime Coverage Scenarios NEAT-NC: NEAT guided Navigation Cells for Robot Path Planning HRDexDB: A Large-Scale Dataset of Dexterous Human and Robotic Hand Grasps ADAPT: Benchmarking Commonsense Planning under Unspecified Affordance Constraints An Intelligent Robotic and Bio-Digestor Framework for Smart Waste Management Efficient closed-form approaches for pose estimation using Sylvester forms World-Value-Action Model: Implicit Planning for Vision-Language-Action Systems A Nonasymptotic Theory of Gain-Dependent Error Dynamics in Behavior Cloning CooperDrive: Enhancing Driving Decisions Through Cooperative Perception SpaceMind: A Modular and Self-Evolving Embodied Vision-Language Agent Framework for Autonomous On-orbit Servicing HiVLA: A Visual-Grounded-Centric Hierarchical Embodied Manipulation System UMI-3D: Extending Universal Manipulation Interface from Vision-Limited to 3D Spatial Perception Towards Multi-Object-Tracking with Radar on a Fast Moving Vehicle: On the Potential of Processing Radar in the Frequency Domain Beyond Conservative Automated Driving in Multi-Agent Scenarios via Coupled Model Predictive Control and Deep Reinforcement Learning Failure Identification in Imitation Learning Via Statistical and Semantic Filtering A Dynamic-Growing Fuzzy-Neuro Controller, Application to a 3PSP Parallel Robot Vision-Language-Action Jump-Starting for Reinforcement Learning Robotic Agents A Mechanistic Analysis of Sim-and-Real Co-Training in Generative Robot Policies ESCAPE: Episodic Spatial Memory and Adaptive Execution Policy for Long-Horizon Mobile Manipulation Evolvable Embodied Agent for Robotic Manipulation via Long Short-Term Reflection and Optimization
Behavioral Research and Practical Models of Drivers' Atte...
Iuliia Kotseruba, John K. Tsotsos · 2021-04-13 · via cs.RO updates on arXiv.org

Driving is a routine activity for many, but it is far from simple. Drivers deal with multiple concurrent tasks, such as keeping the vehicle in the lane, observing and anticipating the actions of other road users, reacting to hazards, and dealing with distractions inside and outside the vehicle. Failure to notice and respond to the surrounding objects and events can cause accidents. The ongoing improvements of the road infrastructure and vehicle mechanical design have made driving safer overall. Nevertheless, the problem of driver inattention has remained one of the primary causes of accidents. Therefore, understanding where the drivers look and why they do so can help eliminate sources of distractions and identify unsafe attention patterns. Research on driver attention has implications for many practical applications such as policy-making, improving driver education, enhancing road infrastructure and in-vehicle infotainment systems, as well as designing systems for driver monitoring, driver assistance, and automated driving. This report covers the literature on changes in drivers' visual attention distribution due to factors, internal and external to the driver. Aspects of attention during driving have been explored across multiple disciplines, including psychology, human factors, human-computer interaction, intelligent transportation, and computer vision, each offering different perspectives, goals, and explanations for the observed phenomena. We link cross-disciplinary theoretical and behavioral research on driver's attention to practical solutions. Furthermore, limitations and directions for future research are discussed. This report is based on over 175 behavioral studies, nearly 100 practical papers, 20 datasets, and over 70 surveys published since 2010. A curated list of papers used for this report is available at \url{https://github.com/ykotseruba/attention_and_driving}.