arXiv subjects

Patricio Reyes Gomez

Publications and source records attributed to Patricio Reyes Gomez.

3 recordsLinked to original sources

arXiv2026

Inconsistent by Design: A Systematic Review of Experimental Design Practices Across Impaired Driving Domains

Impaired driving, including distraction, fatigue, and intoxication, leads to thousands of fatalities annually. Impairment detection and related assistive technologies are rapidly advancing, but their full potential remains unrealized. The inconsistent maturity of impairment research and anomalies within individual domains are key barriers, including variations in taxonomic characterization, experimental data-collection methodologies, and treatment of impairments across studies. We present a unified review of impairment studies, covering 91 studies across nine impairment domains coded on three dimensions: impairment induction methods, scenario hazards, and observable phenomena and metrics. Our key findings include: (1) a proposed performance-degradation vs. event-response paradigm for existing impairment domains, (2) a maturity framework along the three dimensions, and (3) an analysis of impairment research methodologies, revealing anomalies and gaps in the treatment of scenarios and metrics. We further propose recommendations for standardizing methodologies to support future cross-domain research and development of holistic detection and assistive systems.

cs.HC↗

arXiv2026

SimCoachCorpus: A naturalistic dataset with language and trajectories for embodied teaching

High-quality curated datasets are essential for training and evaluating AI approaches, but are often lacking in embodied interactive domains where language and physical action are intertwined. In particular, few datasets capture how people acquire motor skills in embodied tasks through verbal instruction over time. To address this gap, we introduce SimCoachCorpus: a unique dataset of race car simulator driving that enables the investigation of rich phenomena during guided and unguided motor skill acquisition. In this dataset, 29 humans were asked to drive in a driving simulator around a race track for approximately ninety minutes. Fifteen participants received one-on-one instruction from a professional performance driving coach, and 14 participants drove without coaching instruction. SimCoachCorpus includes features such as vehicle state and inputs, map (track boundaries and race-line), and cone landmarks. Additionally, these are synchronized with the coach's concurrent verbal feedback and additional terminal feedback at the end of each lap. We also provide high-quality annotations of high-level coaching categories for each concurrent feedback utterance, ratings on students' compliance with coaching advice, and self-reported cognitive load and emotional state of participants (gathered from surveys during the study). The final dataset includes over 20,000 concurrent feedback utterances, over 400 terminal feedback utterances, and over 40 hours of interactive driving data. Our naturalistic interactive dataset can be used to investigate motor learning dynamics, explore linguistic phenomena, and train computational models of teaching and learning. We demonstrate applications of this dataset for in-context learning, imitation learning, and topic modeling. Data is hosted at https://doi.org/10.7910/DVN/W7VTKZ and code is available at https://github.com/ToyotaResearchInstitute/sim_coach_corpus

cs.RO↗

arXiv2025

A Simulator Dataset to Support the Study of Impaired Driving

Despite recent advances in automated driving technology, impaired driving continues to incur a high cost to society. In this paper, we present a driving dataset designed to support the study of two common forms of driver impairment: alcohol intoxication and cognitive distraction. Our dataset spans 23.7 hours of simulated urban driving, with 52 human subjects under normal and impaired conditions, and includes both vehicle data (ground truth perception, vehicle pose, controls) and driver-facing data (gaze, audio, surveys). It supports analysis of changes in driver behavior due to alcohol intoxication (0.10\% blood alcohol content), two forms of cognitive distraction (audio n-back and sentence parsing tasks), and combinations thereof, as well as responses to a set of eight controlled road hazards, such as vehicle cut-ins. The dataset will be made available at https://toyotaresearchinstitute.github.io/IDD/.

cs.CV↗