@misc{10481/107779,
year = {2025},
month = {10},
url = {https://hdl.handle.net/10481/107779},
abstract = {Driving in snowy conditions challenges both human drivers and autonomous systems.
Snowfall and ice accumulation impair vehicle control and affect driver perception and
performance. Road markings are often obscured, forcing drivers to rely on intuition
and memory to stay in their lane, which can lead to encroachment into adjacent lanes
or sidewalks. Current lane detectors assist in lane keeping, but their performance is
compromised by visual disturbances such as ice reflection, snowflake movement, fog, and
snow cover. Furthermore, testing these systems with users on actual snowy roads involves
risks to driver safety, equipment integrity, and ethical compliance. This study presents a
low-cost virtual reality simulation for evaluating winter lane detection in controlled and
safe conditions from a human-in-the-loop perspective. Participants drove in a simulated
snowy scenario with and without the detector while quantitative and qualitative variables
were monitored. Results showed a 49.9% reduction in unintentional lane departures
with the detector and significantly improved user experience, as measured by the UEQ-S
(p = 0.023, Cohen’s d = 0.72). Participants also reported higher perceived safety, situational
awareness, and confidence. These findings highlight the potential of vision-based lane
detection systems adapted to winter environments and demonstrate the value of immersive
simulations for user-centered testing of ADASs.},
organization = {Canada Research Chair Program – CRC-950-232172 (CRC-2018-00299)},
organization = {Natural Sciences and Engineering Research Council of Canada (NSERC) – Discovery Grants (RGPIN-2018-05917, CRSNG-RGPIN-2019-07206, CRSNG-RGPAS-2019-00114)},
organization = {MCIN/AEI/10.13039/501100011033, UE – PLEISAR-Social project (PID2022-136779OB-C33)},
publisher = {MDPI},
keywords = {Lane detection},
keywords = {winter weather},
keywords = {virtual reality simulation},
title = {VR Human-Centric Winter Lane Detection: Performance and Driving Experience Evaluation},
doi = {10.3390/s25206312},
author = {Ortegon-Sarmiento, Tatiana and Paderewski Rodríguez, Patricia and Kelouwani, Sousso and Gutiérrez Vela, Francisco Luis and Uribe-Quevedo, Álvaro},
}