@inproceedings{3c8d64b5a3614ac699f51486373636d2,
title = "Enjoy the Ride Consciously with CAWA: Context-Aware Advisory Warnings for Automated Driving",
abstract = "In conditionally automated driving, drivers decoupled from driving while immersed in non-driving-related tasks (NDRTs) could potentially either miss the system-initiated takeover request (TOR) or a sudden TOR may startle them. To better prepare drivers for a safer takeover in an emergency, we propose novel context-aware advisory warnings (CAWA) for automated driving to gently inform drivers. This will help them stay vigilant while engaging in NDRTs. The key innovation is that CAWA adapts warning modalities according to the context of NDRTs. We conducted a user study to investigate the effectiveness of CAWA. The study results show that CAWA has statistically significant effects on safer takeover behavior, improved driver situational awareness, less attention demand, and more positive user feedback, compared with uniformly distributed speech-based warnings across all NDRTs.",
keywords = "advisory warning, auditory warning, automated driving, context-aware warning, haptic warning, multimodal adaptive warning, takeover behavior, visual warning",
author = "Erfan Pakdamanian and Erzhen Hu and Shili Sheng and Sarit Kraus and Seongkook Heo and Lu Feng",
note = "Publisher Copyright: {\textcopyright} 2022 Owner/Author.; 14th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications, AutomotiveUI 2022 ; Conference date: 17-09-2022 Through 20-09-2022",
year = "2022",
month = sep,
day = "17",
doi = "https://doi.org/10.1145/3543174.3546835",
language = "الإنجليزيّة",
series = "Main Proceedings - 14th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications, AutomotiveUI 2022",
pages = "75--85",
booktitle = "Main Proceedings - 14th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications, AutomotiveUI 2022",
}