Enjoy the Ride Consciously with CAWA: Context-Aware Advisory Warnings for Automated Driving

Erfan Pakdamanian, Erzhen Hu, Shili Sheng, Sarit Kraus, Seongkook Heo, Lu Feng

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationMain Proceedings - 14th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications, AutomotiveUI 2022
Pages75-85
Number of pages11
ISBN (Electronic)9781450394154
DOIs
StatePublished - 17 Sep 2022
Event14th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications, AutomotiveUI 2022 - Seoul, Korea, Republic of
Duration: 17 Sep 202220 Sep 2022

Publication series

NameMain Proceedings - 14th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications, AutomotiveUI 2022

Conference

Conference14th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications, AutomotiveUI 2022
Country/TerritoryKorea, Republic of
CitySeoul
Period17/09/2220/09/22

Keywords

  • advisory warning
  • auditory warning
  • automated driving
  • context-aware warning
  • haptic warning
  • multimodal adaptive warning
  • takeover behavior
  • visual warning

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Human-Computer Interaction
  • Software
  • Automotive Engineering

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