Attention-guiding Takeover Requests for Situation Awareness in Semi-autonomous Driving

Qingxin Chen, Jialong Li, Kenji Tei

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

In semi-autonomous driving (SAE Level-3), the automated driving system allows drivers to focus on their non-driving-related tasks for the majority of the journey. However, when the system faces situations beyond its operational design domain, the drivers need to manually control the vehicle responding to the takeover request (TOR). Many efforts have been made in previous studies to find a more effective method to initiate the TOR. In this paper, we propose to improve drivers' takeover performance by utilizing attention-guiding techniques when delivering the TOR. A preliminary experiment (N=19) indicates that our method reduced drivers' collision rate and mental workload.

Original languageEnglish
Title of host publicationHRI 2023 - Companion of the ACM/IEEE International Conference on Human-Robot Interaction
PublisherIEEE Computer Society
Pages416-421
Number of pages6
ISBN (Electronic)9781450399708
DOIs
Publication statusPublished - 2023 Mar 13
Event18th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2023 - Stockholm, Sweden
Duration: 2023 Mar 132023 Mar 16

Publication series

NameACM/IEEE International Conference on Human-Robot Interaction
ISSN (Electronic)2167-2148

Conference

Conference18th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2023
Country/TerritorySweden
CityStockholm
Period23/3/1323/3/16

Keywords

  • Attention guidance
  • Point of interest
  • Semi-autonomous driving
  • Situation awareness
  • Takeover request

ASJC Scopus subject areas

  • Artificial Intelligence
  • Human-Computer Interaction
  • Electrical and Electronic Engineering

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