Literature DB >> 34010743

Using eye-tracking to investigate the effects of pre-takeover visual engagement on situation awareness during automated driving.

Nade Liang1, Jing Yang1, Denny Yu1, Kwaku O Prakah-Asante2, Reates Curry2, Mike Blommer2, Radhakrishnan Swaminathan2, Brandon J Pitts3.   

Abstract

Automated driving systems are becoming increasingly prevalent throughout society. In conditionally automated vehicles, drivers may engage in non-driving-related tasks (NDRTs), which can negatively affect their situation awareness (SA) and preparedness to resume control of the vehicle, when necessary. Previous work has investigated engagement in NDRTs, but questions remain unanswered regarding its effect on drivers' SA during a takeover event. The objective of the current study is to use eye-tracking to aid in understanding how visual engagement in NDRTs affects changes in SA of the driving environment after a takeover request (TOR) has been issued. Thirty participants rode in a simulated SAE Level 3 automated driving environment and engaged in three separate pre-TOR tasks (Surrogate Reference Task, Monitoring Task, and Peripheral Detection Task) until presented with a TOR. Situation Awareness Global Assessment Technique (SAGAT) scores and gaze behavior were recorded during the post-TOR segment. Overall, longer times spent viewing the driving scene, and more dispersed visual attention allocation, were observed to be associated with better overall SA. Also, location-based eye tracking metrics show most promise in differentiating between task conditions with significantly different SAGAT scores. Findings from this work can inform the development of real-time SA assessment techniques using eye movements and ultimately contribute to improved operator roadway awareness for next-generation automated transportation.
Copyright © 2021 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Attention allocation; Automated driving; Eye-tracking; Human factors; Non-driving-related tasks; Situation awareness

Mesh:

Year:  2021        PMID: 34010743     DOI: 10.1016/j.aap.2021.106143

Source DB:  PubMed          Journal:  Accid Anal Prev        ISSN: 0001-4575


  1 in total

1.  Visual Attention of Anesthesia Providers in Simulated Anesthesia Emergencies Using Conventional Number-Based and Avatar-Based Patient Monitoring: Prospective Eye-Tracking Study.

Authors:  Arsène Ljubenovic; Sadiq Said; Julia Braun; Bastian Grande; Michaela Kolbe; Donat R Spahn; Christoph B Nöthiger; David W Tscholl; Tadzio R Roche
Journal:  JMIR Serious Games       Date:  2022-03-22       Impact factor: 3.364

  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.