Development of a Situational Awareness Estimation Model Considering Traffic Environment for Unscheduled Takeover Situations

Hiroaki Hayashi, Naoki Oka, Mitsuhiro Kamezaki, Shigeki Sugano

Research output: Contribution to journalArticlepeer-review

Abstract

In semi-autonomous vehicles (SAE level 3) that requires drivers to takeover (TO) the control in critical situations, a system needs to judge if the driver have enough situational awareness (SA) for manual driving. We previously developed a SA estimation system that only used driver’s glance data. For deeper understanding of driver’s SA, the system needs to evaluate the relevancy between driver’s glance and surrounding vehicle and obstacles. In this study, we thus developed a new SA estimation model considering driving-relevant objects and investigated the relationship between parameters. We performed TO experiments in a driving simulator to observe driver’s behavior in different position of surrounding vehicles and TO performance such as the smoothness of steering control. We adopted support vector machine to classify obtained dataset into safe and dangerous TO, and the result showed 83% accuracy in leave-one-out cross validation. We found that unscheduled TO led to maneuver error and glance behavior differed from individuals.

Original languageEnglish
JournalInternational Journal of Intelligent Transportation Systems Research
DOIs
Publication statusAccepted/In press - 2020

Keywords

  • Autonomous driving
  • Cognitive behavior
  • Situational awareness
  • Unscheduled takeover

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Neuroscience(all)
  • Information Systems
  • Automotive Engineering
  • Aerospace Engineering
  • Computer Science Applications
  • Applied Mathematics

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