Time to Arrival as Predictor for Uncertainty and Cooperative Driving Decisions in Highly Automated Driving

Linda Miller, Jasmin Leitner, Johannes Kraus, Jieun Lee, Tatsuru Daimon, Satoshi Kitazaki, Martin Baumann

研究成果: Conference contribution

抄録

Due to the technical advances of automated vehicles (AVs), new uncertainties for human road users arise. To overcome these uncertainties, driving strategies of AVs might be aligned to human interaction styles. In vehicle-vehicle interactions, driving behavior is informed by remaining time gaps between vehicles. This video-based experiment investigated the influence of gap sizes and the measurement method on driving decisions. N=32 participants experienced a highly automated drive in which their AV approached narrow passages. The time to arrival (TTA) of the oncoming traffic was varied. Participants had to decide to drive first or second, indicate their decision certainty, and the situation's criticality. The videos were presented in ascending, descending, and random order. Moreover, participants adjusted the TTA at which they would drive first and second. The results indicated a higher probability of driving first and lower criticality with increasing TTA. Decision certainty was lowest around the 50% threshold, while longer and shorter TTAs resulted in higher certainty. Results differed between the methods. The findings provide guidance for the design of automated systems to mimic human driving behavior.

本文言語English
ホスト出版物のタイトル2022 IEEE Intelligent Vehicles Symposium, IV 2022
出版社Institute of Electrical and Electronics Engineers Inc.
ページ1048-1053
ページ数6
ISBN(電子版)9781665488211
DOI
出版ステータスPublished - 2022
イベント2022 IEEE Intelligent Vehicles Symposium, IV 2022 - Aachen, Germany
継続期間: 2022 6月 52022 6月 9

出版物シリーズ

名前IEEE Intelligent Vehicles Symposium, Proceedings
2022-June

Conference

Conference2022 IEEE Intelligent Vehicles Symposium, IV 2022
国/地域Germany
CityAachen
Period22/6/522/6/9

ASJC Scopus subject areas

  • コンピュータ サイエンスの応用
  • 自動車工学
  • モデリングとシミュレーション

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