論文

査読有り
2021年7月11日

A Comparison of Methods for Sharing Recognition Information and Interventions to Assist Recognition in Autonomous Driving System

2021 IEEE Intelligent Vehicles Symposium (IV)
  • Atsushi Kuribayashi
  • ,
  • Eijiro Takeuchi
  • ,
  • Alexander Carballo
  • ,
  • Yoshio Ishiguro
  • ,
  • Kazuya Takeda

開始ページ
622
終了ページ
629
記述言語
英語
掲載種別
研究論文(国際会議プロシーディングス)
DOI
10.1109/iv48863.2021.9575707
出版者・発行元
IEEE

As research and development related to the practical use of autonomous driving systems (ADS) continue to advance, one of the remaining challenges is achieving both safe and natural autonomous driving. In part, this is due to the difficulty of achieving flawless, automated perception and understanding of the driving environment. In our previous study, we proposed a recognition assistance interface to solve this problem by sharing ADS recognition information with the passenger, allowing them to assist in the recognition stage of the autonomous driving process. In this study, we incorporate our recognition assistance interface into Autoware and test it in a simulated driving environment, using scenarios in which the ADS must recognize the intent of pedestrians and respond to the presence of trash in the road. Natural driving was dened as driving that avoids significant, unnecessary deceleration when performing these challenging recognition tasks. The results of our experiment with 11 participants showed that sharing recognition information with passengers is effective for avoiding unnecessary deceleration and achieving only minor variations in speed when encountering obstacles flagged in error by the recognition system.

リンク情報
DOI
https://doi.org/10.1109/iv48863.2021.9575707
Web of Science
https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000782373100090&DestApp=WOS_CPL
URL
http://xplorestaging.ieee.org/ielx7/9575127/9575130/09575707.pdf?arnumber=9575707
ID情報
  • DOI : 10.1109/iv48863.2021.9575707
  • ISSN : 1931-0587
  • Web of Science ID : WOS:000782373100090

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