Towards Personalized Autonomous Driving: An Emotion Preference Style Adaptation Framework

Jiali Ling, Jialong Li, Kenji Tei, Shinichi Honiden

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

Abstract

Although autonomous driving is expected to pave the way for the future of transportation, it is often met with resistance. One of the reasons for this may be that, as of this writing, autonomous driving still cannot meet the individual needs of people. Furthermore, the unfamiliarity and discomfort when riding in an autonomous vehicle can cause drivers to feel stressed and distrustful of the vehicle. To this end, we propose an Emotion Preference Style Adaptation (EPSA) framework. The framework can analyze and determine a driver's driving preferences from the emotion which is recognized from their EEG signals. And then it will adapt the style of the vehicle's driving behavior to suit the driver's preference.

Original languageEnglish
Title of host publicationProceedings - 2021 IEEE International Conference on Agents, ICA 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages47-52
Number of pages6
ISBN (Electronic)9781665407168
DOIs
Publication statusPublished - 2021
Event2021 IEEE International Conference on Agents, ICA 2021 - Virtual, Online, Japan
Duration: 2021 Dec 132021 Dec 15

Publication series

NameProceedings - 2021 IEEE International Conference on Agents, ICA 2021

Conference

Conference2021 IEEE International Conference on Agents, ICA 2021
Country/TerritoryJapan
CityVirtual, Online
Period21/12/1321/12/15

Keywords

  • DDPG
  • EEG
  • autonomous driving
  • composite reward
  • driving preference
  • emotion recognition
  • personalization

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Information Systems and Management

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