@inproceedings{031543540e7243d3bcd2339e432e7041,
title = "Towards Personalized Autonomous Driving: An Emotion Preference Style Adaptation Framework",
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. ",
keywords = "DDPG, EEG, autonomous driving, composite reward, driving preference, emotion recognition, personalization",
author = "Jiali Ling and Jialong Li and Kenji Tei and Shinichi Honiden",
note = "Funding Information: The research was partially supported by JSPS KAKENHI. Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE International Conference on Agents, ICA 2021 ; Conference date: 13-12-2021 Through 15-12-2021",
year = "2021",
doi = "10.1109/ICA54137.2021.00015",
language = "English",
series = "Proceedings - 2021 IEEE International Conference on Agents, ICA 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "47--52",
booktitle = "Proceedings - 2021 IEEE International Conference on Agents, ICA 2021",
}