{"title":"基于真实驾驶数据的智能驾驶舱大脑启发式驾驶员情绪检测","authors":"Wenbo Li, Yingzhang Wu, Huafei Xiao, Shen Li, Ruichen Tan, Zejian Deng, Wen Hu, Dongpu Cao, Gang Guo","doi":"10.1109/mits.2023.3339758","DOIUrl":null,"url":null,"abstract":"Affective human–vehicle interaction of intelligent cockpits is a key factor affecting the acceptance, trust, and experience for intelligent connected vehicles. Driver emotion detection is the premise of realizing affective human–machine interaction. To achieve accurate and robust driver emotion detection, we propose a novel brain-inspired framework for on-road driver emotion detection using facial expressions. Then, we conduct driver emotion data collection in an on-road context. We develop a data annotation tool, annotate the collected data, and obtain the RoadEmo dataset, a dataset of facial expressions and road scenarios under the driver’s emotional driving. Finally, we validate the detection accuracy of the proposed framework. The experiment results show that our proposed framework achieves excellent detection performance in the on-road driver emotion detection task and outperforms existing frameworks.","PeriodicalId":48826,"journal":{"name":"IEEE Intelligent Transportation Systems Magazine","volume":"75 1","pages":""},"PeriodicalIF":4.3000,"publicationDate":"2023-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Brain-Inspired Driver Emotion Detection for Intelligent Cockpits Based on Real Driving Data\",\"authors\":\"Wenbo Li, Yingzhang Wu, Huafei Xiao, Shen Li, Ruichen Tan, Zejian Deng, Wen Hu, Dongpu Cao, Gang Guo\",\"doi\":\"10.1109/mits.2023.3339758\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Affective human–vehicle interaction of intelligent cockpits is a key factor affecting the acceptance, trust, and experience for intelligent connected vehicles. Driver emotion detection is the premise of realizing affective human–machine interaction. To achieve accurate and robust driver emotion detection, we propose a novel brain-inspired framework for on-road driver emotion detection using facial expressions. Then, we conduct driver emotion data collection in an on-road context. We develop a data annotation tool, annotate the collected data, and obtain the RoadEmo dataset, a dataset of facial expressions and road scenarios under the driver’s emotional driving. Finally, we validate the detection accuracy of the proposed framework. The experiment results show that our proposed framework achieves excellent detection performance in the on-road driver emotion detection task and outperforms existing frameworks.\",\"PeriodicalId\":48826,\"journal\":{\"name\":\"IEEE Intelligent Transportation Systems Magazine\",\"volume\":\"75 1\",\"pages\":\"\"},\"PeriodicalIF\":4.3000,\"publicationDate\":\"2023-12-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Intelligent Transportation Systems Magazine\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1109/mits.2023.3339758\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Intelligent Transportation Systems Magazine","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1109/mits.2023.3339758","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Brain-Inspired Driver Emotion Detection for Intelligent Cockpits Based on Real Driving Data
Affective human–vehicle interaction of intelligent cockpits is a key factor affecting the acceptance, trust, and experience for intelligent connected vehicles. Driver emotion detection is the premise of realizing affective human–machine interaction. To achieve accurate and robust driver emotion detection, we propose a novel brain-inspired framework for on-road driver emotion detection using facial expressions. Then, we conduct driver emotion data collection in an on-road context. We develop a data annotation tool, annotate the collected data, and obtain the RoadEmo dataset, a dataset of facial expressions and road scenarios under the driver’s emotional driving. Finally, we validate the detection accuracy of the proposed framework. The experiment results show that our proposed framework achieves excellent detection performance in the on-road driver emotion detection task and outperforms existing frameworks.
期刊介绍:
The IEEE Intelligent Transportation Systems Magazine (ITSM) publishes peer-reviewed articles that provide innovative research ideas and application results, report significant application case studies, and raise awareness of pressing research and application challenges in all areas of intelligent transportation systems. In contrast to the highly academic publication of the IEEE Transactions on Intelligent Transportation Systems, the ITS Magazine focuses on providing needed information to all members of IEEE ITS society, serving as a dissemination vehicle for ITS Society members and the others to learn the state of the art development and progress on ITS research and applications. High quality tutorials, surveys, successful implementations, technology reviews, lessons learned, policy and societal impacts, and ITS educational issues are published as well. The ITS Magazine also serves as an ideal media communication vehicle between the governing body of ITS society and its membership and promotes ITS community development and growth.