-人工智能支持的智能驾驶舱主动情感交互:用于驾驶员情感识别的中间层特征融合双分支深度学习网络

IF 4.2 2区 工程技术 Q2 ENGINEERING, MANUFACTURING
Ying-Zhang Wu, Wen-Bo Li, Yu-Jing Liu, Guan-Zhong Zeng, Cheng-Mou Li, Hua-Min Jin, Shen Li, Gang Guo
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引用次数: 0

摘要

人工智能(AI)技术的进步推动了汽车智能驾驶舱的快速发展。对驾驶员情绪的主动感知会对道路交通安全产生重大影响。因此,开发驾驶员情绪识别技术对于确保汽车智能驾驶舱高级驾驶员辅助系统(ADAS)的驾驶安全至关重要。人工智能技术的不断进步为实施主动式情感交互技术提供了一个引人注目的途径。本研究引入了多模态驾驶员情感识别网络(MDERNet),这是一种双分支深度学习网络,可在时间上融合驾驶员面部表情特征和驾驶行为特征,实现非接触式驾驶员情感识别。该模型在 CK+、RAVDESS、DEAP 和 PPB-Emo 等公开数据集上进行了验证,可识别离散和维度情绪。结果表明,提出的模型具有先进的识别性能,消融实验证实了模型各组成部分的重要性。所提出的方法为驾驶员情绪识别中的多模态特征融合提供了基本参考,有助于推动汽车智能驾驶舱中的 ADAS。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

·AI-enabled intelligent cockpit proactive affective interaction: middle-level feature fusion dual-branch deep learning network for driver emotion recognition

·AI-enabled intelligent cockpit proactive affective interaction: middle-level feature fusion dual-branch deep learning network for driver emotion recognition

Advances in artificial intelligence (AI) technology are propelling the rapid development of automotive intelligent cockpits. The active perception of driver emotions significantly impacts road traffic safety. Consequently, the development of driver emotion recognition technology is crucial for ensuring driving safety in the advanced driver assistance system (ADAS) of the automotive intelligent cockpit. The ongoing advancements in AI technology offer a compelling avenue for implementing proactive affective interaction technology. This study introduced the multimodal driver emotion recognition network (MDERNet), a dual-branch deep learning network that temporally fused driver facial expression features and driving behavior features for non-contact driver emotion recognition. The proposed model was validated on publicly available datasets such as CK+, RAVDESS, DEAP, and PPB-Emo, recognizing discrete and dimensional emotions. The results indicated that the proposed model demonstrated advanced recognition performance, and ablation experiments confirmed the significance of various model components. The proposed method serves as a fundamental reference for multimodal feature fusion in driver emotion recognition and contributes to the advancement of ADAS within automotive intelligent cockpits.

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来源期刊
Advances in Manufacturing
Advances in Manufacturing Materials Science-Polymers and Plastics
CiteScore
9.10
自引率
3.80%
发文量
274
期刊介绍: As an innovative, fundamental and scientific journal, Advances in Manufacturing aims to describe the latest regional and global research results and forefront developments in advanced manufacturing field. As such, it serves as an international platform for academic exchange between experts, scholars and researchers in this field. All articles in Advances in Manufacturing are peer reviewed. Respected scholars from the fields of advanced manufacturing fields will be invited to write some comments. We also encourage and give priority to research papers that have made major breakthroughs or innovations in the fundamental theory. The targeted fields include: manufacturing automation, mechatronics and robotics, precision manufacturing and control, micro-nano-manufacturing, green manufacturing, design in manufacturing, metallic and nonmetallic materials in manufacturing, metallurgical process, etc. The forms of articles include (but not limited to): academic articles, research reports, and general reviews.
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