基于生理特征映射的驾驶员情绪在线识别模型构建方法

doi:10.3901/JME.2022.20.379

机械工程学报 ›› 2022, Vol. 58 ›› Issue (20): 379-390.doi: 10.3901/JME.2022.20.379

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基于生理特征映射的驾驶员情绪在线识别模型构建方法

王跃飞^1,2, 马伟丽¹, 王文康¹, 王超¹, 肖锴¹

1. 合肥工业大学机械工程学院合肥 230009;
2. 安全关键工业测控技术教育部工程研究中心合肥 230009

收稿日期:2021-03-24 修回日期:2021-09-16 出版日期:2022-10-20 发布日期:2022-12-27
通讯作者: 王跃飞(通信作者)，男，1977年出生，博士，副教授，硕士研究生导师。主要研究方向为车联网与智能汽车、汽车能量智能管理、工业互联网，人机协同控制等。E-mail：yuefeiw@hfut.edu.cn
作者简介:马伟丽，女，1995年出生，硕士研究生。主要研究方向为人机环境工程、制造系统监测控制与管理。E-mail：1058455934@qq.com;王文康，男，1996年出生，硕士研究生。主要研究方向为人机协同控制、智能网联系统。E-mail：319928943@qq.com
基金资助:
国家自然科学基金(61202096)和安徽省重点研究与开发计划(202104a05020018)资助项目。

Online Recognition Model Construction Method of Driver Emotion Based on Physiological Feature Mapping

WANG Yuefei^1,2, MA Weili¹, WANG Wenkang¹, WANG Chao¹, XIAO Kai¹

1. School of Mechanical Engineering, Hefei University of Technology, Hefei 230009;
2. Engineering Research Center of Safety Critical Industrial Measurement and Control Technology of Ministry of Education, Hefei 230009

Received:2021-03-24 Revised:2021-09-16 Online:2022-10-20 Published:2022-12-27

摘要/Abstract

摘要： 高精度驾驶员情绪识别模型是智能车辆安全辅助驾驶系统构建的关键问题之一。针对该问题，提出一种基于生理特征映射的驾驶员情绪在线识别模型构建方法。对不同情绪下驾驶员心电、脉搏生理信号进行时域特征提取，建立生理信号-情绪关联样本库，给出改进式自编码(Improved stacked autoencoder, I-SAE)神经网络架构，构建基于I-SAE神经网络的人员情绪生理表征和离线辨识模型；同步采集行驶过程中不同情绪下驾驶员生理信号和车辆状态数据，利用主成分分析法选取车辆状态特征参数向量，通过I-SAE模型识别处理生理信号数据，将驾驶员情绪的生理表征映射为车辆状态表征，构建车辆状态-驾驶员情绪关联样本库；在此基础上，建立基于学习矢量量化(Learning vector quantization, LVQ)的驾驶员情绪在线识别模型。试验数据表明，该方法构建的驾驶员情绪在线识别模型正确识别率达84%以上，可满足安全距离预警等车辆智能系统需要。

关键词: 驾驶员情绪, 生理信号, 车辆状态, 神经网络

Abstract: High-precision driver’s emotion recognition model is one of the key problems for intelligent vehicle safety assisted driving systems. To solve this problem, a method is proposed to build driver’s emotion online recognition model through physiological feature mapping. The time-domain features of driver’s physiological signals of ECG and pulse under different emotions are extracted, the sample library of physiological signal and emotion correlation is established. An improved stacked autoencoder (I-SAE) neural network is proposed, an emotion model of physiological representation and off-line identification based on I-SAE is built. Both physiological signals and vehicle state data are collected for different emotions during the driving. The vehicle state characteristic parameter vector is made by principal component analysis and the physiological signal data is processed through I-SAE model. The driver’s emotion representation is transferred from physiology to vehicle state, and the correlation sample library of vehicle state and driver emotion are obtained. On this basis, a driver emotion online recognition model based on learning vector quantization (LVQ) is established. The experimental data show that the correct recognition rate of the driver's emotion online recognition model constructed by this method is more than 84%, which can meet the needs of intelligent systems such as vehicle safety distance early warning.

Key words: driver emotion, physiological signal, vehicle state, neural network

中图分类号:

TN915

王跃飞, 马伟丽, 王文康, 王超, 肖锴. 基于生理特征映射的驾驶员情绪在线识别模型构建方法[J]. 机械工程学报, 2022, 58(20): 379-390.

WANG Yuefei, MA Weili, WANG Wenkang, WANG Chao, XIAO Kai. Online Recognition Model Construction Method of Driver Emotion Based on Physiological Feature Mapping[J]. Journal of Mechanical Engineering, 2022, 58(20): 379-390.

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基于生理特征映射的驾驶员情绪在线识别模型构建方法

Online Recognition Model Construction Method of Driver Emotion Based on Physiological Feature Mapping

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