智能驾驶中的行为辅助压力感知方法

人的压力与其行为紧密相关，特别是在智能驾驶时，驾驶员压力感知对实现辅助驾驶具有巨大的应用潜力.现有压力感知方法多用于静态环境，检测过程也缺乏便捷性，难以适应高度动态的智能驾驶应用需求.为了实现智能驾驶中自然、准确和可靠的压力检测，提出一种基于可穿戴系统的行为辅助压力感知方法.该方法基于行为伴随实现压力检测，并基于多指标执行压力状态判别，能够有效提高压力检测准确度.其基本原理在于每个人在不同压力状态下的生理特征和行为模式不同，会对压力相关的PPG数据和行为相关的IMU数据产生独特影响.首先使用嵌入多传感器的可穿戴手套测量驾驶员的生理和运动信息，通过多信号融合技术获得可靠的生理行为指标，最终使用泛化性能较好的SVM模型分类驾驶员的压力状态.基于所提出的方法在模拟驾驶环境下部署了验证实验，实验结果显示，压力分类精确度可达到95%.

Method of Behavioral Correlated Stress Perception in Smart Driving

Driver stress detection has great potential for implementing assisted driving because the stress of the people is closely related to their behavior, especially in smart driving. The existing stress perception methods are often used in static environments and lack of convenience, so it is difficult to satisfy the highly dynamic smart driving environments. This study proposes a behavior-assisted stress perception method based on wearable system to achieve natural, accurate, and reliable stress detection in smart driving. This method based on the behavior and multiple metrics to distinguish stress state, can effectively improve the stress detection accuracy. The basic principle is that each person''s physiological characteristics and behavioral habits under different stress conditions will have unique effects on stress-related PPG data and behavior-related IMU data. The driver''s physiology and motion information are measured using a multi-sensor wearable glove, and then reliable physiological and behavior metrics are obtained through multi-signal fusion techniques. Finally, the SVM model is used to classify the driver''s stress state because of good generalization performance. Based on the proposed method, this study deploys a verification experiment in a simulated driving environment, the experimental results show that the stress classification accuracy can reach 95%.