Detecting stress during real-world driving tasks using physiological sensors

This paper presents methods for collecting and analyzing physiological data during real-world driving tasks to determine a driver's relative stress level. Electrocardiogram, electromyogram, skin conductance, and respiration were recorded continuously while drivers followed a set route through open roads in the greater Boston area. Data from 24 drives of at least 50-min duration were collected for analysis. The data were analyzed in two ways. Analysis I used features from 5-min intervals of data during the rest, highway, and city driving conditions to distinguish three levels of driver stress with an accuracy of over 97% across multiple drivers and driving days. Analysis II compared continuous features, calculated at 1-s intervals throughout the entire drive, with a metric of observable stressors created by independent coders from videotapes. The results show that for most drivers studied, skin conductivity and heart rate metrics are most closely correlated with driver stress level. These findings indicate that physiological signals can provide a metric of driver stress in future cars capable of physiological monitoring. Such a metric could be used to help manage noncritical in-vehicle information systems and could also provide a continuous measure of how different road and traffic conditions affect drivers.     

A Dynamic Programming Algorithm for Scheduling In-Vehicle Messages

In-vehicle information systems (IVISs) can enhance or compromise driving safety. Such systems present an array of messages that range from collision warnings and navigation instructions to tire pressure and e-mail alerts. If these messages are not properly managed, the IVIS might fail to provide the driver with critical information, which could undermine safety. In addition, if the IVIS simultaneously presents multiple messages, the driver may fail to attend to the most critical information. To date, only simple algorithms that use priority-based filters have been developed to address this problem. This paper presents a dynamic programming model that goes beyond the immediate relevance and urgency parameters of the current Society of Automotive Engineers (SAE) message scheduling algorithm. The resulting algorithm considers the variation of message value over time, which extends the planning horizon and creates a more valuable stream of messages than that based only on the instantaneous message priority. This method has the potential to improve road safety because the most relevant information is displayed to drivers across time and not just the highest priority at any given instant. Applying this algorithm to message sets shows that scheduling that considers the time-based message value, in addition to priority, results in substantially different and potentially better message sequences compared with those based only on message priority. This method can be extended to manage driver workload by adjusting message timing relative to demanding driving maneuvers.     

Queuing Network Modeling of Driver Workload and Performance

Drivers overloaded with information significantly increase the chance of vehicle collisions. Driver workload, which is a multidimensional variable, is measured by both performance-based and subjective measurements and affected by driver age differences. Few existing computational models are able to cover these major properties of driver workload or simulate subjective mental workload and human performance at the same time. We describe a new computational approach in modeling driver performance and workload-a queuing network approach based on the queuing network theory of human performance and neuroscience discoveries. This modeling approach not only successfully models the mental workload measured by the six National Aeronautic and Space Administration Task Load Index workload scales in terms of subnetwork utilization but also simulates the driving performance, reflecting mental workload from both subjective- and performance-based measurements. In addition, it models age differences in workload and performance and allows us to visualize driver mental workload in real time. Further usage and implementation of the model in designing intelligent and adaptive in-vehicle systems are discussed.     

On the Roles of Eye Gaze and Head Dynamics in Predicting Driver's Intent to Change Lanes

Driver behavioral cues may present a rich source of information and feedback for future intelligent advanced driver-assistance systems (ADASs). With the design of a simple and robust ADAS in mind, we are interested in determining the most important driver cues for distinguishing driver intent. Eye gaze may provide a more accurate proxy than head movement for determining driver attention, whereas the measurement of head motion is less cumbersome and more reliable in harsh driving conditions. We use a lane-change intent-prediction system (McCall et al., 2007) to determine the relative usefulness of each cue for determining intent. Various combinations of input data are presented to a discriminative classifier, which is trained to output a prediction of probable lane-change maneuver at a particular point in the future. Quantitative results from a naturalistic driving study are presented and show that head motion, when combined with lane position and vehicle dynamics, is a reliable cue for lane-change intent prediction. The addition of eye gaze does not improve performance as much as simpler head dynamics cues. The advantage of head data over eye data is shown to be statistically significant (p < 0.01) 3 s ahead of lane-change situations, indicating that there may be a biological basis for head motion to begin earlier than eye motion during "lane-change"-related gaze shifts.     

基于驾驶员眼动特征的无极调光智能控制技术的安全性

为研究采用无极调光智能控制技术对驾驶员行车安全性的影响,选取周公山隧道进行基于驾驶员眼动特征的试验。采用眼动仪记录驾驶员的眼动数据,对注视持续时间、扫视平均速度、瞳孔直径等参数进行统计分析。试验结果表明,驾驶员在使用无极调光智能控制技术的隧道照明环境中行驶时,眼动特征参数与未使用此技术无明显差异,表明驾驶员通过采用此技术的隧道照明环境时,心理舒适程度、紧张程度与未采用此技术的隧道无明显变化,满足了隧道行车安全性、节能性、舒适性的要求。     

基于ZD832的通用AC输入无变压器AC-DC恒流LED驱动器

ZD832是一种用于驱动白光或RGB串联LED串的高压无变压器AC-DC恒流驱动器IC。它利用85～125VAC或180～240VAC的通用输入电压操作,产生一个可达30mA的可编程恒定输出电流,能为所有形式因数的LED灯和照明系统驱动直达117个串联LED（@VF=1．8V）。     

基于优化概率神经网络的驾驶员疲劳检测

针对驾驶员面部疲劳检测问题，提出了一种基于遗传算法优化概率神经网络(PNN)的驾驶员疲劳检测算法。采用基于HOG特征的人脸检测器检测脸部，使用ERT算法进行关键点定位，计算PERCLOS值、眨眼频率、单位时间内打哈欠的时间占比、点头频率4个疲劳特征参数，将其输入到PNN中进行疲劳判别，并使用遗传算法优化PNN的平滑因子，提高疲劳分类准确率。使用NHTU-DDD数据集和YawDD数据集训练网络，使用自采集样本验证模型泛化性能，实验中与SVM、BP神经网络以及未优化的PNN模型对比，SVM、BP神经网络以及未优化的PNN的准确率分别为95.67%，97.67%，95.33%，所提的优化的PNN模型准确率为98.67%，验证了算法的有效性。     

基于SVM方法的风电场短期风速预测

针对基于支持向量机的风电场短期风速预测进行研究,选择了不同的输入向量（历史风速时间序列,历史风速和温度,历史风速、温度和风向,历史风速、温度和时间）作为输入进行误差对比分析。实测数据及分析结果表明,采用历史风度和温度的二输入模型,预测效果最佳,为风速的短期预测和发电量预测提供了较好的参考价值     

同步磁阻电机作为机床主轴驱动的研究

对现有的交流电机主轴驱动系统进行了概述,提出可用同步磁阻电机作为机床的主轴驱动,并且比较了各种电机主轴驱动的优缺点,指出同步磁阻电机的优势。分析了同步磁阻电机作为主轴驱动的控制方法以及弱磁控制方法,并仿真比较其与永磁同步电机的转矩特性,阐明同步磁阻电机是机床主轴驱动的理想机种。     

基于单片机的线阵CCD驱动模块硬件设计与实现

根据线阵CCD驱动时序的特点,给出一种线阵CCD驱动电路的设计方法。采用具有增强型内核的单片机产生CCD所需的驱动波形,能充分发挥单片机可编程的特点, 为用户提供丰富的驱动信号接口,并实现了电子快门功能。首先介绍CCD驱动模块的基本工作原理、主要特点和驱动时序的设计思路,接着完成驱动模块软硬件的设计,最后通过大量实验验证该驱动模块的有效性。实验结果表明: 该驱动模块所产生的驱动信号满足CCD的需要,当该驱动模块集成到其他无接触测量系统中时, 该测量系统能正常稳定工作,测量结果准确,精度达到了μm级。     

Looking-In and Looking-Out of a Vehicle: Computer-Vision-Based Enhanced Vehicle Safety

This paper presents investigations into the role of computer-vision technology in developing safer automobiles. We consider vision systems, which cannot only look out of the vehicle to detect and track roads and avoid hitting obstacles or pedestrians but simultaneously look inside the vehicle to monitor the attentiveness of the driver and even predict her intentions. In this paper, a systems-oriented framework for developing computer-vision technology for safer automobiles is presented. We will consider three main components of the system: environment, vehicle, and driver. We will discuss various issues and ideas for developing models for these main components as well as activities associated with the complex task of safe driving. This paper includes a discussion of novel sensory systems and algorithms for capturing not only the dynamic surround information of the vehicle but also the state, intent, and activity patterns of drivers     

大功率LED高频驱动电路设计

由于白光LED具有低成本、长寿命和小体积的特性,被迅速应用到了照明和背光等领域,其驱动电路也层出不穷,但大多数驱动源都没有解决效率不高,LED发光亮度不一致,发热量大等问题。该文提出了一种基于恒流二极管的大功率LED高频驱动方案,以带可控端的2THL系列恒流二极管为驱动元件,通过在控制端输入高频脉冲小信号控制恒流二极管通断,从而实现高频恒流驱动大功率LED这一目的。调节脉冲信号占空比即可实现LED调光。该文设计的驱动电路不仅能够保证LED持续、稳定、高效地工作,在一定程度上减小了LED芯片发热量,提高了LED灯具使用寿命,并且对输入电源要求不高,整体可以节能40%左右。     

MOSFET谐振门极驱动电路研究综述

谐振门极驱动电路能够减小高频下MOSFET的驱动损耗。首先介绍了传统电压源驱动及其存在的诸多问题,引出谐振驱动技术。综述了目前现有的谐振门极驱动电路的拓扑结构,并分为电流源型、谐振型和耦合电感型三大类。对于电流源型和谐振型,分别介绍较早提出的拓扑结构及其优缺点,并与后期发展的各种拓扑作对比分析。耦合电感型是在电流源型或谐振型中加入耦合电感来传递能量,这也增加了拓扑的复杂度。考虑谐振门极驱动电路的复杂程度,将拓扑元器件集成到一个芯片中以达到优化。     

Online trained support vector machines‐based generalized predictive control of non‐linear systems

In this work, an online support vector machines (SVM) training method (Neural Comput. 2003; 15 : 2683–2703), referred to as the accurate online support vector regression (AOSVR) algorithm, is embedded in the previously proposed support vector machines‐based generalized predictive control (SVM‐Based GPC) architecture (Support vector machines based generalized predictive control, under review), thereby obtaining a powerful scheme for controlling non‐linear systems adaptively. Starting with an initially empty SVM model of the unknown plant, the proposed online SVM‐based GPC method performs the modelling and control tasks simultaneously. At each iteration, if the SVM model is not accurate enough to represent the plant dynamics at the current operating point, it is updated with the training data formed by persistently exciting random input signal applied to the plant, otherwise, if the model is accepted as accurate, a generalized predictive control signal based on the obtained SVM model is applied to the plant. After a short transient time, the model can satisfactorily reflect the behaviour of the plant in the whole phase space or operation region. The incremental algorithm of AOSVR enables the SVM model to learn the new training data pair, while the decremental algorithm allows the SVM model to forget the oldest training point. Thus, the SVM model can adapt the changes in the plant and also in the operating conditions. The simulation results on non‐linear systems have revealed that the proposed method provides an excellent control quality. Furthermore, it maintains its performance when a measurement noise is added to the output of the underlying system. Copyright © 2006 John Wiley & Sons, Ltd.     

Smart drivers - [IT in-vehicle]

ALTHOUGH computerised vehicle-control mechanisms remain largely closed to infiltration by more mainstream technologies, in-vehicle systems that mediate driving, and dashboard infotainment systems, are increasingly built on the kinds of interfaces that resemble drivers? personal computers, mobile devices, and home entertainment electricals.     

基于SOFM和支持向量机回归的短期负荷预测方法

提出了一种基于自组织特征映射(SOFM)的聚类分析和支持向量机(SVM)的电力系统短期负荷预测方法。该方法首先利用自组织特征映射网络,通过无监督学习策略对训练样本集进行聚类分析,将其分为若干相似子类,再针对每一子类构造一个支持向量机回归模型;使用基于SVM的回归估计算法建立了回归估计函数表达式,给出了基于SOFM和SVM的网络结构;采用河北省某市的实际负荷数据选择样本进行预测。算例表明该方法能够缩短训练时间,提高预测精度。     

Characterization of Driving Behaviors Based on Field Observation of Intersection Left-Turn Across-Path Scenarios

There have been significant research and developments in recent years for intersection-safety solutions that are intended to alert drivers of hazardous situations by utilizing sensing, computing, and communication technologies. Since the effectiveness of intersection-safety systems depends strongly on driver perception and acceptance of the provided warning signal, the understanding of driver actions under the targeted scenario is a central research topic. One significant safety concern at intersections is the left-turn crossing-path scenarios, where a left-turning vehicle is confronted by oncoming traffic. This paper describes the analysis and synthesis of real-world data for such scenarios observed in field observations. Specifically, traffic interactions in left-turn across-path situations are evaluated to compare data from various intersections with different operation and traffic attributes. The analyzed data were characterized to gain insight into a time gap acceptance exhibited by a population of drivers. The knowledge of driving behaviors can provide the guidelines for future investigation as well as a knowledge basis for the selection of warning criteria to allow timely alerts to drivers in the intended safety applications.     

IGBT驱动器在斩波串调系统中的应用研究

研究了以智能门极驱动模块IGD515E为核心的IGBT驱动电路在斩波式内反馈串级调速系统中的典型应用方法,可为该驱动电路的参数设计提供参考,此外给出了较为充分的试验波形予以证明该驱动电路工作稳定,具有优良的驱动和保护性能.     

高频感应加热电源功率器件MOSFET驱动电路

针对多管并联的MOSFET驱动这一难题,通过分析MOSFET开关过程,得出了MOSFET开关转换对驱动电路的要求,以及驱动电流和驱动功率的计算方法,最后采用脉冲变压器法设计了高频感应加热电源功率器件的驱动电路,并给出了电路的设计参数及实验波形.     

步进电机微步恒力矩驱动

为消除步进电机微步驱动中的力矩波动,本文得出了一种能补偿电机谐波力矩和互力矩的驱动电流表达式,使用这种电流波形和带电流负反馈的脉冲宽度调制驱动方式组成了微处理器控制的微步驱动器。实验结果表明它能减小电机步距角,消除电机共振,使普通步进电机在0.25～2500r/min转速范围内平稳运行。