基于个性化行为模型的驾驶疲劳识别方法

为提高疲劳驾驶状态的识别精度,应考虑驾驶人之间的个体差异。以实车驾驶试验条件下车道保持行为中的车速和车道偏离值为输入,以方向盘转角为输出,基于径向基(RBF)神经网络针对每个驾驶人构建正常驾驶状态下的车道保持行为模型,并根据残差对模型的拟合及预测效果进行评价;将疲劳驾驶状态下的车速和车道偏离值输入到上述驾驶行为模型中,可得到模型预测的方向盘转角值,通过分析预测值与实际方向盘转角之间的差异,研究疲劳对驾驶人行为的影响;将预测残差作为输入,建立基于支持向量机(SVM)的疲劳驾驶状态辨识模型。结果表明:所建立的RBF神经网络-SVM识别模型对不同驾驶人疲劳驾驶状态的平均识别率达85%。     

农村公路复杂环境中新老驾驶人视觉特性研究

为研究新老驾驶人在农村公路复杂环境中的视觉特性,采用眼动仪测量若干被试人员在实际驾驶过程中的视觉生理指标,运用统计学和数据挖掘方法分析新老驾驶人的瞳孔直径、注视特性、扫视特性和追随眼动特性,进而辨析新老驾驶人的驾驶行为特性差异。结果表明,在农村公路复杂道路环境中,新老驾驶人的视觉特性和驾驶行为具有明显差异。新驾驶人的瞳孔直径变化剧烈,表现出长时间注视和注视反馈特性;老驾驶人具有较多短时间注视特性,注视前瞻性和反馈特性都很明显,并且具有追随眼动特性。     

女性驾驶人特性与行为机制研究现状及展望

为系统地认识女性驾驶人的驾驶特性,分析其驾驶行为,从女性驾驶人交通事故数据统计、致因和心理、生理、“药驾”等因素,梳理国内外女性驾驶人驾驶特性及行为机制的研究成果,总结主流研究角度、方法和内容,并展望其未来研究趋势。对文献的梳理和分析表明:导致女性驾驶人交通事故的因素主要包括人格、认知等心理特征和视觉能力、应激反应等生理特征,同时“药驾”也影响着驾驶特性与行为。剖析了女性驾驶人特性及行为机制研究目前存在的不足之处,对未来的研究提出了建议。并针对女性驾驶人群体在各种道路交通环境中开展有效的模拟驾驶培训和教育,进而全面提升中国女性驾驶人的交通安全水平。     

视觉与听觉次任务对驾驶人视觉的影响及差异

为研究视觉与听觉次任务对驾驶人视觉的影响及差异性,在虚拟驾驶环境下设计次任务试验,要求被试驾驶人执行多组不同类型、难度的视觉与听觉次任务,同时利用眼动追踪装置采集驾驶人视觉特征参数。在筛选有效数据的基础上,运用统计学与数据挖掘方法比较驾驶人执行不同次任务时,视觉搜索区域面积、瞳孔面积、次任务完成时间的差异并分析统计显著性。结果表明,驾驶人在执行视觉次任务、听觉次任务、无次任务3种状态下,执行视觉次任务时视觉搜索区域面积最小、瞳孔面积变化幅度最大、次任务完成时间最长;无次任务时视觉搜索区域面积最大、瞳孔面积变化幅度最小、执行次任务时间最短;执行听觉次任务时,视觉特征数据居于视觉次任务、无次任务两种状态之间。上述差异具有统计学显著性,主观感知评价与客观数据具有一致性。研究表明,视觉与听觉次任务使驾驶人心理负荷明显增大,且视觉次任务对驾驶人的影响更为明显。     

基于驾驶操作行为的驾驶员疲劳状态识别模型研究

以驾驶疲劳状态监测为研究对象,介绍现有几种疲劳检测方法及其优缺点,提出把驾驶行为操作和驾驶员生理指标相结合建立疲劳识别模型的思想。通过大量模拟器驾驶实验,建立驾驶操作和驾驶员生理指标之间的关系模型,并运用最小二乘法对数学模型进行了参数识别。利用驾驶员生理指标能较好判别驾驶员状态特性的特点,找出驾驶操作行为和驾驶状态之间的关系。研究结果有助于建立驾驶操作行为和驾驶员疲劳状态之间的关系模型。     

视觉分心程度对跟车运动状态的影响

为明确驾驶人在不同复杂度的交通环境下视觉分心程度对跟车运动状态的影响,在驾驶模拟器上搭建城市、乡村和高速道路3种交通环境,利用视线遮挡方法,设置O、1和2s等3种固定时长,量化被试视觉分心程度;对26位被试进行模拟器驾驶试验,采集车辆速度、纵向加速度和车辆位置等参数;采用重复测量一般线性模型,分析视觉分心程度对上述参数...     

驾驶员接管自动驾驶车辆的眼动特性和行为分析

为探究接管自动驾驶车辆期间驾驶员的视觉特性,分析眼动与接管反应操控行为的关系,开展驾驶模拟试验收集驾驶行为及眼动数据。运用统计学方法,分析驾驶员感知不同接管场景的视觉特性,探究接管请求(TOR)前后眼动指标的变化规律;并基于视觉分配和瞳孔变化特性分析驾驶行为,揭示眼动特性与接管反应及驾驶操纵策略的内在联系。结果表明:TOR前,相较于静态场景,驾驶员感知动态场景诱发元素扫视更频繁且平均注视时间更短;此时驾驶员的视觉分配特性与其接管反应行为存在显著相关性。TOR后,驾驶员的注视时间增加,眨眼频率降低,瞳孔直径扩张,眼跳幅度增大;不同场景下驾驶员的瞳孔差异表明其应对动态场景时具备更好的警戒水平和更平稳的操纵策略。     

交通事故鉴定技术在超车类案件中的应用

“超车”泛指偏离原行驶路线、驶入相对方向车道、与被超车并肩行驶、超越后回归本道，集直行、转弯、变更车道之大成的驾驶行为。超车是后车需求引发的交通活动，曲线行驶特性注定车辆将不断发生侵犯他人合法活动空间、不断形成交通交织点和；中突点，驾驶人稍有疏忽或处置不当，轻则扰乱交通秩序危及交通安全，重则发生恶性交通事故，     

基于SEM的双车道公路超车行为安全评价

为提高驾驶人在双车道公路上超车的安全性,基于tau理论适应性分析,提出超车过程中驾驶人不仅根据tau线索估计避碰时间,也利用距离、速度、加速度等判断超车是否安全的试验假设。招募12名受试者,应用驾驶模拟系统试验平台,依据试验假设采集12个与超车行为有关的驾驶行为数据,对其进行无量纲化处理,并利用Bootstrap法对数据进行扩增。结合驾驶行为问卷得分,利用AMOS软件,建立超车行为安全评价的结构方程模型。通过对初始假设模型进行多次修正与评价,得到影响超车行为安全的3个驾驶行为参数。结果表明,超车车辆在超车并道后的速度、与前导车之间的避碰时间及与对向车辆之间的避碰时间这3个驾驶行为参数可作为超车行为安全评价的有效指标。     

基于驾驶模拟的自动驾驶接管行为研究*

为探索有条件自动驾驶对非驾驶相关任务的允准边界,基于实车驾驶模拟器,设计自动驾驶接管试验典型场景,招募30名被试者开展驾驶模拟试验;要求驾驶人执行3种分心形式的驾驶次任务,系统发出接管请求提示后,驾驶人接管车辆控制权以避免险情发生,并分析驾驶人接管反应时间、驾驶负荷以及驾驶绩效等相关数据。结果表明:驾驶次任务涉及的分心形式越复杂,接管过程安全性越差,视觉分心任务与操作分心任务对接管行为影响显著;驾驶人更倾向于选择制动操作接管车辆,次任务分心程度越高,制动接管比例越大;与乘客聊天对接管行为影响不显著,看视频和玩手机游戏均会显著延长接管反应时间,增加工作负荷与车辆纵向减速度,玩手机游戏还会显著提升车辆横向加速度。     

Fatigued driver’s driving behavior and cognitive task performance: Effects of road environments and road environment changes

This study aims to explore the effects of different road environments and their changes on driving behaviors and cognitive task performance of fatigued drivers. Twenty-four participants volunteered in a 2 (road environment) × 3 (fatigue level) within-subjects factorial design simulated driving experiment. Participants were asked to perform basic numerical calculation and distance estimation of traffic signs when driving normally, and provide answers to a questionnaire on fatigue rating. Results show that fatigued drivers faced greater attention demand, were less alert, and tended to overestimate the distance to roadside traffic signs. Fatigue caused by driving in complex road environment had the greatest negative impact on driving behavior and visual distance estimation, and the fatigue transfer effect worsened significantly but differently on both driving behavior and performance of fatigued drivers when switching from a complex to a monotonous road environment and vice versa. Notably, this study shows that fatigued drivers performed relatively better in arithmetic tasks than non-fatigued ones. In addition, when switching from a monotonous to a complex road environment, drivers’ performance in visual distance estimation and arithmetic tasks improved though their driving behavior deteriorated, revealing that the fatigue effect upon drivers might be explained to some extent by their alertness and arousal levels.     

Effects of environment, vehicle and driver characteristics on risky driving behavior at work zones

This study aims to analyze the effects of environment, vehicle and driver characteristics on the risky driving behavior at work zones. A decision tree is developed using the classification and regression tree (CART) algorithm to graphically display the relationship between the risky driving behavior and its influencing factors. This approach could avoid the inherent problems occurred in the conventional logistic regression models and further improve the model prediction accuracy. Based on the Michigan M-94/I-94/I-94BL/I-94BR highway work zone driving behavior data, the decision tree comprising 33 leaf nodes is built. Bad weather, poor road and light conditions, partial/no access control, no traffic control devices, turning left/right and driving in an old vehicle are found to be associated with the risky driving behavior at work zones. The middle-aged drivers, who are going straight ahead in their vehicles with medium service time and equipped with an airbag system, are more likely to take risky behavior at lower work zone speed limits. Further, the middle-aged male drivers engage in risky driving behavior more frequently than the middle-aged female drivers. The number of lanes exhibits opposing effects on risky behavior under different traveling conditions. More specifically, the risky driving behavior is associated with the single-lane road under bad light or weather conditions while drivers are more likely to engage in risky behavior on the multi-lane road under good light conditions.     

大货车驾驶员交通心理与交通安全的研究

笔者从交通心理学的角度,对大货车驾驶员在行驶过程中的心理状况、因攻击性驾驶行为、强烈的冒险动机的驱使、对道路期望心理的失衡及交通安全感偏差等不良心理因素而导致恶性交通事故进行分析。通过研究得出以下结论大货车驾驶员是一特殊的群体,应重视对该群体的教育、培训与管理,从而提高驾驶员的职业道德水平、性格品质、交通安全感及驾驶技能,以改善道路安全环境和交通安全状况、降低道路交通事故发生率。     

海底隧道入口段驾驶员眼动特征分析*

为研究海底隧道入口段驾驶员眼动特征,以海底隧道收费站至入口为研究对象,运用Facelab5.0眼动仪和录像机等设备,采集真实状态下驾驶员眼动特征、行车速度和行车位置数据,并依据道路线形、车辆行车特征和路段标志标线设置,将收费站至入口划分为提速驶离段、换道减速段、缓和段和过渡段（入口段）,分析各区段驾驶员眼动特征及车速变化规律,并建立相应数学模型。研究结果表明:驾驶员驾车通过换道减速段和过渡段时,分别受交织车流与黑洞效应影响,行车速度减小、眼睑闭合度下降、眨眼频率增大;驾驶员行经入口段,车速呈上升-下降-上升-下降的趋势,眼睑闭合度呈增大-减小-增大-减小的趋势,眨眼频率呈减小-增大-减小-增大的趋势,且受交织车流与黑洞效应的影响显著。     

Exploring relationships between microscopic kinetic parameters of tires under normal driving conditions,road characteristics and accident types

IntroductionFreeway accidents are a leading cause of death in China, which also triggers substantial economic loss and an emotional burden to society. However, the internal mechanism of how microscopic kinetic parameters of vehicles influenced by road characteristics determine the occurrence of different types of accidents has not been explicitly studied. This research aimed to explore the “link role” of tire microscopic kinetic parameters in road characteristic variables and traffic accidents to aid in facilitating the traffic design and management, and thus to prevent traffic accident. Method: A mountain freeway in Zhejiang Province, China was used as the research object and the data used in this paper were obtained through a real-time vehicle experiment. Multiple estimation models, including the standard ordered logit (SOL) model, fixed parameters logit (FPL) model, and random parameters logit (RPL) model were established. Results: The findings show that road characteristics will affect the longitudinal kinetic characteristics of the vehicle and, consequently, map the level of risk of rear-end accidents. Driving compensation effects were also identified in this paper (i.e., the drivers tend to be more cautious in complicated driving circumstances). Another finding relating to the mountain freeway is that different tunnel characteristics (e.g., tunnel entrance and tunnel exit) have different effects on different types of traffic accidents. Practical Applications: The framework proposed in this article can provide new insight for researchers to enlarge the research subjects of both explanatory and outcome variables in accident analysis. Future research could be implemented to consider more driving conditions.     

Examination of drivers' cell phone use behavior at intersections by using naturalistic driving data

IntroductionMany driving simulator studies have shown that cell phone use while driving greatly degraded driving performance. In terms of safety analysis, many factors including drivers, vehicles, and driving situations need to be considered. Controlled or simulated studies cannot always account for the full effects of these factors, especially situational factors such as road condition, traffic density, and weather and lighting conditions. Naturalistic driving by its nature provides a natural and realistic way to examine drivers' behaviors and associated factors for cell phone use while driving.MethodIn this study, driving speed while using a cell phone (conversation or visual/manual tasks) was compared to two baselines (baseline 1: normal driving condition, which only excludes driving while using a cell phone, baseline 2: driving-only condition, which excludes all types of secondary tasks) when traversing an intersection.ResultsThe outcomes showed that drivers drove slower when using a cell for both conversation and visual/manual (VM) tasks compared to baseline conditions. With regard to cell phone conversations, drivers were more likely to drive faster during the day time compared to night time driving and drive slower under moderate traffic compared to under sparse traffic situations. With regard to VM tasks, there was a significant interaction between traffic and cell phone use conditions. The maximum speed with VM tasks was significantly lower than that with baseline conditions under sparse traffic conditions. In contrast, the maximum speed with VM tasks was slightly higher than that with baseline driving under dense traffic situations.Practical applicationsThis suggests that drivers might self-regulate their behavior based on the driving situations and demand for secondary tasks, which could provide insights on driver distraction guidelines. With the rapid development of in-vehicle technology, the findings in this research could lead the improvement of human-machine interface (HMI) design as well.     

Assessing driver behavior using shrp2 adverse weather data

IntroductionThis study explored how drivers adapt to inclement weather in terms of driving speed, situational awareness, and visibility as road surface conditions change from dry to slippery and visibility decreases. The proposed work mined existing data from the SHRP 2 NDS for drivers who were involved in weather-related crash and near-crash events. Baseline events were also mined to create related metadata necessary for behavioral comparisons. Methods: Researchers attempted, to the greatest extent possible, to match non-adverse-weather driving scenarios that are similar to the crash and near-crash event for each driver. The ideal match scenario would be at a day prior to the crash during non-adverse weather conditions having the same driver, at the same time of day, with the same traffic level on the same road on which the crash or near-crash occurred. Once the matched scenarios have been identified, a detailed analysis will be performed to determine how a driver’s behavior changed from normal driving to inclement-weather driving. Results: Data collected indicated that, irrespective of site location (i.e., state), most crashes and near-crashes occurred in rain, with only about 12% occurring in snowy conditions. Also, the number of near-crashes was almost double the number of crashes showing that many drivers were able to avoid a crash by executing an evasive maneuver such as braking or steering. Conclusions: Most types of near crashes were rear-end and sideswipe avoidance epochs, as the drivers may have had a difficult time merging or trying to change lanes due to low visibility or traffic. Hard braking combined with swerving were the most commonly used evasive maneuvers, occurring when drivers did not adjust their speeds accordingly for specific situations. Practical applications: Results from this study are expected to be utilized to educate and guide drivers toward more confident and strategic driving behavior in adverse weather.     

基于元胞自动机的人因追尾事故仿真研究

人为因素已经成为导致交通事故发生的关键因素之一,而车辆追尾碰撞是公路上最常见、危害最大的一类事故.本文将驾驶员的心理细分为放松、谨慎和紧张三种状态,设计了状态切换规则以及不同状态下驾驶员的行为规则,建立了一种公路人因追尾事故的元胞自动机仿真模型.随后通过仿真实验研究了交通流参数以及驾驶员驾驶特性对追尾事故发生率的影响,仿真结果表明随着车辆最大允许速度和车辆密度的增加,追尾事故发生率显著上升;跟驰状态下驾驶员的反应延迟与差错率对事故率影响最大,而紧急情况下驾驶员的人为因素对事故率没有显著影响.研究结果对交通中人因事故发生机理研究及人因事故的预防有一定参考价值.     

应用改进的数量化理论预测道路断面事故率

为准确预测道路断面的安全性,建立道路交通事故断面事故率模型。首先选定事故率作为微观预测目标,选取驾驶员的驾龄、车道数、平曲线半径、纵坡度、路面情况、路口路段类型、道路宽度7项因素作为主要影响因素,并且将各影响因素分为若干类目。在数量化理论的基础之上建立改进的数量化理论的道路断面事故率预测模型,最后以某国道222.888～377.387 km段作为算例进行计算,并选取事故多发段333.5～334.0 km处对模型进行具体应用。研究结果表明,对事故影响最大的是该路段中三枝交叉口,其次是3 a(含3 a)以下驾龄及四枝交叉口。     

事故车辆影响下的城市道路交通仿真研究

为研究事故车辆影响下城市道路交通的特征，构建考虑车辆抢道行为的元胞自动机交通流模型，研究给定冲突区域长度下不同进车率和不同事故持续时间对城市道路交通流的影响。研究结果表明:事故车辆会诱发交通瓶颈，对城市交通产生显著干扰并形成拥堵带，且拥堵带向事故车辆上游传递。不同事故持续时间下交通流演化存在差异，道路平均车流量、车流平均密度随着事故持续时间的增加而增加，车辆平均速度随之减小。当道路中车辆较少（pin=0.3）且事故持续时间达到15 min时，交通处于严重拥堵状态；当道路中车辆较多（pin=0.5）、事故持续时间达到5 min时，交通即处于严重拥堵状态。研究结果可为优化城市交通事故处理机制提供依据。