基于驾驶模拟器的使用手机对车辆驾驶可靠度的影响

将驾驶模拟器作为试验平台分析使用手机对驾驶的影响,有40名驾驶员参与了多阶段的试验,利用统计数据对不同通讯方式下完成的不同驾驶任务进行比较分析,得到初步结论:带有干扰性的手机通话最有可能引发道路交通事故,而免提可以起到轻微的改善作用,不能有效改善负面影响;使用手机同时完成双重任务的干扰性远比单一任务的干扰性影响大;男性使用手机对于驾驶安全性影响比对女性大。以试验的相关统计数据为基础,建立了使用手机对驾驶的可靠度模型,综合分析验证了使用手机对于车辆驾驶影响的初步结论。进一步为基于模型的驾驶可靠度进行分级,得到结论:使用手机对驾驶可靠度影响显著,干扰对话对驾驶可靠度影响更加显著;任何形式的通话都会使可靠度迅速减小。     

手机使用对驾驶员行为影响研究综述

驾驶中使用手机通话存在安全隐患,可能直接或间接地导致交通事故.从驾驶时使用手机的原因切入,分析了手机使用对驾驶的影响.手机使用引起驾驶分心从而影响驾驶行为.总结国内外手机对驾驶影响的研究,得出结论.      

驾驶中拨打手机对驾驶人脑力负荷及驾驶行为的影响分析

基于驾驶模拟实验平台,选择高速公路、城市道路2种交通环境,对3名低龄驾驶人正常驾驶与拨打手机驾驶情况下的脑力负荷及驾驶行为表征指标进行了全程监测。每组实验时间约为15min。其中,脑力负荷评估采用心率变异性相关指标,驾驶行为表征采用后视镜使用、转向灯使用、档位变换及速度变化等指标。实验结果表明,相比于正常状态驾驶中拨打手机时心率变异性指标,出现了驾驶人LFNU指标增大、HFNU指标减小、LF/HF比值明显增大、TP增大的现象,规律明显。在排除驾驶疲劳对心电指标产生影响的可能后,可初步判定出现以上现象的原因是拨打手机引起了驾驶人脑力负荷的大幅增加。在驾驶人认知资源有限的情况下,脑力负荷的增大造成了驾驶人在信息获取、转向灯使用、档位变换和车速保持等方面的能力有不同程度的下降。综上所述,驾驶中拨打手机使得驾驶人脑力负荷大幅增加,进而对驾驶行为产生了诸多负面影响,给交通安全带来了隐患。因此,建议相关部门在法律、法规中对驾驶中拨打手机的行为加以限制,以减少交通事故风险。     

驾驶人个体因素对驾驶愤怒情绪影响关系研究

国内外研究表明,对驾驶人的愤怒情绪产生的影响因素进行分析对提高交通安全性有着十分重要的现实意义和价值.对22名志愿者采用给定任务法开展了实车实验,要求2h内完成指定路线的驾驶任务.驾驶过程中要求被试者实时汇报其愤怒等级并进行记录,同时实验车还采集了相关的数据.采用SPSS以及Excel软件对数据进行处理发现:男性驾驶人在驾驶过程中产生愤怒情绪的概率要高于女性驾驶人;年长驾驶人出现驾驶愤怒情绪的概率显著高于年青驾驶人(spearman相关系数为0.618,显著性水平为0.02＜0.05);而随着驾龄的增加驾驶人平均愤怒等级逐渐降低(spearman相关系数为-0.676,显著性水平为0.001＜0.05).同时还发现不同驾照类型对驾驶愤怒情绪也有着较大的影响.     

次任务驾驶对行车安全性影响研究综述

次任务驾驶已成为导致道路交通事故的重要原因.驾驶过程中,次任务操作会在不同程度上占用驾驶人的视觉资源、认知资源和动作资源,分散驾驶人注意力,严重影响行车安全.因此,对次任务驾驶安全性开展相关研究具有重要意义.从移动电话使用、音乐、调控车载设备、喝水抽烟及吃东西等4个方面,对目前国内外次任务驾驶安全研究现状进行了阐述,分析了次任务驾驶安全性评价模型研究进展,概括了次任务驾驶对驾驶安全性影响研究手段和方法的优缺点,并对我国驾驶次任务研究方向作出展望.     

自动驾驶中视觉次任务对年轻驾驶人接管时间的影响

为了了解有条件的自动驾驶中,年轻驾驶人的接管反应特性,分析视觉次任务（3×3箭头次任务和4×4箭头次任务）和接管请求时间（TTC为5 s和7 s）对不同接管时间的影响,基于驾驶仿真平台,设计了包含不同的视觉次任务和接管请求时间的自动驾驶接管场景,针对29名年轻驾驶人进行模拟驾驶试验。使用双因素方差分析来研究不同次任务与不同接管请求时间对接管时间的影响,以及使用Pearson相关性检验来分析不同接管时间的相关性。研究结果表明：与无次任务相比,次任务会显著增加接管时间;不同的次任务对接管时间无影响,次任务与前方有障碍物时的接管请求时间对接管时间无交互作用;与前方无障碍物时的接管相比,前方有障碍物时的接管会显著减少接管反应时间;在驾驶人执行次任务的情况下,不同接管请求时间对转向反应时间和制动反应时间无影响;在前方有障碍物的接管中,驾驶人更倾向于采用制动加转向的组合操作来回避风险,且驾驶人接管回避操作中直接转向和制动加转向的组合操作的比例相同;接管反应时间与制动反应时间有较强的相关性。     

基于自然驾驶的中国驾驶员驾驶使用手机行为研究

手机频繁地出现在人们的日常活动中,甚至驾驶人驾驶时使用手机造成分心驾驶,产生了较大的安全隐患。目前世界范围内约束手机使用的法律、教材种类繁多,但对使用手机对驾驶人视觉及操作的影响缺乏了解。国内对该领域的研究相对较少,且主要停留在通过问卷和蹲点调查进行驾驶使用手机现状调查分析,或通过简单的驾驶模拟试验来进行特定行为特征分析的程度,我国驾驶员在真实驾驶状态下的驾驶使用手机情况和行为特征尚无明确的研究结果。本文依托上海自然驾驶研究数据,利用23名驾驶员2013年的115次出行数据对其驾驶行为特征与安全性进行研究。结果显示,被试驾驶员最常使用的手机功能为发送短信、通话和阅读,三者还对驾驶员的视线分心影响最大,达到最高的平均视线偏离时长;在操作分心方面,发送短信具有远高于其他功能的双手占用率;手机使用对于驾驶速度的波动和车辆在车道内的横向位置波动有极大的影响。     

预警时间对信控交叉口避撞驾驶行为的影响研究

研究不同预警信息发布时间下,不同性别驾驶人对于交叉口迎头侧面避撞情景驾驶行为影响规律,为提高车辆避撞预警系统功效性提供理论依据.基于汽车驾驶模拟器设计实验,招募具有稳定驾驶能力的驾驶人45名,采集7种预警信息发布时间(2.5～5.5 s),将无预警作为控制组,采用C#编程提取能够表征驾驶行为的变量.用方差分析和线性回归方法分析在交叉口迎头侧面碰撞情景下不同预警信息发布时间对驾驶人的制动时间、最大减速度、测试车辆与冲突车辆的最小距离的影响.结果表明,预警信息发布越早,驾驶人的制动时间越长,最大减速度越小,说明较早发布预警信息可以减缓驾驶人采取制动措施的剧烈程度.同时,预警信息发布较早,可以增大车辆间的最小间距,降低碰撞事故发生的可能性.此外,女性驾驶人的驾驶行为比男性驾驶人更加保守.     

基于超效率数据包络分析的重型车驾驶人驾驶安全绩效评价方法

为解决网联环境下重型车驾驶人驾驶安全绩效评价在指标多样性、模型可靠性、评价完整性和结果可溯性等方面的问题，提出一种基于超效率数据包络分析的重型车驾驶人驾驶安全绩效评价框架，包括驾驶行为指标提取方法、包含零值的超效率数据包络分析方法和基于效率前沿分析的驾驶安全绩效提升方案。基于网联环境下重型车自然驾驶数据特征，提取6个行程级的危险驾驶行为指标作为模型输入项，包括：表征激进驾驶的超速行为、急加速行为和急减速行为；表征分心驾驶和疲劳驾驶的打哈欠行为、使用手机行为和吸烟行为。表征驾驶风险暴露因素的行驶时间和行驶里程作为模型输出项。将每个驾驶人视为独立的决策单元，构建3种驾驶绩效评价模型，分别从激进驾驶、分心和疲劳驾驶以及综合驾驶风险3个维度对驾驶绩效进行评价。进一步利用效率前沿分析准确识别低绩效驾驶人，并量化其达到最佳驾驶绩效所需提升的驾驶行为指标。将该框架应用于南京某重型车车队的34名驾驶人，使用连续3个月的网联数据开展驾驶绩效评价。结果表明：该框架能够准确计算驾驶绩效得分，不同驾驶绩效等级驾驶人之间的驾驶行为特征存在显著差异，超速行为和打哈欠行为是影响驾驶绩效评价结果的关键因素，针对低绩效...     

考虑肌电信号的驾驶人弯道操纵行为分析

驾驶人是"人-车-路"闭环系统中的核心。近年来，研发人性化、个性化的汽车驾驶辅助系统逐渐成为行业热点。为了更加透彻地理解弯道驾驶行为特性，为弯道驾驶辅助系统提供功效评估与优化，提出了一种考虑肌电信号的驾驶人弯道行驶过程操纵行为分析方法。招募12名驾驶人在试验场标准路面上进行实车试验，其中包含6名专业试车师与6名普通驾驶人，要求驾驶人分别以30，40，50 km·h^-1的不同初速度驶入U形弯道并自由驾驶。试验过程中记录驾驶人颈部肌电信号数据和车辆运动状态数据，分析转弯行驶车辆侧向运动对不同驾驶能力的驾驶人生理体验的影响，同时进一步探讨不同类型驾驶人在不同入弯速度条件下颈部肌电信号与侧向加速度的关联差异特性。试验结果表明：相同工况下，专业驾驶人和普通驾驶人颈部肌电特征值存在显著差异，专业驾驶人颈部肌电信号特征与车辆侧向加速度呈现一定的线性关系；随着驾驶任务难度的增加，驾驶能力好的驾驶人能够较好地适应任务的变化，在进行纵侧向耦合操纵时能够较好地协调身体生理反应与车辆侧向运动保持较好的关联特性。研究成果为进一步探索并完善驾驶体验评价方法提供了新的研究思路，同时，可为汽车辅助驾驶系统功能设计与智能汽车行驶性能的用户体验测评提供技术支撑。     

Driver behavior formulation in intersection dilemma zones with phone use distraction via a logit-Bayesian network hybrid approach

Use of cellular phone while driving is one of the top contributing factors that induce traffic crashes, resulting in significant loss of life and property. A dilemma zone is a circumstance near signalized intersections where drivers hesitate when making decisions related to their driving behaviors. Therefore, the dilemma zone has been identified as an area with high crash potential. This article utilizes a logit-based Bayesian network (BN) hybrid approach to investigate drivers' decision patterns in a dilemma zone with phone use, based on experimental data from driving simulations from the National Advanced Driving Simulator (NADS). Using a logit regression model, five variables were found to be significant in predicting drivers' decisions in a dilemma zone with distractive phone tasks: older drivers (50–60 years old), yellow signal length, time to stop line, handheld phone tasks, and driver gender. The identified significant variables were then used to train a BN model to predict drivers' decisions at a dilemma zone and examine probabilistic impacts of these variables on drivers' decisions. The analysis results indicate that the trained BN model was effective in driver decision prediction and variable influence extraction. It was found that older drivers, a short yellow signal, a short time to stop line, nonhandheld phone tasks, and female drivers are factors that tend to result in drivers proceeding through intersections in a dilemma zone with phone use distraction. These research findings provide insight in understanding driver behavior patterns in a dilemma zone with distractive phone tasks.     

Modeling phone use prevalence and risk assessment among long-haul truck drivers in India

Research on distracted driving due to phone use has increased substantially over the past decades, however, very little is explored about commercial vehicle drivers (e.g., truck drivers) in this aspect. This study focused on examining the prevalence of phone use habits and the associated crash risk for data collected from 490 Indian truck drivers. The data on demographic details, driving history, phone use habits (in everyday life and during driving), history of receiving any penalty for phone use and incidences of crash occurrence, was collected through face-to-face interviews with the drivers. Binary logistic models were used to identify the factors affecting phone use habits during driving and the associated crash risk. Further, the incidences of receiving a penalty for the phone use were examined through cross-tabulation and chi-square statistics. The results showed that 55% of the drivers used a phone during driving, mainly for talking purpose. The model revealed that education, vehicle size, vehicle ownership and everyday life phone use habits were the significant factors associated with phone use while driving. Regarding the history of penalty receiving incidences, 41% of the drivers who used a phone during driving had received the penalty, and 52% of these penalty-receiving drivers were penalized repetitively. The model results for crash risk showed that the frequent phone users were 29 times more likely to be involved in a crash due to phone use compared to the non-frequent users. The results suggest a double level (legislative and company level) prohibition policy for phone use during driving for the truck drivers and also to enforce strict and effective legislative ban especially on the truck routes.     

驾驶过程中手机使用行为数据采集及分析

驾驶过程中使用手机的行为存在安全隐患,是当今导致交通安全事故的原因之一。文章借助于信息技术开发出采集使用手机行为数据的App,从真实数据出发,客观地分析驾驶员在开车过程中使用手机这一不良驾驶行为的覆盖程度及危险程度,并提出将手机使用行为作为驾驶风险评价因子。     

平直高速公路低交通量下L2自动驾驶对驾驶人心理负荷的影响

为研究驾驶人在L2自动驾驶模式下的心理负荷特性,设计了正常驾驶和次任务驾驶2种状态,进行实车高速道路试验,采集21名被试驾驶人在2种驾驶状态下分别选择手动驾驶和自动驾驶模式的眼动数据、次任务绩效和主观评价数据.采用重复测量一般线性模型,分析不同驾驶模式对上述参数的影响,从客观和主观两方面分析驾驶人的心理负荷变化.结果表...     

Car drivers' road safety performance: A benchmark across 32 countries

The road safety performance of a country and the success of policy measures can be measured and monitored in different ways. In addition to the traditional road safety indicators based on the number of fatalities or injured people in road traffic crashes, complementary road safety performance indicators can be used in relation to vehicles, infrastructure, or road users' behaviour. The last-mentioned can be based on data from roadside surveys or from questionnaire surveys. However, results of such surveys are seldom comparable across countries due to differences in aims, scope, or methodology.This paper is based on the second edition of the E-Survey of Road Users' Attitudes (ESRA), an online survey carried out in 2018, and includes data from more than 35,000 road users across 32 countries. The objective is to present the main results of the ESRA survey regarding the four most important risky driving behaviours in traffic: driving under the influence (alcohol/drugs), speeding, mobile phone use while driving, and fatigued driving. The paper explores several aspects related to these behaviours as car driver, such as the self-declared behaviours, acceptability and risk perception, support for policy measures, and opinions on traffic rules and penalties.Results show that despite the high perception of risk and low acceptability of all the risky driving behaviours analysed, there is still a high percentage of car drivers who engage in risky behaviours in traffic in all the regions analysed. Speeding and the use of a mobile phone while driving were the most frequent self-declared behaviours. On the other hand, driving under the influence of alcohol or drugs was the least declared behaviour. Most respondents support policy measures to restrict risky behaviour in traffic and believe that traffic rules are not being checked regularly enough, and should be stricter.The ESRA survey proved to be a valuable source of information to understand the causes underlying road traffic crashes. It offers a unique database and provides policy makers and researchers with valuable insights into public perception of road safety.     

Trip characteristics impact on the frequency of harsh events recorded via smartphone sensors

The Internet of Things (IoT) constantly offers new opportunities and features to monitor and analyze driver behavior through wide use of smartphones, effective data collection and Big Data analysis, resulting in assessment and improvement of driver behavior and safety. The objective of the present study is to investigate the impact of detailed trip characteristics on the frequency of harsh acceleration and harsh braking events through an innovative smartphone application developed within the framework of BeSmart project. A 200-driver naturalistic experiment spanning 12 months is carried out since July 2019. During the first two months, participants were asked to drive in the way they usually did, without receiving any feedback on their driving behavior from the application. Over the subsequent two months, participants were provided with personalized feedback, a trip list and a scorecard regarding their driving behavior, allowing them to identify their critical deficits or unsafe behaviors. Some of the most important risk factors, such as speed and driving above the speed limit, usage of mobile phone while driving and harsh events (acceleration and braking) are recorded through the application and subsequently analyzed. Generalized Linear Mixed-Effects Models were fitted to the trips of car drivers who made frequent trips for both experiment phases in order to model the frequencies of harsh events. Results indicate that maximum speed, the percentage of speeding duration and total trip duration are positively correlated with both harsh acceleration and harsh braking frequencies. On the other hand, the exposure metric of total trip distance was found to be negatively correlated with both harsh event types. A small positive correlation of the percentage of mobile use duration with harsh accelerations was also detected.     

Which factors lead to driving errors? A structural equation model analysis through a driving simulator experiment

As driving error is a main contributory factor of road accidents, its causes and consequences are of great interest in the road safety decision making process. This paper investigates several factors (including driver distraction, driver characteristics and road environment) that affect overall driving error behaviour and estimates a new unobserved variable which underlines driving errors. This estimation is performed with data obtained from a driving simulation experiment in which 95 participants covering all ages were asked to drive under different types of distraction (no distraction, conversation with passenger, cell phone use) in rural and urban road environment, as well as in both low and high traffic conditions. Driving error was then modeled as a latent variable based on several individual driving simulator parameters. Subsequently, the impact of several risk factors such as distraction, driver characteristics as well as road environment on driving error were estimated directly. The results of this complex model reveal that the impact of driver characteristics and area type are the only statistically significant factors affecting the probability of driving errors. Interestingly, neither conversing with a passenger nor talking on the cell phone have a statistically significant impact on driving error behaviour which highlights the importance of the present analysis and more specifically the development of a measure that represents overall driving error behaviour instead of individual driving errors variables.