BEHAVIORAL TOXICOLOGICAL RESEARCHES ON AUTOMQBILE DRIVERS' DRIVING ACCIDENTS

125 automobile drivers, including 85 drivers with accidents (Accident Group, AG) and 40 drivers without accidents (Nonaccident Group, NAG), and 51 controls (Control Group. CG )were tested with WHO Neurobehavioral Core Test Battery (WHO NCTB). The results showed that there were obvious negative mood states such as tension-anxiety and fatigue in AG and drivers with accidents had more poor neurobehavioral performances, especially attention, response speed and perceptual-motor speed than drivers without accidents and controls. We also found that automobile drivers' neurobehavioral functions got weakened with the increase of their age and got strengthened with the elevation of their educational level. And the functions were inversely correlative to the accidents they cuased. The results of our study suggest that WHO NCTB can be an index of researches on driving accidents that automobile drivers caused and can be used in occupational selection and training of drivers.

Gaze Behavior and Landscape Experience Characteristics of Expressway Drivers

Landscape is the visual impression formed in the mind of the aesthetic subject from what he sees at a certain viewpoint.A good viewpoint field is a place without visual barrier in the visual direction.Under the premise of analyzing aesthetic basis of expressway,starting from behavioral basis of dynamic vision,visual behavior characteristics of drivers at high speed are studied.On this basis,landscape experience tempo and rhythm determined by gaze locus under different linear characteristics are explored,and the impact of spatial alignment on gaze behavior and landscape experience is further analyzed.

Technical review of electric vehicle charging distribution models with considering driver behaviors impacts

Amassive market penetration of electric vehicles(EVs)associated with nonnegligible energy consumption and environmental issues has imposed a big challenge on evaluating electrical power distribution and related transportation facilities improvement in response to the largescale EV charging service need.Strategical deployment of EV charging stations including location and determination ofnumber of slowcharging stations and fast charging stationshas become an emerging concern and one of the most pressing needs in planning.This paper conducts a comprehensive survey of EV charging demand and distribution models with consideration of realistic driver behaviors impacts.This is currently a shortage in academic literature,but indeed has drawn practical attention in the strategic planning process.To address the need,this paper presents an in-depth literature review of relevant studies that have identified different types of EV charging facilities,needs or concerns that are considered into EV charging demand and distribution modeling,alongside critical impacting factor identification,mathematical relationshipsof the contributing factorsandEVchargingdemand and distribution modeling.Key findings from the current literature are summarized with strategies for optimized plan of charging station deployments(i.e.,location and related number of charging station),in an attempt to provide a valuable reference for interested readers.

Using microscopic video data measures for driver behavior analysis during adverse winter weather:opportunities and challenges

This paper presents a driver behavior analysis using microscopic video data measures including vehicle speed, lane-changing ratio, and time to collision. An analytical framework was developed to evaluate the effect of adverse winter weather conditions on highway driving behavior based on automated （computer） and manual methods. The research was conducted through two case studies. The first case study was conducted to evaluate the feasibility of applying an au- tomated approach to extracting driver behavior data based on 15 video recordings obtained in the winter 2013 at three dif- ferent locations on the Don Valley Parkway in Toronto, Canada. A comparison was made between the automated approach and manual approach, and issues in collecting data using the automated approach under winter conditions were identified. The second case study was based on high quality data collected in the winter 2014, at a location on Highway 25 in Montreal, Canada. The results demonstrate the effectiveness of the automated analytical framework in analyzing driver behavior, as well as evaluating the impact of adverse winter weather conditions on driver behavior. This approach could be applied to evaluate winter maintenance strategies and crash risk on highways during adverse winter weather conditions.

Virtual Reality Driving Simulation for Measuring Driver Behavior and Characteristics

This article provides new insights regarding driver behavior, techniques and adaptability. This study has been done because: 1) driving a vehicle is critical and one of the most common daily tasks;2) simulators are used for the purpose of training and researching driver behavior and characteristics;3) the article addresses driver experience by involving new virtual reality technologies. A simulator has been used to assist novice drivers to learn how to drive in a very safe environment, and researching and collecting data for researchers has become easier due to this secure and user-friendly environment. The theoretical framework of this driving simulation has been designed by using the Unity3D game engine (5.4.f3 version) and was programmed with the C# programming language. To make the driving environment more realistic we, in addition, utilized the HTC Vive Virtual reality headset which is powered by Steamvr. We used Unity Game Engine to design our scenarios and maps because by doing this we are able to be more flexible with designing. In this study, we asked 10 people ranging from ages 19 - 37 to participate in this experiment. Four Japanese divers and six non-Japanese drivers engaged in this study, some of which do not have a driver’s license in Japan. A few Japanese drivers have a license and car, while others have a license but no car. In order to analyze the results of this experiment we are used MatlabR2016b to analyze the gathered data. The result of this research indicates that individual’s behavior and characteristics such as controlling the speed, remaining calm and relaxed when driving, driving at appropriate speeds depending on changes in road structures and etc. can affect their driving performance.

Driving Style Recognition Based on Driver Behavior Questionnaire

The ability to classify driver behavior lays the foundation for more advanced driver assistance systems. The present study aims to research driver pattern and classification feature. Driver behavior self-reported investigation was conducted with standardized driver behavior questionnaire (DBQ) by 225 nonprofessional drivers on the internet in Beijing. Questionnaire’s reliability was verified by statistics analysis. Confirmatory factor analysis (CFA) was used to analyze the underlying factor structure. Speed advantage, space occupation, the contend right of way and the contend space advantage were extracted from the ques-tionnaire results to quantify driver characteristics. Based on fuzzy C-means (FCM) algorithm and taking the four factors as pattern features, the number of driver classification distribution was discussed. Then the number of driver classification was determined by statistical indices. The comparison of classification results with the survey finding on whether the driver occurred in traffic accidents within five years shows that the classification result is the same as the actual driving conditions. Finally, correlation between the demographic and types of driving behavior has been analyzed. Female were more likely than male to careful driving, and the older the driver and the less driving experience, the more careful and moderate driving behavior is.

Vehicles, Advanced Features, Driver Behavior, and Safety: A Systematic Review of the Literature

Driver errors contribute to more than 94% of traffic crashes. Automotive companies are striving to enhance their vehicles to eliminate driver errors and reduce the number of crashes. Various advanced features like lane departure warning (LDW), blind spot warning (BSW), over speed warning (OSW), forward collision warning (FCW), lane keep assist (LKA), adaptive cruise control (ACC), cooperative ACC (CACC), and automated emergency braking (AEB) are designed to assist with, or in some cases take over, certain driving maneuvers. They can be broadly categorized into advanced driver assistance system (ADAS) and automated features. Each of these advanced features focuses on addressing a particular task of driving, thereby, aiding the driver, influencing their behavior, and enhancing safety. Many vehicles with these advanced features are penetrating into the market, yet the total reported number of crashes has increased in recent years. This paper presents a systematic review of these advanced features on driver behavior and safety. The review is categorized into 1) survey and mathematical methods to assess driver behavior, 2) field test methods to assess driver behavior, 3) microsimulation methods to assess driver behavior, 4) driving simulator methods to assess driver behavior, and 5) driver understanding and the effectiveness of advanced features. It is followed by conclusions, knowledge gaps, and need for further research.

Evaluating Hazard Response Behavior of a Driver Using Physiological Signals and Car-Handling Indicators in a Simulated Driving Environment

Road traffic accidents are a major cause of casualties and costly implications to all the stakeholders. Research focusing on the driver as one of the causal agent of accidents has been studied for centuries and with the advent of modernized driver assistance technologies. This paper sought to evaluate response of a driver using active-driving performance indicators like reaction time and physiological signal response (surface electromyogram), to understand hazard response behavior. Simulation of driving scenes was done using Unity3D engine and VR Head mounted display. The driver was presented with stimulus (collision objects) of different size and distance. From the results, an event scene that the driver considered hazardous was marked with increased electromyography response distinct from non-event scenes. From the results, we noted an increase in pedal misapplication during hazard response. The proposed approach is applicable in a real time driving analysis for on-road risk level classification.

Generation and Selection of Driver-Behavior-Based Transferable MotionPrimitives

To integrate driver experience and heterogeneous vehicle platform characteristics in a motion-planning algorithm,based on the driver-behavior-based transferable motion primitives(MPs), a general motion-planning framework for offline generation and online selection of MPs is proposed. Optimal control theory is applied to solve the boundary value problems in the process of generating MPs, where the driver behaviors and the vehicle motion characteristics are integrated into the optimization in the form of constraints. Moreover, a layered, unequal-weighted MP selection framework is proposed that utilizes a combination of environmental constraints, nonholonomic vehicle constraints,trajectory smoothness, and collision risk as the single-step extension evaluation index. The library of MPs generated offline demonstrates that the proposed generation method realizes the effective expansion of MP types and achieves diverse generation of MPs with various velocity attributes and platform types. We also present how the MP selection algorithm utilizes a unique MP library to achieve online extension of MP sequences. The results show that the proposed motion-planning framework can not only improve the efficiency and rationality of the algorithm based on driving experience but can also transfer between heterogeneous vehicle platforms and highlight the unique motion characteristics of the platform.

A comparative study of automotive trip characteristics between older drivers and others among densely inhabited district and other areas

This paper examines older driver＇s automotive trip （abbreviation： trip） characteristics which include trip frequency, trip length, destination distribution, and non- home-based （NHB） trips. A two-month experiment of 108 participants was carried out to collect GPS tracking data in Aichi Prefecture, Japan. To identify the effect of living area, a comparative analysis between older drivers and others is conducted in densely inhabited district （DID, i.e., urban） and other areas （non-DID, i.e., suburban, rural, etc）, separately. The present study found that there was no sig- nificant difference between the trip characteristics of older drivers and others who were living in DID. Thus, we suggest that the education of safety driving and the rec- ommendation of public transportation should be given to DID-living older drivers. However, the results of non-DID reflected that older drivers＇ trip frequency, trip length, destination, and NHB trips rate were shorter and lower than others＇. This implies that electric vehicles may be suit- able for promotion among older drivers in suburban and rural area. Furthermore, the regression analysis confirmed that ＂older driver＂ was a significant independent variable on trip frequency, trip length, and NHB trips, and there were interaction effects between ＂older driver＂ and ＂living areas＂ on all trip characteristics.

跟驰事件下网联混驾编队的运行特征及生态安全影响

考虑当前自动驾驶技术成熟度和公众接受程度等问题,网联混驾编队模式可能是自动驾驶编队全面普及应用前的可行的过渡模式之一。为研究网联条件和车队形式对网联混驾编队运行特征及生态安全的影响,文中基于驾驶模拟技术搭建的网联混驾编队实验测试平台,构建由5辆车组成的“领航车-网联L2级人工驾驶车辆、跟驰车-网联自动驾驶车辆”的网联混驾编队模式,并邀请36名被试开展驾驶模拟实验。考虑时域分析侧重呈现指标的时间维度变化,而频域分析可以挖掘指标的频率分布特性,文中选取速度、加速度指标从时域和频域维度分析车辆运行特征,同时选取油耗、速度安全熵指标评价车辆运行的生态安全性。结果表明:网联条件和编队模式均有助于车辆行驶更加平稳,并显著提升其生态安全性;网联混驾编队的综合效能最佳,相较传统单车模式,油耗可降低10.67%,速度安全熵值可降低73.25%。该研究成果可为自动驾驶企业及行业开展网联人机交互终端设计、领航驾驶辅助系统研发、网联混驾编队可行性及有效性测试等提供方案借鉴和平台支持。

高原环境导致的中青年驾驶行为及驾驶绩效特征

为了量化分析高原环境导致的驾驶行为特征差异,寻找驾驶绩效下降时间窗,运用驾驶模拟器和低氧发生器在平原地区模拟海拔3 900 m高原驾驶环境,开展驾驶模拟试验分别收集驾驶人在平原环境和模拟高原环境的驾驶行为数据和心电图(electrocardiogram,ECG)信号数据。通过对比高原实车试验环境驾驶人的心率指标变化,运用雷达图、数值排序图和单因素方差分析方法对平原、高原环境下的驾驶行为特征进行对比分析,引入Jensen-Shannon散度(JS散度)量化2种环境下横向偏移距离、横向加速度、转向盘旋转率和速度这4个指标的概率密度拟合分布之间的差异,使用差分法确定高原环境下的驾驶绩效下降时间,验证了模拟高海拔环境驾驶模拟试验的有效性研究。结果表明:高原环境下横向偏移距离标准差、横向加速度标准差、转向盘旋转率标准差、速度标准差比平原环境下分别增加了0.094 3 m,0.119 0 m/s^(2),0.000 9°/s,0.651 3 km/h,车辆整体稳定性降低;借助JS散度发现横向偏移距离、横向加速度、转向盘旋转率和速度这4个指标在2种环境下的数值分布存在较大距离,分别为0.23,0.11,0.01,0.02,其中车辆的横向运动受负面影响更大;在进入海拔3 900 m的高原地区时,驾驶绩效约在6 min后显著下降。

基于多尺度骨架图和局部视觉上下文融合的驾驶员行为识别方法

识别非驾驶行为是提高驾驶安全性的重要手段之一。目前基于骨架序列和图像的融合识别方法具有计算量大和特征融合困难的问题。针对上述问题,本文提出一种基于多尺度骨架图和局部视觉上下文融合的驾驶员行为识别模型(skeleton-image based behavior recognition network,SIBBR-Net)。SIBBR-Net通过基于多尺度图的图卷积网络和基于局部视觉及注意力机制的卷积神经网络,充分提取运动和外观特征,较好地平衡了模型表征能力和计算量间的关系。基于手部运动的特征双向引导学习策略、自适应特征融合模块和静态特征空间上的辅助损失,使运动和外观特征间互相引导更新并实现自适应融合。最终在Drive&Act数据集进行算法测试,SIBBR-Net在动态标签和静态标签条件下的平均正确率分别为61.78%和80.42%,每秒浮点运算次数为25.92G,较最优方法降低了76.96%。

基于深度学习的驾驶员分心行为检测方法

驾驶员分心行为检测对于开发以驾驶员为中心的人车协同驾驶系统具有至关重要的意义。针对现有基于卷积神经网络的驾驶员分心行为检测模型缺乏全局特征提取能力、泛化性能较弱以及忽视了驾驶场景中不同区域的重要性,构建一种基于深度学习的驾驶员分心行为检测模型,实现对驾驶员分心行为的准确检测。首先,开发了基于HorNet的残差结构,通过高阶空间交互来增强特征表示能力;其次,受人类注意力机制以及现有注意力机制的启发,设计一种自适应加权注意策略来提取与驾驶行为最相关的特征;然后,在现有的分类数据集上训练本文模型,并使用先验知识作为初始权值来改善训练结果,进而提高模型的泛化能力;最后,对驾驶行为特征进行可视化,以提高人们对于本文模型的信任度。实验结果表明,本文模型可以准确地检测驾驶员分心行为,在准确性和可靠性方面明显优于现有方法。

基于自然驾驶数据的驾驶换道风格分析与辨识

为了研究在换道工况下个体驾驶行为及其辨识,基于NGSIM轨迹数据提取532组换道有效数据。分析处理驾驶员换道特征参数,运用K-means算法对驾驶员进行聚类分析,可分为谨慎型、一般型、激进型三类。并对聚类的参数进行统计分析和时频分析验证各类驾驶人的差异性,并提取出有效参数作为辨识模型的输入,基于惯性权重非线性变化和粒子最大速度非线性递减的策略来改进粒子群,建立起基于改进粒子群优化SVM的驾驶人风格辨识模型,对SVM模型的两个关键参数C和g进行优化更新,驾驶人识别准确率可达到96.25%,且与BP神经网络、PSO-SVM、随机森林、KNN算法相比具有更高的识别准确率。

城市道路下分心行为对驾驶员驾驶绩效的影响

为探究城市道路条件下常见驾驶分心行为对驾驶绩效的影响,以16名青年志愿者为被试,开展实车驾驶试验,测量车辆纵向和横向行驶参数,对比分析正常驾驶和分心驾驶状态下的驾驶绩效,并探讨驾驶经验的影响。结果表明:3种分心行为均使横向加速度标准差和纵向加速度标准差增加,而使速度均值降低,说明驾驶员通过速度和转向控制补偿分心状态下的驾驶绩效;交谈对速度均值和纵向加速度均值、阅读广告对速度标准差和横向加速度均值具有显著性影响(p<0.05);接听电话时,经验驾驶员速度标准差明显高于新手(p=0.021);接听电话和阅读广告次任务下,经验驾驶员的横向加速度均值均明显较高(p=0.003,p=0.004),说明经验丰富的驾驶员受分心行为的影响程度更低。

Analysis of rear-end risk for driver using vehicle trajectory data

To explore the relationship between rear-end crash risk and its influencing factors, on-road experiments were conducted for measuring the individual vehicle trajectory data associated with novice and experienced drivers. The rear-end crash potential probability based on the time to collision was proposed to represent the interpretation of rear-end crash risk.One-way analysis of variance was applied to compare the rearend crash risks for novice and experienced drivers. The rearend crash risk models for novice and experienced drivers were respectively developed to identify the effects of contributing factors on the driver rear-end crash risk. Also, the cumulative residual method was used to examine the goodness-of-fit of models. The results show that there is a significant difference in rear-end risk between the novice and experienced drivers.For the novice drivers, three risk factors including the traffic volume, the number of lanes and gender are found to significantly impact on the rear-end crash risk, while significant impact factors for experienced drivers are the vehicle speed and traffic volume. The rear-end crash risk models perform well based on the existing limited data samples.

车辆跟驰行为的主要影响因素研究综述

概述国内外车辆跟驰行为,重点分析L0—L5级车辆跟驰行为的主要影响因素,明确各因素对车辆跟驰行为的影响机制,为解决由驾驶人、车辆特性和车辆自动化等级导致的跟驰行为问题提出合理的解决思路,最后提出综合考虑驾驶人和车辆特性因素影响下L0—L5级车辆的跟驰行为,驾驶不同等级的L0—L5级车辆驾驶人驾驶风格与自动化等级之间的耦合跟驰行为,以及L4—L5级车辆的行为预测与跟踪对车辆跟驰行为的影响等是未来需要进一步研究的主要问题。

基于双路时空网络的驾驶员行为识别

驾驶员危险驾驶行为是恶性交通事故发生的主要原因之一,因此识别驾驶员行为具有工程应用上的重要意义。目前,主流基于视觉的检测方法是对驾驶员行为的局部时空特征进行研究,针对全局空间特征及长时序相关性特征研究较少,这在一定程度上无法结合场景上下文信息对危险驾驶行为进行识别。为了解决上述问题,提出一种基于双路时空网络的驾驶员行为识别方法,整合不同时空通路的优点以提高行为特征丰富度。首先,使用一种改进的双流卷积神经网络(TSN)对时空信息进行表征学习,同时降低提取特征的稀疏性;其次,构建一种基于Transformer的串行时空网络补充长时序相关性信息;最后,联合双路时空网络进行融合决策,增强模型的鲁棒性。实验结果表明,所提方法在驾驶员疲劳检测数据集YawDD、驾驶员分心检测数据集SF-DDDD和最新驾驶员行为识别数据集SynDD1这3个公开数据集上分别取得99.85%、99.94%和98.77%的识别准确率,特别是在SynDD1上,与使用动作识别的网络MoviNet-A0相比识别准确率提升了1.64个百分点;消融实验结果也验证了该方法对驾驶员行为有较高的识别精度。

老年驾驶人认知驾驶能力评价及影响因素研究

我国老年驾驶人数量持续增长,驾驶人结构的变化给交通安全带来了挑战。相比于其他年龄段驾驶人,老年人生心理功能逐渐衰退,更容易发生交通事故。认知功能与驾驶安全表现显著相关。从注意反应能力、执行处理能力、空间感知能力3项认知功能领域出发,研究老年人驾驶特征,设计驾驶模拟实验风险事件,获得认知驾驶行为数据,分析青年人、中年人、老年人驾驶行为特征的差异性;采用主客观结合的方法确定指标权重,提出认知驾驶行为指数计算方法;以驾驶人属性和认知功能为自变量,以认知驾驶行为指数为因变量,建立广义线性混合模型,探究不同因素对认知驾驶能力的影响。结果表明年龄、周驾驶频率、自我调节和TMT-B(Trail Making TestB)与认知驾驶行为指数显著相关,MMSE(Mini-Mental State Examination)为边缘显著相关;老年驾驶人的认知驾驶行为指数受个体特质影响较大;相较于老年人,青年人认知驾驶行为指数更差,中年人更好;周驾驶频率低的人比周驾驶频率高的人认知驾驶行为指数更好;自我调节频率为低和中的驾驶人,比频率为高的驾驶人认知驾驶行为指数更好;TMT-B测量认知正常的驾驶人比认知障碍驾驶人的认知驾驶行为指数更好。该研究从交通事故的人因机理角度出发,探究老年驾驶人面对的认知挑战,提出老年人认知驾驶行为指数计算方法并解析影响因素,为简化老年人驾驶适宜性评价程序、制定驾驶安全干预策略提供参考。