Influence of adverse weather on drivers’ perceived risk during car following based on driving simulations

Adverse weather has a considerable impact on the behavior of drivers,which puts vehicles and drivers in hazardous situations that can easily cause traffic accidents.This research examines how drivers'perceived risk changes during car following under different adverse weather conditions by using driving simulation experiment.An expressway road scenario was built in a driving simulator.Eleven types of weather conditions,including clear sky,four levels of fog,four levels of rain and two levels of snow,were designed.Furthermore,to simulate the carfollowing behavior,three car-following situations were designed according to the motion of the lead car.Seven car-following indicators were extracted based on risk homeostasis theory.Then,the entropy weight method was used to integrate the selected indicators into an index to represent the drivers'perceived risk.Multiple linear regression was applied to measure the influence of adverse weather conditions on perceived risk,and the coefficients were considered as indicators.The results demonstrate that both the weather conditions and road type have significant effects on car-following behavior.Drivers'perceived risk tends to increase with the worsening weather conditions.Under conditions of extremely poor visibility,such as heavy dense fog,the measured drivers'perceived risk is low due to the difficulties in vehicle operation and limited visibility.

Landscape ecological risk assessment and its driving factors in the Weihe River basin,China

Weihe River basin is of great significance to analyze the changes of land use pattern and landscape ecological risk and to improve the ecological basis of regional development.Based on land use data of the Weihe River basin in 2000,2010,and 2020,with the support of Aeronautical Reconnaissance Coverage Geographic Information System(ArcGIS),GeoDa,and other technologies,this study analyzed the spatial-temporal characteristics and driving factors of land use pattern and landscape ecological risk.Results showed that land use structure of the Weihe River basin has changed significantly,with the decrease of cropland and the increase of forest land and construction land.In the past 20 a,cropland has decreased by 7347.70 km2,and cropland was mainly converted into forest land,grassland,and construction land.The fragmentation and dispersion of ecological landscape pattern in the Weihe River basin were improved,and land use pattern became more concentrated.Meanwhile,landscape ecological risk of the Weihe River basin has been improved.Severe landscape ecological risk area decreased by 19,177.87 km2,high landscape ecological risk area decreased by 3904.35 km2,and moderate and low landscape ecological risk areas continued to increase.It is worth noting that landscape ecological risks in the upper reaches of the Weihe River basin are still relatively serious,especially in the contiguous areas of high ecological risk,such as Tianshui,Pingliang,Dingxi areas and some areas of Ningxia Hui Autonomous Region.Landscape ecological risk showed obvious spatial dependence,and high ecological risk area was concentrated.Among the driving factors,population density,precipitation,normalized difference vegetation index(NDVI),and their interactions are the most important factors affecting the landscape ecological risk of the Weihe River basin.The findings significantly contribute to our understanding of the ecological dynamics in the Weihe River basin,providing crucial insights for sustainable management in the region.

Predictors and Risk Perceptions of Using Cell Phones while Driving among Young Adult Drivers

The Philippines is expecting a rise in the number of drivers that use mobile phones while driving.It is known as the“texting capital of the world”.The objectives of this study were to determine the predictors,risk perceptions and the prevalence of cell phone use while driving among trainee residents of the University of the Philippines-Philippine General Hospital.This cross-sectional study employed total enumeration.A survey was first distributed to the target population,followed by a focus group discussion.Chi-square and multiple logistic regression were used to analyze data.Included in the final analysis were 175 drivers aged 25-30 years(mean=27.90+1.34).There was no significant difference in the risk perceptions of cell phone users vs.non-users,and most perceived hands-free devices safer to use(p=0.030).The reported prevalence is 90.68%;drivers have a significant overall unsafe attitude(p=0.007),and an unsafe attitude when using handsets when driving,even when this is known to be dangerous(p=0.003).In conclusion,driving with hands-free devices was perceived to be safer,although drivers have a high overall unsafe attitude.Driving for more than two years and having an unsafe attitude were found to be significant predictors of cell phone use while driving.Countermeasures must take into account these factors when instituting behavioral modification strategies and road safety policies concerning unsafe and distracted driving.

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.

Model calibration concerning risk coefficients of driving safety field model

Driving safety field(DSF) model has been proposed to represent comprehensive driving risk formed by interactions of driver-vehicle-road in mixed traffic environment. In this work, we establish an optimization model based on grey relation degree analysis to calibrate risk coefficients of DSF model. To solve the optimum solution, a genetic algorithm is employed. Finally, the DSF model is verified through a real-world driving experiment. Results show that the DSF model is consistent with driver's hazard perception and more sensitive than TTC. Moreover, the proposed DSF model offers a novel way for criticality assessment and decision-making of advanced driver assistance systems and intelligent connected vehicles.

Driving risk assessment under the connected vehicle environment:a CNN-LSTM modeling approach

Connected vehicle(CV)is regarded as a typical feature of the future road transportation system.One core benefit of promoting CV is to improve traffic safety,and to achieve that,accurate driving risk assessment under Vehicle-to-Vehicle(V2V)communications is critical.There are two main differences concluded by comparing driving risk assessment under the CV environment with traditional ones:(1)the CV environment provides high-resolution and multi-dimensional data,e.g.,vehicle trajectory data,(2)Rare existing studies can comprehensively address the heterogeneity of the vehicle operating environment,e.g.,the multiple interacting objects and the time-series variability.Hence,this study proposes a driving risk assessment framework under the CV environment.Specifically,first,a set of time-series top views was proposed to describe the CV environment data,expressing the detailed information on the vehicles surrounding the subject vehicle.Then,a hybrid CNN-LSTM model was established with the CNN component extracting the spatial interaction with multiple interacting vehicles and the LSTM component solving the time-series variability of the driving environment.It is proved that this model can reach an AUC of 0.997,outperforming the existing machine learning algorithms.This study contributes to the improvement of driving risk assessment under the CV environment.

Study on Assessment Model of Potential Risk in Car Following Scenario

Car following is the most common driving scenario,and rear-end collisions in car following scenario are the most common accidents.TTC(time to collision)is widely employed as the risk indicator in existing rear-end collision warning systems,however,assessment model using only TTC may ignore some high-risk scenarios.Therefore,the concept of potential risk in car following scenario is proposed,and the driver’s maximum brake response time to avoid collision is defined as TM(time margin),which is the basis of the assessment model in this study.One hundred and thirty-nine(139)dangerous car following cases from China-FOT(Field Operation Test)naturalistic driving study database are extracted in reference from detection criteria on dangerous cases,of which TM at the braking moment of preceding vehicle is calculated,thus dangerous domains of two risk levels are determined by the distribution of TM.A potential risk warning system is also designed based on the assessment model,in order to further reduce rear-end collisions in combination with rear-end collision warning system.

Prediction of Point in Time with High Crash Risk by Integration of Bayesian Estimation of Drowsiness, Tracking Error, and Subjective Drowsiness

The aim of this study was to predict drivers＇ drowsy states with high risk of encountering a crash and prevent drivers from continuing to drive under such drowsy states with high risk of crash. While the participants were required to carry out a simulated driving task, EEG （Electro encephalography） （EEG-MPF and EEG-α/β）, ECG （Electrocradiogram） （RRV3）, t racking error, an d subjective rating on drowsiness were measured. On the basis of such measurements, an attempt was made to predict the point in time with high crash risk using Bayesian estimation of posterior probability of drowsiness, tracking error, and subjective drowsiness. As a result of applying the proposed method to the data of each participant, it was verified that the proposed method could predict the point in time with high crash risk before the point in time of crash.

自动驾驶汽车行驶风险评估方法综述

行驶风险评估对自动驾驶系统的安全运行至关重要。将现有行驶风险评估方法分为3类,包括面向单一目标物的、基于可达集的和基于势场论的评估方法。提出5个评价维度,包括计算实时性、结果时效性、应用可行性、内容充分性和场景泛用性,对评估方法进行了全面比较,揭示其特点和适用情况。对自动驾驶行驶风险评估面临的问题以及未来发展趋势进行了分析和展望。

虚实结合的驾驶人风险感知能力评测教学实验设计与实践

驾驶人风险感知能力评测教学实验立足于我国高质量人才培养课程这一核心要素,充分考虑不同道路交通因素对驾驶人安全驾驶的影响,致力于打造虚实结合的线上线下混合式一流教学实验课程。该教学实验基于交通仿真技术,通过线上实验与线下仿真模拟实验相结合的方式,在任课教师的指导下,由学生自主完成课程学习、场景设计、线上或线下实验和实验报告等实验流程。培养学生在课程学习与实验中的创新性与主观能动性,提升学生综合素质与团队协作能力,综合考量学生对道路交通多项学科专业课的掌握情况,并基于现阶段的教学情况对未来提出展望。

Studying Driving Risk Factors using Multi-Source Mobile Computing Data

Traffic congestion can largely be attributed to the issues related with driving behavior,which may cause vehicle crash,stop-and-go traffic due to frequent lane changing behaviors,etc.,and makes the driving behavior research also of significance in the realm of traffic management and demand management.The emergence and subsequent rapid advances with new information and communication technologies(ICT)now offers the capability of collecting high-fidelity and high-resolution trajectory data in a cost-effective manner.In this research,we use a smartphone app to collect data for the purpose of studying driving risk factors.What’s unique about the data in this research is its backend server also estimates traffic speed and volume for each link that the vehicle traverses.In order words,the data collected with build-in GPS modules in the smartphone include not only the vehicle spatial-temporal dimension location,which could be used to correlate the network geography attributes and/or real-time traffic condition,but also the detailed information about the vehicle dynamics including speed,acceleration,and deceleration,whereby a driver’s control and maneuver of a vehicle can be analyzed in detail.Such type of dataset combining both user trajectory and link speed/volume information is rarely seen in prior research,permitting a unique opportunity to link critical traffic congestion factors leading to driving behavior and crash potential.In this paper,the overall research framework used in this research is presented,which mainly includes data collection,data processing,calibration and analysis methodology.A preliminary case study-including data summary statistics and correlation analysis-is also presented.The results of our study will further existing knowledge about driving exposure factors that are closely linked to crash risk,and provide the foundation for advanced forms of Usage Based Insurance.

不同草原防火政策下内蒙古草原火灾发生风险及其驱动因素的研究

草原火灾是草原生态系统中重要的干扰因子之一,不同时期的草原防火政策可能会导致草原火灾发生概率及驱动因素发生变化。本研究基于内蒙古1981~2020年草原火灾数据,以新旧《草原防火条例》实施时间(旧《草原防火条例》1993年10月5日颁布并实施,新《草原防火条例》2008年11月19日颁布并于2009年1月1日起实施)为界线,通过随机森林模型分4个时期(1981~2020年、1981~1993年、1994~2008年、2009~2020年)对内蒙古草原火灾发生概率与驱动因素进行比较与分析,并绘制草原火灾风险等级区划图。结果表明:(1)4个时期建模的全样本AUC在0.930~0.940之间,精度优异。(2)在不同时期,气象因素(日平均相对湿度、气温日较差等)始终是影响草原火灾的主导因素,海拔、距火点最近公路距离等因素也是内蒙古草原火灾发生的重要驱动因素;(3)1981~1993年和1981~2020年草原火灾风险区基本相似,中、高、极高草原火灾风险区主要集中在呼伦贝尔市大部分地区和兴安盟北部,1994~2008年中、高、极高草原火灾风险区主要集中在呼伦贝尔市,而2009~2020年中、高、极高草原火灾风险区主要集中在呼伦贝尔市西部、锡林郭勒盟北部、阿拉善盟东南部、乌海市和鄂尔多斯市东部。

基于模拟驾驶的借道左转驾驶人视觉特性研究

借道左转是一种缓解左转车流压力的新型交通组织方法,通过信号灯时空调配借用对向车道为左转待行区,提高相位内左转车辆通行能力。笔者运用驾驶模拟平台,建立城市道路交叉口借道左转仿真场景;结合眼动追踪系统,获取驾驶人借道左转过程中视觉特性参数的变化;通过瞳孔变化和眼动行为,分析驾驶人心理紧张程度及其与行车风险的关联。结果表明:识读借道左转标识及分隔带开口、到达分隔带开口和到达借用车道停车线几个位置,是驾驶人瞳孔面积变化和持续注视时间、注视兴趣区域变化以及扫视频率骤增的集中点,反映出相应位置驾驶人风险感知强、心理负荷大;借道左转短期注视行为较多,注视时长差距较大,表明该过程驾驶人关注目标多、摄取信息密集且处理难度悬殊,需要进行高强度的信息处理;累计扫视时间占比高于普通左转,且以5°以下小角度扫视为主,反映出借道左转过程环境信息复杂、行车风险较大,驾驶人搜索、对比分析的需求更高,扫视主要是为了捕捉关注目标和补充注视信息。

周车轨迹预测不确定性智能车避撞策略研究

提出了一种基于周车轨迹预测不确定性的智能汽车避撞策略研究方法。轨迹预测模块,将基于物理的轨迹预测模型和数据驱动模型相结合构建物理引导的轨迹预测模型(PG-LSTM),模型输出关于周车预测轨迹的二维高斯分布参数,以表征驾驶员行为的不确定性;风险评估及避撞策略模块,结合轨迹预测模型的输出结果,提出一个新的风险度量--预测驾驶风险PDR和预测相对驾驶风险指数PRDRI作为评估未来风险的参考指标,建立紧急工况下避撞决策机制。通过Carsim搭建复杂紧急工况场景进行仿真实验。仿真结果表明:所提出的驾驶风险评估模型可以准确地辨识复杂行车场景未来驾驶风险,同时基于驾驶风险所提出的避撞决策机制能够提升智能汽车的避撞安全性。

智能车变道决策规划系统的预期功能安全研究

随着智能驾驶汽车快速发展,预期功能安全(safety of the intended functionality,SOTIF)愈发凸显其重要性。自动变道控制系统作为自动驾驶系统的重要组成部分,在决策规划层面存在SOTIF不足的风险。基于ISO21448和系统过程理论(system-theoretic process analysis,STPA),对车辆变道决策规划系统的预期功能安全进行分析,找到潜在的危害触发事件并得到相应的安全目标。针对安全目标进行算法改进,综合考虑车型、车速、路面状况等行驶因素,利用高斯过程回归和模糊综合评价的方法得出目标车辆加速度用以评估当前变道安全性。结合最小变道时间及变道终点确定最优变道轨迹,并在变道过程中实时更新周围车辆行驶状态,利用提出的安全系数判断本车当前的安全状态并采取不同的变道措施,以保证车辆安全变道或在紧急情况无法完成变道时可以安全返回。建立验证场景,对不同场景下功能改进前后系统的风险进行对比。结果表明:功能改进后系统的风险显著降低,变道过程中的安全水平明显提高。

基于风险场的不同认知次任务下接管风险评估模型

为有效评估不同认知次任务下L3级自动驾驶车辆接管时的风险,开展了驾驶接管风险评估模型研究。设计了城市快速路紧急接管场景并开展不同认知次任务下的驾驶模拟试验。考虑轨迹场、势能场和行为场因素,构建了接管风险评估模型。采用接管风险指数法,验证了所建模型的有效性。结合实测数据,量化不同认知次任务和回避操作类型对接管风险场场强的影响。结果表明:被试者进行接管操作后1~9 s内模型接管风险指数分布情况的M-W检验和K-S检验结果均为p<0.05,说明模型可以有效评估车辆在接管过程中的接管风险。此外,模型接管风险指数的均方根误差均值(0.062)小于碰撞时间倒数的均方根误差均值(0.098),说明模型在表征风险的准确性方面要优于碰撞时间倒数。研究结果可为接管过程中的车辆运行风险评估和避撞设计提供借鉴和参考。

基于行车风险场的自动驾驶接管风险评估模型

为评估L3级自动驾驶的接管风险,降低接管过程中的事故率,设计城市快速路接管场景并开展驾驶模拟实验,以行车风险场理论为基础,采用动态和静态风险分布函数反映其他交通单元对接管风险的影响,引入车辆性能概率因子对自动驾驶接管过程中的事故发生概率进行表征,同时考虑接管反应时间的影响,构建自动驾驶接管风险评估模型,基于驾驶模拟实验获取接管反应时间数据和车辆轨迹数据对模型参数进行标定,并与碰撞时间倒数进行对比,验证模型的有效性。结果表明:驾驶员接管后1~9 s内模型计算所得的接管风险指数的变化趋势与碰撞时间倒数一致,但接管过程中风险指数的均方根误差均值(0.059)相较于碰撞时间倒数的均方根误差均值(0.093)下降了37%。构建的模型能够有效评估驾驶员的接管风险,且模型在表征风险的准确性方面优于碰撞时间倒数。

山区公路交叉口驾驶避险决策行为特性分析

为研究驾驶特征指标与驾驶避险行为决策的关联规则以提升驾驶安全,将驾驶避险决策行为划分为纵向“停车避险”和横向“转向避险”,并运用驾驶模拟系统构建12种山区公路交叉口交通冲突虚拟场景,招募38名驾驶人进行试验,采集车辆特征和驾驶人扫视、眨眼、注视等眼动特征数据。基于随机森林构建驾驶避险决策行为模型,然后引入沙普利加和解释(SHapley Additive exPlanation, SHAP)框架进一步分析车辆、眼动特征与驾驶避险行为之间的非线性关系。结果表明:模型对纵、横向避险行为预测的准确率分别为84.77%、94.70%;纵向速度标准差、扫视持续时间标准差、轨迹偏差标准差、侧向速度标准差与驾驶避险决策行为存在明显关联,如纵向速度标准差过大(约大于10 km/h),纵向“停车避险”可能性明显增加。

跟车工况下基于风险评估的人机共驾策略

为了避免驾驶辅助系统对驾驶员造成不必要的干预,结合碰撞风险与驾驶操纵能力引出了纵向跟车工况下的风险评估区的概念,基于驾驶数据的正态分布特性确定了风险评估区边界,进而提出一种新的人机共驾纵向驾驶权分配策略,该策略以碰撞时间倒数(TTCi)为判断前提,如果TTCi超过其阈值,则以风险评估区上边界代表驾驶操纵能力的最大偏差值,根据驾驶员操纵能力的偏差程度分配辅助驾驶系统的控制权。结合Prescan、Matlab/Simulink与罗技G29驾驶模拟器搭建了驾驶员在环仿真平台,以分心驾驶模拟驾驶员操纵能力下降情况,对策略的有效性进行了验证。结果表明,在高速道路跟车工况下,所提出的人机共驾策略能有效避免由于驾驶员操纵能力下降导致的碰撞事件发生。

草原公路驾驶接管风险的模糊推理评估方法研究

为探究草原公路行车场景和非驾驶相关任务对驾驶人接管过程的影响,提出了基于生理特性和接管绩效的模糊逻辑风险评估方法。首先,模拟牛羊出没、前方停车、匝道来车三种草原公路典型场景,并考虑在模拟场景下执行无次任务、听新闻、看视频、玩游戏四种非驾驶相关任务;其次,构建基于生理指标(心率变异性)和接管绩效(接管时间、接管质量)的风险评估指标体系;最后,采用模糊推理建立风险评估模型,量化评价不同场景和非驾驶相关任务组合的风险水平。结果表明,匝道来车场景和玩游戏在驾驶接管风险等级中被评估为高风险和极高风险占比较高。整体而言,高风险和极高风险样本占比达44.2%,反映出草原公路特定场景下执行感官参与维度多的任务具有较高的接管风险隐患。场景和非驾驶相关任务存在交互作用,在复杂场景下从事感官参与维度较高的任务极易导致高风险情况。相较于简单场景,驾驶人在复杂场景中的风险累积更快、波动更大,并在中后段达到峰值。