A road hypnosis identification method for drivers based on fusion of biological characteristics

Risky driving behaviors,such as driving fatigue and distraction have recently received more attention.There is also much research about driving styles,driving emotions,older drivers,drugged driving,DUI(driving under the influence),and DWI(driving while intoxicated).Road hypnosis is a special behavior significantly impacting traffic safety.However,there is little research on this phenomenon.Road hypnosis,as an unconscious state,is can frequently occur while driving,particularly in highly predictable,monotonous,and familiar environments.In this paper,vehicle and virtual driving experiments are designed to collect the biological characteristics including eye movement and bioelectric parameters.Typical scenes in tunnels and highways are used as experimental scenes.LSTM(Long Short-Term Memory)and KNN(K-Nearest Neighbor)are employed as the base learners,while SVM(Support Vector Machine)serves as the meta-learner.A road hypnosis identification model is proposed based on ensemble learning,which integrates bioelectric and eye movement characteristics.The proposed model has good identification performance,as seen from the experimental results.In this study,alternative methods and technical support are provided for real-time and accurate identification of road hypnosis.

Obstructive sleep apnea among commercial vehicle drivers in Malaysia: Issues and initiatives

Obstructive Sleep Apnea (OSA) has been identified by many studies as one of the significant contributing factors for motor vehicle accidents. However, only a small number of studies have been conducted in Malaysia. Objective: This paper aims to highlight the prevalence of OSA among truck drivers and express bus drivers in Malaysia and efforts being undertaken to address issues related to OSA among commercial vehicle drivers. Methodology: Two separate studies were conducted: a cross sectional study among truck drivers and secondly among express bus drivers. The screening process for identifying the high risk group for OSA was done using Berlin questionnaire. Meanwhile, among express bus drivers, OSA was confirmed with sleep study using polysomnography test. Result: Screening of risk group of OSA among truck drivers revealed that 14.6% (19) of drivers were categorized as having high risk of OSA while 85.4% (111) having low risk of OSA. While, in another study, polysomnography test among express bus drivers showed that 83 (28.7%) had mild OSA, 26 (9.0%) had AHI moderate OSA, and 19 drivers (6.6%) severe OSA. Conclusion: This paper highlighted the issues of OSA among commercial vehicle drivers in Malaysia. With an alarming high prevalence, OSA should be a major road safety concern in this country. A special study focusing on sleep and fatigue related crashes may need to be conducted to complement the current studies and full implementation of existing efforts and initiatives to address OSA in road crashes should be realized by the relevant authorities.

Intelligent back-looking distance driver model and stability analysis for connected and automated vehicles

The connected and automated vehicles(CAVs)technologies provide more information to drivers in the car-following(CF)process.Unlike the human-driven vehicles(HVs),which only considers information in front,the CAVs circumstance allows them to obtain information in front and behind,enhancing vehicles perception ability.This paper proposes an intelligent back-looking distance driver model(IBDM)considering the desired distance of the following vehicle in homogeneous CAVs environment.Based on intelligent driver model(IDM),the IBDM integrates behind information of vehicles as a control term.The stability condition against a small perturbation is analyzed using linear stability theory in the homogeneous traffic flow.To validate the theoretical analysis,simulations are carried out on a single lane under the open boundary condition,and compared with the IDM not considering the following vehicle and the extended IDM considering the information of vehicle preceding and next preceding.Six scenarios are designed to evaluate the results under different disturbance strength,disturbance location,and initial platoon space distance.The results reveal that the IBDM has an advantage over IDM and the extended IDM in control of CAVs car-following process in maintaining string stability,and the stability improves by increasing the proportion of the new item.

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.

Different human-simulated characteristics of the driver models in the forward simulation of hybrid electric vehicles

Based on the control theories of PID, fuzzy logic and expert PID, the driver models are built and applied in the forward simulation for hybrid electric vehicles （HEV）. The impact to the vehicle speed tracking and the fuel economy is compared among the different driver models. The different human-simulated characteristics of the driver models are emphatically analyzed. The analysis results indicate that the driver models based on PID, simple fuzzy logic and expert PID are corresponding to the handling characteristics of different drives. The driver models of different human-simulated characteristics bring the handling divergence of drivers with different driving level and habit to the HEV forward simulation, and that is significant to the all-around verification and validation of the control strategy for HEV. System simulation results of different driver models validate the impact of driver models to the dynamic and fuel economy performance of HEV.

Factors Influence Hanoians’ Choice of Non-Motorized Transport Mode to Access Bus Stations

Hanoi’s rapid urbanization has led to a surge in private vehicle ownership, particularly motorcycles, amidst inadequate public transportation infrastructure. Despite government efforts, many still prefer motorized transport, citing mobility and safety concerns, exacerbated by insufficient pedestrian infrastructure. This study examines the motivations behind this reliance on motorized vehicles, particularly motorcycles, in Hanoi. Findings reveal safety and convenience as primary factors driving motorized transport use, especially for accessing bus stations. Economic incentives could promote non-motorized travel and public transport adoption. Policy implications highlight the importance of addressing economic factors and improving access infrastructure to manage motorized vehicle reliance and foster sustainable urban mobility in Hanoi.

Investigation and Optimization of Priority for Non-motor Vehicles in the Old Urban Area of Nanchang

Under the background of"people-oriented"thought and"green transportation",the idea of"priority for non-motor vehicles"came into being,which can improve the riding environment of non-motor vehicle riders to a certain extent.According to the current situation of priority for non-motor vehicles in the old urban area of Nanchang,through field investigation,questionnaire investigation and interview,this study summarized the existing problems,and put forward optimization suggestions for these problems,in order to provide reference for areas with similar conditions.

Connected and Autonomous Vehicles (CAVs) Challenges with Non-motorized Amenities Environments

With the deployment of Connected and Automated Vehicles in the coming decades,road transportation will experience a significant upheaval.CAVs(Connected and Autonomous Vehicles)have been a main emphasis of Transportation and the automotive sector,and the future of transportation system analysis is widely anticipated.The examination and future devel­opment of CAVs technology has been the subject of numerous researches.However,as three essential kinds of road users,pedestrians,bicyclists,and motorcyclists have experienced little to no handling.We explored the influ­ence of CAVs on non-motorized mobility in this article and seven various issues that CAVs face in the environment.

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.

Analysis of Electric and Hybrid Vehicle Usage in Proximity to Charging Infrastructure in Indiana

This paper explores the movement of connected vehicles in Indiana for vehicles classified by the NHTSA Product Information Catalog Vehicle listing as being either electric (EV) or hybrid electric (HV). Analysis of trajectories from July 12-18, 2021 for the state of Indiana observed nearly 33,300 trips and 267,000 vehicle miles travelled (VMT) for the combination of EV and HV. Approximately 53% of the VMT occurred in just 10 counties. For just EVs, there were 9814 unique trips and 64,700 Electric Vehicle Miles Traveled (EVMTs) in total. A further categorization of this revealed that 18% of these EVMTs were on Interstate roadways and 82% on non-interstate roads. Proximity analysis of existing DC Fast charging stations in relation to interstate roadways revealed multiple charging deserts that would be most benefited by additional charging capacity. Eleven roadway sections among the 9 interstates were found to have a gap in available DC fast chargers of 50 miles or more. Although the connected vehicle data set analyzed did not include all EV’s the methodology presented in this paper provides a technique that can be scaled as additional EV connected vehicle data becomes available to agencies. Furthermore, it emphasizes the need for transportation agencies and automotive vendors to strengthen their data sharing partnerships to help accelerate the adoption of EV and reduce consumer range anxiety with EV. Graphics are included that illustrate examples of counties that are both overserved and underserved by charging infrastructure.

Towards Autonomous Vehicles with Advanced Sensor Solutions

Professional truck drivers are an essential part of transportation in keeping the global economy alive and commercial products moving. In order to increase productivity and improve safety, an increasing amount of automation is implemented in modern trucks. Transition to automated heavy good vehicles is intended to make trucks accident-free and, on the other hand, more comfortable to drive. This motivates the automotive industry to bring more embedded ICT into their vehicles in the future. An avenue towards autonomous vehicles requires robust environmental perception and driver monitoring technologies to be introduced. This is the main motivation behind the DESERVE project. This is the study of sensor technology trials in order to minimize blind spots around the truck and, on the other hand, keep the driver’s vigilance at a sufficiently high level. The outcomes are two innovative truck demonstrations: one R & D study for bringing equipment to production in the future and one implementation to the driver training vehicle. The earlier experiments include both driver monitoring technology which works at a 60% - 80% accuracy level and environment perception (stereo and thermal cameras) whose performance rates are 70% - 100%. The results are not sufficient for autonomous vehicles, but are a step forward, since they are in-line even if moved from the lab to real automotive implementations.

无信控交叉口网联车辆动态碰撞风险检测与预警策略

针对无信控交叉口碰撞事故多发的问题,面向网联人工驾驶环境,研究提出了一种基于车车通信的无信控交叉口网联车辆碰撞动态风险检测与预警策略。在构建典型无信控交叉口场景的基础上,设计了一种基于多项式拟合的车辆轨迹模型;融合利用碰撞到达时间和风险暴露时间作为风险检测指标,构建了一种圆形-双圆车辆模型实现车辆碰撞风险检测;综合考虑了驾驶人异质性及其交互行为,提出一种基于博弈论和遗传算法的两级碰撞预警策略;基于SUMO搭建仿真环境对预警策略的有效性及适用性进行测试分析。结果表明,所提策略能够准确识别出所有的碰撞事件并触发预警,预警成功率达到100%;在不同比例驾驶人组成测试工况下,所提预警策略均能显著降低碰撞率和平均碰撞动能。

基于驾驶员意图采集的车辆状态控制系统分析

驾驶员操作意图是整车状态变化的基础,对整车运行具有重要影响。以驾驶员操作意图为研究的出发点,对驾驶员执行车辆各种操作时,车辆的状态进行分析,主动包括启动自检工况,档位变化、加速、制动、转向等工况;基于分析结果对各工况的控制流程进行分析和设计;提出了实现车辆基本功能的控制模式,并对控制电路进行设计。在此基础上,应用实车调试,对控制系统进行了测试。结果可知:对车辆进行准确控制的前提是对驾驶员意图进行准确的分析采集,控制流程应简洁明了,尽可能减少控制器的执行周期,缩短控制周期;实车测试表明系统能准确的实现驾驶员的操作意图,实现加速、减速、制动等,表明所设计的控制系统的可靠性与准确性,为此类设计提供参考。

通用电机控制模块及其驱动软件的研发

电动车的研发是目前的研究热点,它给人们的生活带来了无尽的想象空间,其中各种车辆辅助操作系统为电动车提供了多种保障。提出了一种通用的电机控制模块及其驱动软件的实现原理,它可以方便地接入各种电动车,包括电动汽车和电动自行车,从而给电动自行车辆提供一种低成本的电机解决方案。该系统除了具有行驶、转弯、加速和制动等基本驾驶动作外,还可通过卫星连接,对车辆精准定位。该系统与对应的手机应用软件连接,还能实现多种附加功能,如语音操控、远程操控等功能。

辅助驾驶汽车信息安全检验关键技术研究及测试验证

随着辅助驾驶汽车技术的不断发展和普及,信息安全问题已经成了一个重要的研究方向。本文针对辅助驾驶汽车面临的信息安全隐患,研究提出信息安全检验关键技术,包括检验指标、检验方法和检验标准等,为辅助驾驶汽车安全性评估提供参考;提高辅助驾驶汽车安全性,推动行业的规范化发展,为辅助驾驶汽车的安全性和可靠性提供支持。

基于LQR的智能驾驶汽车横纵向控制研究

为了提高智能驾驶汽车跟踪控制器的稳定性和跟踪精度,提出了一种基于线性二次型调节器(LQR)控制算法和驾驶员预瞄模型的横向跟踪控制策略,结合纵向比例-积分-微分(PID)控制算法实现横纵向控制。首先建立带有前馈的LQR控制器,采用梯度下降优化算法优化LQR控制器权重参数,并在此基础上引入驾驶员预瞄模型,设计了基于经验的预瞄距离自适应控制器;其次建立双PID纵向控制器进行速度控制。最后通过Carsim和Matlab/Simulink联合仿真以及实车测试验证,结果表明:仿真工况下最大横向偏差小于0.035 m,最大航向偏差小于0.09 rad,实车测试工况下也能够良好遵循规划轨迹的整体趋势,速度跟踪效果良好且前轮转角与横摆角速度变化平稳。因此,该控制器能够保证较高精度且平稳的轨迹跟踪,在高速状态下更为明显。

基于视觉指标的雾天高速公路限速值

为解决雾天高速公路能见度降低和驾驶人视距受限的交通安全问题,提出了一种基于驾驶人视觉指标的限速控制方法。首先,对雾天高速公路行驶速度与驾驶人视觉的变化进行了分析,并确定了能综合表征路况信息和能见度水平的视觉指标;其次,通过UC-win/Road模拟了不同能见度下的驾驶环境,并收集了视觉指标和运行速度数据,进一步分析了雾天视觉指标的影响因素;然后,使用SPSS构建了运行速度和视觉指标间的数学模型,根据能见度与视觉指标间的线性关系提出了不同能见度下的视觉指标参考值;最后,基于能见度、交通流量、视觉指标3个因素,提出了一种考虑视觉指标的多条件限速控制标准。结果表明:在能见度低于200 m的环境下,视觉指标明显低于无雾环境下的视觉指标,降低范围在0.578%~1.407%之间;视觉指标与运行速度两变量间的相关系数达到0.863,这说明雾天能见度降低和视觉指标下降对行驶速度有显著影响;预测运行速度与实测速度的相对误差平均值为2.43%,满足预测精度要求。综上所述,本研究提出的限速控制方法同时考虑了能见度、交通流量和驾驶人视觉,对于保障雾天高速公路的安全行驶具有重要的实用价值。

自动紧急转向工况下驾驶员离位响应的一体化仿真研究

为了探究自动紧急转向过程中驾驶员的动态响应,采用主动安全技术和被动安全防护策略相结合的方式,进行特定自动紧急转向场景下驾驶员离位响应的一体化仿真研究.基于模型预测控制算法建立自动紧急转向控制模型,使用PreScan、CarSim和Simulink等软件进行联合仿真,获取车辆在转向过程中的动态响应;结合主动人体模型和驾驶员侧约束系统模型,分析车辆及驾驶员的运动特征以及主动卷收器安全带对驾驶员离位的改善效果.结果表明,在72 km/h的行驶速度下,车辆和驾驶员在纵向换道距离分别为40 m、50 m和60 m的左侧自动紧急转向过程中运动响应分为右倾和左倾两个阶段;右倾阶段车辆横向加速度、侧倾角以及主动人体模型的最大离位量明显高于左倾阶段;随着纵向换道距离的增加,驾驶员头部的最大横向离位量由89.56 mm降低到70.22 mm和53.05 mm;主动卷收器安全带的应用有效改善了驾驶员的离位程度,驾驶员头部最大横向离位量分别降低18.90%、49.56%和67.62%,胸部最大横向离位量分别降低12.70%、41.63%和53.69%,提高了驾驶员在自动紧急转向阶段的安全性.

考虑驾驶员记忆的多前车速度差跟驰模型研究

为了探究驾驶员记忆和多前车速度差对交通流的影响,本文基于全速度差模型(full velocity difference model,FVDM),结合驾驶员记忆因素和多前车对跟驰车的作用,构建了一种考虑了驾驶员记忆和多前车速度差的跟驰模型。通过改进模型的线性稳定性特征,得出改进模型的稳定性条件。再对改进模型下的车流启动和制动过程进行仿真,并与FVDM的仿真结果作对比。然后采用微小扰动法对改进模型进行数值仿真,研究驾驶员记忆因素和多前车速度差对交通流稳定性的影响。最后,利用下一代仿真(next generation simulation,NGSIM)数据标定了改进模型的参数,并预测了其加速度。研究结果表明:驾驶员记忆在一定程度上不利于交通流的稳定,而多前车速度差对稳定交通流具有积极作用;与FVDM相比,改进模型的启动延迟和制动延迟分别降低了10.0%和19.0%,预测精度更高,均方根误差降低了24.3%。

新能源大客车驾驶人混合式培养模式探究

在当前职业教育教学技术手段快速迭代的背景下,高职院校大客车驾驶人产教融合实训基地的传统培训模式已无法满足学员的学习需求,尤其是针对在校的学生学员,培养模式与学员学习习惯之间的矛盾已经显现。本文以高职院校新能源大客车驾驶人培养为例,全面分析了大客车驾驶人的培训模式现状,依托产教融合实训基地提出了线上线下结合的混合式大客车驾驶人培养体系,基于当今校企产业实验基地建设现状,结合线上智能化的学习平台与线下优质的教师、教练资源,形成了混合式职教新能源大客车驾驶人培养模式。