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.

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.

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.

基于改进Faster RCNN的驾驶员手持通话检测

针对现有驾驶员通话行为识别误判率较高的问题,提出一种基于改进Faster RCNN的驾驶员行为检测方法,对驾驶员的违规手持通话进行检测.介绍了针对区域建议网络(RPN)及其损失函数的优化策略,并在原始Faster RCNN上运用多尺度训练、增加锚点数量以及引入残差扩张网络的方法增强网络检测不同尺寸目标的鲁棒性.基于车载平台上采集的驾驶员行为图像,对文中提出的方法进行仿真试验.结果表明:RPN和Faster RCNN通过交替优化共享特征提取网络部分,实现高效的目标检测,相较于原始Faster RCNN,检测精确度提高了3.8%,对环境的适应性更强.

Lateral control of autonomous vehicles based on learning driver behavior via cloud model

In order to achieve the lateral control of the intelligent vehicle, use the bi-cognitive model based on cloud model and cloud reasoning, solve the decision problem of the qualitative and quantitative of the lateral control of the intelligent vehicle. Obtaining a number of experiment data by driving a vehicle, classify the data according to the concept of data and fix the input and output variables of the cloud controller, design the control rules of the cloud controller of intelligent vehicle, and clouded and fix the parameter of cloud controller： expectation, entropy and hyper entropy. In order to verify the effectiveness of the cloud controller, joint simulation platform based on Matlab/Simulink/CarSim is established. Experimental analysis shows that： driver＇s lateral controller based on cloud model is able to achieve tracking of the desired angle, and achieve good control effect, it also verifies that a series of mental activities such as feeling, cognition, calculation, decision and so on are fuzzy and uncertain.

Behavioral adaptation of drivers when driving among automated vehicles

Purpose–This paper aims to explore whether drivers would adapt their behavior when they drive among automated vehicles(AVs)compared to driving among manually driven vehicles(MVs).Understanding behavioral adaptation of drivers when they encounter AVs is crucial for assessing impacts of AVs in mixed-traffic situations.Here,mixed-traffic situations refer to situations where AVs share the roads with existing nonautomated vehicles such as conventional MVs.Design/methodology/approach–A driving simulator study is designed to explore whether such behavioral adaptations exist.Two different driving scenarios were explored on a three-lane highway:driving on the main highway and merging from an on-ramp.For this study,18 research participants were recruited.Findings–Behavioral adaptation can be observed in terms of car-following speed,car-following time gap,number of lane change and overall driving speed.The adaptations are dependent on the driving scenario and whether the surrounding traffic was AVs or MVs.Although significant differences in behavior were found in more than 90%of the research participants,they adapted their behavior differently,and thus,magnitude of the behavioral adaptation remains unclear.Originality/value–The observed behavioral adaptations in this paper were dependent on the driving scenario rather than the time gap between surrounding vehicles.This finding differs from previous studies,which have shown that drivers tend to adapt their behaviors with respect to the surrounding vehicles.Furthermore,the surrounding vehicles in this study are more“free flow’”compared to previous studies with a fixed formation such as platoons.Nevertheless,long-term observations are required to further support this claim.

基于模型预测的智能汽车辅助驾驶控制研究

辅助驾驶控制是智能汽车的重要功能,可有效协助驾驶员完成驾驶操作,减少驾驶疲劳并提升驾驶安全。智能车辆的辅助驾驶控制需要在不干扰驾驶员的情况下,提供合适的车辆运动控制量并防止可能发生的碰撞。文章提出了一种基于模型预测控制的智能汽车辅助驾驶控制方法。该控制方法在驾驶员正常操作时符合驾驶员的操作特性,在可能发生碰撞时纠正驾驶员的转向操作,能够保证安全性且不干扰驾驶员。为了避免与周围车辆的碰撞,基于周围车辆位置建立约束条件,并引入模型预测辅助驾驶控制设计。通过仿真研究验证了所提出的辅助驾驶控制方法,结果表明,所提出的预测控制方法可以帮助驾驶员避免与附近车辆可能发生的碰撞,从而保证智能汽车的行驶安全。

场车司机的不安全行为研究及建议

本文以场车司机易造成安全事故的高危、频发的不安全行为作为研究重点,明确其行为表现、事故特征,提出合理措施和相关建议,以降低场车事故风险。

基于G/S模式的远程车辆行为分析的初步研究

空间信息技术的迅速发展使空间信息融入社会生活应用成为可能,G/S模式是新型的空间信息网络服务模式。本文在研究和扩展G/S模式的基础上,提出一种基于G/S模式的远程车辆行为分析理论,并在智能车辆调度系统中实现应用,最后给出系统测试实例,验证了其可行性。

Road Traffic Accidents (RTAs) Trends on Kathmandu-Bhaktapur Road after Addition of Lanes

Road traffic accidents are the outcome of the factors associated with the traffic system namely road users, road environment and vehicles. Despite good road surface, the Kathmandu-Bhaktapur road has high accident records. The traffic police record shows that 1530 accidents have occurred from July 2009 to June 2012. The area of study of this research is the Kathmandu-Bhaktapur road which starts at Tinkune Kathmandu and ends at Suryabinayak with a length of 9.1 kilometer, a section of Araniko highway heading towards China. The road is the first ever six lane road constructed in Nepal. The objectives of this research work are to identify locations with high accident numbers, to investigate possible causes of accidents, and to propose countermeasures for traffic safety along the Kathmandu-Bhaktapur road.

无控平交口实时风险预警系统对驾驶人行为的影响分析

为检验无控平交口实时风险预警系统(RTCRWS)的警示效果、了解其对驾驶人行为特性的影响;搭建3个道路交叉口虚拟仿真场景进行驾驶模拟实验,获取T_(SDNN)、T_(RMSSD)、N_(LF-norm)、N_(LF/HF)等生理行为数据,采集车速均值、车速标准差和车速变异系数等驾驶操作行为数据。统计分析了被试驾驶人行为特性,研究了RTCRWS对驾驶人行为的影响,应用因子分析方法对不同交通条件下RTCRWS的警示效果进行了综合评价。研究结果表明:RTCRWS对驾驶人群体的警示效果良好,能够提升无控平交口处的安全水平、降低驾驶人通过无控平交口的行驶车速、缓解驾驶人的紧张情绪;不同交通条件下驾驶人对RTCRWS的警示效果存在差异,驾驶人的心理紧张程度按照轻度交通流无预警、轻度交通流有预警和重度交通流有预警的顺序依次减弱。

城市轨道交通全自动运行车辆司机室区域技术方案分析

针对目前城市轨道交通行业内关注较多的FAO(全自动运行)车辆司机室区域的设置方式问题,选取司机室疏散门、侧门、隔断等3个关键部件作为讨论对象,结合FAO需求和FAO车辆的运行特点,详细分析了上述部件在不同设置方式上的优缺点,引用国内FAO轨道交通线路的应用方案加以说明,最后给出了适用不同运营目标的推荐方案。

“非补贴型”政策能否促进新能源汽车企业创新实现?——基于SCP范式分析视角

在新能源汽车“补贴型”政策发展瓶颈的背景下,如何加强引入并有效实施“非补贴型”政策,充分激励起车企的高水平创新与创新绩效实现至关重要。首先对创新层次新能源汽车“非补贴型”政策的工具选择(市场准入、“双积分”、政府采购)与作用机理进行了阐述,进一步基于产业组织中的S-C-P范式,构建了一个完整的新能源车企创新实现框架。结果显示,在前端S-C环节,以直接干预为代表的市场准入政策表现更好,更有利于促进车企的实质性创新行为;而间接引导类的“双积分”政策与政府采购政策更容易偏向企业的策略性创新。在后端S-P环节,政府采购政策对于车企创新短期绩效的作用效果最好,市场准入政策与“双积分”政策则更有利于创新长期绩效的实现。在整体S-C-P框架中,车企创新行为(C)存在部分中介作用,但政策效果从前端创新行为向后端创新绩效的传递效率还偏低。此外,政策协同作用显示,3项政策的“1+1+1”组合效果还并不明显。现有新能源汽车产业政策朝“非补贴”方向的调整是有效且十分必要的,但同时也要注意到不同类型政策创新效果的特征和侧重。

Performance evaluation framework of Wyoming connected vehicle pilot deployment program: summary of Phase 2 pre-deployment efforts and lessons learned

Purpose–This paper aims to present a summary of the performance measurement and evaluation plan of the Wyoming connected vehicle(CV)Pilot Deployment Program(WYDOT Pilot).Design/methodology/approach–This paper identified 21 specific performance measures as well as approaches to measure the benefits of the WYDOT Pilot.An overview of the expected challenges that might introduce confounding factors to the evaluation effort was outlined in the performance management plan to guide the collection of system performance data.Findings–This paper presented the data collection approaches and analytical methods that have been established for the real-life deployment of the WYDOT CV applications.Five methodologies for assessing 21 specific performance measures contained within eight performance categories for the operational and safety-related aspects.Analyses were conducted on data collected during the baseline period,and pre-deployment conditions were established for 1 performance measures.Additionally,microsimulation modeling was recommended to aid in evaluating the mobility and safety benefits of the WYDOT CV system,particularly when evaluating system performance under various CV penetration rates and/or CV strategies.Practical implications–The proposed performance evaluation framework can guide other researchers and practitioners identifying the best performance measures and evaluation methodologies when conducting similar research activities.Originality/value–To the best of the authors’knowledge,this is thefirst research that develops performance measures and evaluation plan for low-volume rural freeway CV system under adverse weather conditions.This paper raised some early insights into how CV technology might achieve the goal of improving safety and mobility and has the potential to guide similar research activities conducted by other agencies.

Safety effects of work zone advisory systems under the intelligent connected vehicle environment:a microsimulation approach

Purpose–This study aims to investigate the safety effects of work zone advisory systems.The traditional system includes a dynamic message sign(DMS),whereas the advanced system includes an in-vehicle work zone warning application under the connected vehicle(CV)environment.Design/methodology/approach–A comparative analysis was conducted based on the microsimulation experiments.Findings–The results indicate that the CV-based warning system outperforms the DMS.From this study,the optimal distances of placing a DMS varies according to different traffic conditions.Nevertheless,negative influence of excessive distance DMS placed from the work zone would be more obvious when there is heavier traffic volume.Thus,it is recommended that the optimal distance DMS placed from the work zone should be shortened if there is a traffic congestion.It was also revealed that higher market penetration rate of CVs will lead to safer network under good traffic conditions.Research limitations/implications–Because this study used only microsimulation,the results do not reflect the real-world drivers’reactions to DMS and CV warning messages.A series of driving simulator experiments need to be conducted to capture the real driving behaviors so as to investigate the unresolved-related issues.Human machine interface needs be used to simulate the process of in-vehicle warning information delivery.The validation of the simulation model was not conducted because of the data limitation.Practical implications–It suggests for the optimal DMS placement for improving the overall efficiency and safety under the CV environment.Originality/value–A traffic network evaluation method considering both efficiency and safety is proposed by applying traffic simulation.

驾驶员在真实交通危险工况中的制动反应时间

本文研究了驾驶员在真实交通危险工况中的制动反应时间。通过对上海地区采集到的真实交通工况进行筛选和分类,得到6种典型的危险工况类型。对这6种危险工况,特别是"前车减速"工况中驾驶员制动反应时间进行了测量和分析。结果表明,驾驶员的制动反应时间随危险工况类型不同而不同。相对于由其他车辆导致的危险,当危险是由行人等易受伤害的道路使用者引起时,驾驶员的制动反应时间更短。在"前车减速"工况中,当车速上升、危险紧急程度增加或者在非路口行驶时,驾驶员制动反应时间有减小的趋势。本研究为开发适合我国特殊交通状况的先进驾驶辅助系统提供依据。

基于驾驶员行为特性的驾驶技能评价指标研究

汽车行驶的过程是人、车、路三者之间相互影响的过程,是由驾驶员操纵有关机械装置来实现的。本文从研究驾驶员行为特性入手,提出以车辆控制能力和感知能力作为驾驶员技能评判的指标,建立驾驶员技能评判模型,为数字化驾驶训练提供理论基础。

交通信号倒计时对车速与交通安全的影响

研究交通信号倒计时对自由流车速及交通安全的影响。选择城市道路交叉口,使用雷达测速设备采集进口道自由流车速等数据。运用描述统计、方差分析、列联表等统计方法,进行数据分析。数据显示,仅考虑单因素影响时,交通违法检测设备存在与否对车速均值有显著影响,而绿灯信号倒计时则无显著影响;考虑多因素相互影响时,在有绿灯信号倒计时但无交通违法检测设备的情况下,车辆速度均值会更高,且绿灯倒计时会对超速率产生影响。红灯倒计时对车速离散程度有明显的影响。结果表明,使用绿灯信号倒计时应同时安装交通违法检测设备,红灯倒计时路口应确保充分的绿灯间隔时间。

基于轨迹-速度耦合策略的复杂道路汽车行驶速度决策

为了提供复杂道路上汽车自动驾驶的目标速度,提出了基于前视轨迹曲率的速度决策算法:首先决策出前视断面的轨迹点,算出每个点位的轨迹曲率并将其作为输入数据;然后从时间最省、驾驶最舒适、定速巡航以及混合模式中选择其一作为决策目标;在速度界限以及纵向/侧向舒适性约束下进行滚动时域优化,决策出前视断面上的期望速度值,随着车辆的行驶前视断面依次向前滚动,最终得到沿行驶距离变化的速度曲线.用复杂山区道路的实测数据验证了模型的精度,以2条公路和1条赛道作为仿真算例,结果表明:(1)通过组合不同的轨迹决策目标和速度决策目标,能够得到多种驾驶模式的速度曲线,进而模拟出多种实际驾驶行为;(2)由于前视轨迹是在通道边界内生成,弯道半径、转角、回旋线、路宽、偏转方向等用于确定通道边界的道路变量,都会改变轨迹特性进而影响行驶速度,因此,本文算法能够适应复杂的道路几何条件.

考虑驾驶员行为特性的行车安全仿真试验

为了分析驾驶员行为特性对行车安全的影响状况,首先建立了基于MATLAB/Simulink和ADAMS联合仿真的人—车—路闭环系统,通过不同车速下的双移线和蛇形线的闭环仿真试验验证了该系统的有效性。此外,在综合国内外学者研究成果的基础上,综合驾驶员的生理、心理特性和疲劳程度将驾驶员的驾驶特性分为12类,并确定相应类型驾驶特性的表征参数。选取某省道设计方案的部分路段为试验路段,对不同类型驾驶员在弯道上的行车安全以及不同疲劳程度的驾驶员在恶劣天气下的行车安全进行正交试验设计,基于人—车—路闭环系统开展虚拟行车仿真试验。试验数据的极差和方差分析结果显示,驾驶员疲劳程度对行车安全状况影响显著,在雨天与冰雪等恶劣天气下行驶时车速是影响行车安全的决定性因素。