普通公路驾驶员期望车速调查分析与建模

为了解普通公路驾驶员的车速选择机理,对驾驶员进行了期望车速问卷调查,对驾驶员的个人信息、车辆特征、以及驾驶员对超速10%的认可态度等可能影响期望车速的因素进行了重点调查。根据限速值的不同,分80、60和40 km/h 3个等级进行了调查,并为各等级公路选取了代表路段,由具有类似驾驶经历的驾驶员认真填写。对435份有效问卷进行了分析,发现期望车速广泛存在于驾驶员心中,驾驶员在行车过程中会使车速尽量维持在期望车速附近。期望车速主要受限速值、驾驶员的驾龄、对超速10%的态度以及车辆性能等因素的影响,驾驶员的性别和年龄对期望车速的影响不显著。采用逐步回归法,得到了期望车速的计算方法。对期望车速的特点、交通安全措施以及调查方案中可供改进的地方进行了总结。     

Comparison of driving characteristics between drivers in Korea and in the united states of America based on driver-vehicle interaction field database

Research and development involving intelligent vehicles of today is geared to safe, driver-friendly and sensitive vehicles that provide a driver with a pleasant and convenient driving environment while preventing him or her from possible risks of accident. In developing convenient and safe vehicles, research on drivers’ driving patterns, reactions and state characteristics depending on road conditions in actual field is essential in order to devise more driver-friendly intelligent vehicles. This paper describes how a driver-vehicle interaction (DVI) field database is built in order to obtain a driver’s input in normal road driving condition on highways, country roads, and city roads, and his or her state information, as well as data on the vehicle and traffic conditions. And the newly built database is compared with the RDCW FOT database established by UMTRI of the US for analysis to suggest that the driving tendencies of drivers in Korea and the road driving conditions are not the same as those in the US, reconfirming the need to establish a DVI field database, which will be used for the development of intelligent vehicles suitable for the Korean environment. The DVI data collected from actual driving in field are anticipated to be widely utilized as basic data for research on various intelligent driving safety systems, advanced driver assistance systems (ADAS) and human-vehicle interface (HVI) that are suitable for the driving environment in Korea.     

驾驶风格对驾驶行为的影响

为了揭示驾驶风格对驾驶行为的影响规律,进而提取表征驾驶风格的特征参数,对不同风格驾驶人在感知层和操作层的驾驶行为数据进行了量化分析。首先,基于驾驶行为问卷对18名中国非职业驾驶人进行了驾驶风格问卷调查,并采用主成分分析、K-均值聚类等方法将被试驾驶人分为谨慎型、正常型和激进型3种类型。接着,被试驾驶人在搭载了SmartEye眼动仪的驾驶模拟器上开展了高速公路行车环境下的驾驶试验,同步采集了感知层的视觉特性参数和操作层的驾驶绩效参数,并采用判断抽样的方式将驾驶样本按照驾驶风格和驾驶模式（换道意图和车道保持）进行了划分,共选取了810组有效样本。最后,采用方差分析法分析了不同风格驾驶人在不同驾驶模式下的注视行为、扫视行为、横向控制特性、纵向控制特性方面相关参数的差异显著性,并提取了不同风格间存在显著差异的参数作为表征驾驶风格的特征参数。研究结果表明：驾驶风格越激进,驾驶人对周围环境关注越少,对车辆的横向控制稳定性越差,急加速和急减速行为发生的频次越高;不同风格驾驶人在意图时窗内对后视镜的注视次数（p=0.002）、方向盘转角熵值（p=0.04）、加速踏板开度（p=0.01）、制动踏板开度（p=0.02）这4个参数的差异均较为显著,因此可作为表征驾驶风格的特征参数。     

基于车辆运行数据的纯电动汽车用户驾驶风格识别与驾驶能耗分析

驾驶风格是用来体现驾驶员在车辆运行状态下对车辆操作的行为特征,对用户驾驶风格进行识别与分析,有利于推进智能驾驶的发展。根据基于116 辆纯电动汽车的车辆运行数据,通过主成分分析方法与K-means 聚类算法,对用户驾驶行为进行分类分析,对驾驶风格进行了分类识别。利用XGBoost 算法构建纯电动汽车驾驶行为与能耗输入模型,利用SHAP 对模型进行解释。结果表明,将驾驶风格聚为3 类具有较好的分类效果,可分别对应冷静型、普通型与激进型;当驾驶员的驾驶风格趋向于激进型时时,车辆的驾驶能耗越高,驾驶风格激进一个层级,车辆百公里电耗增加3~4倍。当驾驶员行车时,其车速越高,油门踏板踩得越深,车辆加速度的绝对值越大,车辆的驾驶能耗越高。驾驶员的驾驶风格越激进,车辆的驾驶能耗越高。     

面向人因的车路协同系统综合测试及影响评估

面向冬奥主干通道兴延高速，以驾驶人适应性为导向，构建一种面向人因的车路协同系统硬件在环效能测试平台，针对多种道路条件、交通状态、特殊事件等面向高速公路设计13种交通情境，从主、客观2个维度实现车路协同系统包括主观感受、高效性、安全性、生态性、舒适性、有效性6个方面的驾驶人适应性评价，分析车路协同驾驶状态下的综合评估指标及影响机理。主观评估结果显示，车路协同技术对驾驶人有积极作用，52%的被试认为车载预警信息可以使行车过程更安全。客观运行结果表明：由于车路协同状态下驾驶人对于前方道路危险状况的可预知性，导致驾驶人提前降速，运行速度降低，效率有所下降；车路协同条件下的加速度和换道次数明显减小，其安全性显著提升；由于车路协同系统避免了驾驶人对于突发危险状况的紧急制动，因此车辆的油耗、排放均明显降低，其生态性改善效果显著；归因于驾驶人对于车路协同系统熟悉程度不足，导致舒适度各系统存在不一致的结论，也表明驾驶人对于车路协同系统的接受度和信任度均有待进一步提高；驾驶人在车路协同条件下可获取不同路段的限速值和超速提示，其有效性表现出明显的优势，速度跟随比有显著提升。所构建的测试平台和指标体系为进一步深层次挖掘车路协同的作用机理奠定了基础。     

北京道路工况下公交车PM和NO_x排放特性试验研究

利用综合性车载排放测试系统完成北京典型柴油公交车在道路工况下排放特性的试验研究。结果表明,匀速行驶工况下的NOx排放率与车速呈正线性相关;平均车速为13.3km/h的低速拥堵工况的NOx排放因子是平均车速为41.1km/h的高速畅通工况的近两倍;研究表明,在低速加速的起步过程和低负荷工况下出现较严重的PM排放,须针对这两种工况优化发动机的燃烧品质。     

An adaptive lateral preview driver model

Successful modelling and simulation of driver behaviour is important for the current industrial thrust of computer-based vehicle development. The main contribution of this paper is the development of an adaptive lateral preview human driver model. This driver model template has a few parameters that can be adjusted to simulate steering actions of human drivers with different driving styles. In other words, this model template can be used in the design process of vehicles and active safety systems to assess their performance under average drivers as well as atypical drivers. We assume that the drivers, regardless of their style, have driven the vehicle long enough to establish an accurate internal model of the vehicle. The proposed driver model is developed using the adaptive predictive control (APC) framework. Three key features are included in the APC framework: use of preview information, internal model identification and weight adjustment to simulate different driving styles. The driver uses predicted vehicle information in a future window to determine the optimal steering action. A tunable parameter is defined to assign relative importance of lateral displacement and yaw error in the cost function to be optimized. The model is tuned to fit three representative drivers obtained from driving simulator data taken from 22 human drivers.     

武汉市公交车典型行驶工况的构建

利用实测的公交车运行数据,建立符合公交车运营特点的行驶工况.首先将连续行驶数据进行短行程划分并计算各短行程的特征值;之后采用主成分分析将12个特征值降为4个主成分,利用相关系数法建立了武汉市公交车的综合行驶工况;同时采用聚类分析对短行程分类,构建了公交车在拥堵道路、较畅通道路、畅通道路3类交通条件下的行驶工况;各工况同实测数据的相关系数均超过了0.98.研究结果表明,该地区公交车平均运行速度为19.46 km/h,各行驶模式下的时间比例分别为:加速26.39%、减速23.61%、匀速33.33%、怠速16.67%.此外将所建立综合工况与燃油消耗量测试工况C-WTVC比较,发现二者在平均速度和怠速时间比例方面存在较大差别.因此有必要针对公交车专门开发测试工况,从而为交通和环保部门的公交运营管理提供指导.      

自然驾驶状态下重型工程车驾驶人驾驶稳定性分析

重型工程车行驶过程中事故风险大,发生恶性事故的概率高,易造成重大生命和经济损失,其运输安全管理问题面临挑战.为探究重型工程车驾驶人驾驶稳定性与相关影响因素之间的关系,开展重型工程车自然驾驶试验,提取车辆运动学、道路条件、驾驶人状态和工作时间等数据;采用速度均值和速度标准差表征驾驶人驾驶稳定性,以睡眠模式、道路线形、道路...     

基于驾驶员信息处理能力的高速公路作业区限速值计算方法

基于人体工程学原理,将驾驶员在高速公路作业区行驶过程中接触的交通信息分为道路几何信息、道路交通运行信息、交通管理控制信息、气候与环境信息及驾驶员自身信息,将作业区各种道路交通信息进行量化处理,在系统分析驾驶员对信息处理模式的基础上,提出了基于驾驶员信息处理能力的高速公路作业区车速速度限制值计算模型。以2006年5月哈大公路入口作业区为例,考虑车道宽度、开放车道数、隔离设施、合流机会、合流冲突及大车影响对驾驶员接受信息的量化影响,计算得出该作业区的限速值为50 km/h,与工程实际中采用的55 km/h吻合较好,验证了本文所提方法的有效性,为高速公路交通管理部门确定车辆速度限制标准提供了一种参考方法。     

基于安全车速差的高速公路特长隧道环境驾驶人行为风险特性研究

为研究高速公路特长隧道环境下驾驶人行为风险特性,选取2座典型特长隧道进行实车试验,通过采集熟练驾驶人和非熟练驾驶人的速度数据,将此作为主观预期车速,结合道路行车环境的客观安全车速,构建基于安全车速差的驾驶人行为风险量化方法。在划分隧道路段为入口段、行车段和出口段的基础上,通过切分行车区间,对比分析出入口段2类驾驶人行为风险变化特性及整个隧道路段和普通高速路段的行为风险变化曲线。结果表明： 1)在隧道内部,相对于非熟练驾驶人,熟练驾驶人表现出更高的行为风险值;在隧道外部,则非熟练驾驶人的行为风险值更高一些。2)所有类型驾驶人在普通高速路段行为风险值最高,在隧道入口段的行为风险值最低。上述结果说明： 在隧道路段,熟悉试验道路的驾驶人车速行为并不安全,行为风险值相对较高。     

Connected variable speed limits control and car-following control with vehicle-infrastructure communication to resolve stop-and-go waves

The vision of intelligent vehicles traveling in road networks has prompted numerous concepts to control future traffic flow, one of which is the in-vehicle actuation of traffic control commands. The key of this concept is using intelligent vehicles as actuators for traffic control systems. Under this concept, we design and test a control system that connects a traffic controller with in-vehicle controllers via vehicle-to-infrastructure communication. The link-level traffic controller regulates traffic speeds through variable speed limits (VSL) gantries to resolve stop-and-go waves, while intelligent vehicles control accelerations through vehicle propulsion and brake systems to optimize their local situations. It is assumed that each intelligent vehicle receives VSL commands from the traffic controller and uses them as variable parameters for the local vehicle controller. Feasibility and effectiveness of the connected control paradigm are tested with simulation on a two-lane freeway stretch with intelligent vehicles randomly distributed among human-driven vehicles. Simulation shows that the connected VSL and vehicle control system improves traffic efficiency and sustainability; that is, total time spent in the network and average fuel consumption rate are reduced compared to (uncontrolled and controlled) scenarios with 100% human drivers and to uncontrolled scenarios with the same intelligent vehicle penetration rates.     

考虑驾驶人风格的换道轨迹规划与控制（双语出版）

基于自动换道控制技术中融合个性化驾驶人风格的研究,建立考虑驾驶人风格的车辆换道轨迹规划及控制模型以提高换道规划控制模型对不同风格驾驶人的适用性,在保证安全性的基础上进一步满足驾驶人的个性化需求。首先通过问卷调查的方式采集得到了212份驾驶人风格量表数据,采用主成分分析法和K均值（K-means）聚类分析法将驾驶人按驾驶风格分为激进型、普通型和谨慎型,并通过驾驶模拟器试验采集不同风格驾驶人分别在自车道前车、目标车道前车和目标车道后车影响下的换道行为数据。然后对椭圆车辆模型进行改进,以描述不同风格驾驶人的行车安全区域,并据此构建3种典型工况下不同风格驾驶人的换道最小安全距离模型,结合驾驶舒适性约束、车辆几何位置约束以及不同风格驾驶人的换道行为数据,以换道纵向位移最短为目标,实现适应驾驶人风格的换道轨迹规划。最后以基于预瞄的路径跟踪模型作为前馈量,设计基于动力学的线性二次型最优（LQR）反馈控制器,通过调节控制权重矩阵实现3种工况下不同驾驶人风格的换道轨迹跟踪。PreScan和MATLAB/Simulink联合仿真结果表明：所设计的考虑驾驶人风格的换道轨迹规划及跟踪控制模型能够实现不同驾驶风格的自动换道轨迹规划及跟踪控制,可满足驾驶人个性化换道需求。     

基于因子模型的高速公路特长隧道驾驶人心理负荷特性研究

为研究驾驶人行驶通过特长隧道环境中的心理负荷变化特性,选取2座典型特长隧道进行实车试验,采集驾驶人实时心电信号,以心率和心率变异性指标分析为基础,通过数据挖掘构建了基于因子分析的心理负荷计算模型,采用心率变异性频域分析结果对模型进行验证。研究结果表明：心率变异性指标在计算心理负荷时比心率指标具有更高的效度和信度,驾驶人在距离隧道入口较远处和距离隧道出口较近时负荷较大;在隧道路段和普通高速路段,熟悉试验道路的驾驶人平均心理负荷小于不熟悉试验道路的驾驶人;被试在隧道路段的平均心理负荷大小依次为入口段、出口段、行车段,熟练驾驶人心理负荷在特长隧道入口前300 m至前180 m范围受到的影响最为明显;非熟练驾驶人心理负荷在入口前300 m至入口后240 m范围受到的影响最为明显。上述结果说明：在隧道出、入口段,尤其是入口段驾驶人负荷过高,也是造成事故数量多的主要原因之一;熟悉道路条件可在一定程度上降低驾驶人心理负荷;普通高速路段虽然行车环境较好,但运行车速过高也会造成驾驶人负荷增加。熟练驾驶人心理负荷在隧道入口前升高,而非熟练驾驶人心理负荷在进入隧道后仍保持较高水平。     

A close examination of speed limit credibility and compliance on UK roads

Speed is at the core of the road safety problem and speed management is a tool for road safety. Speed limits that are more credible are supposed to encourage drivers to comply with speed limits, with consequent benefits for road safety. Credible speed limit has been found to be affected by the features of the road surroundings in previous research. This study investigated, by using a questionnaire, whether or not the current legal speed limit is credible on a variety of current UK road environments and what the difference is between the proposed speed limit and the chosen, self-reported driving speed. The survey result revealed that road layout and the roadside environment affected the intrinsic perception of choice of speed and speed limit. Chosen speed limit and proposed speed are not identical but are related with each other. The higher the speed limit drivers perceived, the higher speed they tended to drive.     

Development of a driving cycle for the measurement of fuel consumption and exhaust emissions of automobiles in Bangkok during peak periods

The exhaust emissions and fuel consumption rates of newly registered automobiles in Thailand are currently assessed using the standard driving cycle of the Economic Commission of Europe (ECE). Because of the highly different driving conditions, the assessment results may not reflect realistic amounts of emissions and fuel consumption for vehicles in Bangkok traffic, which is well known for its congestion. The objective of this study is therefore to develop a new driving cycle for vehicles traveling on Bangkok’s main roads during peak traffic hours. This paper first presents the development of a method for selecting representative road routes with traffic conditions that are representative of traffic in Bangkok for conducting real-world driving speed data collection. These real-world data are obtained by driving a car equipped with a speed-time data logger along those selected road routes. Several driving characteristics, including various profiles of microtrips, are analyzed from the collected speed-time data, and a number of target driving parameters are then defined for use as a set of criteria to justify the best driving cycle. A procedure for generating driving cycles from the analyzed real-driving data is also developed, and the method to select the cycle that is most representative of Bangkok traffic is described. Comparisons found in the study show that the target driving parameters of the newly developed driving cycle are much closer to those obtained from the real-world measured data than those calculated from the presently used European drive cycle. This would imply that the obtained driving cycle will produce more realistic results of the emissions and fuel consumption assessment tests for vehicles traveling in Bangkok. The methods developed in this study for route selection and driving cycle construction can easily be adopted by other big cities to develop their own vehicle driving cycles. Furthermore, although the developed methods are for passenger cars, similar approaches can be applied to develop driving cycles for other types of vehicle, such as city buses and pick-up trucks.     

Real-time characterisation of driver steering behaviour

In recent years the application of driver steering models has extended from the off-line simulation environment to autonomous vehicles research and the support of driver assistance systems. For these new environments there is a need for the model to be adaptive in real time, so the supporting vehicle systems can react to changes in the driver, their driving style, mood and skill. This paper provides a novel means to meet these needs by combining a simple driver model with a single-track vehicle handling model in a parameter estimating filter – in this case, an unscented Kalman filter. Although the steering model is simple, a motion simulator study shows it is capable of characterising a range of driving styles and may also indicate the level of skill of the driver. The resulting filter is also efficient – comfortably operating faster than real time – and it requires only steer and speed measurements from the vehicle in addition to the reference path. Adaptation of the steer model parameters is demonstrated along with robustness of the filter to errors in initial conditions, using data from five test drivers in vehicle tests carried out on the open road.

Abbreviations: ADAS: advanced driver assistance systems; CG: centre of gravity; CAN: controller area network; EKF: extended Kalman filter; GPS: global positioning system; UKF: unscented Kalman filter     

信号交叉口自动驾驶左转车辆类人化全局运动轨迹规划

为了提高信号交叉口自动驾驶车辆左转运动规划的适应性、鲁棒性与类人化程度，提出一种考虑多目标需求的自动驾驶类人化全局运动规划方法。首先，基于西安市北大街信号交叉口规格构建结构化场景，结合车辆运动学模型与道路几何规格定义自动驾驶车辆规范化行驶安全域和车辆运动参数约束条件；其次，根据信号灯状态、道路限速与车辆性能约束制定上游阶段车辆不停车通行规则，以行驶安全、燃油消耗、通行效率与驾驶舒适度作为目标性能函数，构建类人化全局多目标优化模型，通过人类驾驶的车辆预转弯行为耦合上游阶段与转弯阶段；再次，针对非线性运动规划模型变量与约束规模化问题，采用粒子群算法与全联立正交配置有限元方法求解不同阶段车辆运动轨迹的最优解；最后，试验建立Prescan与MATLAB/Simulink联合仿真平台，从多目标性能、适应性以及合理性方面验证该模型的综合性能。结果表明：在以信号灯状态和车辆初速度为变量建立的12种工况下，该模型与人类驾驶车辆、混合运动规划模型相比，平均可分别节省燃油消耗63.7%和29.5%，平均通行延时分别降低3、0.9 s，且轨迹曲率更平缓，最大横向加速度与方向盘转角平方和的平均值最小，证明该模型的多目标性能更好；在以路缘石半径与车道数目为变量建立的7种交叉口规格工况下，所提出模型的车辆轨迹平滑，轨迹安全域边界距离始终大于1.4 m，曲率变化符合期望且峰值小于0.22 m^-1，说明该模型具有较好的适应性；在自由/固定终端时刻条件下，该模型规划的车辆空间路径、速度、曲率及航向角的变化与目标权重变化保持一致，验证了模型的合理性。     

Impact of driving style,behaviour and anger on crash involvement among Iranian intercity bus drivers

In developing countries such as Iran, due to the inadequate infrastructure for rail and air transportation facilities, intercity buses are the most common type of transportation for long distances. Because of the long hours of driving, bus driving is considered a challenging job. Moreover, given the high capacity of these vehicles, a small error from the driver could endanger many passengers' health. So, studying drivers' behaviours can be a key factor in decreasing the risk factors of crash involvement in these drivers. However, few studies have focused on intercity bus drivers' behaviours. This research uses a sample of 254 professional drivers that answered a self-report questionnaire on driving style (MDSI), driving behaviour (DBQ), and driving anger (DAS). A structural equation modelling (SEM) is used to investigate the psychometric properties of these questionnaires. The results show a positive correlation between maladaptive driving styles and driving behaviour, and a negative correlation between adaptive styles and driving behaviour. Significant differences are observed among drivers with and without crash history on their maladaptive driving styles and their driving anger scale. A binary logistic regression model is also developed to predict traffic crashes as a function of driving misbehaviour. The results suggest that factors related to driving anger are the main factors that increase the probability of misbehaviour and traffic crashes. The results also suggest that driving style and driving behaviour significantly predict crash risk among bus drivers. Aggressive driving is associated with an increased probability of crash involvement among intercity bus drivers. The findings can be used to inform the health promotion policies and provide regular interventions designed to improve driving safety among intercity bus drivers.     

基于稳定性和宏观交通流模型的车辆路径规划

为了提升车辆的安全性和能量利用率,从路径规划的层面出发,针对避免车辆遇到极端工况及低效率工况的问题,提出将车辆稳定性判据模型和交通流模型相结合的方法来规划车辆路径,使得车辆在路面湿滑情况下实现快速、安全的行驶。使用交通流模型预测车辆未来将要面临的交通环境变化,再使用稳定性判据模型评估未来交通的安全性,以便为混合动力车辆规划出最快且最安全的路径。具体来讲,为了预测混合动力车辆未来将要面临的车速及车流密度的变化,使用通量矢量分裂格式求解广义Aw-Rascle-Zhang（GARZ）宏观交通流模型。此外,使用驾驶人在环仿真平台PreScan,收集了同一驾驶人在不同车速及不同相对前车距离时给出的前轮转向角响应。基于前轮驱动（FWD）前轮转向（FWS）车辆和全轮转向（AWS）分布式驱动车辆（DDV）的Simulink模型,给出了不同前轮转向角对应的轮胎力饱和因子（δ_TFSC）响应。使用人工神经网络训练不同车速和车流密度对应的δ_TFSC,建立了车辆的稳定性判据模型。使用新建立的稳定性判据模型对交通流模型预测的参数（车流速及车流密度）进行稳定性评估。然后,基于以上的方法优化了车辆行驶路径,以确保车辆在湿滑路面上的行驶安全。最后,使用US-101真实交通流数据来验证交通流模型的预测结果。经实例验证得出：交通流模型与车辆横向稳定性判据模型相结合可以从路径规划的层面保证车辆安全行驶并提升交通系统的通行效率。