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.      

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。     

Driver propensity characterization for different forward collision warning times

The forward collision warning system, which warns danger to the driver after sensing possibility of crash in advance, has been actively studied recently. Such systems developed until now give a warning, regardless of driver’s driving propensity. However, it’s not reasonable to give a warning to every driver at the same time because drivers are different in driving propensity. In this study, to give a warning to each driver differently, three metrics classifying driver’s driving propensity were developed by using the driving data on a testing ground. These three metrics are the predicted time headway, required deceleration divided by the deceleration of the leading vehicle, and the resultant acceleration divided by the deceleration of the leading vehicle. Driving propensity was divided into 3 groups by using these metrics for braking and steering cases. In addition, these metrics were verified by making sure that braking propensity could be classified on public roads as well.     

基于驾驶模拟器的性别对信控铁路道口抵近行为的影响研究

为了分析不同性别驾驶人抵近信号灯控制铁路道口的驾驶行为特性,设计了5个具有不同红灯触发时距的信号灯控制铁路道口的虚拟场景,并进行了驾驶模拟试验,采集24名男性驾驶人和20名女性驾驶人抵近道口的微观驾驶行为数据。基于时空特性将抵近行为划分为月白灯期间、红灯进入时刻、红灯期间3个阶段,利用混合线性模型和二元Logistics线性回归模型对比分析了2组驾驶人抵近道口的制动行为、加速行为和闯红灯行为特性,并探索性别与信号灯控制铁路道口抵近行为的相关关系。结果表明：男性驾驶人更倾向于在月白灯期间提前制动,其提前制动率是女性驾驶人的1.57倍;男性驾驶人在红灯进入时刻和红灯期间的车辆操控行为特性与女性驾驶人无显著性差异;男性驾驶人的总体违法率是女性驾驶人的1.5倍,尤其在红灯触发时距较大（4~6 s）时,女性驾驶人几乎无人违法,而男性驾驶人在3个场景下的违法率仍然达到25.0%、12.5%和16.7%;男性驾驶人闯红灯的严重性更高,驾驶风格更加冒险激进;另外,驾驶人闯红灯行为还受红灯进入时刻位置、制动时刻位置和加速时刻位置显著影响,说明驾驶人在走停决策的制定上更依赖于物理空间位置,与车辆运行速度和驾驶人反应能力无关。研究结果可应用于铁路道口安全管理与防护对策设计。     

Evaluating the effectiveness of a smartphone speed limit advisory application: An on-road study in Port-Harcourt,Nigeria

The development of mobile phone applications that provide speed limit advice and warnings offers opportunities for use of the technology in the improvement of driver safety. This paper looks at the effect of an advisory Intelligent Speed Assistance (ISA) application on driver speeding behaviour. Twenty participants (all males within the age range of 35–60 years) completed a within-group experimental design. Participants drove in real traffic on a 46 km test route which incorporated three-speed limits zones (50 km/h, 60 km/h, and 80 km/h speed limits) and aggregated into 10 different segments. Compared with baseline levels, possible impacts of ISA system functionalities on driver behaviour were studied through appropriate metrics including cumulative speed distribution, mean speed, speed deviation, 85th percentile speed, percentage distanced travelled above the speed limit, and safety benefit estimation. Results indicated the use of the ISA application led to significant improvement in speed limit compliance particularly in the 60 km/h and 80 km/h zones where speeding was eliminated. There were no observed negative effects on driver speeding behaviour from the use of the system. In general, the findings suggest the use of the ISA system, resulted in the adoption of vehicle speeds that are likely to improve road safety.     

Anticipatory speed control model applied to intersection left turns

This paper presents a new speed control model applicable to real-world driving. It is developed for intersection left turns and is based on anticipated acceleration reference (AAR) inputs. This addresses combined visual anticipation of lateral and longitudinal accelerations for the approach to an intersection where both stopping and turning outcomes are possible. The relationship between the AAR and the resulting vehicle accelerations are studied for both stopping and turning events using naturalistic driving data. A closed-loop model is developed, including braking to rest when the left turn is not attempted and for the turn and exit stages when it is. Parameter ranges are estimated, and as a demonstration of model applicability, Monte Carlo simulations are conducted to generate representative left turns using a full simulation model. Extension of the AAR model to other speed control problems, for example, driving on curved roads, is also discussed.     

基于雷达測速技术的混动汽车道路制动性能分析

一汽丰田开发的普锐斯混动汽车,可以将制动能量回收来充电,提高了能源利用率。而汽车的制动性能直接影响汽车使用安全性,是汽车安全行车的重要因素之一,是汽车检测诊断的重点。基于雷达测速技术及装备,在LabVIEW软件平台的支持下,测试混动汽车制动过程中的速度、距离、加速度、时间等参数的变化,对混动汽车道路制动性能进行评价分析。     

驾驶机器人车辆动态制动力矩补偿

为了减小长期自动驾驶过程中制动性能下降带来的影响,提出了一种驾驶机器人车辆动态制动力矩补偿方法。首先建立了以车速和制动踏板力为输入,制动力矩为输出的驾驶机器人车辆制动性能离线自学习模型。然后考虑到驾驶机器人车辆长期自动驾驶导致离线自学习模型可靠性下降,建立了以车速和制动踏板力为输入,制动力矩为输出的扩展自回归在线辨识模型,并采用模糊变遗忘因子递推最小二乘法进行参数辨识。模糊变遗忘因子递推最小二乘法通过引入遗忘因子的方式,对数据施加时变加权系数,以避免出现数据增长导致的数据饱和现象。模糊变遗忘因子控制器以制动力矩辨识误差为输入,经模糊规则推理实时输出合适的遗忘因子进行参数辨识,能够有效均衡驾驶机器人车辆制动性能参数辨识的稳定性与收敛速度。驾驶机器人车辆自动驾驶过程中,根据当前车速与目标车速的大小计算出所需的制动力矩,加上反馈回来的制动力矩误差,并结合当前时刻的车速,利用制动性能离线自学习模型与机械腿逆向运动学模型实时计算出制动电机输出位移量,实现对驾驶机器人车辆制动力矩的在线补偿。仿真与试验结果表明:利用所提出的方法对车辆动态制动力矩进行辨识时,通过调节遗忘因子,辨识结果能够快速收敛且辨识误差较小。在此基础上,控制驾驶机器人车辆进行纵向车速跟踪时,能够有效减小制动性能下降造成的影响,保证控制车速跟踪误差在±1km·h-1之内。     

人-车-路交互下的驾驶人风险响应度建模

人机共驾中，共驾模式的选择和驾驶控制权的分配高度依赖于对驾驶人状态的正确识别。为了分析人机共驾中驾驶人的状态，对行车风险场模型进行重构，通过构建风险场力作用机制，建立包含驾驶人特性、自车特性和外部风险特性的人-车-路闭环系统中的驾驶人风险响应度模型，用于表征驾驶人对风险的认知能力和应对倾向。根据24位驾驶人在跟车和并道2个场景中的驾驶试验结果，对不同风险响应度下驾驶人的驾驶特性进行分析。研究结果表明：驾驶人风险响应度在驾驶过程中具有时变性，在驾驶人个体之间和不同驾驶场景间均存在差异性。在风险响应度分别为低、中、高的3类驾驶片段中，驾驶人在驾驶时的碰撞时间倒数T_TCi和加减速行为均具有显著差异（p<0.05）；风险响应度较高的保守型驾驶中，驾驶人行车时倾向于保持较小的T_TCi（均值为-0.48 s^-1，标准差为1.25 s^-1），单位时间内制动操作最多[均值为0.65次·（15 s）^-1]，总体驾驶风格倾向于规避风险；风险响应度较低的激进型驾驶中，驾驶人行车时倾向于保持最大的T_TCi（均值为0.28 s^-1，标准差为0.42 s^-1），相较于保守型驾驶，单位时间内加速操作较多[均值为0.48次·（15 s）^-1]，制动操作较少[均值为0.50次·（15 s）^-1]，总体驾驶风格倾向于追求行驶效率；风险响应度居中的平衡型驾驶中，驾驶人行车时所保持的T_TCi居中（均值为0.04 s^-1，标准差为0.36 s^-1），单位时间内加速操作[均值为0.23次·（15 s）^-1]和制动[均值为0.41次·（15 s）^-1]操作总数最少，对于行驶效率和行车安全的追求相对均衡。相较于以往将驾驶人作为孤立个体的驾驶人状态评估方法，所提出的驾驶人风险响应度模型可以依据驾驶人在人-车-路交互中的驾驶表现，更为全面地反映驾驶人的个性化驾驶状态。     

车辆转弯制动横向轨迹控制驾驶员模型研究

为了较为真实地反映车辆转弯制动工况,建立了含Pacejka"魔术公式"非线性联合工况轮胎模型的4轮8自由度车辆系统模型,并基于预瞄跟随理论、加速度反馈控制和模糊PID控制技术建立了车辆转弯制动横向轨迹控制驾驶员模型。针对不同初始速度和制动强度,利用MATLAB/Simulink进行了横向轨迹控制仿真分析。分析结果表明,驾驶员控制模型能很好地跟踪横向轨迹,模型的可行性和有效性得到验证,同时不同仿真条件下结果的一致性也说明该控制方法具有较强的自适应能力和鲁棒性,为进一步研究复杂工况下的驾驶员模型及横向轨迹控制提供了一条可行的途径。     

Optimal Control Model Applications and Field Measurements with Respect to Car Driving

The well-known optimal control model has been applied only rarely to car driving, although its structure suits the modelling demands of driving by allowing for a multitask application and providing possibilities for the evaluation of driving in terms of supervisory control. Two series of Supervisory Driver Model predictions are stated for lateral position control in a straight driving scenario with disturbances generated internally by the driver. The first series of model calculations predicts lateral position variations and the time that a driver's vision can be occluded during the observation and control of different combinations of display variables (lateral position, lateral speed, yaw rate, lateral acceleration and yaw acceleration). The second series of predictions concerns two extreme sets of display variables in relation to driving speed and driving experience. Model predictions for the observation and control of all display variables give occlusion times which correspond with data from instrumented car studies with experienced drivers. However, with exclusive observation and control of the lateral position cue, predicted occlusion times are less than found in experimental results of inexperienced drivers. It is suggested that inexperienced drivers are also controlling yaw rate and/or both acceleration cues.     

风险情境下老年驾驶人行为特性研究

为研究风险情境下老年驾驶人与中青年驾驶人行为特性的差异,并确定老年驾驶人的眼动、心理生理、驾驶操作及风险感知等各类行为特性的衰退情况;选取19位老年驾驶人和19位中青年驾驶人作为试验对象,应用眼动仪、生理仪及驾驶模拟平台开展驾驶模拟试验;采集5种风险场景下2组驾驶人的眼动、心理、生理、操作行为与车辆运行数据;对比分析2组驾驶人的注视及扫视等眼动行为特性、心率变异及皮电等心理生理行为特性、制动及转向等操作行为特性、风险反应及敏感度等风险感知行为特性。试验结果表明：2组驾驶人的各类行为特性均随风险等级的增加呈现一定的规律性变化,随着风险等级的增加,2组驾驶人的注视持续时间、皮电均值及增长率、心率增长率和风险敏感度亦随之增加,而扫视、心率变异指标SDNN、制动时间及风险反应时间等指标随风险等级的增加而下降;上述指标的规律性变化说明驾驶人对风险的关注度和敏感度随着风险自身危险性的上升而不断增加,进而做出的反应也就越早,同时伴随着心理紧张程度增加,需要付出的努力也越大,与年龄的高低无关;另一方面,老年驾驶人的各类行为特性出现明显的衰退且与中青年驾驶人存在显著差异,其中老年驾驶人的注视持续时间、扫视幅度、扫视速度等眼动指标分别衰退了37.83%、27.58%、23.80%,皮电均值、心率增长率和SDNN心理生理指标分别衰退了57.67%、20.08%和29.14%,转向熵、车速控制和制动反应时间操作行为指标分别衰退了32.81%、20.34%和49.48%,风险敏感度、判断阈值和风险反应时间风险感知指标分别衰退了13.70%、8.66%和31.80%。通过对风险情境下老年驾驶人的各类行为特性进行详细分析,确定了老年驾驶人各类行为的衰退情况,对老年驾驶人行为特性的研究具有一定借鉴意义。     

基于线性路径跟踪控制的换道避撞控制策略研究

为实现车辆自主避撞,改善道路交通安全状况,提出一种基于线性路径跟踪控制的换道避撞控制策略。为实时确定制动和换道时机,获取跟车状态下自车和前车车速、加速度、相对距离以及驾驶人制动反应时间计算制动安全距离和换道安全距离,并在此基础上分别引入制动危险系数B和换道危险系数S评估制动与换道风险,使得车辆发生追尾碰撞的危险程度和主动干预阈值更直观。根据车辆期望横向加速度和期望横向位移的变化特性,采用5次多项式法规划符合驾驶人换道避撞特性的避撞路径。为保证换道避撞过程中驾驶人的安全舒适,采用最大横向加速度约束换道避撞轨迹。为实现对换道避撞路径的线性跟踪控制,保证车辆的操纵稳定性和横摆稳定性,基于车辆稳态动力学模型建立前馈控制,结合线性反馈控制消除换道路径的位置和横摆角偏差,修正参考路径实现直车道场景追尾避撞控制。仿真和实车交叉验证试验表明：根据车辆期望横向加速度和期望横向位移建立的符合驾驶人换道避撞特性的五次多项式换道路径与驾驶人实际换道避撞路径基本吻合,结合碰撞时间和车间时距的制动避撞控制策略能够在保证车辆行驶安全舒适性的同时有效避免车辆追尾碰撞,减少交通事故的发生。     

ISG hybrid powertrain: a rule-based driver model incorporating look-ahead information

According to European regulations, if the amount of regenerative braking is determined by the travel of the brake pedal, more stringent standards must be applied, otherwise it may adversely affect the existing vehicle safety system. The use of engine or vehicle speed to derive regenerative braking is one way to avoid strict design standards, but this introduces discontinuity in powertrain torque when the driver releases the acceleration pedal or applies the brake pedal. This is shown to cause oscillations in the pedal input and powertrain torque when a conventional driver model is adopted. Look-ahead information, together with other predicted vehicle states, are adopted to control the vehicle speed, in particular, during deceleration, and to improve the driver model so that oscillations can be avoided. The improved driver model makes analysis and validation of the control strategy for an integrated starter generator (ISG) hybrid powertrain possible.     

不同干预方式下右转车辆避让行人效果研究

为探究车辆右转过程中不同干预方式对驾驶人未规避行人行为的改善情况,设计听觉警示、触觉警示、形式惩罚、利益惩罚和道德惩罚5种干预方式,分为控制组、警示组和惩罚组,试验基于眼动仪和模拟驾驶仪展开。定义注视次数、注视点分布信息熵、平均注视时间、视线转移路径、区域关注概率和瞳孔面积6项指标表征驾驶人眼动特性,提取制动踏板深度比例、行车速度2项指标反映车辆运行状态。经方差分析确定各干预方式差异的显著性水平,从注视特性指标、扫视特性指标、瞳孔面积指标、驾驶人制动指标和机动车制动指标5个方面分析不同干预下驾驶人视觉及操纵响应特征,并收集被试反馈的追踪问卷。试验结果表明：不同干预方式对右转车辆未避让行人均有规范作用,各组干预效果由强到弱依次为利益惩罚、道德惩罚、形式惩罚、触觉警示和听觉警示。利益惩罚性主动干预效果优势显著,注视点分布信息熵最高为0.74,右侧平均注视次数为6次,平均注视时间增加至13.25 s,驾驶人对右侧注视概率增加至0.403,瞳孔面积明显增大,制动踏板深度比例维持在0.8,右转车速下降至20 km·h^-1以下,谨慎驾驶程度和避让行人意识均有提升。一致性追踪问卷调查表明,结束试验时32%的驾驶人对利益惩罚印象深刻,驾驶人对其主观认可度高达83%,具有较强的推广性;该干预方式可帮助驾驶人规范驾驶行为,树立避让行人的安全驾驶意识。     

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。     

Minimizing the Path Radius of Curvature for Collision Avoidance

When a driver is suddenly presented with an obstacle in his path, or realizes that his speed is too great for the curved road ahead, commonly he saturates both inputs of steering and braking and thereby jeopardizes his chances of successfully avoiding a collision or negotiating the turn. Although anti-lock braking systems (ABS) avoid saturation of the braking and steerability usually remains, there is evidence to suggest that the vehicle performance with this system could be greatly improved. Could the steering, in addition to the braking, be automatically controlled to improve the performance? Because these threatening situations are so variable, it is very difficult to find a controller which can override both driver inputs and is always beneficial. Using a very simple model of the vehicle, the concept of minimizing the average radius of curvature of the path through controlling both driver inputs is shown to always be beneficial, and worthwhile. The results also carry over to a more realistic model.     

Normal and risky driving patterns identification in clear and rainy weather on freeway segments using vehicle kinematics trajectories and time series cluster analysis

Enhancing traffic safety on freeways is the main goal for all transportation agencies. However, to achieve this goal, many analysis protocols of network screening models need to be improved through considering human factors while analyzing traffic data. This paper introduces one on the new analysis protocol of identifying and discriminating between normal and risky driving in clear and rainy weather. The introduced analysis protocol will consider the effect of human factors on updating the networking screening process of identifying hotspots of crash risk. This paper employs the Second Strategic Highway Research Program (SHRP2) Naturalistic Driving Study (NDS) data to investigate the behavior of normal and risky driving under both rainy and clear weather conditions. Near-crash events on freeways, which were used as Surrogate Measure of Safety (SMoS) for crash risk, were identified based on the changes in vehicle kinematics, including speed, longitudinal and lateral acceleration and deceleration rates, and yaw rates. Through a trajectory-level data analysis, there were significant differences in driving patterns between rainy and clear weather conditions; factors that affected crash risk mainly included driver reaction and response time, their evasive maneuvers such as changes in acceleration rates and yaw rates, and lane-changing maneuvers. A cluster analysis method was employed to classify driving patterns into two clusters: normal and risky driving condition patterns, respectively. Statistical results showed that risky driving patterns started on average one second earlier in rainy weather conditions than in clear weather conditions. Furthermore, risky driving patterns extended in average three seconds in rainy weather conditions, while it was two seconds in clear weather conditions. The identification of these patterns is considered as a primary step towards an automated development that would distinguish between different driving patterns in a Connected Vehicle CV environment using Basic Safety Messages (BSM) and to enhance the network screening analysis for increased crash risk hotspots.     

Role of steering wheel feedback on driver performance: driving simulator and modeling analysis

When driving in curves, how do drivers use the force appearing on the steering wheel? As it carries information related to lateral acceleration, this force could be necessary for drivers to tune their internal model of vehicle dynamics; alternatively, being opposed to the drivers' efforts, it could just help them stabilize the steering wheel position. To assess these two hypotheses, we designed an experiment on a motion-based driving simulator. The steering characteristics of the vehicle were modified in the course of driving, unknown to drivers. Results obtained with standard drivers showed a surprisingly wide range of adaptation, except for exaggerated modifications of the steering force feedback. A two-level driver model, combining a preview of vehicle dynamics and a neuromuscular steering control, reproduces these experimental results qualitatively and indicates that adaptation occurs at the haptic level rather than in the internal model of vehicle dynamics. This effect is related to other theories on the manual control of dynamics systems, wherein force feedback characteristics are abstracted at the position control level. This research also illustrates the use of driving simulation for the study of driver behavior and future intelligent steering assistance systems.     

Role of steering wheel feedback on driver performance: driving simulator and modeling analysis

When driving in curves, how do drivers use the force appearing on the steering wheel? As it carries information related to lateral acceleration, this force could be necessary for drivers to tune their internal model of vehicle dynamics; alternatively, being opposed to the drivers' efforts, it could just help them stabilize the steering wheel position. To assess these two hypotheses, we designed an experiment on a motion-based driving simulator. The steering characteristics of the vehicle were modified in the course of driving, unknown to drivers. Results obtained with standard drivers showed a surprisingly wide range of adaptation, except for exaggerated modifications of the steering force feedback. A two-level driver model, combining a preview of vehicle dynamics and a neuromuscular steering control, reproduces these experimental results qualitatively and indicates that adaptation occurs at the haptic level rather than in the internal model of vehicle dynamics. This effect is related to other theories on the manual control of dynamics systems, wherein force feedback characteristics are abstracted at the position control level. This research also illustrates the use of driving simulation for the study of driver behavior and future intelligent steering assistance systems.