The design of an in-vehicle assistance system to support eco-driving

This driving simulator study was the second of two studies investigating the most effective and acceptable in-vehicle system for the provision of guidance on fuel efficient accelerator usage. Three eco-driving interfaces were selected for test (a second-order display visual display with auditory alerts and two haptic accelerator pedal systems) following a pilot study of 12 different interfaces. These systems were tested in a range of eco-driving scenarios involving acceleration, deceleration and speed maintenance, and assessed through their effects on fuel economy, vehicle control, distraction, and driver subjective feedback. The results suggest that a haptic accelerator pedal system is most effective for preventing over-acceleration, whilst minimal differences were observed between systems in terms of the effect of the assistance provided to prevent under-acceleration. The visual–auditory interface lowered the time spent looking towards the road, indicating a potential negative impact on driver safety from using this modality to provide continuous green driving support. Subjective results were consistent with the objective findings, with haptic pedal systems creating lower perceived workload than a visual–auditory interface. Driver acceptability ratings suggested a slight favouring of a haptic-force system for its usefulness, whereas the more subtle haptic-stiffness system was judged more acceptable to use. These findings offer suggestions for the design of a user-friendly, eco-driving device that can help drivers improve their fuel economy, specifically through the provision of real-time guidance on the manipulation of the accelerator pedal position.     

Energy saving potentials of connected and automated vehicles

Connected and automated vehicles (CAV) are marketed for their increased safety, driving comfort, and time saving potential. With much easier access to information, increased processing power, and precision control, they also offer unprecedented opportunities for energy efficient driving. This paper is an attempt to highlight the energy saving potential of connected and automated vehicles based on first principles of motion, optimal control theory, and a review of the vast but scattered eco-driving literature. We explain that connectivity to other vehicles and infrastructure allows better anticipation of upcoming events, such as hills, curves, slow traffic, state of traffic signals, and movement of neighboring vehicles. Automation allows vehicles to adjust their motion more precisely in anticipation of upcoming events, and save energy. Opportunities for cooperative driving could further increase energy efficiency of a group of vehicles by allowing them to move in a coordinated manner. Energy efficient motion of connected and automated vehicles could have a harmonizing effect on mixed traffic, leading to additional energy savings for neighboring vehicles.     

基于反向传播神经网络的驾驶人人际与情绪危险性模型

为评价和研究驾驶人的人际关系质量对安全驾驶的影响,利用情绪状态可测定的特性,采用反向传播(BP)人工神经网络工具,建立人际-情绪危险性网络模型,并利用530个情绪与人际关系的有效样本作为建模样本占总样本量的80.7%,127个样本作为测试样本,反复对模型进行学习和训练,分别取得了建模样本68.3%的正确率,测试样本70.1%的正确率。研究结果表明:利用BP神经网络建立的人际与情绪危险性模型,在系统关系不明确的状态下仍能达到较为理想的评价结果,可以作为驾驶人尤其是职业驾驶人安全管理和自我检测的有效手段。     

模糊PID控制在参考式扭矩装置伺服加载系统中的应用

将一种增量型模糊PID控制方案应用于参考式扭矩标准装置伺服加载系统。首先分析模糊控制的原理及特点,然后运用模糊规则实现PID参数自整定,接着对控制系统建模仿真,说明模糊PID控制相比传统PID控制的优势,最后通过装置试验验证了方案的实用性。     

重型半挂牵引车典型循环工况研究

随着市场对车辆燃油经济性及排放水平的要求不断提升,设计符合客户实际使用情况的行驶循环工况变得越发重要;本文通过对客户车辆的跟踪数据采集,在获得大量行驶工况数据的基础上,获得速度,加速度等特征参数,利用多元统计理论构建了能够广泛应用于高速物流运输模拟的典型循环工况。     

驾驶经验和气质类型对驾驶行为的影响分析

驾驶人的生理心理因素会对驾驶人的驾驶行为产生一定的影响已经得到了较多认同,但是通过科学实验进行定量化实证分析的研究还较少。论文首先采用量表测量与仪器测试相结合的实验方法,以专业和非专业驾驶人为实验对象,利用气质类型量表测量驾驶人的气质类型;利用实验车在实际道路交通环境下行驶以获得跟驰车辆对前后车的行驶轨迹,并计算得到影响驾驶行为的关键参数。基于实验数据分析了驾驶经验和气质类型对驾驶人的行驶速度、加速度、跟驰距离、时间间隔等方面的影响。在跟驰时驾驶人的速度和加速度选择方面,专业驾驶人对速度和加速度的选择更加具有自主性,并且内向型性格驾驶人的行驶速度和加/减速幅度一般要小于外向型性格的驾驶人。在跟驰时驾驶人对空间和时间距离的选择方面,无论是跟驰距离还是时间间隔,气质类型对非专业驾驶人空间和时间距离选择的影响更加明显,并且各速度段内向型性格驾驶人选择的空间和时间距离一般大于外向型性格驾驶人;相同气质类型的非专业驾驶人选择的时间间隔都要大于专业驾驶人。经研究建立了驾驶人生理心理特性与驾驶行为特性之间的关系,为分析驾驶人行为特性与交通安全之间的关系奠定了良好的基础。     

高速公路平直路段雾霾天气下的IDM跟驰模型分析

雾霾等恶劣气候对交通安全带来严重威胁.为分析雾霾天气下驾驶行为规律,利用高逼真度驾驶模拟器,构建了高速公路平直路段雾霾天气场景,开展了自由加速、紧急制动和跟驰试验;基于采集的试验数据利用遗传算法对IDM (Intelligent Driver Model)跟驰模型进行了标定,并与晴天同路段标定参数进行了对比分析.结果显示,雾霾天气下IDM模型中起步加速度和舒适减速度参数并无显著差异,但安全距离、安全车头时距、期望速度等参数比晴天情况显著减小,由此表明雾霾天气下的交通冲突风险显著增大,应加强雾霾天气下的安全管理.     

高原公路驾驶员视觉特性分析

高原地区自然环境有别于平原地区,使得高原公路相对平原公路的条件复杂,其对驾驶人员的影响也非常显著,为对高原低氧地区驾驶员视觉特性进行研究,选取不同海拔为试验点,利用视力检测仪对长期行驶的车辆进行实地测试。通过对实测数据分析得出:高原公路驾驶员的静视力和动视力的平均值分别为1.18和0.59;动视力相对于静视力的平均下降福度为49.62%,高于绿洲公路和沙漠公路。关联度分析表明:驾驶员特性与海拔、年龄、驾龄、连续驾驶时间有关,并且关联度均为0.73以上。     

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为解决电动汽车驾驶员里程焦虑问题,并为车辆行驶里程预测提供重要依据,本文提出一种基于数据驱动的方法来探讨电动汽车行驶里程和电池SOC之间的关系。首先对采集的原始数据进行删除、插值和平均处理,再对电动汽车行驶里程和电池SOC进行相关性分析并建立模型,利用递推最小二乘法对模型参数进行辨识。利用北京市运营物流电动车的数据对建立的模型及参数辨识结果进行验证。实验结果表明,本文采用的基于数据驱动预测行驶里程的方法是可行的,所建立的行驶里程与电池SOC模型具有较高的准确度。     

基于自然驾驶数据的跨江大桥小客车跟驰特性研究

为明确跨江大桥的跟驰行为特征以及驾驶模式,在重庆菜园坝大桥展开了30位被试的小客车实车驾驶试验,使用华测航姿测量系统和前视碰撞预警系统Mobileye 630采集自然驾驶状态下汽车的连续行驶速度、车头时距和车头间距等数据。通过筛选得到了725条有效跟驰轨迹数据,对比分析发现跨江大桥与城市一般道路的跟驰行为存在一定差异性,明确了菜园坝大桥车头时距和车头间距的分布特征,并且对强跟驰(小于1.6 s)、过渡区间(1.6~2.6 s之间)以及弱跟驰(大于2.6 s)3种跟驰状态和驾驶人性别差异下的跟驰数据进行了分析。结果表明:桥梁段车头时距分布集中在1.6 s处,车头间距分布集中在18 m处;超过1/3的跟驰轨迹处于强跟驰状态,此状态下前车驾驶行为对跟驰车辆具有较强制约性;当车辆处于弱跟驰状态时,前车对于后车的约束性会随车头时距的增大而快速降低;过渡区间的设立更好地揭示了强/弱跟驰状态之间的转变并不是只有一个临界值,而是存在一个转换过程,并且其间车辆跟驰特性的变化与驾驶人本身的操作行为存在较大关联;驾驶人的性别差异对跟驰距离几乎没有影响,但男性驾驶人往往会采取更加冒险的驾驶行为,平均车头时距、车头间距以及相对速度均高于女性驾驶人。