智慧交通与智慧物流

智慧交通和智慧物流是智慧城市建设的重要内容.智慧交通技术的创新和发展，促进了智能交通升级.解决大规模数据计算问题是物流智能化的基础.本次论坛以“智慧交通和智慧物流”为主题，介绍了安全驾驶统一集理论、实践及成果，探讨了新一代信息技术对智能交通发展与创新的推动作用，介绍了无人驾驶技术的总体框架、发展与应用，探讨了智慧物流所面临的大规模数据计算问题及解决方案.     

Human driving data-based design of a vehicle adaptive cruise control algorithm

This paper presents a vehicle adaptive cruise control algorithm design with human factors considerations. Adaptive cruise control (ACC) systems should be acceptable to drivers. In order to be acceptable to drivers, the ACC systems need to be designed based on the analysis of human driver driving behaviour. Manual driving characteristics are investigated using real-world driving test data. The goal of the control algorithm is to achieve naturalistic behaviour of the controlled vehicle that would feel natural to the human driver in normal driving situations and to achieve safe vehicle behaviour in severe braking situations in which large decelerations are necessary. A non-dimensional warning index and inverse time-to-collision are used to evaluate driving situations. A confusion matrix method based on natural driving data sets was used to tune control parameters in the proposed ACC system. Using a simulation and a validated vehicle simulator, vehicle following characteristics of the controlled vehicle are compared with real-world manual driving radar sensor data. It is shown that the proposed control strategy can provide with natural following performance similar to human manual driving in both high speed driving and low speed stop-and-go situations and can prevent the vehicle-to-vehicle distance from dropping to an unsafe level in a variety of driving conditions.     

交通流特征深度认知的车队运行参数优化方法

为提升车队对周围交通流环境的认知能力，获取车队周围多车的运行模式，同时通过改变车队运行参数实现车队群体对周围多车群体运行模式的诱导变化，为提升车队及交通流整体运行效率提供优化策略，提出了基于狄利克雷混和高斯过程的车队周围多车运行模式获取算法，将车队周围多车车辆的复杂运行模式视为混和高斯过程，利用狄利克雷分布作为高斯混和权重的先验分布，建立车队周围多车运行模式速度场，从而获取车队周围多车运行模式并分类；通过比较不同多车运行模式下车队的运行效率，提升车队对所处运行环境的认知能力。研究结果表明：利用非参数贝叶斯算法将复杂的多车运行状态进行分类，获取的车队周围多车运行模式可表现为对车队运行效率产生不同影响的速度场；通过将车队周围的多车运行仿真数据分成多个运行模式，可获取不同多车运行模式下车队及交通流整体运行特性；通过更改车队运行参数，观察不同多车运行模式的占比变化，可获取车队运行参数对所处运行环境改变的影响趋势；车队周围多车运行模式的获取，不仅可以提升车队对周围运行环境的认知能力，使得车队能够选取有利路径行驶，同时能够为车队运行策略的优化提供有效的信息。     

用于智能驾驶系统评价的乘员损伤模型

只有较少的交通事故数据资源被用于建立基于碰撞速度信息的乘员损伤模型，致使所得到的模型精度差。为此，提出了基于车辆变形深度的乘员损伤模型。对美国不同制造年代和车辆级别的事故数据进行聚类分析，论证出车辆变形深度与乘员损伤风险具有相关性。以车辆变形深度为自变量，通过回归分析得到乘员损伤模型。不同种类车辆的乘员损伤模型拟合精度R²约为0.9，证明了该模型的正确性。为进一步验证，以此模型为基础，评价智能驾驶系统的有效性。以自动紧急制动系统为例，对比基于变形深度和速度变化量信息2种方法的有效性计算结果。结果表明：2组结果的平均误差不超过1%，验证了基于变形深度的乘员损伤模型的准确性。该模型仅需要事故数据库中准确的变形深度信息，能够获得更多的事故数据支持，从而可以更好地适应于不同类别智能驾驶系统的评价需求。     

驾驶模拟器在自动驾驶系统中的应用研究

自动驾驶车辆在实际道路上行驶之前的测试阶段是一个至关重要的环节。一个低成本、高效率以及高精度测量的自动驾驶车辆的测试方式,对于自动驾驶车辆的开发具有重要意义。将驾驶模拟器运用到研究自动驾驶车辆测试已是近年来的一个研究热点。基于虚拟驾驶场景的自动驾驶车辆的检测,通过组合虚拟驾驶场景的背景车辆、行人、交通灯、建筑、指示标牌等元素,研究将驾驶模拟器与虚拟驾驶场景的联合应用来测试自动驾驶车辆。设计了典型的交通场景,通过自动驾驶车辆和背景车辆的实时交互,研究自动驾驶车辆的各项性能指标。研究结果表明:该驾驶模拟器可以高度拟合人类驾驶体验,驾驶员通过驾驶模拟器控制背景车辆能够很好的模拟现实中的驾驶行为,对自动驾驶车辆的仿真测试起到了促进作用。     

基于多维信息特征分析的驾驶人认知负荷研究

准确评估驾驶人认知负荷水平，对于深入研究驾驶人行为特性，改善驾驶安全性具有重要意义。现有的驾驶人认知负荷分类方法，大多基于心电、脑电等生理信息和车辆信息，由于特征选择上的单一性，导致驾驶人认知负荷分类模型的分类精度不高。设计基于跟驰场景的不同认知负荷N-back次任务试验，通过采集受试者的生理信号和车辆信号，结合NASA_TLX主观评分和机器学习算法，提出了基于多维信息特征融合的驾驶人认知负荷分类方法。研究表明：基于生理信息和车辆信息的多维信息特征认知负荷分类方法，其精度显著高于传统的基于生理信息的认知负荷分类方法，以多维信息特征为输入，随机森林法以其稳定性好、抗过拟合能力强的特点，表现出优异的分类效果，相比神经网络和支持向量机，具有最高的平均分类精度。     

铁道车辆轴箱弹簧断裂故障对动力学性能的影响

车辆运行过程中,当轴箱弹簧突发断裂故障,会造成动力学状态和性能突变,一系悬挂刚度突然减小,暂时失去承载能力,会威胁车辆行车安全。结合整车的动力学仿真,建立了轴箱弹簧断裂过程的力学模型,对整个断裂过程进行仿真,模拟了轴箱弹簧突然断裂工况下车辆动力学性能变化,分析了轴箱弹簧断裂条件下车辆直线行车安全性以及曲线通过安全性。计算分析结果表明:轴箱弹簧突然断裂导致一系悬挂刚度剧变,引起轮轨垂向力先减小后增大,轮重减载率、脱轨系数等参数增大直至超限,但对轮轨横向力影响不大。     

Towards connected autonomous driving: review of use-cases

Connected autonomous vehicles are considered as mitigators of issues such as traffic congestion, road safety, inefficient fuel consumption and pollutant emissions that current road transportation system suffers from. Connected autonomous vehicles utilise communication systems to enhance the performance of autonomous vehicles and consequently improve transportation by enabling cooperative functionalities, namely, cooperative sensing and cooperative manoeuvring. The former refers to the ability to share and fuse information gathered from vehicle sensors and road infrastructures to create a better understanding of the surrounding environment while the latter enables groups of vehicles to drive in a co-ordinated way which ultimately results in a safer and more efficient driving environment. However, there is a gap in understanding how and to what extent connectivity can contribute to improving the efficiency, safety and performance of autonomous vehicles. Therefore, the aim of this paper is to investigate the potential benefits that can be achieved from connected autonomous vehicles through analysing five use-cases: (i) vehicle platooning, (ii) lane changing, (iii) intersection management, (iv) energy management and (v) road friction estimation. The current paper highlights that although connectivity can enhance the performance of autonomous vehicles and contribute to the improvement of current transportation system performance, the level of achievable benefits depends on factors such as the penetration rate of connected vehicles, traffic scenarios and the way of augmenting off-board information into vehicle control systems.     

Using on-board logging devices to study the longer-term impact of an eco-driving course

In this paper the long-term impact of an eco-driving training course is evaluated by monitoring driving behavior and fuel consumption for several months before and after the course. Cars were equipped with an on-board logging device that records the position and speed of the vehicle using GPS tracking as well as real time as electronic engine data extracted from the controller area network. The data includes mileage, number of revolutions per minute, position of the accelerator pedal, and instantaneous fuel consumption. It was gathered over a period of 10 months for 10 drivers during real-life conditions thus enabling an individual drive style analysis. The average fuel consumption four months after the course fell by 5.8%. Most drivers showed an immediate improvement in fuel consumption that was stable over time, but some tended to fall back into their original driving habits.     

螺旋匝道和螺旋桥的小客车行驶速度特征

为明确螺旋匝道和螺旋桥处的驾驶行为模式和汽车运行特征，在涪陵长江一桥、乌江二桥、重庆融侨大道和涪陵金凯环形高架4处地点开展螺旋匝道实车试验，用车载仪器采集自然驾驶状态下的汽车连续行驶轨迹、速度以及周围行驶环境等信息。基于自然驾驶数据，研究螺旋匝道范围内的速度变化模式、幅值特性以及影响因素。研究结果表明：单车道螺旋匝道的速度变化模式多样化，双车道螺旋匝道的行驶速度在整体上维持稳定，匝道范围内的连续升坡和降坡并未导致速度出现趋势性衰减和趋势性升高；螺旋匝道并入主线时，驾驶人在合流鼻之前有明显的、共性的减速行为，这与现行设计标准中的设计假定相反；除涪陵长江一桥之外，其余3处都是下行速度低于上行速度；螺旋匝道设计速度越低，实测速度与设计速度之间的偏离越严重，并且速度幅值离散化，因此不建议使用20 km·h^-1的匝道设计速度；螺旋匝道运行速度与匝道半径成正相关。