智能网联车辆交通流优化对交通安全的改善

为改善常规驾驶车辆交通流追尾碰撞交通安全状况,提出智能网联车辆（Connected and Automated Vehicles,CAV）与常规车辆构成的混合交通流车队稳定性优化控制方法。基于全速度差模型,应用集成速度与加速度的多前车反馈构建CAV跟驰模型,考虑CAV混合交通流车辆空间分布的随机性,将各类型局部车队稳定性作为优化目标,以局部车队头车速度扰动为系统输入,以尾车速度扰动为系统输出,应用经典控制理论领域的传递函数法推导局部车队稳定性约束条件;分析关于平衡态速度与CAV反馈系数的车队稳定域,以各类型局部车队能够在任意平衡态速度下均稳定为控制目标,对CAV反馈系数输出进行优化控制;设计高速公路上匝道交通瓶颈数值仿真试验,在不同CAV比例等多种条件下,分析CAV混合交通流优化控制对交通流车辆追尾碰撞风险的影响。研究结果表明：CAV混合交通流优化控制可降低车辆追尾碰撞风险,在碰撞时间阈值小于2 s时,100%比例的CAV交通流可将交通流的车辆追尾碰撞风险降低85.81%以上;在碰撞时间阈值大于2 s时,追尾碰撞风险可降低48.22%～78.80%。所提优化控制方法可有效降低CAV车队优化控制的复杂性,为大规模CAV背景下的混合交通流优化控制以及车辆追尾碰撞交通安全提升策略提供直接理论参考。     

In-depth understanding of lane changing interactions for in-vehicle driving assistance systems

Lane-changing events are often related with safety concern and traffic operational efficiency due to complex interactions with neighboring vehicles. In particular, lane changes in stop-and-go traffic conditions are of keen interest because these events lead to higher risk of crash occurrence caused by more frequent and abrupt vehicle acceleration and deceleration. From these perspectives, in-depth understanding of lane changes would be of keen interest in developing in-vehicle driving assistance systems. The purpose of this study is to analyze vehicle interactions using vehicle trajectories and to identify factors affecting lane changes with stop-and-go traffic conditions. This study used vehicle trajectory data obtained from a segment of the US-101 freeway in Southern California, as a part of the Next Generation Simulation (NGSIM) project. Vehicle trajectories were divided into two groups; with stop-and-go and without stop-and-go traffic conditions. Binary logistic regression (BLR), a well-known technique for dealing with the binary choice condition, was adopted to establish lane-changing decision models. Regarding lane changes without stop-and-go traffic conditions, it was identified based on the odd ratio investigation that he subject vehicle driver is more likely to pay attention to the movement of vehicles ahead, regardless of vehicle positions such as current and target lanes. On the other hand, the subject vehicle driver in stop-and-go traffic conditions is more likely to be affected by vehicles traveling on the target lane when deciding lane changes. The two BLR models are adequate for lane-changing decisions in normal and stop-and-go traffic conditions with about 80 % accuracy. A possible reason for this finding is that the subject vehicle driver has a tendency to pay greater attention to avoiding sideswipe or rear-end collision with vehicles on the target lane. These findings are expected to be used for better understanding of driver’s lane changing behavior associated with congested stop-and-go traffic conditions, and give valuable insights in developing algorithms to process sensor data in designing safer lateral maneuvering assistance systems, which include, for example, blind spot detection systems (BSDS) and lane keeping assistance systems (LKAS).     

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。     

雾天驾驶人车辆操纵行为特性及其与追尾风险相关性分析

为分析驾驶人在雾天环境下的车辆操纵行为特性及其与追尾风险的内在关系,设计并开展驾驶模拟试验,采用方差分析,混合效应模型等对晴天、雾天2种环境下驾驶人的车辆操纵行为特性进行对比分析,并利用相关性分析及二元Logistics回归模型对避撞过程中行为间的相互作用及其与追尾风险间的关系进行挖掘.结果表明:雾天环境下驾驶人的车道...     

关于重卡防护装置增加阻尼系统的可行性分析与设计

市场上汽车和挂车后下部防护装置多为刚性连接,当发生车辆追尾碰撞时,不能有效保护乘坐人员的安全,屡屡发生小车钻进大货车尾部伤亡事故.本文目的是提供一种汽车和挂车后下部柔性防护装置,克服已有汽车和挂车后下部防护装置缺点和不足,使汽车发生追尾碰撞时避免发生伤亡事故.此后下部防护装置不仅仅是保护追尾的小车,同时也保护了货车自身...     

网联车对抗神经网络跟驰模型

针对当前混行交通流场景下网联车对前车速度变化的实时性、安全性和车队稳定性较差的状况,提出一种由生成模型和辨别模型构成的网联车生成式对抗网络车辆跟驰模型(GANVFM).其中,生成模型提取跟驰参数中的前车速度、跟驰车速和相对车距计算生成加速度;辨别模型对生成模型生成的加速度参数进行相似度计算,并通过更新函数加以更新.采用...     

Intelligent collision warning using license plate segmentation

Given the increase of vehicles in traffic, traffic accidents have become a crucial and urgent issue for some countries. Particularly, in heavy traffic conditions, rear-end collisions make up the majority of traffic accidents, which make the traffic jam worse. This article proposes a novel approach to rear-end collision warning systems using areas of license plates acquired with a single camera mounted on a car. The edges of the front car's license plate are segmented and a rectangle is sketched to calculate the area, which is used for estimating distance between the cars. Relative speed of the front car is computed using the differences of the rectangles in a specific time. Distance and relative speed are obtained from the estimated areas of the license plates and transferred to the fuzzy inference system to send a warning signal to the driver for collision prevention, in emergency cases. The experiments are greatly encouraging, indicating that number plate segmentation can be utilized to estimate the distance and fuzzy inference system can be developed to create a warning signal to the drivers.     

协同自适应巡航控制车辆占比对下匝道分流区混合交通流安全性的影响分析

在人工驾驶车辆、自适应巡航控制（ACC）车辆和协同自适应巡航控制（CACC）车辆的行车行为特征分析的基础上，运用跟驰模型和换道模型分别构建人工驾驶车辆、ACC车辆及CACC车辆在下匝道分流区混合交通流仿真环境，解析CACC车辆占比对混合交通流安全性的影响。选取全速度差模型、ACC跟驰模型、CACC跟驰模型分别作为人工驾驶车辆、ACC车辆、CACC车辆的纵向跟驰模型，利用随意换道模型、强制换道模型分别构建下匝道分流主线段、远近端区的横向换道模型。基于碰撞时间（TTC）、暴露碰撞时间（TET）、整合碰撞时间（TIT）等参数构建交通流安全性评价指标。利用MATLAB进行数值模拟，仿真分析不同CACC车辆占比下的混合交通流安全性。结果表明:CACC车辆占比为40%~50%时，混合交通流安全性恶化最严重，TET和TIT分别增加约68%和89%，车辆速度离散系数为0.9以上；通过在下匝道分流区设置远端强制换道区（设置长度≤ 1 000 m），可有效降低混合交通流的追尾碰撞风险。      

Automated vehicle collisions in California: Applying Bayesian latent class model

The emerging technology of automated vehicles (AV) has been rapidly advancing and is accompanied by various positive and negative potentials. The new technology is expected to affect costs mainly by reducing the number of collisions and travel time, as well as improving fuel efficiency and parking benefits. On the other hand, safety outcomes from AV deployment is a critical issue. Ensuring the safety of AVs requires a multi-disciplinary approach that monitors every aspect of these vehicles. The California Department of Motor Vehicles has mandated that AV collision reports be made public in recent years. This study collected the scanned collision reports filed by different manufacturers that are assessing AVs in California (September 2014 to May 2019). The collected data offers critical information on AV collision frequencies and associated contributing factors. This study provides an in-depth exploratory analysis of the critical variables. We demonstrated a variational inference algorithm for Bayesian latent class models. The Bayesian latent class model identified six classes of collision patterns. Classes associated with turning, multi-vehicle collisions, dark lighting conditions with streetlights, and sideswipe and rear-end collisions were also associated with a higher proportion of injury severity levels. The authors anticipate that these results will provide a significant contribution to the area of AV and safety outcomes.     

Review of traffic safety evaluation at T-intersections using surrogate safety measures in developing countries context

In recent years, significant research has focused on traffic safety evaluations at unsignalized intersections due to complex and heterogeneous traffic movements as well as driver behaviour at such locations. However, at unsignalized T-intersections, priority traffic rules are less respected, which creates more conflicts. Further, multiple traffic movements such as right turns and through movements with varied driver behaviour results in increases the severity of conflicts. Many research studies have focused on the proactive safety measures in traffic safety evaluations as compared to crash-based analysis. Also, it is observed that Time to Collision (TTC) and Post Encroachment Time (PET) are the predominant types of surrogate safety measures in traffic safety evaluations. From the existing research outcomes, it is understood that these surrogate safety measures may give a better understanding of chain events for crash occurrences, collision mechanisms, and resulting consequences. However, further research is required in order to understand the suitability of such surrogate safety measures based on the complexity of heterogeneous traffic as well as driver behaviour with considerations of turning vehicles, particularly at T-intersections. In this context, this paper critically reviews the recent developments in Surrogate Safety Measures (SSM) and their applications at unsignalized intersections, with a particular focus on the T-intersection. This paper also brings attention to T-intersection safety evaluation with SSM in a developing country context. The outcome of the present study is more useful in the evaluation of traffic safety at T-intersections and suitable safety indicators for the evaluation.     

视线遮挡条件下面向弱势道路使用者的避撞策略研究

由于视线障碍物造成的“鬼探头”事故已经成为当前城市道路交通事故的主要类型之一。针对汽车碰撞视线遮挡条件下横穿的弱势道路使用者(VRU)的场景, 设计了1种基于碰撞时间比和安全制动距离的避撞策略, 建立车辆与VRU的交通状态数学模型, 分析“鬼探头”场景下的制动避撞临界距离。结合临界距离和车辆与VRU的碰撞时间比, 将可以避免碰撞的场景分为3种工况, 分别采用不同的制动减速度, 建立自动紧急制动避撞策略。通过Euro NCAP CPNC测试场景对该策略与传统TTC制动算法进行比较分析。结果表明, 在Euro NCAP CPNC测试场景中, 自车利用该避撞策略在理想情况下能够在更高的车速情况下完成避撞; 在不能避免碰撞的高速行驶工况中较传统TTC算法能够更加有效降低碰撞速度, 同时降低事故重伤风险和死亡风险, 提高车辆的安全性。      

Three-dimensional behaviour simulation of vehicle-to-vehicle collision by dynamic model

Previous studies of safety during vehicle collision pay attention to phenomena in the short time from starting collision, and the behaviour of rollover is studied separately from that at collision. Most simulation of traffic accidents are two-dimensional. Therefore, it is indispensable for vehicle design to analyze three-dimensional and continuous behaviour from crash till stopping. Accordingly, in this study, three-dimensional behaviour of two vehicles at collision is simulated by computer using dynamic models. Here, by comparison of the calculated results with real vehicles' collision test data, it is confirmed that the dynamic model of this study is reliable. It was confirmed in this study that the dynamic model thus established was applicable to various types of collisions and vehicles.     

车车通信环境下考虑交通拥堵状态的碰撞时间混合分布建模研究

碰撞时间（TTC）是评价车车碰撞风险的有效指标，然而该指标分布规律受到交通状态影响。为研究车车（V2V）通信环境下不同交通状态的TTC分布规律，通过构建基于LTE-V技术的车车通信环境，开展实车实验获取4种典型城市道路中的驾驶数据。考虑加速度和航向角建立动态冲突辨识模型，计算车辆以任意角度接近时的TTC值；针对TTC值的结果出现多峰值现象，将交通流分为“拥堵、缓行、畅通”这3种状态，构建了考虑交通流状态的高斯混合模型以描述不同交通状态下的TTC分布规律，并采用最大期望（EM）算法进行参数求解。将所建高斯混合模型与负指数分布、对数正态分布、负指数/对数正态混合分布这3种传统的TTC分布模型进行对比，采用校正决定系数R2评价模型的拟合优度，并通过K-S检验验证模型的有效性。在此基础上，将所建高斯混合模型应用于非车车通信条件下不同交通状态的TTC分布拟合描述，进一步验证模型的适用性。结果表明:车车通信环境下“拥堵、缓行、畅通”这3种交通状态下的高斯分布均值逐渐增大，所处交通场景的碰撞风险依次降低；考虑交通状态的TTC高斯混合模型拟合优度为0.950 5，相较于其他TTC混合分布模型，拟合优度提升了0.057 5。      

乘用车后碰撞安全模拟与结构设计研究

针对我国乘用车后碰撞燃油系统安全要求的强制性标准,研究了乘用车后碰撞安全的一般设计方法。通过某车型三厢改两厢的改型开发案例,利用有限元分析方法,分析验证了改型车的燃油系统安全性。从中总结了乘用车后碰撞安全结构设计的一些原则与要点。     

Exploring pedestrian surrogate safety measures by road geometry at midblock crosswalks: A perspective under mixed traffic conditions

Pedestrian safety is generally assessed using frequency of crashes, based on historical data, for a given transportation facility. However, the lack of good and reliable crash data has hampered its apposite analyses and in evaluating the effectiveness of pedestrian safety programs. To overcome this gap, traffic conflict technique (TCT) which relies mainly on the observations of critical traffic situations for safety analysis were developed. However, the applicability of TCTs and related measures under varying non-lane based heterogeneous traffic conditions prevailing in countries such as India is not widely explored. This paper attempts to evaluate pedestrian safety at urban midblock crosswalk using different surrogate safety measures, including vehicle crossing speed, post encroachment time (PET), yielding compliance of driver as well as pedestrian, and conflict rate. The number of conflicts were observed to increase as the average vehicle crossing speed increases, indicating that pedestrians are extremely vulnerable while crossing the road. The PET value for the smaller vehicles, such as two-wheelers and three-wheelers, is recorded to be lower than the heavy vehicles, such as trucks and buses. With the addition of one lane, there is a significant decrease in the PET value. The average PET values for the vehicle on eight-lane divided road is less than the six-lane divided, four-lane divided, and two-lane undivided roads. Further, the yielding compliance of the driver as well as of the pedestrian depends on the crossing speed of the approaching vehicle and the type of road geometry. Further, the rate and severity of conflicts increased with a decrease in the pedestrian crossing speed. The yielding behaviour of the drivers as well as the pedestrian's yielding compliance varies by location, highlighting the effect of individual and demographic characteristics on pedestrian crossing behaviour.     

Human body modelling for traffic accident analysis

A traffic accident is a complex phenomenon with vehicles and human beings involved. During a collision, the vehicle occupant is exposed to substantial loads, which can cause the occupant injuries that depend on the level of passive safety, as well as on the occupant's individual characteristics. Correct estimation of injury severity demands a validated human body model and known impact conditions. A human body modelling procedure for the purpose of accident analysis is introduced. The occupant body has been modelled as a multibody system with rigid body segments connected. Geometrical and inertial properties of individual body segments were estimated using computed tomography. Frontal impact conditions were simulated on a sled test facility, while the human body dynamic response was measured. Comparison of experimental data and computer simulation revealed an influence of joint resistive properties on the occupant motion in collisions. The difference between measured and simulated response was minimised using optimisation method. Individualised human body modelling procedure enabled better prediction of the occupant motion during vehicle collision and thus more precise estimation of possible injuries in real-life traffic accidents.     

智能网联环境下的混合交通流LWR模型

LWR（Lighthill,Whitham and Richards,LWR）模型可推演交通流宏观状态演化过程,在智能网联环境下混有协同自适应巡航控制（Cooperative Adaptive Cruise Control,CACC）车辆混合交通流LWR模型的研究,可为该混合交通流的宏观动力学特性分析提供理论工具。应用加州伯克利PATH真车试验验证的CACC模型作为CACC车辆跟驰模型,采用智能驾驶人模型（Intelligent Driver Model,IDM）模拟驾驶人在智能网联环境中的"智能"驾驶特性。基于不同CACC车辆比例下的混合交通流基本图,证明混合交通流基本图的切线斜率为交通波在混合车队中传播的波速,建立混合交通流LWR模型的一般性解析框架,得到混有CACC车辆的混合交通流LWR模型。最后,针对LWR模型冲击波特性,在6组平衡态条件下进行数值仿真试验。研究结果表明：所建立的混合交通流LWR模型可较好地描述不同CACC车辆比例时冲击波在混合车队中的传播波速;冲击波波速理论值与仿真均值的相对误差基本控制在10%以内,当冲击波处于由正向波转变为反向波的过渡阶段时,相对误差较大,为19%~26%,但绝对误差仍然较小。研究结果一方面可为混有CACC车辆的交通流宏观状态演化提供理论参考,具有推动该混合交通流其他宏观模型研究进展的积极作用;另一方面,建立的混合交通流LWR模型解析框架能够适应CACC车辆与人工-网联车辆跟驰模型选取的多样性,同时可为其他类型混合交通流LWR模型的建立提供理论支撑。     

乘用车追尾碰撞仿真研究

按照我国乘用车追尾碰撞燃油系统安全法规,针对某款乘用车建立有限元模型,并进行追尾碰撞仿真研究,分析验证其燃油系统的安全性。仿真结果为今后的汽车追尾碰撞仿真及尾部耐撞性研究提供了借鉴。     

自动驾驶车辆混行集聚MAS控制模型

随着车路协同技术和自动驾驶技术的不断发展，越来越多的网联自动驾驶车辆（Connected and Autonomous Vehicle， CAV）涌入道路交通，与传统人工驾驶车辆（Human Pilot Vehicle， HPV）形成混合交通流（Mixed Traffic Stream， MTS）。为在提高MTS交通流量的同时保证交通安全，面向未来的混行交通环境，结合交通工程中人、车、路等要素，设计基于多智能体系统的CAV集聚控制模型（Agglomeration Control Model of Connected and Autonomous Vehicle Based on Multi-Agent System，ACMCAV-MAS）。该模型针对CAV的可控性和HPV的随机性，意在通过集聚控制，促使道路中分散行驶的CAV集聚成行驶条件更优的队列。具体以Agent的形式设计与集聚控制相关的底层车辆Agent（CAV-Agent和HPV-Agent两类）和上层管理Agent。同时，针对同质要素间的匹配和异质要素间的风险规避，区别于常规的无集聚（No Agglomeration，NOA）策略，提出车队级集聚（Platoon Level Agglomeration， PLA）和车道级集聚（Lane Level Agglomeration，LLA）2种策略及相关的CAV-Agent集聚控制算法。基于ACMCAV-MAS及元胞自动机模型，在不同交通流密度和不同CAV-Agent渗透率下进行仿真试验。结果表明：集聚策略能在60%的CAV-Agent渗透率下取得最佳效益，同时，在60 veh·km^-1密度条件下，车队级集聚策略平均能提升38.14%的交通流量，比车道级集聚的提升效果高9.73%，并能在40~50 veh·km^-1的密度范围和50%~70%的CAV-Agent渗透率条件下有效缓解交通拥堵；通过对中高密度交通流下的纵向风险分析，发现2种集聚策略在低CAV-Agent渗透率下的风险发生率无显著差异，且最大风险降低比例都能达到20%以上，然而，在实际交通情况下，集聚策略可能会在一定程度上导致横向碰撞风险的增加。在未来的工作中，将继续探究降低横向碰撞风险的方法，同时着力解决目前仿真框架中对于人工驾驶行为异质性建模不够完善的缺陷，不断优化ACMCAV-MAS，为未来MTS中自动驾驶策略的制定提供理论依据。     

分心对驾驶人交通冲突反应时间的影响(双语出版)

为了研究分心对交通冲突状态下驾驶人反应时间的影响,采用驾驶模拟器构建城市道路交通环境下2种典型冲突形态：侧向行人冲突和纵向追尾冲突,设计认知、视觉以及发短信（认知+视觉复合分心）3种分心任务,在不同行驶车速、跟车时距、前车减速度等紧迫度条件下,采集30名驾驶人应对交通冲突的制动反应时间,分别采用重复测量一般线性模型及线性混合模型进行统计分析。研究结果表明：认知分心使驾驶人应对侧向行人冲突的制动反应时间增加0.09 s,但未观察到其对纵向追尾冲突反应时间的显著性影响;视觉分心与发短信都会延缓驾驶人应对侧向行人（分别增加0.31 s和0.27 s）以及纵向追尾冲突（分别增加0.47 s和0.38 s）的制动反应时间;此外,在纵向追尾冲突中,随着冲突紧迫度提高（前车减速度增大、车头时距减小以及自车速度增大）,驾驶人制动反应时间显著减小。表明驾驶分心延长了驾驶人应对交通冲突的反应时间,容易导致事故的发生,具体而言,认知分心主要延长驾驶人应对侧向冲突的反应时间,涉及视觉的分心同时延长驾驶人应对侧向及纵向冲突的反应时间;视觉分心对驾驶人反应时间的延长显著性高于认知分心,说明视觉分心对行车安全影响更大。