Connected and Autonomous Vehicles (CAVs) Challenges with Non-motorized Amenities Environments

With the deployment of Connected and Automated Vehicles in the coming decades,road transportation will experience a significant upheaval.CAVs(Connected and Autonomous Vehicles)have been a main emphasis of Transportation and the automotive sector,and the future of transportation system analysis is widely anticipated.The examination and future devel­opment of CAVs technology has been the subject of numerous researches.However,as three essential kinds of road users,pedestrians,bicyclists,and motorcyclists have experienced little to no handling.We explored the influ­ence of CAVs on non-motorized mobility in this article and seven various issues that CAVs face in the environment.

Resilient and Safe Platooning Control of Connected Automated Vehicles Against Intermittent Denial-of-Service Attacks

Connected automated vehicles(CAVs)serve as a promising enabler for future intelligent transportation systems because of their capabilities in improving traffic efficiency and driving safety,and reducing fuel consumption and vehicle emissions.A fundamental issue in CAVs is platooning control that empowers a convoy of CAVs to be cooperatively maneuvered with desired longitudinal spacings and identical velocities on roads.This paper addresses the issue of resilient and safe platooning control of CAVs subject to intermittent denial-of-service(DoS)attacks that disrupt vehicle-to-vehicle communications.First,a heterogeneous and uncertain vehicle longitudinal dynamic model is presented to accommodate a variety of uncertainties,including diverse vehicle masses and engine inertial delays,unknown and nonlinear resistance forces,and a dynamic platoon leader.Then,a resilient and safe distributed longitudinal platooning control law is constructed with an aim to preserve simultaneous individual vehicle stability,attack resilience,platoon safety and scalability.Furthermore,a numerically efficient offline design algorithm for determining the desired platoon control law is developed,under which the platoon resilience against DoS attacks can be maximized but the anticipated stability,safety and scalability requirements remain preserved.Finally,extensive numerical experiments are provided to substantiate the efficacy of the proposed platooning method.

Traffic flow of connected and automated vehicles at lane drop on two-lane highway: An optimization-based control algorithm versus a heuristic rules-based algorithm

This paper investigates traffic flow of connected and automated vehicles at lane drop on two-lane highway. We evaluate and compare performance of an optimization-based control algorithm(OCA) with that of a heuristic rules-based algorithm(HRA). In the OCA, the average speed of each vehicle is maximized. In the HRA, virtual vehicle and restriction of the command acceleration caused by the virtual vehicle are introduced. It is found that(i) capacity under the HRA(denoted as C_(H)) is smaller than capacity under the OCA;(ii) the travel delay is always smaller under the OCA, but driving is always much more comfortable under the HRA;(iii) when the inflow rate is smaller than C_(H), the HRA outperforms the OCA with respect to the fuel consumption and the monetary cost;(iv) when the inflow rate is larger than C_(H), the HRA initially performs better with respect to the fuel consumption and the monetary cost, but the OCA would become better after certain time. The spatiotemporal pattern and speed profile of traffic flow are presented, which explains the reason underlying the different performance. The study is expected to help for better understanding of the two different types of algorithm.

Planning and Decision-making for Connected Autonomous Vehicles at Road Intersections:A Review

Planning and decision-making technology at intersections is a comprehensive research problem in intelligent transportation systems due to the uncertainties caused by a variety of traffic participants.As wireless communication advances,vehicle infrastructure integrated algorithms designed for intersection planning and decision-making have received increasing attention.In this paper,the recent studies on the planning and decision-making technologies at intersections are primarily overviewed.The general planning and decision-making approaches are presented,which include graph-based approach,prediction base approach,optimization-based approach and machine learning based approach.Since connected autonomous vehicles(CAVs)is the future direction for the automated driving area,we summarized the evolving planning and decision-making methods based on vehicle infrastructure cooperative technologies.Both four-way signalized and unsignalized intersection(s)are investigated under purely automated driving traffic and mixed traffic.The study benefit from current strategies,protocols,and simulation tools to help researchers identify the presented approaches’challenges and determine the research gaps,and several remaining possible research problems that need to be solved in the future.

AI Based Traffic Flow Prediction Model for Connected and Autonomous Electric Vehicles

There is a paradigm shift happening in automotive industry towards electric vehicles as environment and sustainability issues gainedmomentum in the recent years among potential users.Connected and Autonomous Electric Vehicle(CAEV)technologies are fascinating the automakers and inducing them to manufacture connected autonomous vehicles with self-driving features such as autopilot and self-parking.Therefore,Traffic Flow Prediction(TFP)is identified as a major issue in CAEV technologies which needs to be addressed with the help of Deep Learning(DL)techniques.In this view,the current research paper presents an artificial intelligence-based parallel autoencoder for TFP,abbreviated as AIPAE-TFP model in CAEV.The presented model involves two major processes namely,feature engineering and TFP.In feature engineering process,there are multiple stages involved such as feature construction,feature selection,and feature extraction.In addition to the above,a Support Vector Data Description(SVDD)model is also used in the filtration of anomaly points and smoothen the raw data.Finally,AIPAE model is applied to determine the predictive values of traffic flow.In order to illustrate the proficiency of the model’s predictive outcomes,a set of simulations was performed and the results were investigated under distinct aspects.The experimentation outcomes verified the effectual performance of the proposed AIPAE-TFP model over other methods.

Markov chain-based platoon recognition model in mixed traffic with human-driven and connected and autonomous vehicles

Many vehicle platoons are interrupted while traveling on roads,especially at urban signalized intersections.One reason for such interruptions is the inability to exchange real-time information between traditional human-driven vehicles and intersection infrastructure.Thus,this paper develops a Markov chain-based model to recognize platoons.A simulation experiment is performed in Vissim based on field data extracted from video recordings to prove the model’s applicability.The videos,recorded with a high-definition camera,contain field driving data from three Tesla vehicles,which can achieve Level 2 autonomous driving.The simulation results show that the recognition rate exceeds 80%when the connected and autonomous vehicle penetration rate is higher than 0.7.Whether a vehicle is upstream or downstream of an intersection also affects the performance of platoon recognition.The platoon recognition model developed in this paper can be used as a signal control input at intersections to reduce the unnecessary interruption of vehicle platoons and improve traffic efficiency.

考虑CAV自主停车行为的混合交通均衡配流模型

为了评估网联自动驾驶汽车(CAV)的自主停车行为对交通系统效率的影响,建立了CAV通勤流、人工驾驶汽车(HDV)通勤流、CAV自主停车流3类交通流混行下的交通均衡配流模型.通过引入CAV停车需求内生变量,考虑CAV对路段通行能力的提升效应,从而定量描述CAV停车需求分布以及3类交通流在路段上混行的拥挤效应.分别采用用户均衡、随机用户均衡原则描述CAV、HDV出行者的路径选择行为,采用Logit离散选择模型描述自主停车CAV的停车场选择行为,由此建立多用户混合交通均衡条件以及等价的变分不等式(VI)模型.由于模型中CAV自主停车需求为未知的内生变量,提出一种改进的相继加权平均法求解该模型.最后,通过算例验证了混合交通均衡配流模型及求解算法的有效性.

Autonomous vehicles: challenges, opportunities, and future implications for transportation policies

This study investigates the challenges and opportunities pertaining to transportation policies that may arise as a result of emerging autonomous vehicle （AV） technologies. AV technologies can decrease the transportation cost and increase accessibility to low-income households and persons with mobility issues. This emerging technology also has far-reaching applications and implications beyond all current expectations. This paper provides a comprehensive review of the relevant literature and explores a broad spectrum of issues from safety to machine ethics. An indispensable part of a prospective AV development is communication over cars and infrastructure （connected vehicles）. A major knowledge gap exists in AV technology with respect to routing behaviors. Connected- vehicle technology provides a great opportunity to imple- ment an efficient and intelligent routing system. To this end, we propose a conceptual navigation model based on a fleet of AVs that are centrally dispatched over a network seeking system optimization literature on two fronts： （i） This study contributes to the it attempts to shed light on future opportunities as well as possible hurdles associated with AV technology; and （ii） it conceptualizes a navigation model for the AV which leads to highly efficient traffic circulations.

Pedestrian Facilities Capacity and Level of Service at Intersections in a Connected and Autonomous Vehicle Environment

It is inevitable that Connected and Autonomous Vehicles (CAVs) will be a major focus of transportation and the automotive industry with increased use in future traffic system analysis. Numerous studies have focused on the evaluation and potential development of CAVs technology;however, pedestrians and bicyclists, as two essential and important modes of the road users have seen little to no coverage. In response to the need for analyzing the impact of CAVs on non-motorized transportation, this paper develops a new model for the evaluation of the Level of Service (LOS) for pedestrians in a CAVs environment based on the Highway Capacity Manual (HCM). The HCM provides a methodology to assess the level of service for pedestrians and bicyclists on various types of intersections in urban areas. Five scenarios were created for simulation via VISSIM (a software) that corresponds to the different proportions of the CAVs and different signal systems in a typical traffic environment. Alternatively, the Surrogate Safety Assessment Model (SSAM) was selected for analyzing the safety performance of the five scenarios. Through computing and analyzing the results of simulation and SSAM, the latter portion of this paper focuses on the development of a new model for evaluating pedestrian LOS in urban areas which are based upon HCM standards which are suitable for CAVs environments. The results of this study are intended to inform the future efforts of engineers and/or policymakers and to provide them with a tool to conduct a comparison of capacity and LOS related to the impact of CAVs on pedestrians during the process of a transportation system transition to CAVs.

Online learning-based model predictive trajectory control for connected and autonomous vehicles: Modeling and physical tests

Motivated by the promising benefits of connected and autonomous vehicles (CAVs) in improving fuelefficiency, mitigating congestion, and enhancing safety, numerous theoretical models have been proposed to plan CAVmultiple-step trajectories (time–specific speed/location trajectories) to accomplish various operations. However, limitedefforts have been made to develop proper trajectory control techniques to regulate vehicle movements to follow multiplesteptrajectories and test the performance of theoretical trajectory planning models with field experiments. Without aneffective control method, the benefits of theoretical models for CAV trajectory planning can be difficult to harvest. This studyproposes an online learning-based model predictive vehicle trajectory control structure to follow time–specific speed andlocation profiles. Unlike single-step controllers that are dominantly used in the literature, a multiple-step model predictivecontroller is adopted to control the vehicle’s longitudinal movements for higher accuracy. The model predictive controlleroutput (speed) cannot be interpreted by vehicles. A reinforcement learning agent is used to convert the speed value to thevehicle’s direct control variable (i.e., throttle/brake). The reinforcement learning agent captures real-time changes in theoperating environment. This is valuable in saving parameter calibration resources and improving trajectory control accuracy.A line tracking controller keeps vehicles on track. The proposed control structure is tested using reduced-scale robot cars.The adaptivity of the proposed control structure is demonstrated by changing the vehicle load. Then, experiments on twofundamental CAV platoon operations (i.e., platooning and split) show the effectiveness of the proposed trajectory controlstructure in regulating robot movements to follow time-specific reference trajectories.

Large-Scale Vehicle Platooning:Advances and Challenges in Scheduling and Planning Techniques

Through vehicle-to-vehicle(V2V)communication,autonomizing a vehicle platoon can significantly reduce the distance between vehicles,thereby reducing air resistance and improving road traffic efficiency.The gradual maturation of platoon control technology is enabling vehicle platoons to achieve basic driving functions,thereby permitting large-scale vehicle platoon scheduling and planning,which is essential for industrialized platoon applications and generates significant economic benefits.Scheduling and planning are required in many aspects of vehicle platoon operation;here,we outline the advantages and challenges of a number of the most important applications,including platoon formation scheduling,lane-change planning,passing traffic light scheduling,and vehicle resource allocation.This paper’s primary objective is to integrate current independent platoon scheduling and planning techniques into an integrated architecture to meet the demands of large-scale platoon applications.To this end,we first summarize the general techniques of vehicle platoon scheduling and planning,then list the primary scenarios for scheduling and planning technique application,and finally discuss current challenges and future development trends in platoon scheduling and planning.We hope that this paper can encourage related platoon researchers to conduct more systematic research and integrate multiple platoon scheduling and planning technologies and applications.

Reducing congestion and emissions via roadside unit deployment under mixed traffic flow

It is expected that for a long time the future road trafc will be composed of both regular vehicles(RVs)and connected autonomous vehicles(CAVs).As a vehicle-to-infrastructure technology dedicated to facilitating CAV under the mixed trafc fow,roadside units(RSUs)can also improve the quality of information received by CAVs,thereby infuencing the routing behavior of CAV users.This paper explores the possibility of leveraging the RSU deployment to afect the route choices of both CAVs and RVs and the adoption rate of CAVs so as to reduce the network congestion and emissions.To this end,we frst establish a logit-based stochastic user equilibrium model to capture drivers’route choice and vehicle type choice behaviors provided the RSU deployment plan is given.Particularly,CAV users’perception error can be reduced by higher CAV penetration and denser RSUs deployed on the road due to the improved information quality.With the established equilibrium model,the RSU deployment problem is then formulated as a mathematical program with equilibrium constraints.An active-set algorithm is presented to solve the deployment problem efciently.Numerical results suggest that an optimal RSU deployment plan can efectively drive the system towards one with lower network delay and emissions.

Lane-changing decision rule with the difference of traffic flow's variation in multi-lane highway for connected and autonomous vehicles

Drivers are not far-sighted when they execute lane-changing manipulation.To address this issue,this study proposes a rule to improve vehicles'lane-changing decisions with accurate information of surrounding vehicles(e.g.time headway)-More specifically,connected and autonomous vehicles(CAVs)change lanes in advance if they find severer flow reducing in the lanes,while CAVs should maintain the car-following state if the variations of traffc flow in all lanes have a similar trend.To ilustrate the idea,this study frst calibrates two classic car-following models and a lane-changing model,and then conducts numerical simulations to illustrate the short-sighted decision of drivers.The study incorporates the idea into a lane-changing decision rule by changing the lane-changing model's pa-rameter,and conducts numerical tests to evaluate the effectiveness of the lane-changing decision rule in a multi-lane highway with a bottleneck.The results of this study indicate that the new lane-changing decision rule can substantially improve the throughput of the traffic flow,especially when the inflow exceeds the remaining capacity of the road.The lane-changing rule and results can bring insights into the control of CAVs,as well as the driver assistance system in connected vehicles.

中国智能网联车险之现实困境与创新路径

在智慧交通新时代,智能网联车险面临着新的机遇和挑战。本文分析了国内外智能网联车险的发展现状和特征;从智能网联汽车衍生的新兴风险、车险市场经营管理面临的挑战、车险产品和服务的创新需求三方面,探讨了智能网联车险发展的现实困境。基于国外智能网联车险模式的经验借鉴,提出政府、监管部门、保险公司与科技公司合作共赢,进一步完善智能网联车险相关法律体系,创新监管模式,完善车险生态环境,提供优质车险产品与服务等政策建议。

异质交通流环境下无信号T形交叉口控制策略

针对无信号控制T形交叉口,提出一种异质交通流环境下交叉口的分级限速控制模型.根据异质交通流的车辆运行特性提出分级限速的概念,并建立交叉口的车辆信息矩阵.构建以车流通过冲突区域时间最短为优化目标,以保证冲突车辆安全行驶的时间间隔为约束条件的线性规划模型.通过MATLAB搭建仿真平台对所提出的模型有效性进行试验验证.结果表明:在混合行驶的交通环境下,当车辆到达率为0.30~0.50时,本研究所提模型可以在传统控制模型的基础上提升交叉口通行能力约20%;分级限速控制模型下智能网联车辆渗透率达到0.90时,最多可以增加交叉口约17%的通行能力;所提模型能有效提高道路资源利用效率,保证主路运行通畅的情况下大幅降低支路车辆延误.

考虑智能网联车队要素的交通震荡特性研究

针对智能网联汽车(Connected and Automated Vehicle,CAV)的加入所引发的交通震荡问题,以不同CAV市场渗透率的异质交通流为研究对象,引入CAV车队规模和强度,对异质车队组成进行划分并仿真复现交通震荡现象,采用时空轨迹图和加减速波传播速度可视化交通震荡的演变情况,同时选用加速、减速持续时间衡量交通震荡周期,速度标准差衡量交通震荡振幅.通过设计考虑CAV车队规模、强度和渗透率因素以及头车不同变速模式的交互实验,探究CAV车队要素和头车不同变速模式对交通震荡的影响.研究结果表明:CAV车队规模、强度和渗透率的提高均对震荡周期的减小具有积极影响;车队规模的扩大会增加震荡振幅,而车队强度的增强会减小震荡振幅;随着CAV渗透率的提高,震荡振幅先上升再下降,当渗透率为0.5~0.6时,震荡振幅达到峰值;头车急减速-急加速模式下的交通震荡周期和振幅最小,头车缓慢减速-缓慢加速模式下的交通震荡周期和振幅最大.

利用激光点云的智能网联汽车自主换道横向避障

通过智能网联汽车自主换道横向避障设计,提高车辆自主驾驶安全性能,提出基于激光点云的智能网联汽车自主换道横向避障方法,构建基于全激光雷达的无人驾驶系统的汽车行驶环境激光点云数据采集模型,通过激光雷达激光点云图像检测方法实现对智能网汽车行驶环境感知。结合障碍物检测、车道线检测、可行驶区域检测的方法,分析智能网联下汽车换道障碍物和车道的相关性特征量,通过激光点云的自主寻优方法寻找边界点直到搜索路径达到阈值,再结合直线道路模型和车道线连续性原则实现对汽车自主换道横向避障规划设计。仿真结果表明,采用该方法进行汽车自主环控避障,在5 s时达到收敛状态,障碍物坐标识别误差距离低于0.7,并且该方法的识别精度达到了0.970以上,提高了车道信息的感知能力,准确高效地估计可行驶区域,提高车辆避障能力。

智能网联车专用道内队列控制模型

在智能网联车与人工驾驶车辆混合通行的场景中,为提高混行交通流的通行效率,提出了一种智能网联车专用道内车辆按编队行驶的队列控制模型。根据快速路道路条件和智能网联车的运行模式,通过分析智能网联车专用道内前后两车的运动过程,构建了智能网联车车队创建、加入和巡航控制模型,并求解出专用道内智能网联车形成编队行驶的最优策略。为验证队列控制模型的有效性,利用SUMO仿真平台搭建单车道和多车道仿真场景,模拟智能网联车在专用道内形成车队的过程,统计分析车流的通行效率,其中单车道仿真结果显示,相比于传统CACC跟驰模型,队列控制模型能将车辆编队时间减少23.06%;多车道仿真结果显示,当CAV渗透率在23.22%~40%时,队列控制模型下的车辆平均行程时间缩短17%。这表明在智能网联车专用道内,队列控制模型能更有效地利用专用道的空间资源,从而缩短车辆编队时间,提高智能网联环境下混行交通流的通行效率,故可用于混行交通流下智能网联车专用道的设置。

基于鱼群效应的智能网联车队形成与演化机理研究

随着智能网联车的快速发展,智能网联车与人工驾驶车辆混行下的交通特性分析和协同控制研究已成为热点。本文构建多车道混合交通流元胞自动机仿真模型,对智能网联车队的形成及状态演化过程进行仿真刻画。首先,基于智能网联车的网联化特征,引入鱼群效应来描述4种智能网联车队的形成过程;其次,从车队角度,利用马尔可夫性计算车队规模转移概率,阐述车队状态演化过程;再次,引入Gipps安全距离原则对NaSch模型进行改进,并对智能网联车和智能网联车队进行速度引导;最后,基于实测的车辆到达率,对建立的基于鱼群效应的混合车流元胞自动机模型进行仿真实验。结果表明:智能网联车队可以有效改善混合交通流的运行状态,缓解交通拥堵;在渗透率60%的条件下,智能网联车队规模为3时,与不存在车队相比,拥堵率可减少43.9%,交通流速度约提升43%,并且随着车队规模的增大,交通流平均速度趋于平稳。

基于多智能体一致性的多车道车辆集群编队控制

为安全、高效地控制智能网联汽车(CAV)编队,研究了基于多智能体系统(MAS)一致性的多车道、零散随机分布的车辆集群编队控制策略,并进行数值仿真验证。建立了基于交错式结构的车群期望几何拓扑,提出了包括间距调整阶段、变道控制阶段和队形收敛阶段的3阶段式编队控制流程。基于一致性理论,设计了车群编队控制器,并进行稳定性论证。选取2种典型场景,对三车道场景中初始分布较为极端的车群开展编队研究。结果表明:在该策略的控制过程中,纵向车间距与期望间距误差在0.5 m以内,横向位置与期望位置误差在5 cm以内,且速度在变化后能快速收敛至期望值。因而,该策略能有效控制车群车辆安全、高效地达到车群期望队形。