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.

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.

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.

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.

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.

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.

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.

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.

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.

Robust Platoon Control of Mixed Autonomous and Human-Driven Vehicles for Obstacle Collision Avoidance:A Cooperative Sensing-Based Adaptive Model Predictive Control Approach

Obstacle detection and platoon control for mixed traffic flows,comprising human-driven vehicles(HDVs)and connected and autonomous vehicles(CAVs),face challenges from uncertain disturbances,such as sensor faults,inaccurate driver operations,and mismatched model errors.Furthermore,misleading sensing information or malicious attacks in vehicular wireless networks can jeopardize CAVs’perception and platoon safety.In this paper,we develop a two-dimensional robust control method for a mixed platoon,including a single leading CAV and multiple following HDVs that incorpo-rate robust information sensing and platoon control.To effectively detect and locate unknown obstacles ahead of the leading CAV,we propose a cooperative vehicle-infrastructure sensing scheme and integrate it with an adaptive model predictive control scheme for the leading CAV.This sensing scheme fuses information from multiple nodes while suppressing malicious data from attackers to enhance robustness and attack resilience in a distributed and adaptive manner.Additionally,we propose a distributed car-following control scheme with robustness to guarantee the following HDVs,considering uncertain disturbances.We also provide theoretical proof of the string stability under this control framework.Finally,extensive simulations are conducted to validate our approach.The simulation results demonstrate that our method can effectively filter out misleading sensing information from malicious attackers,significantly reduce the mean-square deviation in obstacle sensing,and approach the theoretical error lower bound.Moreover,the proposed control method successfully achieves obstacle avoidance for the mixed platoon while ensuring stability and robustness in the face of external attacks and uncertain disturbances.

Public perception of connected and automated vehicles:Benefits,concerns,and barriers from an Australian perspective

This study investigates the attitudes and concerns of the Australian public toward connected and autonomous vehicles(CAVs),and the factors influencing their willingness to adopt this technology.Through a comprehensive survey,a diverse group of respondents provided valuable insights toward various CAV scenarios such as riding in a vehicle with no driver,self-driving public transport,self-driving taxis,and heavy vehicles without drivers.The results highlight the significant impact of safety concerns about automated vehicles on individuals’attitudes across all scenarios.Higher levels of concern were associated with more negative attitudes,and a strong correlation between concerns and opposition underlines the necessity of addressing these apprehensions to build public trust and promote CAV adoption.Interestingly,nearly 70%of respondents felt uncomfortable driving next to a CAV,but they displayed more confidence in adopting automated public transport in the near future.Additionally,around 40%of participants indicated a strong willingness to purchase a CAV,primarily driven by the desire to reduce their carbon footprint and safety considerations.Notably,respondents with health conditions or disability exhibited heightened interest(almost double those without health conditions)in CAV technology.Gender differences emerged in attitudes and preferences toward CAVs,with women expressing a greater level of concern and perceiving higher barriers to CAV deployment.This emphasizes the importance of employing targeted approaches to address the specific concerns of different demographics.The study also underscores the role of trust in technology as a significant barrier to CAV deployment,ranking high among respondents’concerns.To overcome these challenges and facilitate successful CAV deployment,various strategies are suggested,including live demonstrations,dedicated routes for automated public transport,adoption incentives,and addressing liability concerns.The findings from this study offer valuable insights for government agencies,vehicle manufacturers,and stakeholders in promoting the successful implementation of CAVs.By understanding societal acceptance and addressing concerns,decision-makers can devise effective interventions and policies to ensure the safe and widespread adoption of CAVs in Australia.Moreover,vehicle manufacturers can leverage these results to consider design aspects that align with passenger preferences,thereby facilitating the broader acceptance and adoption of CAVs in the future.Finally,this research provides a significant contribution to the understanding of public perception and acceptance of CAVs in the Australian context.By guiding decision-making and informing strategies,the study lays the foundation for a safer and more effective integration of CAVs into the country’s transportation landscape.

基于动态距离窗的交叉口CAV轨迹规划算法

针对目前智能网联车(connected and autonomous vehicle,CAV)通过交叉口的轨迹规划算法无法兼顾效率与安全协同最优的问题,根据CAV驶入交叉口通信范围时的不同初始行驶状态引入动态距离窗(dynamic distance windows,DDW)概念,提出适用于可控安全行驶条件下通行效率最优的轨迹规划算法。算法根据CAV初始行驶状态参数和信号灯信息、最大舒适加/减速度和道路限速约束条件,获得CAV初始行驶状态对应的DDW。针对CAV初始位置与停车线上游特定位置间的距离处于DDW范围之内和之外的两种情况,分别设计相应的轨迹规划算法,实现CAV通过交叉口延误最小。仿真结果显示,所提出的算法可有效提高CAV通过交叉口的效率,并且具有更小的速度波动及更平滑的时空轨迹。

Multi-agent reinforcement learning for cooperative lane changing of connected and autonomous vehicles in mixed traffic

Autonomous driving has attracted significant research interests in the past two decades as it offers many potential benefits,including releasing drivers from exhausting driving and mitigating traffic congestion,among others.Despite promising progress,lane-changing remains a great challenge for autonomous vehicles(AV),especially in mixed and dynamic traffic scenarios.Recently,reinforcement learning(RL)has been widely explored for lane-changing decision makings in AVs with encouraging results demonstrated.However,the majority of those studies are focused on a single-vehicle setting,and lane-changing in the context of multiple AVs coexisting with human-driven vehicles(HDVs)have received scarce attention.In this paper,we formulate the lane-changing decision-making of multiple AVs in a mixed-traffic highway environment as a multi-agent reinforcement learning(MARL)problem,where each AV makes lane-changing decisions based on the motions of both neighboring AVs and HDVs.Specifically,a multi-agent advantage actor-critic(MA2C)method is proposed with a novel local reward design and a parameter sharing scheme.In particular,a multi-objective reward function is designed to incorporate fuel efficiency,driving comfort,and the safety of autonomous driving.A comprehensive experimental study is made that our proposed MARL framework consistently outperforms several state-of-the-art benchmarks in terms of efficiency,safety,and driver comfort.

Adaptive Multi-modal Fusion Instance Segmentation for CAEVs in Complex Conditions:Dataset,Framework and Verifications

Current works of environmental perception for connected autonomous electrified vehicles(CAEVs)mainly focus on the object detection task in good weather and illumination conditions,they often perform poorly in adverse scenarios and have a vague scene parsing ability.This paper aims to develop an end-to-end sharpening mixture of experts(SMoE)fusion framework to improve the robustness and accuracy of the perception systems for CAEVs in complex illumination and weather conditions.Three original contributions make our work distinctive from the existing relevant literature.The Complex KITTI dataset is introduced which consists of 7481 pairs of modified KITTI RGB images and the generated LiDAR dense depth maps,and this dataset is fine annotated in instance-level with the proposed semi-automatic annotation method.The SMoE fusion approach is devised to adaptively learn the robust kernels from complementary modalities.Comprehensive comparative experiments are implemented,and the results show that the proposed SMoE framework yield significant improvements over the other fusion techniques in adverse environmental conditions.This research proposes a SMoE fusion framework to improve the scene parsing ability of the perception systems for CAEVs in adverse conditions.

Merging control strategies of connected and autonomous vehicles at freeway on-ramps:a comprehensive review

Purpose–On-ramp merging areas are typical bottlenecks in the freeway network since merging on-ramp vehicles may cause intensive disturbances on the mainline traffic flow and lead to various negative impacts on traffic efficiency and safety.The connected and autonomous vehicles(CAVs),with their capabilities of real-time communication and precise motion control,hold a great potential to facilitate ramp merging operation through enhanced coordination strategies.This paper aims to present a comprehensive review of the existing ramp merging strategies leveraging CAVs,focusing on the latest trends and developments in the research field.Design/methodology/approach–The review comprehensively covers 44 papers recently published in leading transportation journals.Based on the application context,control strategies are categorized into three categories:merging into sing-lane freeways with total CAVs,merging into singlane freeways with mixed traffic flows and merging into multilane freeways.Findings–Relevant literature is reviewed regarding the required technologies,control decision level,applied methods and impacts on traffic performance.More importantly,the authors identify the existing research gaps and provide insightful discussions on the potential and promising directions for future research based on the review,which facilitates further advancement in this research topic.Originality/value–Many strategies based on the communication and automation capabilities of CAVs have been developed over the past decades,devoted to facilitating the merging/lane-changing maneuvers at freeway on-ramps.Despite the significant progress made,an up-to-date review covering these latest developments is missing to the authors’best knowledge.This paper conducts a thorough review of the cooperation/coordination strategies that facilitate freeway on-ramp merging using CAVs,focusing on the latest developments in this field.Based on the review,the authors identify the existing research gaps in CAV ramp merging and discuss the potential and promising future research directions to address the gaps.

An Optimal Distribution of RSU for Improving Self-Driving Vehicle Connectivity

Self-driving and semi-self-driving cars play an important role in our daily lives.The effectiveness of these cars is based heavily on the use of their surrounding areas to collect sensitive and vital information.However,external infrastructures also play significant roles in the transmission and reception of control data,cooperative awareness messages,and caution notifications.In this case,roadside units are considered one of themost important communication peripherals.Random distribution of these infrastructures will overburden the spread of self-driving vehicles in terms of cost,bandwidth,connectivity,and radio coverage area.In this paper,a new distributed roadside unit is proposed to enhance the performance and connectivity of these cars.Therefore,this approach is based primarily on k-means to find the optimal location of each roadside unit.In addition,this approach supports dynamicmobility with a long period of connectivity for each car.Further,this system can adapt to various locations(e.g.,highways,rural areas,urban environments).The simulation results of the proposed system are reflected in its efficiency and effectively.Thus,the system can achieve a high connectivity rate with a low error rate while reducing costs.

Impact of connected and autonomous vehicles on traffic safety of mixed traffic flow:from the perspective of connectivity and spatial distribution

Equipped with high driving automation and advanced communication technologies, connected and autonomous vehicles (CAV) areexpected to possess a shorter reaction time and a wider vision,which are promising to improve traffic safety and efficiency. However,little attention has been paid to the effect of connectivity and spatial distribution on the safety performance of mixed traffic flow. Inthis paper, we attempt to investigate the impact of CAV on traffic safety considering these factors. To this end, a car-following modelfor CAV is proposed first. Then, the cooperative driving strategy for CAVs is designed. Precisely, the feedback gains of the informationare adjusted in real-time and are designed based on the derived stability criterion of the mixed traffic flow. Microscopic simulations ofmixed traffic flow in traffic oscillation are designed and conducted to explore how the distribution and connectivity of CAV affect thesafety performance ofmixed traffic flow. Simulation results show that increasing the penetration rate of CAV is promising to shift thesafety performance ofmixed traffic flow. In addition, the safety performance of mixed traffic flow is related to the spatial distributionand communication range of CAV. Besides, increasing communication range does not inevitably improve the safety performance ofmixed traffic flow when the penetration rate of CAV is low. Moreover, it is also found from the spatial–temporal trajectory of themixedtraffic flow that introducing CAV can mitigate the propagation of the stop-and-go wave and increase the throughput.

Effects of connected and autonomous vehicle merging behavior on mainline human-driven vehicle

Purpose–This study aims to evaluate the influence of connected and autonomous vehicle(CAV)merging algorithms on the driver behavior of human-driven vehicles on the mainline.Design/methodology/approach–Previous studies designed their merging algorithms mostly based on either the simulation or the restricted field testing,which lacks consideration of realistic driving behaviors in the merging scenario.This study developed a multi-driver simulator system to embed realistic driving behavior in the validation of merging algorithms.Findings–Four types of CAV merging algorithms were evaluated regarding their influences on driving safety and driving comfort of the mainline vehicle platoon.The results revealed significant variation of the algorithm influences.Specifically,the results show that the reference-trajectory-based merging algorithm may outperform the social-psychology-based merging algorithm which only considers the ramp vehicles.Originality/value–To the best of the authors’knowledge,this is the first time to evaluate a CAV control algorithm considering realistic driver interactions rather than by the simulation.To achieve the research purpose,a novel multi-driver driving simulator was developed,which enables multi-drivers to simultaneously interact with each other during a virtual driving test.The results are expected to have practical implications for further improvement of the CAV merging algorithm.