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.

Adaptive control for reliable cooperative intersection crossing of connected autonomous vehicles

Rapid advances in vehicle automation and communication technologies enable connected autonomous vehicles(CAVs)to cross intersections cooperatively,which could significantly improve traffic throughput and safety at intersections.Virtual platooning,designed upon car‐following behavior,is one of the promising control methods to promote cooperative intersection crossing of CAVs.Nevertheless,demand variation raises safety and stability concerns when CAVs adopt a virtual platooning control approach.Along this line,this study proposes an adaptive vehicle control method to facilitate the formation of a virtual platoon and the cooperative crossing of CAVs,factoring demand variations at an isolated intersection.This study derives the stability conditions of virtual CAV platoons depending on the time‐varying traffic demand.Based on the derived stability conditions,an optimization model is proposed to adaptively control CAVs dynamics by balancing approaching traffic mobility and safety to enhance the reliability of cooperative crossing at intersections.The simulation results show that,compared to the nonadaptive control,our proposed method can increase the intersection throughput by 18.2%.Also,time‐to‐collision results highlight the advantages of the proposed adaptive control in securing traffic safety.

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.

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.

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.

Connecting tradition with modernity: Safety literature review

Road safety has long been considered as one of the most important issues.Numerous studies have been conducted to investigate crashes with significant progress,whereas most of the work concentrates on the lifespan period of roadways and safety influencing factors.This paper undertakes a systematic literature review from the crash procedure to identify the state-of-the-art knowledge,advantages and disadvantages of crash risk,crash prediction,crash prevention and safety of connected and autonomous vehicles(CAVs).As a result of this literature review,substantive issues in general,data source and modeling selection are discussed,and the outcome of this study aims to provide the summary of crash knowledge with potential insight into both traditional and emerging aspects,and guide the future research direction in safety.

A Trailblazing Framework of Security Assessment for Traffic Data Management

Connected and autonomous vehicles are seeing their dawn at this moment.They provide numerous benefits to vehicle owners,manufacturers,vehicle service providers,insurance companies,etc.These vehicles generate a large amount of data,which makes privacy and security a major challenge to their success.The complicated machine-led mechanics of connected and autonomous vehicles increase the risks of privacy invasion and cyber security violations for their users by making them more susceptible to data exploitation and vulnerable to cyber-attacks than any of their predecessors.This could have a negative impact on how well-liked CAVs are with the general public,give them a poor name at this early stage of their development,put obstacles in the way of their adoption and expanded use,and complicate the economic models for their future operations.On the other hand,congestion is still a bottleneck for traffic management and planning.This research paper presents a blockchain-based framework that protects the privacy of vehicle owners and provides data security by storing vehicular data on the blockchain,which will be used further for congestion detection and mitigation.Numerous devices placed along the road are used to communicate with passing cars and collect their data.The collected data will be compiled periodically to find the average travel time of vehicles and traffic density on a particular road segment.Furthermore,this data will be stored in the memory pool,where other devices will also store their data.After a predetermined amount of time,the memory pool will be mined,and data will be uploaded to the blockchain in the form of blocks that will be used to store traffic statistics.The information is then used in two different ways.First,the blockchain’s final block will provide real-time traffic data,triggering an intelligent traffic signal system to reduce congestion.Secondly,the data stored on the blockchain will provide historical,statistical data that can facilitate the analysis of traffic conditions according to past behavior.

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.

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.

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.

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.

Ecological control strategy for cooperative autonomous vehicle in mixed traffic considering linear stability

Purpose–This paper aims to present a cooperative adaptive cruise control,called stable smart driving model(SSDM),for connected and autonomous vehicles(CAVs)in mixed traffic streams with human-driven vehicles.Design/methodology/approach–Considering the linear stability,SSDM is able to provide smooth deceleration and acceleration in the vehicle platoons with or without cut-in.Besides,the calibrated Virginia tech microscopic energy and emission model is applied in this study to investigate the impact of CAVs on the fuel consumption of the vehicle platoon and trafficflows.Under the cut-in condition,the SSDM outperforms ecological SDM and SDM in terms of stability considering different desired time headways.Moreover,single-lane vehicle dynamics are simulated for human-driven vehicles and CAVs.Findings–The result shows that CAVs can reduce platoon-level fuel consumption.SSDM can save the platoon-level fuel consumption up to 15%,outperforming other existing control strategies.Considering the single-lane highway with merging,the higher market penetration of SSDM-equipped CAVs leads to less fuel consumption.Originality/value–The proposed rule-based control method considered linear stability to generate smoother deceleration and acceleration curves.The research results can help to develop environmental-friendly control strategies and lay the foundation for the new methods.

A review on cooperative perception and control supportedinfrastructure-vehicle system

With the rapid development of connected autonomous vehicles(CAVs),both road infrastructure and transport are experiencing a profound transformation.In recent years,the cooperative perception and control supported infrastructure-vehicle system(IVS)attracted increasing attention in the field of intelligent transportation systems(ITS).The perception information of surrounding objects can be obtained by various types of sensors or communication networks.Control commands generated by CAVs or infrastructure can be executed promptly and accurately to improve the overall performance of the transportation system in terms of safety,efficiency,comfort and energy saving.This study presents a comprehensive review of the research progress achieved upon cooperative perception and control supported IVS over the past decade.By focusing on the essential interactions between infrastructure and CAVs and between CAVs,the infrastructure-vehicle cooperative perception and control methods are summarized and analyzed.Furthermore,the mining site as a closed scenario was used to show the current application of IVS.Finally,the existing issues of the cooperative perception and control technology implementation are discussed,and the recommendation for future research directions are proposed.