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.

Stability of connected and automated vehicles platoon considering communications failures

As a form of a future traffic system,a connected and automated vehicle(CAV)platoon is a typical nonlinear physical system.CAVs can communicate with each other and exchange information.However,communication failures can change the platoon system status.To characterize this change,a dynamic topology-based car-following model and its generalized form are proposed in this work.Then,a stability analysis method is explored.Finally,taking the dynamic cooperative intelligent driver model(DC-IDM)for example,a series of numerical simulations is conducted to analyze the platoon stability in different communication topology scenarios.The results show that the communication failures reduce the stability,but information from vehicles that are farther ahead and the use of a larger desired time headway can improve stability.Moreover,the critical ratio of communication failures required to ensure stability for different driving parameters is studied in this work.

Efficient control of connected and automated vehicles on a two-lane highway with a moving bottleneck

This paper investigates the traffic flow of connected and automated vehicles(CAVs)inducing by a moving bottleneck on a two-lane highway.A heuristic rules-based algorithm(HRA)has been used to control the traffic flow upstream of the moving bottleneck.In the HRA,some CAVs in the control zone are mapped onto the neighboring lane as virtual ones.To improve the driving comfort,the command acceleration caused by virtual vehicle is restricted.Comparing with the benchmark in which the CAVs change lane as soon as the lane changing condition is met,the HRA significantly improves the traffic flow:the overtaking throughput as well as the outflow rate increases,the travel delay and the fuel consumption decrease,the comfort level could also be improved.

Continuum modeling of freeway traffic flows:State-of-the-art,challenges and future directions in the era of connected and automated vehicles

Connected and automated vehicles(CAVs)are expected to reshape traffic flow dynamics and present new challenges and opportunities for traffic flow modeling.While numerous studies have proposed optimal modeling and control strategies for CAVs with various objectives(e.g.,traffic efficiency and safety),there are uncertainties about the flow dynamics of CAVs in real-world traffic.The uncertainties are especially amplified for mixed traffic flows,consisting of CAVs and human-driven vehicles,where the implications can be significant from the continuum-modeling perspective,which aims to capture macroscopic traffic flow dynamics based on hyperbolic systems of partial differential equations.This paper aims to highlight and discuss some essential problems in continuum modeling of real-world freeway traffic flows in the era of CAVs.We first provide a select review of some existing continuum models for conventional human-driven traffic as well as the recent attempts for incorporating CAVs into the continuum-modeling framework.Wherever applicable,we provide new insights about the properties of existing models and revisit their implications for traffic flows of CAVs using recent empirical observations with CAVs and the previous discussions and debates in the literature.The paper then discusses some major problems inherent to continuum modeling of real-world(mixed)CAV traffic flows modeling by distinguishing between two major research directions:(a)modeling for explaining purposes,where making reproducible inferences about the physical aspects of macroscopic properties is of the primary interest,and(b)modeling for practical purposes,in which the focus is on the reliable predictions for operation and control.The paper proposes some potential solutions in each research direction and recommends some future research topics.

On-ramp merging strategy for connected and automated vehicles based on complete information static game

Improper handling of vehicle on-ramp merging may hinder traffic flow and contribute to lower fuel economy,while also increasing the risk of collisions.Cooperative control for connected and automated vehicles(CAVs)has the potential to significantly reduce negative environmental impact while also improve driving safety and traffic efficiency.Therefore,in this paper,we focus on the scenario of CAVs on-ramp merging and propose a centralized control method.Merging sequence(MS)allocation and motion planning are two key issues in this process.To deal with these problems,we first propose an MS allocation method based on a complete information static game whereby the mixed-strategy Nash equilibrium is calculated for an individual vehicle to select its strategy.The on-ramp merging problem is then formulated as a bi-objective(total fuel consumption and total travel time)optimization problem,to which optimal control based on Pontryagin's minimum principle(PMP)is applied to solve the motion planning issue.To determine the proper parameters in the bi-objective optimization problem,a varying-scale grid search method is proposed to explore possible solutions at different scales.In this method,an improved quicksort algorithm is designed to search for the Pareto front,and the(approximately)unbiased Pareto solution for the bi-objective optimization problem is finally determined as the optimal solution.The proposed on-ramp merging strategy is validated via numerical simulation,and comparison with other strategies demonstrates its effectiveness in terms of fuel economy and traffic efficiency.

Intersection control with connected and automated vehicles:a review

Purpose–This paper aims to review the studies on intersection control with connected and automated vehicles(CAVs).Design/methodology/approach–The most seminal and recent research in this area is reviewed.This study specifically focuses on two categories:CAV trajectory planning and joint intersection and CAV control.Findings–It is found that there is a lack of widely recognized benchmarks in this area,which hinders the validation and demonstration of new studies.Originality/value–In this review,the authors focus on the methodological approaches taken to empower intersection control with CAVs.The authors hope the present review could shed light on the state-of-the-art methods,research gaps and future research directions.

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.

Impacts of automated passenger cars on the capacity of a freeway basic section: applicability in the determination of vehicle adjustment factors in mixed traffic

The highway capacity manual(HCM)provides a formula to calculate the heavy vehicle adjustment factor(fHV)as a function of passenger car equivalent factors for the heavy vehicle(ET).However,a significant drawback is that the methodology was established solely based on human-driven passenger cars(HDPC)and human-driven heavy vehicles(HDHV).Due to automated passenger cars(APCs),a new adjustment factor(fAV)might be expected.This study simulated traffic flows at different percentages of HDHVs and APCs to investigate the impacts of HDHVs and APCs on freeway capacity by analyzing their influence on fHV and fAV values.The simulation determined observed adjustment factors at different percentages of HDHVs and APCs(fobserved).The HCM formula was used to calculate(fHCM).Modifications to the HCM formula are proposed,and vehicle adjustment factors due to HDHVs and APCs were calculated(fproposed).Results showed that,in the presence of APCs,while fobserved and fHCM were statistically significantly different,fobserved and fproposed were statistically equal.Hence,this study recommends using the proposed formula when determining vehicle adjustment factors(fproposed)due to HDHVs and APCs in the traffic stream.

Real-time energy-efficient anticipative driving control of connected and automated hybrid electric vehicles

In this paper,we propose a real-time energy-efficient anticipative driving control strategy for connected and automated hybrid electric vehicles(HEVs).Considering the inherent complexities brought about by the velocity profile optimization and energy management control,a hierarchical control architecture in the model predictive control(MPC)framework is developed for real-time implementation.In the higher level controller,a novel velocity optimization problem is proposed to realize safe and energy-efficient anticipative driving.The real-time control actions are derived through a computationally efficient algorithm.In the lower level controller,an explicit solution of the optimal torque split ratio and gear shift schedule is introduced for following the optimal velocity profile obtained from the higher level controller.The comparative simulation results demonstrate that the proposed strategy can achieve approximately 13%fuel consumption saving compared with a benchmark strategy.

CAV driving safety monitoring and warning via V2X-based edge computing system

Driving safety and accident prevention are attracting increasing global interest.Current safety monitoring systems often face challenges such as limited spatiotemporal coverage and accuracy,leading to delays in alerting drivers about potential hazards.This study explores the use of edge computing for monitoring vehicle motion and issuing accident warnings,such as lane departures and vehicle collisions.Unlike traditional systems that depend on data from single vehicles,the cooperative vehicle-infrastructure system collects data directly from connected and automated vehicles(CAVs)via vehicle-to-everything communication.This approach facilitates a comprehensive assessment of each vehicle’s risk.We propose algorithms and specific data structures for evaluating accident risks associated with different CAVs.Furthermore,we examine the prerequisites for data accuracy and transmission delay to enhance the safety of CAV driving.The efficacy of this framework is validated through both simulated and real-world road tests,proving its utility in diverse driving conditions.

On the impact of connected automated vehicles in freeway work zones:a cooperative cellular automata model based approach

Purpose–Freeway work zones have been traffic bottlenecks that lead to a series of problems,including long travel time,high-speed variation,driver’s dissatisfaction and traffic congestion.This research aims to develop a collaborative component of connected and automated vehicles(CAVs)to alleviate negative effects caused by work zones.Design/methodology/approach–The proposed cooperative component is incorporated in a cellular automata model to examine how and to what scale CAVs can help in improving traffic operations.Findings–Simulation results show that,with the proposed component and penetration of CAVs,the average performances(travel time,safety and emission)can all be improved and the stochasticity of performances will be minimized too.Originality/value–To the best of the authors’knowledge,this is the first research that develops a cooperative mechanism of CAVs to improve work zone performance.

Real-time energy optimization of HEVs under-connected environment: a benchmark problem and receding horizon-based solution

In this paper,we propose a benchmark problem for the challengers aiming to energy efficiency control of hybrid electric vehicles(HEVs)on a road with slope.Moreover,it is assumed that the targeted HEVs are in the connected environment with the obtainment of real-time information of vehicle-to-everything(V2X),including geographic information,vehicle-to-infrastructure(V2I)information and vehicle-to-vehicle(V2V)information.The provided simulator consists of an industrial-level HEV model and a traffic scenario database obtained through a commercial traffic simulator,where the running route is generated based on real-world data with slope and intersection position.The benchmark problem to be solved is the HEVs powertrain control using traffic information to fulfill fuel economy improvement while satisfying the constraints of driving safety and travel time.To show the HEV powertrain characteristics,a case study is given with the speed planning and energy management strategy.

Design and experimental validation of event-triggered multi-vehicle cooperation in conflicting scenarios

Platoon control is widely studied for coordinating connected and automated vehicles(CAVs)on highways due to its potential for improving traffic throughput and road safety.Inspired by platoon control,the cooperation of multiple CAVs in conflicting scenarios can be greatly simplified by virtual platooning.Vehicle-to-vehicle communication is an essential ingredient in virtual platoon systems.Massive data transmission with limited communication resources incurs inevitable imperfections such as transmission delay and dropped packets.As a result,unnecessary transmission needs to be avoided to establish a reliable wireless network.To this end,an event-triggered robust control method is developed to reduce the use of communication resources while ensuring the stability of the virtual platoon system with time-varying uncertainty.The uniform boundedness,uniform ultimate boundedness,and string stability of the closed-loop system are analytically proved.As for the triggering condition,the uncertainty of the boundary information is considered,so that the threshold can be estimated more reasonably.Simulation and experimental results verify that the proposed method can greatly reduce data transmission while creating multi-vehicle cooperation.The threshold affects the tracking ability and communication burden,and hence an optimization framework for choosing the threshold is worth exploring in future research.

Suppression strategies in different propagation periods of cyberattacks in merging area under connected environment

In order to ensure the safety of connected and automated vehicles(CAVs)threatened by cyberattack in the confluence area and mitigate the adverse impact of cyberattack propagation,a framework is built to depict the impact of cyberattacks on traffic operation.Based on this framework,corresponding propagation suppression strategies are proposed for different types of cyberattacks in different periods.Under centralized control,game theory is used to solve the confluence sequence corresponding to the strategies.The results show that the proposed method can effectively inhibit the spread of cyberattacks on the premise of security.The initial control effect is the best.Compared with uncontrolled condition,in the 100 timesteps,11 susceptible vehicles are finally added,and the second is the immunity period,in which 10 susceptible vehicles were protected from cyberattack.Outbreak and latency control strategies also protect some vehicles.Under the control strategy of each stage,the peak value of infected vehicles and the duration of cyberattack are improved compared with the uncontrolled strategy.In addition,the traffic efficiency in the confluence area is also improved.This method can also be extended to such road types as diverging section,weaving section and intersection,so as to reduce the impact of cyberattacks on road network scale.

Theoretical Analysis of Cooperative Driving at Idealized Unsignalized Intersections

Cooperative driving is widely viewed as a promising method to better utilize limited road resources and alleviate traffic congestion.In recent years,several cooperative driving approaches for idealized traffic scenarios(i.e.,uniform vehicle arrivals,lengths,and speeds)have been proposed.However,theoretical analyses and comparisons of these approaches are lacking.In this study,we propose a unified group-by-group zipper-style movement model to describe different approaches synthetically and evaluate their performance.We derive the maximum throughput for cooperative driving plans of idealized unsignalized intersections and discuss how to minimize the delay of vehicles.The obtained conclusions shed light on future cooperative driving studies.

Traffic signal coordination control for arterials with dedicated CAV lanes

Purpose–This study aims to make full use of the advantages of connected and autonomous vehicles(CAVs)and dedicated CAV lanes to ensure all CAVs can pass intersections without stopping.Design/methodology/approach–The authors developed a signal coordination model for arteries with dedicated CAV lanes by using mixed integer linear programming.CAV non-stop constraints are proposed to adapt to the characteristics of CAVs.As it is a continuous problem,various situations that CAVs arrive at intersections are analyzed.The rules are discovered to simplify the problem by discretization method.Findings–A case study is conducted via SUMO traffic simulation program.The results show that the efficiency of CAVs can be improved significantly both in high-volume scenario and medium-volume scenario with the plan optimized by the model proposed in this paper.At the same time,the progression efficiency of regular vehicles is not affected significantly.It is indicated that full-scale benefits of dedicated CAV lanes can only be achieved with signal coordination plans considering CAV characteristics.Originality/value–To the best of the authors’knowledge,this is the first research that develops a signal coordination model for arteries with dedicated CAV lanes.

On-board torque management approach to the E-COSM benchmark problem with a prediction-based engine assignment

This paper proposes an energy management strategy for the benchmark problem of E-COSM 2021 to improve the energy efficiency of hybrid electric vehicles(HEVs)on a road with a slope.We assume that HEVs are in a connected environment with real-time vehicle-to-everything information,including geographic information,vehicle-to-infrastructure information and vehicle-to-vehicle information.The benchmark problem to be solved is based on HEV powertrain control using traffic information to achieve fuel economy improvements while satisfying the constraints of driving safety and travel time.The proposed strategy includes multiple rules and model predictive control(MPC).The rules of this strategy are designed based on external environment information to maintain safe driving and to determine the driving mode.To improve fuel economy,the optimal energy management strategy is primarily considered,and to perform real-time energy management via RHC-based optimization in a connected environment with safety constraints,a key issue is to predict the dynamics of the preceding vehicle during the targeted horizon.Therefore,this paper presents a real-time model-based optimization strategy with learning-based prediction of the vehicle’s future speed.To validate the proposed optimization strategy,a powertrain control simulation platform in a traffic-in-the-loop environment is constructed,and case study results performed on the constructed platform are reported and discussed.