Cooperative Merging Research Articles

Position Uncertainty-Tolerant Cooperative Merging Application for Mixed Multilane Traffic

To date, numerous cooperative merging techniques have been developed, with multiple studies demonstrating their potential to improve traffic safety, efficiency, and environmental impact at merging points. However, many of these techniques make assumptions that will almost certainly be questionable in the real world, namely: 1) relative positioning between vehicles will be sufficiently accurate at all times; 2) the merging application-equipped vehicles will not change lanes before reaching the merge point, nor will other vehicles change lanes into their lane; and 3) vehicles with merging-assist technology will not mix with vehicles without the technology. This paper proposes a cooperative merging application which is capable of functioning even when the vehicle's positioning accuracy is severely degraded and all vehicles are free to change lanes into and out of the merging traffic streams. Furthermore, our strategy demonstrates safety, efficiency, and environmental benefits for the overall traffic stream even with technology penetration rates as low as 20%.

Cooperative Merging Strategy Between Connected Autonomous Vehicles in Mixed Traffic

Cooperative Merging Strategy Between Connected Autonomous Vehicles in Mixed Traffic

Exploring work zone late merge strategies with and without enabling connected vehicles technologies

Typical traffic control and operations at work zones are not effective in mitigating the work zone bottleneck, as the common early merge behavior limits utilizing the available capacity in the closed lane. Recently, using late merge strategy is encouraged to fully utilize the closed lane, therefore increasing efficiency of the work zone. In practice, the late merge strategy does not work effectively due to the lack of compliance by drivers. This paper is aimed at exploring two work zone late merge strategies with and without enabling connected vehicles (CV) technology. This first strategy is the late merge without CV enabled. The second strategy incorporates CV-enabled cooperative merge to enhance the work zone efficiency through communications between vehicles in the open and closed lanes. The CV-enabled strategy employs a rule based decentralized control algorithm. The paper has implemented both late merge strategies in microsimulation and evaluated the operational performances. Three major performance measures, which are throughput, delay and queue length, are used in the evaluation. The results indicate that the operational performance of the CV-enabled cooperative late merge is superior to the late merge strategy without CV in most cases, especially when traffic demand is moderate, while both late merge strategies outperform the traditional early merge strategy. Moreover, effects of the control variables including the traffic demand, heavy vehicle percentage, lane distribution percentage, late merge length, reduction of gap acceptance factor, and compliance rate, have been analyzed. Ultimately, sensitivity analysis has been performed to investigate the trend of effects from the significant variables control.

Childhood-onset of primary Sjögren's syndrome: phenotypic characterization at diagnosis of 158 children.

To characterize the phenotypic presentation at diagnosis of childhood-onset primary SS. The Big Data Sjögren Project Consortium is an international, multicentre registry using worldwide data-sharing cooperative merging of pre-existing clinical SS databases from the five continents. For this study, we selected those patients in whom the disease was diagnosed below the age of 19 years according to the fulfilment of the 2002/2016 classification criteria. Among the 12 083 patients included in the Sjögren Big Data Registry, 158 (1.3%) patients had a childhood-onset diagnosis (136 girls, mean age of 14.2 years): 126 (80%) reported dry mouth, 111 (70%) dry eyes, 52 (33%) parotid enlargement, 118/122 (97%) positive minor salivary gland biopsy and 60/64 (94%) abnormal salivary US study, 140/155 (90%) positive ANA, 138/156 (89%) anti-Ro/La antibodies and 86/142 (68%) positive RF. The systemic EULAR Sjögren's syndrome disease activity index (ESSDAI) domains containing the highest frequencies of active patients included the glandular (47%), articular (26%) and lymphadenopathy (25%) domains. Patients with childhood-onset primary SS showed the highest mean ESSDAI score and the highest frequencies of systemic disease in 5 (constitutional, lymphadenopathy, glandular, cutaneous and haematological) of the 12 ESSDAI domains, and the lowest frequencies in 4 (articular, pulmonary, peripheral nerve and CNS) in comparison with patients with adult-onset disease. Childhood-onset primary SS involves around 1% of patients with primary SS, with a clinical phenotype dominated by sicca features, parotid enlargement and systemic disease. Age at diagnosis plays a key role in modulating the phenotypic expression of the disease.

Decentralized cooperative driving automation: a reinforcement learning framework using genetic fuzzy systems

Cooperative Automated Driving System (ADS)-equipped Vehicles (CoAVs) can be a promising solution to traffic congestion. We present a reinforcement learning strategy using Genetic Fuzzy Systems (GFS) for cooperative merge of vehicles onto a highway in high density, mixed-autonomy traffic conditions. The CoAVs are trained to make their own decisions purely based on information available from its surrounding vehicles, thus making this a decentralized system. The GFS module in each CoAV makes recommendations on the acceleration and lane-change decisions. The CoAVs are trained on different ADS behavioral parameters, such as aggressiveness of lane change, different CoAV market penetration rates (MPRs) and traffic congestion levels. The results show the effectiveness of increasing the MPR. It is noticed that the CoAVs trained with different ADS behavioral parameters can generate completely different cooperative maneuvers. The results also show that the trained CoAVs can cooperate even with human-driven vehicles to improve the overall traffic performance.

A Remarkable Fluorescence Quenching Based Amplification in ATP Detection through Signal Transduction in Self-Assembled Multivalent Aggregates.

Signal transduction is essential for the survival of living organisms, because it allows them to respond to the changes in external environments. In artificial systems, signal transduction has been exploited for the highly sensitive detection of analytes. Herein, a remarkable signal transduction, upon ATP binding, in the multivalent fibrillar nanoaggregates of anthracene conjugated imidazolium receptors is reported. The aggregates of one particular amphiphilic receptor sensed ATP in high pm concentrations with one ATP molecule essentially quenching the emission of thousands of receptors. A cooperative merging of the multivalent binding and signal transduction led to this superquenching and translated to an outstanding enhancement of more than a millionfold in the sensitivity of ATP detection by the nanoaggregates; in comparison to the "molecular" imidazolium receptors. Furthermore, an exceptional selectivity to ATP over other nucleotides was demonstrated.

New England merge: a novel cooperative merge control method for improving highway work zone mobility and safety

Given the aging infrastructure and the anticipated growing number of highway work zones in the United States, it is important to investigate methods to improve work zone mobility and safety. Data suggests that inappropriate merge maneuvers are a major contributing factor to highway work zone crashes that often lead to severe congestion and delay. This research proposes a New England Merge (NEM) for highway work zone control, which requires vehicles to behave cooperatively and create safe merging gaps when approaching lane closure points caused by work zones. Based on VISSIM microscopic simulations considering varying input traffic demands, the NEM is compared with late merge, early merge, and no control in terms of average delay, mean travel time, and throughput. Additionally, the safety performance of NEM is analyzed using surrogate safety measures such as vehicle trajectory, density, acceleration, and distance headway. A typical type of highway work zone is modeled in this study, which represents a two-lane highway with the right lane closed. The modeling results show that overall NEM significantly outperforms all other merge control methods in terms of both safety and mobility measures. Similar to late merge and early merge, implementing the NEM would benefit from driver cooperation. The applicability of NEM can be substantially improved with the wide adoption of cooperative adaptive cruise control (i.e., level 1 automation) technology.

Evaluating the Effectiveness of Integrated Connected Automated Vehicle Applications Applied to Freeway Managed Lanes

The purpose of this study is to define an operational concept involving connected automated vehicle (CAV) operation on freeway managed lanes. Despite the low projected market penetration of CAVs during the next decade, the use of managed lane facilities has the potential to support the realization of increased mobility benefits by their very nature. The proposed CAV operation involves platoons of equipped vehicles governed by integrated CAV applications, including cooperative adaptive cruise control (CACC), cooperative merge, and speed harmonization. This study proposes an algorithm for integrating CAV applications. Through microscopic simulation, the study particularly examines the effectiveness of CACC, CACC plus cooperative merge, and the addition of speed harmonization under different penetration rates. Simulation results show the effectiveness of the bundled application to enhance system throughput and reduce delay, even with low CAV penetration rates. The speed harmonization shows the greatest effects on delay reduction at medium-to-high penetration rates and some benefits even at low penetration rates. The conclusions provide operational insights and guidance for traffic management centers to implement CAV-based traffic control in the future.

A Hierarchical Model-Based Optimization Control Approach for Cooperative Merging by Connected Automated Vehicles

Gap selection and dynamic speed profiles of interacting vehicles at on-ramps affect the safety and efficiency of highway merging sections. This paper puts forward a hierarchical control approach for Connected Automated Vehicles (CAVs) to achieve efficient and safe merging operations. A tactical layer controller employs a second-order car-following model with a cooperative merging mode to represent a cooperative merging process and generates an optimal vehicle merging sequence and time instants when on-ramp CAVs start to adapt their speeds and positions to prepare merging into the target gaps respectively. An operational layer controller is designed based on Model Predictive Control (MPC). It uses a third-order vehicle dynamics model and optimizes desired accelerations for CAVs and the time instants when the on-ramp CAVs initiate the lane-changing executions respectively. Both the tactical layer controller and operational layer controller derive their control commands by minimizing an objective function for different time horizons. The objective function penalizes deviations of CAVs’ inter-vehicle gaps to their desired values, relative speeds to their direct predecessors, and actual or desired accelerations, subject to constraints on velocities, actual or desired accelerations, and inter-vehicle gaps. The performance of the proposed hierarchical control framework and a benchmark on-ramp merging method using a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">first-in-first-out</i> rule to determine the merging sequence is demonstrated under 135 scenarios with different initial conditions, desired time gap settings, and numbers of on-ramp vehicles. The experimental results show the superiority of the hierarchical control approach.

Safety, Energy, and Emissions Impacts of Adaptive Cruise Control and Cooperative Adaptive Cruise Control

Connected and automated vehicle technologies have the potential to significantly improve transportation system performance. In particular, advanced driver-assistance systems, such as adaptive cruise control (ACC) and cooperative adaptive cruise control (CACC), may lead to substantial improvements in performance by decreasing driver inputs and taking over control of the vehicle. However, the impacts of these technologies on the vehicle- and system-level energy consumption, emissions, and safety have not been quantified in field tests. The goal of this paper is to study the impacts of automated and cooperative systems in mixed traffic containing conventional, ACC, and CACC vehicles. To reach this goal, experimental data based on real-world conditions are collected (in tests conducted by the Federal Highway Administration and the U.S. Department of Transportation) with presence of ACC, CACC, and conventional vehicles in a vehicle platoon scenario and a cooperative merging scenario. Specifically, a platoon of five vehicles with different vehicle type combinations is analyzed to generate new knowledge about potential safety, energy efficiency, and emission improvement from vehicle automation and cooperation. Results show that adopting the CACC system in a five-vehicle platoon substantially reduces the driving volatility and reduces the risk of rear-end collision which consequently improves safety. Furthermore, it decreases fuel consumption and emissions compared with the ACC system and manually-driven vehicles. Results of the merging scenario show that while the cooperative merging system slightly reduces the driving volatility, the fuel consumption and emissions can increase because of sharper accelerations of CACC vehicles compared with manually-driven vehicles.

Cooperative merging control via trajectory optimization in mixed vehicular traffic

A major challenging issue related to the emerging mixed traffic vehicular system, composed of connected and automated vehicles (CAVs) together with human-driven vehicles, is the lack of adequate modeling and control framework, especially at traffic bottlenecks such as highway merging areas. A hierarchical control framework for merging areas is first outlined, where we assume that the merging sequence is decided by a higher control level. The focus of this paper is the lower level of the control framework that establishes a set of control algorithms for cooperative CAV trajectory optimization, defined for different merging scenarios in the presence of mixed traffic. To exploit complete cooperation flexibility of the vehicles, we identify six scenarios, consisting of triplets of vehicles, defined based on the different combinations of CAVs and conventional vehicles. For each triplet, different consecutive movement phases along with corresponding desired distance and speed set-points are designed. Through the movement phases, the CAVs engaged in the triplet cooperate to determine their optimal trajectories aiming at facilitating an efficient merging maneuver, while complying with realistic constraints related to safety and comfort of vehicle occupants. Distinct models are considered for each triplet, and a Model Predictive Control scheme is employed to compute the cooperative optimal control inputs, in terms of acceleration of CAVs, accounting also for human-driven vehicles’ uncertainties, such as drivers’ reaction time and desired speed tracing error. Simulation investigations demonstrate that the proposed cooperative merging algorithms ensure efficient and smooth merging maneuvers while satisfying all the prescribed constraints.

Leveraging existing high-occupancy vehicle lanes for mixed-autonomy traffic management with emerging connected automated vehicle applications

Transportation agencies have started including phased deployment of connected and automated vehicle (CAV) technologies in their transportation plans and programs. While theoretical analyses have indicated significant benefits of CAVs for improving system performance, deploying these technologies at existing or adapted highway facilities concerns more than technological issues. This study introduces the concepts and operational strategies of using managed lanes, High-Occupancy Vehicle (HOV) lanes in particular, for mixed-autonomy traffic management. The study enhances existing and develops new algorithms for three selected freeway CAV applications as a CAV technology integration, including speed harmonization, cooperative adaptive cruise control (CACC), and cooperative merge. Instead of evaluation on a synthetic segment, this study reports a large-scale simulation-based real-world case study to investigate CAV early deployment opportunities on Interstate 66 outside the Beltway. The simulation results show that, for all scenarios, individual and bundled CAV applications can significantly improve traffic performance in terms of delay and throughput. CACC platooning is the most effective individual strategy to improve traffic performance. The bundled CAV applications can benefit the system, even with low CAV market penetration, and fully handle more than 130 percent of the existing demand with high CAV market penetration rates. Additionally, left-side dedicated ramps and shared managed lane operational strategies are also beneficial, even during early deployment stages.

Epidemiological profile and north-south gradient driving baseline systemic involvement of primary Sjögren's syndrome.

To characterize the systemic phenotype of primary Sjögren's syndrome at diagnosis by analysing the EULAR-SS disease activity index (ESSDAI) scores. The Sjögren Big Data Consortium is an international, multicentre registry based on worldwide data-sharing cooperative merging of pre-existing databases from leading centres in clinical research in Sjögren's syndrome from the five continents. The cohort included 10007 patients (9352 female, mean 53 years) with recorded ESSDAI scores available. At diagnosis, the mean total ESSDAI score was 6.1; 81.8% of patients had systemic activity (ESSDAI score ≥1). Males had a higher mean ESSDAI (8.1 vs 6.0, P < 0.001) compared with females, as did patients diagnosed at <35 years (6.7 vs 5.6 in patients diagnosed at >65 years, P < 0.001). The highest global ESSDAI score was reported in Black/African Americans, followed by White, Asian and Hispanic patients (6.7, 6.5, 5.4 and 4.8, respectively; P < 0.001). The frequency of involvement of each systemic organ also differed between ethnic groups, with Black/African American patients showing the highest frequencies in the lymphadenopathy, articular, peripheral nervous system, CNS and biological domains, White patients in the glandular, cutaneous and muscular domains, Asian patients in the pulmonary, renal and haematological domains and Hispanic patients in the constitutional domain. Systemic activity measured by the ESSDAI, clinical ESSDAI (clinESSDAI) and disease activity states was higher in patients from southern countries (P < 0.001). The systemic phenotype of primary Sjögren's syndrome is strongly influenced by personal determinants such as age, gender, ethnicity and place of residence, which are key geoepidemiological players in driving the expression of systemic disease at diagnosis.

Penetration effect of connected and automated vehicles on cooperative on‐ramp merging

Earlier work has established a centralised cooperative merging framework of optimally coordinating two strings of connected and automated vehicles (CAVs) passing through the on-ramp merging zone. The proposed merging strategy is capable of making a good trade-off between performance and computational cost. In this study, the authors address the problem of optimally coordinating CAVs under mixed traffic conditions, where both CAVs and human-driven vehicles (non-CAVs) travel on the roads, so as to enhance efficiency while guaranteeing safety constraints. A hierarchical cooperative merging framework is proposed for CAVs, which integrates merging sequence scheduling strategies (high level) and motion planning methods (low level). The impacts of CAV penetration (i.e. the fraction of CAVs relative to all vehicles) on throughput, delay, fuel consumption and emission are also investigated under different traffic demands. Simulation-based case studies indicate that the performance improvement becomes more significant as the CAV penetration rate increases and about 30% CAV penetration can effectively mitigate the shockwave and reduce traffic congestion.

Two-stage on-board optimization of merging velocity planning with energy management for HEVs

This paper proposes a two-stage hierarchy control system with model predictive control (MPC) for connected parallel HEVs with available traffic information. In the first stage, a coordination of on-ramp merging problem using MPC is presented to optimize the merging point and trajectory for cooperative merging. After formulating the merging problem into a nonlinear optimization problem, a continuous/GMRES method is used to generate the real-time vehicle acceleration for two considered HEVs running on main road and merging road, respectively. The real-time acceleration action is used to calculate the torque demand for the dynamic system of the second stage. In the second stage, an energy management strategy (EMS) for powertrain control that optimizes the torque-split and gear ratio simultaneously is composed to improve fuel efficiency. The formulated nonlinear optimization problem is solved by sequential quadratic programming (SQP) method under the same receding horizon. The simulation results demonstrate that the vehicles can merge cooperatively and smoothly with a reasonable torque distribution and gear shift schedule.

A Rule-Based Cooperative Merging Strategy for Connected and Automated Vehicles

Connected and automated vehicles (CAVs) show great potential to improve both traffic efficiency and safety by sharing information. This paper addresses the problem of coordinating two strings of vehicles at highway on-ramps efficiently and safely in the longitudinal direction. A rule-based adjusting algorithm is proposed to achieve a near-optimal merging sequence for vehicles coming from the mainline and entering through the ramp. Optimality analysis indicates that the proposed method performs very well compared with the global optimal solutions. Furthermore, to investigate the effectiveness and robustness of the proposed method, simulation-based case studies are carried out under both balanced and unbalanced scenarios. The results are compared with two other control strategies (i.e., rule-based methods and optimization-based methods) in terms of throughput, delay, computational cost, and fuel consumption.

Cooperative Merging Strategy for Connected Vehicles at Highway On-Ramps

AbstractAs traffic demands increase rapidly on highways, effective merging strategies are necessary to cooperate with intelligent vehicles and improve traffic operations. Existing merging algorithm...

Trajectory optimization of connected and autonomous vehicles at a multilane freeway merging area

Merging areas on freeways are primary locations for bottlenecks due to vehicles’ mandatory lateral conflicts. These critical conflicts, however, are potentially avoidable with Connected and Autonomous Vehicle (CAV) technology. However, current literature using CAV technology mostly focuses on merging maneuvers between a single-lane mainline and an incoming ramp. An algorithm dealing with multilane merging areas is absent. In this paper, an online system control algorithm for multilane freeway merging areas is presented with a CAV environment based on optimizing vehicles’ lane changing and car following trajectories. First, the lane flow distribution is adjusted upstream of the merging point under a rule-based lane changing decision, which eventually balances downstream lane flow distribution. A Cooperative Lane Changing Control (CLCC) optimization model is proposed to ensure safe and smooth lane changing execution. Second, a Cooperative Merging Control (CMC) model is adopted, analyzed, and generalized to conduct merging maneuvers around the merging point. Third, a dynamic moving border point method is designed to coordinate the consecutive execution of the CLCC and CMC models. To validate the proposed algorithm, a simulation platform based on VISSIM is developed for online computation and visualization. A typical two-lane freeway merging area is studied. Results under various demand scenarios demonstrate that the proposed algorithm outperforms previous cooperative merging algorithms consistently with respect to delays and average travel speeds.

Vehicle Platooning and Cooperative Merging

Abstract This paper presents a solution to the vehicle merging problem using distributed controllers. In order to guarantee collision avoidance, a state feedback law is utilised to generate externally positive dynamics for the controlled vehicles. These vehicles are temporarily connected via a communication network that enables them to exchange information. The networked control scheme handles the lane switching scenario with a feed-forward control law to create a gap for the lane changing vehicle. It is shown that the proposed controllers guarantee a collision-free vehicle merging.

DynamicMap\xa02.0: A Traffic Data Management Platform Leveraging Clouds, Edges and Embedded Systems

Various embedded sensors such as LiDAR, cameras and GPS are mounted on vehicles today. And, vehicles with communication equipment can exchange sensor data between not only surrounding vehicles but also road-side-units. To achieve traffic safety and comfort driving, utilization of these traffic data are important for autonomous driving and advanced driving assistance systems. Our research group has been developing a city-scale traffic data management platform named DynamicMap 2.0. This paper proposes a software architecture of our platform. Our platform uniformly treats road maps, static information, dynamic information and predicted information. Road maps consist of three-level granularities: links, lanes and physical shapes. Especially, a data representation of lane-level road maps is newly proposed. Static information includes fixed properties about objects on the map. Dynamic information includes streaming data from sensors. And, predicted information is computed by prediction algorithms and simulations. Our platform provides query language for searching these traffic data. A developer of traffic applications writes a query (continuous query or one-shot query) which specifies integration and filtering of data. As a use case of our platform, we developed a prototype of a cooperative merging assistance system at intersections, and verify that our platform operates correctly.