Performance Of Platoon Research Articles

Robust cooperative control strategy for a platoon of connected and autonomous vehicles against sensor errors and control errors simultaneously in a real-world driving environment

In a real-world driving environment, a platoon of connected and autonomous vehicles (CAVs) is subject to many internal and external disturbances, resulting in uncertain vehicle dynamics. In general, the disturbances can be categorized into two types: disturbances due to vehicle sensor errors (e.g., GPS error) and disturbances due to vehicle control errors (e.g., actuator delay). In the literature, many control strategies have been proposed to improve the robustness of the CAV platoon against uncertain vehicle dynamics induced by these disturbances. However, most of these strategies only consider one type of disturbance and cannot tackle both types of disturbances simultaneously. Furthermore, they are designed to maximize the benefits of each vehicle in the platoon independently, which can deteriorate the performance of the platoon. To address these problems, this study proposes a robust cooperative control (RCC) strategy to maneuver the vehicles in the platoon cooperatively to counteract the impacts of both types of disturbances. The RCC strategy is developed based on a minimax problem, where the maximization subproblem seeks to find the worst inputs for the uncertainty terms in the vehicle dynamics equation to minimize the platoon performance, while the minimization subproblem seeks to find the optimal control decisions for all subsequent vehicles to maximize the platoon performance in the worst case. To solve the minimax problem, this study proposes a globally convergent solution algorithm. It can solve the minimax problem very efficiently to enable real time deployment of the RCC strategy. Numerical application indicates that compared to the existing methods, the RCC strategy can dramatically improve the robustness of the CAV platoon against the uncertain vehicle dynamics induced by both vehicle state detection errors and vehicle control errors. Therefore, it can maneuver the CAV platoon safely and efficiently in a real-world driving environment.

Enhancing platoon performance: A novel approach to speed and direction control using V2X communication

Autonomous vehicle platoons have become an increasingly popular solution to enhance driving system performance and facilitate the safe integration of these vehicles on the roads. To ensure efficient platoon traffic, it is essential to effectively manage both speed and direction within this group of vehicles. It is in this context that this research is situated. Our primary objective is to enhance the performance of speed and direction controllers, as the ability of vehicles to adjust these parameters in a coordinated manner is crucial for the success of platoon traffic. To achieve this, we have developed a novel speed control approach based on neural networks and fuzzy logic, utilizing V2X communication. By incorporating environmental parameters through vehicle-to-vehicle communication and considering the specific goals of the platoon, our neuro-fuzzy system can accurately calculate optimal speeds and directions for the vehicles. Our experiments have demonstrated the effectiveness of this approach compared to traditional and advanced methods, improving both energy efficiency and temporal coordination of autonomous vehicles within the platoon.

A detection and rerouting mechanism for platoon control of non‐linear autonomous vehicles under denial of service attacks

AbstractThis paper presents a novel detection and rerouting mechanism for distributed adaptive platoon control of non‐linear autonomous connected vehicles under denial of service (DoS) attacks. DoS attacks can cause delays or losses of data packets due to blocked communication channels, leading to reducing platoon performance or even collisions among vehicles. To tackle this issue, the proposed mechanism detects and reroutes communication topology depending on the real‐time topology and the number of link failures. Real‐time detection divides the scenario of DoS attacks into three parts. According to the different scenarios, rerouting mechanisms will be utilized. A controller adapted to real‐time variable communication topology is also designed in this scheme. The adjacency matrix of the real‐time communication topology generated by the rerouting mechanism is used to update the controller so that the platoon can remain in a stable state without being affected by DoS attacks. In addition, the sliding mode controller and the observer are designed by solving linear matrix inequalities, and the platoon stability and internal stability are proven. Numerical simulation studies demonstrate that the proposed mechanism and control design can reduce the vehicle state estimate error and platoon‐tracking error to ideal states under DoS attacks. The proposed method solves the problem that the existing methods have not considered the number of link failures and the inability to restore communication when the communication topology is paralyzed.

Behavioral Decision-Making Approach for Vehicle Platoon Control: Two Noncooperative Game Models

Game theory has been extensively studied in recent years for its potential benefits on solving interactions between autonomous vehicles in platoon control. In this paper, we propose two behavioral decision-making approach based on non-cooperative game theory with both the complete information and incomplete information for cooperative vehicle platoon systems. The non-cooperative game payoff function takes the platooning performances of economy, comfort, safety, and further achieving self-driving functions into consideration. For the driving situations with incomplete information, a belief pool is constructed to represent the action probability for different behavioral types of vehicles, which will be updated by combining the driving intention identification with a Bayesian probability formula. With this, the stable strategies can be obtained for the two potentially conflicting parties, ensuring that neither of them has a motivation to change their driving behavior. Finally, the simulation results demonstrate that with the proposed behavioral decision-making approaches with complete information, the cooperative platoon performance can be markedly improved; with incomplete information, the platoon can determine the behavioral types of conflicting vehicles and complete collaborative decision-making approaches for solving not only the simple road-rights conflicting problems, but also for the extended vehicle platoon driving scenarios in a more complex transportation system.

Truck platoon analysis for autonomous trucks

Selection of optimum platoon pattern based on types of trucks inside the platoon, the number of trucks in the platoon, headway distance, interplatoon distance as well as the use of different lateral wander modes for autonomous trucks has been analyzed. The objective of this research is to study the impacts of axle configurations, truck grouping, headway distance and lateral wander options on the performance of truck platoons. Four different headway distances from 2 to 5 m are compared. The first platoon PT-1 only consists of semi trailers, the second platoon PT-2 only consists of rigid body trucks and the third platoon PT-3 consists of equally distributed random traffic mix. Analysis has been conducted using the dload subroutine for projecting zero wander and uniform wander movements for each truck in the platoon on a three layered pavement crossection at vehicle speeds of 90 km/h for a total of 15 years of pavement lifetime consisting of 1.4 million equivalent single axle loads in finite element software ABAQUS. Results show that PT-3 platoon yields the minimum accumulation of damaging strains when compared against other platoon types. A headway distance of 5 m is suggested when using a zero wander mode and 3 m when using a uniform wander mode. In case of zero wander mode, fatigue life of the pavement decreases by 1.2 years and the use of uniform wander mode delays the rutting by 1.6 years, thereby increasing lifetime of the pavement.

Discrete Multi-Objective Switching Topology Sliding Mode Control of Connected Autonomous Vehicles With Packet Loss

Information flow topology is crucial in the control of connected autonomous vehicles. It has a substantial influence on the platoon's performance, especially in scenarios with imperfect communication. To address this issue, this study provides a real-time switching topology technique for improving the platoon's performance under various types of imperfect communication situations and constant packet dropout rates. First, a discrete sliding mode controller with a double power reaching law is designed for a nonlinear heterogeneous vehicle dynamic model with packet loss. Then, Lyapunov analysis is applied to ensure the platoon's stability and string stability. Finally, a two-step switching topology framework is introduced. The first step is to design an offline Pareto optimal topology search with some predicted imperfect communication scenarios, where the platoon's tracking ability, fuel consumption, and driving comfort are optimised using a multi-objective evolutionary algorithm based on decomposition with two opposing adaptive mechanisms. In the second step, the optimal topology is switched and selected in real-time from among the previously acquired Pareto optimal topology candidates, to reduce the control cost. To validate the proposed approach, numerical simulations are conducted. According to the results, compared to a standard robust sliding mode controller, the suggested technique enhances platoon tracking ability by 97.73 percent, fuel efficiency by 9.96 percent, and driving comfort by 20.18 percent, respectively.

Prescribed-Time Performance Recovery Fault Tolerant Control of Platoon With Nominal Constraints Guarantee

The specific restrictions are breached in the case of vehicle platoon faults and result in unacceptable system performance degradation. This paper proposed a novel prescribed time performance recovery fault tolerant control method to ensure nominal platoon performance under multiple faults, including actuator faults with deferred backup actuator switching and leader-follower link faults in consideration. A novel barrier function based prescribed time sliding mode controller is devised to assure platoon consensus errors and convergence time within prescribed constraints under normal conditions at first. Under multiple faults conditions, to tackle with leader-follower link faults problem, a novel distributed recursive estimator is proposed to estimate the leader’s states and recover the previous leader-follower platooning control protocol in a prescribed time. Besides, in the presence of actuator failures, the nominal constraints violated problem under faults is put into consideration. Owing to the unavoidable deferred actuator replacement time, the previous platoon consensus error constraints are violated and cause platoon performance degradation. Under such circumstances, by exploiting one novel barrier function-based sliding mode controller with an error shifting function, the unfavorable exceeding platoon consensus errors can be recovered into the nominal constraints domains within a prescribed time. Numerical simulations and hardware-in-loop (HIL) experiments are demonstrated to validate the effectiveness and superiority of our performance recovery fault tolerant control algorithms.

Cooperative Adaptive Cruise Control in a Mixed-Autonomy Traffic System: A Hybrid Stochastic Predictive Approach Incorporating Lane Change

This paper presents a stochastic and predictive control design approach for connected and automated vehicles (CAVs) in a mixed-autonomy traffic environment, where CAVs are able to react properly to uncertain maneuvers of human-driven vehicles (HVs). The proposed fully-automated cooperative adaptive cruise control (CACC) design leverages a discrete hybrid stochastic model predictive controller that automatically determines the vehicle's operating mode based on onboard sensors data and information received through vehicle-to-vehicle (V2V) communication. Operating modes include free following, warning, danger, emergency braking, and lane change. Although the controller mainly focuses on maintaining the desired velocity and distance among CAVs, it also allows HVs to perform lane-change maneuvers and merge into the platoon's lane when needed. In response to an HV's position in the lane and its probabilistic behavior, the controller may switch the CAV's operating mode to react accordingly. Considering free-following and emergency-braking modes leads to efficient and safe autonomous driving. Switching between warning, danger, and lane-change modes along with adjusting the steering angle to perform a lane-change maneuver, when needed, robustifies the platoon's performance against unexpected human-driven vehicle maneuvers. Simulation studies are conducted to validate the efficacy of the proposed control design approach. The performance of the proposed control design approach is also compared to a switching control using simulation studies.

Intelligent reflecting surface-assisted federated learning in multi-platoon collaborative networks

Inspired by mobile edge computing (MEC), edge learning has gained a momentum by directly performing model training at network edge without sending massive data to a centralized data center. However, the quality of model training will be affected by the limited communication and computing resources of network edge. In this paper, how to improve the training performance of a federated learning system aided by intelligent reflecting surface (IRS) over vehicle platooning networks is studied, where multiple platoons train a shared federated learning model. Multi-platoon cooperation can alleviate the pressure of data processing caused by the limited computing resources of single platoon. Meanwhile, IRS can enhance the inter-platoon communication in a cost-effective and energy-efficient manner. Firstly, the federated learning optimization problem of maximizing the learning accuracy is formulated by jointing platoon scheduling, bandwidth allocation and phase shifts at the IRS to maximize the number of scheduled platoon. Specifically, in the proposed learning architecture each platoon updates the learning model with its own data and uploads it to the global model through IRS-based wireless networks. Then, a method based on sequential optimization algorithm (SOA) and a group-based optimization method are analyzed for single IRS aided and large-scale IRS aided communication, respectively. Finally, a platoon scheduling scheme is designed based on the communication reliability and computing reliability of platoons. Simulation results demonstrate that large-scale IRS assisted communication can effectively improve the reliability of multi-user communication networks. The scheduling scheme based on learning reliability balances the communication performance and computing performance of platoons.

String Stable and Collision-Safe Model Predictive Platoon Control

Automated vehicle platooning bears high potential to increase traffic efficiency, improve road safety, and reduce fuel consumption. To realize platoons with small inter-vehicle distances, collision safety is the most crucial concern and needs to be considered carefully. Moreover, it is desired to attenuate disturbances along the platoon which is known as string stability. While model predictive control concepts achieve efficient, situation-aware, and safe platooning, establishing string stability properties is difficult. In this work string stability is characterized for a generic feedback setting. A workflow to design an extended time gap spacing policy is proposed for a safety-extended distributed model predictive platooning controller. It provides safe, tightly-packed platoon operation with robust string stability near steady-state even without vehicle-to-vehicle- V2V-communication. Platoon performance is further improved by exploiting V2V-communication. Finally, the resulting closed-loop platoon dynamics are validated in a high-fidelity co-simulation study.

Exploring the impact of damping on Connected and Autonomous Vehicle platoon safety with CACC

Connected and Autonomous Vehicle (CAV) platooning has gained attention for its potential for vastly enhancing capacity and safety of future transport systems. Previous research on CAV platoon control approximates stability as safety during CAV operations and primarily addressed instability and stability properties. Nevertheless, it is vital to note that stability does not stand for absolutely safe, since the behaviors of CAV platoons could also be impacted by the oscillation characteristics of damping that are essential for CAV platoons to boost reliability against variant continuous disturbances. To explore the impact of damping on CAV platoon safety, the time-domain analysis approach is introduced to reveal the relationship between damping characteristics and CAV platoon safety performance with Cooperative Adaptive Cruise Control (CACC) based on the constant time headway policy under stable conditions. Numerical simulations are carried out to verify the impacts of the damping on CAV platoon performance and evaluate the safety of a platoon possessing stability under different damping properties. Further, different control parameters based on damping and stability properties are applied to CAV platoons and their safety is evaluated by calculating the Deceleration Rate to Avoid a Crash (DRAC), Time Exposed Time-to-Collision (TET) and Time-Integrated Time-to-Collision (TIT) as surrogate safety measures of the rear-end collision risk. Numerical analysis results show that it could better maintain platoon safety, when control parameters make the damping ratio between 1 and 1.1.

Predictive longitudinal following control for ship platoon considering diesel engine driven propeller reversal

To realize synchronous movement of ship platoons, a cooperative Longitudinal Following Control (LFC) method based on Model Predictive Control (MPC) considering the diesel engine driven propeller reversal is studied. To describe the complex process of diesel engine driven propeller rotation, we propose a diesel engine driven propeller rotation model with taking engine fuel injection signal as an input and rotation speed as an output, and the rotation direction switching logic is expressed with a function. Then, a unified LFC predictive model is proposed in combination of the propeller rotation model, propeller propulsion model and ship following model. An objective function considering multiple constraints, energy consumption, and platoon performance is designed, and a dynamic optimization problem is solved with a nonlinear optimization function, where a discrete process (rotation direction switching) and a continuous process motion control are handled simultaneously. Four cases have been studies in simulation experiments with a standard container ship and 12K98ME diesel engine. The results show that the proposed MPC based LFC considering the Propeller Reversal (MPC-LFC-PR) can effectively achieve the ship platooning control in comparison of the MPC based LFC Not considering the Propeller Reversal (MPC-LFC-NPR) and the Proportional–Integral Derivative based LFC considering the Propeller Reversal (PID-LFC-PR).

Secure and collision-free multi-platoon control of automated vehicles under data falsification attacks

This paper addresses the secure and safe distributed cooperative control problem of multiple platoons of automated vehicles under unknown data falsification attacks on driving commands. First, a general multi-platoon control framework is developed, which accommodates longitudinal and lateral vehicle dynamics, inter- and intra-platoon information exchanges, falsified driving commands, and unknown external disturbances. To deal with the unknown falsified driving commands on the platoon performance, a neural-network-based adaptive control strategy is developed to compensate their adverse effects. In order to avert both longitudinal and lateral collisions under various maneuvering scenarios, a built-in avoidance mechanism is then designed for each platoon vehicle. Furthermore, a secure and anti-collision multi-platoon control design approach is proposed to ensure the desired inter- and intra-platoon tracking performance with a collision-free guarantee. It is formally proved that the inter- and intra-platoon tracking errors converge to small neighborhood around zero. Finally, several comparative simulation cases are presented to verify the effectiveness and merits of the proposed multi-platoon control approach.

Real-time deployable and robust cooperative control strategy for a platoon of connected and autonomous vehicles by factoring uncertain vehicle dynamics

In real-world driving environments, a platoon of connected and autonomous vehicles (CAVs) will be subject to many disturbances (e.g., aerodynamic drag, dynamic road gradients) that can prevent them from achieving desired control accurately. These disturbances can cause uncertainties in the vehicle dynamics, reducing platoon safety and mobility. To address this problem, this study proposes a robust cooperative control (RCC) strategy, developed as a minimax problem, to ensure the safe and efficient maneuvering of a CAV platoon in the worst-case situation due to uncertainties in the vehicle dynamics. The maximization subproblem of the minimax problem determines the inputs of the uncertainties to maximize the cost function (i.e., the platoon performance indicator), while the minimization subproblem seeks to optimize the control decisions for all following vehicles in the platoon to minimize the cost function using inputs of the uncertainties from the maximization subproblem. A novel partition-based method of feasible direction is proposed to solve the minimax problem. It is globally convergent and computationally efficient, enabling the RCC strategy to be deployed in real time. The conditions for robust stability of the CAV platoon are also analyzed. Results from numerical studies show that compared to a control strategy that ignores uncertainties in vehicle dynamics, the RCC strategy can substantially improve the platoon performance under disturbances. Hence, it can be used to enable the safe and efficient maneuvering of CAV platoons in a real-world driving environment.

Study on the local and global performance of homogeneous platoon with a bidirectional ring interconnection

This paper investigates the local and global performance of the platoon where multiple identical vehicles move in a closed roadway and cooperate based on nearest-neighbour interactions. The information from the front neighbour, namely position, speed, and acceleration, is weighted non-equally component-wise to that from the back neighbour, which is quantified by the asymmetry level on each component. The local performance refers to the locally propagating attenuation for the bidirectional control, which captures whether the disturbance is amplified when propagating from a vehicle to its immediate neighbours. The global performance refers to the system transfer scalability, which characterises the scalability of system transfer matrix. Based on forward and backward wave transfer functions, several analytical necessary conditions of the locally propagating attenuation are obtained. Furthermore, two of these necessary conditions happen to be the same with the conditions for scalably asymptotic stability. Under mild assumptions, the locally propagating attenuation is sufficient for scalably asymptotic stability, the latter of which in turn implies the system transfer scalability. Numerical calculations further show that it is favourable for both performance measures to select the asymmetry levels on speed and acceleration with small magnitudes and located around the centre line of the band-like region of scalably asymptotic stability.

Steady-state performance and dynamic performance of heterogeneous platoons under a connected environment

Under the vehicle-to-vehicle (V2V) environment, the steady-state performance and the dynamic performance are both crucial for the traffic system. This paper proposes an in-depth methodology to provide some insights into the comprehensive changes of steady-state performance and dynamic performance of a heterogeneous platoon, which contains human-driven vehicles (HDVs) and connected and automated vehicles (CAVs). To further understand the impacts of various feedback information, an improved full velocity difference model that incorporates multiple feedback communicating information is developed. Based on the transfer function, the stability and steady-state error are proposed to characterize the steady-state performance, while the degree of fluctuation and duration of fluctuation are used to measure the dynamic performance. On the one hand, a key finding is that there are inconsistencies in steady-state and dynamic performance under particular feedback information. The acceleration or spacing feedback information can enhance the steady-state performance while degrading the dynamic performance. The speed difference feedback information yields the opposite result. On the other hand, it is discovered that compared to several types of feedback information, combining just one type of information is enough to boost the platoon performance. When the number of CAVs is considerable, combining two types of information will considerably increase the duration time. Therefore it is important to find the key feedback information for the platoon performance. The main purpose of this paper is to present a methodology of platoon performance analysis instead of discussing the various traffic scenarios. As a result, while certain results may alter in some specific scenarios, the proposed methodology and core contribution remain applicable to various traffic scenarios.

Delay-Aware Multi-Layer Multi-Rate Model Predictive Control for Vehicle Platooning Under Message-Rate Congestion Control

Vehicle platooning is an enabler technology for increasing road capacity, improving safety and reducing fuel consumption. Platoon control is a two-layered system where each layer runs under a different communication standard and rate – (i) the upper-layer operates under a specific V2V communication standard such as IEEE 802.11p and (ii) the lower-layer operates over high-speed in-vehicle communication networks such as FlexRay, CAN. The upper-layer, under 802.11p, uses periodic Cooperative Awareness Messages (CAMs) for exchanging vehicle motion information (i.e., acceleration, velocity and so on), the rate of which is adapted depending on the network congestion level. With over 70% channel load, the CAMs experience significant delay and packet loss, jeopardizing the stability of the platoon control. Under such high congestion, the European Telecommunications Standard Institute (ETSI) proposes to engage Decentralized Congestion Control (DCC) to control the channel load. We propose a platoon control and DCC scheme to tackle this scenario. Our contribution is three-fold. First, we propose a multi-layer platoon model explicitly augmenting the communication delay in the state-space. Second, the augmented delay-aware platoon model is integrated in the state-of-the-art multi-layer multi-rate model predictive control (MPC) for the upper-layer. Third, we adopt a message-rate congestion control scheme to keep the channel load under a given threshold. We use the proposed delay-aware MPC scheme under the message-rate congestion control scheme which may lead to switching under dynamic network conditions. Using the proposed technique, we show that platoon performance can be maintained under high network congestion while maintaining string stability.

Improving the Performance of Cooperative Platooning With Restricted Message Trigger Thresholds

Cooperative Vehicular Platooning (Co-VP) is one of the most prominent and challenging applications of Intelligent Traffic Systems. To support such vehicular communications, the ETSI ITS G5 standard specifies event-based communication profiles, triggered by kinematic parameters such as speed. The standard defines a set of threshold values for such triggers but no careful assessment in realistic platooning scenarios has been done to confirm the suitability of such values. In this work, we investigate the safety and performance limitations of such parameters in a realistic platooning co-simulation environment. We then propose more conservative threshold values, that we formalize as a new profile, and evaluate their impact in the longitudinal and lateral behaviour of a vehicular platoon as it carries out complex driving scenarios. Furthermore, we analyze the overhead introduced in the network by applying the new threshold values. We conclude that a pro-active message transmission scheme leads to improved platoon performance for highway scenarios, notably an increase greater than 40% in the longitudinal performance of the platoon, while not incurring in a significant network overhead. The obtained results also demonstrated an improved platoon performance for for semi-urban scenarios, including obstacles and curves, where the heading error decreases in 26%, with slight network overhead.

Heavy-duty truck platooning on hilly terrain highways: Methods for assessment and improvement

Platooning heavy-duty trucks is a proven method to reduce fuel consumption on flat ground, but a significant portion of the U.S. highway system covers hilly terrain. The effort described in this paper uses experimentally gathered single truck data from a route with hilly terrain and an experimentally-validated two-truck platoon simulation framework to analyze control methods for effective platooning on hilly terrain. Specifically, this effort investigates two platoon control aspects: (1) the lead truck’s vehicle speed control and (2) the platoon’s transmission shifting algorithm. Three different types of lead truck speed control strategies are analyzed using the validated platoon model. Two are commercially available cruise control strategies – conventional constant set speed cruise control (CCC) and flexible set speed cruise control (FCC). The third lead truck speed control strategy was developed by the authors in this paper. It uses look-ahead grade information for an entire route to create an energy-optimal speed profile for the lead truck which is called long-horizon predictive cruise control (LHPCC). Then, a two-truck platoon transmission shifting strategy that coordinates the shift events – Simultaneous Shifting (SS) – is introduced and compared to a commercially available shifting strategy using the validated platoon model. This shifting strategy demonstrates further improvements in the platoon performance by improving the platoon gap control. A summary of these simulations demonstrates that the performance of the platoon can be improved by three methods: adding speed flexibility to the lead truck speed control method, using look-ahead road grade information to generate energy-optimal speed targets for the lead truck, and coordinating the timing of the transmission shifts for each truck in the platoon.

A single-layer approach for joint optimization of traffic signals and cooperative vehicle trajectories at isolated intersections

A joint control approach that simultaneously optimizes traffic signals and trajectories of cooperative (automated) vehicle platooning at urban intersections is presented in this paper. In the proposed approach, the signal phase lengths and the accelerations of the controlled platoons are optimized to maximize comfort and minimize travel delay within the signal cycle, subject to motion constraints on speeds, accelerations and safe following gaps. The red phases are initially considered as logic constraints, and then recast as several linear constraints to enable efficient solutions. The proposed approach is solved by mixed integer linear programming (MILP) techniques after linearization of the objective function. The generated outputs of the MILP problem are the optimal signal timings and the optimal accelerations of all vehicles. This joint control approach is flexible in incorporating multiple platoons and traffic movements under different traffic demand levels and it does not require prespecified terminal conditions on position and speed at the signal cycle tail. The performance of the proposed control approach is verified by simulation at a standard four-arm intersection under the balanced and unbalanced vehicle arrival rates from different arms, taking the released traffic movement numbers, turning proportions, signal cycle lengths and the controlled vehicle numbers into account. The simulation results demonstrate the platoon performance of the joint controller (such as split, merge, acceleration and deceleration maneuvers) under the optimal signals. Based on the simulation results, the optimal patterns of trajectories and signals are explored, which provide insights into the optimal traffic control actions at intersections in a cooperative vehicle environment. Furthermore, the computational performance of the proposed control approach is analyzed, and the benefits of the proposed approach on the average travel delay, throughput, fuel consumption, and emission are proved by comparing with the two-layer approaches using the car following model, the signal optimization models, and the state-of-the-art approach.