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Hierarchical and Distributed Eco-Driving Approach for Mixed Vehicle Clusters at Unsignalized Intersections
Hierarchical and Distributed Eco-Driving Approach for Mixed Vehicle Clusters at Unsignalized...
Yu, Jie;Luo, Yugong;Kong, Weiwei;Jiang, Fachao
Hindawi Journal of Advanced Transportation Volume 2023, Article ID 7114792, 16 pages https://doi.org/10.1155/2023/7114792 Research Article Hierarchical and Distributed Eco-Driving Approach for Mixed Vehicle Clusters at Unsignalized Intersections 1,2 2 1 1 Jie Yu , Yugong Luo , Weiwei Kong , and Fachao Jiang College of Engineering, China Agricultural University, Beijing 100083, China State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, China Correspondence should be addressed to Weiwei Kong; firstname.lastname@example.org Received 13 March 2022; Revised 23 October 2022; Accepted 24 March 2023; Published 22 April 2023 Academic Editor: Arkatkar Shriniwas Copyright © 2023 Jie Yu et al. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. To improve the driving efciency and energy-saving characteristics for large-scale mixed trafc fows under diferent market penetration rates (MPRs) of intelligent and connected vehicles (ICVs) at unsignalized intersections, considering the cooperative eco-driving performance between ICVs and human-driven vehicles (HDVs) with time-varying speed characteristics, the hi- erarchical and distributed cooperative eco-driving architecture is frst established in this paper, consisting of a cloud decision layer and a vehicle control layer. For the cloud decision layer, the multivehicle model-free adaptive predictive cooperative driving (MFAPCD) method is designed by using only the driving data of the HDVs and ICVs formation based on compact form dynamic linearization (CFDL) technique, thereby improving trafc efciency. Furthermore, the CFDL integral terminal sliding mode predictive control (CFDL-ITSMPC) scheme is utilized to predict the time-varying driving speed of HDVs, and then, the CFDL predictive control (CFDL-PC) scheme is utilized to predict the expected control variables of ICVs formation. For the vehicle control layer, based on the anticipated driving speed obtained from the cloud decision layer, the nonlinear distributed model predictive control (NDMPC) method is utilized for distributed optimal control of each vehicle formation, to achieve optimization in terms of energy saving. Simulation results show that, compared with the signal time assignment strategy, the method can increase the average velocity by about 15.22% and decrease the average fuel consumption by about 36.43% under diferent MPRs and trafc volumes. MPRs and trafc volumes oriented to signal-controlled 1. Introduction intersections, such as the timing and optimization of traf- Te ICVs enabled by the new generation of information and fc signals for cooperative driving of hybrid vehicles [2–4]. communication technology can provide new ideas for However, with the continuous improvement of the com- solving problems such as high time consuming and poor munication network infrastructure and the intelligent level energy saving for vehicles to pass through intersections . of ICVs, the transportation system will be more intelligent, Given the current trafc situation, the transition from to- and the trafc lights will be replaced by the infrastructure called intersection manager. Moreover, there are also several day’s largely human-driven trafc to purely automated trafc will be a gradual process, with the fact that we may scholars focused on a multivehicle cooperative driving experience mixed trafc shortly. Terefore, in such a tran- method to improve the driving efciency or energy-saving sitional period, it is necessary to design a driving scheme to for mixed trafc fows at such nonsignal-controlled in- coordinate the mixed trafc fows of ICVs and HDVs, which tersections. Zohdy and Rakha  proposed an improved is of great signifcance for improving trafc efciency and cooperative adaptive cruise control (iCACC) system, and the reducing fuel consumption. trafc efciency and fuel consumption of intersections under At present, several driving efciencies and energy-saving diferent MPRs were discussed, in which HDVs with given improvement methods have been proposed under diferent driving states can maintain a safe driving distance from 2 Journal of Advanced Transportation environment and vehicles; for the model-based methods, the ICVs. Qian et al.  proposed a priority-based coordination system with the hypothesis that the driving state of HDVs is precise model information requirement for HDVs driving behavior of the entire mixed trafc fow might restrict its accurately known and can maintain a safe distance from their leading ICVs to enhance intersection efciency. Yang practical applications. and Oguchi  proposed a trafc model for predicting total In summary, to improve the driving efciency and vehicle delay, which afects the observable driving states of energy-saving for mixed trafc fows under diferent MPRs HDVs by solving the optimal speed of ICVs to reduce the and trafc volumes at unsignalized intersections, consid- trafc delay. Although the previous strategies can achieve the ering the cooperative control performance of ICVs forma- improvement of driving efciency and energy saving of the tion and HDVs with time-varying speed characteristics, the hierarchical and distributed cooperative eco-driving scheme mixed trafc fow with the given HDVs’ driving state at intersections, due to many unavoidable factors such as sight- is established in this paper. Te main contributions of this paper are as follows: a hierarchical and distributed co- line insufciency and driving habit diference, the driver’s driving behavior is dynamic and random in many cases, operative eco-driving architecture, which contains two layers of optimization objectives: cloud decision layer and which lead to safety accident, trafc jams, even high con- sumptions at intersection conficting zone. Nevertheless, the vehicle control layer, can achieve the global comprehensive negative impacts of random driving behavior of HDVs on optimization of trafc efciency and energy saving for large- improving driving efciency and energy saving for mixed scale mixed trafc fows under diferent MPRs and trafc trafc fows have not been considered in the previous volumes at unsignalized intersections. Especially, in the studies. Terefore, higher requirements need to be put cloud decision layer, a multivehicle MFAPCD approach of forward for cooperative driving between ICVs and HDVs nonlinear multivehicle systems is proposed to achieve the prediction of the time-varying driving speed for HDVs and with random driving behavior . Various research studies have been developed to im- the anticipated driving speed for ICVs formation to improve the trafc efciency. Te control method designed in this prove the trafc efciency for ICVs and HDVs with random driving behavior at unsignalized intersections, and the study only used the online I/O data of mixed vehicles during driving based on the CFDL technology to handle the existing methods can be classifed into three categories: (1) learning-based methods [9–11], which leverage machine complex, nonlinear, and uncertain issues of multivehicle learning frameworks, such as deep reinforcement learning, cooperative driving afected by the random driving speed of to train the cooperative control strategy for ICVs. For ex- the mixed trafc fow. ample, the reinforcement learning agent learned a policy for Te rest of this paper is organized as follows: Section 2 IM to let ICVs at unsignalized intersections give up their presents the system architecture. Section 3 presents the right of way and yield to other HDVs to optimize trafc fow multivehicle MFAPCD scheme to realize the improvement of the trafc efciency for mixed trafc fows. Section 4 . (2) Model-based methods [12, 13], which adopt the perspective of rigorous control theory based on the con- presents the NDMPC method to realize the optimization of energy saving for ICV formation. Numerical experiments trolled model and ofer certain insights for the ICV control problem in mixed trafc. Such as, the uncertain maneuver of are given in Section 5, and we conclude the paper in the HDVs based on the driver behavior model was regarded Section 6. as disturbance, and a receding horizon merging control strategy for ICVs to address the problems of safety and trafc 1.1. Abbreviation. Te abbreviation in Table 1 is used efciency of the mixed trafc merging was proposed . (3) throughout this paper. Other methods, such as, an intersection integrated man- agement system was proposed, which used the partially observable Markov decision process (POMDP) modeling 2. Hierarchical and Distributed Eco- method to estimate the driver intention of HDVs, thereby Driving Architecture decreasing the uncertainties in decision-making and plan- ning for ICVs, and then, the trafc efciency was enhanced As shown in Figure 1, a hierarchical and distributed eco- ; in addition, a game theory-based decision-making driving architecture is developed by considering two layers: dynamic was developed to achieve more realistic models the cloud decision layer and the vehicle control layer. With of human behavior when making conficting maneuvers at this architecture, the anticipated safety driving speed of intersections, and incorporate it into ICVs’ motion planning mixed trafc fow (cloud decision layer) and the multivehicle algorithms and further to improve the trafc efciency . optimal speed control of ICVs formation (vehicle control However, the previous studies mainly focus on improving layer) can be organically combined, which makes it possible trafc efciency under mixed trafc fows but have not yet to achieve the global comprehensive optimization of trafc considered the comprehensive improvement of trafc ef- efciency and energy-saving for mixed vehicles at unsign- ciency and energy-saving for ICVs and HDVs with time- alized intersections. varying speed characteristics under diferent MPRs and For the cloud decision layer, there is an edging com- trafc volumes. In addition, for the learning-based methods, puting (EC) control system at intersections, which can the shortages include that the training process is usually collect the global status information (position and speed) of computationally demanding, and the resulting strategies mixed vehicles entering the intersection zone through V2I might rely on historical information of the trafc communication technology, in which the time-varying Journal of Advanced Transportation 3 Table 1: Te major acronyms used in this paper. Abbreviations Descriptions MPRs Market penetration rates ICVs Intelligent and connected vehicles HDVs Human-driven vehicles MFAPCD Model-free adaptive predictive cooperative driving CFDL Compact form dynamic linearization CFDL-ITSMPC CFDL integral terminal sliding mode predictive control CFDL-PC CFDL predictive control NDMPC Nonlinear distributed model predictive control iCACC Improved cooperative adaptive cruise control EC Edging computing MFAC Model-free adaptive control PPD Pseudopartial-derivative RSUs Road-side unit IDM Intelligent driver model MPC Model predictive control STA Signal time assignment W-E Western entrance to the eastern exit N-S Northern entrance to the southern exit Time-varying Driving states Cloud Decision Layer driving states of information of HDVs ICVs Decision information Status information Multi-vehicle MFAPCD scheme for mixed multi-vehicle systems • Observation and prediction for HDVsĎ driving speed • Anticipated driving speed planning for ICVs formations Control command issuance Anticipated driving speed Position and speed Vehicle Control Layer Optimal and cooperative control of formation driving vehicle vehicle vehicle ······ controller 1 controller 2 controller N Energy-saving of each ······ vehicle-subsystems Figure 1: Hierarchical and distributed cooperative control architecture. driving speed of HDVs is observed and predicted by the the intersection zone without collision is distributed and multivehicle MFAPCD method (that is reconstructed HDVs guided. status information in EC controller). On this basis, the For the vehicle control layer, we designed the distributed confict-free order and anticipated safety driving speed for controller that focuses on the optimization and cooperative mixed vehicles are calculated, and the anticipated safety control for multivehicle formation based on the NDMPC driving speed of the corresponding ICV formation to cross method according to the anticipated driving speed 4 Journal of Advanced Transportation information obtained from the upper level. With this discrete nonlinear system, and the partial derivative of each method, the large-scale systems with multivehicle groups are component of (n + 2) variable is continuous. Moreover, decoupled into several vehicle subsystems that can interact equation (1) satisfes the generalized Lipschitz continuous with each other. On this basis, the fuel consumption, driving condition. For any k ≠ k , k , k ≥ 0 and ∆u (k), we have 1 2 1 2 h � � � � safety, and passenger comfort of each vehicle subsystem are � � � � � � � � �y (k + 1) − y (k)� ≤ b�u (k + 1) − u (k)�, (2) h h h h comprehensively considered. where b > 0. 3. MultivehicleMFAPCDSchemeforImproving For all k, when u (k) ≠ 0, there is a time-varying pa- Traffic Efficiency in Cloud Decision Layer rameter Φ based on PPD, so that the equation is trans- c,h formed into the CFDL data model by the following equation: Motivated by the concept of the model-free adaptive control (3) (MFAC), which does not need a precise model and iden- ∆y (k + 1) � Φ (k)∆u (k) + d (k)T , h,i c,h h 0 tifcation process, and has the advantages of small calcula- where ∆y (k + 1) � y (k + 1) − y (k); y represents the h h h h tion burden, convenient implementation, and simple positions of HDVs;∆u is the speed increment control input controller parameter on-line tuning algorithm [16–18], the h of the HDVs’ the state information reconstruction system in multivehicle MFAPCD scheme is proposed in this study. Te the EC controller. In addition, due to the dynamic and main idea of this method is that build an equivalent CFDL random nature of manual driving behavior, road-side unit data model at each operation point of the closed-loop (RSUs) sensors cannot accurately observe changing speeds nonlinear system based on the novel concept of [19, 20]. Similar to the practice in reference , the un- pseudopartial-derivative (PPD). Ten, the system’s PPD is certain maneuver of the HDVs is regarded as a disturbance online estimated by using system online I/O data, and the in this study, and d is an unknown additional disturbance. controller is further designed using the CFDL-ITSMPC and 0 In equation (3), since d (k) is unknown and ∆y (k + 1) CFDL-PC according to the equivalent CFDL data model. 0 h under the action of ∆u (k) is also unknown, thereby re- ducing the cooperative driving control performance through 3.1. Observation and Prediction for HDVs’ Time-Varying EC controller to mixed vehicles with conficting driving Driving Speed Based on CFDL-ITSMPC. In the EC con- directions. To calculate the time-varying velocity of HDVs, troller, the HDVs (1, ..., n) entering the intersection area are the CFDL-ITSMPC approach is proposed, which mainly frst considered as a class of multiple-input and multiple- includes two parts: output (MIMO) discrete-time nonlinear systems: y (k + 1) � f y (k), . . . , y k − n , u (k), . . . , u k − n , h h h y h h u 3.1.1. Time-Varying Velocity Observation of HDVs. For d(k), d(k − 1), . . . , d k − n , equation (1), let Φ � Φ (k) + Φ (k), where Φ is the c,h c,h c,h c,h estimation error of Φ , and then equation (3) can be re- c,h (1) written as ∆y (k + 1) � Φ (k)∆u (k) + d(k), where h c,h h where u (k) ∈ R represents the system input at the time k; d(k) � Φ (k)∆u (k) + d (k) representing the total dis- h c,h h 0 y (k + 1) ∈ R represents the system output at the time turbance. Te disturbance observer  shown in the fol- k + 1; d(k) is the unknown perturbation and d(k) is lowing equation is designed to estimate the driving speed bounded; n , n , and n are the unknown integers; f(·) � and disturbance information of HDVs entering the in- y u d (f (. . .) . . . f (. . .)) ∈ R ↦R stands for the tersection, respectively: 1 n n +n +2 n n y u − 1 ⎧ ⎨ z (k + 1) � z (k) + θk υ