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Vehicle networking and autonomous driving are hot areas of scientific research today, and they complement each other and play an important role in people's intelligent travel. Intelligent driving vehicle can enhance road safety, effectively reduce traffic flow and fuel consumption, and promote the overall social development. It has great application value in urban traffic system. The traffic condition of a city directly affects the economic development of the city and the improvement of people's quality of life. As the "core" of the urban traffic network, intersections are the frequent places where traffic jams occur. Game theory, as a win-win theory, mainly solves the problem of multiperson and multi-objective with contradictory objective functions and can be used to study the optimal signal control strategy. Aiming at this problem, the potential conflict behaviors of intelligent driving vehicles when turning left at urban intersections are analyzed and a decision model is established. A long-term trajectory prediction model of straight vehicles is established based on the Gaussian process regression model (GPR) considering the vehicle motion pattern. Combined with trajectory prediction, a decision-making process (model) for intelligent driving vehicles based on conflict resolution and a multifactor driving action selection method are proposed. A coordination algorithm based on game theory is designed for conflicting vehicles. The proposed algorithm is verified by the self-developed intelligent vehicle hardware simulation platform. The simulation results show that the PID method based on digital identification and positioning makes the intelligent vehicle obtain good system step response, can improve the disturbance tracking ability of intersection turning analysis, meet the requirements of turning control system, and reduce the complexity and randomness of parameter design, which is better than the traditional fuzzy control method. [ABSTRACT FROM AUTHOR]

In order to improve the decision-making and control effect of autonomous vehicles, in this paper, combined with literature research and process analysis, the control algorithm of autopilot vehicle is analyzed, and the driving process is analyzed combined with the flow method. In order to improve the effect of autonomous driving, with the support of improved algorithms, an integrated decision-making control system for autonomous vehicles under multi-task constraints in intelligent traffic scenarios is constructed, and system performance is improved by simulating autonomous driving decisions in a variety of complex situations. Moreover, this paper designs the road driving model according to actual needs, sets the functional modules of the entire system, and build the overall framework of the system. Finally, in order to study the integrated decision-making effect of this system, this paper conducts test research by designing a simulation test method. From the simulation test results, it can be seen that the intelligent decision-making system for autonomous vehicles constructed in this paper has certain effects. [ABSTRACT FROM AUTHOR]

For the automated vehicles, the user experience on comfort plays an important role for the market acceptance. Generally, for the experienced drivers who already form some certain driving preferences during the longtime driving, they will feel apparent discomfort if the automated vehicles drive very differently from them. Therefore, it is of great significance for comfort driving if the automated vehicles could learn the driving preferences of the users. Fortunately, we enter the era of traffic big data, from the cyber physical system (CPS) perspective, we almost can get whatever data we need to map human drivers from physical space to cyberspace. In this paper, we build a general driving model based on deep convolutional fuzzy systems (DCFS), and design an online driving preferences learning algorithm based on the high-dimensional on-board data. For the verification of the method, we apply this method to design a personalized lane keeping controller (PLKC) with considering the guaranteed stability. Fifteen volunteers participate in the experiments on the Prescan-based simulation platform, and the results show that the PLKC has the online learning ability to the fixed and the time-varying lateral driving preferences. [ABSTRACT FROM AUTHOR]

This paper investigates the globally exponential stability of delayed switched systems via Razumikhin technique. According to mode-dependent dwell average time and multiple Lyapunov functions, new Razumikhin-type stability results are deduced. In contrast to the existing Rzumikhin-type results, the proposed ones are less conservative. In order to demonstrate the applicability, some stability and stabilization results for switched neural networks with time-varying delays are also derived. Several numerical examples are also employed to show the effectiveness and superiority of the obtained results. [ABSTRACT FROM AUTHOR]

To enhance the efficiency and stability of traffic flow in the vicinity of traffic signals under mixed traffic, a macroscopic model considering the characteristics of the vicinity of traffic signals is proposed from the perspective of cyber-physical systems (CPS). In this model, the effects of traffic lights and vehicle lane changing behavior are analyzed. Among them, the remaining time of the signal light, the distance between the vehicle and the signal light, and the urgency of lane change are considered to build a macro model. Finally, the simulation results show that compared with the traditional macroscopic model, the proposed model can improve the stability of traffic flow in the vicinity of traffic signals and effectively alleviate traffic congestion. [ABSTRACT FROM AUTHOR]

This paper proposes a unified theoretical framework to study the problem of finite/fixed-time drive–response anti-synchronization for a class of Markovian stochastic neural networks. State feedback switching controllers without the sign function are designed to achieve the finite/fixed-time anti-synchronization of the addressed systems. Compared with the existing synchronization criteria, our results indicate that the controllers via the switching control without the sign function are given with less conservativeness, and the controllers without any sign function can deal with the chattering problem. By employing Lyapunov functional method and properties of the Weiner process, several finite/fixed-time synchronization criteria are presented and the corresponding settling times are calculated as well. Finally, three numerical examples are provided to illustrate the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

This paper is concerned with multistability and attraction basins of discrete-time neural networks with nonmonotonic piecewise linear activation functions. Under some reasonable conditions, the addressed networks have (2 m + 1) n equilibrium points. (m + 1) n of which are locally asymptotically stable, and the others are unstable. The attraction basins of the locally asymptotically stable equilibrium points are given in the form of hyperspherical regions. These results here, which include existence, uniqueness, locally asymptotical stability, instability and attraction basins of the multiple equilibrium points, generalize and improve the earlier publications. Finally, an illustrative example with numerical simulation is given to show the feasibility and the effectiveness of the theoretical results. The theoretical results and illustrative example indicate that the activation functions improve the storage capacity of neural networks significantly. [ABSTRACT FROM AUTHOR]

This paper focuses on the global exponential synchronization problem for a class of inertial reaction-diffusion coupled neural networks with proportional delay. Through a variable transformation, the inertial reaction-diffusion neural networks are transformed into neural networks with first-order time and space derivative of the states. By taking new Lyapunov-Krasovskii functional, utilizing Wirtinger inequality, a sufficient criterion is obtained to make the addressed networks globally exponentially synchronized onto an isolated node via periodically intermittent feedback controllers. The width index, convergence rate and control gain are given through rigorous mathematical proof. Wirtinger inequality is employed to deal with the reaction-diffusion terms in a symmetric matrix form. The obtained results are easy to be verified by linear matrix inequality toolbox. The results here are also applicable to feedback control for general reaction-diffusion neural networks and inertial neural networks without any other conditions. Finally, the effectiveness and merits of the devised controllers are validated by two simulation experiments. [ABSTRACT FROM AUTHOR]

In traffic systems, cooperative driving has attracted the researchers’ attention. A lot of works attempt to understand the effects of cooperative driving behavior and/or time delays on traffic flow dynamics for specific traffic flow models. This paper is a new attempt to investigate analyses of linear stability and weak nonlinearity for the general car-following model with consideration of cooperation and time delays. We derive linear stability condition and study how the combinations of cooperation and time delays affect the stability of traffic flow. Burgers’ equation and Korteweg de Vries’ (KdV) equation for car-following model considering cooperation and time delays are derived. Their solitary wave solutions and constraint conditions are concluded. We investigate the property of cooperative optimal velocity (OV) model which estimates the combinations of cooperation and time delays about the evolution of traffic waves using both analytic and numerical methods. The results indicate that delays and cooperation are model-dependent, and cooperative behavior could inhibit the stabilization of traffic flow. Moreover, delays of sensing relative motion are easy to trigger the traffic waves; delays of sensing host vehicle are beneficial to relieve the instability effect to a certain extent. [ABSTRACT FROM AUTHOR]

In fact, driving process is a typical cyber physical process which couples tightly the cyber factor of traffic information with the physical components of the vehicles. Meanwhile, the drivers have situation awareness in driving process, which is not only ascribed to the current traffic states, but also extrapolates the changing trend. In this paper, an extended car-following model is proposed to account for drivers’ situation awareness. The stability criterion of the proposed model is derived via linear stability analysis. The results show that the stable region of proposed model will be enlarged on the phase diagram compared with previous models. By employing the reductive perturbation method, the modified Korteweg de Vries (mKdV) equation is obtained. The kink–antikink soliton of mKdV equation reveals theoretically the evolution of traffic jams. Numerical simulations are conducted to verify the analytical results. Two typical traffic Scenarios are investigated. The simulation results demonstrate that drivers’ situation awareness plays a key role in traffic flow oscillations and the congestion transition. [ABSTRACT FROM AUTHOR]

In this paper, the general nonlinear car-following model with multi-time delays is investigated in order to describe the reactions of vehicle to driving behavior. Platoon stability and string stability criteria are obtained for the general nonlinear car-following model. Burgers equation and Korteweg de Vries (KdV) equation and their solitary wave solutions are derived adopting the reductive perturbation method. We investigate the properties of typical optimal velocity model using both analytic and numerical methods, which estimates the impact of delays about the evolution of traffic congestion. The numerical results show that time delays in sensing relative movement is more sensitive to the stability of traffic flow than time delays in sensing host motion. [ABSTRACT FROM AUTHOR]

Development of highly automated and intelligent vehicles can lead to the reduction of driver workload. However, it also causes the out-of-the-loop problem to drivers, which leaves drivers handicapped in their ability to take over manual operations in emergency situations. This contribution puts forth a new switched driving strategy to avoid some of the negative consequences associated with out-of-the-loop performance by having drivers assume manual control at periodic intervals. To minimize the impact of the transitions between automated and manual driving on traffic operations, a switched cooperative driving model towards human vehicle copiloting situation is proposed by considering the vehicle dynamics and the realistic intervehicle communication in a cyberphysical view. The design method of the switching signal for the switched cooperative driving model is given based on the Lyapunov stability theory with the comprehensive consideration of platoon stability and human factors. The good agreement between simulation results and theoretical analysis illustrates the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

Transportation cyber‐physical system (T‐CPS) is a spatiotemporal discrete‐continuous hybrid system. However, the hybrid T‐CPSs have some analysis and modelling problems, especially the problems related to the spatiotemporal characteristics. Existing coloured Petri net approaches of traffic control cannot effectively analyse dynamic changes of cyber systems and physical entities in space and time. Moreover, some problems, related to the state space explosion and the large complex T‐CPSs, have not been well solved. This study develops the innovative methods for T‐CPS design and modelling via the development and application of coloured spatiotemporal Petri nets (CSTPNs). The proposed research ideas involve the CSTPN theory creation, the development of traffic intersection coordination control system using CSTPNs, the traffic simulation analysis and the implementation of T‐CPS‐based CSTPNs. The experimental results show that the new spatiotemporal theories and approaches of the traffic coordination control based on CSTPNs have excellent potentials to address the issues related to the spatiotemporal discrete‐continuous characteristics of T‐CPS. Moreover, these innovative methods with a good validity can be easily applied into practise for the development of T‐CPS. [ABSTRACT FROM AUTHOR]

Lattice hydrodynamic model is a typical continuum traffic flow model, which describes the jamming transition of traffic flow properly. Previous studies in lattice hydrodynamic model have shown that the use of control method has the potential to improve traffic conditions. In this paper, a new control method is applied in lattice hydrodynamic model from a transportation cyber–physical system approach, in which only one lattice site needs to be controlled in this control scheme. The simulation verifies the feasibility and validity of this method, which can ensure the efficient and smooth operation of the traffic flow. [ABSTRACT FROM AUTHOR]

New information technology paradigms like vehicle-to-vehicle and vehicle-to-infrastructure communication are applied to improve traffic operations at an intersection, as a typical cyber-physical system which enable an efficient traffic state estimation and traffic control. In this paper, we try to investigate the optimization of signaled intersection from the cyber-physical system perspective. Specifically, physical system properties be considered in cyber space, then we propose a model prediction control based framework for optimization of vehicle in vicinity of intersection. Under this framework, variable speed limit method based control strategy for individual vehicle is introduced, and a cost function reflecting fuel consumption and traffic efficiency is proposed as a case study. Numerical examples demonstrate the effectiveness of the proposed framework. [ABSTRACT FROM AUTHOR]

In order to obtain consecutive vehicle safety states (CVSSs) for a vehicle safety warning system (VSWS), a novel CVSS calculation scheme, tightly coupling the vehicle's cyber system and its physical dynamics (the cyber-physical system (CPS)), has been developed. The proposed scheme objectively divides the dynamic vehicle distance changes into a cyber process and a physical process, defines the notions of minimum safety distance and safety constants and then improves the dynamic vehicle safety distance calculation scheme using the vehicle velocity, acceleration, the velocity difference and other parameters in the consecutive vehicle distance for scenarios of staying in the same lane and changing to a different lane. The simulation results show that the proposed scheme is practical and effective, and can offer advice for VSWS designs. [ABSTRACT FROM AUTHOR]

In this paper, a new car following model is presented by considering the average speed effect of preceding vehicles group in cyber–physical systems (CPS) environment. The effect of this new consideration upon the stability of traffic flow is examined through linear stability analysis. A modified Korteweg–de Vries (mKdV) equation was derived via nonlinear analysis to describe the propagating behavior of traffic density wave near the critical point. Good agreement between the simulation and the analytical results shows that average speed of preceding vehicles group leads to the stabilization of traffic systems, and thus can efficiently suppress the emergence of traffic jamming. [ABSTRACT FROM AUTHOR]

Single target tracking based on computer vision helps to collect, analyse and exploit target information. The SwinTrack algorithm has received widespread attention as one of the twin network algorithms with the best trade‐off between tracking accuracy and speed, but it also suffers from the insufficient fusion of deep and shallow features leading to loss of shallow information and insufficient use of temporal information leading to inconsistency between target and template. Semantic information and detailed information are combined and multiple convolutional forms are introduced to propose a multi‐level feature fusion strategy to effectively fuse features in space. Besides, based on the idea of feedback, a dynamic template branching approach is also designed to fuse temporal features and enhance the representation of target features. The effectiveness of this method was verified on the OTB100 and GOT10K datasets. [ABSTRACT FROM AUTHOR]

By controlling automated driving vehicles, the human driving vehicles could be indirectly controlled. This control method is different with the methods in homogeneous traffic. In this paper, a CPS-Based H ∞ pinning approach for mixed traffic is proposed. In order to better guide and restrain HVs, the driver factors are taken into consideration. Numerical simulations under different scenarios illustrated the effectiveness of proposed control method. • A new cyber–physical description of traffic is proposed. • The impact of vehicle behavior on traffic from the perspective of CPS is given. • A novel H ∞ pinning approach for mixed traffic is proposed. • The impact of the spatial distribution of CAVs on mixed traffic is studied. [ABSTRACT FROM AUTHOR]

Control of mixed traffic, consisting of manual driving vehicles and automated driving vehicles, remains a major challenge. In this paper, a novel pinning control strategy for mixed platoon to achieve indirect control of manual driving vehicles is proposed. Besides the control algorithm of pinning nodes, the impact of the spatial distribution of automated driving vehicles on traffic is studied. The approach is compatible with the existing control of automated driving vehicles in mixed vehicle platoon. Numerical simulation results illustrated the effectiveness of the proposed pinning control strategy. • A new cyber–physical description of mixed platoon is proposed. • A novel pinning control framework for mixed traffic is given. • The impact of the spatial distribution of automated driving vehicles on mixed platoon is studied. [ABSTRACT FROM AUTHOR]

In this paper, the car following model is investigated by considering the vehicle dynamics in a cyber physical view. In fact, that driving is a typical cyber physical process which couples the cyber aspect of the vehicles' information and driving decision tightly with the dynamics and physics of the vehicles and traffic environment. However, the influence from the physical (vehicle) view was been ignored in the previous car following models. In order to describe the car following behavior more reasonably in real traffic, a new car following model by considering vehicle dynamics (for short, D-CFM) is proposed. In this paper, we take the full velocity difference (FVD) car following model as a case. The stability condition is given on the base of the control theory. The analytical method and numerical simulation results show that the new models can describe the evolution of traffic congestion. The simulations also show vehicles with a more actual acceleration of starting process than early models. [ABSTRACT FROM AUTHOR]

To address the out-of-the-loop problem of the automated driving, a human–machine cooperative scheme for car-following control of the connected and automated vehicles (CAVs) is proposed. The proposed scheme can keep the drivers always in the loop and improve the car-following performance. To be specific, the driving automation system (artificial driver) is assigned to the task of velocity tracking and the human driver is responsible for headway adjustment. For the velocity tracking task, a feedforward–feedback control strategy was designed firstly by considering the advantages of the accurate perception and communication of CAVs, then an H ∞ suboptimal control method was developed to optimize the controller parameters according to the desired performance index, further the controller was fine-tuned based on the idea of human-simulated intelligent control (HSIC) to improve the dynamic performance of the velocity tracking. For the operator's headway adjusting task, the stability analysis based on the Lyapunov function proved that the simple proportional feedback control can be assumed by the driver to ensure the system stability under the cooperation of automated velocity tracking. The experiments based on the driving simulator demonstrated that human–machine cooperative scheme for car-following can reduce the tracking error of vehicle distance effectively, and the human driver can be kept in the control loop with a smaller operating load. [ABSTRACT FROM AUTHOR]

In this paper, a novel driver model for lane keeping is proposed to replicate the steering behavior of expert drivers. Specifically, a feedforward-feedback control scheme mocking expert drivers is adopted: the feedforward controller plays a leading role, which is a data-driven model based on deep convolutional fuzzy systems (DCFS), and for the sake of human-simulation and guaranteed stability, a supervisory feedback controller is designed, which works only if the state hits the set boundary. Comparing with the previous driver models, the key novelty of the paper is to introduce the "motor intermittency" of human behavior into driver modeling, which is an important issue for biological modeling of the drivers. Simulations on the joint platform of PreScan and CarSim show that the newly presented driver model has better matching performance to the expert drivers comparing with the two different types of advanced model predictive control (MPC) controllers. The proposed driver model has the potential application for semi-automated vehicles to provide human-like qualities for automated driving, which may be one of the essential points to promote the comfort when the driver hands over the steering authority, and improve the transition smoothness in the scenario of human vehicle co-piloting. • A new driver model reproducing the "motor intermittency" of human behavior is proposed. • DCFS are proved to be universal approximators. • A hybrid modeling approach including data-driven and mechanism modeling is used. • The proposed model is verified to have better matching performance with the expert drivers. [ABSTRACT FROM AUTHOR]

This paper investigates the optimized merging sequence (MS) and on-ramp merging strategy in mixed traffic with vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I). To this end, a cooperative merging sequence optimization method is first proposed based on the reverse auction. In the method, a hybrid optimized computing structure is proposed to provide the foundation for connected and human-driving vehicles (CHVs) and connected and automated vehicles (CAVs) to obtain the MS more efficiently. And a cooperative merging strategy based on all cooperative merging vehicles under mixed traffic conditions is proposed. In particular, the downstream vehicles in the strategy can change their original velocities to actively participate in the cooperative merging process according to the merging requirements of on-ramp vehicles. And the vehicles in this strategy are all subject to state constraints to avoid the adverse effects of cooperative merging behavior on the following traffic on the main road. Results of numerical experiments illustrate that the merging sequence optimization method can reduce the time to obtain the optimal MS, and the increased computational efficiency is affected by CAV penetration. In addition, in mixed traffic conditions, the cooperative merging strategy can reduce fuel consumption and the time required for merging. [ABSTRACT FROM AUTHOR]

This paper presents a traffic flow control scheme of connected vehicles to stabilize a traffic system with on-ramps from a macromodel point of view. Firstly, this paper establishes a lattice hydrodynamic model with on-ramps, and an output feedback controller is designed based on the characteristics of connected vehicles. Then, by using the Lyapunov–Krasovskill functional analysis method, this paper studies the delay-dependent convergence conditions of the control method. String stability is defined as the nonamplification of the downstream disturbance of a traffic flow when it propagates upstream. The influence of the on-ramps is regarded as a stochastic disturbance in this paper. Through a theoretical analysis, the control conditions that can ensure the string stability of the traffic system under the influence of on-ramp are obtained. Finally, numerical experiments are carried out to compare the traffic states of the traffic system with and without control. The results show that the proposed control can effectively suppress the instability of the traffic system. [ABSTRACT FROM AUTHOR]

• A new driver model is developed from the view of human factors. • An MPC-based optimized switched driving strategy is proposed. • The simulator experiment is conducted to verify the new driver longitudinal model. • The performance index of switched driving is smaller than the human and ACC driving. • The real-time of algorithm can meet the requirement of engineering practice. In vehicle longitudinal control, improved comfort and reduced operation workload for human drivers are achieved with the ACC (Adaptive Cruise Control), which still requires the driver to maintain full attention on monitoring, meanwhile, the risk of distraction and fatigue rises resulting from the long-term supervising task, which has a strong impact on the successful takeover. The key point to make full use of ACC advantages and make up human's weakness on supervising task is the reasonable arrangement of control time of ACC and human driver. In this paper, an MPC (Model Predictive Control) based optimized switching strategy of longitudinal driving authority transition is proposed, which aims to provide proper advice for human drivers to handover and takeover control authority, through minimizing the overall index consisted of the operation workload, fuel consumption, takeover risk and tracking errors. In addition, a new driver longitudinal model considering actual reaction time delay and insensitive distance perception of human drivers is proposed as well, which combines with ACC to constitute a typical switched control system called the switched driving model as the predictive model for MPC. The stable condition of the new driver longitudinal model is derived by using describing function method and a sufficient condition to ensure steady switched system is given by using the Lyapunov method and LMI approach. The results of simulator experiments show that the new driver model describes the car-following behavior of real human driver better. What's more, the simulation results demonstrate that the performance index of switched driving is smaller than the human driving and ACC driving only, and the optimization time is short enough to meet the requirement of engineering practice. [ABSTRACT FROM AUTHOR]

Vehicle detection plays an important role in intelligent transportation systems and security. Using the original Single Shot MultiBox Detector (SSD) directly for vehicle detection, lacks accuracy and stability. Moreover, most of the state‐of‐the‐art methods need cost a lot of time to inference. Vehicle detection is often used in complex traffic environments. Therefore, faster detection speed and higher detection accuracy are required. This study is aimed at developing a trade‐off between accuracy and speed vehicle detection framework based on the SSD algorithm. To improve the multi‐scale detection performance of SSD, semantic information, detailed features and receptive fields are combined to propose the feature pyramid enhancement strategy (FPES). On the other hand, the cascade detection mechanism is proposed to strengthen the positioning capability of SSD and an adaptive threshold acquisition method for object detection module (ODM) stage to improve model accuracy. Finally, a more efficient convolutional network is deployed through network slimming. Experimental results demonstrate that the proposed framework achieves state‐of‐the‐art performance on UA‐DETRAC and Udacity benchmarks. Interestingly, the inference time is the lowest for the proposed method than the state‐of‐the‐art methods, promising its application for fast and effective vehicle detection. [ABSTRACT FROM AUTHOR]

With the incredible advancement of information technology, the communication between vehicles (V2V), vehicle and infrastructure (V2I) improves the traffic efficiency and safety. In this work, we present a modified coupled map car-following model with the control terms of the velocity difference and headway difference under V2X (vehicle to everything) environment. Then we study the impact of the control term on the stability and dynamic performance, respectively. The stability of the modified model is analyzed and the conditions for the stability of the traffic flow are presented. Besides, the simulations are carried out from the micro and macro perspectives. The numerical simulation results are consistent with the theory, and the validity of the model is verified. Moreover, in order to study the macro properties of traffic, through sumo numerical experiments, the evolution of the macro quantity of the traffic system has also been studied. [ABSTRACT FROM AUTHOR]

Connected cooperative driving is known as the promising way to mitigate traffic congestion, enhance driving safety and improve fuel economy. However, before vehicle-to-vehicle (V2V) communication technology became widely applied, vehicles could not always communicate with the front cars due to the uncertainty of vehicle type and communication function. Towards the non-connected situation and making the most of the on-board sensors of the automated vehicles (AVs), an auto-regression (AR) model was adopted to predict the velocity of the preceding vehicle at the next moment, then a new longitudinal car-following control scheme is given from the perspective of cyber physical system to improve the longitudinal following performance. The sufficient condition ensuring a better performance is acquired by local stability analysis and the impact of velocity prediction errors of the AR model is analyzed through a nonlinear partial differential equation. The experiments based on the US-101 dataset and numerical simulations were carried out and the results are in great agreement with the theoretical analysis, which reveals that applying AR model to predicting the velocity of the preceding vehicle at the next moment can improve the car-following performance of AVs without the support of communication devices. [ABSTRACT FROM AUTHOR]

This paper investigates the globally exponential stability of switched neural networks with time-varying delays. By virtue of mode-dependent average dwell time, some significant criteria of exponential stability are obtained for delayed switched neural networks with only stable subsystems or with both stable subsystems and unstable subsystems. The proposed theoretical results could be utilized not only to verify the globally exponential stability of switched neural networks, but also to design appropriate switching signal to guarantee the globally exponential stabilization. They can explicitly reflect the effect of mode-dependent average dwell time on the stability of switched neural networks. In contrast to the previous results, the proposed criteria are more straightforward and effective in the real-world application. Three numerical examples are introduced to illustrate the effectiveness of the proposed results. [ABSTRACT FROM AUTHOR]

This paper studies the reachability realization problem of discrete-time switched linear systems. A new constructive approach is developed to design switching sequence such that the reachable subspace is realized. Compared with the existing results, the length of the designed switching sequence is smaller and the proposed algorithm has fewer calculations. One numerical example is presented to illustrate the effectiveness of the obtained results. [ABSTRACT FROM AUTHOR]

This paper investigates the exponential synchronization of chaotic systems with time delays via periodically intermittent control. Under the new differential inequality, some novel synchronization criteria are derived. In contrast to the existing works, the proposed results are less conservative because they can obtain more precise synchronized rate under the identical control conditions and remove the restrictions on the control period (or the control width) and the time delay. By using special parameters, the feasible region D(ξ), which guarantees the response system synchronizes with the drive system with synchronized rate 0.5ξ, is obtained. The Lu chaotic attractor and a first-order chaotic system with time delay are presented to demonstrate the effectiveness of the proposed results. [ABSTRACT FROM AUTHOR]

Trajectories control for vehicles in the vicinity of a signalized intersection under the mixed traffic scenario can improve fuel efficiency and shorten travel time (TT). The emergence of Connected and Automated Vehicles (CAVs) enables control of trajectories from the platoon level. At the same time, the actuator faults are not conducive to implementing trajectories control, and fault-tolerant control of transportation systems under 100% penetration rate (PR) of CAVs is no longer applicable. Hence, mixed vehicular platoons are investigated under V2X in this paper. An adaptive fault-tolerant platoon controller is constructed under the mixed traffic scenario. Meanwhile, the platoon splitting and merging strategies for trajectories control considering the non-connected characteristics of AVs are designed. The presented methods are verified through simulations under different conditions and comparison simulations. • An adaptive fault-tolerant platoon controller for mixed traffic is constructed, which ensures the implementation of trajectories control. • The platoon splitting and merging strategies are designed to address the non-connected characteristics of AVs. • The collision-avoid strategy is modified by the merging strategy that considers the effective range of vehicles' sensors. [ABSTRACT FROM AUTHOR]

This paper investigates the stability of a class of variable-time switched systems whose switching times are dependent on system states. To overcome the difficulty that switching times are not explicit, we construct a new fixed-time switched impulsive system whose stability is equivalent to the stability of the variable-time switched system. In the light of the mode-dependent average dwell time and the constructed fixed-time switched impulsive systems, sufficient conditions for exponential stability of variable-time switched systems are obtained. Finally, two applications are employed to indicate the effectiveness of the proposed results. [ABSTRACT FROM AUTHOR]

In this paper, a macro hydrodynamic model suitable for tunnel traffic in the network environment is proposed. Firstly, according to the characteristics of tunnel traffic, we establish the corresponding lattice hydrodynamics model, and design the corresponding control strategy according to the information obtained by connected vehicles. Through the theoretical analysis, the internal stability conditions of the traffic system are obtained. After the external disturbance caused by the tunnel is considered, a robust H-infinity (H ∞) control strategy is proposed, and the corresponding robust stability conditions are obtained. The string stability is also studied to ensure that the instantaneous disturbance is not amplified. Numerical simulation compares the evolution of the traffic system with and without control. The results reflect that the control strategy proposed in this paper can effectively maintain the stability of tunnel traffic system. [ABSTRACT FROM AUTHOR]

Traffic estimation and traffic simulation are essential parts of the intelligent transportation system. In recent years, massive traffic data has brought many data-based traffic estimation methods, but few are utilized in traffic simulation. The cause is that the data-based traffic simulation has high requirements for data quality, needing the trip information of all vehicles, not met by common traffic data. Fortunately, the electronic registration identification (ERI) data of the vehicle can satisfy. Therefore, we utilize ERI data to study the traffic estimation and simulation for the road network. The core of this research is the travel time estimation model, which is constructed by combining traffic theory models and the deep neural network. The traffic theory models are a group of linear models, which represent the relationship between traffic flow or traffic density and travel time. The deep neural network can extract the temporal and spatial characteristics of the road network traffic by Moving Average Convergence-Divergence (MACD) and Graph Convolutional Network (GCN), respectively. We named the travel time estimation model MGCN. Then, we employed MGCN for traffic simulation. In the experiment section, we employed Chongqing ERI data to verify the research content. Compared with some baseline methods, our method is superior in travel time estimation and traffic simulation. [ABSTRACT FROM AUTHOR]

Connected vehicles can obtain the position and velocity of multiple preceding vehicles. According to the characteristics of connected vehicles, this paper proposes the new control term and improves the original car-following model. The model considers the optimized acceleration and velocity difference information among the preceding vehicles. Meanwhile, linear stability analysis of the proposed model is executed and the stability criterion is given. It shows that the model has better stability compared with the existing car-following models. Three scenarios including car-following evolution, starting process and braking process are simulated, and the response time, space headway and velocity fluctuations are analyzed separately. The numerical simulation results show that the controlling strategy proposed in this paper has a noticeable effect on the stability of the traffic system. In addition, the calibration results based on the measured data show that the proposed model is more consistent with the actual traffic conditions. [ABSTRACT FROM AUTHOR]

Mixed traffic flows behave uncertainty and random feature, which lead to traditional forecast method has low prediction precision. This paper proposed a new multi-type vehicles forecast method of the short-term freeway traffic flow. Based on the change law of traffic flow of the various type vehicles was different, it used improved time series method to forecast the flow value of large type car and trailer, and used second exponential smoothing method to forecast the flow value of small type car and middle type car, then obtained the prediction value of the mixed traffic flow by weight average method on the basic of vehicle conversion factor. Finally, compared with nonparametric regression model and traditional time series model, the results of experiment shows that the new method can increase the forecasting accuracy of short-term freeway traffic flow on weekdays or holidays, and the results can supply the foundation to further improve the ability of highway management and control. [ABSTRACT FROM AUTHOR]

Accurate short-term traffic flow prediction is an important basis of intelligent transportation systems (ITS) such as transportation operations and urban planning applications. However, due to the lack of complete directly measured data on urban traffic flow, existing studies cannot adequately mine the dynamic spatial-temporal correlations characterizing traffic flows in urban road networks. Electronic registration identification (ERI), which is an emerging technology for uniquely identifying a vehicle, can help collect the travel records of all vehicles. This inspires us to employ ERI big data for traffic flow prediction. In this paper, we propose a dynamic spatial-temporal feature optimization method with ERI big data for short-term traffic flow prediction based on a gradient–boosted regression tree, called DSTO-GBRT. Firstly, the framework of DSTO-GBRT is built. Secondly, we analyze the dynamic spatial-temporal correlations among the current prediction point and upstream correlative points using the Pearson correlation coefficient (PCC). Thirdly, to eliminate the linear correlations among features, we exploit principal component analysis (PCA) to optimize the original training data and obtain optimized training data. In the experiment, real-world ERI big data from Chongqing are employed for the proposed DSTO-GBRT method. Compared with ST-GBRT, ARIMA, DSTO-BPNN and DSTO-SVM, the results demonstrate that DSTO-GBRT can provide timely and adaptive prediction even in rush hour, when traffic conditions change rapidly. Furthermore, compared with DSO-GBRT and DTO-GBRT, the results show that the proposed DSTO-GBRT method is more accurate. [ABSTRACT FROM AUTHOR]

Considering vehicle dynamics and external disturbances, cooperative driving system of homogeneous vehicles is investigated in the vicinity of traffic signals under V2X. This paper analyzes vehicle dynamics and dynamic response performance and investigates the topology structures between vehicles and vehicles and roadside infrastructures. A cooperative driving model with external disturbances and communication delays is proposed for homogeneous vehicles. By using Lyapunov–Krasovskii stability theory, we investigate the robust analysis of the proposed model which is represented by the form of linear matrix inequality. The distributed robust H ∞ control strategy is designed to suppress the impact of external disturbance. The validity and feasibility of the proposed control strategy is verified by simulation experiments, which has the great potential to improve speed tracking performance and fuel efficiency. • Cooperative driving model for homogeneous vehicles is proposed. • We present sufficient conditions for the robust H ∞ controller to ensure robust stability. • The distributed robust control strategy of the proposed model is designed. [ABSTRACT FROM AUTHOR]

Abstract The simulation of pedestrian evacuation in emergency is of great significance to the public facilities design and emergency plan implementation. For study pedestrian behaviors under emergency, this paper first analyzes emergency and pedestrian characteristics. In order to describe the spread effect of emergency on pedestrian evacuation process, the concept of emergency diffusion field is put forward. In addition, the concept of panic coefficient is put forward in order to depict the effect of pedestrian panic psychology on the evacuation process. Then, the floor field model consisted of static floor field, dynamic floor field and emergency diffusion field is established, the corresponding updating rules are defined. Finally, the experiments results reproduce the pedestrian evacuation process under different conditions, and the influence factors in the process of pedestrian evacuation are analyzed, verify the validity of the model Highlights • The concept of emergency diffusion field and panic coefficient are put forward. • The floor field model consisted of static floor field, dynamic floor field and emergency diffusion field is established. • The experimental results verify the validity of the model. [ABSTRACT FROM AUTHOR]

Dynamic traffic prediction is an important section of the urban intelligent transportation system. Although there have been many studies in this area, it is still a challenge for the urban road network considering the complexity of urban traffic and the lack of high-quality traffic data. Electronic Registration Identification of Vehicles (ERI) is an emerging traffic information acquisition technology based on Radio Frequency Identification (RFID). It can identify each vehicle accurately and generate high-quality traffic data. We employ ERI data to realize the dynamic prediction of traffic density and travel time for the urban road network. First of all, we study the temporal characteristics model of traffic through the Markov chain. Secondly, combining the Expectation–Maximization algorithm and logistic regression classifier, we classify the training data into different traffic scenes and build the spatial characteristics model for each traffic scene. The model parameters are obtained by the particle swarm optimization algorithm. Then, the trained temporal and spatial models are combined to conduct dynamic traffic prediction. Finally, the real data of Chongqing is utilized to verify the proposed method. The experimental results show that the proposed method has a good prediction accuracy and is suitable for all kinds of roads in the road network. Besides, the constructed model has good interpretability for real traffic. • This paper studies the prediction of traffic density and travel time. • We use novel traffic data, Electronic Registration Identification data of vehicles. • The temporal and spatial characteristics of urban traffic are considered. • The interpretability of the prediction model is highlighted. [ABSTRACT FROM AUTHOR]

Unlicensed taxis are private vehicles that are not duly licensed or permitted by the jurisdiction in which they operate. Trajectory data contain rich behavior features of mobile objects, which are valuable for unlicensed taxi detection. (Electronic Registration Identification) ERI of the motor vehicle is a way to collect trajectory data. ERI's advantage is that it can record all kinds of vehicles traveling in the city, including taxis, private vehicles. As a pilot city of ERI in China, Chongqing has formed a massive ERI trajectory dataset of motor vehicles. This dataset provides us with an opportunity to detect and analyze unlicensed taxis from a data-driven aspect. In this paper, we complete two main works: detecting city-wide unlicensed taxis and analyzing them. Firstly, we build an unlicensed taxis detection model based on an ensemble learning approach, random forest(RF). The goal of ensemble learning is to improve prediction, generalizability, and robustness over a single classifier. We employ taxis and commuting private vehicles as training samples. The core idea is that unlicensed taxis and taxis are similar in many aspects. We also innovatively utilize the POI information as an input feature to the unlicensed taxi detection model. With the comparison of some baseline models, we have proved our model's superiority on the ERI dataset. So, we apply the detection model on a real-world dataset and detect the city-wide potential unlicensed taxis. Secondly, we conduct some statistical analysis with the detected potential unlicensed taxis. We find that unlicensed taxis do behave very much like taxis. The hot areas of taxis and unlicensed taxis are not the same, which provides vital information for further traffic management. [ABSTRACT FROM AUTHOR]