A fundamental challenge in car-following (CF) modeling lies in accurately representing the multiscale complexity of driving behaviors, particularly the intradriver heterogeneity where a single driver's actions fluctuate dynamically under varying conditions. While existing models, both conventional and data-driven, address behavioral heterogeneity to some extent, they often emphasize interdriver heterogeneity or rely on simplified assumptions, limiting their ability to capture the dynamic heterogeneity of a single driver under different driving conditions. To address this gap, we propose a novel data-driven CF framework that systematically embeds discrete driving regimes (e.g., steady-state following, acceleration, cruising) into vehicular motion predictions. Leveraging high-resolution traffic trajectory datasets, the proposed hybrid deep learning architecture combines gated recurrent units (GRUs) for discrete driving regime classification with long short-term memory networks (LSTMs) for continuous kinematic prediction, unifying discrete decision-making processes and continuous vehicular dynamics to comprehensively represent interdriver and intradriver heterogeneity. Driving regimes are identified using a bottom-up segmentation algorithm and dynamic time warping (DTW), ensuring robust characterization of behavioral states across diverse traffic scenarios. Comparative analyses demonstrate that the framework significantly reduces prediction errors for multiple metrics while reproducing critical traffic phenomena, such as stop-and-go wave propagation and oscillatory dynamics.

Hard shoulder running (HSR) has emerged as a sustainable and cost-effective strategy for improving expressway capacity. To address the limitations of existing approaches in capturing short-term traffic fluctuations and the coarse granularity of conventional sensor data, a short-term congestion prediction–driven, scenario-adaptive dynamic HSR (D-HSR) control framework based on multi-segment expressway video surveillance data is proposed. Specifically, a YOLOv8-DeepSORT pipeline is employed to extract real-time traffic flow parameters from video streams. Acongestion warning model is then developed to define dynamic control thresholds for HSR activation. Two distinct traffic scenarios are considered: recurrent and incident-induced congestion. The corresponding HSR activation decisions are formulated as time series forecasting (TSF) and traffic condition assessment (TCA) tasks, respectively. To enhance temporal modeling performance, S-Mamba is introduced as a high-capacity deep sequence model that enables more responsive and accurate traffic state predictions. The proposed strategy is implemented and evaluated on a calibrated simulation of the G25 section of the Changchun to Shenzhen expressway. Compared with the next-best model and rule-based baselines, the proposed method achieves a 1.95% and 31.13% reduction in average fuel consumption and a 4.06% and 58.53% reduction in average travel time, respectively. The results validate the effectiveness of the proposed strategy in facilitating intelligent and D-HSR operations for congestion mitigation.

In transportation systems, accurately estimating the remaining useful life (RUL) of critical components, such as aircraft engines, Battery Management Systems (BMSs), is crucial for the safe and reliable operation and manufacturing of transportation vehicles. However, most existing research overlooks the underlying physical information, which is vital for more precise RUL prediction. To fill this gap, this paper proposes a physics-informed method for predicting the RUL of key components of transportation vehicles. By integrating the Mamba network with a multi-head attention mechanism, we capture and emphasize key features and trends in the equipment’s operational state, improving prediction accuracy. Additionally, we introduce a Physics-Informed Neural Network (PINN) framework to model the underlying physical relationships between RUL and sensor data, incorporating these relationships as a regularization term in the loss function to enhance predictive capability and interpretability. We conducted experimental validation using the C-MAPSS aircraft engine dataset (operation) and the transportation vehicle chip manufacturing dataset (manufacture). The results show that the proposed method significantly improves the accuracy of RUL prediction, providing strong support for the intelligent maintenance and reliability management of key components in transportation vehicles.

This article introduces a novel fuzzy logic-enhanced neuroadaptive sliding mode control (FLENNSMC) framework, developed for vehicular platoon systems subject to a confluence of challenges. Leveraging the synergistic integration of fuzzy logic's interpretive strengths and neural networks' adaptive learning capabilities, FLENNSMC effectively addresses nonlinear dynamics, stochastic disturbances, actuator faults, and stringent asymmetric spacing constraints. We propose a Takagi-Sugeno (T-S) fuzzy model to structure the learning process and a fuzzy logic-enhanced RBFNN (FLERBFNN) for robust approximation of unknown functions, including unmodeled dynamics and fault signals. The controller design incorporates a fault-tolerant control mechanism for enhanced robustness, an asymmetric barrier Lyapunov function (BLF) to strictly enforce spacing constraints, and a Nussbaum function to compensate for actuator faults with unknown directions. The fuzzy logic-enhanced structure allows for localized and efficient learning, which reduces computational burden and improves adaptation speed. Through a rigorous stochastic Lyapunov-Krasovskii stability analysis, we derive sufficient LMI-based conditions for the uniform ultimate boundedness (UUB) of tracking errors in the mean square sense and guarantee a mixed H-infinity/passivity performance. Extensive simulations on a 2-D multilane vehicular platoon demonstrate the superior performance of the proposed FLENNSFC compared to conventional neuroadaptive control approaches, particularly highlighting the benefits of fuzzy logic in structuring the learning process and handling complex uncertainties. Simulation code is available at https://github.com/zhanganguo/FLENNSMC-Platoon-Control-Simulation.

Switching communication topologies can cause instability in vehicle platoons, as vehicle information may be lost during the dynamic switching process. This highlights the need to design a controller capable of maintaining the stability of vehicle platoons under dynamically changing topologies. However, capturing the dynamic characteristics of switching topologies and obtaining complete vehicle information for controller design while ensuring stability remains a significant challenge. In this study, we propose an observer-based distributed model predictive control (DMPC) method for vehicle platoons under directed Markovian switching topologies. Considering the stochastic nature of the switching topologies, we model the directed switching communication topologies using a continuous-time Markov chain. To obtain the leader vehicle's information for controller design, we develop a fully distributed adaptive observer that can quickly adapt to the randomly switching topologies, ensuring that the observed information is not affected by the dynamic topology switches. Additionally, a sufficient condition is derived to guarantee the mean-square stability of the observer. Furthermore, we construct the DMPC terminal update law based on the observer and formulate a string stability constraint based on the observed information. Numerical simulations demonstrate that our method can reduce tracking errors while ensuring string stability.

The difficulty of vehicle trajectory prediction mainly lies in the shared spatial-temporal relationships among vehicles. To address this task effectively, extracting the spatial-temporal details that affect the inter-vehicle motion, such as motion intentions and interactions, is crucial. This paper proposes a Mamba-driven differential Transformer model with an intention coupling decoder (ICMDT). Differential Transformer determines final attention scores by calculating the difference between two independent softmax attention maps, effectively suppressing the noise in spatial-temporal features. ICMDT integrates the strengths of differential Transformer and Mamba, concentrating on encoding spatial-temporal features while eliminating redundant information. It also enhances global information aggregation and spatial interaction modeling. Additionally, an intention coupling decoder is proposed to align motion intentions with spatial-temporal features, achieving connections between features and intention query instances. This decoder facilitates accurate multi-modal trajectory predictions. ICMDT has demonstrated superior performance across four real-world datasets, surpassing multiple metrics. For instance, improvements of 45.92% to 57.89% in long-term prediction (3∼5s) are achieved using the RMSE metric, which is quite promising. Notably, our experiments reveal that the intention coupling decoder consistently enhances the prediction accuracy of several leading prediction models, providing new insights for the future development of vehicle trajectory prediction algorithms.

Limited transmission bandwidth and actuator faults in intelligent and connected vehicles (ICVs) pose significant challenges to controller design. To address these issues, this paper proposes a transmission-efficient fault-tolerant control strategy for a platoon of ICVs. Adaptive laws are developed to estimate the bounds of unknown fault characteristics, which eliminates the need for prior fault knowledge. A quantized event-triggered (QET) mechanism that integrates a hysteresis quantizer and a dynamic event-triggered mechanism (DETM) is designed to improve signal transmission efficiency. By appropriately designing the dynamic variable in the DETM, the Zeno behavior is avoided. Furthermore, with the aid of smooth functions, an adaptive controller is constructed to address the impact of unknown actuator faults, as well as signal deviations induced by the QET mechanism. Simulation results in both representative and real-world driving scenarios demonstrate that the proposed strategy significantly reduces communication burden while ensuring robustness against unknown actuator faults.

This paper proposes a novel hierarchical controller for connected truck platoons. To this end, the predecessor following topology is used to characterize the communication connectivity between connected trucks. Then, a longitudinal efficient controller consisting of upper-level and lower-level controllers is proposed. In particular, the upper-level controller is designed based on the kinematic model to handle the car-following interactions between connected trucks and delays in communication and input. The lower-level controller comprises a feedforward and a feedback control law. The feedforward control law converts the desired acceleration from the upper-level controller into the vehicle throttle or braking pressure using the inverse dynamic model, while the feedback control law compensates for the control error caused by unknown vehicle parameters. In addition, in the linear region, the internal stability is analyzed based on the second-order kinematic model using s-domain analysis and linearization method, respectively. Then, the string stability is proved. The influence of parameters on the stability performance is extensively discussed using the stability diagram. Finally, the feasibility of the proposed controller is verified via co-simulations in PreScan and TruckSim, in terms of acceleration, velocity, and spacing error profiles.

Vehicle trajectory prediction (VTP) poses unique spatial-temporal modeling challenges, as a human-like prediction requires considering fine-grained interactions. Prior models have often used various attention mechanisms to extract key spatial-temporal interactions from the foreground, thus missing background information processing. However, this may result in the model’s insufficient generalization ability in a specific modality. Here this paper presents VCIFormer, a velocity-aware complementary interaction Transformer designed to enhance vehicle trajectory prediction by capturing complex spatial-temporal interactions between foreground and background with context awareness. VCIFormer combines inverse attention with traditional spatial-temporal attention as a complementary mechanism, applying bidirectional optimization to capture foreground and background attention flows. An adaptive visual mask is also developed to align attention allocation with human visual patterns at varying velocities. It enables the model to prioritize critical regions analogous to human driving behavior. Moreover, a context-aware encoder, consisting of a surround-aware module and a motion-enhancement module, is incorporated to provide additional interaction cues and spatial information. VCIFormer is evaluated on six real-world datasets (NGSIM, HighD, RounD, ExiD, Argoverse, and nuScenes) and attains state-of-the-art performance in critical metrics. For example, in comparison to baseline models, there are significant improvements in ADE and FDE by 2.84-18.18% and 9.88-13.33% on the NGSIM, HighD, RounD, and ExiD datasets, respectively. In sum, compared with previous architectures, VCIFormer presents a more effective combination of spatial-temporal interaction layers and context awareness for VTP.

Mobile edge computing (MEC)-assisted vehicle-to-everything (V2X) communication has been proposed as it can reduce the computation overhead of vehicles by offloading partial tasks. However, the performance improvement of such systems is still challenging due to the limited spectrum resources and computation capabilities. To this end, we study a mode selection and resource allocation (RA) problem in MEC-assisted V2X networks with limited energy and bandwidth constraints. Our goal is to minimize the delay of vehicle-to-infrastructure (V2I) links under the constraints of the maximum transmission bandwidth, the minimum data rate, the maximum transmit power, and the mode selection factors. To solve the mixed-integer nonlinear programming problem, an alternative optimization method is employed to decompose it into two subproblems: a radio RA subproblem and a task offloading subproblem. Then, the former subproblem is converted into a convex problem via the variable substitution approach, while the latter one is converted into a convex problem via variable relaxation and successive convex approximation. Finally, an iteration-based RA algorithm is proposed. Simulation results show that the proposed algorithm reduces latency by 77.9% compared to the RA algorithm without MEC and by 68.9% compared to the RA algorithm without mode selection.

Generative Adversarial Networks (GAN) have been widely used in traffic data imputation to improve the accuracy of data imputation. However, existing GAN-based models often suffer from mode collapse and cannot fully reflect the complex characteristics of real-world traffic, which affects the quality of data imputation. To address these challenges, we incorporate the Diffusion Model (DM) into the GAN framework, integrating the traffic dynamics modeling process within the Diffusion-GAN network. Based on this, we propose a Diffusion-TGAN speed data imputation model to generate individual vehicle speeds. Combined with the generated vehicle speed, the group trajectory reconstruction result is further given. The model uses the forward process of DM to generate condition vectors to guide the training of GAN generator. Subsequently, the discriminator of GAN takes the traffic dynamics constraints into account during adversarial training. Traffic dynamics modeling aims to make the generated speed data consistent with the real traffic characteristics. Experiments on multiple data sets show that the proposed model effectively imputes in the spatio-temporal speed data, and reduces the RMSE of the speed considering the position by 23.4% compared with the common GAN model, and reduces the RMSE by 39.7% in the trajectory reconstruction respectively. The code and our model are available at GitHub.

This paper presents a novel consensus control algorithm for a vehicle platoon, taking into account human physiological-psychological comfort. To this end, this paper incorporates state constraints into the optimal velocity-based platoon controller, which can effectively improve the comfort and consensus. Moreover, this paper employs the barrier Lyapunov function (BLF) to prove the stability of the proposed controller, providing more strict stability guarantees. According to theoretical analysis, numerical experiments are conducted to demonstrate the performance of the proposed control algorithm and the benchmark algorithm. The comparative results show that the proposed control algorithm can produce better-uniformed states and bounded state errors.

On-ramp merging areas are typical bottlenecks in the freeway network. Vehicles merging on the ramp usually lead to reduced traffic efficiency, increased risk of collisions and fuel consumption. Most previous studies have mainly addressed single-lane merging, neglecting the complexity of multilane on-ramp entrances. To solve this problem, graph convolutional proximal policy optimization for connected and automated vehicles (GCAV-CPO) is proposed in a multilane on-ramp merging scenario in a mixed-autonomy traffic environment. This system is a distributed reinforcement learning framework that integrates vehicle graph structures with multiagent reinforcement learning (MARL) to coordinate the merging of connected and automated vehicles (CAVs) in a multilane traffic environment. An eco-friendly Markov decision process is developed, taking into account factors, such as energy consumption, travel time and safety, and incorporating a lane-changing factor into the reward function. Our method is evaluated under three different traffic pressure modes and four CAV penetration rates to thoroughly demonstrate its generalization ability. Experimental results show that the proposed GCAV-CPO significantly outperforms current baselines in terms of increasing traffic efficiency and safety, as well as in reducing energy consumption.

This article addresses the lateral control problem of nonlinear autonomous ground vehicles (AGVs) with time-varying velocities. A novel solution is provided for making nonlinear AGVs drive smoothly along a predetermined path in the presence of external disturbances. First, a new controller based on the homogeneous polynomial technique is designed to improve the control flexibility so as to enhance the control effect of the vehicle facing the change in road curvature. Second, a more relaxed stability criterion is derived by using convex optimization techniques and relaxation techniques to ensure the exponential stability of the closed-loop vehicle system. Finally, the MATLAB/Simulink-CarSim co-simulation platform is used to verify the proposed control strategy's effectiveness.

In this paper, a hierarchical control strategy of vehicle platoons is presented, in which the longitudinal and lateral coupling property of vehicles is taken into account. A three-degree-of-freedom dynamic model of vehicles is approximated to a “global” linear model by the Koopman operator theory. A synchronous distributed predictive control scheme of vehicle platoons is proposed as an upper-level controller, where both the linear vehicle model and a linear parametric-varying lane-keeping model are adopted to predict the dynamic of vehicles, and keep vehicles in the designated lane. Thus, it can avoid the solution of nonlinear optimization problems and reduce the computational burden accordingly. A lower-level controller is designed, where the desired longitudinal control force determined by the upper-level controller is transformed into the desired throttle angle and brake pressure through an inverse longitudinal dynamics model of vehicles. The joint simulation results by PreScan, CarSim and MATLAB/Simulink show that when the leader vehicle accelerates or decelerates, the following vehicles in the platoon can keep the same velocity as the leader vehicle, and maintain the desired safety distance between the front and rear vehicles. In addition, joint simulation in the curved road scenario show that the performance of lane keeping can be guaranteed for vehicle platoons with the proposed control strategy.

Existing studies on Autonomous Intersection Management (AIM) primarily focus on regular vehicles (e.g., cars), while ignoring bus priority demands. This paper aims to optimize the bus operations at autonomous intersection with trajectory planning and priority control. First, an intersection trajectory planning approach is proposed for turning movements within the intersection considering significant passenger volume and large size of buses. A two-stage trajectory planning method is adopted that employs a combination of transition and circular curves to ensure the smooth turning movements for vehicles within the intersection. Next, a bus priority control model for autonomous intersection (AIM-BP) is developed to minimize weighted combinations of total bus delay and car delay. In particular, the model incorporates the introduction of the dynamic bus lane designed to clear the vehicles in front of buses, thereby creating a relatively exclusive space for the buses. The proposed model simultaneously optimizes the lane choice on the road section, the route choice within the intersection and the time to enter the intersection for each vehicle, while determining whether to deploy the dynamic bus lane and which lane serves as the dynamic bus lane in the approach. The model is formulated as a Mixed Integer Linear Programming (MILP) problem, compiled in AMPL (A Mathematical Programming Language) and solved by CPLEX. Results demonstrate significant reductions in average bus delay and passenger delay under the proposed model, and sensitivity analysis further examines its effectiveness.

This article proposes a finite-time based sliding-mode controller (FTSMC) and disturbance observer (FTDO) for connected vehicle (CV) platoon with uncertain dynamics. In particular, a recursive structure consisting of first-level and second-level sliding mode surfaces (SMSs) is developed for the chattering of the conventional SMC. Herein, the first-level SMS is designed based on a proportional-integral-derivative SMS considering the spacing error and interactive behaviors of vehicles, and the second-level SMS is based on an integral terminal SMS. Simultaneously, the disturbances suffered from the ego-vehicle uncertainty and nonlinearity are estimated by the FTDO. The FTSMC and FTDO are proposed to regulate the CV platoon in finite time. Meanwhile, the CV platoon can ensure the finite stability and string stability via rigorous analysis. Finally, the feasibility of the proposed controller is verified by extensive simulations and co-simulations, and small-scaled experiments.

This paper proposes a generalized car-following (CF) model to depict the dynamics of traffic flow that includes human-driven vehicles (HDVs), connected vehicles (CVs) and connected and autonomous vehicles (CAVs). Notably, the model integrates human reaction times, information delays, and status data from multiple preceding vehicles endowed with communication capabilities. Then, by utilizing the perturbation method, the Intelligent Driver Model (IDM) as an example is taken in this CF model to determine the stability condition of the mixed traffic based on CAV penetration rate and their spatial distribution. Finally, comprehensive co-simulation using PreScan and MATLAB/Simulink is developed to explore the impact of varying CAV penetration rates across seven distinct spatial distributions on traffic capacity and dynamic performance. The findings underscore the efficacy of our proposed model in analyzing mixed traffic scenarios comprising HDVs, CVs, and CAVs. Increasing CAV penetration rates can lead to improved stability, capacity, and dynamic performance within mixed traffic environments. Notably, at the CAV penetration rate below 60%, the spatial distribution labeled as CAVs-HDVs-CVs (where CAVs lead the traffic flow, followed by HDVs, then CVs) demonstrates superior dynamic performance, whereas the HDVs-CVs-CAVs configuration (with HDVs leading, followed by CVs, then CAVs) performs worst. However, it’s noteworthy that spatial distribution scarcely affects dynamic performance when the CAV penetration rate exceeds 60%.

In a mixed traffic environment of human and autonomous driving, it is crucial for an autonomous vehicle to predict the lane change intentions and trajectories of vehicles that pose a risk to it. However, due to the uncertainty of human intentions, accurately predicting lane change intentions and trajectories is a great challenge. Therefore, this paper aims to establish the connection between intentions and trajectories and propose a dual Transformer model for the target vehicle. The dual Transformer model contains a lane change intention prediction model and a trajectory prediction model. The lane change intention prediction model is able to extract social correlations in terms of vehicle states and outputs an intention probability vector. The trajectory prediction model fuses the intention probability vector, which enables it to obtain prior knowledge. For the intention prediction model, the accuracy can be improved by designing the multi-head attention. For the trajectory prediction model, the performance can be optimized by incorporating intention probability vectors and adding the LSTM. Verified on NGSIM and highD datasets, the experimental results show that this model has encouraging accuracy. Compared with the model without intention probability vectors, the impact of the model on NGSIM dataset and highD dataset in RMSE is improved by 57.27% and 58.70% respectively. Compared with two existed models, evaluation metrics of the intention prediction can be improved by 7.40-10.09% on NGSIM dataset and 2.17-2.69% on highD dataset within advanced prediction time 1s. This method provides the insights for designing advanced perceptual systems for autonomous vehicles.

The fifth industrial revolution (Industry 5.0) integrates humans and machines to satisfy the increasing customization demands of the manufacturing complexity using an optimized robotized manufacturing process. Industry 5.0 make use of collaborative robots (cobots) for optimizing productivity and ensuring safety. At the same time, unmanned aerial vehicles (UAVs) are predicted to be the main part of industry 5.0 in the forthcoming days. Regardless of high mobility and energy-limited UAVs for wireless communication as significant advantages, different issues are also existing in the UAV networks, such as security, reliability, etc. Several research works have focused on resolving security issues in UAV communication to support safety-critical applications. With this motivation, this article presents an artificial intelligence-based UAV-borne secure communication with classification (AIUAV-SCC) framework for industry 5.0 environment. The proposed AIUAV-SCC model involves two major phases namely image steganography-based secure communication and deep learning (DL)-based classification. At the initial stage, a new image steganography technique with multilevel discrete wavelet transformation, quantum bacterial colony optimization based optimal pixel selection, and encryption processes take place. Next, in the second stage, the Bayesian optimization (BO)-based SqueezeNet model is applied for the classification of securely received UAV images where the parameters in the SqueezeNet method are optimally tuned by the utilize of the BO technique. To validate the performance of the presented model, extensive simulations are applied using the UC Merced dataset (UCM) aerial dataset and the outcomes are investigated under several dimensions. The outcomes make sure the goodness of the presented model on test UCM aerial dataset over the compared methods.

The string stability of cooperative adaptive cruise control (CACC) platoons is largely affected by complex driving environment and abnormal driving behaviors. Fast and repetitive driving-state changes always occur during the period of changing driving states (such as leaving a platoon or lane-change), due to errors made in driver decisions or automatic driving system. This research proposes a framework which combines recognition of driving states with platoon operations and risk-prediction in order to reduce disturbance and unnecessary platoon operations resulting from driving-state jitters. First of all, long short-term memory (LSTM) neural networks were used in this research combined with a time-window in order to recognize driving states. Based on this research, the LSTM mode with an added time-window was found to be able to effectively reduce comparatively the jitters of recognition results. After that, an integrated mode which incorporates a recognition mode with danger probabilities was demonstrated to present better platoon operations. Monte Carlo simulation and importance sampling method will be given to predict platoons’ and vehicles’ trajectories and compute danger probabilities. In addition, an innovative strategy is implemented to identify an additional leader and execute a platoon splitting in order to improve driving smoothness, if a vehicle is recognized in an abnormal car-following state with a high danger-probability. In summary, this research has conducted extensive numerical tests to evaluate performances of the proposed system and the analysis results show that the proposed strategies will effectively increase smoothness and safety for a multi-platooning system.

This paper proposes a new car-following (CF) model to capture the realistic behaviors of connected and automated vehicles (CAVs), whose communication topology (CT) among vehicles is characterized by graph theory in the V2V communication environment. By considering the heterogeneous time delays under the fixed and switching CTs, a generalized CF model is proposed. Based on the Lyapunov–Krasovskii method, a convergence analysis has been implemented for this new CF model with multiple time delays to obtain the convergence condition. Meanwhile, provides an estimate of the time delay bound. Finally, numerical experiments are performed under three typical fixed CTs (i.e., PF topology, BDLF topology, and TPLF topology) and the corresponding switching topology. Results support that the proposed CF model is capable of accurately reproducing the velocity, acceleration, position, and space headway profiles of CAVs traffic flow.

This article develops a new platoon control strategy for heterogeneous connected vehicles (CVs) subject to time delays and external disturbances. Specifically, based on the third-order vehicle model, a novel platoon controller is developed by embedding the variable time headway (VTH) spacing policy and the nonlinear motion coupling interactions between CVs. Simultaneously, an integral sliding mode (ISM) controller is developed to resist the disturbances. Then, the condition of asymptotic stability for the CV platoon and the upper bound of communication delay are deduced by using the Lyapunov theorem. Also, the string stability is proved by using the infinity-norm method. Finally, extensive simulations and co-simulations are provided to show the validity of the developed controller. Moreover, experiments with intelligent micro vehicles are conducted further to validate the practical feasibility of the developed controller.

This paper proposes a longitudinal platoon controller for connected vehicles (CVs) by considering the information of multiple preceding vehicles and the car-following interactions between CVs. The stability of the proposed controller is analyzed using the Routh criterion. For the verification, we develop an integrated platoon control framework for CVs in a V2V/V2I communication environment. The proposed framework consists of two main components: simulation platform and experimental platform. In particular, the simulation platform is developed based on the TransModeler software, and the experimental platform is designed using the self-developed V2X devices. Finally, a scenario of platoon forming is taken as an example and is conducted in simulation platform and experimental platform, respectively. Results demonstrate the effectiveness of the proposed controller with respect to the trajectory and velocity profiles.

Vehicular platoon control can effectively achieve group consensus, improve vehicular running safety and increase road capacity. However, some constraints exist in practical situations due to the limitations of traffic environment in time-varying metrics (time-delay, packet-dropout or interruption) in wireless communication systems. In this work, a distributed model predictive control (MPC) algorithm is proposed for connected vehicle platoon with a focus on switching communication topologies and control strategy under abnormal communications. Firstly, the predecessor-leader following is selected as the basic communication topology, by which the switching communication topology and the desired vehicle spacing policy are established. Secondly, the platoon control algorithm of connected vehicles is established and a set of constraints is analyzed. Thirdly, the${\mathcal{ L}}_{2} $-norm string stability criterion and the asymptotic stability criterion are considered within the proposed MPC. Finally, a co-simulation platform for connected vehicle platoon is developed based on Prescan/Matlab/V2X communication simulator. In addition, the platoon control algorithm is tested in three traffic scenarios including normal communication, leading vehicle with abnormal communication and following vehicle with abnormal communication. The experiments demonstrate that the communication topologies in different communication environments can be switched well in real time through the proposed platoon control algorithm. In addition, the string stability, the consistency of vehicle spacing, speed and acceleration are proven to be guaranteed simultaneously.

In this article, we consider a class of decentralized nonsmooth composite optimization problems over undirected graphs. The global optimization problem is to minimize the sum of local objective functions consisting of a Lipschitz-differentiable convex function and two possibly nonsmooth convex functions, one of which contains a bounded linear operator. The goal is to solve the global optimization problem through decentralized computation and communication over a network of agents without a central coordinator. Through using triple proximal splitting operators to deal with the nonsmooth terms, we come up with a novel decentralized algorithm with uncoordinated stepsizes, where the stepsizes with independent upper bounds are also distributed for agents or edges over the communication network. Furthermore, we establish the sublinear convergence rate for the proposed algorithm in terms of the first-order optimality residual in a nonergodic sense. Simulation experiments on a constrained quadratic programming problem and an optimal load-sharing problem are carried out to verify the correctness of the theoretical results.

Unstable vehicle-to-vehicle (V2V) communication connections are a vital phenomenon in connected vehicle (CV) environments which lead to the change of communication topologies and delays among electric connected vehicles (ECVs). This paper aims to evaluate the effects of the switching period of communication topology and delay on the dynamic performance and energy consumption of an ECV traffic stream considering the characteristics of car-following (CF) theory. To this end, a communication topology characterization method is developed by using the beacon transmission mechanism, graph theory, and probability theory. Then, a new CF model incorporating the effects of the communication topologies and delays is proposed to capture the interactions under a CV environment. The stability of the proposed model is analyzed by using the perturbation method. Finally, extensive simulations are implemented to be separately discussed by considering the effects of different switching periods of communication topologies and delays.

This paper focuses on platoon control of multi-vehicle systems in a realistic vehicle-to-vehicle/vehicle-to-infrastructure (V2V/V2I, or V2X) communication environment. To this end, the communication probability of the beacon transmission is analyzed based on the carrier sense multiple access with collision avoidance (CSMA/CA) mechanism. Then, a new car-following model is proposed by considering the communication probability to capture the car-following behavior of connected vehicles (CVs) equipped with V2X communications. The stability of the proposed car-following model is analyzed using the perturbation method. In addition, a nonlinear consensus-based longitudinal control algorithm is designed by considering the interactions between CVs, and an artificial function-based lateral control algorithm is presented. The convergence of the longitudinal and lateral control algorithms is analyzed using the Hurwitz stable theorem and Lyapunov technique, respectively. Also, the string stability of the vehicular platoon is performed based on the infinity-norm method. Finally, field experiments are conducted using four CVs under the scenarios of platoon forming, vehicle merging, and vehicle diverging. The results verify the effectiveness of the proposed method in terms of the trajectory and velocity profiles.

In cloud computing, data is duplicated to prevent data loss. One way to achieve data consistency in such a distributed computing systems is to use a blockchain. Based on practical Byzantine fault tolerance (PBFT), a specific type of blockchain, this paper proposes a synchronous Byzantine fault tolerance (SBFT) algorithm that not only maintains data consistency, but also has much higher efficiency than other general blockchain algorithms. We provide experimental results that demonstrate the algorithm's data consistency, efficiency, and reliability.

This paper investigates formation control protocols for autonomous vehicular strings with vehicle-to-vehicle (V2V) communication connections. To this end, a four-layer framework is first proposed to illustrate the cooperative mechanism within and across strings. Then, cooperative control protocols are designed based on vehicle role, i.e., leader or follower, in vehicular multi-string. In particular, longitudinal controllers are designed for single string and multiple strings by incorporating inter-vehicle gap and velocity difference of the follower vehicle with respect to the preceding vehicle and the lead vehicle. In addition, lateral controllers are proposed for single string and multiple strings based on the artificial function method. The proposed protocols ensure that follower vehicles asymptotically track the leader within each string, while different vehicular strings can form a desired platoon pattern. The study further analyzes the stability and consensus of the proposed control protocols using the Routh-Hurwitz stable criterion and the Lyapunov technique. Numerical experiments are performed for two cooperative mechanisms-parallel and serial. Results from numerical experiments illustrate the effects of the proposed control protocols on road throughput and demonstrate their effectiveness for position and velocity consensuses.

This paper proposes a distributed nonlinear consensus delay-dependent control algorithm for a connected vehicle (CV) platoon. In particular, considering that the behavior of the following vehicle is associated with the longitudinal inter-vehicle gap with respect to the preceding vehicle, a nonlinear function is designed to characterize the car-following interactions between CVs. Then, a nonlinear consensus algorithm is proposed by incorporating the car-following interactions and heterogeneous time delays. The delay-dependent convergence condition of the proposed control algorithm is analyzed using the Lyapunov-Krasovskii method, and an estimate of the delay bound is provided. Under the proposed algorithm, not only can the consensus of CVs be guaranteed but also the behavior of vehicles is consistent with traffic flow theory. Finally, an example using a 10-vehicle platoon is provided under three scenarios: no time delays, heterogeneous time delays, and homogeneous time delays. Results from extensive simulations verify the effectiveness of the proposed control algorithm in terms of the position, velocity, and acceleration/deceleration profiles.

In previous work on joint multiple point sets registration, the multiple point sets are often formulated by a Gaussian mixture model (GMM) and the registration is then cast to a clustering problem, which aims to exploit global relationships on the multiple point sets. However, local relationships on the multiple point sets are ignored in the state-of-the-art joint multiple point sets registration techniques. In this paper, the multiple point sets are assumed to be generated from a GMM. Local features of the multiple point sets, such as shape context, are proposed to infer the membership probabilities of the GMM. The problem of joint multiple point sets registration can be performed by maximum likelihood of the GMM. The parameters of GMM and registration are estimated by an expectation maximization algorithm. Comprehensive experiments demonstrate that our proposed method has better performance than the state-of-the-art methods.

In this paper, the problem of clustering in distributed sensor networks with uncertain measurements is considered. It is assumed that each node in the sensor network can be described as a mixture of some elementary conditions. Therefore, the measurements of the sensors can be modeled using a Gaussian mixture model, in which the uncertainty on the attributes is represented by the belief functions. We present a novel algorithm, called distributed evidential expectation maximization (DEEM) algorithm, for the estimation of the Gaussian components in the mixture model. The effectiveness of the proposed algorithm is demonstrated through simulations of sensor networks with uncertain data.

This paper proposes a new car-following (CF) model incorporating the effects of lateral gap and roadside device communication to capture the characteristics of electric vehicle (EV) traffic stream in transportation-cyber-physical systems. Stability of the proposed CF model is analyzed using the perturbation method. Furthermore, the energy consumption of the EV traffic stream is investigated based on the drive cycles produced by the proposed model. Numerical experiments analyze three scenarios: start, stop, and evolution processes for the scenarios of no lateral gap, lateral gap, and lateral gap with roadside device, respectively. Results demonstrate that: 1) the nonlane-discipline-based model is more responsive than the lane-discipline-based model; 2) the nonlane-discipline-based model for the EV traffic stream consumes more energy in the acceleration phase and recuperates more energy in the deceleration phase compared with the lane-discipline-based model; and 3) the nonlane-discipline-based model with roadside device communication for EV traffic stream consumes more energy in the acceleration phase and recuperates more energy in the deceleration phase than the model without roadside devices.

Considering the traffic safety in the scenario of arterial road with on-ramp, this study proposes a time-to-collision (TTC) based vehicular collision warning algorithm under connected environment. In particular, the information of vehicles of interest, i.e., position, traveling direction and velocity, is assumed to be collected by the roadside device via the vehicle-to-infrastructure (V2I) communications. Then, the TTC of a pair of vehicles in arterial road and on-ramp is estimated based on the position, traveling direction and velocity difference of that pair of vehicles. Consequently, the TTC warning messages can be disseminated to vehicles within the communication range of the roadside device, so as to reduce the risk of collision. The proposed algorithm can be used in the cooperative vehicle infrastructure systems (CVIS) to improve the traffic safety.

An extended-state-observer-based double-loop integral sliding-mode controller for electronic throttle (ET) is proposed by factoring the gear backlash torque and external disturbance to circumvent the parametric uncertainties and nonlinearities. The extended state observer is designed based on a nonlinear model of ET to estimate the change of throttle opening angle and total disturbance. A double-loop integral sliding-mode controller consisting of an inner loop and an outer loop is presented based on the opening angle and opening angle change errors of ET through Lyapunov stability theory. Numerical experiments are conducted using simulation. The results show that the accuracy and the response time of the proposed controller are better than those of the back-stepping and sliding mode control.

Car-following models play an important role in the field of road traffic system, and one of its important issues is the vehicle formation. To conduct the analysis of vehicle formation, this paper introduces the concept of zero dynamics and gives a detailed analysis of the full velocity difference (FVD) model. Meanwhile, the stability analysis of FVD model based on the Lyapunov function is also discussed, and the result validates that the vehicle formation can achieve asymptotic stability.