Search Results (66)

This study focuses on investigating the adaptive sliding mode control (SMC) problem for connected vehicles with input delays and unknown time-varying control coefficients. As a result of wear and tear of mechanical components, throttle response lags, and the internal data processing time of the controller, input delays widely exist in vehicle actuators. Since input delays may lead to instability of the vehicle platoon, an improved integral compensation mechanism (ICM) with the adjustment factor for input delays is developed to improve the platoon’s robustness. As the actuator efficiency, drive mechanism, and load of the vehicle may change during operation, the control coefficients of vehicle dynamics are usually unknown and time-varying. A novel adaptive updating mechanism utilizing a radial basis function neural network (RBFNN) is designed to deal with the unknown time-varying control coefficients, thereby improving the vehicle platoon’s tracking performance. By integrating the improved ICM and the RBFNN-based adaptive updating mechanism (RBFNN−AUM), an innovative distributed adaptive control scheme using sliding mode techniques is proposed to guarantee that the convergence of state errors to a predefined region and accomplish the vehicle platoon’s control objectives. Comparative numerical results confirm the effectiveness and superiority of the developed control strategy over existing method. Full article

The growing deployment of connected and autonomous vehicles (CAVs) requires coordinated control strategies that jointly address safety, mobility, and energy efficiency. This paper presents a novel two-stage cooperative control framework for autonomous hybrid electric vehicle (HEV) platoons based on machine learning. In the first stage, a metric learning-based distributed model predictive control (ML-DMPC) strategy is proposed to enable cooperative longitudinal control among heterogeneous vehicles, explicitly incorporating inter-vehicle interactions to improve speed tracking, ride comfort, and platoon-level energy efficiency. In the second stage, a multi-agent twin-delayed deep deterministic policy gradient (MATD3) algorithm is developed for real-time energy management, achieving an optimal power split between the engine and battery while reducing Q-value overestimation and accelerating learning convergence. Simulation results across multiple standard driving cycles demonstrate that the proposed framework outperforms conventional distributed model predictive control (DMPC) and multi-agent deep deterministic policy gradient (MADDPG)-based methods in fuel economy, stability, and convergence speed, while maintaining battery state of charge (SOC) within safe limits. To facilitate future experimental validation, a dSPACE-based hardware-in-the-loop (HIL) architecture is designed to enable real-time deployment and testing of the proposed control framework. Full article

Mixed traffic flow composed of connected and automated vehicles (CAVs) and human-driven vehicles (HDVs) represents a core characteristic of intelligent transportation systems. However, its operational efficiency is significantly constrained by lane management strategies and CAV cooperative driving behaviors. To investigate this, a cellular automata-based simulation model is developed that integrates multiple car-following rules, a lane-changing strategy, and a platoon coordination mechanism. Through a systematic comparison of 13 lane management strategies in one-way two-lane and three-lane configurations, this study analyzes the influence mechanisms of lane allocation and cooperative driving on traffic flow, considering fundamental diagram characteristics, operating speed, CAV degradation behavior, and maximum platoon size. The results indicate that the performance of different strategies exhibits phased evolution with increasing CAV penetration rates. At low penetration rates, providing relatively independent space for HDVs effectively suppresses random disturbances and improves throughput. At medium to high penetration rates, dedicated CAV lanes—especially those with spatial continuity—enable cooperative platoons to fully leverage their advantages, leading to significant improvements in traffic capacity and operational stability. These findings demonstrate an optimal alignment between cooperative driving mechanisms and lane configurations, offering theoretical support for highway lane management in mixed traffic environments. Full article

Digital Twins (DTs) are increasingly used to support real-time decision making in connected mobility systems, where network latency and uncertainty limit the effectiveness of conventional control strategies. This paper proposes a conceptual model for a DT-driven Co-Pilot designed to provide adaptive speed recommendations in congested urban traffic. The system combines live data from a mobile client with a prediction engine that executes multiple short-horizon SUMO simulations in parallel, enabling the DT to anticipate local traffic evolution faster than real time. A lightweight clock-alignment mechanism and latency evaluation over LAN, Cloudflare-tunneled connections, and 4G/5G networks demonstrate that the Co-Pilot can operate reliably using existing communication infrastructures. Experimental results show that moderate speeds (35–50 km/h) yield throughput and delay performance comparable to higher speeds, while improving flow stability—an important property for safe platooning and collaborative driving. The parallel execution of ten SUMO instances completes within 2–3 s for a 600 s simulation horizon, confirming the feasibility of embedding domain-specific ITS logic into a predictive DT architecture. The findings demonstrate that Digital Twin–based anticipatory simulation can compensate for communication latency and support real-time speed coordination, providing a practical pathway toward scalable, deployable DT-enabled traffic assistance systems. Full article

Vehicle platoons offer significant benefits in connected vehicle environments, including reduced travel time, increased throughput, mitigated congestion, and lower energy consumption. To adapt to dynamic traffic conditions, platoon formations must be adjusted flexibly; this process is facilitated by traffic management centers via real-time control and cloud-based data transmission. Given communication constraints, we propose a minimum information set that is sufficient to maintain and adjust platoon formations and that supports data storage, computation, and representation of state changes within platoons. This set comprises two components: the Property Set and the Instruction Set. The Property Set collects vehicle-level and platoon-level attributes, whereas the Instruction Set, which includes communication and control subsets, enables formation maintenance and adjustment. We design a series of algorithms structured along timeline-based and task-based frameworks to specify transition rules and task execution modes across states, thereby describing the complete life cycle of a platoon from independent driving through formation and reorganization to dissolution. Finally, we develop an integrated scenario algorithm and apply it to two representative cases: highway platooning and intersection merging and separation. The results indicate that the proposed Minimum Platoon Set has substantial potential for platoon management, providing a solid theoretical foundation and practical guidance for optimizing platoon control. Full article

The rapid increase in global vehicle usage has intensified challenges such as traffic congestion, frequent accidents, and energy consumption, highlighting the need for safe and efficient platooning strategies. Conventional adaptive cruise control (ACC), while widely adopted, suffers from string instability that amplifies disturbances along a platoon. Communication-based cooperative ACC (CACC) can theoretically guarantee string stability at short headways, but its dependence on costly and unreliable vehicle-to-vehicle (V2V) links limits large-scale deployment. Radar-only CACC using single-model Kalman Filter (KF) alleviates this dependency, yet its estimation accuracy degrades under abrupt maneuvers due to model mismatch. To overcome these limitations, this paper proposes a Multi-Q Interacting Multiple Model Kalman Filter (Multi-Q IMM-KF) approach that adaptively blends multiple motion models to ensure robust acceleration estimation across diverse driving conditions. A four-vehicle platoon simulation in CarSim–Simulink demonstrates that the Multi-Q IMM-KF CACC significantly reduces spacing error propagation and improves velocity tracking compared with ACC and Nominal KF-CACC, offering a cost-effective and communication-resilient solution for practical platoon control. Full article

There have been many attempts to model the flow of vehicular traffic in analogy to the flow of fluids. Given the evident change in distance between vehicles driving in platoons, the compressibility of traffic flow is inferred and, considering the reaction time-scales of the driver (human or autonomous), it is argued that this compressibility is increased as relative velocities increase—giving the lag in imposed redirection by the driver and the controller units a higher relative importance. Therefore, a supersonic compressible flow field has been opted for as the most analogous base flow. On this point, added to by the overall extreme similarities of the two above-mentioned flows, the non-dimensional group of the traffic Mach number M_T has been defined in the present research, providing the possibility of calculating a suggested flow field and its corresponding shockwave systems, for any given obstacle ahead of the traffic flow. This suggested flow field is then taken as the basis to obtain trajectories designed for avoiding collision with the obstacle, and in compliance with the physics of the underlying analogous fluid flow phenomena, namely the internal supersonic compressible flow around a double wedge. It should be noted that herein we do not model the traffic flow but propose these trajectories for more optimal collision avoidance, and therefore the above-mentioned similarities (explained in detail in the manuscript) suffice, without the need to rely on full analogies between the two flows. The manuscript further analyzes the applicability of the proposed analogy in the path-planning process for an autonomous passenger vehicle, through dynamics and control of a full-planar vehicle model with an autonomous path-tracking controller. Simulations are performed using realistic vehicle parameters and the results show that the fluid flow analogy is compatible with the vehicle dynamics, as it is able to follow the target path generated by fluid flow calculations with minor deviations. Simulation results demonstrate that the proposed method produces smooth and dynamically consistent trajectories that remain stable under varying traffic scenarios. The controller achieves accurate path tracking and rapid convergence, confirming the feasibility of the fluid-flow analogy for real-time vehicle control. Full article

The development of the Connected and Autonomous Vehicle (CAV) and Hybrid Electric Vehicle (HEV) provides a new effective means for the optimization of eco-driving strategies. However, the existing research has not effectively considered the cooperative speed optimization and power allocation problem of the Connected and Autonomous Plug-in Hybrid Electric Vehicle (CAPHEV) platoon. To this end, a hierarchical eco-driving strategy is proposed, which aims to enhance driving efficiency and fuel economy while ensuring the safety and comfort of the platoon. Firstly, an improved car-following model is proposed, which considers the motion states of multiple preceding vehicles. On this basis, a platoon cooperative car-following decision-making method based on model predictive control is designed. Secondly, a distributed energy management strategy is constructed, and a bionic optimization algorithm based on the behavior of nutcrackers is introduced to solve nonlinear problems, so as to solve the energy distribution and management problems of powertrain systems. Finally, the tests are conducted under the driving cycle of the Urban Dynamometer Driving Schedule (UDDS) and the Highway Fuel Economy Test (HWFET). The results show that the proposed strategy can ensure the driving safety of the CAPHEV platoon in different scenes, and has excellent tracking accuracy and driving comfort. Compared with the rule-based strategy, the equivalent energy consumption of UDDS and HWFET is reduced by 20.7% and 5.5% in the battery’s healthy charging range, respectively. Full article

Ensuring dynamic risk management for intelligent connected vehicles (ICVs) in complex urban environments is critical as autonomous driving technology advances. This study presents three key contributions: (1) a comprehensive risk indicator system, constructed using entropy-based weighting, extracts 13-dimensional data on abnormal behaviors (e.g., speed, acceleration, position) to enhance safety and efficiency; (2) a multidimensional risk quantification method, simulated under single-vehicle and platooning modes on a CARLA-SUMO co-simulation platform, achieved >98% accuracy; (3) a cloud takeover strategy for high-level autonomous vehicles, directly linking risk assessment to real-time control. Analysis of 56,117 risk data points shows a 32% reduction in safety risks during simulations. These contributions provide methodological innovations and substantial data support for ICV field testing. Full article

Autonomous vehicles (AVs) have emerged as a promising sustainable urban mobility solution, expected to lead to enhanced road safety, smoother traffic flows, less traffic congestion, improved accessibility, better energy utilization and environmental performance, as well as more efficient passenger and freight transportation, in terms of time and cost, due to better fleet management and platooning. However, challenges also arise, mostly related to data privacy, security and cyber-security, high acquisition and infrastructure costs, accident liability, even possible increased traffic congestion and air pollution due to induced travel demand. This paper presents the results of a survey conducted among 654 residents who experienced an autonomous bus (AB) service in the city of Trikala, Greece, in order to assess their willingness to use (WTU) and willingness to pay (WTP) for ABs, through testing a range of factors based on a literature review. Results useful to policy-makers were extracted, such as that the intention to use ABs was mostly shaped by psychological factors (e.g., users’ perceptions of usefulness and safety, and trust in the service provider), while WTU seemed to be positively affected by previous experience in using ABs. In contrast, sociodemographic factors were found to have very little effect on the intention to use ABs, while apart from personal utility, users’ perceptions of how autonomous driving will improve the overall life standards in the study area also mattered. Full article

Cooperative driving systems can coordinate individual vehicles on the road in a platoon, holding significant promise for enhancing traffic efficiency and lowering the energy consumption of vehicle movements. For an extended period, vehicles on the road will consist of a mix of traditional gasoline and electric vehicles. To explore the economic driving strategies for diverse vehicles on the road, this paper introduces a collaborative eco-driving system that takes into account the energy consumption traits of vehicles. Unlike prior research, this paper puts forward a lane change decision-making approach that integrates energy modeling and speed prediction. This method can effectively capture the speed variations in the vehicle ahead and facilitate lane changes with energy efficiency in mind. The system encompasses three vital functions: vehicle cooperative architecture, ecological trajectory planning, and power system control. Specifically, eco-speed planning is carried out in two stages: the initial stage is executed globally, with cooperative speed optimization performed based on the energy consumption characteristics of different vehicles to determine the economical speed for vehicle platoon driving. The subsequent stage involves local speed adaptation, where the vehicle platoon dynamically adjusts its speed and makes lane change decisions according to local driving conditions. Ultimately, the generated control information is fed into the powertrain control system to regulate the vehicle. To assess the proposed collaborative eco-driving system, the algorithms were tested on highways, and the results substantiated the system’s efficacy in reducing the energy consumption of vehicle driving. Full article

For the commercialization of connected vehicles and smart cities, extensive research is carried out on autonomous driving, Vehicle-to-Everything (V2X) communication, and platooning. However, limitations remain, such as restrictions to highway environments, and studies are conducted separately due to challenges in ensuring reliability and real-time performance under external influences. This paper proposes a cooperative autonomous driving system based on V2X network implemented in the CARLA simulator, which simulates an urban environment to optimize vehicle-embedded systems and ensure safety and real-time performance. First, the proposed Throttle–Steer–Brake (TSB) driving technique reduces the computational overhead for following vehicles by utilizing the control commands of a leading vehicle. Second, a V2X network is designed to support object perception, cluster escape, and joining. Third, an urban perception system is developed and validated for safety. Finally, pseudonymized vehicle identifiers, Advanced Encryption Standard (AES), and the Edwards-curve Digital Signature Algorithm (EdDSA) are employed for data reliability and security. The system is validated in processing time and accuracy, confirming feasibility for real-world application. TSB driving demonstrates a computation speed approximately 466 times faster than conventional waypoints-based driving. Accurate urban perception and V2X communication enable safe cluster escape and joining, establishing a foundation for cooperative autonomous driving with improved safety and real-time capabilities. Full article

As autonomous driving technology advances, connected and autonomous vehicles (CAVs) will coexist with human-driven vehicles (HDVs) for an extended period. The deployment of CAVs will alter traffic flow characteristics, making it crucial to investigate their impacts on mixed traffic. This study develops a hybrid control framework that integrates a platoon control strategy based on the “catch-up” mechanism with lane management for CAVs. The impacts of the proposed hybrid control framework on mixed traffic flow are evaluated through a series of macroscopic simulations, focusing on fundamental diagrams, traffic oscillations, and safety. The results illustrate a notable increase in road capacity with the rising market penetration rate (MPR) of CAVs, with significant improvements under the hybrid control framework, particularly at high MPRs. Additionally, traffic oscillations are mitigated, reducing shockwave propagation and enhancing efficiency under the hybrid control framework. Four surrogate safety measures, namely time to collision (TTC), criticality index function (CIF), deceleration rate to avoid a crash (DRAC), and total exposure time (TET), are utilized to evaluate traffic safety. The results indicate that collision risk is significantly reduced at high MPRs. The findings of this study provide valuable insights into the deployment of CAVs, using control strategies to improve mixed traffic flow operations. Full article

With the rapid increase in vehicle ownership and increasingly stringent emission regulations, addressing the energy consumption of and emissions from commercial vehicles have become critical challenges. This study introduces a multi-objective cooperative adaptive cruise control (CACC) strategy, designed for intelligent connected commercial vehicle platoons, operating in event-triggered conditions. A hierarchical control framework is utilized: the upper layer handles reference speed planning based on vehicle dynamics and constraints, while the lower layer uses distributed model predictive control (DMPC) to manage vehicle following. DMPC is chosen for its ability to manage distributed platoons by enabling vehicles to make local decisions, while maintaining system-wide coordination. Additionally, adaptive particle swarm optimization (APSO) is employed during the optimization process to solve the optimal problem efficiently. APSO is employed for its computational efficiency and adaptability, ensuring quick convergence to optimal solutions with reduced overheads. An event-triggering mechanism is integrated to further reduce the computational demands. The simulation results show that the proposed approach reduces fuel consumption by 8.05% and NO_x emissions by 10.15%, while ensuring stable platoon operation during dynamic driving conditions. The effectiveness of the control strategy is validated through extensive simulations, highlighting superior performance compared to conventional methods. Full article

Quantifying and predicting driving risks for connected autonomous vehicles (CAVs) is critical to ensuring the safe operation of traffic in complex environments. This study first establishes a car-following model for CAVs based on molecular force fields. Subsequently, using a convolutional neural network and long short-term Memory (CNN-LSTM) deep-learning model, the future trajectory of the target vehicle is predicted. Risk is quantified by employing models that assess both the collision probability and collision severity, with deep-learning techniques applied for risk classification. Finally, the High-D dataset is used to predict the vehicle trajectory, from which the speed and acceleration of a target vehicle are derived to forecast driving risks. The results indicate that the CNN-LSTM model, when compared with standalone CNN and LSTM models, demonstrates a superior generalization performance, a higher sensitivity to risk changes, and an accuracy rate exceeding 86% for medium- and high-risk predictions. This improved accuracy and efficacy contribute to enhancing the overall safety of connected vehicle platoons. Full article