Search Results (281)

Personal Rapid Transit (PRT) and Group Rapid Transit (GRT) are classes of fully automated public transport systems, where passengers can travel in small vehicles on an interconnected, grade-separated network of guideways, non-stop, from origin to destination. PRT and GRT are considered sustainable as they are low-emission and able to attract car drivers. The parameterized cost modeling framework developed in this paper has the advantage that profitability of different PRT/GRT systems can be rapidly verified in a transparent way and in function of a variety of relevant system parameters. This framework may contribute to a more transparent, rapid, and low-cost evaluation of PRT/GRT schemes for planning and decision-making purposes. The main innovation is the introduction of the “peak hour network saturation” S: the number of vehicles in circulation during peak hour divided by the maximum number of vehicles running at line speed with minimum time headways. It is an index that aggregates the main uncertainties in the planning process, namely the demand level relative to the supply level. Furthermore, a maximum S can be estimated for a PRT/GRT project, even without a detailed demand estimation. The profit per trip is analytically derived based on S and a series of more certain parameters, such as fares, capital and maintenance costs, daily demand curve, empty vehicle share, and physical properties of the system. To demonstrate the ability of the framework to analyze profitability in function of various parameters, we apply the methods to a single vehicle PRT, a platooned PRT, and a mixed PRT/GRT. The results show that PRT services with trip length proportional fares could be profitable already for $S>0.25$. The PRT capacity, profitability, and robustness to tripled infrastructure costs can be increased by vehicle platooning or GRT service during peak hours. 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

This study examines the extended dissipativity analysis for newly designed mixed traffic systems (MTSs) utilizing the coupling memory sampled-data control (CMSDC) approach. The traffic flow creates a platoon, and the behavior of human-driven vehicles (HDVs) is presumed to adhere to the optimal velocity model, with the acceleration of a single-linked automated vehicle regulated directly by a suggested CMSDC. The ultimate objective of this work is to present a CMSDC approach for optimizing traffic flow amidst disruptions. The primary emphasis is on the proper design of the CMSDC to ensure that the closed-loop MTS is extended dissipative and quadratically stable. A more generalized CMSDC methodology incorporating a time delay effect is created using a Bernoulli-distributed sequence. The existing Lyapunov–Krasovskii functional (LKF) and enhanced integral inequality methods offer sufficient conditions for the suggested system to achieve an extended dissipative performance index. The suggested criteria provide a comprehensive dissipative study, evaluating $L_2-L_{\infty }$, $H_{\infty }$, passivity, and dissipativity performance. A simulation example illustrates the accuracy and superiority of the proposed controller architecture for the MTS. Full article

External Human–Machine Interfaces (eHMIs) enhance pedestrian safety in interactions with autonomous vehicles (AVs) by signaling crossing risk based on time-to-arrival (TTA), categorized as low, medium, or high. This study compared five eHMI configurations (single-level low, medium, high; two-level low-medium, medium-high) against a three-level (low-medium-high) configuration to assess their impact on pedestrians’ crossing decisions, mental workload (MW), and situation awareness (SA) in vehicle platoon scenarios under full and partial eHMI penetration. In a video-based experiment with 24 participants, crossing decisions were evaluated via temporal gap selection, MW via P300 event-related potentials in an auditory oddball task, and SA via the Situation Awareness Rating Technique. The three-level configuration outperformed single-level medium, single-level high, two-level low-medium, and two-level medium-high in gap acceptance, promoting safer decisions by rejecting smaller gaps and accepting larger ones, and exhibited lower MW than the two-level medium-high configuration under partial penetration. No SA differences were observed. Although the three-level configuration was generally appreciated, future research should optimize presentation to mitigate issues from rapid signal changes. Notably, the single-level low configuration showed comparable performance, suggesting a simpler alternative for real-world eHMI deployment. Full article

This work studies the issue of distributed fault-tolerant control for a vehicle platoon with actuator faults, saturation, and external disturbances. As the degrees of wear, age, and overcurrent of a vehicle actuator might change during the working process, it is more practical to consider the actuator faults to be time-varying rather than constant. Considering a situation in which actuator faults may cause partial actuator effectiveness loss, a novel adaptive updating mechanism is developed to estimate this loss. A new nonlinear observer is proposed to estimate external disturbances without requiring us to know their upper bounds. Since non-zero initial spacing errors (ISEs) may cause instability of the vehicle platoon, a novel exponential spacing policy (ESP) is devised to mitigate the adverse effects of non-zero ISEs. Based on the developed nonlinear observer, adaptive updating mechanism, radial basis function neural network (RBFNN), and the ESP, a novel nonlinear observer-based distributed adaptive fault-tolerant control strategy is proposed to achieve the objectives of platoon control. Lyapunov theory is utilized to prove the vehicle platoon’s stability. The rightness and effectiveness of the developed control strategy are validated using a numerical example. Full article

This study proposes an adaptive car-following model based on the unscented Kalman filter algorithm to enable coordinated speed control in vehicle platoons and to address key limitations present in conventional car-following models. Traditional models generally assume a fixed maximum speed within the optimal velocity function, which constrains effective platoon speed regulation across road segments with varying speed limits and lacks adaptability to dynamic scenarios such as changes in the platoon leader’s speed or substitution of the lead vehicle. The proposed adaptive model utilizes state estimation based on the unscented Kalman filter to dynamically identify each vehicle’s maximum achievable speed and to adjust inter-vehicle constraints, thereby enforcing a unified speed reference across the platoon. By estimating these maximum speeds and transmitting them to individual follower vehicles via vehicle-to-vehicle communication, the model promotes smooth acceleration and deceleration behavior, reduces headway variability, and mitigates shockwave propagation within the platoon. Simulation studies—covering both single-leader acceleration and intermittent acceleration scenarios—demonstrate that, compared with conventional car-following models, the adaptive model based on the unscented Kalman filter achieves superior speed synchronization, improved headway stability, and smoother acceleration transitions. These enhancements lead to substantial improvements in traffic flow efficiency and string stability. The proposed approach offers a practical solution for coordinated platoon speed control in intelligent transportation systems, with promising application prospects for real-world implementation. Full article

With the development of intelligent transportation systems, platooning can reduce vehicle aerodynamic drag by decreasing spacing between vehicles, improving transportation efficiency and reducing emissions. However, it is difficult for existing models to enable dynamic adjustment and real-time feedback. Therefore, this study proposes a digital twin system for real-time drag coefficient prediction using stacking ensemble learning. First, 2000 datasets of pressure distributions and drag coefficients under varying spacings were obtained through simulations. Then, an online prediction model for the aerodynamic performance of platooning vehicles was then constructed, realizing real-time drag coefficient prediction, and verifying the model performance using computational fluid dynamics data. The results indicate that the model proposed achieves 98.56% prediction accuracy, significantly higher than that of the traditional BP model (75.78%), and effectively captures the nonlinear relationship between vehicle spacing and drag coefficient. The influence mechanism of vehicle spacing on the aerodynamic performance of platooning vehicles revealed in this study enables high-precision real-time prediction under dynamic parameters. Full article

Existing studies on platoon trajectory optimization of connected and automated vehicles face challenges in balancing computational efficiency, privacy, and safety. This study proposes a distributed optimization method that decomposes the platoon trajectory planning problem into independent individual vehicle tasks while ensuring safe inter-vehicle following gaps and maximizing travel efficiencyand ride comfort. The individual vehicle problems independently optimize their trajectory to improve computational efficiency, and only exchange dual variables related to safe following gaps to preserve privacy. Simulation experiments were conducted under single-platoon scenarios with different simulation horizons, as well as multi-platoon and platoon-merging scenarios, to analyze the control performance of the distributed method in contrast to the centralized method. Simulation results demonstrate that the mean computation time is reduced by 50% and the fuel consumption is decreased by 4% compared to the centralized method while effectively maintaining the safe inter-vehicle following gaps. The distributed method shows its scalability and adaptability for large-scale problems. Full article

The connected and automated vehicles (CAV) smoothing mixed traffic flow has gained attention, and a thorough assessment of these control algorithms is necessary. Our previous research proposed the time-varying model predictive control (TV-MPC) strategy, which considers the time-varying driving style of human driven vehicles (HDV), performing better than current baseline models. Due TV-MPC can be applied to any traffic congestion scenario and the dynamic modeling that considers driving style, can be easily transferred to other control algorithms. Thus, TV-MPC enable to represent typical control algorithms in mixed traffic flow. This study investigates the performance of TV-MPC under diverse disturbance characteristics and mixed platoons. Firstly, quantifying mixed traffic flow with different CAV penetration rates and platooning intensities by a Markov chain model. Secondly, by constructing evaluation indicators for micro-level operation of mixed traffic flow, this paper analyzed the impact of TV-MPC on the operation of mixed traffic flow through simulation. The results demonstrate that (1) CAV achieve optimal control at specific positions within mixed traffic flow; (2) higher CAV penetration enhances TV-MPC performance; (3) dispersed CAV distributions improve control effectiveness; and (4) TV-MPC excels in scenarios with significant disturbances. Full article

The article focuses on the issue of the sliding mode control of a vehicle platoon with matched and mismatched disturbances. A novel finite-time disturbance observer-based sliding mode control scheme is developed to effectively mitigate the adverse impact of disturbances and achieve the control goals of a platoon. As matched and mismatched disturbances might decrease the control performance or even cause the instability of a vehicle platoon, a finite-time disturbance observer (FTDO) is designed to effectively reduce the effects of both types of disturbances. Unlike previous studies, the proposed FTDO in this article has the capability to directly estimate disturbances without the need to know the precise upper bounds of the disturbances. A feedforward compensation term, derived from disturbance estimation, is incorporated into the FTDO-based sliding mode control scheme to solve the issue of the degradation of control performance. The controlled vehicle platoon’s stability is proven through the Lyapunov approach, which means that the control goals of the platoon can be achieved under the developed FTDO-based sliding mode control scheme. Finally, a numerical example is conducted to confirm the efficacy of the developed control scheme. Full article

This paper proposes a practical design guideline for selecting control parameters in adaptive cruise control (ACC) systems to ensure both individual vehicle stability and string stability in vehicle following systems with homogeneous longitudinal dynamics. The primary control objective is to regulate spacing errors under a constant time-gap policy, which is commonly adopted in ACC applications. By employing a simple proportional-derivative (PD) controller, we present a clear methodology for tuning the proportional and derivative gains. The proposed approach demonstrates that string stability can be effectively achieved using this straightforward control structure, making it highly applicable for assisting practitioners in selecting appropriate parameters for real-world platooning scenarios. We provide a rigorous analysis of the necessary and sufficient conditions for selecting PD gains, along with practical guidelines for implementation. The effectiveness of the design guideline is further validated through simulations conducted in realistic driving scenarios. Full article

This paper addresses the resource allocation challenges in autonomous vehicle (AV) networks for delay-sensitive perception tasks. Current vehicular networks face sub-optimal resource distribution and excessive communication overhead, hindering the performance of AVs. We propose an integrated approach that combines a platoon-based system model with optimization techniques using Deep Q-Networks (DQN) and Particle Swarm Optimization (PSO). The platoon-based model enables AVs to share resources effectively, while the DQN and PSO models optimize task offloading and reduce overhead. Simulation results across various traffic scenarios demonstrate that the PSO algorithm outperforms traditional methods in task completion rates, overhead minimization, and platoon formation. This approach offers a significant advancement in enhancing AV network performance and ensuring timely task execution. Full article

In this paper, we study the stability of the stochastically heterogeneous traffic flow involving connected and automated vehicles (CAVs) and human-driven vehicles (HDVs). Taking the stochasticity of vehicle arrivals and behaviors into account, a general robust $H_{\infty }$ platoon controller is proposed to address the communication delay and unexpected disturbances such as prediction or perception errors on HDV motions. To simplify the problem complexity from a stochastically heterogeneous traffic flow to multiple long vehicle control problems, three types of sub-platoons are identified according to the CAV arrivals, and each sub-platoon can be treated as a long vehicle. The car-following behaviors of HDVs and CAVs are simulated using the optimal velocity model (OVM) and the cooperative adaptive cruise control (CACC) system, respectively. Later, the robust $H_{\infty }$ platoon controller is designed for a pair of a CAV long vehicle and an HDV long vehicle. The time-lagged system and the closed-loop system are formulated and the $H_{\infty }$ state feedback controller is designed. The robust stability and string stability of the heterogeneous platoon system are analyzed using the $H_{\infty }$ norm of the closed-loop transfer function and the time-lagged bounded real lemma, respectively. Simulation experiments are conducted considering various settings of platoon sizes, communication delays, disturbances, and CAV penetration rates. The results show that the proposed $H_{\infty }$ controller is robust and effective in stabilizing disturbances in the stochastically heterogeneous traffic flow and is scalable to arbitrary sub-platoons in various CAV penetration rates in the heterogeneous traffic flow of road vehicles. The advantages of the proposed method in stabilizing heterogeneous traffic flow are verified in comparison with a typical car-following model and the linear quadratic regulator. Full article

This study proposes a stability index to quantitatively evaluate the psychological stability of drivers during truck platooning. Truck platooning is a technique in which a manually driven lead truck is followed by automated trucks using V2X communication and onboard sensors. While significant technical advances have been made in truck platooning, research on drivers’ psychological comfort remains limited. Due to the shorter intervehicle time gaps compared to conventional trucking, truck platooning raises concerns regarding drivers’ psychological stability. The proposed index quantifies this aspect and is validated using a driving simulator. Both the stability index calculations and the survey consistently indicate that when the time gap decreases to 0.6 s (approximately 15 m) or less, drivers’ psychological stability deteriorates. However, increasing the time gap beyond 0.6 s does not significantly improve it. Furthermore, this study investigates the effect of the see-through functionality, which provides real-time front-view footage of the preceding vehicle to the following driver. The results confirm that this functionality enhances psychological stability, even under a short time gap. The stability index should serve as a practical indicator for designing truck platooning systems that consider drivers’ psychological stability and may be extended to various advanced driving technologies, including general vehicle platooning. Full article

The emergence of autonomous vehicles offers the potential to eliminate traditional traffic lanes, enabling vehicles to navigate freely in two-dimensional spaces. Unlike conventional traffic constrained by physical lanes, autonomous vehicles rely on real-time data exchange within platoons to adopt cooperative movement strategies, similar to synchronized flocks of birds. Motivated by this paradigm, this paper introduces an innovative traffic flow model based on the principles of particle swarm intelligence. In the proposed model, each vehicle within a platoon is treated as a particle contributing to the collective dynamics of the system. The motion of each vehicle is determined by the following two key factors: its local optimal velocity, influenced by the preceding vehicle, and its global optimal velocity, derived from the average of the optimal velocities of M vehicles within its observational range. To implement this framework, we develop a novel particle swarm optimization algorithm for autonomous vehicles and rigorously analyze its stability using linear system stability theory, as well as evaluate the system’s performance through four distinct indices inspired by traditional control theory. Numerical simulations are conducted to validate the theoretical assumptions of the model. The results demonstrate strong consistency between the proposed swarm intelligent model and the Bando model, providing evidence of its effectiveness. Additionally, the simulations reveal that the stability of the traffic flow system is primarily governed by the learning parameters $c_1$ and $c_2$, as well as the field of view parameter M. These findings underscore the potential of the swarm intelligent model to improve traffic flow system dynamics and contribute to the broader application of autonomous traffic systems management. In addition, it is worth noting that this paper explores the operational control of an AV platoon from a theoretical perspective, without fully considering passenger comfort, as well as “soft” instabilities (vehicles joining/leaving) and “hard” instabilities (technical failures/accidents). Future research will expand on these related aspects. Full article