The continuous expansion of urban areas and population growth have created an urgent need for innovative solutions in traffic management. Addressing fluctuating mobility demands and optimizing resource allocation in real time are fundamental challenges for modern cities. To address these issues, this paper introduces a low computational cost mobility-on-demand (MoD) rebalancing solution designed to dynamically adapt to varying demand across the traffic network. The proposed algorithm continuously evaluates both the current state of the traffic network and projected future demand to optimize rebalancing times. It operates using two adjustable parameters: one for requesting additional vehicles and another for allowing nodes to dispatch rebalancing vehicles. Simulation results demonstrate a significant reduction in maximum waiting times compared to scenarios without rebalancing. Additionally, the proposed solution outperforms existing methods, including reinforcement learning approaches such as deep deterministic policy gradient (DDPG), and Model Predictive Control (MPC), which require significantly longer training times. This efficiency enhances operational responsiveness, making the proposed system a more practical and scalable solution for real-world urban mobility challenges.

The safety and stability of autonomous vehicles heavily rely on the proper functioning of their components. Distributed driven vehicles are particularly susceptible to actuator faults due to their multiple actuators, which elevate the risk of performance degradation and potential loss of vehicle control. This paper proposes a novel approach that integrates a robust trajectory tracking controller tolerant to both the steering actuator and in-wheel motors with reinforcement learning. Using a Linear Parameter Varying control framework, the proposed controller accounts for time-varying vehicle speeds, nonlinearities arising from tire behaviour and actuator faults. The controller relies only on data accessible from standard on-board sensors found in commercial vehicles and employs a static output- feedback control approach, ensuring practical implementation. System stability under faulty conditions is analyzed using the Lyapunov method, guaranteeing robustness to disturbances via $\mathscr {H}_{\infty }$ criteria. The controller performance is enhanced by a Deep Deterministic Policy Gradient reinforcement learning agent, which redistributes the wheel torques. The proposed methodology is evaluated across various driving scenarios under different fault conditions. The results demonstrate its robustness in handling faults during high-speed manoeuvres, where it maintains vehicle stability, achieving better tracking performance than other fault-tolerant approaches. Furthermore, the reinforcement learning agent further reduces tracking errors, enhancing the accuracy and performance beyond what the proposed robust fault-tolerant controller can achieve alone.

This paper proposes a novel centralized trajectory tracking LPV controller for automated vehicles based on Observer-Based Robust Model Predictive Control (OB-RMPC), combining longitudinal and lateral control of the vehicle through a coupled dynamics vehicle model with the aim of applying it during high-velocity evasive maneuvers. For this purpose, minimization of the trajectory tracking errors has been pursued through a cost function which combines the MPC estimated states and control input costs with an $H_{\infty }$ condition. Since not all vehicle states are available for easy measurement, an observer-based approach has been followed for the controller synthesis. First, a Luenberger observer is designed for unknown states estimation. Then, the OB-RMPC optimization problem is tackled by addressing the dynamics of the estimation error and estimated states. Quadratic boundedness condition, augmented system robust positive invariance, and actuator saturation constraints are managed through the use of Linear Matrix Inequalities (LMIs) formulation. A mechanism to update the stability boundaries of the estimation error set is implemented online. Finally, to mitigate the computational demands of the OB-RMPC strategy, an online algorithm is proposed. The control action is calculated based on the states estimation, the optimized bound of the estimation error and an offline-constructed look-up table. The proposed strategy is validated through simulations in Matlab (R) Simulink and CarSim (R) under different high-velocity driving scenarios. The obtained results emphasize the superior tracking effectiveness of the proposed control method compared to other state-of-the-art control strategies in high-velocity maneuvers without compromising lateral stability.

This paper addresses the problem of integrated motion planning and trajectory tracking for multiple intelligent vehicles in unsignalized intersections. A depth-first spanning tree method is employed to determine the optimal sequence for vehicle traversal across the intersection. It must be acknowledged that the strategy presented here does not guarantee the prevention of collisions. Therefore, a centralized planning algorithm is designed for collision-free planning of speed profiles for each vehicle. It is essential that vehicles are able to follow the designated routes with precision to avoid the creation of additional conflict points at intersections. To address this issue, a novel trajectory tracking method is introduced. Given the inherent limitations in measuring each vehicle state, and the necessity of constraining both the input and the vehicle states to ensure system stability, this work derives the linear matrix inequality conditions for the design of a robust static output- feedback trajectory tracking controller, taking into account the aforementioned constraints. In order to validate the proposed method, cooperative driving in intersections has been evaluated under co-simulations with Simulink and Unreal Engine. In comparison to alternative state-of-the-art methodologies, the proposed method has the potential to reduce the time of arrival of the last vehicle by up to 25.63%. Furthermore, the lateral deviation and velocity tracking errors have been reduced by up to 50%.

Magnetorheological dampers (MR) have become essential components in recent years for vibration control in dynamic systems. Their effective application relies on the calibration of models using experimental data. However, these models are often assumed to have deterministic parameters, which can negatively impact the reliability of the control system by neglecting uncertainties. In this work, we propose a methodology to assess the reliability of a vehicle semi-active suspension system under parameter uncertainties in an MR damper model. First, we calibrate the Kwok's MR damper model using Bayesian methods across a wide range of input currents, excitation frequencies, and displacements. Then, we design a procedure to identify the strongest nonlinear response of the semi-active suspension system, enabling the construction of surrogate models for uncertainty propagation without excessive computational cost. Finally, we apply this approach to estimate the probability density functions (PDFs) of the MR damper model parameters for untested input currents. This study demonstrates the application of existing uncertainty quantification techniques to improve the identification of parameter uncertainties in smart semi-active suspension systems.

This paper presents a novel approach to signalized traffic management that optimizes energy efficiency while considering driver preferences. In order to address the heterogeneous nature of vehicles, our methodology is applicable to both electric and internal combustion engine (ICE) vehicles. Traffic flow in signalized intersections is improved, taking into account that drivers want to reach the desired destination as soon as possible, under consideration of the acceleration preference of each individual driver. Furthermore, road safety is ensured by maintaining a safe distance from the preceding vehicle. Simulation results demonstrate that our approach significantly improves energy efficiency and reduces fuel consumption, while also accommodating driver preferences to enhance overall satisfaction. A comparison with train-like velocity profiles revealed energy reductions of 2.14%, 8.37%, and 9.67% for electric, gasoline, and diesel vehicles, respectively, when the proposed methodology was employed.

The objective of this paper is to propose a reliable control solution that addresses the issue of communication disconnections in a heterogeneous vehicle platoon. For each vehicle in the platoon, predecessor-leader following (PLF) communication is selected. In the event of a communication failure, the vehicle transitions to the predecessor-following (PF) topology until communication with the leader is reestablished. To address this issue, a controller has been developed with the objective of adapting to the current communication topology on each vehicle. Event-triggering is implemented to reduce the amount of control orders given to the throttle/brake actuator pedals on each vehicle. Constant time spacing is selected for vehicle separation. A novel method is proposed to ensure string stability of the platoon during the controller design phase. Closed-loop stability of the proposed controlled vehicle platoon is guaranteed under Lyapunov criterion. Robustness against external disturbances and sensor measurement errors is guaranteed under $\mathscr {H}_\infty$ criterion. Simulations demonstrate that the proposed platoon control methodology can enhance road safety in the event of network disconnections. In the most unfavorable circumstances, the separation error is reduced by 40% in comparison to model predictive control techniques and by 73% in comparison to the intelligent driver model.

While the increased availability of traffic data is allowing us to better understand urban mobility, research on data-driven and predictive modeling is also providing new methods for improving traffic management and reducing congestion. In this paper, we present a hybrid predictive modeling architecture, namely GAT-LSTM, by incorporating graph attention (GAT) and long short-term memory (LSTM) networks for handling traffic prediction tasks. In this architecture, GAT networks capture the spatial dependencies of the traffic network, LSTM networks capture the temporal correlations, and the Dayfeature component incorporates time and external information (such as day of the week, extreme weather conditions, holidays, etc.). A key attention block is designed to integrate GAT, LSTM, and the Dayfeature components as well as learn and assign weights to these different components within the architecture. This method of integration is proven effective at improving prediction accuracy, as shown by the experimental results obtained with the PeMS08 open dataset, and the proposed model demonstrates state-of-the-art performance in these experiments. Furthermore, the hybrid model demonstrates adaptability to dynamic traffic conditions, different prediction horizons, and various traffic networks.

This paper proposes a novel design method for a magnetorheological (MR) damper-based semi-active suspension system. An improved MR damper model that accurately describes the hysteretic nature and effect of the applied current is presented. Given the unfeasibility of installing sensors for all vehicle states, an MR damper current controller that only considers the suspension deflection and deflection rate is proposed. A linear matrix inequality problem is formulated to design the current controller, with the objective of enhancing ride safety and comfort while guaranteeing vehicle stability and robustness against any road disturbance. A series of experiments demonstrates the enhanced performance of the proposed MR damper model, which exhibits greater accuracy than other state-of-the-art damper models, such as Bingham or bi-viscous. An evaluation of the vehicle behavior under two simulated road scenarios has been conducted to demonstrate the performance of the proposed output feedback MR damper-based semi-active suspension system.

This paper presents an investigation into the problem of controlling a heterogeneous vehicle platoon, focusing on two aspects: the impact of noise in sensor measurements and the effect of road slope, wind, and rolling resistance on the longitudinal dynamics of each vehicle. To maintain an adequate separation between each follower, a static output feedback (SOF) controller is designed within the predecessor-leader-follower vehicle platoon topology. In order to mitigate the impact of sensor measurement noise and external disturbances on the system, robust control theory is incorporated into the controller design. The engine’s control input is adjusted to compensate for external road disturbances affecting the longitudinal dynamics of each vehicle. This estimation is achieved through the use of a Kalman filter. Closed-loop stability of the heterogeneous vehicle platoon is ensured through a Lyapunov functional analysis. Simulations have shown that the proposed methodology achieves smoother platoon following than a strategy that does not consider or compensate the effect of disturbances on the longitudinal dynamics of the vehicle.

Vehicle platooning is a promising solution to improve traffic behaviour for autonomous vehicles. The advantages of this mode of transportation include decreased travel time, reduced pollution, improved fuel consumption, and the potential to avoid traffic congestion. However, the technology is not yet mature enough due to several technical challenges that complicate its development in real systems, such as vehicle heterogeneity, the presence of disturbances such as wind on vehicle longitudinal dynamics, and delays and interruptions in communication networks, among others. This paper examines the individual effects of each aspect to determine their order of relevance and establish a framework for designing robust vehicle platoon controllers to handle such effects.

Ambulances management plays a critical role for the proper performance of medical health systems during pandemic situations, such as the COVID-19, so an optimized routing planning to attend patient calls is crucial. Electric ambulances offer the advantage of improving air quality in smart cities by reducing the emission of pollutants and greenhouse gases. Besides, electric ambulances are more economical than fuel-powered ambulances. This paper develops a numerical planning model to solve the electric ambulances routing problem using a numerical planner, with the aim of attending the maximum number of patients in the shortest possible time and also optimizing the energy management of electric ambulances. Moreover, the proposed model also considers the vehicle disinfection time required during pandemic situations.

In recent years, there has been a significant integration of advanced technology into the automotive industry, aimed primarily at enhancing safety and ride comfort. While a notable proportion of these driver-assist systems focuses on skid prevention, insufficient attention has been paid to addressing other crucial scenarios, such as rollovers. The accurate estimation of slip and roll angles plays a vital role in ensuring vehicle control and safety, making these parameters essential, especially with the rise of modern technologies that incorporate networked communication and distributed computing. Furthermore, there exists a lag in the transmission of information between the various vehicle systems, including sensors, actuators, and controllers. This paper outlines the design of an IoT architecture that accurately estimates the sideslip angle and roll angle of a vehicle, while addressing network transmission delays with a networked control system and an event-triggered communication scheme. Experimental results are presented to validate the performance of the IoT architecture proposed. The event-triggered scheme of the IoT solution is used to decrease data transmission and prevent network overload.

In this paper, a robust state-feedback event-triggered fault-tolerant controller is designed for the active suspension of a vehicle in order to guarantee roll stability. Actuator faults have been modelled as a polytope. Stability and robustness of the system have been under Lyapunov and H_∞ criteria, respectively. The validation of the proposed controller has been done with the vehicle dynamics simulation software CarSim.

This article presents a research on the problem of enhancing ride safety and comfort during driving of a vehicle using an active suspension control system under a networked communication. An integral event-triggered condition is defined to reduce the network usage over time, a Dynamic Output Feedback Controller is designed under the $H_\infty$ criteria and Lyapunov-Krasovskii functionals to guarantee the system stability, actuator faults are considered for the controller design. The control algorithm is solved in terms of Linear Matrix Inequalities. In order to prove a practical feasibility, control performance characteristics for vibration suppression are evaluated under various road conditions.

Daily traffic forecasting is crucial for optimizing traffic operations, providing traveler information, assisting traffic control on events, supporting incident management, etc. However, we are still confronted with many challenges, mainly due to the medium- and long-term forecasting horizon, which requires details and trends, and the difficulty of obtaining complete traffic data for large networks. In this paper, we propose a distributed points data driven model and apply a hybrid graph attention based long-short term memory (GAT -LSTM) architecture to carry out a 24-hour traffic forecasting task. External factors of holidays, extreme weather changes, and vacation habits of local people and other special periods of time are also encoded to improve forecasting accuracy. A case study with real-world traffic datasets from Madrid city is introduced to demonstrate the validity and usability of the model. Experimental results are also used to reveal the characteristics of the medium- and long-term forecasts from the perspective of the spatial and temporal granularities of traffic data.

This paper proposes a multi-input multi-output (MIMO) method for path tracking control of autonomous vehicles under network-induced delays while taking into account the roll dynamics to improve both the driving safety and the passenger comfort. The steering control is directly applied to the front wheels, while the anti-roll moment is exerted by an active suspension. The asynchronous phenomenon caused by the sampling process and the time-varying vehicle speed are explicitly taken into account in the control design using a polytopic linear parameter-varying (LPV) control approach. Moreover, to avoid using costly vehicle sensors and complex control structures, a static output feedback (SOF) control scheme is considered. An effective event-triggering mechanism is also proposed to alleviate the communication burden of the vehicle networked control system. Based on augmented Lyapunov-Krasovskii functional, the control design conditions are derived to guarantee the vehicle closed-loop stability under the effects of transmission delays, event-triggered control signals and time-varying parameters. The design procedure is reformulated as an iterative optimization problem involving linear matrix inequality (LMI) constraints, which can be effectively solved with available numerical solvers. The proposed event-triggered SOF controller is evaluated with the vehicle dynamics simulation software CarSim under several dynamic scenarios. A comparative study with related vehicle control results is performance to emphasize the effectiveness of the control method in terms of path tracking performance, driving safety and comfort, and data communication efficiency of the vehicle networked control system.

Tire slip control is one of the most critical topics in vehicle dynamics control, being the basis of systems such the Anti-lock Braking System (ABS), Traction Control System (TCS) or Electronic Stability Program (ESP). The highly nonlinear behavior of tire–road contact makes it challenging to design robust controllers able to find a dynamic stable solution in different working conditions. Furthermore, road conditions greatly affect the braking performance of vehicles, being lower on slippery roads than on roads with a high tire friction coefficient. For this reason, by knowing the value of this coefficient, it is possible to change the slip ratio tracking reference of the tires in order to obtain the optimal braking performance. In this paper, an H∞ controller is proposed to deal with the tire slip control problem and maximize the braking forces depending on the road condition. Simulations are carried out in the vehicular dynamics simulator software CarSim. The proposed controller is able to make the tire slip follow a given reference based on the friction coefficient for the different tested road conditions, resulting in a small reference error and good transient response.

El control del deslizamiento de los neumáticos es una de las áreas más críticas dentro del control de la dinámica vehicular, siendo la base de sistemas como el antibloqueo de frenos (ABS), el control de tracción (TCS) o el control de estabilidad (ES

This paper presents an investigation of the event-triggered control problem for an active suspension system featuring a networked communication architecture and Dynamic Output Feedback Controller (DOFCs) while taking into account the possibility of failure in actuators. The event-triggering condition determines when it is necessary to transmit the observed variables of the plant to the controller, in order not to saturate the network. A Dynamic Output Feedback Controller is synthesized under LMI restrictions to guarantee the system stability with the H∞ criteria, using the Lyapunov–Krasovskii functional approach. As the failures in actuators affect the state space model of the system, a novel polytopic model is employed to approach the plant function. In order to prove a practical feasibility, control performance characteristics for vibration suppression are evaluated under various road conditions.