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The enhanced model reference adaptive control (EMRAC) algorithm is an effective full‐state adaptive solution to control plants affected by nonlinear unmodelled dynamics and persistent disturbances. The EMRAC strategy improves the tracking performance by equipping the MRAC algorithm with adaptive switching and adaptive integral control actions. However, the need for the plant state prevents the applicability of the EMRAC algorithm to engineering control problems where only the plant output is measurable. To cover this gap and extend the range of plants controllable by EMRAC solutions, this article presents an output‐based EMRAC algorithm leveraging the closed‐loop (CL) MRAC formulation. The robustness of the closed‐loop control system is analytically analysed, not only with respect to plant parameter uncertainties and square measurable disturbances, but also to ℒ∞$$ {\mathcal{L}}_{\infty } $$ unmodelled terms and disturbances. The ultimate boundedness of the closed‐loop control system is assessed with respect to ℒ∞$$ {\mathcal{L}}_{\infty } $$ unknown nonlinear terms and disturbances, by using Lyapunov theory for Filippov systems, as the adaptive switching control action makes the closed‐loop system discontinuous. To assess the effectiveness of the CL‐EMRAC strategy to impose reference trajectories despite the unmodelled plant dynamics and persistent bounded disturbances, the problem of vehicles' direct yaw moment control is used as an engineering case study. The closed‐loop tracking performance is also quantitatively evaluated through a set of key performance indicators and compared to those provided by four benchmark controllers, that is, two LQ‐based strategies and two MRAC‐based control solutions. The CL‐EMRAC and benchmark controllers are implemented and tested in a co‐simulation environment based on a high‐fidelity IPG CarMaker vehicle model. [ABSTRACT FROM AUTHOR]

The coupled, dynamic loads from vehicles travelling on a bridge can cause excessive vibration of the bridge and limit its service life. The coupled vehicle–bridge interaction (VBI) is strongly determined by the properties of the vehicle suspension, the vehicle speed, and the bridge flexibility. A bridge-friendly vehicle is designed to minimize the contact force between the tires and the bridge, enhancing the vehicle’s performance and protecting the bridge from damage by optimizing the vehicle suspension system. In this paper, we investigate the concept of bridge-friendly vehicle by optimally tuning the parameters of the vehicle suspension to mitigate the bridge vibration. The vehicle is modeled with an inerter–spring–damper (ISD) suspension. A time-frozen technique is applied to obtain the time-varying displacement transmissibility functions of the coupled system, and effects of the suspension inerter on the time–frequency characteristics of the transmissibility are discussed. The fixed-point principle is applied to derive the optimal values of the ISD, which are shown to depend only on a priori known structural parameters of the bridge and vehicle parameters. Numerical simulations of the coupled system responses are presented to demonstrate the effectiveness of the tuned ISD in mitigating the vibration of the vehicle and the bridge. Effects of considering the inerter in the suspension model in this tuning problem for bridge-friendliness are also discussed. [ABSTRACT FROM AUTHOR]

The temperature of axle box bearing is always one of the indicators used to monitor whether the high-speed train is running safely. In this paper, a thermal model of axle box bearing considering the change of bearing lubrication state and the operating condition of the high-speed train was established. Based on the analysis of the generation mechanism of frictional heat inside the bearing, the influence of proportion of grease involved in lubrication was introduced for the first time and a detailed temperature transfer system of the axle box system was established. At the same time, through the axle box bearing-vehicle system dynamics model, the vertical and lateral loads of the axle box bearing under different operating condition were obtained as input parameters of the axle box bearing thermal model, the temperature distribution curve of the bearing under different operating conditions was obtained. Finally, based on the long-term wireless transmission device system (WTDS) temperature monitoring data, the calculation results of the established model were verified. The verification results showed the reliability of the model established in this paper, and showed the necessity of considering the change of the internal lubrication state of the bearing when evaluating the axle box bearing temperature. [ABSTRACT FROM AUTHOR]

The advent of autonomous vehicles has brought about significant advancements in transportation technology, promising safer and more efficient means of travel. However, their full integration into society depends on the accuracy of path-tracking realised by a lateral controller. Lateral control is achieved by regulating the steering angle to minimise the lateral error between the vehicle and a target point at a look-ahead distance on the reference path. This paper investigates the look-ahead distance as it is considered a key parameter that impacts vehicle performance, stability, and energy consumption. A qualitative analysis is performed to deduct a set of rules to adapt the look-ahead distance to three parameters: vehicle velocity, road curvature, and road adherence. Then, an original explicit mathematical formulation is developed for the look-ahead distance as a function of the considered parameters. A fuzzy logic decision for the look-ahead distance is further established and compared with the formulated one. Both approaches are implemented on a look-ahead distance-based lateral controller based on the super-twisting sliding mode control. Simulation results carried out in a joint simulation between Simulink/MatLab and SCANeR $ ^{{\rm TM}} $ TM Studio vehicle dynamics simulator demonstrate the effectiveness of the developed model on vehicle performance, stability, computational efficiency, and energy consumption. [ABSTRACT FROM AUTHOR]

Recent advances in mobile mapping systems have greatly enhanced the efficiency and convenience of acquiring urban 3D data. These systems utilize LiDAR sensors mounted on vehicles to capture vast cityscapes. However, a significant challenge arises due to occlusions caused by roadside parked vehicles, leading to the loss of scene information, particularly on the roads, sidewalks, curbs, and the lower sections of buildings. In this study, we present a novel approach that leverages deep neural networks to learn a model capable of filling gaps in urban scenes that are obscured by vehicle occlusion. We have developed an innovative technique where we place virtual vehicle models along road boundaries in the gap-free scene and utilize a ray-casting algorithm to create a new scene with occluded gaps. This allows us to generate diverse and realistic urban point cloud scenes with and without vehicle occlusion, surpassing the limitations of real-world training data collection and annotation. Furthermore, we introduce the Scene Gap Completion Network (SGC-Net), an end-to-end model that can generate well-defined shape boundaries and smooth surfaces within occluded gaps. The experiment results reveal that 97.66% of the filled points fall within a range of 5 centimeters relative to the high-density ground truth point cloud scene. These findings underscore the efficacy of our proposed model in gap completion and reconstructing urban scenes affected by vehicle occlusions. [ABSTRACT FROM AUTHOR]

This paper focuses on the cooperative driving challenges of connected and automated vehicles (CAVs) at single-lane roundabouts. First, a geometric path planning method is proposed for CAVs navigating a single-lane roundabout. Based on this method, a vehicle roundabout model is established. Four potential traffic scenarios for CAVs are established, and the optimal arrival times at conflict points are analyzed. By correlating the optimal arrival times at conflict points with the optimal entry times into the roundabout, the multi-vehicle coordination problem in complex intersections is simplified to a speed control issue during entry. Utilizing the principles of optimal control and Pontryagin minimization, two speed optimization strategies are proposed. Finally, MATLAB is employed for simulation analysis. The results indicate that the control strategy proposed in this paper enables the system to clearly identify potential conflicts between vehicles and implement an optimal control strategy, ensuring that vehicles can navigate the roundabout efficiently in terms of time and fuel without collisions. Additionally, the minimum time interval is established at 0.2 seconds to completely prevent vehicle collisions. In this study, the fusion problem involving two vehicles at a single conflict point is further expanded to encompass multiple vehicles at multiple conflict points. Thus, the efficient scheduling of multiple vehicles in single-lane roundabouts is realized. [ABSTRACT FROM AUTHOR]

The traffic environment in tunnel entrance area is complex, with prominent issues related to safety and efficiency. However, existing models find it difficult to accurately capture the traffic situation in this area. Therefore, this paper establishes a vehicle dynamics model for the entrance area of a tunnel based on the social force model and artificial potential field. This model can characterize the deceleration behavior of vehicles at the entrance of the tunnel and the interaction between vehicles. By considering the special effects of the tunnel environment, the simulation experiments successfully reproduce the congestion phenomenon in the entrance of the tunnel. Based on this model, this paper proposes a coordinated driving model under vehicle to vehicle (V2V) communication environment, and the simulation results show that this model can effectively alleviate the congestion in the entrance area of the tunnel and improve traffic efficiency. [ABSTRACT FROM AUTHOR]

In response to the issues of low merging success rates and poor safety in the on-ramp merging scenario within autonomous driving, we propose an on-ramp merging model for unmanned vehicles based on the Multi-Agent Proximal Policy Optimization (MAPPO) algorithm. Firstly, we introduce an Action-Mask (AM) to prevent the sampling of invalid actions during merging, thus enhancing safety by ensuring only valid actions are considered. Secondly, we incorporate noise advantage values to encourage unmanned vehicles to thoroughly explore the environment and avoid being trapped in local optimal solutions. Experimental results demonstrate that the AM-MAPPO algorithm model improves both safety and traffic efficiency. [ABSTRACT FROM AUTHOR]

In response to the fact that autonomous vehicles cannot avoid obstacles by emergency braking alone, this paper proposes an active collision avoidance method for autonomous vehicles based on model predictive control (MPC). The method includes trajectory tracking, adaptive cruise control (ACC), and active obstacle avoidance under high vehicle speed. Firstly, an MPC-based trajectory tracking controller is designed based on the vehicle dynamics model. Then, the MPC was combined with ACC to design the control strategies for vehicle braking to avoid collisions. Additionally, active steering for collision avoidance was developed based on the safety distance model. Finally, considering the distance between the vehicle and the obstacle and the relative speed, an obstacle avoidance function is constructed. A path planning controller based on nonlinear model predictive control (NMPC) is designed. In addition, the alternating direction multiplier method (ADMM) is used to accelerate the solution process and further ensure the safety of the obstacle avoidance process. The proposed algorithm is tested on the Simulink and CarSim co-simulation platform in both static and dynamic obstacle scenarios. Results show that the method effectively achieves collision avoidance through braking. It also demonstrates good stability and robustness in steering to avoid collisions at high speeds. The experiments confirm that the vehicle can return to the desired path after avoiding obstacles, verifying the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

Nowadays, control is pervasive in vehicles, and a full and accurate knowledge of vehicle states is crucial to guarantee safety levels and support the development of Advanced Driver-Assistance Systems (ADASs). In this scenario, real-time monitoring of the vehicle sideslip angle becomes fundamental, and various virtual sensing techniques based on both vehicle dynamics models and data-driven methods are widely presented in the literature. Given the need for on-board embedded device solutions in autonomous vehicles, it is mandatory to find the correct balance between estimation accuracy and the computational burden required. This work mainly presents different physical KF-based methodologies and proposes both mathematical and graphical analysis to explore the effectiveness of these solutions, all employing equal tire and vehicle simplified models. For this purpose, results are compared with accurate sensor acquisition provided by the on-track campaign on passenger vehicles; moreover, to truthfully represent the possibility of using such virtual sensing techniques in real-world scenarios, the vehicle is also equipped with low-end sensors that provide information to all the employed observers. [ABSTRACT FROM AUTHOR]

The electrification of commercial vehicles has led to new wheel torque allocation options for propulsion and braking of articulated heavy vehicles. Each unit or axle can be individually braked or propelled whilst the other units or axles are excluded from such action. This may achieve the best energy efficiency. However, this can also lead to potential yaw stability problems such as jackknifing and trailer swing, especially under bad loading and weather conditions. This paper describes the above instabilities and introduces a nonlinear single-track model to study the vehicle dynamics of the tractor-semitrailer combination. The effects of different vehicle and environment parameters are analysed with this vehicle model. A safe operating envelope for limiting the wheel forces is obtained using this vehicle model. Since the vehicle model introduced in this paper is computationally effective, it can be run online in real vehicles, with an instantaneous safe operating envelope obtained for the momentary conditions. Thus, yaw instabilities such as jackknifing and trailer swing may be avoided. [ABSTRACT FROM AUTHOR]

To address the issues of curvature discontinuity and terminal tire non-return in the parallel parking of autonomous vehicles, a novel parallel parking path planning method based on the combination of the quintic polynomial curve and an improved sigmoid function was proposed. First, a vehicle kinematic model was established. Second, considering the position, front wheel angle, and yaw angle constraints during the parking process, a hybrid superimposed curve was designed. The parking path planning problem was converted into an optimal control problem, with the maximum curvature and curvature at both ends as objective functions, and the parameters were optimized using the simulated annealing algorithm. Subsequently, for path tracking control, the simulated annealing algorithm was used to optimize the prediction time horizon of the model predictive control algorithm. Finally, a series of actual vehicle experiments were conducted based on the Apollo Autonomous Driving Developer Suite, and the effectiveness of the proposed path planning method was validated. Therefore, this method can provide a certain reference for automatic parking path planning technology. [ABSTRACT FROM AUTHOR]

To solve the complicated steering control of small vehicles in the agriculture and difficult steering on complex roads, this study designed a wheel-track composite vehicle. The vehicle incorporated a novel power differential steering mechanism with dual driving, enabling steering through the differential rotation of the rear two wheels. The vehicle is simple to control, small in size, and is able to work under the conditions of complex roads, such as hills, mountains, and muddy land. The study initially focused on presenting the design, theoretical analysis, and dynamic simulation analysis of the power differential steering mechanism with dual driving. Subsequently, the vehicle underwent modeling and simulation using UG software to validate the reasonability of the values. Finally, utilizing test data, four mathematical models for the actual steering radius of the vehicle on four road surfaces were derived through neural network fitting. The maximum relative error between the model results and the actual steering radius value was reported to be 3.53%. The advantages of the vehicle included continuous radius steering, deceleration and torsion increase, differential lock, etc. This made it well-suited for applications in all-terrain military and civilian vehicles, as well as various special equipment mobile platforms equipped with walking devices. [ABSTRACT FROM AUTHOR]

In this paper, the removal effects and activation energy of Ce and Pd doping on pollutants (CO, C3H6, and NO) were comparatively analyzed by using characterization methods and constructed kinetic equations. Furthermore, the problems of the water influence mechanism on the NSR process were also discussed. The results show the following: (1) Pd doping effectively improves the removal of CO (80%) and C3H6 (71%) in the low-temperature section of the catalyst (150–250 °C) compared to Ce doping, while Ce doping exhibits excellent low-temperature conversion of NO. (2) The reaction activation energy of the LaKMnPdO3 catalyst was 9784 kJ/mol, which was significantly lower than that of the LaKMnCeO3 catalyst. (3) The presence of H2O has an important enhancement effect in the storage performance of the LaKMnPdO3 catalyst for NOx but decreases the catalytic reduction of NO. It provides a solution for the effective treatment of the increasing problems of particulate matter and ozone pollution. [ABSTRACT FROM AUTHOR]

Unexpected tire blowout on expressway is one of the most dangerous scenarios for heavy load multi-axle trucks, especially under the inexperienced driver's improper intervention. To this end, this paper proposes an autonomous tire blowout controller for three-axle trucks based on model predictive control (MPC) method. First of all, aiming at balancing the computing load and the path-tracking accuracy, an equivalent chassis is transformed based on the stable two-dimensional vehicle dynamic model. Second, a tire blowout–oriented vehicle high-fidelity dynamic model is established based on the equivalent chassis, in which the vehicle lateral, yaw, and roll motions are all considered at the same time. Then, an MPC-based dynamic control strategy is proposed to achieve effective tire blowout suppression with the consideration of vehicle rollover stability. Afterward, a transient vertical tire force shock is employed to simulate the real tire blowout behavior, on which two typical scenarios are constructed based on the TruckSim–Simulink co-simulation platform. The simulation results illustrate the feasibility and effectiveness of the proposed method. In two different dangerous tire blowout conditions, the lateral load transfer rate (LTR) value of the vehicle is reduced by 3.45% and 40.87%, respectively, while the trajectory tracking error is reduced at the same time. [ABSTRACT FROM AUTHOR]

This paper proposes an event-triggered model predictive-preview control strategy for autonomous vehicle trajectory tracking. First, the dynamic equation is established based on the preview road curvature and the vehicle's 2 degree-of-freedom relationship. A model predictive tracking controller is designed by predicting the system dynamics in the future. Second, to reduce the computational burden of the controller, the triggering conditions are designed according to the system's stability and feasibility. The proposed event-triggered model predictive-preview control strategy not only makes the autonomous vehicles maintain tracking accuracy but also reduces the number of online optimization. Then, asymptotically stable of closed-loop systems are proved by using Lyapunov's theorem. Finally, simulation experiments show that the proposed strategy has some advantages over traditional model predictive control in terms of improving vehicle tracking accuracy and reducing algorithm complexity. [ABSTRACT FROM AUTHOR]

This study presents a comprehensive investigation into the impact of active aerodynamic load balancing (AALB) on the cornering performance of high-performance vehicles. The research explores the use of active asymmetric aerodynamic devices, specifically split and tilted rear wing concepts, capable of manipulating vertical wheel loads and counteracting effects of lateral load transfer during cornering. The performance potential of AALB is assessed through quasi-steady static coupling of aerodynamic data with a detailed vehicle dynamics model. The findings show that inside bias operating states of the split rear wing and tilted rear wing concepts, which favor loads on the inside tires, can improve cornering velocities up to 0.5% and 2% compared to high symmetric operating states, respectively. Noteworthy, through effective distribution of aerodynamic loads, the inside bias operating states produce less downforce and drag, thereby reducing the propulsion power required to overcome drag by 15%–20%, depending on the cornering condition. The tilted rear wing concept demonstrates the highest AALB capability and most consistent response to its control strategy, accredited to its ability to generate vertical and horizontal aerodynamic force components. It can, therefore, achieve over 1% higher maximum cornering velocities compared to the split rear wing, while also offering efficiency benefits. Overall, the research highlights the effectiveness of AALB in improving cornering performance and efficiency, offering valuable insights for the development of advanced active aerodynamic solutions in automotive design and paving the way for future advancements in the field. [ABSTRACT FROM AUTHOR]

In order to improve the delivery efficiency of logistics, this paper proposed a vehicle routing problem with drone and time window considered by the time window, UAV (unmanned aerial vehicle) power change and multi-point continuous delivery of UAV. Then, this paper proposed a hybrid particle swarm optimization algorithm with local search to solve it. Based on the hybrid particle swarm optimization algorithm, the proposed algorithm transformed the problem solution space into the algorithm search space by constructing an efficient encoding and decoding strategy. Further, the proposed algorithm combined a single point insertion strategy, a vehicle replacement strategy and a UAV replacement strategy to form a local search strategy to improve the optimization ability. The experimental results show that the proposed model is more efficient than the model of pure vehicle distribution and saves 31.51% of the cost. The proposed algorithm is better than the four comparison algorithms, and its highest optimization rate is 82.08%. [ABSTRACT FROM AUTHOR]

Providing safe, smooth, and efficient trajectories for autonomous vehicles has long been a question of great interest in the field of autopiloting. In dynamic and ever-changing urban environments, safe and efficient trajectory planning is fundamental to achieving autonomous driving. Nevertheless, the complexity of environments with multiple constraints poses challenges for trajectory planning. It is possible that behavior planners may not successfully obtain collision-free trajectories in complex urban environments. Herein, this paper introduces spatio–temporal joint optimization-based trajectory planning (SJOTP) with multi-constraints for complex urban environments. The behavior planner generates initial trajectory clusters based on the current state of the vehicle, and a topology-guided hybrid A* algorithm applied to an inflated map is utilized to address the risk of collisions between the initial trajectories and static obstacles. Taking into consideration obstacles, road surface adhesion coefficients, and vehicle dynamics constraints, multi-constraint multi-objective coordinated trajectory planning is conducted, using both differential-flatness vehicle models and point-mass vehicle models. Taking into consideration longitudinal and lateral coupling in trajectory optimization, a spatio–temporal joint optimization solver is used to obtain the optimal trajectory. The simulation verification was conducted on a multi-agent simulation platform. The results demonstrate that this methodology can obtain optimal trajectories safely and efficiently in complex urban environments. [ABSTRACT FROM AUTHOR]

Transforming an up-to-date vehicle into a measurement system is a rewarding task due to the large number of different sensors in the onboard control and diagnostic systems. These procedures are not performed by a single control unit; it is necessary to share the signal values over a communication network, to which an external device can be connected to record the real traffic. The paper aims to use these recorded data for 1 DOF longitudinal vehicle and powertrain model validation. For repeatability, three city routes are selected: plain road, smaller road grade, and higher road grade in both directions. Therefore, the drivetrain system is tested in a high load range, even with long-term recuperation. The altitude changes are recorded with a DGPS system. By the recorded measurements, the vehicle and the drivetrain model can be calibrated, such as the air drag parameters, the rolling resistances, and the efficiencies of the drivetrain. The validation criteria are defined for speed tracking, and the relative tolerance of the cumulated energy should be below 10%. At the end of the day, a developed model is ready for energetic analysis or control strategy design. The energy balance of the applied cycles is also presented to prove that. [ABSTRACT FROM AUTHOR]

The bird's-eye view (BEV) method, which is a vision-centric representation-based perception task, is essential and promising for future Autonomous Vehicle perception. It has advantages of fusion-friendly, intuitive, end-to-end optimization and is cheaper than LiDAR. The performance of existing BEV methods, however, would be deteriorated under the situation of a tire blow-out. This is because they quite rely on accurate camera calibration which may be disabled by noisy camera parameters during blow-out. Therefore, it is extremely unsafe to use existing BEV methods in the tire blow-out situation. In this paper, we propose a geometry-guided auto-resizable kernel transformer (GARKT) method, which is designed especially for vehicles with tire blow-out. Specifically, we establish a camera deviation model for vehicles with tire blow-out. Then we use the geometric priors to attain the prior position in perspective view with auto-resizable kernels. The resizable perception areas are encoded and flattened to generate BEV representation. GARKT predicts the nuScenes detection score (NDS) with a value of 0.439 on a newly created blow-out dataset based on nuScenes. NDS can still obtain 0.431 when the tire is completely flat, which is much more robust compared to other transformer-based BEV methods. Moreover, the GARKT method has almost real-time computing speed, with about 20.5 fps on one GPU. [ABSTRACT FROM AUTHOR]

This article considers a path tracking control problem for autonomous vehicles constrained to maintain a safe distance from the next vehicle in the lane. The control design problem is solved by considering a mathematical model of the vehicle that jointly describes the lateral and longitudinal dynamics. Specifically, the ℒ2$$ {\mathcal{L}}_2 $$ optimal controller is designed aimed at minimizing the orientation and position tracking errors with respect to a given reference path. Moreover, the controller is able to ensure that the vehicle can both follow a speed profile and automatically adjusts the vehicle speed to maintain a proper distance from the next vehicle. The lateral dynamic is regulated by a 2‐DOF feedforward/feedback controller, where the feedforward law is implemented by exploiting an inverse kinematic model of the vehicle. On the other side, the feedback action, along with the action of the controller in charge to regulate the longitudinal dynamics, is the ℒ2$$ {\mathcal{L}}_2 $$ optimal gain‐scheduling state‐feedback controller. In particular, a parameter‐dependent quadratic Lyapunov function approach is used to reduce the conservativeness of the solution and a suitable convex LMI optimization problem is formulated for the synthesis. The vehicle is described by a seven‐degrees‐of‐freedom (7DOF) full‐car model, with the tire‐generated forces described by the classical Pacejka's Magic Formula. In order to assess the performance of the proposed control strategy, simulations have been undertaken in Matlab/Simulink by considering an autonomous vehicle driving into an urban scenario. [ABSTRACT FROM AUTHOR]

Lane-keeping assistance (LKA) systems aim to address the vehicle deviation problem from normal lanes due to driver distraction, and their performance is affected by factors such as the stability of the steering system and external interference. This study proposed a vehicle LKA strategy based on the electric power steering system (EPS) to reveal the relationship between the control strategy and the performance of LKA systems. Firstly, an EPS system, a vehicle three-degree-offreedom dynamics model, and a driver model were established for the nonlinear electromechanically coupled vehicle EPS system. Both the serial lane-keeping control strategy for the EPS system controller based on robust sliding mode observers and the model prediction controller based on vehicle trajectory were devised. Then, based on various driving conditions, the vehicle's intervention coefficient outputs were dynamically adjusted in accordance with the lateral safety deviation and the driver's manual force. Finally, the intervention coefficients of the lane-keeping system were designed, and the accuracy of the control strategy was verified through experiments. Results show that after adding lane-keeping auxiliary control, the wheel turning angle is reduced by a maximum of 9°, the traverse angular velocity is decreased by a maximum of 0.52 rad, and the vehicle lateral offset is reduced by a maximum of 3.4 m. This study provides a significant reference for constructing vehicle models and evaluating the performance of LKA control strategies. [ABSTRACT FROM AUTHOR]

In the paper, some selected results concerning the ellipses of adhesion for various conditions of motion of a vehicle model, have been presented. The analysis, regarding the phenomena between the road and the wheels of the given motor vehicle's model, was previously carried out in the earlier paper by the author. However, they proved useful for the second part of research based on determination of the ellipses of adhesion. This can enable answering the question whether the road conditions may affect the maximum longitudinal and lateral forces combining such ellipses in the contact plane between the road and the wheels. The analysis has been presented based on the previously obtained results. [ABSTRACT FROM AUTHOR]

The vibration of high-speed trains induced by track irregularities is a main factor affecting ride comfort. Especially, the long-wavelength track irregularities can reduce the passengers' ride comfort by increasing the vibration of the high-speed trains. A three-dimensional human body model coupled to a vehicle body and tracks is established to form a coupled human-seat-vehicle-track system for investigating the influence of the long-wavelength track irregularities on ride comfort. The parameters of the seated human model are determined by measurement based on the multi-island GA method. The coupled model is then used to evaluate the ride comfort of high-speed trains using ISO 2631 under the excitation of the track longitudinal level irregularities. The effects of primary, secondary suspension, and seat support vertical stiffness and damping on ride comfort are analysed. The results show that the track's longitudinal irregularities lead to high human body vibration levels and large ride comfort indices (lower ride comfort), especially when the irregularity excitation frequency is near the natural frequency of the high-speed train. The passengers' ride comfort is more sensitive to the vertical secondary suspension stiffness than the other suspension parameters. [ABSTRACT FROM AUTHOR]

• A comprehensive vertical dynamics model of the electric vehicle considering the integrated powertrain system is proposed. • The limitations of traditional vertical dynamics model of electric vehicle in the high frequency range are revealed. • The effects of mounting stiffness and damping on the vibration of chassis and integrated powertrain system are explored. • A multi-objective optimization model is designed to minimize the vibration of chassis and integrated powertrain system. Integrated powertrain system, mainly composed of driving motor and reducer, is one of the excitation sources to electric vehicle vibration but is limited in coupling with the vertical vehicle dynamic in the existing research, which restricts the reliability of dynamics investigation of vehicle and integrated powertrain system. This work proposes a novel vertical dynamic model of the electric vehicle considering the integrated powertrain system, enabling the way to access the coupled vibration among the chassis, unsprung mass, and integrated powertrain system. The road irregularity, unbalanced magnetic pull, and gear mesh excitation are developed and are further applied to the novel vehicle dynamic model with ten degrees of freedom (DOFs). Then, the dynamic response between the novel model and traditional one is compared. Further, the effects of the stiffness and damping of suspension and mounting on the vibration of chassis and integrated powertrain system are explored by numerical calculation. Finally, a multi-objective optimization model is proposed and is driven by the dragonfly algorithm to minimize the vibration of chassis and integrated powertrain system. The research results shown that there will be present the new complex dynamic characteristics with considering the excitation from the integrated powertrain system. The suspension and mounting parameters have the nonlinear effects on the vehicle vibration. And the system vibration can be globally optimized with the optimization algorithm. [ABSTRACT FROM AUTHOR]

The huge growth in the number of vehicles is causing serious traffic management problems. Existing roads must handle traffic more than expected which presents serious challenges including congestion and safety. Intelligent traffic management systems were proposed as a solution to solve such problems. This intelligent system is charged to estimate vehicle density and manage the traffic flow accordingly. A wide variety of sensors was deployed for the traffic management system such as cameras, ultrasonic sensors, radar, infrared sensors, and acoustic sensors. In this work, an intelligent traffic flow management system was proposed based on data provided by public surveillance cameras. For this purpose, a real-time vehicle detection and counting model based on You Look Only Once (YOLO) v6 was deployed. The proposed model was trained and evaluated on different publicly available datasets such as BDD 100K and KITTI. Extensive experimentations proved the efficacy of the proposed model for detecting vehicles while operating under real-time constraints. The proposed traffic flow management system was evaluated using real videos for different traffic scenarios, and good results were achieved. [ABSTRACT FROM AUTHOR]

Multi-intelligent vehicles forming and maintaining a specific formation during traveling can save energy consumption and road resources, and improve traffic efficiency and driving safety. Centered around the Cooperative Vehicle Infrastructure(CVI) framework of the Internet of Vehicles(IOV), this paper delves into the control objectives of forming multi-intelligent vehicle formation, explores the theory of formation shaping process, and investigates the optimization of energy consumption in the formation shaping process. First, the clustering design is used to take the kinematic process of vehicles as an optimization process from the bionics, taking the instantaneous consumption indexes as the optimization objectives. Then, optimize the acceleration inputs of the vehicle formation process using the improved Artificial Fish Swarm Algorithm(AFSA), and iterate with the clustering algorithm part, so as to realize the formation shaping process with energy consumption as the optimization objective. Finally, ajoint simulation platform built by Simulation of Urban Mobility (SUMO) and Python is used for simulation verification, and the results show that the formation shaping process can be realized and achieve the optimal energy consumption under the scenarios proposed in this paper, thus verifying the validity of the clustering model of vehicle formation shaping and the energy consumption optimization algorithm designed in this paper. [ABSTRACT FROM AUTHOR]

The traditional Model Predictive Control (MPC) algorithm, grounded in precise mathematical models, faces challenges attributed to uncertainties in vehicle model parameters and modeling errors. These limitations result in suboptimal accuracy of the path tracking controller, particularly in complex driving environments. To address this issue, a tube-based model predictive control strategy (Tube MPC) is proposed. Initially, nominal dynamic modeling of intelligent vehicles is conducted, followed by linearized derivations. Leveraging the resulting linearized nominal model, the MPC cost function is solved to derive the control law for the nominal system. Subsequently, an error model between the actual and nominal systems is established, integrating various target constraints for path tracking and formulating a robust objective function. Utilizing the linear matrix inequality optimization method, a state feedback-assisted control law is derived to mitigate steady-state lateral position error. This approach ensures a more precise alignment between the actual and nominal states, ultimately enhancing tracking accuracy. Finally, experimental validations are conducted in the Carsim/Simulink simulation environment, encompassing diverse vehicle speed conditions and driving scenarios to assess the proposed controller against traditional Model Predictive Control. The experimental results demonstrate that, while ensuring vehicle stability, this control method exhibits superior tracking accuracy. [ABSTRACT FROM AUTHOR]

This paper proposes an optimal lane-changing and control strategy to solve the collision avoidance of intelligent vehicles in highway driving. A quintic polynomial lane-changing trajectory model is established in the Frenet frame, and the trajectory planning problem is transformed into the optimisation problem of lane-changing target coordinates. To consider the multiple environmental factors on vehicle lane-changing, the feasible region of the lane-changing target point was generated based on the lateral acceleration limitation and the safe distance constraints. The performance evaluation indexes were established, considering efficiency, comfort, lateral slip, and lane offset. The multi-objective optimisation problem is solved in the feasible region, and the optimisation results were applied to the collision detection process. In addition, considering the nonlinear characteristics of tire force caused by sudden lateral movements of vehicles at high-speed, a vehicle dynamics model based on the Magic Formula is established, and Model Predictive Control is applied to track the expected trajectory. Simulation results show that this strategy can plan suitable lane-changing trajectories in high-speed working conditions and achieve vehicle collision avoidance. [ABSTRACT FROM AUTHOR]

Research and development of innovative suspension components for rail vehicles have involved huge investments in recent years. Research efforts have focussed on designing and optimising suspension systems to deal with the new challenges introduced in railway dynamics by the continuous increase in vehicle speed. In particular, yaw dampers have been a relevant research topic due to their influence on vehicle stability. In this context, this paper aims to propose a Hardware-In-the-Loop (HIL) methodology for testing yaw dampers under experimental conditions close to real operating scenarios, comparing the influence of different prototypes on the stability of high-speed rail vehicles. A reduced vehicle model is proposed for real-time integration in HIL tests. This model provides a reference stroke to be imposed on the prototype tested, considering the actual damping force provided by the device being analysed. Two yaw damper prototypes are introduced to validate the proposed methodology by means of comparative analysis at different vehicle speeds. The experimental results provided by the HIL test bench are then compared with corresponding analysis done using Multi-Body (MB) simulations. The proposed HIL methodology has proved to be able to test physical prototypes and define guidelines to assist damper manufacturers developing and optimising yaw damper components. [ABSTRACT FROM AUTHOR]

A parametric Adaptive Model Predictive Controller (AMPC) based on Particle Swarm Optimization-Back Propagation (PSO-BP) neural network has been developed in this paper, the primary focus is on improving the trajectory tracking performance of autonomous vehicles under varying road conditions. The PSO-BP neural network is employed for real-time adjustment of the controller's predictive horizon and sampling time. A vehicle dynamics model is established and an improved tracking control algorithm involving road curvature feedforward is proposed. In the design of AMPC, the real-time update of tire lateral stiffness is achieved through the adoption of the Recursive Least Squares (RLS) method, ensuring the precision of trajectory tracking for the vehicle under varying operating conditions. The simulation platform, which combines Carsim and Simulink, was employed for validating the proposed approach. The findings reveal that the proposed controller can promptly adjust the predictive horizon and sampling time according to the vehicle's state. Through the employed estimation strategy, real-time adjustments of tire lateral stiffness are achieved, allowing for dynamic alterations of vehicle speed and front wheel angle in response to road curvature. As a result, this approach significantly enhances control accuracy and lateral steering stability, especially in large curvature conditions. [ABSTRACT FROM AUTHOR]

In this paper, we present a data-driven modeling method for lateral motion control of unknown vehicle models. Vehicle's motion can be modeled linearly but this model has complex and nonlinear characteristic. Therefore, it is necessary to know the exact information of the car chassis and requires a knowledge and understanding of dynamics. To solve these drawbacks, we linearly represent full vehicle's lateral dynamics which include nonlinear behavior using dynamic mode decomposition (DMD), one of the data driven modeling methods. To determine the validity of the model obtained using the DMD method, we conducted a simulation of the comparison of the output states between the existing model and the model obtained through DMD modeling, using the scenario of a dynamic maneuver called a double line change during lateral motion of a vehicle. After determination of validation is completed, we designed a lane keeping system by applying a model predictive control to specifically evaluate the model of the proposed method. Performance was derived by comparing the error caused by the vehicle driving on the course with the controller of the simulation. The performance of the proposed approach has been evaluated through simulations and is useful when the model is inaccurate. [ABSTRACT FROM AUTHOR]

This study aimed to determine a method to suppress thermal runaway in electric vehicles by passing water directly inside the battery case. A scaled-down model experiment was conducted using a lithium-ion battery pack consisting of six 18650 cells, which is equal to about one-thousandth of an electric vehicle's charging capacity. The heat generation rate, heat transfer coefficient, and time constant for cooling were measured using a simple model for the cooling methods, thermal runaway stages, and state of charge. When thermal runaway occurred during natural cooling, the battery temperature rose to 630 °C at a rate of 116 °C/s. Through water injection, the thermal runaway was quickly suppressed with a time constant of about 3 s and a heat transfer coefficient of 3400 W/m2·K. The water effectively prevented chain explosions and kept harmful gases emitted from the batteries. It was found that it is difficult to completely suppress thermal runaway using the latent heat of the stagnant water in the spaces between cylindrical batteries. If the experimental results of this study were to be applied to an actual vehicle, it is expected that thermal runaway could be suppressed with a time constant of about 170 s and 1 ton of water. [ABSTRACT FROM AUTHOR]

In this paper, we consider an estimation problem of sideslip angle and cornering stiffness of a vehicle from measurements of yaw rate, lateral acceleration and steering angle. Firstly, a vehicle dynamical model is expressed as a linear parameter‐varying system with cornering stiffness as uncertain parameters. Then, we show a method to estimate the parameters and the state based on moving horizon estimation. We provide a simple algorithm to solve the estimation problem. The effectiveness of the estimation method is evaluated through numerical simulations. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

The investigation on vehicle–bridge interaction systems (VBIS) plays an important role in evaluating the performance of bridges and/or vehicles. However, due to the limitations of traditional simulation methods and testing methods, the impact of vehicle jumping (VJ) on the VBIS has never been precisely disclosed. In order to accurately and comprehensively reveal the impact of VJ on the dynamic performance of VBIS, one real-time hybrid simulation method (RHSM) for VBIS considering the impact of VJ (VBIS-VJ) is proposed in this paper. In the proposed method, the vehicle system is chosen as the experimental substructure loaded by servo-hydraulic actuator loading system in real-time, whilst the bridge system is chosen as the numerical substructure numerically simulated in one computer. During the vehicle jumping, the vehicle and bridge vibrate freely respectively, otherwise, the experimental substructure and numerical substructure are synchronized at the coupling points in terms of force equilibrium and deformation compatibility in RHSM for VBIS-VJ. Firstly, the principle and numerical models of RHSM for VBIS-VJ are proposed. Furthermore, numerical simulations of RHSM for VBIS-VJ are conducted for verifying the feasibility of the proposed method. Finally, the influence of jumping position, jumping height, and driving speed on the VBIS is disclosed by hybrid simulations. It is shown that compared with the ever-known investigation methods, the proposed method could economically and accurately disclose the dynamic performance of VBIS-VJ. It is shown from the simulation results that compared with the referenced results, the quantitative errors of the proposed method with quarter vehicle model and half vehicle model are less than 2.5152% and 3.5599% respectively, which prove the effectiveness and accuracy of proposed RHSM for VBIS-VJ. The peak displacement of the bridge at a quarter-span and mid-span exceeds the three-quarters span by about 1.9–2.9 mm. The influence of jumping position of a quarter span and midspan of bridge is much huger than that in the three-quarters span. The dynamic response of bridge increases with the increase the jumping height, but the deformation of the bridge is reduced when the jumping height is more than 0.05 m. Meanwhile, the requirements for experimental loading equipment are acquired through the hybrid simulations, which could be used as a reference for selecting the loading system of the RHSM for VBIS-VJ. [ABSTRACT FROM AUTHOR]

The rapid advancement of electric vehicles (EVs) accentuates the criticality of efficient thermal management systems for electric motors, which are pivotal for performance, reliability, and longevity. Traditional thermal modeling techniques often struggle with the dynamic and complex nature of EV operations, leading to inaccuracies in temperature prediction and management. This study introduces a novel thermal modeling approach that utilizes a multihead attention mechanism, aiming to significantly enhance the prediction accuracy of motor temperature under varying operational conditions. Through meticulous feature engineering and the deployment of advanced data handling techniques, we developed a model that adeptly navigates the intricacies of temperature fluctuations, thereby contributing to the optimization of EV performance and reliability. Our evaluation using a comprehensive dataset encompassing temperature data from 100 electric vehicles illustrates our model's superior predictive performance, notably improving temperature prediction accuracy. [ABSTRACT FROM AUTHOR]

In the field of intelligent transportation, the planning of traffic flows that meet energy-efficient driving requirements necessitates the acquisition of energy consumption data for each vehicle within the traffic flow. The current methods for calculating vehicle energy consumption generally rely on longitudinal dynamics models, which require comprehensive knowledge of all vehicle power system parameters. While this approach is feasible for individual vehicle models, it becomes impractical for a large number of vehicle types. This paper proposes a digital model for vehicle driving energy consumption using vehicle speed, acceleration, and battery state of charge (SOC) as inputs and energy consumption as output. The model is trained using an optimized CNN-BiLSTM-Attention (OCBA) network architecture. In comparison to other methods, the OCBA-trained model for predicting PHEV driving energy consumption is more accurate in simulating the time-dependency between SOC and instantaneous fuel and power consumption, as well as the power distribution relationship within PHEVs. This provides an excellent framework for the digital modeling of complex power systems with multiple power sources. The model requires only 54 vehicle tests for training, which is significantly fewer than over 2000 tests typically needed to obtain parameters for power system components. The model's prediction error for fuel consumption under unknown conditions is reduced to 5%, outperforming the standard error benchmark of 10%. Furthermore, the model demonstrates high generalization capability with an R2 value of 0.97 for unknown conditions. [ABSTRACT FROM AUTHOR]

Creating accurate emission models capable of capturing the variability and dynamics of modern propulsion systems is crucial for future mobility planning. This paper presents a methodology for creating THC and NOx emission models for vehicles equipped with start–stop technology. A key aspect of this endeavor is to find techniques that accurately replicate the engine's stop stages when there are no emissions. To this end, several machine learning techniques were tested using the Python programming language. Random forest and gradient boosting methods demonstrated the best predictive capabilities for THC and NOx emissions, achieving R2 scores of approximately 0.9 for engine emissions. Additionally, recommendations for effective modeling of such emissions from vehicles are presented in the paper. [ABSTRACT FROM AUTHOR]

Considering the requirements pertaining to the trafficability of off-road vehicles on rough roads, and since their roll stability deteriorates rapidly when turning violently or passing slant roads due to a high center of gravity (CG), an efficient anti-slip control (ASC) method with superior instantaneity and robustness, in conjunction with a rollover prevention algorithm, was proposed in this study. A nonlinear 14 DOF vehicle model was initially constructed in order to explain the dynamic coupling mechanism among the lateral motion, yaw motion and roll motion of vehicles. To acquire physical state changes and friction forces of the tires in real time, corrected LuGre tire models were utilized with the aid of resolvers and inertial sensors, and an adaptive sliding mode controller (ASMC) was designed to suppress each wheel's slip ratio. In addition, a model predictive controller (MPC) was established to forecast rollover risk and roll moment in reaction to the change in the lateral forces as well as the different ground heights of the opposite wheels. During experimentation, the mutations of tire adhesion capacity were quickly discerned and the wheel-hub drive motors (WHDM) and ASC maintained the drive efficiency under different adhesion conditions. Finally, a hardware-in-the-loop (HIL) platform made up of the vehicle dynamic model in the dSPACE software, semi-active suspension (SAS), a vehicle control unit (VCU) and driver simulator was constructed, where the prediction and moving optimization of MPC was found to enhance roll stability effectively by reducing the length of roll arm when necessary. [ABSTRACT FROM AUTHOR]