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The precision of path tracking in high‐speed intelligent vehicles is significantly influenced by model mismatch arising from factors like parameter uncertainty, model simplification, external disturbances, and other sources. In this paper, we propose a novel robust control strategy that integrates the compensation function observer (CFO) with the model predictive control (MPC) method, utilizing an optimized vehicle dynamics model (opt‐model) to address this challenge, called OCMPC. Initially, we establish the opt‐model to design predictive model by leveraging suspension kinematics and compliance (K&C) data collected from a miniature pure electric vehicle. Remarkably, the opt‐model exhibits improved accuracy compared to the conventional vehicle dynamics model (con‐model) while preserving the same degrees of freedom (DOF). Next, we incorporate CFO into the path tracking process of high‐speed intelligent vehicles, enabling dynamic real‐time observation of the model mismatch between the prediction model and the actual vehicle. CFO can capture the dynamics of the vehicle, including nonlinearities and uncertainties, without placing a heavy computing burden on the controller. This observed mismatch is subsequently employed for feed‐forward compensation, facilitating the attainment of optimal control values. Ultimately, we validate the effectiveness of our proposed method in enhancing path tracking accuracy for high‐speed intelligent vehicles through co‐simulation using Simulink and Carsim. [ABSTRACT FROM AUTHOR]

ABSTRACT This article considers the trajectory tracking problem of automated guided vehicles with unknown‐but‐bounded noises and proposes a set‐membership filtering (SMF) based model predictive control strategy. A 2‐DOF vehicle model is first established with considering the unknown‐but‐bounded process and measurement noises. Then, a trajectory tracking control scheme is proposed which consists of two parts, that is, a set‐membership observer and a model predictive controller. The proposed set‐membership observer is able to deal with the unknown‐but‐bounded noises and obtain the state estimation ellipsoid for the considered automated guided vehicle. Further, the designed model predictive controller utilizes the estimated ellipsoid to compute the control sequence. Finally, simulation results demonstrate the effectiveness and superiority of the proposed method. [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]

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]

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]

Hybrid wheel–track systems have found extensive applications due to the advantages a combination of wheels and tracks. However, the coupling influence between the wheeled and tracked mechanisms poses a challenge to stable and efficient controller design and implementation. This paper focuses on the lateral dynamic control of a vehicle in scenarios where both tracks and wheels are in contact with the ground. A dynamic model of a vehicle is first established based on the tire brush model and linearized general track model. Based on the dynamic model, a novel adaptive model predictive control (AMPC) method is designed considering the coupling and nonlinearity of the wheels and tracks to simultaneously regulate both mechanisms. Compared with traditional model predictive control approaches, the AMPC controller takes the side-slip angle and slip ratio as constraints to prevent the vehicle from reaching unstable states. Simulations are conducted to validate the effectiveness of the controller, and the results indicate that the controller has the capacity to optimize the objective's yaw-rate response while maintaining lateral vehicle stability and preventing slip by imposing constraints. [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]

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]

Nonlinear Model Predictive Control (NMPC) is effective for local planning of automated vehicles, especially when there exist dynamical objects and multipe requirements. But it requires many computation resources for numerical optimization, which limits its practical application becase of the limited power of onboard unit. To extend the application range of the NMPC based local planner, the coupled nonlinear vehicle dynamics model is adopted based on the numerical analysis, which conversely requires much more discretization poits for acceptable accuracy. For better computation efficiency, Lagrange polynomials are used to discretize the vehicle dynamics model and objective function with less points and fine numerical accuracy. Furthermore, an adaptive strategy is designed to determine the order of Lagrange polynomials according to running state by numerical analysis of discretization error. Both acceleration effect and performance of the local planner designed by NMPC are validated by experimental tests under scenarios with multiple dynamical obstacles. The test results show that compared with the original one the accuracy and efficiency are improved by 74% and 60%, respectively. [ABSTRACT FROM AUTHOR]

Trajectory tracking on a low-speed vehicle using the model predictive control (MPC) algorithm usually assumes a simple road terrain. This assumption does not correspond to the actual road situation, leading to low tracking accuracy. Therefore, a trajectory tracking method considering road curvature based on MPC is proposed in this paper. In this method, the controller can automatically switch between MPC types. Linear model predictive control (LMPC) is selected for small road curvatures, while nonlinear model predictive control (NMPC) is employed for large road curvatures. In addition, the NMPC algorithm in this work considers the effect of road curvature on tracking accuracy, making it suitable for tracking time-varying curvature roads. To verify the feasibility of the algorithm, simulation comparisons with the basic MPC model were carried out at different testing roads and vehicle longitudinal speeds. The results indicate that the method significantly improves trajectory tracking accuracy, all while ensuring real-time calculations. The intelligent switching capability of control models based on road curvature allows its application to track trajectories on arbitrarily complex roads. [ABSTRACT FROM AUTHOR]

In a typical traffic scenario, automated vehicles are required to interact with surrounding traffic participants, e.g. human-driven vehicles, pedestrians, etc. In this paper, a novel adaptive authority allocation strategy considering the social behaviours of surrounding vehicles is proposed for the shared steering control (SSC) of automated vehicles. First, a Koopman-based potential-field-driven distributed model predictive control (K-PF-DMPC) method is proposed for the modelling of vehicle interaction to describe surrounding vehicle's social behaviour. This method effectively deals with the nonlinearity embedded in the non-cooperative game-based vehicle interaction model, so that the analytical form of Nash equilibrium can be derived for a fast online solution. Then, the SSC system of the automated vehicle is implemented by a weighted summation method. To balance the driving performance and intervention degree while considering the surrounding vehicles' social behaviours, a hybrid fuzzy strategy (HFS) is proposed for allocating the control authority between the driver and automation. The value of authority allocation coefficient is calculated by fusing the outputs from two shared fuzzy controllers, i.e. the enhanced and weakened shared fuzzy controllers, in accordance to the belief of the surrounding vehicle's type. Several numerical simulations and driver-in-the-loop simulator experiments are conducted for validation. Results show that the proposed strategy can provide appropriate steering intervention to the driver owing to the consideration of the surrounding vehicle's social behaviour and obtains the best driver-automation collaboration performance compared with the comparative groups. [ABSTRACT FROM AUTHOR]

To solve the problem of trajectory tracking lateral control in autonomous driving technology, a model predictive control (MPC) controller trajectory tracking lateral control method combined with a deep deterministic policy gradient algorithm (DDPG) is proposed in this paper. This method inputs the real-time state of the vehicle into DDPG to achieve real-time automatic optimization of the prediction time domain and control time domain parameters of the MPC controller, and then affects the specific performance of the MPC controller in trajectory tracking lateral control. Specifically, the state space, action space, and reward function of DDPG are defined, and the automatic driving trajectory tracking lateral controller is designed in combination with the vehicle dynamics model. To reduce the exploration space of DDPG and improve the training efficiency of the entire model, the technique of advantage-disadvantage experience separation and extraction is introduced. Finally, the proposed method was trained and verified in various scenarios, and compared with two other lateral control methods for autonomous driving. The results showed that the learning and training time of the trajectory tracking lateral control method based on DDPG-MPC was shorter than that of the DDPG-based method, and the evaluation indicators in the trajectory tracking control process were better than those of the DDPG-based method and original MPC-based method. [ABSTRACT FROM AUTHOR]

The development of intelligent transportation technology has provided a significant impetus for autonomous driving technology. Currently, autonomous vehicles based on Model Predictive Control (MPC) employ motion control strategies based on sampling time, which fail to fully utilize the spatial information of obstacles. To address this issue, this paper proposes a dual-layer MPC vehicle collision-free trajectory tracking control strategy that integrates spatial kinematics and vehicle dynamics. To fully utilize the spatial information of obstacles, we designed a vehicle model based on spatial kinematics, enabling the upper-layer MPC to plan collision avoidance trajectories based on distance sampling. To improve the accuracy and safety of trajectory tracking, we designed an 8-degree-of-freedom vehicle dynamic model. This allows the lower-layer MPC to consider lateral stability and roll stability during trajectory tracking. In collision avoidance trajectory tracking experiments using three scenarios, compared to two advanced time-based algorithms, the trajectories planned by the proposed algorithm in this paper exhibited predictability. The proposed algorithm can initiate collision avoidance at predetermined positions and can avoid collisions in predetermined directions, with all state variables within safe ranges. In terms of time efficiency, it also outperformed the comparative algorithms. [ABSTRACT FROM AUTHOR]

This work investigates commercial vehicle platoon predictive cruise control for highways. We propose a cloud-based platoon predictive cruise control method (CPPCC). A two-layered control architecture of the CPPCC is proposed as a platoon predictive cruise speed planning layer in the cloud and a platoon stabilization control layer. The CPPCC communication topology is proposed to achieve coupled control of the hierarchical architecture. The speed planning layer is a dynamic planning (DP) algorithm based on road slope in the rolling distance domain. The lower layer is a stability control algorithm to meet the stability requirements of vehicle platoon driving; the vehicle side is distributed model predictive control (DMPC). The CPPCC is validated by real road and vehicle data models, and comparative experiments with the traditional predecessor-leader following–cruise control (PLF-CC) platoon and predecessor following–cruise control (PF-CC) platoon. The speed error of the vehicle platoon was maintained at [−0.25, 0.30] (m=s) and the space error at [−0.13, 0.66] (m) in platoon stability. Against the comparison method, the CPPCC saved fuel by over 5.13% and achieved an overall operational efficiency improvement of 5.71%. [ABSTRACT FROM AUTHOR]

Precise trajectory prediction is pivotal for autonomous vehicles operating in real-world traffic conditions, and can help them make the right decisions to ensure safety on the road. However, state-of-the-art approaches consider limited information about the historical movements of vehicles. On highways, drivers make their next judgments according to the behavior of the ambient vehicles. Thus, vehicles need to consider temporal and spatial interactions to reduce the risk of future collisions. In the current work, a trajectory prediction method is put forward in accordance with a graph attention mechanism. We add the absolute and relative motion information of vehicles to the input of the model to describe the vehicles' past motion states more accurately. LSTM models are employed to process the historical motion information of vehicles, as well as the temporal correlations in interactions. The graph attention mechanism is applied to capture the spatial correlations between vehicles. Utilizing a decoder rooted in an LSTM framework, the future trajectory distribution is generated. Evaluation on the NGSIM US-101 and I-80 datasets substantiates the superiority of our approach over existing state-of-the-art algorithms. Moreover, the predictions of our model are analyzed. [ABSTRACT FROM AUTHOR]

Despite its excellent performance in path tracking control, the model predictive control (MPC) is limited by computational complexity in practical applications. The neural network control (NNC) is another attractive solution by learning the historical driving data to approximate optimal control law, but a concern is that the NNC lacks security guarantees when encountering new scenarios that it has never been trained on. Inspired by the prediction process of MPC, the deviation sequence neural network control (DS-NNC) separates the vehicle dynamic model from the approximation process and rebuilds the input of the neural network (NN). Taking full use of the deviation sequence architecture and the real-time vehicle dynamic model, the DS-NNC is expected to enhance the adaptability and the training efficiency of NN. Finally, the effectiveness of the proposed controller is verified through simulations in Matlab/Simulink. The simulation results indicate that the proposed path tracking NN controller possesses adaptability and learning capabilities, enabling it to generate optimal control variables within a shorter computation time and handle variations in vehicle models and driving scenarios. [ABSTRACT FROM AUTHOR]

In this paper, we compared the linear and nonlinear motion prediction models of a long combination vehicle (LCV). We designed a nonlinear model predictive control (NMPC) for trajectory-following and off-tracking minimisation of the LCV. The used prediction model allowed coupled longitudinal and lateral dynamics together with the possibility of a combined steering, propulsion and braking control of those vehicles in long prediction horizons and in all ranges of forward velocity. For LCVs where the vehicle model is highly nonlinear, we showed that the control actions calculated by a linear time-varying model predictive control (LTV-MPC) are relatively close to those obtained by the NMPC if the guess linearisation trajectory is sufficiently close to the nonlinear solution, in contrast to linearising for specific operating conditions that limit the generality of the designed function. We discussed how those guess trajectories can be obtained allowing off-line fixed time-varying model linearisation that is beneficial for real-time implementation of MPC in LCVs with long prediction horizons. The long prediction horizons are necessary for motion planning and trajectory-following of LCVs to maintain stability and tracking quality, e.g. by optimally reducing the speed prior to reaching a curve, and by generating control actions within the actuators limits. [ABSTRACT FROM AUTHOR]

The control goal of the vehicle platoon is to maintain the same speed and desired distance. Most current studies are based on simplified vehicle models, and the leader's state is also rarely considered. However, under complex working conditions, such as low adhesion or curves, the lateral stability of the platoon will be difficult to guarantee, and tracking errors of desired speed and spacing may further increase. To solve the above problems, a new hierarchical coordinated control strategy is proposed. Taking distributed drive electric vehicles (DDEVs) as research objects, the upper control level establishes a stability situation assessment model according to the vehicle's dynamic characteristics. At the medium control level, variable weight model predictive control (MPC) coordinates conflicts between longitudinal tracking and lateral stability. A correction term is also introduced to revise the prediction model. At the same time, the weight of different control objectives of the leader and following vehicle was adjusted, respectively. Torque distribution is carried out at the lower level controller. Finally, the control strategy is tested on a hardware-in-the-loop (HIL) platform. The results show that the proposed control strategy can ensure lateral stability while improving the tracking performance of the vehicle platoon. [ABSTRACT FROM AUTHOR]

This study demonstrates the applicability of an explicit model predictive controller (ExMPC) to the air mass flow control in a fuel cell electric vehicle. The two control tasks comprise the supply of air to the fuel cell stack for the electrochemical reaction as well as the dilution of hydrogen in the outlet gas flow of the cathode subsystem. With the aim of achieving zero control deviation for steady states, the linear model in the state observer is extended with two perturbations. During the experimental validation of the proposed controller, fast dynamics are achieved with the ExMPC, which generally exploits the limits of the control variables. After the calibration of the ExMPC, the final comparison to a set of reference PI controllers reveals a significant improvement in the closed-loop behavior of the cathode subsystem during step changes in the controller setpoints. • An explicit model predictive controller is applied to a fuel cell electric vehicle. • Delays for control and controlled variables are considered. • The operating limits of the control variables are exploited during dynamic operation. • Improved closed-loop behavior is achieved in comparison to reference PI controllers. [ABSTRACT FROM AUTHOR]

Aiming to promote the control performance of traditional fixed-parameter model predictive control (MPC), a new longitudinal and lateral coupling path tracking control method for the autonomous vehicle is proposed. First, a dynamic model of the autonomous vehicle, which can describe the longitudinal and lateral motion states, is established. Secondly, both the tire side-slip angle and centroid side-slip angle constraints are introduced in the path tracking controller design, which improves the accuracy of the prediction model at high speed. Furthermore, the key parameters of the proposed path tracking controller are optimized. The simulation and experimental results show that compared with the traditional MPC tracking control method, the lateral error and directional error of the proposed optimized path tracking control method under high speed decrease by 19.17 % and 11.89 %, respectively, which improves the path tracking performance significantly. [ABSTRACT FROM AUTHOR]

Advanced driving algorithms, control strategies, and their optimization in self-driving vehicles in various scenarios are hotspots in current research; 4WS-4WD (four-wheel steering and four-wheel drive) is another hotspot in the study of new concept models; and the nonlinear dynamic characteristics of self-driving vehicles (AVs) are prominent in the fast cornering mode, which leads to a significant reduction in the accuracy and stability of trajectory tracking. Based on these research backgrounds, this paper proposes a control strategy optimization idea based on the 4WS4WD vehicle and its optimization model. The main content includes the establishment of a 3D vehicle model that takes into account vehicle load transfer and position change, and the establishment of a hierarchical control strategy based on the optimized NMPC and 4WS4WD models. The controller consists of two parts: an upper tracking controller based on the new vehicle model and NMPC, and a lower decoupled controller. The tracking control effect of the algorithmic control strategy based on the model and controller is validated in the high-speed serpentine motion mode and double-shift linear motion mode on the joint simulation platform of Car Sim and Simulink. [ABSTRACT FROM AUTHOR]

The present work deals with the design of a traction system for a hybrid vehicle using a PMSM-type motor, including an energy regeneration stage, using a Neutral Point Piloted (NPP) inverter. Optimal operation allows the motor to be supplied in the traction stage with the correct energy in such a way that it operates properly; on the other hand, during braking, it facilitates the transfer of energy in regenerative mode to the DC bus, storing the greatest amount of energy in the storage elements, which, in this case, refers to a battery bank. It also includes a DC-DC bidirectional boost interleaved converter to regulate the DC voltage levels both in traction and in braking. Its fundamental characteristic is that with a reduced number of switching devices, it allows for the reduction in DC voltage of the DC bus with adequate characteristics, constant and stable, regardless of abrupt changes in the output voltage reference value. Also, other features include oscillation control, that is, reducing or increasing oscillations according to operating conditions. Its transient operation is excellent, since the settling time is considerably reduced, which implies that the voltage of the DC bus does not cause mishaps in the operation of the motor. In its operation as a boost converter, in the event of any voltage value of the DC bus, the converter raises the voltage value according to the reference conditions. Similarly, control allows the voltage to be held at a stable value regardless of changes in the set point. This work presents a novel energy management scheme for permanent magnet electric drive based on a Model Predictive Control strategy, thus contributing to the effective energy management of a standard hybrid electric vehicle driving cycle. Results obtained using a real-time Hardware-in-the-Loop (HIL) platform showed the effectiveness of Model Predictive Control in dealing with power flow management while ensuring control of electric drive in all the required speed–torque profiles. [ABSTRACT FROM AUTHOR]

A pre-emptive braking control method is proposed to improve the stability of autonomous vehicles during emergency collision avoidance, aiming to imitate the realistic human driving experience. A linear model predictive control is used to derive the front wheel steering angle to track a predefined fifth-degree polynomial trajectory. Based on a two-degrees-of-freedom (DOF) vehicle dynamics model, the maximum stable vehicle speed during collision avoidance can be determined. If the actual vehicle speed exceeds the maximum stable vehicle speed, braking action will be applied to the vehicle. Furthermore, four-wheel steering (4WS) control and direct yaw moment control (DYC) are employed to further improve the stability of the vehicle during collision avoidance. Simulation results under a double lane change scenario demonstrate that the control system incorporating pre-emptive braking, 4WS, and DYC can enhance the vehicle stability effectively during collision avoidance. Compared to the 2WS system without pre-emptive braking control, the maximum stable vehicle speed of the integrated control system can be increased by at least 56.9%. The proposed integrated control strategy has a positive impact on the safety of autonomous vehicles, and it can also provide reference for the research and development of autonomous driving systems. [ABSTRACT FROM AUTHOR]

In recent years, with the rapid growth in the number of electric vehicles (EVs), the large-scale grid connection of EVs has had a profound impact on the power grid. As a flexible energy storage resource, EVs can participate in auxiliary services of the power grid via vehicle-to-grid (V2G) technology. Due to the uncertainty of EVs accessing the grid, it is difficult to accurately control their charging and charging behaviors at both the day-ahead and real-time stages. Aiming at this problem, this paper proposes a two-stage scheduling strategy framework for EVs. In the presented framework, according to historical driving data, a day-ahead scheduling model based on distributionally robust optimization (DRO) is first established to determine the total power plan. In the real-time scheduling stage, a real-time scheduling model based on model predictive control (MPC) is established to track the day-ahead power plan. It can reduce the impact of EVs' uncertainties. This strategy can ensure the charging demand of users is under the control of the charging and discharging behaviors of EVs, which can improve the accuracy of controlling EVs. The case study shows that the scheduling strategy can achieve accurate and fast control of charging and discharging. At the same time, it can effectively contribute to the security and stability of grid operations. [ABSTRACT FROM AUTHOR]

The high construction and operation costs of urban rail transit have prompted scholars to explore new urban rail transit systems and operating modes. Based on the new straddle-type rapid transit system proposed by our team, the collaborative control of intelligent formation vehicles is discussed in this paper. The mathematical formulation of straddle-type rapid transit vehicle (SRTV) operating in intelligent formation operation mode (IFOM) was first constructed, and then the formation vehicle safety protection strategy based on limited speed difference (LSD) was analyzed. Subsequently, based on the Model Predictive Control (MPC) algorithm, a formation collaborative operation controller was established for IFOM operation. Based on the typical operating scenarios of formation vehicles exiting, cruising, and entering the station, formation performance evaluation indicators were adopted, with a focus on optimizing the time for formation vehicles to exit and enter the station. The simulation results show that the formation operates well and the vehicles in the formation can safely and stably travel in formation, proving that the use of LSD speed limitation strategy and MPC formation controller can ensure the safety and stability of SRTV vehicle formation. [ABSTRACT FROM AUTHOR]

This paper proposes a control strategy for active lateral secondary suspension that uses preview data. Based on a derived analytical model, a model predictive controller (MPC) is implemented. The influence of the track irregularities upon carbody lateral dynamics is considered explicitly. The controller developed is applied to a full-scale rail vehicle model. Ride comfort is evaluated according to EN 12299. Multibody simulations show that there is a significant increase in continuous ride comfort on poor-quality tracks. [ABSTRACT FROM AUTHOR]

Trajectory prediction is an essential element for driving autonomous vehicles, and various trajectory prediction models have emerged with the development of deep learning technology. Convolutional neural network (CNN) is the most commonly used neural network architecture for extracting the features of visual images, and the latest models exhibit high performances. This study was conducted to identify an efficient CNN backbone model among the components of deep learning models for trajectory prediction. We changed the existing CNN backbone network of multiple-trajectory prediction models used as feature extractors to various state-of-the-art CNN models. The experiment was conducted using nuScenes, which is a dataset used for the development of autonomous vehicles. The results of each model were compared using frequently used evaluation metrics for trajectory prediction. Analyzing the impact of the backbone can improve the performance of the trajectory prediction task. Investigating the influence of the backbone on multiple deep learning models can be a future challenge. [ABSTRACT FROM AUTHOR]

Accurate control of front wheel angle is essential in intelligent vehicle electric‐hydraulic power steering (EHPS) system, because it has a significant impact on the safety, comfort, and reliability of vehicle steering. However, the accurate control of front wheel angle is a challenging problem due to the non‐linearity of hydraulic power in steering system and the uncertainty of steering resistance. In this paper, a model predictive control (MPC) method for front wheel angle based on steering resistance estimation is proposed. Firstly, through theoretical analysis and practical experiment of EHPS system model, the relationship between front wheel angle and steering wheel angle and the non‐linear characteristics of hydraulic power are obtained. Secondly, a sliding mode observer is established to estimate the front axle lateral tyre force and steering resistance, which is verified by MATLAB/TruckSim co‐simulation. Then a controller based on the MPC theory is designed and applied to the accurate control unit of front wheel angle in the EHPS system for real‐time control. Finally, the simulation and test platform results show that the control algorithm proposed in this paper can precisely control the vehicle's front wheel turning angle within a certain frequency range. [ABSTRACT FROM AUTHOR]

Braking control of urban rail vehicles with multiple carriages is critical to ensure the safe operation of urban rails. However, existing decentralized braking control methods lead to the inconsistent speed and excessive coupler force among carriages, which compromises the operation safety of urban rails. To address this issue, a cooperative braking strategy is proposed for urban rail vehicles based on Koopman model predictive control method. First, a cyber‐physical model is established, where the physical layer characterizes the dynamic model of multiple carriages using the Koopman operator. The cyber layer represents the communication topology of carriages with graph theory. Second, a cooperative braking controller is designed with the distributed model predictive control method. An optimisation problem is formulated to minimize the speed inconsistency and relative displacement difference among carriages. Third, extensive simulations under various scenarios, including normal and extreme operating conditions, are conducted to verify the effectiveness of the proposed method. Simulation results show that, compared with the classical decentralized method, the proposed braking method reduces the average relative displacement by 78% while reducing the speed error by 29%. [ABSTRACT FROM AUTHOR]

In this paper, the authors investigate the issue of constructing and incorporating an accurate vehicle dynamic model for model predictive control (MPC) with an application to autonomous vehicle path following. To achieve the desired performance, MPC employs a precise dynamic model. However, the uncertainty of complex systems and their operating environments presents a challenge to the development of an adequately accurate vehicle dynamic model. This paper proposes a Deep Gaussian Process Regression (DGPR) method to improve model precision. Meanwhile, the learning model is incorporated into a novel MPC framework to enhance closed‐loop performance. High‐fidelity simulations using CarSim‐MATLAB demonstrate the validity of the proposed approach in terms of enhancing the path following performance and lateral stability under the condition of large curvature at medium to high speeds on roads with different friction coefficients when compared to the nominal MPC approach. [ABSTRACT FROM AUTHOR]

Fuel-saving-oriented collaborative driving is a highly promising yet challenging endeavor that requires satisfying the driver's operational intentions while surpassing the driver's fuel-saving performance. In light of this challenge, the paper introduces an innovative collaborative driving strategy tailored to the objective of fuel conservation in the context of commercial vehicles. An enhancement to this strategy involves the development of a network prediction model for vehicle speed, leveraging insights from driver style recognition. Employing the predicted speed as a reference, a model-predictive-control-based optimal controller is designed to track the reference while optimizing fuel consumption. Furthermore, a straightforward yet effective collaborative rule is proposed to ensure alignment with the driver's intention. Subsequently, the proposed control scheme is validated through simulation and real-world driving data, revealing that the human–machine cooperative driving controller saves 4% more fuel than human drivers. [ABSTRACT FROM AUTHOR]

When an intelligent vehicle changes lanes, the state of other vehicles may change, which increases the risk of collision. Therefore, real-time local path replanning is needed at this time. Based on model predictive control (MPC), a lane-change trajectory replanning strategy was proposed, which was divided into a lane-change trajectory correction strategy, a lane-change switchback strategy and forward active collision avoidance strategy according to collision risk. Based on the collision risk function of the rectangular safety neighborhood, the objective functions were designed according to the specific requirements of different strategies. The vehicle lateral controller based on MPC and the vehicle longitudinal motion controller were established. The longitudinal velocity was taken as the joint point to establish the lateral and longitudinal integrated controller. The trajectory planning module, trajectory replanning module and trajectory tracking module were integrated in layers, and the three trajectory replanning strategies of lane-change trajectory correction, lane-change switchback and forward active collision avoidance were respectively simulated and verified. The simulation results showed the trajectory replanning strategy achieves collision avoidance under different scenarios and ensures the vehicle's driving stability. The trajectory tracking layer achieves accurate tracking of the conventional lane-change trajectory and has good driving stability and comfort. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of path following and dynamic obstacle avoidance for an underwater biomimetic vehicle-manipulator system (UBVMS). Firstly, the general kinematic and dynamic models of underwater vehicles are presented; then, a nonlinear model predictive control (NMPC) scheme is employed to track a reference path and collision avoidance simultaneously. Moreover, to minimize the tracking error and for a higher degree of robustness, improved extended state observers are used to estimate model uncertainties and disturbances to be fed into the NMPC prediction model. On top of this, the proposed method also considers the uncertainty of the state estimator, while combining a priori estimation of the Kalman filter to reasonably predict the position of dynamic obstacles during short periods. Finally, simulations and experimental results are carried out to assess the validity of the proposed method in case of disturbances and constraints. [ABSTRACT FROM AUTHOR]

In this paper, a model predictive control (MPC) approach for controlling automated vehicle steering during path tracking is presented. A (linear parameter-varying) LPV vehicle plant model including steering dynamics is proposed to determine the system evolution matrices. The steering dynamics are modeled in two different ways by using first-order lag and a second-order lag; the application of the first-order system resulted in a slightly more accurate path-following. Additionally, a cascade MPC structure is applied in which two MPCs are used; the second-order steering dynamics are separated from the path-following controller in a second MPC. Both steering system models and the cascade MPC are evaluated in simulation and on a test vehicle. The reference trajectory is calculated based on a fixed predefined path by transforming the necessary path segment to the vehicle ego coordinate system, thereby describing the reference for the path-following task in a novel way. The MPC method computes the optimal steering angle vector at each time step for following the path. The longitudinal dynamics is controlled separately by a PI controller. After simulation evaluation, experimental tests were conducted on a test vehicle on an asphalt surface. Both simulation and experimental results prove the effectiveness of the proposed reference definition method. The effect of the applied steering system models is evaluated. The inclusion of the steering dynamics in the prediction model resulted in a significant increase in controller performance. Finally, the computational requirements of the proposed control and modeling methods are also discussed. [ABSTRACT FROM AUTHOR]

This paper proposes a novel prioritisation model predictive control for improving the handling and stability of all-wheel-drive vehicles configured with an electric motor and an open differential per axle with differential braking capability. The configuration of the powertrain provides a significant handling improvement by optimising front/rear torque distribution. The controller gives a high priority to front/rear torque distribution and, if needed, activates the differential braking. A coupled force prediction model of the vehicle handling dynamics is developed to capture the interaction of longitudinal and lateral tyre forces. Since differential braking causes speed drop, energy waste, and noise, two control actuations are prioritised: (1) front/rear torque shifting, (2) differential braking. Appropriate stability constraints are defined for the vehicle yaw rate and sideslip, dividing the sideslip-yaw rate phase plane into three regions – stable, marginal, unstable – based on which three control objectives are defined. Then, the priorities of the control actuations and the control objectives are combined, and a model predictive control structure is developed for this multi-objective multi-actuation control problem. The predictive controller prioritises the objectives and actuations through the prediction horizon. The performance of the proposed controller is thoroughly evaluated through numerical and full vehicle experimental studies. [ABSTRACT FROM AUTHOR]

In order to improve the path following performance and solve the stability problems of autonomous distributed drive electric buses, this paper proposed a path following and lateral-yaw-roll stability integrated control method. Firstly, to cope with the varying speed during different maneuvers and roll motion of autonomous distributed drive electric buses, a nonlinear 4-DOF vehicle dynamic model which includes rolling and longitudinal movement is established. Secondly, a hierarchical control method is built, in the upper layer, a model predictive controller (MPC) is designed to generate the control inputs, which is obtained through an optimization problem including path following error and vehicle stability condition, especially the roll stability for buses, and for the lower layer, the torque of each drive wheel is distributed through solving an optimization problem about drive requirement and tire utilization. Finally, the validation of the proposed method is carried out through TruckSim-Simulink co-simulation, the results illustrate that both following accuracy and vehicle stability are obtained more effectively than the popular linear MPC method, especially roll stability is guaranteed under extreme condition. [ABSTRACT FROM AUTHOR]

The paper presents a novel charging load prediction model for electric vehicles that takes into account traffic conditions and ambient temperature, which are often overlooked in conventional EV load prediction models. Additionally, the paper investigates the impact of disordered charging on distribution networks. Firstly, the paper creates a traffic road network topology and speed-flow model to accurately simulate the driving status of EVs on real road networks. Next, we calculate the electric vehicle power consumption per unit kilometer by considering the effects of temperature and vehicle speed on electricity consumption. Then, we combine the vehicle's main parameters to create a single electric vehicle charging model, use the Monte Carlo method to simulate electric vehicle travel behavior and charging, and obtain the spatial and temporal distribution of total charging load. Finally, the actual traffic road network and typical distribution network in northern China are used to analyze charging load forecast estimates for each typical functional area under real vehicle–road circumstances. The results show that the charging load demand in different areas has obvious spatial and temporal distribution characteristics and differences, and traffic conditions and temperature factors have a significant impact on electric vehicle charging load. [ABSTRACT FROM AUTHOR]

In this paper, we present the adaptation of the terminal component learning-based model predictive control (TC-LMPC) architecture for autonomous racing to the Formula Student Driverless (FSD) context. We test the TC-LMPC architecture, a reference-free controller that is able to learn from previous iterations by building an appropriate terminal safe set and terminal cost from collected trajectories and input sequences, in a vehicle simulator dedicated to the FSD competition. One major problem in autonomous racing is the difficulty in obtaining accurate highly nonlinear vehicle models that cover the entire performance envelope. This is more severe as the controller pushes for incrementally more aggressive behavior. To address this problem, we use offline and online measurements and machine learning (ML) techniques for the online adaptation of the vehicle model. We test two sparse Gaussian process regression (GPR) approximations for model learning. The novelty in the model learning segment is the use of a selection method for the initial training dataset that maximizes the information gain criterion. The TC-LMPC with model learning achieves a 5.9 s reduction (3%) in the total 10-lap FSD race time. [ABSTRACT FROM AUTHOR]

In this paper, we present a study of the license plate recognition (LPR) system for Taiwanese vehicles using a cascade of You Only Look Once version 4 (YOLOv4) detectors. The LPR system is composed of a vehicle detection model, a license plate (LP) detection model, an LP corner prediction model, and an LPR model. Herein, the pretrained YOLOv4 model was directly applied to vehicle detection. The YOLOv4 framework was adopted in the LP detection and LP recognition models, performing transfer learning on each model. Furthermore, to enhance the accuracy of the LPR system, an LP corner prediction model was developed in this study to predict the four corner positions of an LP to perform a perspective transformation on the plate for alignment purposes. The experimental results show that our LPR system achieves an accuracy of 98.88% when tested on 2049 images of the application-oriented LP dataset, outperforming most LPR systems reported in the literature. [ABSTRACT FROM AUTHOR]

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Efficient path tracking plays a key role in the overall ride experience of Autonomous Vehicles (AVs). Model Predictive Control (MPC) is one of the most competent techniques which is capable of handling multiple variables and constraints. The performance of this controller heavily relies on the proper choice of the vehicle model used to predict future states. This work proposes a novel MPC framework for the effective adoption of vehicle models to achieve a compromise between MPC's performance and computational cost. To this aim, a Switched MPC (SMPC) using vehicle models with different levels of complexity and fidelity is developed. The SMPC uses a novel supervision scheme to adopt the appropriate vehicle model based on the models' prediction error and MPC's solution time. Two different configurations of SMPC are implemented: (1) A Fuzzy Logic System (FLS), and (2) An adaptive switching supervisor. The outcomes show that both approaches can perform path tracking accurately on roads with varying curvatures for different AV speeds. Moreover, comparisons with the conventional MPCs show that the proposed controllers can perform comparably with selective use of a complex vehicle model. Notably, they benefit from a higher computation efficiency from sidestepping the most complex models during path tracking. [ABSTRACT FROM AUTHOR]

Wheel odometry is a simple and low-cost localization technique that can be used for localization in GNSS-deprived environments; however, its measurement accuracy is affected by many factors, such as wheel slip, wear, and tire pressure changes, resulting in unpredictable and variable errors, which in turn affect positioning performance. To improve the localization performance of wheel odometry, this study developed a wheel odometry error prediction model based on a transformer neural network to learn the measurement uncertainty of wheel odometry and accurately predict the odometry error. Driving condition characteristics including features describing road types, road conditions, and vehicle driving operations were considered, and models both with and without driving condition characteristics were compared and analyzed. Tests were performed on a public dataset and an experimental vehicle. The experimental results demonstrate that the proposed model can predict the odometry error with higher accuracy, stability, and reliability than the LSTM and WhONet models under multiple challenging and longer GNSS outage driving conditions. At the same time, the transformer model's overall performance can be improved in longer GNSS outage driving conditions by considering the driving condition characteristics. Tests on the experimental vehicle demonstrate the model's generalization capability and the improved positioning performance of dead reckoning when using the proposed model. This study explored the possibility of applying a transformer model to wheel odometry and provides a new solution for using deep learning in localization. [ABSTRACT FROM AUTHOR]

This paper proposes a double-layer model predictive control (MPC) algorithm for the integrated path planning and trajectory tracking of autonomous vehicles on roads. The upper module is responsible for generating collision-free lane trajectories, while the lower module is responsible for tracking this trajectory. A simplified vehicle model based on the friction cone is proposed to reduce the computation time for trajectory planning in the upper layer module. To achieve dynamic and accurate collision avoidance, a polygonal distance-based dynamic obstacle avoidance method is proposed. A vertical load calculation method for the tires is introduced to design the anti-rollover constraint in the lower layer module. Numerical simulations, with static and dynamic obstacle scenarios, are conducted on the MATLAB platform and compared with two state-of-the-art MPC algorithms. The results demonstrate that the proposed algorithm outperforms the other two algorithms regarding computation time and collision avoidance efficiency. [ABSTRACT FROM AUTHOR]

In order to balance the performance index and computational efficiency of the active suspension control system, this paper offers a fast distributed model predictive control (DMPC) method based on multi-agents for the active suspension system. Firstly, a seven-degrees-of-freedom model of the vehicle is created. This study establishes a reduced-dimension vehicle model based on graph theory in accordance with its network topology and mutual coupling constraints. Then, for engineering applications, a multi-agent-based distributed model predictive control method of an active suspension system is presented. The partial differential equation of rolling optimization is solved by a radical basis function (RBF) neural network. It improves the computational efficiency of the algorithm on the premise of satisfying multi-objective optimization. Finally, the joint simulation of CarSim and Matlab/Simulink shows that the control system can greatly minimize the vertical acceleration, pitch acceleration, and roll acceleration of the vehicle body. In particular, under the steering condition, it can take into account the safety, comfort, and handling stability of the vehicle at the same time. [ABSTRACT FROM AUTHOR]

• Dynamic model of the tracked vehicle considered this type of forces which is traction, hydrodynamic, centrifugal, lateral friction, longitudinal resistive, bulldozing, and compaction force. • Path tracking is vital in autonomous vehicles since it is designed to deliver appropriate steering, throttle, and brake input to control the vehicle's direction and speed along a predefined path. • Model Predictive Control (MPC) may handle system limits and future predictions. • The objective of a path tracking controller is to eliminate any variation in vehicle direction from the specified path. This paper provides a review of path tracking strategies used in autonomous vehicle control design. Several elements of modelling process and path tracking control are examined, including the vehicle model implemented, the path tracking control algorithms used, and the criteria for evaluating the controller's performance. Path tracking control is classified into several forms based on its methodology and linearity. Vehicle models are grouped into numerous types based on the linearity and the intended behaviour to be observed. This study explores each of these strategy in terms of the applicability and disadvantages/advantages. The main challenges in the field of path tracking control are defined and future research directions are offered based on the critical reviews. Based on the entire review, a model-based controller based on a linear vehicle model and assessed with hardware-in-the-loop (HIL) is suggested. This review is aimed to serve as a starting point for determining which controllers to use in path tracking control development for an autonomous tracked vehicle. [ABSTRACT FROM AUTHOR]

This study proposes an approach to the yaw and lateral stability of the vehicle that considers their steady-state trend. This study designs a new linear time-varying model predictive control (LTV-MPC) method based on the 2DOF vehicle model. The tire model curve of the magic formula is simplified into two straight lines to replace the nonlinear characteristic of tires and reduce the solution time of the controller under the premise of a good tracking effect. The lateral stiffness of tires is selected according to the predicted trend of stable state of the vehicle. Simulation results show that, under extreme conditions, the proposed method can effectively track the reference vehicle sideslip angle and yaw rate. The real-time performance of the controller is substantially improved compared with NMPC. [ABSTRACT FROM AUTHOR]

This paper addresses the on-line minimum-time motion planning and control of a black-box racing vehicle model. We present a hierarchical control framework, composed of a high-level non-linear model predictive controller (NMPC) based on an advanced kineto-dynamical vehicle model, a low-level neural network to compute the inverse steering dynamics and a longitudinal controller for the low-level tracking of speed profiles. An off-line identification procedure, consisting of simulated manoeuvres, is defined to learn the high-level and low-level models. A closed-loop simulation is setup to control the black-box vehicle near the limits of handling along a racetrack. Simulation results are compared with the off-line solution of a minimum-time-optimal control problem. [ABSTRACT FROM AUTHOR]

Since the coupling relationship of excitation sources is complicated, meantime the motor torque changes quickly under the acceleration condition, the problem of torsional vibration is prominent. This paper studies an active damping control (ADC) strategy for a parallel hybrid electric vehicle (HEV) under acceleration condition. Primarily, a full-order dynamic model is built, and the corresponding motion equations are derived. Moreover, a controller design–oriented model is established based on model reduction algorithm. Furthermore, a method that considers time delay characteristics of actuator based on model predictive control (MPC) theory is proposed to solve the torsional vibration problem. The controller handles delay characteristics of an actuator by state-space reorganization method, and the optimal control sequence is obtained by solving the objective function. Finally, the controller is tested using Simulink simulation and hardware-in-loop simulation platform, which mainly includes the verification of model reduction and vibration damping effect. The results show that simplified third-order model has a good consistency with the original full-order model in time and frequency domain. Meanwhile, the designed controller has a considerable damping effect and ensures the comfort performance of the vehicle. This study provides an important reference for vibration control of the hybrid powertrain. [ABSTRACT FROM AUTHOR]