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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]

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]

Road excitation is the basic input of vehicle vibration, which greatly impacts on vehicle ride comfort. In addition, it can cause vertical vibration of the vehicle, leading to deformation of the powertrain mount. Similarly, the longitudinal movement of the powertrain can also cause deformation of the powertrain mount. Due to the effect of the powertrain mount, there is a mutual coupling between the vehicle in the longitudinal and vertical directions, which has a great influence on the smoothness and comfort of the vehicle. In order to reveal the impact of random road excitation on the vehicle dynamic characteristics of a dual clutch transmission (DCT) vehicle under starting and shifting conditions, a longitudinal-vertical coupled dynamic model of the DCT vehicle considering the powertrain mount was established. This model includes dynamic torque model of the engine, powertrain mount model, transmission system model, tire model and road excitation model. The influence of random road excitation on the vehicle's dynamic characteristics is analyzed under starting, shifting, and different starting and shifting intentions. The simulation results of the longitudinal-vertical coupling dynamic DCT vehicle model considering road excitation are compared with vehicle test results, the model is found to accurately reflect the influence of road excitation on the dynamic performance of DCT vehicles under starting and shifting conditions, thus verifying the correctness of the model. [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]

Vehicle mass is an important parameter for motion control of intelligent vehicles, but is hard to directly measure using normal sensors. Therefore, accurate estimation of vehicle mass becomes crucial. In this paper, a vehicle mass estimation method based on fusion of machine learning and vehicle dynamic model is introduced. In machine learning method, a feedforward neural network (FFNN) is used to learn the relationship between vehicle mass and other state parameters, namely longitudinal speed and acceleration, driving or braking torque, and wheel angular speed. In dynamics-based method, recursive least square (RLS) with forgetting factor based on vehicle dynamic model is used to estimate the vehicle mass. According to the reliability of each method under different conditions, these two methods are fused using fuzzy logic. Simulation tests under New European Driving Cycle (NEDC) condition are carried out. The simulation results show that the estimation accuracy of the fusion method is around 97%, and that the fusion method performs better stability and robustness compared with each single method. [ABSTRACT FROM AUTHOR]

To improve the multidirectional motion control accuracy and driving stability of Distributed Drive Autonomous Vehicles (DDAVs) under extreme conditions, the extreme speed estimation method based on dynamic boundary and the multidirectional motion coupling control law design method based on multi degrees of freedom vehicle dynamic model are proposed. The extreme speed estimation method identifies the stable state of DDAVs by the dynamic boundary composed of yaw rate, sideslip angle and roll angle, and then estimates the extreme speed of the vehicle. The design method of multidirectional motion coupling control law adopts eight-degrees-of-freedom (8-DOF) vehicle dynamic model to design path tracking control law, speed tracking control law, yaw stability control law, and active suspension control law at the same time, so as to realize multidirectional motion coupling control. Based on the above method, the Multidirectional Motion Coupling Control System (MMCCS) of DDAVs is designed. The effectiveness of the proposed method is verified by double-line-shifting and serpentine driving simulations under different road adhesion conditions. The superiority of the method is proved by comparing the existing integrated control method. [ABSTRACT FROM AUTHOR]

An eight-degree-of-freedom vehicle dynamic model with electromechanical coupling was established for a four-wheel-independent-drive electric vehicle (4WID-EV). Based on the single-point preview optimal curvature theory, an adaptive fuzzy PID driver model with lateral acceleration feedback was designed, and the control of vehicle trajectory tracking was achieved in the driver-vehicle-road closed-loop coupling model. With the sideslip angle and yaw rate as control variables, the upper layer of a fuzzy sliding mode controller and the lower layer of an optimal distribution controller of the yaw moment were designed. The optimal longitudinal forces of four driving wheels were determined to achieve vehicle-handling stability. J-turn, fishhook, and snake-shaped pile simulations were carried out in MATLAB/Simulink. The results showed that the fuzzy sliding mode controller significantly improved the driving stability of the system, and it had a better anti-chattering ability than the sliding mode control strategy. The established lateral acceleration feedback adaptive fuzzy PID driver model had good trajectory tracking ability. Integration of the two controllers can better achieve both trajectory tracking and driving stability of a 4WID-EV. [ABSTRACT FROM AUTHOR]

A positioning system estimates the position and orientation of a vehicle. Autonomous driving systems plan paths and control the vehicle based on the information from the positioning system. Recently, methods of estimating the position in urban areas have been actively studied. In U-turn sections, which are common in urban areas, vehicles perform a rotation to reverse the direction of travel. Through these sections, drivers can reduce the travel distance and save time but with a high risk of an accident. Despite there being a need for the development of autonomous driving schemes for U-turn sections, the existing research is limited. This study proposes an interacting multiple model (IMM) filter-based position estimation algorithm for urban U-turn sections. To reflect the dynamic characteristics of a vehicle during U-turn maneuvers, a multiple vehicle model was used. This model includes kinematic and dynamic vehicle models. The state estimates of the vehicle model and gyroscope are combined using an IMM filter. The position estimation algorithm developed in this study is verified via experiments. The experimental results indicate that, during urban U-turn maneuvers, the position estimation accuracy of the IMM filter-based algorithm is improved than that of the single vehicle model. [ABSTRACT FROM AUTHOR]

Articulated tracked vehicles (ATVs) exhibit enhanced steerability but reduced yaw stability. Apart from the track-terrain contact, the dynamics of the articulated frame steering (AFS) system strongly influences the stability of ATVs. We developed a detailed hydraulic steering system and a yaw-plane ATVs model for vehicle stability research. The transient characteristic of track-ground contact was considered. The multibody dynamics model by Recurdyn/Track software was compared with the established model to verify the model correctness at time-domain and frequency-domain. The result suggests that the vehicle yaw stability can be evaluated in terms of the oscillation frequency, damping ratio, overshoot, and steady-state deviation of articulation angle response. A multi-objective optimization is proposed to obtain the optimized structural parameters of AFS system by employing response surface method (RSM) and non-dominated sorting genetic algorithm II (NSGA-II). The optimal solutions revealed that more compact mechanism design of AFS system contributes to a compromise stability performance with 4.44 % decrease in the oscillation frequency and 10.3 % decrease in the damping ratio. [ABSTRACT FROM AUTHOR]

Utmost models for vehicle steering detailed in the writing accept consistent travel times. Plainly, disregarding the way that the movement time between two areas does not depend just on the separation voyaged, yet on numerous different variables including time, sways the use of the models to genuine issues. In the present research, a multi-target dynamic vehicle-directing issue with fuzzy time series is displayed. In this issue, majority of the work where information is known ahead of time, some setoff ongoing solicitations arrive arbitrarily after some time and the dispatcher does not have any deterministic or probabilistic data on the area and size of them until they arrive. The manuscript utilizes an immediate understanding of the multi-target dynamic vehicle-directing issue with fuzzy time series as a multi-target issue where the required armada measure, generally all out voyaging separation, and hold-up time forced on vehicles are limited, and the general clients' inclinations for administration are boosted. The presentation of the proposed methodology is assessed in various strides on different test issues summed up from a lot of static occasions in the writing. In the initial step, the exhibition of the proposed methodologies is checked in static conditions and after that, different presumptions and improvements are included progressively, and changes are analyzed. Computational tests on informational collections represent the productivity and adequacy of the proposed methodology. [ABSTRACT FROM AUTHOR]

Recent research has developed experimentally verified dynamic models for skid-steered wheeled vehicles and used these results to derive a power model for this important class of all-terrain vehicles. As presented in this paper, based on the torque limitations of the vehicle motors, the dynamic model can be used to develop payload and terrain-dependent minimum turn radius constraints and the power model can be used to predict the energy consumption of a given trajectory. This paper uses these results along with sampling based model predictive optimization to develop an effective methodology for generating dynamically feasible, energy efficient trajectories for skid-steered autonomous ground vehicles (AGVs) and compares the resultant trajectories with those based on the standard distance optimal trajectories. The simulated and experimental results consider an AGV moving at a constant forward velocity on both wood and asphalt surfaces under various payloads. The results show that a small increase in the distance of a trajectory over the distance optimal trajectory can result in a dramatic savings in the AGV's energy consumption. They also show that distance optimal planning can often produce trajectories that violate the motor torque constraints for skid-steered AGVs, which can result in poor navigation performance. [ABSTRACT FROM AUTHOR]