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This paper proposes a monitoring procedure based on characterizing state probability distributions estimated using particle filters. The work highlights what types of information can be obtained during state estimation and how the revealed information helps to solve fault diagnosis tasks. If a failure is present in the system, the output predicted by the model is inconsistent with the actual output, which affects the operation of the estimator. The heterogeneity of the probability distribution of states increases, and a large proportion of the particles lose their information content. The correlation structure of the posterior probability density can also be altered by failures. The proposed method uses various indicators that characterize the heterogeneity and correlation structure of the state distribution, as well as the consistency between model predictions and observed behavior, to identify the effects of failures.The applicability of the utilized measures is demonstrated through a dynamic vehicle model, where actuator and sensor failure scenarios are investigated. [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]

This work is focused on the simulation of a complete hybrid bus vehicle model performing a real-world driving cycle. The simulation framework consists of a coupled co-simulation environment, where all the vehicle sub-system models are linked to achieve a real time exchange of input and output signals. In the vehicle model also the electric devices of the powertrain and accumulation system are included. This co-simulation platform is applied to investigate the hybridization of a 12-m city bus, performing a typical urban driving mission. A comparison between the conventional powertrain is performed against the hybridized version, to highlight the advantages and challenges. In particular, the novelty of this modeling approach is that the IC engine simulation does not rely on pre-processed look-up tables, but exploits a high-fidelity one-dimensional thermo-fluid dynamic model. However, it was necessary to develop a fast simulation methodology to exploit this predictive tool, achieving a low computational cost. The 1D engine model is first validated against the experimental engine map data available, showing a good model predictivity. Then the 1D engine model and the other models of the powertrain are coupled to the vehicle model, in order to follow the prescribed velocity profile of the driving cycle. The complete model is applied under different conditions, to evaluate the impact on performance and emissions and assess the simulation predictivity. [ABSTRACT FROM AUTHOR]

In complex driving scenarios, automated vehicles should behave reasonably and respond adaptively with high computational efficiency. In this paper, a computational efficient motion planning method is proposed, which considers traffic interaction and accelerates calculation. Firstly, the behavior is decided by connecting the points on the unequally divided road segments and lane centerlines, which simplifies the decision-making process in both space and time span. Secondly, as the dynamic vehicle model with changeable longitudinal velocity is considered in the trajectory generation module, the C/GMRES algorithm is used to accelerate the calculation of trajectory generation and realize on-line solving in nonlinear model predictive control. Meanwhile, the motion of other traffic participants is more accurately predicted based on the driver's intention and kinematics vehicle model, which enables the host vehicle to obtain a more reasonable behavior and trajectory. The simulation results verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

A path following control strategy for a four-wheel-independent-drive electrical vehicle (4WID-EV) based on backstepping and model predictive control is presented, which can ensure the accuracy of path following and maintain vehicle stability simultaneously. Firstly, a 2-DOF vehicle dynamic model and a path following error model are built and the desired yaw rate is obtained through backstepping. Then, a model predictive controller is adopted to track the desired yaw rate and obtain the optimal front wheel steering and external yaw moment. Meanwhile, an optimal torque distribution algorithm is carried out to allocate it to each tire. Finally, the effectiveness and superiority of the strategy is validated via CarSim–Simulink joint simulation. Results show that the strategy has higher following accuracy, smaller sideslip angle, and better yaw rate tracking. [ABSTRACT FROM AUTHOR]

Local path planning is a key task for the motion planners of autonomous vehicles since it commands the vehicle across its environment while avoiding any obstacles. To perform this task, the local path planner generates a trajectory and a velocity profile, which are then sent to the vehicle's actuators. This paper proposes a new local path planner for autonomous vehicles based on the Attractor Dynamic Approach (ADA), which was inspired by the behavior of movement of living beings, along with an algorithm that takes into account four acceleration policies, the ST dynamic vehicle model, and several constraints regarding the comfort and security. The original functions that define the ADA were modified in order to adapt it to the non-holonomic vehicle's constraints and to improve its response when an impact scenario is detected. The present approach is validated in a well-known simulator for autonomous vehicles under three representative cases of study where the vehicle was capable of generating local paths that ensure the security of the vehicle in such cases. The results show that the approach proposed in this paper is a promising tool for the local path planning of autonomous vehicles since it is able to generate trajectories that are both safe and efficient. [ABSTRACT FROM AUTHOR]

Considering the actual situation of moving vehicles acting on road surfaces, a dynamic model of a heavy vehicle–road coupling system was established based on the traditional vehicle–road vibration model. Firstly, a seven-degree-of-freedom vehicle model was established, and the vibration characteristics of the road subsystem were considered part of the whole system. The excitation effect of road roughness on the vehicle model was considered, and the dynamic model of the coupling system was finally obtained by combining the displacement compatibility principle of the contact relationship between the wheels and the road surface. The results show that the maximum bending value of the surface course reaches 2.37 mm. The maximum shear stress in the middle part of the surface course is 43,858 Pa. The vertical dynamic stress in the middle part of the surface course is larger, reaching 119,373 Pa, while the value of the vertical dynamic stress in the subgrade is much smaller, reaching 5824 Pa. The coupling dynamic model can reflect the relationship between the moving vehicle and the road and the dynamic performance, which not only provides theoretical support for the design parameters of heavy-duty vehicles but also provides a reference for the design of road durability. [ABSTRACT FROM AUTHOR]

This study addresses the Hyperloop characterized by a capsule-type vehicle, superconducting electrodynamic suspension (SC-EDS) levitation, and driving in a near-vacuum tube. Because the Hyperloop is different from conventional transportation, various considerations are required in the vehicle-design stage. Particularly, pre-investigation of the vehicle dynamic characteristics is essential because of the close relationship among the vehicle design parameters, such as size, weight, and suspensions. Accordingly, a 1/10 scale Hyperloop vehicle system model, enabling the analysis of dynamic motions in the vertical and lateral directions, was developed. The reduced-scale model is composed of bogies operated by Stewart platforms, secondary suspension units, and a car body. To realize the bogie motion, an operation algorithm reflecting the external disturbance, SC-EDS levitation, and interaction between the bogie and car body, was applied to the Stewart platform. Flexible rubber springs were used in the secondary suspension unit to enable dynamic characteristic analysis of the vertical and lateral motion. Results of the verification tests were compared with simulation results to examine the fitness of the developed model. The results showed that the developed reduced-scale model could successfully represent the complete dynamic characteristics, owing to the enhanced precision of the Stewart platform and the secondary suspension allowing biaxial motions. [ABSTRACT FROM AUTHOR]