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Since the large-scale application of fully autonomous vehicles is difficult to be commercialized in the short term, human-vehicle shared control (HVSC) is a promising technique. To implement the control authority allocation and observe the driver characteristic, it is essential to develop an efficient HVSC dynamic model with the driver's neuromuscular characteristic (NMS). To further our previous research, a simplified HVSC dynamic model is proposed in this paper. This model simplifies the non-critical NMS parameters such as muscle spindle feedback, which has no significant feedback effect while retaining essential NMS characteristics such as stretch reflection and intrinsic properties. The model consists of a model predictive controller (MPC) coupled with a driver NMS model and a 2 DOF vehicle model. The stability is proved by Lyapunov stability theory. Moreover, a field experiment was conducted for validation of the model. The V-Box is utilized to measure the vehicle's state signals, such as steering wheel angle and pedal stroke. Subsequently, the adaptive genetic algorithm (AGA) is employed to identify the model parameters based on the experimental results. The comparison between the experiment and the model output shows that the proposed model can accurately represent the driver's NMS characteristics and vehicle dynamic parameters. This paper will serve as a theoretical basis for the control authority allocation for L3 class autonomous vehicles. [ABSTRACT FROM AUTHOR]

A human-like trajectory could give a safe and comfortable feeling for the occupants in an autonomous vehicle especially in corners. The research of this paper focuses on planning a human-like trajectory along a section road on a test track using optimal control method that could reflect natural driving behaviour considering the sense of natural and comfortable for the passengers, which could improve the acceptability of driverless vehicles in the future. A mass point vehicle dynamic model is modelled in the curvilinear coordinate system, then an optimal trajectory is generated by using an optimal control method. The optimal control problem is formulated and then solved by using the Matlab tool GPOPS-II. Trials are carried out on a test track, and the tested data are collected and processed, then the trajectory data in different corners are obtained. Different TLCs calculations are derived and applied to different track sections. After that, the human driver's trajectories and the optimal line are compared to see the correlation using TLC methods. The results show that the optimal trajectory shows a similar trend with human's trajectories to some extent when driving through a corner although it is not so perfectly aligned with the tested trajectories, which could conform with people's driving intuition and improve the occupants' comfort when driving in a corner. This could improve the acceptability of AVs in the automotive market in the future. The driver tends to move to the outside of the lane gradually after passing the apex when driving in corners on the road with hard-lines on both sides. [ABSTRACT FROM AUTHOR]

The existing powertrain mounting models of the heavy-duty truck for optimizing the mounts parameters cannot well describe the deformation of the chassis frame. To overcome this disadvantage, a new full vehicle model is proposed which embraces the view of system modeling and includes the frame flexibility. The model can achieve optimal parameters of the Powertrain Mounting System for isolating the vibration transmitted from the powertrain to the chassis. A model reduction technique, improved reduced system, is used to obtain a reduced model of the frame to represent its original large-scale finite element analysis model for the accessibility of time-efficient solutions of the model. The reduced frame model is integrated with the powertrain mounting and suspension model to form the full dynamic model of the vehicle. The accuracy and effectiveness of the proposed model are evaluated by its original vehicle model built in software ADAMS and an existing rigid model with rigid foundation. A hybrid model optimization strategy is also presented to tune the dynamic parameters of the powertrain mounts using the developed coupling model. The simulation results show that the proposed coupling model has better representation of the dynamic characteristics of the real vehicle system, and the presented hybrid model optimization strategy can obtain better optimization results compared with the existing rigid model with rigid foundation. In addition, the application of the proposed model can also be extended to the vibration control and the structural fatigue prediction. [ABSTRACT FROM AUTHOR]

This article deals with the design of a partial preview active suspension control algorithm for the improvement of vehicle ride comfort. Generally, while preview-controlled active suspension systems have even greater potential than feedback-controlled systems, their main challenge is obtaining preview information of the road profile ahead. A critical drawback of the "look-ahead" sensors is an increased risk of incorrect detection influenced by water, snow, and other soft obstacles on the road. In this work, a feasible wheelbase preview suspension control algorithm without information about the road elevation has been developed based on a novel 3-degree-of-freedom full-car dynamic model which incorporates only the vehicle body dynamics. The main advantage of the employed vehicle model is that the system disturbance input vector consists of vertical wheel accelerations that can be measured easily. The measured acceleration information of the front wheels is used for predictive control of the rear suspension to stabilize the body motion. The suspension state estimator has also been designed to completely remove the effect of unknown road disturbance on the state estimation error. The estimation performance of an observer is verified via a simulation study and field tests. The performance of the proposed suspension controller is evaluated on a frequency domain and time domain via a simulation study. It is shown that the vehicle ride comfort can be improved more by the proposed wheelbase preview control approach than by the feedback approach. [ABSTRACT FROM AUTHOR]

The performance parameters of suspension systems must be properly matched to ensure the handling and stability performance of a vehicle. Based on real vehicle measured data, a parameterized vehicle dynamic model is built, and the validity of the parameterized vehicle dynamic model is verified by comparing simulation results with real vehicle test results. Seven representative steady-state and transient single evaluation indicators of handling and stability of the vehicle are selected. The key parameters of McPherson suspension system, which significantly affects steady-state and transient handling and stability performance, are selected through a sensitivity analysis. Their contribution rates for each single evaluation indicator are calculated based on 81 simulation tests using the parameterized vehicle dynamic model. A comprehensive evaluation indicator system for the whole vehicle is established. This system contains the seven steady-state and transient single handling and stability evaluation indicators that are obtained using a quadratic response surface fitting for the selected key parameters. The comprehensive evaluation indicator system is used to show whether a vehicle has good steady-state and desirable transient responses. Moreover, a generalized multi-dimension adaptive learning particle swarm optimization is proposed to search for the global optimum of the comprehensive evaluation indicator system across the search space with rapid convergence. Optimization results show that a comprehensive handling and stability performance are improved, and simulation results of the parameterized vehicle dynamic model that is modified in accordance with the optimization results verify the improvement of the steady-state steering driving behavior and transient yaw response of the vehicle. In conclusion, the comprehensive evaluation indicator system is feasible, and the generalized multi-dimension adaptive learning particle swarm optimization is effective for the optimization design of the key parameters of the McPherson suspension system. [ABSTRACT FROM AUTHOR]