Driver characteristics have been the research focus for automotive control. Study on identification of driver characteristics is provided in this paper in terms of its relevant research directions and key technologies involved. This paper discusses the driver characteristics based on driver’s operation behavior, or the driver behavior characteristics. Following the presentation of the fundamental of the driver behavior characteristics, the key technologies of the driver behavior characteristics are reviewed in detail, including classification and identification methods of the driver behavior characteristics, experimental design and data acquisition, and model adaptation. Moreover, this paper discusses applications of the identification of the driver behavior characteristics which has been applied to the intelligent driver advisory system, the driver safety warning system, and the vehicle dynamics control system. At last, some ideas about the future work are concluded.

In this paper, a systematic design with multiple hierarchical layers is adopted in the integrated chassis controller for full drive-by-wire vehicles. A reference model and the optimal preview acceleration driver model are utilised in the driver control layer to describe and realise the driver's anticipation of the vehicle's handling characteristics, respectively. Both the sliding mode control and terminal sliding mode control techniques are employed in the vehicle motion control (MC) layer to determine the MC efforts such that better tracking performance can be attained. In the tyre force allocation layer, a polygonal simplification method is proposed to deal with the constraints of the tyre adhesive limits efficiently and effectively, whereby the load transfer due to both roll and pitch is also taken into account which directly affects the constraints. By calculating the motor torque and steering angle of each wheel in the executive layer, the total workload of four wheels is minimised during normal driving, whereas the MC efforts are maximised in extreme handling conditions. The proposed controller is validated through simulation to improve vehicle stability and handling performance in both openand closed-loop manoeuvres.

Model predictive control (MPC) is advantageous for designing an electrical vehicle path-tracking controller, but the high computational complexity, mathematical problem, and parameterization challenge adversely affect the control performance. Hence, based on a fully actuated-by-wire electrical vehicle (FAW-EV), a novel path-tracking controller based on improved MPC with a Laguerre function and exponential weight (LEMPC) is designed. The massive optimization control parameters of MPC with a long control horizon are reduced by introducing a fitting orthogonal sequence consisting of Laguerre functions, thereby substantially reducing the computational complexity without sacrificing the tracking accuracy. An exponential weight with decreasing characteristic is introduced to MPC to solve the mathematical problem, thereby improving the robustness of the path tracking controller. In addition, the parameterization access for online adjusting path tracking control performance can be provided by the proposed method. The path tracking motion realization for FAW-EV is subsequently illustrated. Finally, several simulations are implemented to verify the advantages of the proposed method. INDEX TERMS Path track, electrical vehicle, model predictive control (MPC), Laguerre function, exponential weight.

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Vehicle mass is a critical parameter for economic cruise control. With the development of active control, vehicle mass estimation in real-time situations is becoming notably important. Normal state estimators regard system error as white noise, but many sources of error, such as the accuracy of measured parameters, environment and vehicle motion state, cause system error to become colored noise. This paper presents a mass estimation method that considers system error as colored noise. The system error is considered an unknown parameter that must be estimated. The recursive least squares algorithm with two unknown parameters is used to estimate both vehicle mass and system error. The system error of longitudinal dynamics is analyzed in both qualitative and quantitative aspects. The road tests indicate that the percentage of mass error is 16%, and, if the system error is considered, the percentage of mass error is 7.2%. The precision of mass estimation improves by 8.8%. The accuracy and stability of mass estimation obviously improves with the consideration of system error. The proposed model can offer online mass estimation for intelligent vehicle, especially for heavy-duty vehicle (HDV).

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