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Reasonably foreseeable misuse by persons, as a primary aspect of safety of the intended functionality (SOTIF), has a significant effect on cooperation performance for lane keeping. This paper presents a novel human–machine cooperative control scheme with consideration of SOTIF issues caused by driver error. It is challenging to balance lane keeping performance and driving freedom when driver error occurs. A safety evaluation strategy is proposed for safety supervision, containing assessments of driver error and lane departure risk caused by driver error. A dynamic evaluation model of driver error is designed based on a typical driver model in the loop to deal with the uncertainty and variability of driver behavior. Additionally, an extension model is established for determining the cooperation domain. Then, an authority allocation strategy is proposed to generate a dynamic shared authority and achieve an adequate balance between lane keeping performance and driving freedom. Finally, a model predictive control (MPC)-based controller is designed for calculating optimal steering angle, and a steer-by-wheel (SBW) system is employed as an actuator. Numerical simulation tests are conducted on driver error scenarios based on the CarSim and MATLAB/Simulink software platforms. The simulation results demonstrate the effectiveness of the proposed method.

According to the feature that the torque of each wheel for the in-wheel motor drive electric vehicle is independently controllable, the left and right steering wheels’ driving force is changed to provide steering power, and the differential drive assisted steering is implemented. Considering the effect on vehicle stability by differential drive assisted steering system, the Carsim/Simulink was utilized to establish the model of the in-wheel motor drive electric vehicle, and a two-layer extension coordinated control system was designed for the stability of automotive differential drive assisted steering system. In the upper-layer controller, according to vehicle driving conditions, the extension coordination controller was established. The extension distance was computed in one-dimensional extension set instead of two-dimensional extension set, so as to solve the correlation function to determine the controllers’ output weights. In the lower-layer controller, the differential drive assisted steering controller was established based on steering wheel torque direct control strategy. Then the yaw moment controller was set up based on the switching of yaw rate control and sideslip angle control according to the different conditions of vehicle in the extension domain and non-domain. Next, the optimal drive torque of four wheels was distributed based on quadratic programming and three different constraint conditions were selected according to different domains. The Carsim and Matlab/Simulink were utilized to carry out the simulation under the double lane conditions with different road friction coefficient. The simulation results show that compared with differential drive assisted steering system working alone, when the road adhesion coefficient was 0.8, the control system can improve the road tracking ability that the lateral and longitudinal deviation respectively decrease by 50% and 30% , and the root mean square values of yawing angular velocity, the center of mass of side-slip angle and lateral acceleration were obviously decreased by 54.9%, 21.4% and 22.3% respectively; when the road adhesion coefficient was 0.4, the system can avoid vehicle’s instability and improve the driving safety.

Studying on online monitoring and fault diagnosis system for monitoring vibration, noise, temperature, torque, stress parameters of the driving axle's key parts has a very important practical significance. The system is composed of hardware and software, which are designed into several modules, including slow-speed data acquiring and saving module, fault diagnosis module, high-speed data acquiring module, database module, reliability prediction module, graphical output module. Combined with studies on fault mechanism of hypoid gears, the system can monitor the states of driving axle, and can also diagnoses different kind of faults of driving axle.

Based on the analysis of the shortcomings of traditional automotive dynamic models, a multi-rigid body model is built, the adaptive control strategy based on neural networks is put forward and the neural identifier and controller are designed in the paper. In order to improve the control effects, a compensating network is added to the control system. Simulation and experiment results show that adaptively controlled semi-active suspensions with nonlinear neural networks can achieve obvious vibration reducing effects.

The exist researches of the magneto-rheological semi-active suspension(MSAS) control mainly focus on the design of control laws,which aim at obtaining an optimal control strategy to improve the ride comfort and handling stability.In the controller design,the stability of the MSAS system cannot be confirmed owing to the control input time delay considered little.In this paper,a quarter vehicle MSAS model with time-delay is built.Therefore,through formulating the sprung mass acceleration suitably as the optimization object,suspension deflection and tyre dynamic load and coulomb damping force as the constraint objects,with considering the control input time-delay,a delay-dependent state feedback H2/H∞ controller is designed.According to Lyapunov-Krasovskii functional theory,the sufficient conditions for asymptotic stability and the existence of delay-dependent H2/H∞ controller are obtained,and the controller design is transformed into the minimization problem for linear function through linear matrix inequality(LMI).Random road excitation simulations and experiments are carried out.The simulation and experiment results show that the design can preserve the closed-loop stability and achieve the performances for MSAS system in spite of the existence of the control input time-delay.The present study can provide an important basis and method for research on time-delay problem in MSAS and other chassis subsystems..

The vision-guided intelligent vehicle acquires the road information by machine vision,and follows the road automatically. Ordinary feedback control only uses the present road information,so the intelligent vehicle can hardly work efficiently in complicated or emergent conditions.By previewing the on-line acquired road information,future navigation information can be utilized in order to improve safety and efficiency.So road information is classified into present information and future information, and they can be used in feedback and preview respectively.A preview-feedback controller is designed.The controller can automatically adjust the preview distance and the vehicle speed according to the previewed road,so tracking accuracy and driving stability can be improved.The simulation and experiments show that this controller has better accuracy and stability than the ordinary feedback controller.

A vehicle nonlinear lateral and vertical dynamics model is built under crosswind disturbance. With the L2-gain method of disturbance suppression, the nonlinear H_∞output feedback controller is designed for electrical power steering (EPS) and active suspension system (ASS) with considerations of random road input and strong crosswind disturbance. Simula- tions for driver-vehicle-road closed-loop system are carried out by using Matlab soft'ware. The results show that the designed controller can suppress the disturbances effectively and improve handling stability, handiness and ride characteristics of a vehicle.