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After obtaining the optimal trajectory through the lane change decision and trajectory planning, the last key technology for the automatic lane change assist system is to carry out the precise and rapid steering actuation according to the front wheel angle demand. Therefore, an automatic lane change system model including a BLDCM (brushless DC motor) model, a steering system model and a vehicle dynamics model is first established in this paper. Electromagnetic characteristics of the motor, the moment of the inertia and viscous friction etc. are considered in these models. Then, a SMC (Sliding Mode Control) algorithm for the steering system is designed to follow the steering angle input. The control torque of the steering motor is obtained through the system model according to steering angle demand. After that, the control current is calculated considering of electromagnetic characteristics of the BLDCM. Debugging and optimization of the control algorithm are done through simulations.

The automatic lane change assist system is an intelligent driving assistance technology oriented to traffic safety, which requires trajectory planning of the lane change maneuver based on the lane change decision. A typical scene of lane change for overtaking is selected, where the front vehicle in the same lane and the rear vehicle in the left lane are deemed to be potential dangerous vehicles through the lane change. Lane change trajectory equation is first established according to the general law of steering wheel angle through lane changes. Based on the relative position, velocity and acceleration information of the dangerous vehicles and the lane change vehicle, motions of these surrounding dangerous vehicles are predicted. At the same time, a multi-objective optimization function is established based on the relative longitudinal safety boundary. The objectives are the minimum safety distance, the lane change time and the front wheel angle.

Urethane is utilized to bond the windshield and backlite to the vehicle frame. The contribution of different modulii of glass bonding urethane adhesives on the stiffness characteristics of the vehicle is studied through finite element analysis. The modal analyses of a finite element body-in-white model with different modulii of urethanes are performed, and the analyses show that high modulus urethane makes notable contribution to the vehicle stiffness. The optimized modulus of urethane adhesive is suggested based on the analyses.

In this paper, the MDO analysis is applied to the design integration in terms of NVH and crash safety. Considering the difference of characteristics in these two fields, all the design variables are used to construct the response surfaces in the NVH analysis, while for crash safety analysis, these design variables are split into three groups, front, side and rear, to reduce the computational cost. Many of the structure performance indices in the NVH and crash safety fields are included as the constraints in the MDO analysis, which proves that the design integration of these two fields through MDO analysis is feasible. A comparison of crash safety optimization and crash safety-NVH MDO is done through a parallel-coordinate diagram. The difference provides the CAE engineers to find the guidance to modify the original design architecture.

The around view monitor (AVM) system for the long-body road vehicle with multiple articulated carriages usually suffers from the incomplete distortion rectification of fisheye cameras and the irregular image stitching area caused by the change of relative position of the cameras on different carriages while the vehicle is in motion. In response to these problems, a set of calibration and stitching methods of AVM are proposed. First, a radial-distortion-based rectification method is adopted and improved. This method establishes two lost functions and solves the model parameters with the two-step optimization method. Then, AVM system calibration is conducted, and the perspective transformation matrix is calculated. After that, a static basic look-up table is generated based on the distortion rectification model and perspective transformation matrix.