The longitudinal collision avoidance controller can avoid or mitigate vehicle collision accidents effectively via auto brake, and it is one of the key technologies of autonomous vehicles. Moreover, the vehicle lateral stability is very crucial in emergency scenarios. Due to complex traffic conditions and various road frictions, emergency brake may cause a vehicle to lose its lateral stability. Therefore, this paper proposes a lateral-stability-coordinated collision avoidance control system (LSCACS) based on the model predictive control (MPC). First, the proposed LSCACS decides which control mode to be implemented based on vehicle dynamics states, including a normal driving mode, a full auto brake mode, and a brake and stability mode. The MPC is used in the upper controller to calculate the desired deceleration and additional yaw moment. The lower controller calculates the desired tire forces of four wheels and realizes them by certain wheel cylinder hydraulic pressures. The LSCACS is validated by hardware-in-the-loop (HIL) tests, and the results show LSCACS's effectiveness and great performance of the collision avoidance and lateral stability.

A novel optimal energy management strategy (EMS) for plug-in hybrid electric vehicle (PHEV) is proposed in this paper, which takes the battery health into consideration for prolonging its service life. The integrated control framework combines batch-wise iterative learning control (ILC) and time-wise model predictive control (MPC), referred as 2D-MPILC. The major advantages of the proposed method are shown with better performance as well as faster convergence speed by taking into account the time-wise feedback control with-in the current batch. Then, the MPILC method is applied for PHEV with the ability to make continuous period-to-period improvements. Its performances will approach to DP-based method after a learning process with a satisfying real-time processing capacity. The results in real-world city bus routines verify the effectiveness of the proposed EMS for greatly improving the performance of PHEV.

The automated steering control technology is crucial for autonomous vehicles, and lane-keeping control systems have been developed extensively, but various traffic conditions, and obstacles ahead of the ego vehicle would cause serious traffic collision accidents. Therefore, this paper proposes a virtual fluid-flow-model (VFFM) based lane-keeping integrated with collision avoidance control system (LKCA) to realize the function of both lane-keeping and collision avoidance. Firstly, a novel lane-keeping model is proposed which is inspired by viscous fluid flowing between two parallel plates or around a cylinder. Then, based on the proposed VFFM-based lane-keeping model, the ego vehicle’s path can be planned, and it ensures the performance of lane-keeping and obstacle-avoidance. Finally, an optimal-preview-driver-model-based path tracking controller is designed to track the desired VFFM-based path. The proposed control system is evaluated by both co-simulations of MATLAB/Simulink & CarSim and real-bus tests. Results show the effectiveness of the proposed control system, and it can ensure lane-keeping performance on the premise of obstacle-avoidance.