In this paper, we aim to investigate the path following control problem for four-wheel-independent-drive electric vehicles (4WID-EVs) with consideration of modeling error and complex driving scenario. In order to guarantee operational safety and performance of robustness, a super-twisting secondorder sliding mode (SOSM) control strategy is employed to suppress the heavy chattering problem existing in the traditional sliding mode control (SMC). To degrade the lumped disturbance, a nonlinear disturbance observer (NDOB) technique is proposed to estimate the disturbance and compensate the modeling error. Then, a composite control strategy which combines the SMC algorithm and NDOB is proposed base on stability analysis of the corresponding closed-loop system. Finally, simulation results and comparisons show that the super-twisting SOSM control algorithm with the NDOB technique can achieve better tracking performance and suppress the chattering.

A four-wheel steer-by-wire vehicle (FSV), which is a combination of a steer-by-wire (SBW) system and a four wheel steering (4WS) system, not only can improve vehicle safety and maneuverability, but also steering flexibility. Considering the parameters, uncertainties of vehicle speed, and tire cornering stiffness, weighted function is solved to express the uncertain system. Aiming at the multiple-input multiple-output (MIMO) system, the structured singular value μ is used to research FSV under multiple perturbations in this paper. Based on μ control strategy, μ controller is designed to track the desired sideslip angle and yaw rate. Thus, the vehicle gets better performance. Compared with SBW and 4WS systems, FSV has better state response under steering angle step input simulation experiment. The advantages of μ control compared with Hoo control on FSV have been explained in the simulation experiments. Furthermore, experiment results show that the designed controller can make the vehicle well track the reference model and improve the vehicle maneuverability.

Recently, inferring lane change intention has received considerable attention. Due to the high nonlinearity and complexity of traffic contexts, traditional methods cannot satisfy the requirements of long-term prediction tasks and lack the ability of capturing nonlinear temporal dependencies. This paper proposes an intention inference model based on Recurrent Neural Networks (RNN), to tackle time series prediction problems. Considering dynamic interaction among surrounding vehicles, our model takes the sequence motion information of surrounding vehicles as inputs and calculates the congestion of different lanes, integrated with vehicle states of the object vehicle. To illustrate the availability of the proposed RNN intention inference model, a motion planning controller considering intention was developed. A Nonlinear Model Predictive Control (NMPC) was established to planning a safe, sub-optimal path for autonomous driving vehicle under collision avoidance constraints. The experiments on the proposed model were conducted, based on two RNN structure Long-Short Term Memory (LSTM) and Generalized Recurrent Unit (GRU), by Tensorflow with NGSIM data. The motion planning controller is modeled and simulated by Carsim with Simulink for some typical scenarios. Subsequently, experimental results demonstrate that RNN achieves best performance, inferring intention with 96% accuracy, compared with other approaches.

Connected automated vehicle (CAV) platoon control is becoming increasingly prevalent because of its unique advantages in reducing fuel consumption and improving traffic efficiency. A novel control framework for CAV platoon control is designed in this paper. First, a model predictive control (MPC)-based method is proposed to obtain the optimal velocity of the whole platoon, in which both reducing fuel consumption and improving transport efficiency are taken into account in the optimization process. Then, a distributed adaptive triple-step nonlinear control strategy is investigated from the perspective of multi-agent system control. The adaptive performance of the control strategy can guarantee string stability of the CAV platoon under the premise of the existence of dynamic uncertainties. Various simulation conditions with heterogeneous dynamic disturbances are designed to validate the proposed control strategy, and the results show that the proposed control strategy can be robust to dynamic disturbances while ensuring the string stability of the CAV platoon.

Random network delay will introduce uncertainty into trajectory tracking model of the autonomous vehicle, which seriously deteriorates the vehicle's control system stability and trajectory tracking accuracy. In this paper, considering steering angle oscillation caused by random network delay, trajectory tracking system robustness and stability is analyzed and a linear uncertain time-delay system is established. Comprehensively considering control system accuracy, robustness, and computational efficiency in the rolling optimization of Model Predictive Control (MPC), Adaptive Model Predictive Control for Uncertain model (UM-AMPC) algorithm is proposed to predict control variables for the next sampling time and alleviate the target angle discontinuity. This is achieved by operating target angle signal and augmented state variables, which are received by the lower nodes during the period from the current sampling time to network delay upper bound. The hardware-in-the-loop simulation results show that the proposed algorithm can effectively guarantee system stability and tracking accuracy of the autonomous vehicle under random network delay.