This article describes a human-like automated lane-changing system to mimic the lane-changing maneuver of human drivers in order to make automated vehicles more realistic. In this system, both the personalized factors of human drivers and the traffic environmental factors are considered to derive a personalized human-like lane-changing trajectory planning model, which includes a longitudinal driving behavior model and a lateral lane-changing trajectory planning model. The longitudinal model is derived based on the traffic environment information to predict the longitudinal speed of human-driven vehicles in the lane-changing processes, and also to analyze the longitudinal driving habits of drivers in different driving scenarios. The lateral model plans a suitable lateral lane-changing trajectory preferred by the driver. The personalized parameters of the longitudinal and lateral models are calibrated by the driving data with multi-dimensional time series regression method. The simulation results show that the proposed method can well realize the human-like and personalized lane-changing trajectory planning.

This paper describes a lane-changing motion planning model for intelligent vehicles under constraints of collision avoidance in dynamical driving environments. The key innovation is decoupling the longitudinal and lateral motion planning to realize trajectory re-planning in a normal lane-changing process to prevent collisions. The longitudinal planning model decides a collision-free termination point of motion through planning the longitudinal acceleration and velocity. Taking the termination time as input, the lateral planning model plans the optimal reference trajectory for normal lane-changing maneuvers, or re-plans the lane-changing trajectory to eliminate potential accidents. When traffic states have variations that may bring about the collisions, the termination point can be updated through the longitudinal planning model, based on which the lateral planning model makes adjustments to the pre-planned trajectory to complete lane-changing successfully, or return its original lane. Simulation results show that the proposed model not only can handle the general motion planning problem, but also can re-plan trajectories in emergent conditions to ensure safety, while vehicle dynamics retain in a stable state during lane-changing or returning maneuvers.

The Four Wheel Independent Steering/Driving (4WIS/4WID) electric vehicle has the advantage that the rotation angle and driving torque of each wheel can be independently and accurately controlled. In this paper, a trajectory tracking strategy based on the hierarchical control method is designed. In the path tracking layer, the nonlinear state feedback controller is used, and the neural network Proportion Integration Differentiation (NNPID) controller is designed to track the desired path and to obtain the desired yaw rate. By tracking the desired yaw rate and vehicle speed, the terminal sliding mode controller in vehicle dynamics control layer calculates the desired resultant tire force. In the tire force distribution layer, the multiple optimization objectives, including vehicle stability performance objective, energy-saving performance objective, and tire wear energy consumption objectives are determined and the weight coefficient is adaptive to different working conditions based on fuzzy logic theory. Finally, the wheel steering angle and driving torque of each wheel are calculated by the nonlinear three-degree-of-freedom vehicle model. Simulation results show that it realizes the adaptive control of tire force while tracking the desired trajectory, improves the stability and energy saving of the vehicle, and effectively reduces tire wear.

Research on game-based human-machine shared driving (HMSD) has traditionally been constrained by stringent game theory assumptions, high requirements for automated steering controllers, and the need to acquire the human driver’s target trajectory. To address these issues, this paper proposes a bargaining game-based HMSD control authority allocation strategy for a steer-by-wire vehicle. First, two types of lateral path tracking controllers are developed to simulate the human driver and the automatic steering system. Then, a bargaining game-based HMSD control authority allocation strategy is created using the Nash bargaining solution. Finally, a Simulink/CARSIM co-simulation platform and a driver-in-the-loop experimental bench are utilized to verify the proposed strategy. The results show that the proposed bargaining game-based HMSD control authority allocation strategy has potential advantages over existing game-based HMSD, including decoupling of human-machine interaction, lower requirements for automated steering controllers, and feasible game modeling ability. Moreover, the proposed strategy demonstrates excellent performance in terms of shared driving, which extends the applicability of HMSD in various operating maneuvers. Both the human driver and the automatic steering system benefit from the proposed HMSD system, which could increase the acceptance and trustworthiness of the human driver with the HMSD system and reduce the human driving load to make the driver-vehicle interaction more reliable and smoother.

Vehicle state and tire-road adhesion are of great use and importance to vehicle active safety control systems. However, it is always not easy to obtain the information with high accuracy and low expense. Recently, many estimation methods have been put forward to solve such problems, in which Kalman filter becomes one of the most popular techniques. Nevertheless, the use of complicated model always leads to poor real-time estimation while the role of road friction coefficient is often ignored. For the purpose of enhancing the real time performance of the algorithm and pursuing precise estimation of vehicle states, a model-based estimator is proposed to conduct combined estimation of vehicle states and road friction coefficients. The estimator is designed based on a three-DOF vehicle model coupled with the Highway Safety Research Institute(HSRI) tire model; the dual extended Kalman filter (DEKF) technique is employed, which can be regarded as two extended Kalman filters operating and communicating simultaneously. Effectiveness of the estimation is firstly examined by comparing the outputs of the estimator with the responses of the vehicle model in CarSim under three typical road adhesion conditions(high-friction, low-friction, and joint-friction). On this basis, driving simulator experiments are carried out to further investigate the practical application of the estimator. Numerical results from CarSim and driving simulator both demonstrate that the estimator designed is capable of estimating the vehicle states and road friction coefficient with reasonable accuracy. The DEKF-based estimator proposed provides the essential information for the vehicle active control system with low expense and decent precision, and offers the possibility of real car application in future.