The rapid development of the active front-wheel steering technique makes it possible to pursue precise, fast, and reliable path-tracking control for autonomous vehicles. To mitigate the impact of time-varying model parameters and system disturbances on the control performance, this paper proposes a continuous-time solving scheme for the autonomous path- tracking problem, which combines the robust model predictive control (RMPC) technique, non-parallel distributed compensation (non-PDC) method, and polytopic model. First, considering the time-varying longitudinal vehicle velocity, a polytopic model is utilized to construct a vehicle path-tracking system with a simpler structure. Then, a state-feedback controller for the continuous system model is developed based on the non- PDC method. By using the Lyapunov stability proof to transform the optimal control problem into the linear matrix inequalities (LMIs) solving form, an RMPC method is used to ensure path-tracking accuracy. Moreover, a robust compensation control is introduced to improve the anti-disturbance and transient response of the system. The virtual simulation by Carsim/ Simulink and field vehicle tests are conducted to assess the proposed controller. Both the simulation and field vehicle tests prove the effectiveness of the proposed controller in improving the path-tracking performance.

To improve the safety, comfort, and efficiency of the intelligent transportation system, particularly in complex traffic environments where autonomous vehicles (AVs) and human-driven vehicles (HVs) coexist, a game theoretic trajectory planning framework is proposed in this paper. Firstly, the game framework including non-cooperative games between AVs and HVs, as well as partial cooperative games between ego AV and other AVs is constructed. Secondly, a longitudinal game strategy for HVs is established with consideration of the driver's longitudinal handling characteristics and personalized aggressiveness, with which the driving behaviors of HVs can be accurately predicted. Thirdly, the longitudinal game strategies for AVs at different positions are designed, with consideration of the safety constraints of AVs and surrounding vehicles, as well as the objectives of comfort and traffic efficiency. Then, the interactive games of ego AV, HVs, and other AVs in different types of mixed-driving environments are solved based on the Stackelberg game and partial cooperative game methods. Finally, the optimal lane-changing (LC) gaps and longitudinal speed trajectories of AVs are obtained. Human-in-the-loop experiment results demonstrate the effectiveness of the proposed trajectory planning framework in lane-changing scenarios involving HVs with different aggressiveness and response delays.

Human-machine mutual trust has become one of the important factors restricting steer-by-wire vehicles due to the removal of mechanical connections. To this end, this paper proposes a hierarchical shared steering control framework based on the human-machine mutual trust evaluation. The upper level of the framework aims to evaluate the human-machine mutual trust level. The driver's trust level in the machine is evaluated by the human-machine steering difference. And the machine's trust level in the driver is evaluated by driver skills. The lower level is to dynamically optimize the authority allocation considering varying human-machine mutual trust states. The fuzzy method is adopted to calculate the reference value of the human-machine authority based on the mutual trust level. Through minimizing the lateral acceleration and tracking error, the fuzzy rule database for the authority reference level is further tuned offline. Furthermore, to improve the smoothness of authority transfer and path tracking, the model prediction control is used to optimize the human-machine authority levels online. The proposed authority allocation strategy is verified with the driver-in-the-loop test bench. The results show the effectiveness of improving driving performance under different human-machine mutual trust levels.

Independently driven electric vehicles (IDEVs) with typical redundancy characteristics provide more potential to improve global performance. However, the model uncertainties would cause extra energy consumption and even destroy vehicle safety. This study presents a hierarchical control scheme to simultaneously guarantee the vehicle energy conservation and handling stability. In the upper layer, the tire nonlinear characteristics are reconstructed by the polytopic method to facilitate the controller design. Then a robust controller is developed to address the model uncertainties and ensure the vehicle handling stability by integrating the direct yaw moment control (DYC) system and the active front wheel steering (AFS) system. Furthermore, the stability performance of independently driven electric vehicles is analyzed and quantified with different regions in the tire slip angles phase plane. Considering the uncertain longitudinal velocity, the lower layer utilizes a linear time-varying model predictive controller (LTV-MPC) to develop the torque allocation strategy and realize energy conservation. The control authority for stability performance and energy conservation is dynamically adjusted by assessing the vehicle safety state. Hardware-in-the loop (HIL) tests are carried out to verify the effectiveness. The test results demonstrate the proposed method is feasible to balance different control objectives.

The implementation of direct yaw moment control (DYC) in distributed drive electric vehicles brings greater potential for enhancing vehicle maneuverability and stability performance. Therefore, this paper introduces a robust framework for driver assistance control by integrating the active front-wheel steering (AFS) system and DYC, aiming to improve the overall performance. First, the driver steering behavior is modeled and incorporated into the path-tracking system of the distributed drive electric vehicle (DDEV). This integration facilitates a comprehensive consideration of the driver characteristics during the controller design. Moreover, the uncertain model parameters are represented using a reduced number of polytopic vertices. Through field tests, the integrated control scheme showcases its superiority in improving the vehicle's lateral motion capability. Subsequently, to effectively balance the optimization objectives concerning path-tracking accuracy, stability performance, and control efforts, a robust control paradigm is systematically constructed by adjusting the weight coefficients. The optimal assistance inputs for AFS/DYC are derived by solving the linear matrix inequalities (LMIs), ensuring the system's robustness against disturbances. The robust invariant set is further employed to satisfy the system constraints. Finally, a hardware-in-the-loop test platform is developed. Comparative results demonstrate the capacity of the proposed method to enhance human-machine co-driving performance.

Heterogeneous vehicle platoons, consisting of a human-driven vehicle (HDV) as the leader and connected automated vehicles (CAVs) as followers, present a promising solution to address various challenges arising from fully autonomous driving. In this paper, we propose a novel LSTM-based distributed model predictive control (DMPC) platooning method. Initially, we develop and train a vehicle acceleration prediction model based on a long short-term memory (LSTM) network using real-world driving data. Subsequently, the predicted acceleration sequence of the leading HDV is integrated into the DMPC-based platoon control model for the following CAVs. To validate the effectiveness of our method, we conduct simulation experiments using real-world driving data. The results demonstrate that, with a time headway of 1 s, the maximum speed error and maximum spacing error of the heterogeneous vehicle platoon using the proposed LSTM-based DMPC are reduced by at least 5.8% and 5.9%, respectively, compared to the traditional DMPC method. Furthermore, the LSTM-based DMPC outperforms the Transformer-based DMPC method, resulting in a 1.0% reduction in maximum speed error and a 0.7% reduction in maximum spacing error. The proposed method effectively dampens oscillation caused by the leading HDV and enhances tracking accuracy.