This paper investigates the lane keeping control of autonomous ground vehicles (AGVs) considering the rollover prevention and input saturation. An enhanced state observer-based sliding mode control (SMC) strategy is proposed to achieve the control purpose and maintain the lane keeping errors as well as the roll angle within the prescribed performance boundaries. Three contributions are made in this paper. First, a prescribed performance function (PPF) is proposed in the controller design, aiming to implement the error transformation so as to constrain the controlled variables within the prescribed performance boundaries. Second, a modified sliding surface is developed incorporating two nonlinear functions, whose specialities and benefits are taken advantage of: one is a barrier function to restrict the load transfer ratio (LTR) in a safe boundary to guarantee the roll stability; another is a monotonely decreasing function to adaptively change the damping ratio of the closed-loop system to improve the transient performance, including reducing the transient overshoots and steady-state errors. Third, a modified multivariable adaptive SMC controller is proposed to achieve the integrated lane-keeping and roll control in the presence of the input saturation and bound-unknown disturbances. The stability of the closed-loop system is rigorously proved via the Lyapunov function. Finally, the effectiveness of the proposed control strategy is verified with a high-fidelity and full-car model via the CarSim platform.

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.

The uncertainties of driver's behavior seriously affect road safety and bring significant challenges to the human-machine cooperative control. This paper proposes a human-machine shared control framework considering driver's time-varying characteristics to improve the co-driving cooperation performance. Firstly, the driving intention is introduced to describe the driver's involvement level through using Gauss-Bernoulli restricted Boltzmann machine method. And the index of driving ability is proposed to evaluate driver skills based on path-tracking errors. Then, a novel human-machine authority allocation strategy is designed by combining the two driving behavior characteristics and used to construct the driver-vehicle interaction system. Subsequently, a T-S fuzzy robust state-feedback shared control system is developed considering time-varying driver behaviors and vehicle states. Finally, the proposed shared steering system is validated by the driver-in-the-loop test bench. The results show that the proposed control method can reduce human-machine conflicts and has obvious superiority in improving performance of driving comfort, path tracking, and vehicle stability for the co-driving vehicles.

Path planning is a critical part for improving the driving safety and driver comfort of autonomous vehicles (AVs), especially in complex maneuvering conditions. In addition, different drivers have different preferences for AVs, thus, how to provide personalized trajectories for different drivers is a vital issue for AVs. The collision-free path planning problem in conditions with large road curvatures is investigated in this paper, with the consideration of environmental safety constraints, drivers’ comfort, vehicle actuator constraints, etc. Firstly, a Driver-Vehicle-Road (DVR) system is established based on the combination of the kinematic vehicle model and the two-point visual preview driver model, such that the driver's individual handling characteristics can be considered in the controller. The kinematic vehicle model is modified to have the similar understeering characteristics with those of the nonlinear full car models, and then the proposed DVR system can satisfy different groups of drivers and cars. Secondly, for environmental constraints, a new artificial potential field (APF) method is proposed, which can form a banana-shaped 3-D dangerous imaginary mountain and a lane boundary cliff suitable for arbitrary curvature roads to generate a collision-free evasive path. Finally, the Linear-Time-Varying (LTV) model predictive control (MPC) method is adopted to design the path planner. The CarSim-Simulink joint simulation illustrates that with the proposed planner, the host vehicle is capable of avoiding obstacles with a safer and more comfortable maneuver on large curvature roads. And the proposed path planner can provide individually safe trajectories for different drivers with good maneuverability.

High-speed cruising scenarios with mixed traffic greatly challenge the road safety of autonomous vehicles (AVs). Unlike existing works that only look at fundamental modules in isolation, this work enhances AV safety in mixed-traffic high-speed cruising scenarios by proposing an integrated framework that synthesizes three fundamental modules, i.e., behavioral decision-making, path-planning, and motion-control modules. Considering that the integrated framework would increase the system complexity, a bootstrapped deep Q-Network (DQN) is employed to enhance the deep exploration of the reinforcement learning method and achieve adaptive decision making of AVs. Moreover, to make AV behavior understandable by surrounding HDVs to prevent unexpected operations caused by misinterpretations, we derive an inverse reinforcement learning (IRL) approach to learn the reward function of skilled drivers for the path planning of lane-changing maneuvers. Such a design enables AVs to achieve a human-like tradeoff between multi-performance requirements. Simulations demonstrate that the proposed integrated framework can guide AVs to take safe actions while guaranteeing high-speed cruising performance.