Trajectory tracking and stability control are two essential functions of autonomous vehicles, and there is inevitable mutual interference between them, especially under extreme conditions. Towards addressing this challenge, this paper proposes a novel cooperative strategy of trajectory tracking and stability control for four-wheel independent drive (4WID) autonomous vehicles. An adaptive trajectory tracking controller is designed based on the model predictive control (MPC) theory, in which the tire cornering stiffness is modified in real-time leverages the lateral force estimated by the square-root cubature Kalman filter (SCKF). Then, the vehicle stability controller is designed based on the sliding mode control (SMC), in which the tire force saturation constraint and the relative weight of yaw rate and sideslip angle are considered. Furthermore, a weight adaptive criterion with normalized stability index is defined to develop the cooperative strategy of trajectory tracking and stability control. The feasibility and adaptability of the proposed method to different extreme conditions are verified by hardware-in-the-loop (HIL) test and CarSim-Simulink co-simulation. Finally, an experimental case is presented to demonstrate the realizability of the proposed method.

Steer-by-Wire (SBW) systems are an integral and essential component of intelligent electrified vehicles and are critical to ensure safe vehicle operations. In this paper, a fault-tolerant control method for SBW is proposed to mitigate the adverse influence of front wheel steering angle sensor faults based on the Kalman filtering technique. First, a linearized vehicle model is derived based on tire characteristics and is further combined with a steering actuator model to provide the modelling foundation for estimating the front wheel steering angle. Considering multi-source noises and their significant influence on the front wheel steering angle sensor, the Butterworth filter is employed to filter out the high-frequency noises. The front wheel steering angle estimation is realized by incorporating the measurements of the yaw rate and lateral acceleration of vehicle and the current of steering motor. In particular, a dynamic threshold mechanism is adopted to activate the fault diagnosis and fault-tolerant compensation based on the residual theory. Finally, a linear time-varying model predictive controller is presented to execute the trajectory tracking task for an intelligent electrified vehicle. The effectiveness of the proposed scheme is verified under the single lane change and double lane change maneuvers during the trajectory tracking.

This paper presents a hierarchical motion planning approach based on discrete optimization method. Well-coupled longitudinal and lateral planning strategies with adaptability features are applied for better performance of on-road autonomous driving with avoidance of both static and moving obstacles. In the path planning level, the proposed method starts with a speed profile designing for the determination of longitudinal horizon, then a set of candidate paths will be constructed with lateral offsets shifting from the base reference. Cost functions considering driving comfort and energy consumption are applied to evaluate each candidate path and the optimal one will be selected as tracking reference afterwards. Re-determination of longitudinal horizon in terms of relative distance between ego vehicle and surrounding obstacles, together with update of speed profile, will be triggered for re-planning if candidate paths ahead fail the safety checking. In the path tracking level, a pure-pursuit-based tracking controller is implemented to obtain the corresponding control sequence and further smooth the trajectory of autonomous vehicle. Simulation results demonstrate the effectiveness of the proposed method and indicate its better performance in extreme traffic scenarios compared to traditional discrete optimization methods, while balancing computational burden at the same time.

This paper presents a constrained robust H_∞ controller design of active suspension system for in-wheel-independent-drive electric vehicles considering control constraint and parameter variation. In the active suspension system model, parameter uncertainties of sprung mass are analyzed via linear fraction transformation, and the perturbation bounds can be also limited, then the uncertain quarter-vehicle active suspension model where the in-wheel motor is suspended as a dynamic vibration absorber is built. The constrained robust H_∞ feedback controller of the closed-loop active suspension system is designed using the concept of reachable sets and ellipsoids, in which the dynamic tire displacements and the suspension working spaces are constrained, and a comprehensive solution is finally derived from H_∞ performance and robust stability. Simulations on frequency responses and road excitations are implemented to verify and evaluate the performance of the designed controller; results show that the active suspension with a developed H_∞ controller can effectively achieve better ride comfort and road-holding ability compared with passive suspension despite the existence of control constraints and parameter variations.

In this paper, an improved bidirectional RRT vehicle path planning method for smart vehicle is proposed. In this method, the resultant force of the artificial potential field is used to determine the search direction to improve the search efficiency. Different kinds of constraints are considered in the method, including the vehicle constraints and the vehicle driving environment constraints. The collision detection based on separating axis theorem is used to detect the collision between the vehicle and the obstacles to improve the planning efficiency. The cubic B-spline curve is used to optimize the path to make the path’s curvature continuous. Both simulation and experiment are implemented to verify the proposed improved bidirectional RRT method. In the simulation analysis, this paper’s method can generate the smoothest path and takes the shortest time compared with the other two methods and it can be adaptive to the complicated environment. In the real vehicle experiment, we can see from the test results that this paper’s method can be applied in practice on the smart electric vehicle platform; compared with others’ algorithm, this paper’s algorithm can generate shortest and smoothest path.