A novel control strategy is proposed for the autonomous vehicle equipped with the front and rear independent steering systems to enhance the capability of collision avoidance and improve path tracking and handling stability under critical maneuvers. The novel control system includes a path replanning controller, a path tracking controller, and a stability controller. The path replanning controller is designed to generate a collision-free trajectory by optimizing a cost function. The cost function can be updated quickly and iteratively, according to the dynamic fitting matrix and measurement signals. The path tracking controller and the stability controller with considering multi-constraints are designed to generate the steering angle of the front wheel and the rear wheel, respectively, which improve the ability of path tracking and handling stability of the autonomous vehicle under extreme conditions. The real-time effectiveness of the proposed control strategy is verified and evaluated in Hardware-in-the-loop (HIL) simulation environment. The function of collision avoidance of the control algorithm is verified in the scenario of the preset obstacle artificially on the globally planned path. The proposed control strategy, presents superior performance against two other control strategies based on model predictive control algorithm, and also exhibits satisfactory performance in terms of path tracking as well as handling stability of the autonomous vehicle.

In this paper, we present an optimal path planning and speed control strategy for motion planning of unmanned ground vehicles to follow the desired path in various driving environments. The novelty of this work is that it integrates the longitudinal speed and lateral motion control by maximizing the longitudinal progression and minimizing the lateral path tracking errors and formulates a standard quadratic optimization problem. Firstly, a dynamic programming approach is introduced to convexify the corridor constraints in path pre-selection and replanned for obstacle avoidance. The idea is to find a convex feasible set for the optimization problem using the convex constraints. And then a standard quadratic convex problem is formulated to optimize the longitudinal motion and steering angle control in prediction and control horizon. The longitudinal progression and lateral tracking errors are decomposed in orthogonal curvilinear coordinates. The centerline as well as road borders are also described by using cubic spline curves. Some challenge scenarios including static and moving participating obstacle vehicles are tested and validated for the proposed motion planning strategy. The feasibility and performance of the proposed strategy are demonstrated by numerical experiments in terms of the stability and maneuverability of unmanned ground vehicles in different driving conditions.