Research on the cooperative adaptive cruise control (CACC) algorithm is primarily concerned with the longitudinal control of straight scenes. In contrast, the lateral control involved in certain traffic scenes such as lane changing or turning has rarely been studied. In this paper, we propose an adaptive cooperative cruise control (CACC) algorithm that is based on the Frenet frame. The algorithm decouples vehicle motion from complex motion in two dimensions to simple motion in one dimension, which can simplify the controller design and improve solution efficiency. First, the vehicle dynamics model is established based on the Frenet frame. Through a projection transformation of the vehicles in the platoon, the movement state of the vehicles is decomposed into the longitudinal direction along the reference trajectory and the lateral direction away from the reference trajectory. The second is the design of the longitudinal control law and the lateral control law. In the longitudinal control, vehicles are guaranteed to track the front vehicle and leader by satisfying the exponential convergence condition, and the tracking weight is balanced by a sigmoid function. Laterally, the nonlinear group dynamics equation is converted to a standard chain equation, and the Lyapunov method is used in the design of the control algorithm to ensure that the vehicles in the platoon follow the reference trajectory. The proposed control algorithm is finally verified through simulation, and validation results prove the effectiveness of the proposed algorithm.
In order to address the obstacle avoidance problem of driverless vehicles in hospital environment, a local path planning method based on model predictive control is proposed. Firstly, the potential field model of driving environment factors including obstacles, environmental vehicles, roads, and target points is established by using artificial potential field theory. Then, based on model predictive control algorithm combined with driving environment potential field, trajectory planning and tracking are transformed into a unified constrained optimization problem. The objective function and constraint conditions of local path planning for unmanned vehicles are designed, and roll is introduced to the dynamic optimization mechanism. The simulation results show that the error between the path planning and the expected path is less than 0.1 m, the time consumption is at least 3.3 s, and it has strong robustness, which can effectively solve the obstacle avoidance problem of local path of unmanned vehicles.
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