Aiming at the trajectory tracking problem of self-driving cars under structured roads, an integrated decision-making and control method for self-driving based on the convex approximation obstacle avoidance principle is proposed. First, based on the convex approximation obstacle avoidance principle, the safety constraints are optimized, the feasible trajectory domain is appropriately reduced, and only the part of the feasible points related to the interaction with a specific week vehicle is retained. Then, in combination with the model predictive control algorithm, a linearized bicycle kinematic model is established in the low-speed scenario, and with the goal of minimizing the trajectory tracking error, the shapes of the self-driving car and the weekly vehicle, the road geometric constraints, and the safety constraints are considered, and multiple optimal control problems are constructed in relation to the static paths. The optimal control problems in relation to the static paths are constructed, and the constraints are handled using an external penalty function that is solved based on the sequential quadratic programming method to select the optimal trajectory for tracking. The results of highway simulation experiments on the Carla simulation platform show that the proposed integrated decision control method for autonomous driving based on the convex approximation obstacle avoidance principle can effectively reduce the decision risk in autonomous driving, although the overtaking efficiency has been reduced, but the driving safety has been adequately guaranteed and the trajectory tracking performance has not been affected.