An integrated approach for vehicle trajectory planning and tracking control is proposed in this paper, aiming at enhancing the ability to avoid obstacles by autonomous vehicles when they are in a complex dynamic environment. And the method combines the improved driving risk field model with model predictive control, and integrates speed planning, trajectory planning, and tracking control into a multi-objective optimization problem. First, considering the impact of dynamic traffic conditions on driving risk, an improved driving risk model is constructed by introducing the time to collision (TTC) indicator as well as the parameters of vehicle intrinsic properties and motion state attributes. Next, a dynamic vehicle model of three degrees of freedom (3DOF) is established. Based on the model predictive control (MPC) algorithm, the corresponding cost function and constraint conditions are designed by considering the multi-dimensional objectives of vehicle stability, comfortableness, driving safety, etc. Finally, different traffic scenarios were set up for simulation verification using the Simulink + Prescan + Carsim joint simulation platform. A comparative analysis was conducted between the integrated control method proposed in this study and the hierarchical control method and integrated control without considering TTC method, and the results indicate that the control method mentioned in this paper can achieve dynamic obstacle avoidance for autonomous vehicles in various traffic scenarios. And it can also achieve a more accurate and more stable driving trajectory and thus ensure the driving comfortableness and safety. And the improved driving risk field model mentioned in this paper draws a more accurate image of driving risks in a complex environment compared with the traditional driving risk field model which could improve the driving safety and applicability of autonomous vehicles in dynamic traffic environments.
Xu et al. (Fri,) studied this question.