Trajectory generation strategies for safe autonomous driving in urban scenario

In this dissertation we develop novel strategies based on nonlinear optimal control techniques for trajectory generation of autonomous vehicles. These strategies are designed to enable the development of autonomous vehicles that navigate dynamic environments while enhancing safety and passenger comfort. In the first part of the work, we introduce a family of reduced-order car models suited for trajectory generation strategies. We derive the equations of motion for both kinematic and dynamic bicycle models, the latter of which includes tire modeling for a more realistic representation of vehicle behavior. We re-write the kinematic model in terms of longitudinal and lateral coordinates, aligning them with the way humans perceive and control vehicle motion. In the second part, we propose an optimization-based strategy to address merging maneuvers in busy intersections. We describe vehicle dynamics in terms of longitudinal and transverse coordinates and introduce a "virtual target vehicle" constrained to move within the target lane for merging. We formulate an optimal control problem in terms of longitudinal and lateral coordinates, including the kinematic position error between the autonomous vehicle and the virtual target vehicle. We also use obstacle predictions to enforce suitable kinematic constraints for generating collision-free trajectories. We show the efficacy of the proposed strategy through a set of numerical computations and highlight the main features of the generated trajectories. In the third part, we present a real-time maneuver generation algorithm. Given a planar road geometry with static and moving obstacles along it, we are interested in finding collision-free maneuvers that satisfied the vehicle dynamics and subject to physical and comfort limits. Based on longitudinal and transverse coordinates, we propose a novel collision avoidance constraint and formulate a suitable optimal control problem. The optimization problem is solved by using a nonlinear optimal control technique that generates (local) optimal trajectories. We demonstrate the efficacy of the proposed algorithm by providing numerical computations on a simulated scenario. Experimental results are presented to demonstrate the efficiency of the proposed algorithm both in terms of computational effort and dynamic features captured. In the fourth part, we address the lane change maneuver using a parametric model predictive control approach. We recognize that successful lane changes involve both the decision of when to initiate these maneuvers and the generation of collision-free trajectories. Our approach combines decision-making and planning tasks, guiding the low-level policy through upper-level policy search. Additionally, we incorporate self-supervised learning techniques to adapt to dynamic, online scenarios, ensuring the vehicle can handle unexpected changes in its environment. We provide numerical results that highlight the effectiveness of this approach in improving vehicle maneuvering in dynamic environments.