However, these approaches typically yield [24] a straightforward geometric path that must later be accompanied by an appropriate timing law in a subsequent planning phase. In the existing literature, these two processes are often treated separately, focusing on one phase at a time. Nevertheless, for instance in [36], the challenge of determining a suitable timing law has been addressed as part of the path planning problem by incorporating an auxiliary time coordinate within the configuration space. This paper, however, proposes a method that integrates both steps, as was done in some of the authors' previous works; see [1] and the references therein. Further classification of existing solutions depends on how the configuration space for planning is discretized using various methods. These methods aim to generate a set of candidate collision-free points, cells, or lattices [24] for the planning process. Approaches include probabilistic roadmaps [37], vector field histograms--which have shown robustness against uncertainty in [38]--and the widely used [22] visibility graph method. The latter is specifically adapted for autonomous vehicle systems (AVSs) in [39] by accurately defining obstacle approximations that consider the kinematics of AVSs. Various techniques can be integrated with different approximations of obstacles or no-go areas, such as rectangular shapes [40] or ellipsoids [41]. These approaches can successfully handle, to a certain extent, replanning in unstructured scenarios [42]. In addition to the classification previously presented, motion planning algorithms for vehicles frequently depend on the definition of worst-case state sets [17] that are proven to be collision-free and consider various phenomena that may affect vehicle performance. Finally, the interested reader can find further information in the survey [43]. This study employs a solution tested on robots [1], which is characterized by its ability to ensure specific constraints on vehicle states (e.g., maximum lateral velocity) that are often recommended for defining the Operational Design Domain (ODD) and enhancing driving comfort. Even before the explosion of interest in artificial intelligence, robotics and autonomous driving technologies have benefited from automated learning mainly in the form of supervised learning. Among the first two categories, most AI-based solutions for motion planning deal with environment sensing [44] and path planning rather than trajectory planning [45]. For example, ref. [46] relied on two different neural networks, one of which was used for path planning and another of which providea a collision avoidance feature to the motion of a ground vehicle. Even further, in planning, the greatest part of artificial intelligence-based solution focuses on the deliberative layer, for example, ref. [47], wherein they relied on model predictive control, with a focus on providing a natural driving style, and [48] which reached similar results in a line-change scenario. In [49], a hybrid-A\({}^{\star}\) was proposed in which the heuristic function of the A\({}^{\star}\) algorithm was improved through an artificial intelligence approach, even though it was limited to path planning. Only a handful works combine kinodynamic feasibility with artificial intelligence for improving search speed. For example, an interesting approach can be found in [50], wherein they performed the trajectory planning of a car, performed through an A\({}^{\star}\) in a 4D space, by pruning the search space through a support vector machine (SVM), while [51] focused on solving the inherently nonprovable safety of trajectories obtained by an artificial intelligence model. Apart from supervised and unsupervised learning, reinforcement learning (RL)-based approaches offer interesting advantages in terms of adaptability. For example, ref.