In this context, the European Union, through its latest directives, has set a clear goal of reducing greenhouse gasses emissions: initially by 30% and later increased to 55% by 2030 compared to 2021 levels. Achieving such an ambitious target cannot be reasonably accomplished without the widespread adoption of electric vehicles, the conscious and intelligent use of energy storage systems for electric traction [11], and the implementation of assisted and/or autonomous driving technologies. The main push arises from its expected benefits, which include reducing incidents (94% of which are related to driver errors [12]) and emissions [13]. After years of extensive research, the SAE J3016[14] standard was finally published in 2014. This standard outlines six levels of vehicle automation (ranging from 0 to 5) for classification purposes. The third level, as defined by the SAE J3016, does not require constant human supervision and has been commercially available since the late 2010s. This level of automation is capable of performing complex safety-related tasks like lane-keeping with minimal human involvement [15]. Autonomous driving is a complex and interdisciplinary effort that involves finding solutions to a variety of challenges, particularly in urban environments [16]. The decision-making process in autonomous driving systems (ADSs) typically involves a layered approach that includes a local motion planning layer, which depends on the feedback control of vehicle systems [17]. This work tackles local motion planning, whose input is a structured environment in which both static and moving obstacles are represented by rectangular embeddings [18; 19; 20], hence assuming that a low-level control law exists, along with environment sensing and goals from the deliberative layer. Local motion planning is a foundational technology for autonomous driving, supporting the already commercially available J3016 level 2 features like self-parking, as well as level 3 applications where the automated driving system (ADS) handles intricate tasks within a specified operational design domain (ODD) with a human fallback, such as overtaking obstacles or slower vehicles. Motion planning for an autonomous driving system (ADS) usually consists of two phases [21; 22]. The first phase is path planning, where an appropriate geometric path is determined between a starting position and a destination. The second phase involves associating this path with a suitable timing law [1]. From a computational perspective, these two phases can present significant challenges [23]. Numerous solutions have been proposed in the literature [24], many of which have been adapted from robotic motion planning techniques [25] or share similarities with Unmanned Aerial Vehicle (UAV) motion planning to some extent [26; 27]. However, unlike robotic motion, autonomous vehicles have greater demands when it comes to performance and safety. Motion planning solutions can be categorized based on how they ensure the feasibility of trajectories, particularly in terms of adhering to specific constraints. For instance, some solutions are grounded in geometric considerations, frequently utilizing Bezier curves or Lagrange polynomials [28] or ad hoc Dubin paths, which can embed complex constraints [29]. Other methods involve graph-based solutions that construct decision trees of potential trajectories, which are selected based on a cost function that can target different aspects of planning, ranging from fuel consumption [30] to passengers' comfort [31]. There are also approaches based on model-predictive control (MPC) [32]. In the realm of graph-based methods, optimization algorithms such as [PERSON]'s algorithm or A* and its variants [33] are notable for their applicability to replanning [34]. These algorithms can utilize complex heuristic functions that consider various factors, including penalties for risky maneuvers [35].