  As vehicle automation advances, motion planning algorithms face escalating
challenges in achieving safe and efficient navigation. Existing Advanced Driver
Assistance Systems (ADAS) primarily focus on basic tasks, leaving unexpected
scenarios for human intervention, which can be error-prone. Motion planning
approaches for higher levels of automation in the state-of-the-art are
primarily oriented toward the use of risk- or anti-collision constraints, using
over-approximates of the shapes and sizes of other road users to prevent
collisions. These methods however suffer from conservative behavior and the
risk of infeasibility in high-risk initial conditions. In contrast, our work
introduces a novel multi-objective trajectory generation approach. We propose
an innovative method for constructing risk fields that accommodates diverse
entity shapes and sizes, which allows us to also account for the presence of
potentially occluded objects. This methodology is integrated into an
occlusion-aware trajectory generator, enabling dynamic and safe maneuvering
through intricate environments while anticipating (potentially hidden) road
users and traveling along the infrastructure toward a specific goal. Through
theoretical underpinnings and simulations, we validate the effectiveness of our
approach. This paper bridges crucial gaps in motion planning for automated
vehicles, offering a pathway toward safer and more adaptable autonomous
navigation in complex urban contexts.
