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As vehicle automation advances, motion planning algorithms face escalatingchallenges in achieving safe and efficient navigation. Existing Advanced DriverAssistance Systems (ADAS) primarily focus on basic tasks, leaving unexpectedscenarios for human intervention, which can be error-prone. Motion planningapproaches for higher levels of automation in the state-of-the-art areprimarily oriented toward the use of risk- or anti-collision constraints, usingover-approximates of the shapes and sizes of other road users to preventcollisions. These methods however suffer from conservative behavior and therisk of infeasibility in high-risk initial conditions. In contrast, our workintroduces a novel multi-objective trajectory generation approach. We proposean innovative method for constructing risk fields that accommodates diverseentity shapes and sizes, which allows us to also account for the presence ofpotentially occluded objects. This methodology is integrated into anocclusion-aware trajectory generator, enabling dynamic and safe maneuveringthrough intricate environments while anticipating (potentially hidden) roadusers and traveling along the infrastructure toward a specific goal. Throughtheoretical underpinnings and simulations, we validate the effectiveness of ourapproach. This paper bridges crucial gaps in motion planning for automatedvehicles, offering a pathway toward safer and more adaptable autonomousnavigation in complex urban contexts.

Overcoming the Fear of the Dark: Occlusion-Aware Model-Predictive Planning for Automated Vehicles Using Risk Fields