  Navigating unsignalized intersections in urban environments poses a complex
challenge for self-driving vehicles, where issues such as view obstructions,
unpredictable pedestrian crossings, and diverse traffic participants demand a
great focus on crash prevention. In this paper, we propose a novel state
representation for Reinforcement Learning (RL) agents centered around the
information perceivable by an autonomous agent, enabling the safe navigation of
previously uncharted road maps. Our approach surpasses several baseline models
by a sig nificant margin in terms of safety and energy consumption metrics.
These improvements are achieved while maintaining a competitive average travel
speed. Our findings pave the way for more robust and reliable autonomous
navigation strategies, promising safer and more efficient urban traffic
environments.
