Autonomous driving has obtained significant attention in recent years, fueled by advancements in computational power that enable new opportunities. This progress allows for the application of complex machine learning (ML) models to enhance autonomous driving stacks and address the limitations of traditional methods. Path planning, particularly for obstacle avoidance, is a key area where ML can offer more robust and adaptable solutions compared to the geometric approaches often used in current systems like Autoware Universe. These geometric methods can exhibit significant limitations in dynamic scenarios, creating a need for the adaptive solutions that ML can provide. A critical challenge in autonomous navigation remains the development of path-planning mechanisms capable of generating safe, efficient, and comfortable trajectories in real time.