  We present a framework for dynamic trajectory generation for autonomous
navigation, which does not rely on HD maps as the underlying representation.
High Definition (HD) maps have become a key component in most autonomous
driving frameworks, which include complete road network information annotated
at a centimeter-level that include traversable waypoints, lane information, and
traffic signals. Instead, the presented approach models the distributions of
feasible ego-centric trajectories in real-time given a nominal graph-based
global plan and a lightweight scene representation. By embedding contextual
information, such as crosswalks, stop signs, and traffic signals, our approach
achieves low errors across multiple urban navigation datasets that include
diverse intersection maneuvers, while maintaining real-time performance and
reducing network complexity. Underlying datasets introduced are available
online.
