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We present a framework for dynamic trajectory generation for autonomousnavigation, which does not rely on HD maps as the underlying representation.High Definition (HD) maps have become a key component in most autonomousdriving frameworks, which include complete road network information annotatedat a centimeter-level that include traversable waypoints, lane information, andtraffic signals. Instead, the presented approach models the distributions offeasible ego-centric trajectories in real-time given a nominal graph-basedglobal plan and a lightweight scene representation. By embedding contextualinformation, such as crosswalks, stop signs, and traffic signals, our approachachieves low errors across multiple urban navigation datasets that includediverse intersection maneuvers, while maintaining real-time performance andreducing network complexity. Underlying datasets introduced are availableonline.

TridentNetV2: Lightweight Graphical Global Plan Representations for Dynamic Trajectory Generation