TridentNet: A Conditional Generative Model for Dynamic Trajectory Generation
This work addresses scalability and generalization issues for autonomous vehicle trajectory generation, though it appears incremental by building on existing methods.
The paper tackles the lack of generalization in end-to-end autonomous vehicle models by introducing TridentNet, a conditional generative model that uses lightweight map representations and geometric constraints to generate feasible trajectories, achieving low relative errors.
In recent years, various state of the art autonomous vehicle systems and architectures have been introduced. These methods include planners that depend on high-definition (HD) maps and models that learn an autonomous agent's controls in an end-to-end fashion. While end-to-end models are geared towards solving the scalability constraints from HD maps, they do not generalize for different vehicles and sensor configurations. To address these shortcomings, we introduce an approach that leverages lightweight map representations, explicitly enforcing geometric constraints, and learns feasible trajectories using a conditional generative model. Additional contributions include a new dataset that is used to verify our proposed models quantitatively. The results indicate low relative errors that can potentially translate to traversable trajectories. The dataset created as part of this work has been made available online.