TridentNet: 動的軌道生成のための条件付き生成モデル
TridentNet: A Conditional Generative Model for Dynamic Trajectory Generation
軽量な地図表現と幾何学的制約を活用し、条件付き生成モデルで走行可能な軌道を学習する手法を提案。検証用の新データセットも公開した。
著者: David Paz, Hengyuan Zhang, Henrik I. Christensen
分類: cs.RO
原文アブストラクト
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.
