TridentNetV2: 動的軌道生成のための軽量グラフ型グローバルプラン表現
TridentNetV2: Lightweight Graphical Global Plan Representations for Dynamic Trajectory Generation
HDマップに依存せず、軽量なグラフ型グローバルプランとシーン表現からリアルタイムに自車周辺の実行可能軌道分布を生成する自律ナビゲーション手法を提案した論文。
著者: David Paz, Hao Xiang, Andrew Liang, Henrik I. Christensen
分類: cs.RO
原文アブストラクト
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.
