都市運転における離散的行動モードのための実行可能な統合予測と計画
Tractable Joint Prediction and Planning over Discrete Behavior Modes for Urban Driving
マルチモーダル軌道予測モデルを再学習なしで閉ループ計画に統合し、離散的な行動モード上で反応的な計画を行う手法を提案。動的な合流シナリオで従来のプランナーが陥る「凍結ロボット問題」を回避し、CARLAの高密度交通シナリオで最先端性能を達成。
著者: Adam Villaflor, Brian Yang, Huangyuan Su, Katerina Fragkiadaki, John Dolan, Jeff Schneider
分類: cs.RO, cs.LG
原文アブストラクト
Significant progress has been made in training multimodal trajectory forecasting models for autonomous driving. However, effectively integrating these models with downstream planners and model-based control approaches is still an open problem. Although these models have conventionally been evaluated for open-loop prediction, we show that they can be used to parameterize autoregressive closed-loop models without retraining. We consider recent trajectory prediction approaches which leverage learned anchor embeddings to predict multiple trajectories, finding that these anchor embeddings can parameterize discrete and distinct modes representing high-level driving behaviors. We propose to perform fully reactive closed-loop planning over these discrete latent modes, allowing us to tractably model the causal interactions between agents at each step. We validate our approach on a suite of more dynamic merging scenarios, finding that our approach avoids the $\textit{frozen robot problem}$ which is pervasive in conventional planners. Our approach also outperforms the previous state-of-the-art in CARLA on challenging dense traffic scenarios when evaluated at realistic speeds.
