LookOut: 自動運転のための多様な未来予測と計画
LookOut: Diverse Multi-Future Prediction and Planning for Self-Driving
交通シーンの多様な未来を生成モデルで予測し、それらに対する安全な反応を保証する非保守的な軌道計画を最適化する自動運転システムを提案した論文。
著者: Alexander Cui, Sergio Casas, Abbas Sadat, Renjie Liao, Raquel Urtasun
分類: cs.RO, cs.AI, cs.CV, cs.LG
原文アブストラクト
In this paper, we present LookOut, a novel autonomy system that perceives the environment, predicts a diverse set of futures of how the scene might unroll and estimates the trajectory of the SDV by optimizing a set of contingency plans over these future realizations. In particular, we learn a diverse joint distribution over multi-agent future trajectories in a traffic scene that covers a wide range of future modes with high sample efficiency while leveraging the expressive power of generative models. Unlike previous work in diverse motion forecasting, our diversity objective explicitly rewards sampling future scenarios that require distinct reactions from the self-driving vehicle for improved safety. Our contingency planner then finds comfortable and non-conservative trajectories that ensure safe reactions to a wide range of future scenarios. Through extensive evaluations, we show that our model demonstrates significantly more diverse and sample-efficient motion forecasting in a large-scale self-driving dataset as well as safer and less-conservative motion plans in long-term closed-loop simulations when compared to current state-of-the-art models.
