安全な自動運転のための分布認識型目標予測と適合モデルベース計画
Distribution-aware Goal Prediction and Conformant Model-based Planning for Safe Autonomous Driving
自動運転の学習を認識・目標予測・計画に分解し、正規化フローで多様な目標分布を予測してモデルベース計画を行う手法を提案し、CARLAのCARNOVELベンチマークで最高性能を達成した。
著者: Jonathan Francis, Bingqing Chen, Weiran Yao, Eric Nyberg, Jean Oh
分類: cs.RO, cs.AI, cs.CV, cs.LG, cs.SY, eess.SY
原文アブストラクト
The feasibility of collecting a large amount of expert demonstrations has inspired growing research interests in learning-to-drive settings, where models learn by imitating the driving behaviour from experts. However, exclusively relying on imitation can limit agents' generalisability to novel scenarios that are outside the support of the training data. In this paper, we address this challenge by factorising the driving task, based on the intuition that modular architectures are more generalisable and more robust to changes in the environment compared to monolithic, end-to-end frameworks. Specifically, we draw inspiration from the trajectory forecasting community and reformulate the learning-to-drive task as obstacle-aware perception and grounding, distribution-aware goal prediction, and model-based planning. Firstly, we train the obstacle-aware perception module to extract salient representation of the visual context. Then, we learn a multi-modal goal distribution by performing conditional density-estimation using normalising flow. Finally, we ground candidate trajectory predictions road geometry, and plan the actions based on on vehicle dynamics. Under the CARLA simulator, we report state-of-the-art results on the CARNOVEL benchmark.
