不確実で変化する意図を持つ歩行者のリアルタイム予測モデリングとロバストな回避
Real-Time Predictive Modeling and Robust Avoidance of Pedestrians with Uncertain, Changing Intentions
歩行者の意図変化をチェンジポイント検出とクラスタリングで素早く捉え、オンライン学習で未知の動きも予測。確率的制約付き計画でリアルタイムに安全な軌道を生成し、実機実験で動的環境でのロバストな走行を実証した。
著者: Sarah Ferguson, Brandon Luders, Robert C. Grande, Jonathan P. How
分類: cs.RO
原文アブストラクト
To plan safe trajectories in urban environments, autonomous vehicles must be able to quickly assess the future intentions of dynamic agents. Pedestrians are particularly challenging to model, as their motion patterns are often uncertain and/or unknown a priori. This paper presents a novel changepoint detection and clustering algorithm that, when coupled with offline unsupervised learning of a Gaussian process mixture model (DPGP), enables quick detection of changes in intent and online learning of motion patterns not seen in prior training data. The resulting long-term movement predictions demonstrate improved accuracy relative to offline learning alone, in terms of both intent and trajectory prediction. By embedding these predictions within a chance-constrained motion planner, trajectories which are probabilistically safe to pedestrian motions can be identified in real-time. Hardware experiments demonstrate that this approach can accurately predict pedestrian motion patterns from onboard sensor/perception data and facilitate robust navigation within a dynamic environment.
