実時間で確率的保証付き歩行者予測の技術報告
Technical Report for Real-Time Certified Probabilistic Pedestrian Forecasting
シーン内の履歴軌跡から学習した常微分方程式の重み付き和により歩行者を確率的に予測する手法を提案し、リアルタイム動作と長期予測精度の向上を実証した。
著者: Henry O. Jacobs, Owen K. Hughes, Matthew Johnson-Roberson, Ram Vasudevan
分類: cs.RO, math.OC
原文アブストラクト
The success of autonomous systems will depend upon their ability to safely navigate human-centric environments. This motivates the need for a real-time, probabilistic forecasting algorithm for pedestrians, cyclists, and other agents since these predictions will form a necessary step in assessing the risk of any action. This paper presents a novel approach to probabilistic forecasting for pedestrians based on weighted sums of ordinary differential equations that are learned from historical trajectory information within a fixed scene. The resulting algorithm is embarrassingly parallel and is able to work at real-time speeds using a naive Python implementation. The quality of predicted locations of agents generated by the proposed algorithm is validated on a variety of examples and considerably higher than existing state of the art approaches over long time horizons.
