地図と社会的文脈を用いた市街地車両の行動系列予測
Action Sequence Predictions of Vehicles in Urban Environments using Map and Social Context
HDマップを活用して走行データから行動系列を自動生成する手法を提案し、Argoverseに適用して大規模データセットを構築するとともに、位置・速度・地図・社会的文脈を統合した行動系列予測ネットワークを提案した。
著者: Jan-Nico Zaech, Dengxin Dai, Alexander Liniger, Luc Van Gool
分類: cs.CV, cs.RO
原文アブストラクト
This work studies the problem of predicting the sequence of future actions for surround vehicles in real-world driving scenarios. To this aim, we make three main contributions. The first contribution is an automatic method to convert the trajectories recorded in real-world driving scenarios to action sequences with the help of HD maps. The method enables automatic dataset creation for this task from large-scale driving data. Our second contribution lies in applying the method to the well-known traffic agent tracking and prediction dataset Argoverse, resulting in 228,000 action sequences. Additionally, 2,245 action sequences were manually annotated for testing. The third contribution is to propose a novel action sequence prediction method by integrating past positions and velocities of the traffic agents, map information and social context into a single end-to-end trainable neural network. Our experiments prove the merit of the data creation method and the value of the created dataset - prediction performance improves consistently with the size of the dataset and shows that our action prediction method outperforms comparing models.
