PePScenes：3D歩行者行動予測のための新データセットとベースライン
PePScenes: A Novel Dataset and Baseline for Pedestrian Action Prediction in 3D
自動運転データセットnuScenesに2D/3Dバウンディングボックスと行動アノテーションを追加した歩行者横断行動予測用データセットを構築し、複数モダリティを統合するハイブリッドニューラルネットのベースラインを提案した論文。
著者: Amir Rasouli, Tiffany Yau, Peter Lakner, Saber Malekmohammadi, Mohsen Rohani, Jun Luo
分類: cs.CV, cs.RO
原文アブストラクト
Predicting the behavior of road users, particularly pedestrians, is vital for safe motion planning in the context of autonomous driving systems. Traditionally, pedestrian behavior prediction has been realized in terms of forecasting future trajectories. However, recent evidence suggests that predicting higher-level actions, such as crossing the road, can help improve trajectory forecasting and planning tasks accordingly. There are a number of existing datasets that cater to the development of pedestrian action prediction algorithms, however, they lack certain characteristics, such as bird's eye view semantic map information, 3D locations of objects in the scene, etc., which are crucial in the autonomous driving context. To this end, we propose a new pedestrian action prediction dataset created by adding per-frame 2D/3D bounding box and behavioral annotations to the popular autonomous driving dataset, nuScenes. In addition, we propose a hybrid neural network architecture that incorporates various data modalities for predicting pedestrian crossing action. By evaluating our model on the newly proposed dataset, the contribution of different data modalities to the prediction task is revealed. The dataset is available at https://github.com/huawei-noah/PePScenes.
