歩行者の行動予測のための二重・意味推論フレームワークBiPed
Bifold and Semantic Reasoning for Pedestrian Behavior Prediction
マルチモーダルデータを二重エンコードし、シーンの意味解析で相互作用をモデル化して歩行者の軌跡と行動を同時予測する手法を提案し、PIE/JAADで最先端性能を達成した。
著者: Amir Rasouli, Mohsen Rohani, Jun Luo
分類: cs.CV, cs.RO
原文アブストラクト
Pedestrian behavior prediction is one of the major challenges for intelligent driving systems. Pedestrians often exhibit complex behaviors influenced by various contextual elements. To address this problem, we propose BiPed, a multitask learning framework that simultaneously predicts trajectories and actions of pedestrians by relying on multimodal data. Our method benefits from 1) a bifold encoding approach where different data modalities are processed independently allowing them to develop their own representations, and jointly to produce a representation for all modalities using shared parameters; 2) a novel interaction modeling technique that relies on categorical semantic parsing of the scenes to capture interactions between target pedestrians and their surroundings; and 3) a bifold prediction mechanism that uses both independent and shared decoding of multimodal representations. Using public pedestrian behavior benchmark datasets for driving, PIE and JAAD, we highlight the benefits of the proposed method for behavior prediction and show that our model achieves state-of-the-art performance and improves trajectory and action prediction by up to 22% and 9% respectively. We further investigate the contributions of the proposed reasoning techniques via extensive ablation studies.
