2020/12/06 by Amir Rasouli, Rasouli, Amir, Mohsen Rohani +3 · 4 citations
Computer Science · Engineering · #Autonomous Vehicle Technology and Safety #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Human Pose and Action Recognition #Robotics (cs.RO) #Traffic and Road Safety #cs.CV #cs.RO
paper · pdf · doi:10.48550/arxiv.2012.03298
ICCV 2021. 11 pages; 5 Figures; 5 tables
openalex publication_date 2020/12/06 · arxiv created 2021/08/09 · arxiv updated 2021/08/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.