Predicting Pedestrian Crossing Intention With Feature Fusion and Spatio-Temporal Attention

Predicting vulnerable road user behavior is an essential prerequisite for deploying Automated Driving Systems (ADS) in the real-world. Pedestrian crossing intention should be recognized in real-time, especially for urban driving. Recent works have shown the potential of using vision-based deep neural network models for this task. However, these models are not robust and certain issues still need to be resolved. First, the global spatio-temporal context that accounts for the interaction between the target pedestrian and the scene has not been properly utilized. Second, the optimal strategy for fusing different sensor data has not been thoroughly investigated. This work addresses the above limitations by introducing a novel neural network architecture to fuse inherently different spatio-temporal features for pedestrian crossing intention prediction. The authors fuse different phenomena such as sequences of RGB imagery, semantic segmentation masks, and ego-vehicle speed in an optimal way using attention mechanisms and a stack of recurrent neural networks. The optimal architecture was obtained through exhaustive ablation and comparison studies. Extensive comparative experiments on the JAAD and PIE pedestrian action prediction benchmarks demonstrate the effectiveness of the proposed method, where state-of-the-art performance was achieved. The authors' code is open-source and publicly available: https://github.com/OSU-Haolin/Pedestrian_Crossing_Intention_Prediction

• Record URL:
  http://dx.doi.org/10.1109/TIV.2022.3162719
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/23798858
• Supplemental Notes:
  • Copyright © 2022, IEEE.
• Authors:
  • Yang, Dongfang
  • Zhang, Haolin
  • 0000-0002-8549-1974
  • Yurtsever, Ekim
  • Redmill, Keith A
  • Özgüner, Ümit
• Publication Date: 2022-6

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 221-230
• Serial:
  • IEEE Transactions on Intelligent Vehicles
  • Volume: 7
  • Issue Number: 2
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 2379-8858
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=7274857

Subject/Index Terms

• TRT Terms: Behavior; Data fusion; Image processing; Mathematical prediction; Neural networks; Pedestrian movement; Pedestrians; Vulnerable road users
• Subject Areas: Data and Information Technology; Highways; Pedestrians and Bicyclists; Planning and Forecasting;

Filing Info

• Accession Number: 01855636
• Record Type: Publication
• Files: TRIS
• Created Date: Aug 24 2022 3:01PM