Pedestrian Trajectory Prediction Combining Probabilistic Reasoning and Sequence Learning

• Published in: IEEE transactions on intelligent vehicles Vol. 5; no. 3; pp. 461 - 474
• Main Authors: Li, Yang, Lu, Xiao-Yun, Wang, Jianqiang, Li, Keqiang
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.09.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptation models; Bayes methods; Bayesian analysis; Decision making; Dynamic Bayesian network; Intelligent vehicles; pedestrian behavior; Predictive models; Reasoning; Roads; sequence learning; Switches; Trajectory; trajectory prediction; vulnerable road users; Weighting methods
• ISSN: 2379-8858; 2379-8904
• DOI: 10.1109/TIV.2020.2966117

Pedestrian behavior prediction is essential to enable safe and efficient driving of intelligent vehicles on urban traffic environment. This article presents a novel framework for pedestrian trajectory prediction, which integrates Dynamic Bayesian network and Sequence-to-Sequence model through an adaptive online weighting method. Dynamic Bayesian network utilizes environmental features and kinematic information to infer the pedestrian's motion intentions through probabilistic reasoning. Sequence-to-Sequence model views trajectory predictions as sequence generation tasks, in which the future trajectories are generated relying on the observed trajectories. A real-world pedestrian motion dataset is employed for model validations and it is also enlarged through data augmentation techniques to enable training of data-driven approaches. We compare our model with several typical baselines methods and results show that our model outperforms those baselines. The average error and the final destination error with one-second prediction are 0.04m and 0.10m in crossing scenarios, and 0.06m and 0.17m in stopping scenarios, respectively. The study expects to provide guidelines for the decision-making of intelligent vehicles in order to protect vulnerable road users.