Pedestrian Behavior Prediction Using Deep Learning Methods for Urban Scenarios: A Review
Journal article, 2023

The prediction of pedestrian behavior is essential for automated driving in urban traffic and has attracted increasing attention in the vehicle industry. This task is challenging because pedestrian behavior is driven by various factors, including their individual properties, the interactions with other road users, and the interactions with the environment. Deep learning approaches have become increasingly popular because of their superior performance in complex scenarios compared to traditional approaches such as the social force or constant velocity models. In this paper, we provide a comprehensive review of deep learning-based approaches for pedestrian behavior prediction. We review and categorize a large selection of scientific contributions covering both trajectory and intention prediction from the last five years. We categorize existing works by prediction tasks, input data, model features, and network structures. Besides, we provide an overview of existing datasets and the evaluation metrics. We analyze, compare, and discuss the performance of existing work. Finally, we point out the research gaps and outline possible directions for future research.
Marie Skłodowska-Curie Actions; Innovative Training Network (ITN); Project name: SHAPE-IT; Grant number: 860410; Publication date: 12 June 2023; DOI: 10.1109/TITS.2023.3281393

survey

neural networks

deep learning

trajectory

Pedestrian behavior prediction

automated vehicles

intention

Author

Chi Zhang

Chalmers, Computer Science and Engineering (Chalmers), Interaction Design and Software Engineering

Christian Berger

Chalmers, Computer Science and Engineering (Chalmers), Interaction Design and Software Engineering

IEEE Transactions on Intelligent Transportation Systems

1524-9050 (ISSN) 1558-0016 (eISSN)

Vol. 24 10 10279-10301

Supporting the interaction of Humans and Automated vehicles: Preparing for the Environment of Tomorrow (Shape-IT)

European Commission (EC) (EC/H2020/860410), 2019-10-01 -- 2023-09-30.

Areas of Advance

Information and Communication Technology

Transport

Infrastructure

ReVeRe (Research Vehicle Resource)

Subject Categories

Robotics

Computer Science

Computer Vision and Robotics (Autonomous Systems)

DOI

10.1109/TITS.2023.3281393

More information

Latest update

7/11/2024