Analyzing Factors Influencing Pedestrian Behavior in Urban Traffic Scenarios using Deep Learning
Paper i proceeding, 2023

Pedestrians are the most vulnerable road users in urban traffic scenarios and need to be protected from potentially hazardous situations. It is essential for automated vehicles and modern driver-assistance systems to better predict pedestrians’ behavior to prevent road crashes. Predicting pedestrian behavior is challenging because their behavior can be influenced by many factors. In recent years, deep learning (DL) methods as powerful tools have been utilized by many researchers to improve such predictions, but few researchers have analyzed the factors that influence pedestrian behavior prediction in DL. This paper uses DL to predict and analyze the factors that influence pedestrian behavior, especially the interactions between pedestrians and other road users. We focus on real-world urban traffic and use the publicly available Waymo Open Dataset for training, testing, and analyzing.
Marie Skłodowska-Curie Actions; Innovative Training Network (ITN); Project name: SHAPE-IT; Grant number: 860410; Publication date: 13 December 2023; DOI: 10.1016/j.trpro.2023.11.637

automated vehicles

trajectory prediction

behavior analysis

pedestrian interactions

Deep learning

pedestrian behavior prediction

Författare

Chi Zhang

Chalmers, Data- och informationsteknik, Interaktionsdesign och Software Engineering

Christian Berger

Chalmers, Data- och informationsteknik, Interaktionsdesign och Software Engineering

Transportation Research Procedia

23521457 (ISSN) 23521465 (eISSN)

Vol. 72

TRA Lisbon 2022 Conference Proceedings Transport Research Arena (TRA Lisbon 2022)
Lisboa, Portugal,

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

Europeiska kommissionen (EU) (EC/H2020/860410), 2019-10-01 -- 2023-09-30.

Styrkeområden

Informations- och kommunikationsteknik

Transport

Ämneskategorier

Data- och informationsvetenskap

Transportteknik och logistik

Datorseende och robotik (autonoma system)

DOI

10.1016/j.trpro.2023.11.637

Mer information

Senast uppdaterat

2024-05-30