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Pedestrian Intention Recognition and Action Prediction Using a Feature Fusion Deep Learning Approach
University of Skövde, School of Informatics. (Skövde Artificial Intelligence Lab (SAIL))
University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment. (Skövde Artificial Intelligence Lab (SAIL))ORCID iD: 0000-0003-2949-4123
2021 (English)In: USB Proceedings The 18th International Conference on Modeling Decisions for Artificial Intelligence: MDAI 2021, Umeå / [ed] Vicenç Torra; Yasuo Narukawa, 2021, p. 89-100Conference paper, Published paper (Refereed)
Abstract [en]

Recognizing Pedestrians intention to cross a street and predicting their imminent crossing action are major challenges for advanced driver assistance systems (ADAS) and Autonomous Vehicles (AV). In this paper we address these problems by proposing a new neural network architecture that uses feature fusion. The approach is used to recog[1]nise/predict the pedestrians intention/action 1.5 sec (45 frames) ahead. We evaluate our approach on the recently suggested benchmark by Rasouli et al. and show that our approach outperforms current state of the art models. We observe further improved results when the model is trained and tested on a stronger balanced subset of the PIE dataset.

Place, publisher, year, edition, pages
2021. p. 89-100
Keywords [en]
Intention Recognition, ADAS, Deep Learning, Feature Fusion
National Category
Computer and Information Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
URN: urn:nbn:se:his:diva-20644ISBN: 978-91-527-1027-2 (electronic)OAI: oai:DiVA.org:his-20644DiVA, id: diva2:1602716
Conference
The 18th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2021, Umeå, Sweden, September 27–30, 2021
Note

Supporting Institutions:

Department of Computing Sciences, Umeå University

The European Society for Fuzzy Logic and Technology (EUSFLAT)

The Catalan Association for Artificial Intelligence (ACIA)

The Japan Society for Fuzzy Theory and Intelligent Informatics (SOFT)

The UNESCO Chair in Data Privacy 

Available from: 2021-10-13 Created: 2021-10-13 Last updated: 2022-04-11Bibliographically approved

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Steinhauer, H. Joe

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CiteExportLink to record
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Citation style
  • apa
  • apa-cv
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
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  • asciidoc
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