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Predicting Cyclist Trajectory on Shared Road for Conflict Discovery and Proactive Alerts
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering.
2023 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

The growing popularity of cycling as a sustainable transportation choice has inten- sified the focus on cyclist safety. Despite this mounting concern, the development of cyclist-side decision support systems aimed at enhancing cyclist safety remains limited. This study aims to bridge this gap by conducting a comprehensive literature review of machine learning-based trajectory prediction methods applied to pedestrians. Build- ing upon this groundwork, the study develops cyclist trajectory prediction models and assesses a cyclist decision support system within a simulated environment. Our study involves a comparative analysis of trajectory prediction models, examining the impact of including social context and scene context in comparison to a baseline model that solely considers the cyclist’s individual dynamics. The evaluation metrics used encompass the Final Displacement Error and Average Displacement Errors. Our analysis reveals a significant improvement in prediction accuracy through the incorporation of social and scene contexts, while social contexts exert a more substantial influence on accurate trajectory prediction. Moreover, the results suggest that when integrating social context, both the proximity of surrounding road users and the density of neighborhoods influ- ence the prediction accuracy. The developed trajectory prediction model is integrated into a simulated environment, coupled with a conflict detection algorithm, to construct a cyclist-decision support system. This integration facilitates the timely provision of collision alerts to cyclists, highlighting potential conflict scenarios. Through simulation- based evaluations, instances of trajectory prediction and conflict detection failures are identified. These findings not only contribute to a comprehensive understanding of system limitations but also pave the way for future research avenues. 

Place, publisher, year, edition, pages
2023. , p. 61
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:ltu:diva-103222OAI: oai:DiVA.org:ltu-103222DiVA, id: diva2:1817523
Subject / course
Student thesis, at least 30 credits
Educational program
Master Programme in Green Networking and Cloud Computing
Examiners
Available from: 2023-12-07 Created: 2023-12-06 Last updated: 2023-12-07Bibliographically approved

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Citation style
  • apa
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  • de-DE
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  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
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  • Other locale
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  • asciidoc
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