Leveraging Deep Learning and Multimodal Large Language Models for Near-Miss Detection Using Crowdsourced Videos

Leveraging Deep Learning and Multimodal Large Language Models for Near-Miss Detection Using Crowdsourced Videos

Near-miss traffic incidents, positioned just above "unsafe acts" on the safety triangle theory, offer crucial predictive insights for preventing crashes. However, these incidents are often underrepresented in traffic safety research, which tends to focus primarily on actual crashes. This s...

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Bibliographic Details
Main Authors: Shadi Jaradat, Mohammed Elhenawy, Huthaifa I. Ashqar, Alexander Paz, Richi Nayak
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Open Journal of the Computer Society
Subjects:
Near-miss detection
crowdsource
proactive approach
convolutional neural network (CNN)
vision transformer
LSTM
Online Access:https://ieeexplore.ieee.org/document/10820995/
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