Denoising Recurrent Neural Networks for Classifying Crash-Related Events

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With detailed sensor and visual data from automobiles, a data-driven model can learn to classify crash-related events during a drive. We propose a neural network model accepting time-series vehicle sensor data and forward-facing videos as input for learning classification of crash-related events and varying types of such events. To elaborate, a novel recurrent neural network structure is introduced, namely, denoising gated recurrent unit with decay, in order to deal with time-series automobile sensor data with missing value and noises. Our model detects crash and near-crash events based on a large set of time-series data collected from naturalistic driving behavior. Furthermore, the model classifies those events involving pedestrians, a vehicle in front, or a vehicle on either side. The effectiveness of our model is evaluated with more than two thousand 30-s clips from naturalistic driving behavior data. The results show that the model, including sensory encoder with denoising gated recurrent unit with decay, visual encoder, and attention mechanism, outperforms gated recurrent unit with decay, gated CNN, and other baselines not only in event classification and but also in event-type classification.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2020-07
Language
English
Article Type
Article
Citation

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, v.21, no.7, pp.2906 - 2917

ISSN
1524-9050
DOI
10.1109/TITS.2019.2921722
URI
http://hdl.handle.net/10203/281982
Appears in Collection
CS-Journal Papers(저널논문)
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