Driving Safety Risk Prediction Using Cost-Sensitive With Nonnegativity-Constrained Autoencoders Based on Imbalanced Naturalistic Driving Data

A large number of studies have shown that most vehicle collisions are caused by drivers' abnormal operations. To ensure the safety of all people on the road network as much as possible, it is crucial to be able to predict the drivers' driving safety risks in real time. In this paper, we pr...

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Bibliographic Details
Published in:IEEE transactions on intelligent transportation systems Vol. 20; no. 12; pp. 4450 - 4465
Main Authors: Chen, Jie, Wu, ZhongCheng, Zhang, Jun
Format: Journal Article
Language:English
Published: New York IEEE 01.12.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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Accident reconstruction
Accidents
adaptive search
Automobile safety
class imbalance
Computer crashes
Drivers
driving safety risk prediction
Feature extraction
naturalistic driving study
Real-time systems
Risk levels
Roads
Safety
Sliding
Time series analysis
Vehicle safety
Vehicles
Windows (intervals)
ISSN:1524-9050, 1558-0016
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Summary:A large number of studies have shown that most vehicle collisions are caused by drivers' abnormal operations. To ensure the safety of all people on the road network as much as possible, it is crucial to be able to predict the drivers' driving safety risks in real time. In this paper, we propose a novel cost-sensitive L 1 /L 2 -nonnegativity-constrained deep autoencoder network for driving safety risk prediction. Unfortunately, with existing research methods, the size of the sliding time window is too large, the feature extraction is relatively subjective, and class imbalances occur, which leads to low identification accuracy, long prediction times, and poor applicability. We first propose using a three-layer L 1 /L 2 -nonnegativity-constrained autoencoder to adaptively search the optimal size of the sliding window and then construct a deep L 1 /L 2 -nonnegativity-constrained autoencoder network to automatically extract the hidden features of the driving behaviors. Finally, we build a new L 1 /L 2 -nonnegativityconstrained focal loss classifier to predict the driving behaviors under different safety risk levels. The results from the public 100-Car naturalistic driving study dataset indicate that our method can effectively find the optimal window size, reduce the data volume and reconstruction error, and extract more distinctive features. Furthermore, this method effectively curbs the class imbalance, improves the driving safety risk prediction performance, reduces overfitting, shortens the prediction time, and improves the timeliness.
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ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2018.2886280