Attention-based deep neural network for driver behavior recognition
W Xiao, H Liu, Z Ma, W Chen - Future Generation Computer Systems, 2022 - Elsevier
Driver behavior recognition is crucial for traffic safety in intelligent transportation systems. To
understand the driver distraction behavior, deep learning methods has been used to learn …
understand the driver distraction behavior, deep learning methods has been used to learn …
FDAN: Fuzzy deep attention networks for driver behavior recognition
W Xiao, G Xie, H Liu, W Chen, R Li - Journal of Systems Architecture, 2024 - Elsevier
Driver behavior is an essential factor affecting traffic safety, and driver behavior monitoring
systems (DMSs) are widely exploited in intelligent transportation systems to reduce the risk …
systems (DMSs) are widely exploited in intelligent transportation systems to reduce the risk …
Driver activity recognition for intelligent vehicles: A deep learning approach
Driver decisions and behaviors are essential factors that can affect the driving safety. To
understand the driver behaviors, a driver activities recognition system is designed based on …
understand the driver behaviors, a driver activities recognition system is designed based on …
Feature refinement for image-based driver action recognition via multi-scale attention convolutional neural network
Y Hu, M Lu, X Lu - Signal Processing: Image Communication, 2020 - Elsevier
Driver distraction has currently been a global issue causing the dramatic increase of road
accidents and casualties. However, recognizing distracted driving action remains a …
accidents and casualties. However, recognizing distracted driving action remains a …
End-to-end driving activities and secondary tasks recognition using deep convolutional neural network and transfer learning
Drivers' decision and their corresponding behaviors are important aspects that can affect the
driving safety, and it is necessary to understand the driver behaviors in real-time. In this …
driving safety, and it is necessary to understand the driver behaviors in real-time. In this …
AB-DLM: an improved deep learning model based on attention mechanism and BiFPN for driver distraction behavior detection
T Li, Y Zhang, Q Li, T Zhang - IEEE Access, 2022 - ieeexplore.ieee.org
Driver distraction behavior causes a large number of traffic accidents every year, resulting in
economic losses and injuries. Currently, the driver still plays an important role in the driving …
economic losses and injuries. Currently, the driver still plays an important role in the driving …
Joint deep neural network modelling and statistical analysis on characterizing driving behaviors
Google defines the concept of autonomous driving as one of the applications of big data.
Specifically, with the input sensor data, the autonomous vehicles can be provided with the …
Specifically, with the input sensor data, the autonomous vehicles can be provided with the …
Driver behavior analysis via two-stream deep convolutional neural network
JC Chen, CY Lee, PY Huang, CR Lin - Applied Sciences, 2020 - mdpi.com
According to the World Health Organization global status report on road safety, traffic
accidents are the eighth leading cause of death in the world, and nearly one-fifth of the traffic …
accidents are the eighth leading cause of death in the world, and nearly one-fifth of the traffic …
End-to-end deep learning for driver distraction recognition
In this paper, an end-to-end deep learning solution for driver distraction recognition is
presented. In the proposed framework, the features from pre-trained convolutional neural …
presented. In the proposed framework, the features from pre-trained convolutional neural …
Enhancing driver distraction recognition using generative adversarial networks
Distracted driving is among the primary causes for serious car accidents. Among the leading
cause of death among teenagers today are traffic accidents and major part of them are …
cause of death among teenagers today are traffic accidents and major part of them are …