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 …
Vehicles driving behavior recognition based on transfer learning
Due to the complexity of experiments to test driving behaviors and the high cost of data
collection for some types of vehicles, eg, heavy-duty freight vehicles, it is normally hard to …
collection for some types of vehicles, eg, heavy-duty freight vehicles, it is normally hard to …
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 …
Vehicle driving behavior recognition based on multi-view convolutional neural network with joint data augmentation
Y Zhang, J Li, Y Guo, C Xu, J Bao… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
This paper proposes a method for vehicle driving behavior recognition based on a six-axis
motion processor. This method uses deep-learning technology to learn the sample data …
motion processor. This method uses deep-learning technology to learn the sample data …
Driving behaviour recognition from still images by using multi-stream fusion CNN
Y Hu, M Lu, X Lu - Machine Vision and Applications, 2019 - Springer
Abnormal driving behaviour is a leading cause of serious traffic accidents threatening
human life and public property globally. In this paper, we investigate the use of a deep …
human life and public property globally. In this paper, we investigate the use of a deep …
Driver identification using only the CAN-Bus vehicle data through an RCN deep learning approach
N Abdennour, T Ouni, NB Amor - Robotics and Autonomous Systems, 2021 - Elsevier
In the recent years, many studies claim that humans have a unique driving behavior style
that could be used as a fingerprint in recognizing the identity of the driver. With the rising …
that could be used as a fingerprint in recognizing the identity of the driver. With the rising …
Distracted driver classification using deep learning
M Alotaibi, B Alotaibi - Signal, Image and Video Processing, 2020 - Springer
One of the most challenging topics in the field of intelligent transportation systems is the
automatic interpretation of the driver's behavior. This research investigates distracted driver …
automatic interpretation of the driver's behavior. This research investigates distracted driver …
Driver behavior detection and classification using deep convolutional neural networks
Driver behavior monitoring system as Intelligent Transportation Systems (ITS) have been
widely exploited to reduce the traffic accidents risk. Most previous methods for monitoring …
widely exploited to reduce the traffic accidents risk. Most previous methods for monitoring …
HCF: A hybrid CNN framework for behavior detection of distracted drivers
C Huang, X Wang, J Cao, S Wang, Y Zhang - IEEE access, 2020 - ieeexplore.ieee.org
Distracted driving causes a large number of traffic accident fatalities and is becoming an
increasingly important issue in recent research on traffic safety. Gesture patterns are less …
increasingly important issue in recent research on traffic safety. Gesture patterns are less …
Human-factors-in-driving-loop: Driver identification and verification via a deep learning approach using psychological behavioral data
Driver identification has been popular in the field of driving behavior analysis, which has a
broad range of applications in anti-thief, driving style recognition, insurance strategy, and …
broad range of applications in anti-thief, driving style recognition, insurance strategy, and …