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 distraction detection using capsule network
With the onset of the new technological age, the distractions caused due to handheld
devices have been a major cause of traffic accidents as they affect the decision-making …
devices have been a major cause of traffic accidents as they affect the decision-making …
Aggregating CNN and HOG features for real-time distracted driver detection
MR Arefin, F Makhmudkhujaev… - 2019 IEEE …, 2019 - ieeexplore.ieee.org
Detecting distracted behaviors of drivers, and warning them in real-time can reduce the
number of road accidents. Recently, Convolutional Neural Network (CNN) has been …
number of road accidents. Recently, Convolutional Neural Network (CNN) has been …
Vision based detection of driver cell phone usage and food consumption
B Wagner, F Taffner, S Karaca… - IEEE transactions on …, 2021 - ieeexplore.ieee.org
Distracted driving is a problem which yearly causes a large amount of road traffic crashes
with high rates of fatalities and injured persons. Recently, car manufacturers started to …
with high rates of fatalities and injured persons. Recently, car manufacturers started to …
Application of deep learning for characterization of drivers' engagement in secondary tasks in in-vehicle systems
Distracted driving (ie, engaging in secondary tasks) is an epidemic that threatens the lives of
thousands every year. Data collected from vehicular sensor technologies and through …
thousands every year. Data collected from vehicular sensor technologies and through …
Detecting and recognizing driver distraction through various data modality using machine learning: A review, recent advances, simplified framework and open …
Driver distraction is one of the main causes of fatal traffic accidents. Therefore, the ability to
detect driver inattention is essential in building a safe yet intelligent transportation system …
detect driver inattention is essential in building a safe yet intelligent transportation system …
A personalized model for driver lane-changing behavior prediction using deep neural network
J Gao, H Zhu, YL Murphey - 2019 2nd International Conference …, 2019 - ieeexplore.ieee.org
Lane changes are stressful maneuvers for drivers, particularly during high-speed traffic
flows. Modeling driver's lane-changing decision and implementation process is challenging …
flows. Modeling driver's lane-changing decision and implementation process is challenging …
Detecting distraction in drivers using electroencephalogram (EEG) signals
SP Kumar, J Selvaraj, R Krishnakumar… - 2020 fourth …, 2020 - ieeexplore.ieee.org
Driver distraction is considered as major factors in most of the traffic accidents. Driving errors
may arise due to the distraction of the drivers. The aim of this paper is to analyze the EEG …
may arise due to the distraction of the drivers. The aim of this paper is to analyze the EEG …
Detecting human driver inattentive and aggressive driving behavior using deep learning: Recent advances, requirements and open challenges
MH Alkinani, WZ Khan, Q Arshad - Ieee Access, 2020 - ieeexplore.ieee.org
Human drivers have different driving styles, experiences, and emotions due to unique
driving characteristics, exhibiting their own driving behaviors and habits. Various research …
driving characteristics, exhibiting their own driving behaviors and habits. Various research …