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Driver behavior classification: A systematic literature review
Driver behavior is receiving increasing attention because of the staggering number of road
accidents. Many road safety reports regard human behavior as the most important factor in …
accidents. Many road safety reports regard human behavior as the most important factor in …
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 …
How did COVID-19 impact driving behaviors and crash Severity? A multigroup structural equation modeling
Risky driving behaviors such as speeding and failing to signal have been witnessed more
frequently during the COVID-19 pandemic, resulting in higher rates of severe crashes. This …
frequently during the COVID-19 pandemic, resulting in higher rates of severe crashes. This …
A review of driving style recognition methods from short-term and long-term perspectives
Driving style recognition provides an effective way to understand human driving behaviors
and thereby plays an important role in the automotive sector. However, most works fail to …
and thereby plays an important role in the automotive sector. However, most works fail to …
Early identification and detection of driver drowsiness by hybrid machine learning
Drunkenness or exhaustion is a leading cause of car accidents, with severe implications for
road safety. More fatal accidents could be avoided if fatigued drivers were warned ahead of …
road safety. More fatal accidents could be avoided if fatigued drivers were warned ahead of …
MTP-GO: Graph-based probabilistic multi-agent trajectory prediction with neural ODEs
Enabling resilient autonomous motion planning requires robust predictions of surrounding
road users' future behavior. In response to this need and the associated challenges, we …
road users' future behavior. In response to this need and the associated challenges, we …
E2DR: A deep learning ensemble-based driver distraction detection with recommendations model
M Aljasim, R Kashef - Sensors, 2022 - mdpi.com
The increasing number of car accidents is a significant issue in current transportation
systems. According to the World Health Organization (WHO), road accidents are the eighth …
systems. According to the World Health Organization (WHO), road accidents are the eighth …
[HTML][HTML] Driver fatigue detection systems using multi-sensors, smartphone, and cloud-based computing platforms: a comparative analysis
Internet of things (IoT) cloud-based applications deliver advanced solutions for smart cities
to decrease traffic accidents caused by driver fatigue while driving on the road …
to decrease traffic accidents caused by driver fatigue while driving on the road …
Driver drowsiness prediction based on multiple aspects using image processing techniques
The majority of the accidents were happening perpetually due to driver drowsiness over the
decades. Automation has been playing key role in many fields to provide conformity and …
decades. Automation has been playing key role in many fields to provide conformity and …
[PDF][PDF] Deriving Driver Behavioral Pattern Analysis and Performance Using Neural Network Approaches.
It has been observed that driver behavior has a direct and considerable impact upon factors
like fuel consumption, environmentally harmful emissions, and public safety, making it a key …
like fuel consumption, environmentally harmful emissions, and public safety, making it a key …