AIMC Topic: Distracted Driving

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An intelligent network framework for driver distraction monitoring based on RES-SE-CNN.

Scientific reports Feb 26, 2025
As the quantity of motor vehicles and drivers experiences a continuous upsurge, the road driving environment has grown progressively more complex. This complexity has led to a concomitant increase in the probability of traffic accidents. Ample resear...
Humans Automobile Driving Distracted Driving Neural Networks, Computer Accidents, Traffic
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A machine learning approach to understanding the road and traffic environments of crashes involving driver distraction and inattention (DDI) on rural multilane highways.

Journal of safety research Nov 14, 2024
INTRODUCTION: Driver distraction and inattention (DDI) are major causes of road crashes, especially on rural highways. However, not all instances of distracted or inattentive driving lead to crashes. Previous studies indicate that DDI-related driving...
Humans Distracted Driving Rural Population Machine Learning Attention Accidents, Traffic Automobile Driving
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CBAM VGG16: An efficient driver distraction classification using CBAM embedded VGG16 architecture.

Computers in biology and medicine Aug 1, 2024
Driver monitoring systems (DMS) are crucial in autonomous driving systems (ADS) when users are concerned about driver/vehicle safety. In DMS, the significant influencing factor of driver/vehicle safety is the classification of driver distractions or ...
Neural Networks, Computer Deep Learning Humans Automobile Driving Attention Distracted Driving
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HSDDD: A Hybrid Scheme for the Detection of Distracted Driving through Fusion of Deep Learning and Handcrafted Features.

Sensors (Basel, Switzerland) Feb 26, 2022
Traditional methods for behavior detection of distracted drivers are not capable of capturing driver behavior features related to complex temporal features. With the goal to improve transportation safety and to reduce fatal accidents on roads, this r...
Deep Learning Support Vector Machine Distracted Driving Algorithms
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E2DR: A Deep Learning Ensemble-Based Driver Distraction Detection with Recommendations Model.

Sensors (Basel, Switzerland) Feb 26, 2022
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 highest top cause of death around the world. More than 80% of road accide...
Attention Automobile Driving Accidents, Traffic Distracted Driving Deep Learning
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A hybrid neural network for driving behavior risk prediction based on distracted driving behavior data.

PloS one Jan 25, 2022
Distracted driving behavior is one of the main factors of road accidents. Accurately predicting the risk of driving behavior is of great significance to the active safety of road transportation. The large amount of information collected by the sensor...
Distracted Driving Risk-Taking Neural Networks, Computer Models, Biological Humans Accidents, Traffic Attention
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A Hybrid Deep Learning Model for Recognizing Actions of Distracted Drivers.

Sensors (Basel, Switzerland) Nov 8, 2021
With the rapid spreading of in-vehicle information systems such as smartphones, navigation systems, and radios, the number of traffic accidents caused by driver distractions shows an increasing trend. Timely identification and warning are deemed to b...
Humans Neural Networks, Computer Deep Learning Distracted Driving Accidents, Traffic Automobile Driving
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Optimally-Weighted Image-Pose Approach (OWIPA) for Distracted Driver Detection and Classification.

Sensors (Basel, Switzerland) Jul 15, 2021
Distracted driving is the prime factor of motor vehicle accidents. Current studies on distraction detection focus on improving distraction detection performance through various techniques, including convolutional neural networks (CNNs) and recurrent ...
Distracted Driving Neural Networks, Computer Accidents, Traffic
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Safety critical event prediction through unified analysis of driver and vehicle volatilities: Application of deep learning methods.

Accident; analysis and prevention Dec 29, 2020
Transportation safety is highly correlated with driving behavior, especially human error playing a key role in a large portion of crashes. Modern instrumentation and computational resources allow for the monitorization of driver, vehicle, and roadway...
Distracted Driving Accidents, Traffic Humans Automobile Driving Deep Learning
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A hierarchical machine learning classification approach for secondary task identification from observed driving behavior data.

Accident; analysis and prevention Dec 13, 2018
According to NHTSA, more than 3477 people (including 551 non-occupants) were killed and 391,000 were injured due to distraction-related crashes in 2015. The distracted driving epidemic has long been under research to identify its impact on driving be...
Cell Phone Male Female Behavior Observation Techniques Adult Accidents, Traffic Humans Deep Learning Distracted Driving Text Messaging
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