AIMC Topic: Automobile Driving
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Detecting Emotional Arousal and Aggressive Driving Using Neural Networks: A Pilot Study Involving Young Drivers in Duluth.
Sensors (Basel, Switzerland)
Nov 5, 2024
Driving is integral to many people's daily existence, but aggressive driving behavior increases the risk of road traffic collisions. Young drivers are more prone to aggressive driving and danger perception impairments. A driver's physiological state ...
Predicting lane change maneuver and associated collision risks based on multi-task learning.
Accident; analysis and prevention
Nov 4, 2024
The lane-changing (LC) maneuver of vehicles significantly impacts highway traffic safety. Therefore, proactively predicting LC maneuver and associated collision risk is of paramount importance. However, most of the previous LC risk prediction researc...
An integrated framework for driving risk evaluation that combines lane-changing detection and an attention-based prediction model.
Traffic injury prevention
Oct 2, 2024
OBJECTIVE: In recent years, the increase in traffic accidents has emerged as a significant social issue that poses a serious threat to public safety. The objective of this study is to predict risky driving scenarios to improve road safety.
A Real-Time Embedded System for Driver Drowsiness Detection Based on Visual Analysis of the Eyes and Mouth Using Convolutional Neural Network and Mouth Aspect Ratio.
Sensors (Basel, Switzerland)
Sep 27, 2024
Currently, the number of vehicles in circulation continues to increase steadily, leading to a parallel increase in vehicular accidents. Among the many causes of these accidents, human factors such as driver drowsiness play a fundamental role. In this...
Sample selection of adversarial attacks against traffic signs.
Neural networks : the official journal of the International Neural Network Society
Sep 3, 2024
In the real world, the correct recognition of traffic signs plays a crucial role in vehicle autonomous driving and traffic monitoring. The research on its adversarial attack can test the security of vehicle autonomous driving system and provide enlig...
Analyzing the heterogenous effects of factors on high-range speeding likelihood of taxi speeders: Does explainable deep learning provides more insights than random parameter approach?
Accident; analysis and prevention
Aug 24, 2024
The random parameters Generalized Linear Model (GLM) is frequently used to model speeding characteristics and capture the heterogenous effects of factors. However, this statistical approach is seldom employed for prediction and generalization due to ...
Research on low-power driving fatigue monitoring method based on spiking neural network.
Experimental brain research
Aug 23, 2024
Fatigue driving is one of the leading causes of traffic accidents, and the rapid and accurate detection of driver fatigue is of paramount importance for enhancing road safety. However, the application of deep learning models in fatigue driving detect...
Considering multi-scale built environment in modeling severity of traffic violations by elderly drivers: An interpretable machine learning framework.
Accident; analysis and prevention
Aug 13, 2024
The causes of traffic violations by elderly drivers are different from those of other age groups. To reduce serious traffic violations that are more likely to cause serious traffic crashes, this study divided the severity of traffic violations into t...
Analysis of harsh braking and harsh acceleration occurrence via explainable imbalanced machine learning using high-resolution smartphone telematics and traffic data.
Accident; analysis and prevention
Aug 8, 2024
Harsh driving events such as harsh brakings (HBs) and harsh accelerations (HAs) are promising Surrogate Safety Measures, already extensively utilised in road safety research. However, their occurrence relative to normal driving conditions has not bee...
Exploring driving behavioral characteristics in pre-, in-, and post-conflict stages based on car-following trajectory data.
Ergonomics
Aug 7, 2024
This study investigates driving behaviour in different stages of rear-end conflicts using vehicle trajectory data. Three conflict stages (pre-, in-, and post-conflict) are defined based on time-to-collision (TTC) indicator. Four indexes are selected ...
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