AIMC Topic: Automobile Driving

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Predicting car accident severity in Northwest Ethiopia: a machine learning approach leveraging driver, environmental, and road conditions.

Scientific reports Jul 1, 2025
Road traffic accidents (RTAs) in Northwest Ethiopia, a region with a fatality rate of 32.2 per 100,000 residents, pose a critical public health challenge exacerbated by infrastructural deficits and environmental hazards. This study leverages machine ...
Young Adult Adult Male Machine Learning Female Ethiopia Accidents, Traffic Automobile Driving Middle Aged Adolescent Humans
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Interval type-2 intelligent fuzzy vehicle speed controller design using headlamp reflection detection and an adaptive neuro-fuzzy inference system.

PloS one Jun 2, 2025
In this study, we present an algorithm to estimate the distance between a vehicle and a target object using light from headlights captured by a camera. In situations with limited distance data, we also design a fuzzy controller using the adaptive neu...
Neural Networks, Computer Algorithms Automobile Driving Fuzzy Logic Humans
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Advanced traffic conflict analysis for safety evaluation at roundabouts under mixed traffic using extreme value theory.

Accident; analysis and prevention May 16, 2025
Roundabout safety evaluation in non-lane-based, heterogeneous traffic conditions in low-middle-income countries brings challenges due to unavailable/unreliable crash data, thereby switching to the utilization of safety surrogates. This study employed...
Accidents, Traffic Safety Video Recording Automobile Driving Humans Models, Theoretical Environment Design
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Could vehicles analyze driving risks using human fuzzy semantic logic? A data-knowledge-driven new perspective.

Accident; analysis and prevention May 6, 2025
Accurate risk identification is crucial for ensuring the safe operation of Host vehicles (HoVs) in environments shared with Neighboring vehicles (NeVs). Traditional risk identification mechanisms typically rely on large amounts of precise numerical d...
Semantics Automobiles Accidents, Traffic Automobile Driving Risk Assessment Fuzzy Logic Humans
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Dynamic cross-domain transfer learning for driver fatigue monitoring: multi-modal sensor fusion with adaptive real-time personalizations.

Scientific reports May 6, 2025
Driver fatigue is one of the most common causes of road accidents, which means that there is a great need for robust and adaptive monitoring systems. Current models of fatigue detection suffer from domain-specific limitations in generalizing across d...
Accidents, Traffic Humans Algorithms Automobile Driving Fatigue Electrocardiography Neural Networks, Computer Machine Learning Electroencephalography
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A framework for real-time traffic risk prediction incorporating cost-sensitive learning and dynamic thresholds.

Accident; analysis and prevention May 5, 2025
In recent years, researchers have explored an innovative approach that leverages real vehicle trajectory data to simultaneously derive traffic state and risk level for real-time risk prediction, which is crucial for traffic safety. However, existing ...
Automobile Driving Risk Assessment Accidents, Traffic Humans Algorithms Reproducibility of Results Machine Learning
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Investigating the influence of socioeconomic factors on the relationships between road characteristics and traffic crash frequency and severity-- A hybrid structural equation modelling - artificial neural networks approach.

Accident; analysis and prevention May 5, 2025
Traffic crashes result from complex interactions between driver, roadway, and environmental factors, which traditional methods often fail to capture. This paper investigates the influence of road, weather, and socioeconomic factors on traffic crashes...
Humans Neural Networks, Computer Environment Design Accidents, Traffic Weather Latent Class Analysis Socioeconomic Factors Automobile Driving Ohio
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A dense multi-pooling convolutional network for driving fatigue detection.

Scientific reports May 3, 2025
Driver fatigue is one of the major causes of traffic accidents, particularly for drivers of large vehicles, who are more susceptible to fatigue due to prolonged driving hours and monotonous conditions during their journeys. Existing vision-based driv...
Humans Neural Networks, Computer Automobile Driving Algorithms Fatigue Accidents, Traffic
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HATNet: EEG-Based Hybrid Attention Transfer Learning Network for Train Driver State Detection.

IEEE transactions on cybernetics Apr 23, 2025
Electroencephalography (EEG) is widely utilized for train driver state detection due to its high accuracy and low latency. However, existing methods for driver status detection rarely use the rich physiological information in EEG to improve detection...
Algorithms Humans Attention Adult Electroencephalography Neural Networks, Computer Signal Processing, Computer-Assisted Automobile Driving Male Machine Learning
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Deep reinforcement learning for decision making of autonomous vehicle in non-lane-based traffic environments.

PloS one Apr 16, 2025
Existing research on decision-making of autonomous vehicles (AVs) has mainly focused on normal road sections, with limited exploration of decision-making in complex traffic environments without lane markings. Taking toll plaza diverging area as an ex...
Decision Making Automobiles Computer Simulation Reinforcement Machine Learning Humans Deep Learning Automobile Driving Algorithms
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