AIMC Topic: Automobile Driving
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Could vehicles analyze driving risks using human fuzzy semantic logic? A data-knowledge-driven new perspective.
Accident; analysis and prevention
Aug 1, 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...
Collision risk prediction and takeover requirements assessment based on radar-video integrated sensors data: A system framework based on LLM.
Accident; analysis and prevention
Aug 1, 2025
There are safety risks when drivers take over the control of autonomous driving vehicles, and reducing unnecessary takeovers is essential to improve driving safety. This study seeks to develop an interpretable system framework for collision risk pred...
A framework for real-time traffic risk prediction incorporating cost-sensitive learning and dynamic thresholds.
Accident; analysis and prevention
Aug 1, 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 ...
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
Aug 1, 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...
A game theoretical model to examine pedestrian behaviour and safety on unsignalised slip lanes using AI-based video analytics.
Accident; analysis and prevention
Jul 1, 2025
Left-turn slip lanes, also known as channelised right-turn lanes in right-hand driving countries, are widely implemented to facilitate left-turning at signalised intersections. However, pedestrian safety on slip lanes is not well known. At unsignalis...
An explainable machine learning framework for predicting driving states using electroencephalogram.
Medical engineering & physics
Jun 1, 2025
OBJECTIVES: Understanding drivers' cognitive load is essential for enhancing road safety, as cognitive demands fluctuate across different driving scenarios, potentially impacting performance, and safety, particularly for drivers with neurological dis...
Deep-ATM DL-LSTM: A novel adaptive thresholding model with dual-layer LSTM architecture for real-time driver drowsiness detection using skin conductance signals.
Computers in biology and medicine
Jun 1, 2025
Driver drowsiness detection systems are crucial for road safety. However, existing machine learning models struggle to adjust thresholds for Skin Conductance (SC) adaptively signals due to insufficient feature extraction of tonic and phasic responses...
Driver Steering Intention Prediction for Human-Machine Shared Systems of Intelligent Vehicles Based on CNN-GRU Network.
Sensors (Basel, Switzerland)
May 20, 2025
In order to mitigate human-machine conflicts and optimize shared control strategy in advance, it is essential for the shared control system to understand and predict driver behavior. This paper proposes a method for predicting driver steering intenti...
Real-time driver drowsiness detection using transformer architectures: a novel deep learning approach.
Scientific reports
May 20, 2025
Driver drowsiness is a leading cause of road accidents, resulting in significant societal, economic, and emotional losses. This paper introduces a novel and robust deep learning-based framework for real-time driver drowsiness detection, leveraging st...
Evaluation of data collection and annotation approaches of driver gaze dataset.
Behavior research methods
May 14, 2025
Driver gaze estimation is important for various driver gaze applications such as building advanced driving assistance systems and understanding driver gaze behavior. Gaze estimation in terms of gaze zone classification requires large-scale labeled da...
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