The paper introduces a sophisticated deep learning method to drive behavior classification with sequential sensor signals that surpasses all competitors in terms of classification accuracy and, more importantly, in terms of detecting patterns over time and achieving an optimal mix of recall and accuracy for all types of driving behavior.
This research establishes a statistically robust and deployable foundation for next-generation intelligent transportation systems by coupling Bayesian learning theory with edge computing design.
S. M. Hosseini, V. Kiani, Hadi Sadoghi-Yazdi· Computing· 0 citations
The obtained results demonstrate the effectiveness of ensemble learning methods for driver behavior recognition under secondary-road conditions and highlight their potential for integration into intelligent driver monitoring and Advanced Driver Assistance Systems (ADASs).
A. Potams, Raymond Ghandour, Z. Al Barakeh et al.· Technologies· 0 citations
IYOLO, an enhanced YOLOv8-based framework for simultaneous detection and classification of vehicles, drivers, and passengers on highways, aiming to distinguish drivers from passengers and establish one-to-one vehicle-driver associations is proposed.
Yang Zhang, Peihua Lv, Hongjin Ren et al.· International Conference on...· 0 citations
Driving style, reflected in recurrent patterns of acceleration, braking, speed selection, following distance, gear use, and lane-changing behavior, plays an important role in road safety and is also associated with fuel consumption, emissions, passenger comfort, and vehicle wear. This paper presents a structured review...
T. Ghiță, R. Boboc, M. Duguleană· Electronics· 0 citations
Driving drowsiness is a major cause of road accidents, underscoring the need for reliable detection systems to enhance road safety. This paper introduces a novel hybrid architecture, adaptive neuro-fuzzy inference system (ANFIS) (EM)–LSTM, which integrates the complete fuzzy inference process with recurrent modeling, c...
Mouhcine Lakhloufi, E. Mellouli· Bulletin of Electrical Engin...· 0 citations
Automated driver behavior profiling is a strategically important problem within intelligent transportation systems, road safety engineering, and fleet-based emissions governance. This study presents a Heterogeneous Out-of-Fold (OOF) Stacking Ensemble augmented with inter-model probabilistic disagreement as a first-clas...
Md Mohibul Alam, A. O. Balogun, N. Arshad et al.· International Journal of Adv...· 0 citations
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