2026· Computer Modeling in Engineering & Sciences· 0 citations· 66 references
TL;DR
Quantitative and qualitative analyses, including state-of-the-art comparison, cross-validation, statistical analysis, and computational complexity evaluation, highlight the system’s accuracy, modularity, and suitability for real-world deployment.
Abstract
: Transportation has become an essential component of modern daily life, with continuous advancements aimed at reducing travel time and improving mobility. However, this increased convenience has also contributed to a rise in road accidents, often caused by driver distraction, fatigue, and age-related cognitive decline. These concerns have driven growing interest in Artificial Intelligence (AI)-based real-time driver monitoring systems designed to enhance road safety. Despite recent progress, several challenges remain, including limitations in detection accuracy, inadequate temporal reasoning, and high computational complexity. To address these challenges, we utilize the EfficientNetV2-S model for backbone feature extraction due to its high performance, compact model size, and fast inference speed. The model leverages Squeeze-and-Excitation (SE) attention to enhance feature learning capabilities; however, SE attention captures only channel information and overlooks important spatial features. To overcome this limitation, our framework incorporates a Driver-Monitoring Coordinate Attention (DM-CA) mechanism with modifications that encode features along both height and width directions. Beyond frame-level classification, the proposed system integrates temporal memory and reasoning to convert frame-level predictions into behavior-level insights. A risk-aware decision module evaluates the drivers state based on duration, context, and driving conditions, enabling goal-driven adaptive interventions such as warnings or alerts, supported by a feedback adaptation mechanism. We evaluated the framework on UTKFace, Fatigue, modified Fatigue, and 100-driver datasets, demonstrating its effectiveness in understanding driver distraction. Quantitative and qualitative analyses, including state-of-the-art comparison, cross-validation, statistical analysis, and computational complexity evaluation, highlight the system’s accuracy, modularity, and suitability for real-world deployment.
This research establishes a statistically robust and deployable foundation for next-generation intelligent transportation systems by coupling Bayesian learning theory with edge computing design.
Seyed Mohammad Hosseini, V. Kiani, Hadi Sadoghi-Yazdi· Computing· 0 citations
Anomaly detection has become an important area of research because of its relevance across a wide range of fields, including surveillance, transportation, healthcare, and public safety. With the rapid expansion of urban environments, the need for effective monitoring systems has increased significantly. Modern cities depend heavily on surveillance infrastructure, particularly CCTV cameras, to observe traffic conditions on roads, highways, and public intersections. While these systems generate a continuous stream of visual data, relying on human operators to monitor them is both impractical and inefficient. Continuous observation can lead to fatigue, reduced attention, and delayed responses, especially when dealing with large-scale surveillance networks. These limitations highlight the necessity for automated systems capable of identifying unusual events accurately and in real time. In this context, the present study focuses on the detection of road accidents using deep learning techniques applied to surveillance video data. Road accidents remain a major global issue, contributing to loss of life, physical injuries, traffic disruption, and economic costs. A critical factor in reducing the impact of such incidents is the speed at which they are detected and reported. Delays in identifying accidents often result in slower emergency response times, which can worsen outcomes. Therefore, there is a clear need for intelligent systems that can recognize accident scenarios as they occur and promptly alert the relevant authorities.
N. Navaneetha· International journal of res...· 1 citation
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
Smart city transportation has become an important part of improving road safety and traffic management. This project focuses on detecting traffic accidents using a deep learning ensemble approach that combines I3DConvLSTM2D with RGB and optical flow information. By analyzing both the appearance of vehicles and their movement, the system can identify accident events more accurately than traditional methods. It is designed to work in real time, making it suitable for surveillance cameras and smart city environments. The proposed model also addresses challenges such as limited training data and different road conditions, helping improve its reliability in practical situations. Early accident detection allows emergency services to respond quickly, reducing the impact of road accidents and improving public safety. Overall, this system demonstrates how artificial intelligence can support modern transportation by providing faster, more accurate, and efficient accident detection while contributing to safer roads and better traffic monitoring.
CHENCHETI INDHU, Dr.M.Ramesh· International Journal of Eng...· 0 citations
The proposed framework comprises sensor fusion, image processing, feature extraction, deep neural network inference and explainability mechanisms such as Gradient-weighted Class Activation Mapping, Local Interpretable Model-Agnostic Explanations and SHapley Additive exPlanations.
Johan Håstad's mentor Arne Andersson, Börje Langefors· International Journal of Eme...· 0 citations
Driver behavior detection is essential for improving road safety in Intelligent Transportation Systems (ITSs). This paper proposes an attention-enhanced CNN integrated with Explainable Artificial Intelligence (XAI) to achieve reliable and interpretable driver behavior classification. Furthermore, the model also uses a multi-head attention mechanism to ensure that it captures local spatial features as well as long-range dependencies in the activities of the driver. The State Farm Distracted Driver dataset is experimented with using stratified 5-fold cross-validation. The proposed MHA-CNN model has a mean accuracy of 99.48% on all folds and an overall high levels of precision, recall, and F1-score in all ten distraction classes. Grad-CAM is used to produce attribution maps to improve interpretability by showing semantically important parts of the image, including hands, face, and mobile devices, and can give a visual explanation of why models make decisions. In addition, an ablation investigation is performed with k-fold validation to assess the value of the multi-head attention mechanism. Its model can run at 122 frames per second with 42.92 million parameters, showing that the suggested method is a reasonable tradeoff between accuracy, efficiency, and transparency and can be applied in real-time driver monitoring applications.
A. Al mamun, Md Shahidul Islam Shabuz, Mohamed N. Rahaman et al.· Algorithms· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
