Visual Dominance and Emerging Multimodal Approaches in Distracted Driving Detection: A Review of Machine Learning Techniques

Distracted driving continues to be a significant cause of road traffic injuries and fatalities worldwide, even with advancements in driver monitoring technologies. Recent developments in machine learning (ML) and deep learning (DL) have primarily focused on visual data to detect distraction, often neglecting the complex, multimodal nature of driver behavior. This systematic review assesses 74 peer-reviewed studies from 2019 to 2024 that utilize ML/DL techniques for distracted driving detection across visual, sensor-based, multimodal, and emerging modalities. The review highlights a significant prevalence of visual-only models, particularly convolutional neural networks (CNNs) and temporal architectures, which achieve high accuracy but show limited generalizability in real-world scenarios. Sensor-based and physiological models provide complementary strengths by capturing internal states and vehicle dynamics, while emerging techniques, such as auditory sensing and radio frequency (RF) methods, offer privacy-aware alternatives. Multimodal architecture consistently surpasses unimodal baselines, demonstrating enhanced robustness, context awareness, and scalability by integrating diverse data streams. These findings emphasize the need to move beyond visual-only approaches and adopt multimodal systems that combine visual, physiological, and vehicular cues while keeping in checking the need to balance computational requirements. Future research should focus on developing lightweight, deployable multimodal frameworks, incorporating personalized baselines, and establishing cross-modality benchmarks to ensure real-world reliability in advanced driver assistance systems (ADAS) and road safety interventions.

An Improved ResNet50 Model for Predicting Pavement Condition Index (PCI) Directly from Pavement Images

Accurately predicting the Pavement Condition Index (PCI), a measure of roadway conditions, from pavement images is crucial for infrastructure maintenance. This study proposes an enhanced version of the Residual Network (ResNet50) architecture, integrated with a Convolutional Block Attention Module (CBAM), to predict PCI directly from pavement images without additional annotations. By incorporating CBAM, the model autonomously prioritizes critical features within the images, improving prediction accuracy. Compared to the original baseline ResNet50 and DenseNet161 architectures, the enhanced ResNet50-CBAM model achieved a significantly lower mean absolute percentage error (MAPE) of 58.16%, compared to the baseline models that achieved 70.76% and 65.48% respectively. These results highlight the potential of using attention mechanisms to refine feature extraction, ultimately enabling more accurate and efficient assessments of pavement conditions. This study emphasizes the importance of targeted feature refinement in advancing automated pavement analysis through attention mechanisms.