Search Results (139)

Drowsy driving and sudden medical emergencies are major contributors to traffic accidents, necessitating continuous, non-intrusive driver monitoring. Since current technologies often struggle to balance accuracy with practicality, this study presents the design, fabrication, and validation of a smart steering wheel prototype. The device integrates dry-contact electrocardiogram (ECG), photoplethysmography (PPG), and inertial sensors to facilitate multimodal physiological monitoring. The system underwent a two-stage evaluation involving a single participant: laboratory validation benchmarking acquired signals against medical-grade equipment, followed by real-world testing in a custom electric research vehicle to assess performance under dynamic conditions. Laboratory results demonstrated that the prototype captured high-quality signals suitable for reliable heart rate variability analysis. Furthermore, on-road evaluation confirmed the system’s operational functionality; despite increased noise from motion artifacts, the ECG signal remained sufficiently robust for continuous R-peak detection. These findings confirm that the multimodal smart steering wheel is a feasible solution for unobtrusive driver monitoring. This integrated platform provides a solid foundation for developing sophisticated machine-learning algorithms to enhance road safety by predicting fatigue and detecting adverse health events. Full article

Driver fatigue detection based on electroencephalography (EEG) signals has gained increasing attention for enhancing road safety. However, existing deep learning models often treat EEG data as generic time-series inputs, neglecting the inherent hierarchical and spatial–temporal structure of brain activity, which limits their interpretability and generalization. To address this, we propose a novel Residual Polynomial Network (RPN) that explicitly models the positive and negative activation patterns in EEG signals through a polarity-aware architecture. The RPN integrates polarity decomposition, residual learning, and hierarchical feature fusion to capture discriminative neurophysiological dynamics while maintaining model transparency. Extensive experiments are conducted on a real-world driving fatigue dataset using a subject-wise 10-fold cross-validation protocol. Results show that the proposed RPN achieves an average classification accuracy of 97.65%, outperforming conventional machine learning and deep learning baselines including SVM, KNN, DT, and LSTM. Ablation studies confirm the effectiveness of each component, and Sankey diagram analysis provides interpretable insights into feature-to-class mappings. This work not only advances the state of the art in EEG-based fatigue detection but also offers a more transparent and physiologically plausible deep learning framework for brain signal analysis. Full article

Driving fatigue poses a serious threat to road safety. To detect fatigue accurately and thereby improve vehicle safety, this paper proposes a novel semi-dry electrode with the ability to automatically replenish the conductive fluid for monitoring driving fatigue. This semi-dry electrode not only integrates the advantages of both wet and dry electrodes but also incorporates an automatic conductive fluid replenishment mechanism. This design significantly extends the operational lifespan of the electrode while mitigating the limitations of manual replenishment, particularly the risk of signal interference. Additionally, this study adopts a transfer learning approach to detect driving fatigue by analyzing electroencephalography (EEG) signals. The experimental results indicate that this method effectively addresses the issue of data sparsity in real-time fatigue monitoring, overcomes the limitations of traditional algorithms, shows strong generalization performance and cross-domain adaptability, and achieves faster response times with enhanced accuracy. The semi-dry electrode and transfer learning algorithm proposed in this study can provide rapid and accurate detection of driving fatigue, thereby enabling timely alerts or interventions. This approach effectively mitigates the risk of traffic accidents and enhances both vehicle and road traffic safety. Full article

Driver fatigue is a primary factor in traffic accidents and poses a serious threat to road safety. To address this issue, this paper proposes a multi-feature fusion fatigue detection method based on an improved YOLOv8 model. First, the method uses an enhanced YOLOv8 model to achieve high-precision face detection. Then, it crops the detected face regions. Next, the lightweight PFLD (Practical Facial Landmark Detector) model performs keypoint detection on the cropped images, extracting 68 facial feature points and calculating key indicators related to fatigue status. These indicators include the eye aspect ratio (EAR), eyelid closure percentage (PERCLOS), mouth aspect ratio (MAR), and head posture ratio (HPR). To mitigate the impact of individual differences on detection accuracy, the paper introduces a novel sliding window model that combines a dynamic threshold adjustment strategy with an exponential weighted moving average (EWMA) algorithm. Based on this framework, blink frequency (BF), yawn frequency (YF), and nod frequency (NF) are calculated to extract time-series behavioral features related to fatigue. Finally, the driver’s fatigue state is determined using a comprehensive fatigue assessment algorithm. Experimental results on the WIDER FACE and YAWDD datasets demonstrate this method’s significant advantages in improving detection accuracy and computational efficiency. By striking a better balance between real-time performance and accuracy, the proposed method shows promise for real-world driving applications. Full article

Enhancing road safety requires an accurate assessment of the drivers’ mental states. The Driver Status Test App (DSTA) is designed to detect conditions such as intoxication, fatigue, and cognitive impairment in simulated driving environments. Utilizing a dual-platform approach, it integrates mobile data collection via React Native and Firebase with web-based management using React and TypeScript. The mobile application conducts real-time assessments of cognitive and motor functions, while the web interface offers data visualization, trend analysis, and results exportation. DSTA evaluates driver impairment through metrics such as tracking, precision, balance, and choice reaction, producing an objective impairment score. These assessments are rapid, scalable, and adaptable for various research and regulatory purposes. The composite scoring framework differentiates between impaired and unimpaired states, making DSTA valuable for driver training programs, regulatory assessments, and autonomous vehicle research, where monitoring human factors is crucial. Full article

Driver fatigue is a critical factor contributing to traffic accidents. Therefore, real-time and accurate recognition of driver fatigue states holds significant importance. This paper proposes a novel driver fatigue detection method based on electroencephalogram (EEG) signals and an Interpretable Dynamic System Recurrent Network (IDSRN). The IDSRN integrates the temporal modeling capability of traditional Recurrent Neural Networks (RNNs) with the nonlinear function approximation advantages of polynomial networks, enabling effective extraction of nonlinear dynamic features from EEG signals. This study collected EEG data from drivers under varying fatigue states and constructed input vectors suitable for classification tasks through preprocessing and feature extraction. The extracted features were subsequently fed into the IDSRN model for training and testing, with comparative analyses conducted against traditional methods (e.g., SVM, CNN, and standard RNN). Experimental results demonstrate that the IDSRN outperforms other models in recognition accuracy (average: 92.3%), convergence speed, and robustness, significantly improving the efficacy of driver fatigue-state identification. Full article

Long COVID (LC), also known as post-acute sequelae of COVID-19 infection (PASC), is a heterogeneous and debilitating chronic disease that currently affects 10 to 20 million people in the U.S. and over 420 million people globally. With no approved treatments, the long-term global health and economic impact of chronic LC remains high and growing. LC affects children, adolescents, and healthy adults and is characterized by over 200 diverse symptoms that persist for months to years after the acute COVID-19 infection is resolved. These symptoms target twelve major organ systems, causing dyspnea, vascular damage, cognitive impairments (“brain fog”), physical and mental fatigue, anxiety, and depression. This heterogeneity of LC symptoms, along with the lack of specific biomarkers and diagnostic tests, presents a significant challenge to the development of LC treatments. While several biological abnormalities have emerged as potential drivers of LC, a causative factor in a large subset of patients with LC, involves reservoirs of virus and/or viral RNA (vRNA) that persist months to years in multiple organs driving chronic inflammation, respiratory, muscular, cognitive, and cardiovascular damages, and provide continuous viral antigenic stimuli that overstimulate and exhaust CD4⁺ and CD8⁺ T cells. In this review, we (i) shed light on persisting virus and vRNA reservoirs detected, either directly (from biopsy, blood, stool, and autopsy samples) or indirectly through virus-specific B and T cell responses, in patients with LC and their association with the chronic symptomatology of LC; (ii) explore potential mechanisms of inflammation, immune evasion, and immune overstimulation in LC; (iii) review animal models of virus reservoirs in LC; (iv) discuss potential T cell immunotherapeutic strategies to reduce or eliminate persistent virus reservoirs, which would mitigate chronic inflammation and alleviate symptom severity in patients with LC. Full article

Fatigue and distracted driving are among the leading causes of traffic accidents, highlighting the importance of developing efficient and non-intrusive driver monitoring systems. Traditional camera-based methods are often limited by lighting variations, occlusions, and privacy concerns. In contrast, millimeter-wave (mmWave) radar offers a non-contact, privacy-preserving, and environment-robust solution, providing a forward-looking alternative. This study introduces a novel deep learning model, RTSFN (radar-based temporal-spatial fusion network), which simultaneously analyzes the temporal motion changes and spatial posture features of the driver. RTSFN incorporates a cross-gated fusion mechanism that dynamically integrates multi-modal information, enhancing feature complementarity and stabilizing behavior recognition. Experimental results show that RTSFN effectively detects dangerous driving states with an average F1 score of 94% and recognizes specific high-risk behaviors with an average F1 score of 97% and can run in real-time on edge devices such as the NVIDIA Jetson Orin Nano, demonstrating its strong potential for deployment in intelligent transportation and in-vehicle safety systems. Full article

Driving fatigue is a crucial factor affecting road traffic safety. Accurately assessing the driver’s fatigue status is critical for accident prevention. This paper explores the assessment methods of driving fatigue under different conditions based on multimodal physiological and behavioral data. Physiological data such as heart rate, brainwave, electromyography, and pupil diameter were collected through experiments, as well as behavioral data such as posture changes, vehicle acceleration, and throttle usage. The results show that physiological and behavioral indicators have significant sensitivity to driving fatigue, and the fusion of multimodal data can effectively improve the accuracy of fatigue detection. Based on this, a comprehensive driving fatigue assessment model was constructed, and its applicability and reliability in different driving scenarios were verified. This study provides a theoretical basis for the development and application of driver fatigue monitoring systems, helping to achieve real-time fatigue warnings and protections, thereby improving driving safety. Full article

Driving fatigued is a prevalent issue frequently contributing to traffic accidents, prompting the development of automated fatigue detection methods based on various data sources, particularly reliable physiological signals. However, challenges in accuracy, robustness, and practicality persist, especially for cross-subject detection. Multimodal data fusion can enhance the effective estimation of driver fatigue. In this work, we leverage the advantages of multimodal signals to propose a novel Multimodal Attention Network (TMU-Net) for driver fatigue detection, achieving precise fatigue assessment by integrating electroencephalogram (EEG) and electrooculogram (EOG) signals. The core innovation of TMU-Net lies in its unimodal feature extraction module, which combines causal convolution, ConvSparseAttention, and Transformer encoders to effectively capture spatiotemporal features, and a multimodal fusion module that employs cross-modal attention and uncertainty-weighted gating to dynamically integrate complementary information. By incorporating uncertainty quantification, TMU-Net significantly enhances robustness to noise and individual variability. Experimental validation on the SEED-VIG dataset demonstrates TMU-Net’s superior performance stability across 23 subjects in cross-subject testing, effectively leveraging the complementary strengths of EEG (2 Hz full-band and five-band features) and EOG signals for high-precision fatigue detection. Furthermore, attention heatmap visualization reveals the dynamic interaction mechanisms between EEG and EOG signals, confirming the physiological rationality of TMU-Net’s feature fusion strategy. Practical challenges and future research directions for fatigue detection methods are also discussed. Full article

Mind wandering (MW) is a significant safety risk in driving, yet research on its scope, underlying mechanisms, and mitigation strategies remains fragmented across disciplines. In this review guided by the PRISMA framework, we analyze findings from 64 empirical studies to address these factors. The presented study quantifies the prevalence of MW in naturalistic and simulated driving environments and shows its impact on driving behaviors. We document its negative effects on braking reaction times and lane-keeping consistency, and we assess recent advancements in objective detection methods, including EEG signatures, eye-tracking metrics, and physiological markers. We also identify key cognitive and contextual risk factors, including high perceived risk, route familiarity, and driver fatigue, which increase MW episodes. Also, we survey emergent countermeasures, such as haptic steering wheel alerts and adaptive cruise control perturbations, designed to sustain driver engagement. Despite these advancements, the MW research shows persistent challenges, including methodological heterogeneity that limits cross-study comparisons, a lack of real-world validation of detection algorithms, and a scarcity of long-term field trials of interventions. Our integrated synthesis, therefore, outlines a research agenda prioritizing harmonized measurement protocols, on-road algorithm deployment, and rigorous evaluation of countermeasures under naturalistic driving conditions. Full article

Accurately identifying driver fatigue in complex driving environments plays a crucial role in road traffic safety. To address the challenge of reduced fatigue detection accuracy in complex cabin environments caused by lighting variations, we propose YOLO-FDCL, a novel algorithm specifically designed for driver fatigue detection under complex lighting conditions. This algorithm introduces MobileNetV4 into the backbone network to enhance the model’s ability to extract fatigue-related features in complex driving environments while reducing the model’s parameter size. Additionally, by incorporating the concept of structural re-parameterization, RepFPN is introduced into the neck section of the algorithm to strengthen the network’s multi-scale feature fusion capabilities, further improving the model’s detection performance. Experimental results show that on the YAWDD dataset, compared to the baseline YOLOv8-S, precision increased from 97.4% to 98.8%, recall improved from 96.3% to 97.5%, mAP@0.5 increased from 98.0% to 98.8%, and mAP@0.5:0.95 increased from 92.4% to 94.2%. This algorithm has made significant progress in the task of fatigue detection under complex lighting conditions. At the same time, this model shows outstanding performance on our self-developed Complex Lighting Driving Fatigue Dataset (CLDFD), with precision and recall improving by 2.8% and 2.2%, respectively, and improvements of 3.1% and 3.6% in mAP@0.5 and mAP@0.5:0.95 compared to the baseline model, respectively. Full article

Pupil detection is a key technology in fields such as human–computer interaction, fatigue driving detection, and medical diagnosis. Existing pupil detection algorithms still face challenges in maintaining robustness under variable lighting conditions and occlusion scenarios. In this paper, we propose a novel pupil detection algorithm, ViMSA, based on the ViM model. This algorithm introduces weighted feature fusion, aiming to enable the model to adaptively learn the contribution of different feature patches to the pupil detection results; combines ViM with the MSA (multi-head self-attention) mechanism), aiming to integrate global features and improve the accuracy and robustness of pupil detection; and uses FFT (Fast Fourier Transform) to convert the time-domain vector outer product in MSA into a frequency–domain dot product, in order to reduce the computational complexity of the model and improve the detection efficiency of the model. ViMSA was trained and tested on nearly 135,000 pupil images from 30 different datasets, demonstrating exceptional generalization capability. The experimental results demonstrate that the proposed ViMSA achieves 99.6% detection accuracy at five pixels with an RMSE of 1.67 pixels and a processing speed exceeding 100 FPS, meeting real-time monitoring requirements for various applications including operation under variable and uneven lighting conditions, assistive technology (enabling communication with neuro-motor disorder patients through pupil recognition), computer gaming, and automotive industry applications (enhancing traffic safety by monitoring drivers’ cognitive states). Full article

Driver drowsiness is a major contributor to road accidents, often resulting from delayed reaction times and impaired cognitive performance. This study introduces EffRes-DrowsyNet, a hybrid deep learning model that integrates the architectural efficiencies of EfficientNetB0 with the deep representational capabilities of ResNet50. The model is designed to detect early signs of driver fatigue through advanced video-based analytics by leveraging both computational scalability and deep feature learning. Extensive experiments were conducted on three benchmark datasets—SUST-DDD, YawDD, and NTHU-DDD—to validate the model’s performance across a range of environmental and demographic variations. EffRes-DrowsyNet achieved 97.71% accuracy, 98.07% precision, and 97.33% recall on the SUST-DDD dataset. On the YawDD dataset, it sustained a high accuracy of 92.73%, while on the NTHU-DDD dataset, it reached 95.14% accuracy, 94.09% precision, and 95.39% recall. These results affirm the model’s superior generalization and classification performance in both controlled and real-world-like settings. The findings underscore the effectiveness of hybrid deep learning models in real-time, safety-critical applications, particularly for automotive driver monitoring systems. Furthermore, EffRes-DrowsyNet’s architecture provides a scalable and adaptable solution that could extend to other attention-critical domains such as industrial machinery operation, aviation, and public safety systems. Full article

The incorporation of Driver Monitoring Systems (DMSs) in vehicles is fundamental to enhancing road safety by continuously assessing driver behavior and identifying signs of fatigue or distraction. However, as these technologies evolve, they also present considerable cybersecurity challenges. This research undertakes an extensive Threat Analysis and Risk Assessment (TARA) of DMSs, adhering to the ISO/SAE 21434 standard, to methodically detect and assess potential security threats. A total of 115 threats were recognized and classified into 95 low-risk, 16 medium-risk, and 4 high-risk scenarios, underscoring key vulnerabilities in data transmission, sensor reliability, and communication frameworks. To mitigate these risks, we suggest a range of countermeasures, including enhanced encryption techniques, stringent authentication protocols, and reinforced access control mechanisms, designed to strengthen the security posture of DMSs in practical applications. This study introduces a structured framework for evaluating and addressing cybersecurity threats in alignment with industry regulations, facilitating the dependable and safeguarded implementation of DMSs in future vehicle architectures while contributing to ongoing progress in automotive cybersecurity. Full article