Mohamed Ksantini

Electroencephalography (EEG)-based Brain–Computer Interfaces offer direct insight into a driver’s mental state, relevant to autonomous-vehicle perception stacks. This work addresses vigilance/drowsiness specifically, one of several driver states relevant to Level 3 takeover readiness. Detecting drowsiness before handover in Level 3 vehicles requires balancing high-dimensional EEG features against the real-time demands of a lightweight safe-stop trigger mechanism. We present a complete pipeline: a standardized 22-feature-per-channel schema extracted across four driving-related EEG datasets, an autoencoder achieving over 95% dimensionality reduction with minimal performance loss, and a classifier driving a constant-deceleration kinematic profile used to obtain a measurable latency figure, evaluated under a fully subject-disjoint, leakage-free protocol. Leave-one-subject-out cross-validation on the vigilance dataset yields a mean macro-F1 of 0.793 ± 0.163 (0.763 ± 0.155 on an alternative run, within expected autoencoder-retraining variance). Extending compression evaluation to all four datasets shows preserved or improved performance on three, with a small cost on the fourth. The full decision chain completes in under 1 ms (mean 0.13 ms) on standard hardware; this covers the perception-to-trigger budget only, excluding solver latency that an optimization-based planner (MPC/CBF-QP) would add downstream. These findings show cognitive-state-aware safe-stop triggering is implementation-viable, while highlighting the calibration and cross-machine validation needed for deployment.

Retinopathy of prematurity (ROP) is one of the notable causes of vision impairment among kids. The retinal zone, among the signs of ROP, is clinically considered a better predictor of severe forms than staging. This study explores five convolutional neural network (CNN) models used for computerized ROP zone classification based on fundus images of the retina. A total database of 1,365 images drawn from Al Amal Eye Center, located in Baghdad-Iraq, was trained into three classes mimicking three varied ROP zones. The images were used to finetune the models on top of pre-trained VGG16, VGG19, Xception, Inception-ResNetV2, and Inception-V3 models, whose network sizes and configurations were varied among the five models. Compared to minimal works done, the Inception-V3 model yielded the highest accuracy and reached 94.04% on zone detection. Computerized detection of the retinal lesions among pre-terms is fundamental to guiding the regimen plan that may incorporate laser therapy, intravitreal implantation, or close observation with intervention on demand. Merging computerized interpretation with the expertise of pediatric ophthalmologists may guide more consistent decisions on the management plan of the ROP. Future work will include external validation on independent multi-center datasets to assess generalizability.

Autonomous vehicles (AVs) are expected to operate safely and efficiently in complex urban environments characterized by dynamic and uncertain elements such as pedestrians, cyclists and adverse weather. Although current neural network-based decision-making algorithms, fuzzy logic and reinforcement learning have shown promise, they often struggle to handle ambiguous situations, such as partially hidden road signs or unpredictable human behavior. This paper proposes a new hybrid decision-making framework combining multi-agent reinforcement learning (MARL) and explainable artificial intelligence (XAI) to improve robustness, adaptability and transparency. Each agent of the MARL architecture is specialized in a specific sub-task (e.g., obstacle avoidance, trajectory planning, intention prediction), enabling modular and cooperative learning. XAI techniques are integrated to provide interpretable rationales for decisions, facilitating human understanding and regulatory compliance. The proposed system will be validated using CARLA simulator, combined with reference data, to demonstrate improved performance in safety-critical and ambiguous driving scenarios.

American scientific publishing group

Electroencephalography (EEG)-based brain-computer interfaces (BCIs) hold significant potential for enhancing driver safety through real-time monitoring of cognitive and affective states. However, the development of reliable BCI systems for Advanced Driver Assistance Systems (ADAS) depends on the availability of high-quality, publicly accessible EEG datasets collected during driving tasks. Existing datasets lack standardized parameters and contain demographic biases, which undermine their reliability and prevent the development of robust systems. This study presents a multidimensional benchmark analysis of seven publicly available EEG driving datasets. We compare these datasets across multiple dimensions, including task design, modality integration, demographic representation, accessibility, and reported model performance. This benchmark synthesizes existing literature without conducting new experiments. Our analysis reveals critical gaps, including significant age and gender biases, overreliance on simulated environments, insufficient affective monitoring, and restricted data accessibility. These limitations hinder real-world applicability and reduce ADAS performance. To address these gaps and facilitate the development of generalizable BCI systems, this study provides a structured, quantitative benchmark analysis of publicly available driving EEG datasets, suggesting criteria and recommendations for future dataset design and use. Additionally, we emphasize the need for balanced participant distributions, standardized emotional annotation, and open data practices.

This research work presents the design and the development of a cost-effective autonomous mobile robot for locating misplaced objects within unstructured indoor environments. The tools integrated into the proposed system for perception and localization are a hardware architecture equipped with LiDAR, an inertial measurement unit (IMU), and wheel encoders. The system also includes an ROS2-based software stack enabling autonomous navigation via the NAV2 framework and Adaptive Monte Carlo Localization (AMCL). For real-time object detection, a lightweight YOLO11n model is developed and implemented on a Raspberry Pi 4 to enable the robot to identify common household items. The robot’s motion control is achieved by a fuzzy logic-enhanced PID controller that dynamically modifies gain values based on navigation conditions. Remote supervision, task management, and real-time status monitoring are provided by a user-friendly Flutter-based mobile application. Simulations and real-world experiments demonstrate the robustness, modularity, and responsiveness of the robot in dynamic environments. This robot achieves a 3 cm localization error and a 95% task execution success rate.

The Takagi–Sugeno observer is based on fuzzy logic, which allows for a higher degree of interpretability compared to purely mathematical or computational models. The rules and their membership functions can be understood and modified by human operators, providing a degree of transparency that is valuable in practical applications. For a specific class of Bilinear Systems (BS), we examine the observer design problem under small disturbances in this work. Under some appropriate conditions, we prove that the resulting error system is globally exponentially stable to a small compact set. An application to the case of fractional order with a numerical example is also proposed to strengthen the validity of the main result.

The continuous evolution of autonomous vehicles technologies has significantly elevated the capabilities of intelligent transportation and road safety. Among these advancements, driving automation systems have played a vital role within vehicles, encompassing a diversity of functionality extending from systems assisting the driver called Advanced Driver Assistance Systems to Automated Driving Systems offering full control over various driving functions. Among these systems, Traffic Sign Recognition (TSR) system plays a significant role in terms of functionality and development. This survey focuses on the development of TSR system including both detection and classification methods considering diverse techniques based on color, shape, machine learning and deep learning algorithms and offers insights into the various approaches. Hence a comparative synthesis is established to discuss thorough of them along. Furthermore, this paper presents a comprehensive study for TSR systems using two hardware platforms: Raspberry Pi and Nano Jetson to analyze their performances and provide insights into the most suitable hardware architectures for deploying efficient and reliable TSR systems. This paper concludes by advocating for a synergistic combination of the two best methods of detection and classification of road signs in terms of accuracy and processing time to build a new real time TSR methodology that effectively address this challenge and ultimately improve TSR performance implemented on the best board.

A significant cause of blindness in preterm infants is retinal retinopathy of prematurity (ROP). Early detection and intervention are essential for preventing visual loss. This study proposes an advanced approach for predicting ROP stages using deep learning algorithms and belief function theory. Three steps comprise the suggested technique: image pre-processing, feature extraction using deep learning models, and classification utilizing belief function theory. We used a dataset of 3720 retinal images from premature infants and achieved a classification accuracy of 95.57% for predicting ROP stages. Our results demonstrate the effectiveness of deep learning algorithms and belief function theory in ROP diagnosis. This strategy can increase the efficacy and precision of ROP diagnosis, improving the treatment course for premature infants at risk of vision loss.