Autonomous driving technologies are rapidly advancing, driven by the need for safer, more efficient, and intelligent transportation systems. A fundamental component of these systems is the perception module, which enables vehicles to understand and react to their surrounding environment. Object detection, in particular, is essential for identifying dynamic and static elements on the road, such as pedestrians, vehicles, traffic signs, and obstacles. In this work, we explore and enhance the capabilities of state-of-the-art deep learning-based object detectors within the YOLO (You Only Look Once) family, focusing on the latest versions: YOLOv10 and YOLOv11. We fine-tuned and optimized multiple variants of each model—namely, nano (n), small (s), medium (m), and large (l)—to improve detection accuracy and computational efficiency for real-time autonomous driving applications. The models were trained and evaluated on a diverse road object, and performance was measured using key metrics including precision, mean Average Precision (mAP), and Precision-Recall curves. Experimental results reveal that the fine-tuned YOLOv10n achieved a peak class-level precision of 1.00 at a specific confidence threshold (0.997), indicating that perfect precision was observed for certain classes under high-confidence conditions, while the overall mean precision and mAP metrics reflect more balanced model performance, while YOLOv11s attained the best result within its group with a precision of 0.91 at a threshold of 0.972. These findings demonstrate the potential of tailored YOLO architectures to meet the demanding requirements of real-world autonomous navigation systems.

Adversarial examples remain a major obstacle to the reliable deployment of deep neural networks, yet widely used one-step attacks such as FGSM rely on a fixed perturbation budget that ignores how vulnerability varies across inputs and architectural paradigms. In this paper, we proposeFuzzy-FGSM(FFGSM), a single-step, interpretable adversarial attack that adaptively allocates the$\ell \_\infty$perturbation budget on a per-sample basis. FFGSM couples the FGSM sign direction with a lightweight Mamdani fuzzy inference system that maps two human-interpretable indicators-the input-gradient norm (GN), capturing local sensitivity, and the model confidence (CN), capturing prediction certainty-into a bounded, sample-specific perturbation magnitude$\hat{\epsilon }\in [0,\epsilon \_{\max }]$. We evaluate FFGSM under$\epsilon \_{\max }=8/255$across three datasets (CIFAR-10, STL-10, and Tiny-ImageNet) and four architectures (ResNet-18, ResNet-50, VGG-16, and DenseNet-121). Our results demonstrate that FFGSM consistently outperforms transfer-based and black-box baselines, achieving peak Attack Success Rates (ASR) of up to$85.53\%$on DenseNet-121/Tiny-ImageNet, while remaining surprisingly close to the strong multi-step AutoAttack ensemble of about$80.2\%$vs.$81.7\%$ASR on CIFAR-10/ResNet18, and$64.5\%$vs.$66.8\%$on STL-10/ResNet50. Notably, FFGSM remains remarkably competitive with the strong multi-step Auto-Attack ensemble, often trailing by only a 1–$4\%$margin despite being a single-step method. Furthermore, cross-model transferability analysis reveals that FFGSM effectively exploits the “transferability gap,” showing that residual-based models generate more universal adversarial directions than sequential models like VGG-16. Runtime measurements confirm that the fuzzy controller adds only a negligible overhead (approx.$5.4\%$–$7.7\%$), preserving the simplicity and real-time efficiency of single-step generation.

Fifth generation and beyond networks are envisioned to support multi industrial Internet of Things (IIoT) applications with a diverse quality-of-service (QoS) requirements. Network slicing is recognized as a flagship technology that enables IIoT networks with multiservices and resource requirements by allowing the network-as-infrastructure transition to the network-as-service. Motivated by the increasing IIoT computational capacity, and taking into consideration the QoS satisfaction and private data sharing challenges, federated reinforcement learning (RL) has become a promising approach that distributes data acquisition and computation tasks over distributed network agents, exploiting local computation capacities and agent's self-learning experiences. This article proposes a novel deep RL scheme to provide a federated and dynamic network management and resource allocation for differentiated QoS services in future IIoT networks. This involves IIoT slices resource allocation in terms of transmission power (TP) and spreading factor (SF) according to the slices QoS requirements. Toward this goal, the proposed deep federated Q-learning (DFQL) is reached into two main steps. First, we propose a multiagent deep Q-learning-based dynamic slices TP and SF adjustment process that aims at maximizing self-QoS requirements in term of throughput and delay. Second, the deep federated learning is proposed to learn multiagent self-model and enable them to find an optimal action decision on the TP and the SF that satisfy IIoT virtual network slice QoS reward, exploiting the shared experiences between agents. Simulation results show that the proposed DFQL framework achieves efficient performance compared to the traditional approaches.

The future fifth-generation (5G) networks are expected to support a huge number of connected devices with various and multitude services having different quality of service (QoS) requirements. Communication in Industry 4.0 is one of the flagships and special applications of the 5G due to the specificity of the industrial environment as well as the variety of its services such as safety communication, robot's communications, and machine monitoring. In this context, we propose a new resource allocation for the future Industry 4.0 based on software-defined networking and network function virtualization technologies, machine learning tools and the slicing paradigm where each slice of the network is dedicated to a category of services having similar QoS requirement level. In this article, the proposed solution ensures the allocation of the resources to the slices depending on their requirements in terms of bandwidth, delay, and reliability. Toward this goal, our solution is performed in three main steps: first, Internet of Things (IoT) devices assignment to the slices step based on online Gaussian mixture model clustering algorithm, second, inter-slices resources reservations step based on mini-batch gradient descent, and third, intra-slices resources allocations based on the max-utility algorithm. We have performed extensive simulations in a realistic industrial scenario using NS3 simulator. Numerical results show the effectiveness of our proposed solution in terms of reducing packet error rate, energy consumption, and in terms of increasing the percentage of served devices in delay comparing to the traditional approaches.