In an urban and uncontrolled environment, the presence of mixed traffic of autonomous vehicles, classical vehicles, vulnerable road users, e.g., pedestrians, and unprecedented dynamic events makes it challenging for the classical autonomous vehicle to navigate the traffic safely. Therefore, the realization of collaborative autonomous driving has the potential to improve road safety and traffic efficiency. However, an obvious challenge in this regard is how to define, model, and simulate the environment that captures the dynamics of a complex and urban environment. Therefore, in this research, we first define the dynamics of the envisioned environment, where we capture the dynamics relevant to the complex urban environment, specifically, highlighting the challenges that are unaddressed and are within the scope of collaborative autonomous driving. To this end, we model the dynamic urban environment leveraging a probabilistic graphical model (PGM). To develop the proposed solution, a realistic simulation environment is required. There are a number of simulators—CARLA (Car Learning to Act), one of the prominent ones, provides rich features and environment; however, it still fails on a few fronts, for example, it cannot fully capture the complexity of an urban environment. Moreover, the classical CARLA mainly relies on manual code and multiple conditional statements, and it provides no pre-defined way to do things automatically based on the dynamic simulation environment. Hence, there is an urgent need to extend the off-the-shelf CARLA with more sophisticated settings that can model the required dynamics. In this regard, we comprehensively design, develop, and implement an extension of a classical CARLA referred to as CARLA+ for the complex environment by integrating the PGM framework. It provides a unified framework to automate the behavior of different actors leveraging PGMs. Instead of manually catering to each condition, CARLA+ enables the user to automate the modeling of different dynamics of the environment. Therefore, to validate the proposed CARLA+, experiments with different settings are designed and conducted. The experimental results demonstrate that CARLA+ is flexible enough to allow users to model various scenarios, ranging from simple controlled models to complex models learned directly from real-world data. In the future, we plan to extend CARLA+ by allowing for more configurable parameters and more flexibility on the type of probabilistic networks and models one can choose. The open-source code of CARLA+ is made publicly available for researchers.

With the advancement in various technological fronts, we are expecting the design goals of smart cities to be realized earlier than expected. Undoubtedly, communication networks play the crucial role of backbone to all the verticals of smart cities, which is why we are surrounded by terminologies such as the Internet of Things, the Internet of Vehicles, the Internet of Medical Things, etc. In this paper, we focus on implanting intelligence in 5G and beyond mobile networks. In this connection, we design and develop a novel data-driven predictive model which may serve as an intelligent slicing framework for different verticals of smart cities. The proposed model is trained on different machine learning algorithms to predict the optimal network slice for a requested service resultantly assisting in allocating enough resources to the slice based on the traffic prediction.

Emotion recognition from electroencephalography (EEG) signals is an important component of emotionally intelligent human–computer interaction systems. However, existing approaches often rely on handcrafted features or conventional deep learning architectures that struggle to generalize across subjects due to the high variability of EEG signals. Most prior studies focus primarily on binary emotion classification and provide limited investigation of more complex multi-class scenarios. This work presents EmoCaps, an end-to end deep learning framework based on a capsule network with a self-attention–guided routing mechanism to learn discriminative representations directly from raw EEG signals. The proposed model is evaluated on the DEAP dataset under both subject dependent and subject-independent settings and across binary and multi-class emotion recognition tasks involving valence, arousal, and dominance dimensions. Experimental results demonstrate that EmoCaps consistently outperforms several representative deep learning models. In subject-dependent binary classification, the proposed approach achieves accuracies of up to 97%, while in the more challenging subject-independent setting it exceeds 80% accuracy across emotional dimensions. The framework also achieves strong performance in four-class and eight-class emotion recognition tasks and provides, to the best of our knowledge, the first reported results for subject independent multi-class emotion recognition on this dataset. Although the capsule-based architecture introduces higher computational cost, the proposed model significantly improves robustness and generalization across subjects. These results highlight the potential of EmoCaps for real-world emotion-aware applications in healthcare, adaptive learning, and affective computing systems.