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Equipping autonomous agents for dynamic interaction and navigation is a significant challenge in intelligent transportation systems. This study aims to address this by implementing a brain-inspired model for decision making in autonomous vehicles. We employ active inference, a Bayesian approach that models decision-making processes similar to the human brain, focusing on the agent's preferences and the principle of free energy. This approach is combined with imitation learning to enhance the vehicle's ability to adapt to new observations and make human-like decisions. The research involved developing a multi-modal self-awareness architecture for autonomous driving systems and testing this model in driving scenarios, including abnormal observations. The results demonstrated the model's effectiveness in enabling the vehicle to make safe decisions, particularly in unobserved or dynamic environments. The study concludes that the integration of active inference with imitation learning significantly improves the performance of autonomous vehicles, offering a promising direction for future developments in intelligent transportation systems. [ABSTRACT FROM AUTHOR]

Deploying unmanned aerial vehicles (UAVs) as aerial base stations is an exceptional approach to reinforce terrestrial infrastructure owing to their remarkable flexibility and superior agility. However, it is essential to design their flight trajectory effectively to make the most of UAV-assisted wireless communications. This paper presents a novel method for improving wireless connectivity between UAVs and terrestrial users through effective path planning. This is achieved by developing a goal-directed trajectory planning method using active inference. First, we create a global dictionary using traveling salesman problem with profits (TSPWP) instances executed on various training examples. This dictionary represents the world model and contains letters representing available hotspots, tokens representing local paths, and words depicting complete trajectories and hotspot order. By using this world model, the UAV can understand the TSPWP's decision-making grammar and how to use the available letters to form tokens and words at various levels of abstraction and time scales. With this knowledge, the UAV can assess encountered situations and deduce optimal routes based on the belief encoded in the world model. Our proposed method outperforms traditional Q-learning by providing fast, stable, and reliable solutions with good generalization ability. [ABSTRACT FROM AUTHOR]

Autonomous vehicles (AVs) rely on advanced sensory systems, such as Light Detection and Ranging (LiDAR), to function seamlessly in intricate and dynamic environments. LiDAR produces highly accurate 3D point clouds, which are vital for the detection, classification, and tracking of multiple targets. A systematic review and classification of various clustering and Multi-Target Tracking (MTT) techniques are necessary due to the inherent challenges posed by LiDAR data, such as density, noise, and varying sampling rates. As part of this study, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology was employed to examine the challenges and advancements in MTT techniques and clustering for LiDAR point clouds within the context of autonomous driving. Searches were conducted in major databases such as IEEE Xplore, ScienceDirect, SpringerLink, ACM Digital Library, and Google Scholar, utilizing customized search strategies. We identified and critically reviewed 76 relevant studies based on rigorous screening and evaluation processes, assessing their methodological quality, data handling adequacy, and reporting compliance. As a result of this comprehensive review and classification, we were able to provide a detailed overview of current challenges, research gaps, and advancements in clustering and MTT techniques for LiDAR point clouds, thus contributing to the field of autonomous driving. Researchers and practitioners working in the field of autonomous driving will benefit from this study, which was characterized by transparency and reproducibility on a systematic basis. [ABSTRACT FROM AUTHOR]

In recent days, it is becoming essential to ensure that the outcomes of signal processing methods based on machine learning (ML) data-driven models can provide interpretable predictions. The interpretability of ML models can be defined as the capability to understand the reasons that contributed to generating a given outcome in a complex autonomous or semi-autonomous system. The necessity of interpretability is often related to the evaluation of performances in complex systems and the acceptance of agents' automatization processes where critical high-risk decisions have to be taken. This paper concentrates on one of the core functionality of such systems, i.e., abnormality detection, and on choosing a model representation modality based on a data-driven machine learning (ML) technique such that the outcomes become interpretable. The interpretability in this work is achieved through graph matching of semantic level vocabulary generated from the data and their relationships. The proposed approach assumes that the data-driven models to be chosen should support emergent self-awareness (SA) of the agents at multiple abstraction levels. It is demonstrated that the capability of incrementally updating learned representation models based on progressive experiences of the agent is shown to be strictly related to interpretability capability. As a case study, abnormality detection is analyzed as a primary feature of the collective awareness (CA) of a network of vehicles performing cooperative behaviors. Each vehicle is considered an example of an Internet of Things (IoT) node, therefore providing results that can be generalized to an IoT framework where agents have different sensors, actuators, and tasks to be accomplished. The capability of a model to allow evaluation of abnormalities at different levels of abstraction in the learned models is addressed as a key aspect for interpretability. [ABSTRACT FROM AUTHOR]

This paper investigates self-organizing binary majority consensus disturbed by faulty nodes with random and persistent failure. We study consensus in ordered and random networks with noise, message loss and delays. Using computer simulations, we show that: (1) explicit randomization by noise, message loss and topology can increase robustness towards faulty nodes; (2) commonly-used faulty nodes with random failure inhibit consensus less than faulty nodes with persistent failure; and (3) in some cases, such randomly failing faulty nodes can even promote agreement. [ABSTRACT FROM AUTHOR]

Smart cameras are key sensors in Internet of Things (IoT) applications and often capture highly sensitive information. Therefore, security and privacy protection is a key concern. This paper introduces a lightweight security approach for smart camera IoT applications based on elliptic-curve (EC) signcryption that performs data signing and encryption in a single step. We deploy signcryption to efficiently protect sensitive data onboard the cameras and secure the data transfer from multiple cameras to multiple monitoring devices. Our multi-sender/multi-receiver approach provides integrity, authenticity, and confidentiality of data with decryption fairness for multiple receivers throughout the entire lifetime of the data. It further provides public verifiability and forward secrecy of data. Our certificateless multi-receiver aggregate-signcryption protection has been implemented for a smart camera IoT scenario, and the runtime and communication effort has been compared with single-sender/single-receiver and multi-sender/single-receiver setups. [ABSTRACT FROM AUTHOR]