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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]

The combination of different sensory information to predict upcoming situations is an innate capability of intelligent beings. Consequently, various studies in the Artificial Intelligence field are currently being conducted to transfer this ability to artificial systems. Autonomous vehicles can particularly benefit from the combination of multi-modal information from the different sensors of the agent. This paper proposes a method for video-frame prediction that leverages odometric data. It can then serve as a basis for anomaly detection. A Dynamic Bayesian Network framework is adopted, combined with the use of Deep Learning methods to learn an appropriate latent space. First, a Markov Jump Particle Filter is built over the odometric data. This odometry model comprises a set of clusters. As a second step, the video model is learned. It is composed of a Kalman Variational Autoencoder modified to leverage the odometry clusters for focusing its learning attention on features related to the dynamic tasks that the vehicle is performing. We call the obtained overall model Cluster-Guided Kalman Variational Autoencoder. Evaluation is conducted using data from a car moving in a closed environment and leveraging a part of the University of Alcalá DriveSet dataset, where several drivers move in a normal and drowsy way along a secondary road. [ABSTRACT FROM AUTHOR]

Approximately, one-third to half of all food produced globally is wasted. In developed countries, roughly up to half of this food waste comes from consumers. In response to this, the UN has set goals to raise consumer awareness and reduce food waste by 50% before 2030. Our objective is to evaluate how emerging technologies could improve awareness in households. Inspired by future sensing possibilities, we envision a community-based social system that captures in-home food availability and waste patterns and uses this information to support awareness and sustainability. In this work, we describe an evaluation of a component that could be part of such a system. This component or concept, called Social Recipes , aims at encouraging food sharing by suggesting groups of related consumers recipes that are based on ingredients from different individuals or households. To evaluate Social Recipes , we conducted 3 user studies to see how it could raise awareness and reduce food waste and to suggest implications for its design. In the first two studies, we evaluated expected impacts of the concept. The third study was a home deployment, where Social Recipes were sent using technological probes for a more realistic experience. Here, we also evaluated it against the more common method of influence strategy in sustainability research that is restricted to feedback (i.e., eco-feedback). Our main findings showed that Social Recipes has raised awareness of in-home food availability and triggered food-related conversations among participants resulting in knowledge gain. However, Social Recipes alone was not perceived as effective in directly reducing food waste. And therefore, for the design of a community-based social system, we suggest another component to be added to the system that provides eco-feedback. This component was perceived as more effective in reducing food waste with impacts on awareness of waste generation and social surveillance. Overall, the aim of this work is to contribute to an understanding of how Social Recipes could impact consumers and how to design a community-based social (recipe) system that can be integrated in consumers daily activities for effective but pleasurable food waste prevention. [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]

• Mathematical formulation of Lines of attraction and flow models for high dimensional data. • Computation of Generalized State space by using Null Force Filter and learning of linear attractors. • Estimation of transition matrices from the intersection of the lines of attraction and learning of Hierarchical-Generalize Dynamic Bayesian Network. • Application of the data-driven probabilistic learning model on Autonomous vehicle. • Detecting abnormalities efficiently from the environment and compare the learning model with the state-of-art methods. A novel approach is presented for learning probabilistic transition matrices from temporal data series as switching models based on Generalized States (GS). An observed data sequence is analyzed by a reference filter whose errors are clustered. Each cluster is associated with a dynamic flow model, which described as a parametric linear attractor. The set of linear attractors define the Hierarchical Generalized Dynamic Bayesian Network (H-GDBN), which encodes a learned model of the obtained sequence. A Markov Jump Particle Filter (MJPF) uses H-GDBN's probabilistic information to make inferences at a multilevel scale and facilitates the detection of abnormalities. This paper shows how transition matrices can be obtained as an integral part of the clustering step by employing the advantages of GSs, enabling a unique optimal criterion for learning flow models at discrete and continuous levels of H-GDBN. For evaluating the proposed method, odometry and proprioceptive control data from an autonomous vehicle are employed to learn H-GDBNs. Learned H-GDBN are used to detect abnormalities when vehicle encounter any abnormal situation. Performance evaluation based on ROC curves is provided to select the optimal transition matrix. [ABSTRACT FROM AUTHOR]

The evolution of Intelligent Transportation Systems in recent times necessitates the development of self-awareness in agents. Before the intensive use of Machine Learning, the detection of abnormalities was manually programmed by checking every variable and creating huge nested conditions that are very difficult to track. This paper aims to introduce a novel method to develop self-awareness in autonomous vehicles that mainly focuses on detecting abnormal situations around the considered agents. Multi-sensory time-series data from the vehicles are used to develop the data-driven Dynamic Bayesian Network (DBN) models used for future state prediction and the detection of dynamic abnormalities. Moreover, an initial level collective awareness model that can perform joint anomaly detection in co-operative tasks is proposed. The GNG algorithm learns the DBN models' discrete node variables; probabilistic transition links connect the node variables. A Markov Jump Particle Filter (MJPF) is applied to predict future states and detect when the vehicle is potentially misbehaving using learned DBNs as filter parameters. In this paper, datasets from real experiments of autonomous vehicles performing various tasks used to learn and test a set of switching DBN models. • Self-awareness models considering pair-based features of the vehicles. • Initial level collective awareness model for joint anomaly detection. • Data-driven Dynamic Bayesian Network models for anomaly detection in driving tasks. • The Markov Jump Particle Filter for anomaly detection of vehicles. • Anomaly detection without exhaust coding for self-state variables for large projects. [ABSTRACT FROM AUTHOR]