Optimizing path planning for energy efficiency is critical for achieving sustainable vehicular transportation. This paper presents a novel framework for evaluating the impact of path planning algorithms (PPAs) on energy consumption within a simulated environment. We leverage the CARLA simulator to conduct a comparative analysis between the widely used A* and a Hybrid Genetic Algorithm (HGA) across diverse vehicular scenarios. This investigation aims to quantify the influence of PPA selection on vehicle energy expenditure, enabling data-driven optimization for energy minimization. We leverage an offline energy estimation model to further streamline the comparison of the two PPAs. Extensive simulations are employed to demonstrate the efficiency and adaptability of the proposed framework. The findings contribute to the development of energy-efficient path-planning strategies, promoting advancements toward sustainable transportation systems.

Unmanned aerial vehicle (UAV) swarms are experiencing a remarkable growth as powerful assets in search and rescue (SAR) missions, offering rapid situationa

When constructing a vehicular network, safety, efficiency and reliability represent the fundamental requirement. The usually encountered Long Range (LoRa) communication protocol has overcome the coverage limitations while focusing on the trade-offs between dependable transmissions and resource expenditure. Such a system supports battery-powered edge devices to monitor the coverage area. Through the LoRa Wide Area Network (LoRaWAN) architecture, a more flexible and locality-driven network can be structured. This work establishes a broadcast-based discovery system for a vehicular environment to simulate potential imminent dangers. This study examines a crisis management system that provides detailed insights into the resource intensity associated with non-discrete events. The research primarily focuses on optimizing network capacity and effectively disseminating critical information to those affected by events involving vehicles.

Modern autonomous and intelligent vehicles increasingly require reliable in-cabin monitoring systems capable of interpreting complex physiological and behavioral signals to safeguard driver well-being. This paper proposes a time-aware, ontology-driven framework that unifies multimodal physiological, behavioral, and cognitive indicators into a single semantic model capable of transparent, rule-based driver-state inference. The proposed system combines GCIs for interpretable physiological threshold classification with SWRL rules for contextual and numeric reasoning, enabling transparent inference of fatigue, attention level, and unresponsiveness across sequential time windows. A 4D Description Logic architecture structures each measurement as a temporally indexed state, supporting longitudinal analysis over time-series data. To ensure robustness and scalability, real-world physiological data are augmented with synthetic physiological time series generated via TimeGAN, while the inferred driver states condition a diffusion-based image generation module for visual interpretation. The framework is evaluated through a comprehensive validation stack, including competency questions, structural consistency checks, performance profiling, image quality assessment using a self-supervised ARNIQA model and cross-model consistency using visual extractors with an LLM, demonstrating high logical consistency, accurate state inference, and stable performance across multiple distinct actors with 16 temporal slices. Results confirm that the proposed ontology-based framework provides a transparent, extensible, and computationally tractable foundation for in-cabin driver state assessment, enabling future integration with advanced Human-Machine Interface (HMI) systems and real-time intelligent vehicle applications.