This paper introduces a cutting-edge approach that combines facial recognition and drowsiness detection technologies with Internet of Things capabilities, including 5G/6G connectivity, aimed at bolstering vehicle security and driver safety. The delineated two-phase project is tailored to strengthen security measures and address accidents stemming from driver distraction and fatigue. The initial phase is centered on facial recognition for driver authentication before vehicle initiation. Following successful authentication, the subsequent phase harnesses continuous eye monitoring features, leveraging edge computing for real-time processing to identify signs of drowsiness during the journey. Emphasis is placed on video-based identification and analysis to ensure robust drowsiness detection. Finally, the study highlights the potential of these innovations to revolutionize automotive security and accident prevention within the context of intelligent transport systems.

Efficient real-time computer vision-based passenger flow analysis is increasingly important for the management of intelligent transportation systems and smart cities. This paper presents the design and implementation of a system for real-time object detection, tracking, and people counting in tram stations. The proposed approach integrates YOLO-based detection with a lightweight tracking module and is deployed on an NVIDIA Jetson Nano device, enabling operation under resource constraints and demonstrating the potential of edge AI. Multiple YOLO versions, from v3 to v11, were evaluated on data collected in collaboration with Metropolitano de Tenerife. Experimental results show that YOLOv5s achieves the best balance between detection accuracy and inference speed, reaching 96.85% accuracy in counting tasks. The system demonstrates the feasibility of applying edge AI to monitor passenger flow in real time, contributing to intelligent transportation and smart city initiatives.

This paper presents a ubiquitous, blockchain-based system designed to improve transparency, traceability and trust in supply chains involving autonomous vehicles (AVs). The framework integrates Internet of Things (IoT) sensors, radio-frequency identification (RFID) and QR identifiers, global positioning system (GPS) tracking, and mobile communications with smart contracts implemented on the Ethereum 2.0 blockchain. The main contributions are as follows: (1) an architecture enabling real-time monitoring and automated verification of logistics transactions; (2) a proof of concept integrating blockchain, the IoT and Android-based OBUs; and (3) a quantitative analysis of gas and smart contract execution costs. Experimental tests show gas consumption ranging from 21,000 to 5,000,000 units and transaction costs ranging from 0.0001 to 0.0033 ETH, confirming the system’s technical feasibility and cost-efficiency. As well as cost and efficiency, the process improved transparency, real-time traceability and decentralized verification, confirming the system’s efficacy for supply chains involving autonomous vehicles.

This research explores the development and implementation of an artificial intelligence (AI)-based system for obstacle detection and traffic light recognition in the tram network of Tenerife, Canary Islands, Spain. The motivation for this study lies in the need to improve safety and operational efficiency in public transport systems through the use of current object detection technologies. For this purpose, a comprehensive review of current AI-based object detection technologies is carried out, with a focus on deep learning methods. As a result of this analysis, a You Only Look Once (YOLO) model, optimized using transfer learning, is selected and implemented in a real-time software application running on a Jetson Nano device. This system allows fast and accurate detection of obstacles and traffic signals, providing immediate alerts to operators. The objective of the system is to design and evaluate a system capable of assisting trams operators in identifying potential risks in real time through timely alerts, contributing to accident reduction and service optimization. The results obtained demonstrate the feasibility and effectiveness of using deep learning techniques to strengthen safety and operational efficiency in trams. Furthermore, this study demonstrates how advanced computer vision techniques can be successfully applied in public transportation environments, laying the groundwork for future improvements in driver assistance systems. This research contributes to the field of AI in transportation by demonstrating a practical application of these technologies in public transit systems, highlighting their potential to significantly improve driving assistance systems.