Search Results (35)

Horizontal dilution of precision (HDOP) is a widely used quality indicator of Global Navigation Satellite System (GNSS) positioning, considering only satellite geometry. In this study, HDOP was simulated using GNSS almanacs and high-resolution digital surface models (DSMs) along three European road sections: Oslo— Svinesund Bridge (Norway); Hamburg city center (Germany); and Rotterdam—Dutch–German border (Netherlands). This study was accomplished as part of the MODI project, which is a cross-border initiative to accelerate Cooperative, Connected, and Automated Mobility (CCAM). Our analysis revealed excellent or good overall GNSS performance in the study areas, particularly on highway sections with 99–100% of study points having a median HDOP that is categorized as excellent (HDOP < 2) or good (HDOP < 5). However, the road section in Hamburg’s city center presents challenges. When GPS is used alone, 8% of the study points experience weak or poor HDOP, and there are study points where the system is available (HDOP < 5) less than 50% of the time. Combining GNSS constellations significantly improved system availability, reaching 95% for 99% of the study points in Hamburg. To validate our simulations, we compared results with GNSS observations from a survey vehicle in Hamburg. Initial low correlation was attributed to the reception of signals from non-line-of-sight satellites. By excluding satellites with low signal-to-noise ratios, the correlation increased significantly, and reasonable agreement was obtained. We also examined the impact of using a 10 m DSM instead of a 1 m DSM in Hamburg. While the coarser spatial resolution offers computational benefits, it may miss critical details for accurate assessment of satellite visibility. Full article

This paper analyzes the exploitability, economic viability, and impact of fleet-based 5G network extensions implemented in maritime environments, focusing on the Baltic Sea and Mediterranean as a case study. Through cost–benefit analysis and business model validation, we demonstrate how multi-hop 5G connectivity can reduce communication costs while improving service quality for maritime operators. Our findings indicate that implementing vessel-based 5G relay stations can achieve 80–90% coverage in key maritime corridors with a break-even period of 2–3 years. The study reveals that combining vessel-to-vessel relaying with strategic floating base stations can reduce connectivity costs by up to 40% compared to traditional satellite solutions, while enabling new revenue streams through premium services. We provide a detailed economic framework for evaluating similar implementations across different maritime routes and suggest policy recommendations for facilitating cross-border 5G maritime networks and introduce key use cases value creation for network extension. Full article

Today’s urban environments are complex, highly congested traffic scenarios that suffer from multiple unsolved problems such as traffic jams and congestion. These problems pose a significant increase in the risks and probability of traffic accidents in modern cities, which have experienced an enormous growth in the number of vehicles. This work introduces a centralized arbitration framework designed for Cooperative Connected Automated Vehicles (CCAVs) to make real-time decisions and resolve conflicts among various driving strategies or behaviors to facilitate resource reservation based on their collaborative actions. Cooperation and arbitration are two of the most important areas of research that seek to provide tools and mechanisms for the optimization and control of traffic flow at critical locations such as intersections and traffic circles. The approach presented, fully implemented on ROS and capable of constructing a software-defined traffic control environment, is able to supervise in a distributed manner how any CCAV operates with the infrastructure, potentially reducing the number of vehicles waiting and harmonizing the traffic flow. The methodology proposed surpasses traditional driver-in-the-loop cooperation by delivering a higher level of automation for collaborative traffic behavior. This approach demonstrably reduces average waiting time by 13% and increases the total utilization of the traffic emplacement by 70% compared to the classic simulated traffic light model. The solution presented was tested on the Carla simulator, with a complete ROS-based vehicle automation solution that provides promising results for CCAV coordination in complex traffic scenarios through a general framework of behavior-based collaboration. Full article

Cooperative, Connected, and Automated Mobility (CCAM) is set to play a key role in the future of transportation, contributing to the achievement of sustainable development goals. Moreover, Artificial Intelligence (AI), a transformative technology with applications across various industries, can significantly enhance CCAM operations. Additionally, passenger demand forecasting, a critical aspect of mobility research, will become even more essential as CCAM adoption continues to grow in the next years. Therefore, the present research study, in order to deal with the issue of passenger demand forecasting in CCAM, proposes the Principal Component Random Forest (PCRF) methodology, which is based on AI, as it leverages a well-established statistical methodology such as the Principal Components Analysis with a flagship traditional machine learning technique, which is Random Forest. The application of PCRF in four European pilot sites within the European Union-funded SHOW project demonstrated its high accuracy and effectiveness as reflected by the average normalized error of approximately 15%. Full article

The combination of connectivity and automation allows connected and autonomous vehicles (CAVs) to operate autonomously using advanced on-board sensors while communicating with each other via vehicle-to-vehicle (V2V) technology to enhance safety, efficiency, and mobility. One of the most promising features of CAVs is cooperative adaptive cruise control (CACC). This system extends the capabilities of conventional adaptive cruise control (ACC) by facilitating the exchange of critical parameters among vehicles to enhance safety, traffic flow, and efficiency. However, increased connectivity introduces new vulnerabilities, making CACC susceptible to cyber-attacks, including false data injection (FDI) attacks, which can compromise vehicle safety. To address this challenge, we propose a secure observer-based control design leveraging Lyapunov stability analysis, which is capable of mitigating the adverse impact of FDI attacks and ensuring system safety. This approach uniquely addresses system security without relying on a known lead vehicle model. The developed approach is validated through simulation results, demonstrating its effectiveness. Full article

Background: With urban logistics facing challenges such as high delivery volumes and driver shortages, autonomous driving emerges as a promising solution. However, the integration of autonomous vans and robots into existing fulfillment processes and platforms remains largely unexplored. Method: This paper addresses this gap by developing and piloting a comprehensive blueprint architecture tailored for autonomous mobility in urban last-mile delivery. The proposed framework integrates autonomous vehicle operations, data processing, and stakeholder collaboration. Results: Through initial implementation and piloting, we demonstrate the practical applicability and advantages of this architecture. Conclusions: This study contributes to the understanding of essential data, services, and tools, providing a valuable guideline for Logistics Service Providers aiming to implement autonomous last-mile delivery solutions. Full article

Nowadays, traffic management challenges in the era of digital transport are rising, as the interactions of various stakeholders providing such technologies play a pivotal role in shaping traffic dynamics. The objective of this paper was to present a game-theory-based framework for modeling and optimizing urban traffic in road networks, considering the co-existence and interactions of different players composed of drivers of conventional vehicles, central governing authorities with traffic management capabilities, and competitive or cooperative connected mobility private service providers. The scope of this work was to explore and present the outcomes of diverse mixed equilibrium conditions in the road network of the city of Thessaloniki (Greece), integrating the principles of user equilibrium, system optimum, and Cournot oligopoly. The impacts of varying network attributes were systematically analyzed to provide quantitative indicators representing the overall network performance. Analysis of the results provided insights into the sensitivity and the resilience of the road network under various prevalence schemes of drivers of conventional vehicles, representing the user equilibrium characteristics, or drivers relying on traffic guidance provided by a central governing authority, representing the system optimum principles as well as the cooperation and competition schemes of private connected mobility providers with certain market shares in the network. Full article

This paper examines the impact of Vehicle-to-Everything (V2X) communications on vehicle cooperation, focusing on increasing the robustness and feasibility of Cooperative, Connected, and Automated Vehicles (CCAVs). V2X communications enable CCAVs to obtain a holistic environmental perception, facilitating informed decision making regarding their trajectory. This technological innovation is essential to mitigate accidents resulting from inadequate or absent communication on the roads. As the importance of vehicle cooperation grows, the European Telecommunications Standards Institute (ETSI) has been standardizing messages and services for V2X communications, in order to improve the synchronization of CCAVs actions. In this context, this preliminary work explores the use of Maneuver Coordination Messages (MCMs), under standardization by ETSI, for cooperative path planning. This work presents a novel approach by implementing these messages as well as the associated Maneuver Coordination Service (MCS) with a Cooperative Driving System to process maneuver coordination. Additionally, a trajectory approach is introduced along with a message generation mechanism and a process to dynamically handle collisions. This was implemented in an Artery V2X simulation framework combining both network communications and SUMO traffic simulations. The obtained results demonstrate the effectiveness of using V2X communications to ensure the safety and efficiency of Cooperative Intelligent Transportation Systems (C-ITS). Full article

Population growth and environmental burden have turned the efforts of cities globally toward smarter and greener mobility. Cooperative and Connected Automated Mobility (CCAM) serves as a concept with the power and potential to help achieve these goals building on technological fields like Internet of Things, computer vision, and distributed computing. However, its implementation is hindered by various challenges covering technical parameters such as performance and reliability in tandem with other issues, such as safety, accountability, and trust. To overcome these issues, new distributed and decentralized approaches like blockchain and smart contracts are needed. This paper identifies a comprehensive inventory of CCAM challenges including technical, social, and ethical challenges. It then describes the most prominent methodologies using blockchain and smart contracts to address them. A comparative analysis of the findings follows, to draw useful conclusions and discuss future directions in CCAM and relevant blockchain applications. The paper contributes to intelligent transportation systems’ research by offering an integrated view of the difficulties in substantiating CCAM and providing insights on the most popular blockchain and smart contract technologies that tackle them. Full article

Connected and Autonomous Vehicles (CAVs) technology has the potential to transform the transportation system. Although these new technologies have many advantages, the implementation raises significant concerns regarding safety, security, and privacy. Anomalies in sensor data caused by errors or cyberattacks can cause severe accidents. To address the issue, this study proposed an innovative anomaly detection algorithm, namely the LSTM Autoencoder with Gaussian Mixture Model (LAGMM). This model supports anomalous CAV trajectory detection in the real-time leveraging communication capabilities of CAV sensors. The LSTM Autoencoder is applied to generate low-rank representations and reconstruct errors for each input data point, while the Gaussian Mixture Model (GMM) is employed for its strength in density estimation. The proposed model was jointly optimized for the LSTM Autoencoder and GMM simultaneously. The study utilizes realistic CAV data from a platooning experiment conducted for Cooperative Automated Research Mobility Applications (CARMAs). The experiment findings indicate that the proposed LAGMM approach enhances detection accuracy by 3% and precision by 6.4% compared to the existing state-of-the-art methods, suggesting a significant improvement in the field. Full article

The cooperative, connected, and automated mobility (CCAM) infrastructure plays a key role in understanding and enhancing the environmental perception of autonomous vehicles (AVs) driving in complex urban settings. However, the deployment of CCAM infrastructure necessitates the efficient selection of the computational processing layer and deployment of machine learning (ML) and deep learning (DL) models to achieve greater performance of AVs in complex urban environments. In this paper, we propose a computational framework and analyze the effectiveness of a custom-trained DL model (YOLOv8) when deployed in diverse devices and settings at the vehicle-edge-cloud-layered architecture. Our main focus is to understand the interplay and relationship between the DL model’s accuracy and execution time during deployment at the layered framework. Therefore, we investigate the trade-offs between accuracy and time by the deployment process of the YOLOv8 model over each layer of the computational framework. We consider the CCAM infrastructures, i.e., sensory devices, computation, and communication at each layer. The findings reveal that the performance metrics results (e.g., 0.842 mAP@0.5) of deployed DL models remain consistent regardless of the device type across any layer of the framework. However, we observe that inference times for object detection tasks tend to decrease when the DL model is subjected to different environmental conditions. For instance, the Jetson AGX (non-GPU) outperforms the Raspberry Pi (non-GPU) by reducing inference time by 72%, whereas the Jetson AGX Xavier (GPU) outperforms the Jetson AGX ARMv8 (non-GPU) by reducing inference time by 90%. A complete average time comparison analysis for the transfer time, preprocess time, and total time of devices Apple M2 Max, Intel Xeon, Tesla T4, NVIDIA A100, Tesla V100, etc., is provided in the paper. Our findings direct the researchers and practitioners to select the most appropriate device type and environment for the deployment of DL models required for production. Full article

In the typical landscape of road transportation, about 90% of traffic accidents result from human errors. Vehicle automation enhances road safety by reducing driver fatigue and errors and improves overall mobility efficiency. The advancement of autonomous vehicle technology will significantly impact traffic safety, potentially saving more than 30,000 lives annually in the United States alone. The widespread acceptance of autonomous and connected autonomous vehicles (AVs and CAVs) will be a process spanning multiple decades, requiring their coexistence with traditional vehicles. This study explores the mobility and safety performance of CAVs in mixed-traffic environments using the cumulative-anticipative car-following (CACF) model. This research compares the CACF model with established Wiedemann 99 and cooperative adaptive cruise control (CACC) models using a VISSIM platform. The simulations include single-lane and multi-lane networks, incorporating sensitivity tests for mobility and safety parameters. The study reveals increased throughput, reduced delays, and enhanced travel times with CACF, emphasizing its advantages over CACC. Safety analyses demonstrate CACF’s ability to prevent traffic shockwaves and bottlenecks, emphasizing the significance of communication range and acceleration coefficients. The research recommends early investment in vehicle-to-infrastructure (V2I) communication technology, refining CACC logic, and expanding the study to diverse road scenarios. Full article

Traffic-calming measures (TCMs) are non-invasive devices designed to improve road mobility and urban areas on a human scale. Despite their potential, they have been in use for a long time and now have to deal with the latest technological innovations in the automotive field, such as cooperative driving technologies (CDTs), to improve energy efficiency in cities. The goal of this study is to explore the safety and operational performances of TCMs featuring CDTs in urban areas. An urban-scale road network close to a seaside area in the City of Mazara del Vallo, Italy, was properly redesigned and simulated in AIMSUN to assess several design solutions, where connected and automated vehicles (CAVs) have been employed as a more energy-efficient public transportation system. Preliminarily, the fine-tuning process of model parameters included CAVs and human-operated vehicles (HOVs) flowing through the network up to saturation conditions. The safety of the planned solutions was tested by using surrogate measures. The micro-simulation approach allowed us to know in advance and compare the operational and safety performances of environmentally friendly solutions involving TCMs and CDTs. These results can also support urban road decision makers in pivoting urban-traffic-calming-based design solutions featuring cooperative driving technologies toward energy efficiency transitions for smart cities. Full article

This paper proposes a distributed coordination scheme for connected vehicles, including automated vehicles (AVs) and manual vehicles (MVs), at signal-free intersections. The cooperation issue of vehicles at an intersection is formulated into a multi-objective optimization problem that aims to eliminate conflicts and improve traffic mobility and fuel economy. For this purpose, the future trajectories of AVs and MVs are predicted by the respective car-following models, and are shared with neighboring vehicles in conflict relationships. The proposed scheme optimizes the sum of the performance of AVs within the cooperative zone in a prediction horizon. A distributed optimization algorithm in the receding horizon is presented to obtain the local optimal solutions, and is tested in simulations with different demand levels and penetration rates of AVs. The results show that the proposed scheme reduces travel time by 29.7–45.5% and 34.5–49.2%, and decreases fuel consumption by 27.6–35.3% and 21.6–29.9% under 70–100% penetration rates of AVs, compared to the no-control operation and fixed-time signal control strategy. In addition, a comparison simulation with the strategy of jointly optimizing the vehicle trajectory and signal timing is conducted to evaluate the relative merits of the proposed scheme. Full article

The rapid integration of Connected and Automated Vehicles (CAVs) into modern transportation systems necessitates a robust and systematic approach to assess the quality of the underlying digital infrastructure. In the presented work, we propose a methodology and evaluation of framework that can be used to assess digital infrastructure segments based on their readiness for the deployment of CAVs. The methodology encompasses a comprehensive framework that collects, processes, and evaluates diverse data sources, including real-time traffic, communication, and environmental data. The proposed framework is developed based on experimental data and provides a systematic approach to assess infrastructure readiness for CAVs. The proposed methodology is applied in a system for detecting the readiness status of digital infrastructure from a Cooperative, Connected, and Automated Mobility (CCAM) perspective. The system can determine the percentage of non-compliance of technical service requirements in terms of latency, bandwidth, and localization accuracy. Thanks to this, we can determine in advance in which state the current digital infrastructure is and which services can be currently operated, and thus locate the segments of the route in which the telecommunication systems need to be supported. Full article