The AI framework was successfully shown to reorder and present messages based on real-time context, improving the clarity and usefulness of information provided to the driver, and support a hybrid C-V2X architecture as a robust model for future smart highway deployments.
Abstract
This project advances connected vehicle applications by developing and testing an enhanced RampCast system, a comprehensive traffic management system using C-V2X technology for Indiana highways. The system features a dual-mode architecture integrating both short-range (PC5) and long-range cellular (Uu) Cellular Vehicle-to-Everything (C-V2X) communication pathways, utilizing commercial-grade Cohda MK6 hardware and adhering to SAE J2735 standards to ensure interoperability. A key innovation is the integration of an AI-based prioritization framework, which leverages a large language model to enhance the contextual relevance of traffic messages. This AI system introduces two intelligent agents: one to dynamically estimate the appropriate display distance for an event based on its severity, and another to prioritize the order of messages based on urgency and potential driver impact. Field tests conducted on I-65 and I-70 in Indianapolis validated the system’s hybrid design. Results confirmed that the PC5 link provides very low latency (around 25 ms), ideal for time-critical alerts, while the Uu link ensures highly reliable coverage in complex environments, albeit with higher latency (around 45 ms). The AI framework was successfully shown to reorder and present messages based on real-time context, improving the clarity and usefulness of information provided to the driver. These findings support a hybrid C-V2X architecture as a robust model for future smart highway deployments.
The introduction of Connected and Autonomous Vehicles (CAVs) into the existing traffic system represents one of the greatest challenges of modern road traffic engineering. Beyond their role as active traffic participants, CAVs can also be regarded as mobile (floating) sensors, effectively turning the vehicle fleet itself into a distributed, city-wide and motorway-wide sensing infrastructure. The transition from fully human-driven vehicles to fully autonomous vehicles will take decades, giving rise to a prolonged mixed-traffic period in which vehicles with different levels of automation share the same road space. This paper analyses the parameters and measures used for evaluating the throughput, environmental impact, and safety of traffic networks at different CAV penetration rates. It further reviews studies that rely exclusively on data collected from CAVs acting as mobile sensors, examining data-aggregation and traffic-state-estimation methods used to reconstruct macroscopic traffic parameters such as flow, density, headway, and speed. Additionally, measures for evaluating specific use cases for CAVs including mobility-on-demand services and their cost comparison with human-driven taxi operations are also addressed. The energy and emissions implications of CAV deployment, including the added burden of sensing hardware and system-level rebound effects, are also examined. Based on the synthesis performed, a set of representative CAVs penetration rates is proposed as a standardised framework for future mixed-traffic flow evaluations.
Lucija Bukvić, M. Gregurić, Filip Vrbanić et al.· Vehicles· 0 citations
—Efficient traffic signal control is essential for reducing congestion, emissions and travel delays in modern urban environments. Traditional Vehicle-to-Infrastructure (V2I) systems are limited by infrastructure coverage, while Vehicle-to-Vehicle (V2V) communication alone lacks global signal-state awareness. This study proposes a hybrid V2V-V2I communication model that enhances Green Light Optimal Speed Advisory (GLOSA) performance by allowing vehicles to relay Signal Phase and Timing (SPaT) information in low-infrastructure or obstructed environments. A mathematical formulation describing vehicle motion, inter-vehicle message propagation and signal-state transitions is developed, complemented by a detailed algorithmic description of the hybrid control logic. The model is implemented using SUMO with the Krauss microscopic car-following model to simulate real-world driving behavior. Results demonstrate that the hybrid model reduces stop frequency, travel time, fuel consumption, and CO₂ emissions compared to standalone V2I and V2V schemes. The added V2V relaying mechanism improves prediction accuracy, particularly in scenarios with limited roadside units. The findings highlight the feasibility, scalability and ecological benefits of integrating multi-source communication in intelligent traffic systems, offering a promising solution for future connected urban mobility.
Abdullah Alsaleh· Journal of Advances in Infor...· 0 citations
This study integrates predictive traffic inference, GIS-based coordination, and adaptive guidance generation into a scalable ITS architecture, providing a viable foundation for next-generation smart expressway management.
S. Hirekhan, A. Hirekhan, C. Waghmare et al.· African Journal Of Applied R...· 0 citations
European safety regulation now permits a large share of automated-driving homologation evidence to be produced virtually, provided a validated physical-virtual facility generates it. We present a deployed hybrid Vehicle-in-the-Loop (ViL) platform that couples a real instrumented vehicle with a CARLA-based digital twin (DT) through a V2X message pipeline, and we report its first integrated operation on a public-road-representative test track. A real vehicle streams ETSI-compliant CAM/CPM messages into the DT, where a GPU-accelerated Cooperative Perception (CP) module fuses them into a probabilistic occupancy grid during scenario runtime. We demonstrate the platform on a multi-vehicle double T-intersection scenario, characterise the CP workload across nominal, rain and night conditions and five localization-noise levels, and discuss the platform's current architectural limits and the engineering targets they define. The results show that CP substantially widens field-of-view (FoV) coverage and improves occupied-cell recall, and that beyond a moderate localization-noise threshold, positioning uncertainty, and not weather, becomes the dominant error source. We outline the platform's trajectory toward a Mediterranean operational design domain (ODD) testing service.
A. Bolovinou, Giorgos Hadjipavlis, M. Antonopoulos et al.· arXiv.org· 0 citations
With increased need in computing requirements on the Infrastructure (IX) and not having enough sensing computation on vehicles, IX based Automated Vehicle Marshalling (AVM) achieves faster deployment of the SAE Level-4 driver-less vehicles in the geo-fenced Operational Domain (ODD) areas. With IX based AVM, it would open-up for multiple new use-cases in the automotive industry such as plant marshalling, depot marshalling, hands-free charging, valet parking, airport parking, rental cars parking etc. To enable the IX based AVM with the inputs from infrastructure sensors, it would require standard messages (SAE, ETSI) to be communicated wirelessly between the Remote Vehicle Operation (RVO) server and Automated Vehicle (AV). Selection of Wireless communication plays a critical role in the safety aspects of autonomous vehicle operations. In this paper, we present a feasibility assessment focusing on the wireless Key Performance Indicators (KPIs) of Latency and Packet Error Rate (PER), alongside an analysis of the associated advantages and challenges for multiple wireless communication technologies. These technologies include Cellular Vehicle to Everything (C-V2X) operating via its PC5 direct communication interface, Cellular-Uu public base stations, Cellular-Uu public Distributed Antenna Systems (DAS) integrated with Multi-Access Edge Compute (MEC), Citizens Broadband Radio Service (CBRS), and Wireless Fidelity (Wi-Fi).
Krishna Bandi, Vyas Darshan Shenoy· International Conference on...· 0 citations
A demand-driven signal control strategy is developed to allocate green time based on real-time vehicle demand, eliminating wasted signal phases and providing a scalable and intelligent solution for modern smart city traffic systems.
Friday Idakwo David, S. T. Apeh, Oduware Okosun· E3S Web of Conferences· 0 citations
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