arXiv subjects

Marco Savarese

Publications and source records attributed to Marco Savarese.

5 recordsLinked to original sources

arXiv2026

Feasibility Assessment of Remote Driving via Latency Analysis of ITS-G5 and Cellular Networks in the MASA Living Lab

Remote driving has gained increasing attention as a key enabler for connected and automated vehicles. Yet its practical deployment hinges on wireless networks' ability to guarantee low, predictable latency. In this paper, we present an extensive latency analysis of ITS-G5 and cellular (5G) technologies within the Modena Automotive Smart Area (MASA), a real-world, city-scale testbed equipped with a distributed intelligent transportation infrastructure. By conducting controlled experiments under varying network loads and traffic conditions, we measure network and end-to-end latency components relevant to remote driving, in which the uplink consists of a continuous video stream transmitted from the vehicle to the remote operator, and the downlink conveys control commands back to the car. Measurements conducted under diverse conditions reveal how latency and variability differ across the two technologies and how infrastructure coverage impacts video-stream transmission performance. Based on the observed latency distributions and reliability metrics, we assess the practical feasibility and safety margins of remote driving in mixed network environments. The results provide actionable insights for future teleoperation deployments and motivate hybrid communication strategies that combine the strengths of ITS-G5 and cellular networks.

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arXiv2026

Measurement-Based Performance Evaluation of SmartRSUs with Heterogeneous Antenna Architectures for V2X Communications

This paper presents a measurement-based performance evaluation of two custom Smart Roadside Units (SmartRSUs) featuring different V2X antenna architectures. The first configuration integrates GNSS and communication antennas into an all-in-one rooftop module, whereas the second uses external dual ITS-G5 (IEEE 802.11p) antennas operating at 5.9~GHz and a dedicated GNSS antenna. Both systems are built upon a proprietary On-Board Unit (OBU) platform adapted for infrastructure deployment. The experimental campaign evaluates key V2X communication metrics, including coverage, received signal strength indicator (RSSI), packet loss, and end-to-end latency in both transmission (OBU-to-infrastructure) and reception (infrastructure-to-OBU) directions. To ensure objective validation, a commercial off-the-shelf V2X Roadside Unit is co-located on the same infrastructure and used as a performance benchmark, providing ground-truth reference measurements under identical environmental conditions through a controlled co-located deployment. Results highlight the impact of antenna design and placement on communication reliability and latency, revealing trade-offs between integrated and external antenna configurations in real-world deployment scenarios. The findings provide practical insights for the design and optimization of next-generation SmartRSUs in cooperative intelligent transportation systems (C-ITS).

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arXiv2026

Experimental Insights into UDP-Based Video and Control Traffic over IEEE 802.11p ITS-G5

Vehicular applications such as cooperative driving, teleoperation, and real-time perception increasingly rely on low-latency wireless communication. In this context, ITS-G5, based on IEEE 802.11p, represents a key technology for enabling direct vehicle-to-vehicle and vehicle-to-infrastructure communication. Despite its relevance, experimental studies focusing on the performance of UDP-based traffic over IEEE 802.11p under realistic conditions remain limited. This paper presents an experimental evaluation of UDP transmission over an IEEE 802.11p ITS-G5 testbed composed of Raspberry Pi-based onboard units and commercial roadside units. The analysis investigates the impact of different modulation and coding schemes (MCS). It also evaluates two network-layer configurations (IPv4 unicast and IPv6 multicast) and the use of CAKE for active queue management. In addition to synthetic traffic generated with iPerf, the evaluation includes real-time video streaming using MPEG-TS over UDP to emulate latency-sensitive vehicular applications. Results show that the modulation scheme is the dominant factor influencing latency at low traffic loads, while the choice of transmission mode and IP version becomes increasingly significant under congested conditions. Higher-order modulations significantly reduce latency and variability, whereas IPv6 multicast exhibits greater delay dispersion than IPv4 unicast. Furthermore, active queue management does not seem to improve delay predictability. These findings provide practical insights for configuring ITS-G5 networks supporting latency-sensitive vehicular services.

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arXiv2026

CAMASA: A CAM-based Dataset from the MASA Living Lab

Trajectory prediction is a key enabler of autonomous and cooperative driving systems. However, most existing benchmarks are either sensor-centric, geographically constrained, or based on synthetic mobility traces that do not capture real-world V2X communication dynamics. This paper introduces CAMASA, a large-scale infrastructure-based dataset derived from Cooperative Awareness Messages (CAMs) and Decentralized Environmental Notification Messages (DENMs) collected within the Modena Automotive Smart Area (MASA). The dataset comprises more than 40 million CAMs and 2 million DENMs recorded under authentic urban traffic conditions over multiple months. We present a rigorous preprocessing pipeline that includes filtering, pseudonym reconciliation to account for ETSI privacy-driven stationID changes, and temporal normalization to 10 Hz trajectories, suitable for motion forecasting and time-series analysis. With over 14,000 km of reconstructed vehicle paths and tens of thousands of unique station IDs, CAMASA provides a statistically significant empirical foundation for research on Cooperative Intelligent Transportation Systems (C-ITS). Beyond trajectory prediction, the dataset enables calibration of microscopic urban traffic simulators (e.g., SUMO) and supports the development of realistic Intelligent Transportation Systems (ITS) Digital Twins by jointly modeling mobility patterns and V2X communication coverage in real deployments.

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arXiv2025

RI-PIENO -- Revised and Improved Petrol-Filling Itinerary Estimation aNd Optimization

Efficient energy provisioning is a fundamental requirement for modern transportation systems, making refueling path optimization a critical challenge. Existing solutions often focus either on inter-vehicle communication or intra-vehicle monitoring, leveraging Intelligent Transportation Systems, Digital Twins, and Software-Defined Internet of Vehicles with Cloud/Fog/Edge infrastructures. However, integrated frameworks that adapt dynamically to driver mobility patterns are still underdeveloped. Building on our previous PIENO framework, we present RI-PIENO (Revised and Improved Petrol-filling Itinerary Estimation aNd Optimization), a system that combines intra-vehicle sensor data with external geospatial and fuel price information, processed via IoT-enabled Cloud/Fog services. RI-PIENO models refueling as a dynamic, time-evolving directed acyclic graph that reflects both habitual daily trips and real-time vehicular inputs, transforming the system from a static recommendation tool into a continuously adaptive decision engine. We validate RI-PIENO in a daily-commute use case through realistic multi-driver, multi-week simulations, showing that it achieves significant cost savings and more efficient routing compared to previous approaches. The framework is designed to leverage emerging roadside infrastructure and V2X communication, supporting scalable deployment within next-generation IoT and vehicular networking ecosystems.

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