Vehicular Ad Hoc Networks (VANETs)

Neueste Paper

• Trustworthy Data- and ML-Ops for Intelligent Transportation Systems and Logistics

  Antonio Emanuele Cin\`a, Giovanni Scodeller, Cecilia Caterina Pasquale, Silvia Siri, Davide Anguita, Fabio Roli, Simona Sacone, Luca Oneto · 2. Oktober 2026

  The rapid evolution of Intelligent Transportation Systems and Logistics (ITS\&L) has become a cornerstone of the modern social economy, relying heavily on the integration of Data, Artificial Intelligence (AI), and, more specifically, Machine Learning (ML). This paper provides a comprehensive review …

• SemRD-V2X: Closure-Guided Communication with Bounded Inference for Cooperative Perception

  Hu Xu, Chun Li, Siyuan Qiu, Zeyan Li, Jianfeng Xu · 29. September 2026

  Vehicle-to-Everything (V2X) cooperative perception improves 3-D detection by sharing intermediate features, but dense remote features may repeat context that the ego agent can infer locally. Most communication-efficient designs optimize masks or codes empirically, leaving a more basic question open:…

• Dense Coverage, Sparse Refinement: Byte-Constrained Cooperative Perception

  Melih Yazgan, Timon M\"uller, J. Marius Z\"ollner · 25. September 2026

  Collaborative perception improves autonomous perception by sharing intermediate Bird's-Eye-View (BEV) features across connected agents, but dense feature exchange is difficult to deploy under strict Vehicle-to-Everything (V2X) bandwidth limits. Existing efficient methods typically either compress th…

• On the security and privacy of LLMs in Mobility

  Mauro Conti, Lorenzo Perinello, Umberto Salviati · 23. September 2026

  The mobility sector is undergoing a paradigm shift driven by advances in Generative Artificial Intelligence. With a global market valued at approximately 2.9 trillion dollars annually, considering only cars, the integration of these technologies has the potential to impact more than 1.5 billion vehi…

• NS3Learn: Transferring 5G NR Mode-2 Reception Realism from ns-3 to the Veins/SUMO Stack for Connected-Vehicle Safety Assessment

  Rasheed Bello, Arthur Mukwaya, Gurcan Comert, Varghese Vaidyan, Vijay Bendigeri, Anthony Dontoh, Jagruti Sahoo, Judith Mwakalonge · 18. September 2026

  Connected-vehicle safety evaluations rely on coupled traffic and network simulations, but standard channel models ignore radio resource competition in 5G NR sidelink Mode-2, reporting unrealistically high message delivery in dense traffic. This study introduces resource-competition losses without re…

• QoS-Aware Federated Learning for Multimodal In-Cabin Interaction in Smart Vehicles

  Baran Can G\"ul, Mert Nak{\i}p, Nasser Jazdi, Michael Weyrich · 18. September 2026

  Modern smart vehicles leverage multimodal sensors, ranging from high-bandwidth vision systems to low-rate physiological monitors, to provide personalized in-cabin services. However, integrating high-fidelity multimodal fusion with collaborative training is often hindered by the heterogeneous and tim…

• AI-Driven Real-Time Relay Optimisation in Smart Urban NR-V2X Networks via Learning-to-Optimise Graph Neural Networks

  Giambattista Amati, Federica Mangiatordi, Emiliano Pallotti, Simone Angelini · 18. September 2026

  Reliable and low-latency communication is a fundamental requirement for smart city services and Industry 4.0 applications enabled by NR-V2X networks. However, limited Road-Side Unit (RSU) deployment and complex urban propagation conditions often prevent Connected and Automated Vehicles (CAVs) from m…

• Situation Awareness for Intelligent Data Distribution in Connected Vehicles

  Falk Dettinger, Akshay Narla, Michael Weyrich · 9. September 2026

  The limitations of on-board sensors and blind spots caused by occlusion cause the reduction of perception quality in autonomous vehicles. In such cases, cooperative perception provides additional data via Vehicle-to-Everything communication to enhance local perception, causing a large volume of data…

• Trust as a Field: A Macroscopic Representation for Vehicular Networks

  Md Mahmudul Islam, Shaurya Agarwal · 19. August 2026

  Trust assessment is a fundamental component of cooperative and connected vehicle systems. However, existing approaches operate primarily at the level of individual vehicles, making it difficult to reason about trust evolution across road segments. In this paper, we propose a spatio-temporal trust-fi…

• Handover Analysis for Vehicular Communication with Explainability on the Fly

  Ali Fuat Sahin, Semiha Tedik Başaran, Tufan Kumbasar · 18. August 2026

  Handover (HO) management in vehicular networks requires fast and reliable decision-making under highly dynamic conditions. While machine learning (ML) approaches can improve HO detection by capturing complex relationships among various key performance indicators (KPIs), their black-box nature limits…

• Extend the Safety Horizon for Intelligent Transportation Systems through Semantic-Aware Cooperative Perception

  Chun-Yeow Yeoh, Chee Keong Tan, Joanne Mun-Yee Lim, Heng-Siong Lim · 18. August 2026

  Cooperative perception enables vehicles and infrastructure to exchange sensor data via Vehicle-to-Everything (V2X) communication, extending sensing coverage beyond occlusions and mitigating blind spots. While critical for autonomous driving and safety, practical deployments often rely on bandwidth-e…

• Hierarchical Multi-Task Federated Learning in VANETs

  M. Saeid HaghighiFard, Sinem Coleri · 11. August 2026

  Vehicular Ad hoc Networks (VANETs) increasingly rely on federated learning (FL) to enable collaborative intelligence without sharing raw sensory data. However, most existing vehicular FL frameworks assume that all vehicles train a single global model for a common task, which limits their applicabili…

• Behavioral Residualization for Unsupervised Intrusion Detection in Automotive CAN Networks

  Chandan Hegde, Mukundh R Reddy · 7. August 2026

  Modern vehicles rely on the Controller Area Network (CAN) bus, whose design prioritizes low cost and real-time performance but provides no message authentication or encryption. An attacker with physical or remote access can therefore inject arbitrary frames, making intrusion detection an important d…

• An Inline Control Architecture for Language Models in Intelligent Transportation Systems

  Narendra Kumar Dewangan, Mounira Msahli · 6. August 2026

  Vehicle-to-everything (V2X) systems increasingly incorporate large language models (LLMs) for semantic tasks such as message summarization, operator assistance, and decision support at roadside units and edge nodes. Although these components are not part of safety-critical control loops, they introd…

• Sensitivity Analysis of GRU, LSTM and Transformer Encoder in Classification of Automated Driving Systems

  Bidhya Shrestha, Christos Papadopoulos · 3. August 2026

  Automated driving systems (ADSs) are becoming ubiquitous. Future Software Defined Vehicles (SDVs) may be able to run multiple ADSs, both native and aftermarket such as Comma.ai's Openpilot. Monitoring systems to independently verify which automated driving system is active are important for safety m…

• GNN-based Multi-Agent Control of Traffic Shockwaves in Sparse Vehicular Ad-hoc Networks

  Prachi Nandi, Madhuri Malakar, Sonakshi Satpathy, Pabitra Mohan Khilar · 28. Juli 2026

  Traffic shockwaves are stop-and-go waves that propagate upstream through the streams of vehicles and are one of the major causes of traffic congestion, fuel inefficiency, and increased accident rates in modern transportation systems. Although Connected and Autonomous Vehicles (CAVs) offer a promisin…

• AI-Driven Multi-Hop Relay Selection for Smart Urban NR-V2X Networks via Learning-to-Optimize Graph Neural Networks

  Giambattista Amati, Federica Mangiatordi, Simone Angelini, Emiliano Pallotti, Pierpaolo Salvo · 24. Juli 2026

  Reliable and low-latency NR-V2X communications are essential for smart mobility in dense urban environments. However, limited Road-Side Unit (RSU) density, frequent non-line-of-sight conditions, and highly dynamic vehicular topologies often prevent many Connected and Automated Vehicles (CAVs) from m…

• Low-Latency Relay Selection in NR-V2X Vehicular Communications via Graph Isomorphism Networks with Edge Features

  Giambattista Amati, Federica Mangiatordi, Emiliano Pallotti, Simone Angelini, Pierpaolo Salvo, Paola Vocca · 17. Juli 2026

  Reliable, low-latency uplink connectivity is a key requirement for C-V2X networks in dense urban environments, where fast channel variations and blockages often degrade direct vehicle-to-infrastructure links. Multi-hop relaying can restore coverage, but relay-link activation under radio, capacity, a…

• Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking

  Siyuan Wang, Lei Lei, Pranav Maheshwari, Sam Bellefeuille, Kan Zheng · 7. Juli 2026

  Radio resource allocation (RRA) is a critical function in cellular vehicle-to-everything (C-V2X) networks, where vehicles must share limited wireless resources to support safety-critical communications. Multi-agent reinforcement learning (MARL) has emerged as a promising approach for this problem. H…

• Exploiting Overlapping Fields of View for Redundancy-Aware Uplink Transmission in Vehicular 6G

  Hamidreza Mazandarani, Masoud Shokrnezhad, Tarik Taleb, Onur Günlü · 1. Juli 2026

  Emerging uplink-dominant 6G use cases, such as cooperative vehicular streaming, require efficient transmission of high-volume visual data over limited wireless resources. While semantic communications can reduce traffic by prioritizing task-relevant content, most existing approaches treat users inde…

• CooperScene: Multi-Modal Cooperative Autonomy Benchmark with C-V2X Communication Characterization

  Bo Wu, Ruoshen Mo, Justin Yue, Yanyu Zhang, Janice Nguyen, Guoyuan Wu, Amit Roy-Chowdhury, Matthew J. Barth, Hang Qiu · 1. Juli 2026

  Cellular vehicle-to-everything (C-V2X) enables cooperative perception, prediction, and planning beyond the field of view of individual agents. However, existing datasets often overlook the complexities of real-world deployment, such as limited communication bandwidth and its dynamics, heterogeneous …

• CAN We Trust Your Results? A Cross-Dataset Study of Automotive IDS Evaluation

  Beatrix Koltai, Gergely Acs, Andras Gazdag · 30. Juni 2026

  The increasing connectivity of modern vehicles has made securing in-vehicle communication networks a critical challenge. Intrusion Detection Systems (IDS) have been widely studied as a defense mechanism for detecting malicious activities on the Controller Area Network (CAN) bus. However, the evaluat…

• In-Vehicle Digital Twin-Based Collision Warning Framework with Sybil Attack Detection

  Mohammad Imtiaz Hasan, Abyad Enan, Jean Michel Tine, Araf Rahman, M Sabbir Salek, Mashrur Chowdhury · 30. Juni 2026

  Connected Vehicles (CVs) rely extensively on communication technologies to enable data-driven predictive analyses for enhancing performance and safety. These communication channels can be exploited by adversaries to launch cyberattacks such as Sybil attacks, which could threaten both safety-critical…

• DinoLink: A Token-Centric Representation Compression Framework for Bandwidth-Constrained Collaborative V2X Perception

  Tianle Zhu, Haohua Que, Handong Yao, Hongyi Xu, Zhipeng Bao · 26. Juni 2026

  High-precision remote perception is often hindered by the severe bandwidth constraints of Vehicle-to-Everything (V2X) networks. We propose \textit{DinoLink}, a token-centric compression framework that replaces raw pixel streaming with discrete semantic communication for vehicle-cloud collaborative i…

• A Survey on Deep Multi-Task Learning in Connected Autonomous Vehicles

  Jiayuan Wang, Farhad Pourpanah, Q. M. Jonathan Wu, Ning Zhang · 9. Juni 2026

  Connected autonomous vehicles (CAVs) must simultaneously perform multiple tasks, such as perception, prediction, planning, and control, to ensure safe and reliable navigation in complex environments. Moreover, through vehicle-to-everything (V2X) communication, cooperative perception and driving amon…