This paper surveys how integrating Edge Computing, Machine Learning, and Deep Learning can enhance the security and resilience of complex Internet of Vehicles (IoV) networks.

The paper proposes a trust-aware federated hybrid intrusion detection framework using multiple ML models at distributed edge nodes to proactively secure highly connected Intelligent Transport Systems.

This survey reviews how Large and Multi-modal Language Models (LLMs/MM-LLMs) are being applied to integrate diverse data sources for enhanced decision support in transportation systems management and…

This paper reviews the current state of cybersecurity for EV charging infrastructure, analyzing existing machine learning countermeasures and proposing future directions to overcome data limitations i…

The paper proposes CANGuard, a hybrid CNN-GRU-Attention deep learning model, to accurately detect sophisticated Denial-of-Service and spoofing attacks targeting critical in-vehicle CAN bus networks.

This paper provides a comprehensive review of network optimization aspects for Connected and Autonomous Vehicles (CAVs), aiming to clarify misconceptions and outline future research directions.

The paper proposes DAIRE, a lightweight AI model, for highly efficient, real-time detection and classification of various cyberattacks targeting the vulnerable Controller Area Network (CAN) in the Int…

ReasonLight is a multimodal foundation model-enhanced RL framework that enables zero-shot traffic signal control by semantically refining RL-proposed actions using heterogeneous sensor and camera data…

This paper identifies and demonstrates eight novel attack scenarios exploiting the ISO 15765-2 transport protocol over CAN, showing that three can successfully induce denial of diagnostic services in…

This paper analyzes the potential downsides of integrating advanced AI and smart capabilities across the Edge-Cloud continuum in modern industry, focusing specifically on security vulnerabilities, sid…

The paper introduces EvoXXLTraffic, an ultra-large, sensor-evolving dataset that simulates real-world road network growth, demonstrating that existing state-of-the-art traffic forecasting models fail…

The paper proposes NETRA, a cost-effective, internet-independent system using probabilistic sensor fusion and edge-AI classification on Raspberry Pi platforms to achieve high-accuracy, real-time detec…

This paper presents an open-source computer vision pipeline for classifying vehicle body types from naturalistic roadway video.

DeepIPCv3 is a novel multi-modal framework that fuses LiDAR and DVS event streams using cross-modal attention to achieve state-of-the-art, highly reactive avoidance maneuvers for sudden pedestrian cro…

The paper proposes IPEK, a context-aware trust mechanism for VANETs, which significantly improves detection of intelligent attackers by incorporating event and location severity into trust calculation…

The paper proposes a communication-centric 6G-LLM architecture for tactical autonomous defense vehicles, demonstrating significant improvements in coordination and communication efficiency over conven…

The paper develops a novel deep reinforcement learning framework, SMamba-DDPG, to accurately model vehicle-type-specific pedestrian crash avoidance behavior, finding that pedestrians react faster and…

The paper introduces a genetic algorithm framework to calibrate complex urban traffic simulations using only sparse real-world traffic observations, eliminating the need for detailed employment data.

This paper analyzes the privacy challenges posed by Integrated Sensing and Communication (ISAC) in 6G networks by classifying sensitive data into three levels (location, behavioral, and physiological)…

AlphaTransit introduces a novel search-based planning framework that combines Monte Carlo Tree Search (MCTS) with a neural policy-value network to efficiently design high-quality, city-scale bus trans…