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 a proactive, resilient architecture for autonomous vehicles by integrating redundancy, diversity, and adaptive reconfiguration to defend against various cyber and physical attacks.

This paper proposes a systematic joint workflow combining HARA and TARA to comprehensively identify and analyze risks stemming from inherent limitations of Deep Neural Networks (DNNs) used in autonomo…

This paper proposes a decentralized Multi-Agent Reinforcement Learning framework with a Graph Neural Network for connected and autonomous vehicles to reduce traffic shockwaves.

This paper analyzes incidents involving autonomous vehicles (AVs) and identifies gaps in current safety paradigms, suggesting the need for human-interactive autonomy.

This paper investigates the feasibility of using 5G networks for teleoperated driving (ToD) and identifies the impact of bandwidth and TDD frame structure on ToD performance.

This paper systematically analyzes 48 studies on perception attacks against autonomous vehicles, revealing that the increasing reliance on multi-sensor fusion creates new, complex vulnerabilities that…

The paper introduces a novel adversarial attack that uses static, view-dependent camouflage on a vehicle to induce consistent feature drift, causing autonomous systems to predict false, yet plausible,…

A hierarchical deep reinforcement learning architecture called MAPS is proposed for coordinating autonomous vehicles at unsignalized intersections, achieving collision-free navigation and reducing tra…

The paper introduces a stealthy, scenario-realistic data fabrication attack that subtly manipulates object poses in shared perception data to induce unsafe driving behaviors in connected and autonomou…

The paper introduces AgenticRL, a self-refining reinforcement learning framework that uses a multimodal GPT agent to automatically design, refine, and deploy reward functions for complex UAV navigatio…

This paper compares the efficiency of different routing algorithms for collectively routed fleets of autonomous vehicles and proposes a market design to encourage social welfare oriented cooperation.

SARAD proposes a novel safety-aware hybrid framework that combines Large Language Models (LLMs) and Deep Reinforcement Learning (DRL) to improve autonomous driving decision-making by replacing random…

This paper presents a two-stage recovery approach for camera-only low-cost unmanned ground vehicles to restore guideline tracking when lines are lost.

The paper introduces CityGen, a diffusion-based framework that enables zero-label city adaptation for autonomous driving by synthesizing city-style data conditioned on HD maps and visual prompts, sign…

The paper introduces an uncertainty-aware framework that uses regulated expert advice to guide safe and efficient exploration for autonomous driving policies, significantly improving performance in co…

This paper demonstrates that reasoning-enabled Vision-Language-Action (VLA) models for autonomous driving are highly vulnerable to realistic input perturbations, significantly compromising both reason…

This paper systematically analyzes the high cross-architecture transferability of physical adversarial attacks on Vision-Language Models (VLMs) used in autonomous driving, demonstrating that attacks e…