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

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 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,…

This paper proposes Rollout-Retrieval Lifelong Policy Learning (R$^2$LPL), a framework for continual policy improvement in autonomous driving by retrieving corrective targets from recoverable mistakes…

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…

The paper introduces a structured multi-level visual perturbation framework to systematically analyze how dependent VLA-based driving behavior is on visual information, revealing uneven visual groundi…

This paper integrates unsupervised dictionary learning as a post-hoc interpretability module in end-to-end driving models to extract semantically meaningful concepts and improve driving performance.

This paper presents CARLA-GS, a modular corner-case synthesis pipeline for autonomous driving that decouples visual representation, semantic reasoning, and physics-based execution while maintaining ti…

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…

The paper proposes a multi-resolution end-to-end deep neural network for autonomous driving that dynamically adjusts input resolution to optimize the critical tradeoff between prediction accuracy and…

The paper proposes CARA, an interpretable framework for collision anticipation in autonomous driving using domain-grounded risk concepts, aligning them with video frames, and organizing them into evol…

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…

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.

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

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