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 CityTrajBench, a unified benchmark framework that standardizes the evaluation of city-scale vehicle trajectory generation, demonstrating that no single generation model dominates…

RiskFlow is a novel framework that generates realistic and safety-critical multi-agent traffic scenarios by reformulating trajectory generation as a single-pass transport problem in the action space.

The paper proposes a novel render-free framework that conditions video diffusion models directly on compressed 3D human mesh tokens, enabling robust 3D-aware human motion control without relying on re…

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.

The paper proposes GC-MoE, a graph-conditioned Mixture of Experts framework, to improve traffic forecasting by assigning personalized, specialized forecasting experts to individual road segments.

PhyGenHOI introduces a novel framework that generates physically accurate and visually faithful 4D Human-Object Interactions by coupling generative human motion with explicit physical object simulatio…

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 an enhanced Wasserstein GAN with Gradient Penalty (SA-JS-WGAN-GP) incorporating Self-Attention and Jensen-Shannon Divergence to synthesize diverse network traffic data, significantl…

The paper introduces PaSBench-Video, a comprehensive streaming video benchmark designed to rigorously test multimodal LLMs' ability to issue proactive safety warnings, finding that current models stru…

The paper introduces TWGuard, a linguistic context-optimized safety guardrail model, demonstrating that tailoring AI safety mechanisms to specific local linguistic contexts significantly improves perf…

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,…

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 demonstrates that fine-tuning safety guard models on benign data can catastrophically collapse their safety alignment, proposing Fisher-Weighted Safety Subspace Regularization (FW-SSR) to ac…

This paper provides the first non-vacuous generalization analysis for the Stochastic Variance Reduced Gradient (SVRG) method by establishing sharp, data-dependent algorithmic stability bounds, thereby…

The paper introduces GenAI-FDIA, a comprehensive framework that benchmarks various physics-informed generative models to synthesize high-fidelity False Data Injection Attacks (FDIA) for power systems,…

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 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 proposes SafeDIG, a robust safety steering framework that adapts Diffusion Transformers for text-to-image generation by treating safety control as position-aware sparse feature transfer, ens…