DarkVesselNet is a novel multi-modal deep learning framework that fuses SAR, optical, and AIS data to accurately detect vessels that do not report their presence via Automatic Identification System (A…

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 advocates for a paradigm shift toward joint Spatial Representation Learning (SRL) that unifies raster imagery and structured vector data into a single embedding space for developing more sem…

The paper demonstrates a coordinated, cross-modal spoofing attack that successfully deceives state-of-the-art multi-sensor fusion systems in autonomous vehicles by making multiple sensors agree on a f…

DELOS is a novel contrastive-learning framework that efficiently and sensitively detects shallow, intermediate-to-long-period exoplanet transits in Kepler photometry, significantly outperforming tradi…

The paper proposes HADT, a novel transformer-based architecture using differential attention and relational tokenization, to enable adaptive and real-time autonomous resource management for heterogene…

The paper proposes a cross-layer behavioral fingerprinting framework that fuses physical and network data to detect comprehensive attacks in dense LEO satellite constellations, achieving high detectio…

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…

OrbitBFT introduces a novel two-stage hierarchical BFT consensus protocol that enables scalable and robust Byzantine Fault-Tolerant coordination for large-scale Low Earth Orbit satellite constellation…

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.

FLORO is a multimodal geospatial foundation model that learns transferable remote sensing representations from a small, diverse corpus, achieving strong performance across various sensor types and res…

The paper proposes a pose-conditioned, permutation-equivariant denoiser to accurately reconstruct work zone geometry using noisy Ultra-Wideband (UWB) range data from connected and autonomous vehicles…

The paper proposes RPCASSM, a novel state space model leveraging Robust PCA (RPCA) to accurately detect and segment infrared small targets by separately modeling background and target information base…

CIPER proposes a unified transformer framework to simultaneously perform cross-view image retrieval and precise 3-DoF pose estimation, overcoming the limitations of cascaded, separate methods.

The paper introduces SkyShield, the first front-view monocular semantic occupancy benchmark for low-altitude urban UAV flight, along with a novel metric and model to address the unique safety challeng…

The paper proposes Energy-Aware NECO, a single-pass hybrid detector that combines geometric ratio and logit-based energy scores to achieve superior pixel-wise out-of-distribution detection for semanti…

PixVOD proposes a fully parallelizable, pixel-distributed framework for visual odometry and depth estimation that performs computations directly on the sensor using Gaussian Belief Propagation.

The paper presents an end-to-end system that translates high-level operator intents into low-level, safe routing constraints for LEO mega-constellations, achieving high accuracy and safety guarantees.

The paper identifies a fundamental mismatch between standard pairwise ranking metrics (like AP and FPR-95) and the true assignment objective in multi-view object association, proposing a Sinkhorn-base…

This paper reviews cybersecurity vulnerabilities in CubeSats, proposing TinyML-based, resource-efficient intrusion detection systems to address limitations of traditional security measures.