Aeronáutica y espacio

A Survey on Deep Learning Techniques for Action Anticipation

- The ability to anticipate possible future human actions is essential for a wide range of applications, including autonomous driving and human-robot interaction. Consequently, numerous methods have...

Representations Before Pixels: Semantics-Guided Hierarchical Video Prediction

- Accurate future video prediction requires both high visual fidelity and consistent scene semantics, particularly in complex dynamic environments such as autonomous driving. We present Re2Pix, a...

OpenDT: Exploring Datacenter Performance and Sustainability with a Self-Calibrating Digital Twin

- Datacenters are the backbone of our digital society, but raise numerous operational challenges. We envision digital twins becoming primary instruments in datacenter operations, continuously and...

Using Unwrapped Full Color Space Palette Recording to Measure Exposedness of a Vehicle Exterior Parts for External Human Machine Interfaces

- One of the concerns with autonomous vehicles is their ability to communicate their intent to other road users, specially pedestrians, in order to prevent accidents. External Human-Machine Interfaces...

MapATM: Enhancing HD Map Construction through Actor Trajectory Modeling

- High-definition (HD) mapping tasks, which perform lane detections and predictions, are extremely challenging due to non-ideal conditions such as view occlusions, distant lane visibility, and adverse...

On Switched Event-triggered Full State-constrained Formation Control for Multi-vehicle Systems

- Vehicular formation control is an important component of intelligent transportation systems (ITSs). In practical implementations, the controller design needs to satisfy multiple state constraints,...

BridgeSim: Unveiling the OL-CL Gap in End-to-End Autonomous Driving

- Open-loop (OL) to closed-loop (CL) gap (OL-CL gap) exists when OL-pretrained policies scoring high in OL evaluations fail to transfer effectively in closed-loop (CL) deployment. In this paper, we...

LIDARLearn: A Unified Deep Learning Library for 3D Point Cloud Classification, Segmentation, and Self-Supervised Representation Learning

- Three-dimensional (3D) point cloud analysis has become central to applications ranging from autonomous driving and robotics to forestry and ecological monitoring.Although numerous deep learning...

Energy-Efficient Federated Edge Learning For Small-Scale Datasets in Large IoT Networks

- Large-scale Internet of Things (IoT) networks enable intelligent services such as smart cities and autonomous driving, but often face resource constraints. Collecting heterogeneous sensory data,...

SignReasoner: Compositional Reasoning for Complex Traffic Sign Understanding via Functional Structure Units

- Accurate semantic understanding of complex traffic signs-including those with intricate layouts, multi-lingual text, and composite symbols-is critical for autonomous driving safety. Current models,...

FishRoPE: Projective Rotary Position Embeddings for Omnidirectional Visual Perception

- Vision foundation models (VFMs) and Bird's Eye View (BEV) representation have advanced visual perception substantially, yet their internal spatial representations assume the rectilinear geometry...

MAVEN-T: Multi-Agent enVironment-aware Enhanced Neural Trajectory predictor with Reinforcement Learning

- Trajectory prediction remains a critical yet challenging component in autonomous driving systems, requiring sophisticated reasoning capabilities while meeting strict real-time deployment constraints....

Hardware Utilization and Inference Performance of Edge Object Detection Under Fault Injection

- As deep learning models are deployed on resource constrained edge platforms in autonomous driving systems, reli able knowledge of hardware behavior under resource degradation becomes an essential...

Decentralized Opinion-Integrated Decision making at Unsignalized Intersections via Signed Networks

- In this letter, we consider the problem of decentralized decision making among connected autonomous vehicles at unsignalized intersections, where existing centralized approaches do not scale...

VAGNet: Vision-based accident anticipation with global features

- Traffic accidents are a leading cause of fatalities and injuries across the globe. Therefore, the ability to anticipate hazardous situations in advance is essential. Automated accident anticipation...

Neural Distribution Prior for LiDAR Out-of-Distribution Detection

- LiDAR-based perception is critical for autonomous driving due to its robustness to poor lighting and visibility conditions. Yet, current models operate under the closed-set assumption and often fail...

Long-SCOPE: Fully Sparse Long-Range Cooperative 3D Perception

- Cooperative 3D perception via Vehicle-to-Everything communication is a promising paradigm for enhancing autonomous driving, offering extended sensing horizons and occlusion resolution. However, the...

Learning Vision-Language-Action World Models for Autonomous Driving

- Vision-Language-Action (VLA) models have recently achieved notable progress in end-to-end autonomous driving by integrating perception, reasoning, and control within a unified multimodal framework....

LMGenDrive: Bridging Multimodal Understanding and Generative World Modeling for End-to-End Driving

- Recent years have seen remarkable progress in autonomous driving, yet generalization to long-tail and open-world scenarios remains a major bottleneck for large-scale deployment. To address this...

Integrated photonic 3D tensor processing engine

- Optical computing leverages high bandwidth, low latency, and power efficiency, which is considered as one of the most effective solutions for accelerating deep learning tasks. However, mainstream...

Fail2Drive: Benchmarking Closed-Loop Driving Generalization

- Generalization under distribution shift remains a central bottleneck for closed-loop autonomous driving. Although simulators like CARLA enable safe and scalable testing, existing benchmarks rarely...