End-to-End driving is a promising paradigm as it circumvents the drawbacks associated with
modular systems, such as their overwhelming complexity and propensity for error …

The popularity of adapting deep neural networks (DNNs) in solving hard problems has
increased substantially. Specifically, in the field of computer vision, DNNs are becoming a …

The applications of Vision-Language Models (VLMs) in the field of Autonomous Driving (AD)
have attracted widespread attention due to their outstanding performance and the ability to …

With recent breakthroughs in deep neural networks numerous tasks within autonomous
driving have exhibited remarkable performance. However deep learning models are …

Resource management, security, and privacy stand as fundamental pillars for the reliable
and secure operation of efficient semantic communications (SC) system. By addressing …

Adversarial training is extensively utilized to improve the adversarial robustness of deep
neural networks. Yet mitigating the degradation of standard generalization performance in …

Visual Simultaneous Localization and Map** (vSLAM) plays a pivotal role in numerous
emerging applications, including autonomous driving and robotic navigation. It mainly …

At present the perception system of autonomous vehicles is grounded on 3D vision
technologies along with deep learning to process depth information. Although deep learning …

The convergence of artificial intelligence (AI) and Earth observation (EO) technologies has
brought geoscience and remote sensing into an era of unparalleled capabilities. AI's …

Recent years have witnessed increasing interest in adversarial attacks on images, while
adversarial video attacks have seldom been explored. In this paper, we propose a sparse …