The emergence of autonomous driving represents a pivotal milestone in the evolution of the
transportation system, integrating seamlessly into the daily lives of individuals due to its …

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 …

Multi-sensor fusion has been widely used by autonomous vehicles (AVs) to integrate the
perception results from different sensing modalities including LiDAR, camera and radar …

Deep learning models with backdoors act maliciously when triggered but seem normal
otherwise. This risk, often increased by model outsourcing, challenges their secure use …

Controller Area Network (CAN) protocol is widely used in vehicles as an efficient standard
enabling communication among Electronic Control Units (ECUs). However, the CAN bus is …

3D object detection plays an important role in autonomous driving; however, its vulnerability
to backdoor attacks has become evident. By injecting''triggers''to poison the training dataset …

Object detectors are vulnerable to backdoor attacks. In contrast to classifiers, detectors
possess unique characteristics, architecturally and in task execution; often operating in …

Autonomous mobile systems increasingly rely on deep neural networks for perception and
decision-making. While effective, these systems are vulnerable to adversarial machine …

Neural Radiance Field (NeRF) represents a significant advancement in computer vision,
offering implicit neural network-based scene representation and novel view synthesis …

The increasing adoption of 3D point cloud data in various applications, such as autonomous
vehicles, robotics, and virtual reality, has brought about significant advancements in object …