Autonomous vehicles (AVs), defined as vehicles capable of navigation and decision-making
independent of human intervention, represent a revolutionary advancement in transportation …

The rapid development of Artificial Intelligence (AI) technology has enabled the deployment
of various systems based on it. However, many current AI systems are found vulnerable to …

Autonomous driving systems have witnessed significant development during the past years
thanks to the advance in machine learning-enabled sensing and decision-making …

With the rapid developments of artificial intelligence (AI) and deep learning (DL) techniques,
it is critical to ensure the security and robustness of the deployed algorithms. Recently, the …

Adversarial attack methods based on point manipulation for 3D point cloud classification
have revealed the fragility of 3D models yet the adversarial examples they produce are …

It is known that deep neural networks (DNNs) are vulnerable to adversarial attacks. The so-
called physical adversarial examples deceive DNN-based decision makers by attaching …

Perception plays a pivotal role in autonomous driving systems, which utilizes onboard
sensors like cameras and LiDARs (Light Detection and Ranging) to assess surroundings …

Modern autonomous driving systems rely heavily on deep learning models to process point
cloud sensory data; meanwhile, deep models have been shown to be susceptible to …

Contrastive learning (CL) can learn generalizable feature representations and achieve state-
of-the-art performance of downstream tasks by finetuning a linear classifier on top of it …

As self-driving systems become better, simulating scenarios where the autonomy stack may
fail becomes more important. Traditionally, those scenarios are generated for a few scenes …