Abstract The integration of Deep Learning (DL) algorithms in Autonomous Vehicles (AVs)
has revolutionized their precision in navigating various driving scenarios, ranging from anti …

This paper systematically analyzes the security risks associated with artificial intelligence
(AI) components in autonomous vehicles (AVs). Given the increasing reliance on AI for …

Multi-sensor fusion (MSF) is widely used in autonomous vehicles (AVs) for perception,
particularly for 3D object detection with camera and LiDAR sensors. The purpose of fusion is …

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 …

All vehicles must follow the rules that govern traffic behavior, regardless of whether the
vehicles are human-driven or Connected Autonomous Vehicles (CAVs). Road signs indicate …

Autonomous Vehicles (AVs) using LiDAR-based object detection systems are rapidly
improving and becoming an increasingly viable method of transportation. While effective at …

LiDAR sensors are widely used in many safety-critical applications such as autonomous
driving and drone control, and the collected data called point clouds are subsequently …

Autonomous Driving Systems (ADS) represent a revolutionary advancement in
transportation and offer unprecedented safety and convenience. Real-world physical attacks …

LiDAR-based perception is crucial to ensure the safety and reliability of autonomous driving
(AD) systems. Though some adversarial attack methods against LiDAR-based detectors …

LiDAR (Light Detection And Ranging) is an indispensable sensor for precise long-and wide-
range 3D sensing, which directly benefited the recent rapid deployment of autonomous …