Autonomous vehicles (AV) are game-changing innovations that promise a safer, more
convenient, and environmentally friendly mode of transportation than traditional vehicles …

Autonomous Vehicles (AVs) increasingly use LiDAR-based object detection systems to
perceive other vehicles and pedestrians on the road. While existing attacks on LiDAR-based …

Great advances in deep neural networks (DNNs) have led to state-of-the-art performance on
a wide range of tasks. However, recent studies have shown that DNNs are vulnerable to …

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 …

Collaborative perception, which greatly enhances the sensing capability of connected and
autonomous vehicles (CAVs) by incorporating data from external resources, also brings forth …

Reproducibility is crucial to the advancement of science; it strengthens confidence in
seemingly contradictory results and expands the boundaries of known discoveries …

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 …

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

Autonomous vehicles (AVs) are predicted to change transportation; however, it is still difficult
to maintain robust situation awareness in a variety of driving situations. To enhance AV …

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