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

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

In addition to red-green-blue (RGB) camera sensors, light detection and ranging (LiDAR)
plays an important role in autonomous vehicles (AVs) to perceive their surroundings. Deep …

Intellectual Property (IP) includes ideas, innovations, methodologies, works of authorship
(viz., literary and artistic works), emblems, brands, images, etc. This property is intangible …

The prevailing way to test a self-driving vehicle (SDV) in simulation involves non-reactive
open-loop replay of real world scenarios. However, in order to safely deploy SDVs to the …

Predicting the trajectories of surrounding objects is a critical task for self-driving vehicles and
many other autonomous systems. Recent works demonstrate that adversarial attacks on …

Large-scale data is crucial for learning realistic and capable driving policies. However, it can
be impractical to rely on scaling datasets with real data alone. The majority of driving data is …

As autonomous driving technology matures, the safety and robustness of its key
components, including trajectory prediction is vital. Although real-world datasets such as …