Recently significant progress has been made in vehicle prediction and planning algorithms
for autonomous driving. However, it remains quite challenging for an autonomous vehicle to …

Predicting the future trajectories of surrounding vehicles based on their history trajectories is
a critical task in autonomous driving. However, when small crafted perturbations are …

The robustness of deep neural networks has received significant interest recently, especially
when being deployed in safety-critical systems, as it is important to analyze how sensitive …

Trajectory generation and prediction are two in-terwoven tasks that play important roles in
planner evaluation and decision making for intelligent vehicles. Most existing methods focus …

The rapid advancement of edge and cloud computing platforms, vehicular ad-hoc networks,
and machine learning techniques have brought both opportunities and challenges for next …

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 …

Drive‐by structural health monitoring (SHM) is a cost‐efficient alternative to the direct SHM
of short‐to medium‐size bridges requiring no sensors to be installed on the structure …

Trajectory generation and trajectory prediction are two critical tasks for autonomous
vehicles, which generate various trajectories during development and predict the trajectories …

The recent development of cloud computing and edge computing shows great promise for
the Connected and Automated Vehicle (CAV), by enabling CAVs to offload their massive on …

Neural networks are being applied to a wide range of tasks in autonomous systems, such as
perception, prediction, planning, control, and general decision making. While they may …