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 …

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 …

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 …

Connected and automated vehicles (CAVs) provide various valuable and advanced
services to manufacturers, owners, mobility service providers, and transportation authorities …

Cooperative object transportation using multiple robots has been intensively studied in the
control and robotics literature, but most approaches are either only applicable to …

The capacity of highways has been an ever-present constraint in the 21st century, bringing
about the issue of safety with greater likelihoods of traffic accidents occurring. Furthermore …

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 …

Abstract Machine learning-based techniques have shown great promises in perception,
prediction, planning, and general decision-making for improving task performance of …

Connected Vehicle (CV) technologies are under rapid deployment across the globe and will
soon reshape our transportation systems, bringing benefits to mobility, safety, environment …