Impacts of vehicle automation and connectivity have been studied widely from the
perspectives of fuel economy, ecology, and safety. Synthesis of various segments of this …

In this work we put forward a predictive trajectory planning framework to help autonomous
vehicles plan future trajectories. We develop a partially observable Markov decision process …

With rapid development in hardware (sensors and processors) and AI algorithms, automated
driving techniques have entered the public's daily life and achieved great success in …

Autonomy and connectivity are expected to enhance safety and improve fuel efficiency in
transportation systems. While connected vehicle-enabled technologies, such as coordinated …

As an important part of a smart city, the Intelligent Transportation System (ITS) is the key to
the sustainable development of urban transportation considering the fuel consumption as …

In this study, we develop a comprehensive framework to model the impact of cyberattacks on
safety, security, and head-to-tail stability of connected and automated vehicular platoons …

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 …

Connected and automated vehicles (CAVs) and human-driven vehicles (HVs) are expected
to coexist in the near future. CAV-dedicated lanes and phases have been explored to …

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 …

Neural networks have shown great promises in planning, control, and general decision
making for learning-enabled cyber-physical systems (LE-CPSs), especially in improving …