Automation is increasingly at the forefront of transportation research, with the potential to
bring fully autonomous vehicles to our roads in the coming years. This comprehensive …

In recent years, the level of acceptance of autonomous vehicles (AVs) has changed with the
advent of new sensor technologies and the proportional increase in market perception of …

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 …

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

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 …

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 …

Multimodal transportation systems require an effective journey planner to allocate multiple
passengers to transport operators. One example is mobility-as-a-service, a new mobility …

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 …

Neural network-based driving planners have shown great promises in improving task
performance of autonomous driving. However, it is critical and yet very challenging to ensure …

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 …