In the era of technological transformation, mobility and transportation systems are becoming
more intelligent and greener. Thanks to powerful technologies and tools, electric and …

In this paper, an automated driving system (ADS) data acquisition and analytics platform for
vehicle trajectory extraction, reconstruction, and evaluation based on connected automated …

Deep learning-based intelligent vehicle perception has been developing prominently in
recent years to provide a reliable source for motion planning and decision making in …

Recent advancements in Vehicle-to-Everything communication technology have enabled
autonomous vehicles to share sensory information to obtain better perception performance …

This research proposes an integrated behavioral and motion planner with proactive right-of-
way acquisition capability. The proposed planner overcomes the shortcomings of …

Potential field theory, as a theory that can also be applied to vehicle control, is an emerging
risk quantification approach to accommodate the connected and self-driving vehicle …

We propose and validate a novel car following model based on deep reinforcement
learning. Our model is trained to maximize externally given reward functions for the free and …

Intelligent Transportation Systems (ITS) operate within a highly intricate and dynamic
environment characterized by complex spatial and temporal dynamics at various scales …

This study reviews the research works published in the last five years on Digital Twin (DT)
technology for intelligent transportation systems, focusing on the use of DT in electromobility …

Currently, autonomous vehicles (AVs) have gained considerable research interest in motion
planning (MP) to control driving. Deep learning (DL) is a subset of machine learning …