The significant contribution of human errors, accounting for approximately 94%(with a
margin of±2.2%), to road crashes leading to casualties, vehicle damages, and safety …

Autonomous driving related researches require the analysis and usage of massive amounts
of driving scenario data. Compared to raw data collected by sensors, scenario data provide …

Predicting the trajectory of other road users, especially vulnerable road users (VRUs), is an
important aspect of safety and planning efficiency for autonomous vehicles. With recent …

In autonomous vehicle (AV) technology, the ability to accurately predict the movements of
surrounding vehicles is paramount for ensuring safety and operational efficiency …

Predicting the motion of surrounding vehicles is an important function of autonomous
vehicles. However, most of the current state-of-the-art trajectory prediction models rely …

Predicting the future trajectories of risky maneuvers such as cut-in is of great significance for
intelligent vehicles to alarm potential crashes in advance. A more critical issue is the use of …

Data science (DS) devotes to extract useful data from noisy one to form actionable insights. It
has broad applications in many domains such as internet search, tourism and social media …

Accurate prediction of agent motion trajectories is crucial for autonomous driving,
contributing to the reduction of collision risks in human-vehicle interactions and ensuring …

Vehicle Trajectory Prediction (VTP) is one of the key issues in the field of autonomous
driving. In recent years, more researchers have tried applying Deep Learning methods and …

To handle two shortcomings of existing methods,(i) nearly all models rely on the high-
definition (HD) maps, yet the map information is not always available in real traffic scenes …