In order to drive safely in a dynamic environment, autonomous vehicles should be able to
predict the future states of traffic participants nearby, especially surrounding vehicles, similar …

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 …

Identifying and evaluating the potential risks in the surrounding environment is critical for
intelligent vehicles' safety and user experience. This paper provides a comprehensive …

Motion prediction for the leading vehicle is a critical task for connected autonomous
vehicles. It provides a method to model the leading-following vehicle behavior and analysis …

Predicting the trajectory of neighboring vehicles is closely related to the driving safety of
intelligent vehicles and supports driving assistance. This article proposes a dual-attention …

This paper describes an optimal lane-changing strategy for intelligent vehicles under the
constraints of collision avoidance in complex driving environments. The key technique is …

Lane-changing (LC) is a critical task for autonomous driving, especially in complex dynamic
environments. Numerous automatic LC algorithms have been proposed. This topic …

In highway scenarios, an alert human driver will typically anticipate early cut-in/cut-out
maneuvers of surrounding vehicles using visual cues mainly. Autonomous vehicles must …

Autonomous vehicle technologies have evolved quickly over the last few years, with safety
being one of the key requirements for their full deployment. However, ensuring their safety …

Lane change and crash risk prediction are critical technologies for autonomous driving. An
attention-based LSTM model is proposed in this paper for lane change behavior prediction …