This paper presents an interactive motion predictor to infer the intention of cut-in vehicles
using a bidirectional long short-term memory (Bi-LSTM) module. The proposed predictor …

Understanding human drivers' intentions and predicting their future motions are significant to
connected and autonomous vehicles and traffic safety and surveillance systems. Predicting …

Predicting the trajectories of surrounding vehicles is important to avoid or mitigate collision
with traffic participants. However, due to limited past information and the uncertainty in future …

Accurate vehicle trajectory prediction can benefit a variety of intelligent transportation system
applications ranging from traffic simulations to driver assistance. The need for this ability is …

In this paper, we propose a deep learning based vehicle trajectory prediction technique
which can generate the future trajectory sequence of surrounding vehicles in real time. We …

To safely and efficiently navigate through complex traffic scenarios, autonomous vehicles
need to have the ability to predict the future motion of surrounding vehicles. Multiple …

Vehicle trajectory prediction has been an active research area in autonomous driving. In a
real traffic scene, autonomous vehicle needs to predict future motion of surrounding vehicles …

Driving intention prediction from a bird's-eye view has always been an active research area.
However, existing research, on one hand, has only focused on predicting lane change …

Fast and accurate long-term trajectory prediction of surrounding vehicles (SVs) is critical to
autonomous driving systems. In high-density traffic flows, strongly correlated vehicle …

The behavior prediction of the surrounding vehicles is crucial when planning a minimal-risk
path when realizing a collision-avoidance system. Herein, we propose a multiple model …