This study aims to improve the accuracy of trajectory prediction in lane-changing scenarios
compared to the state-of-the-art. Lane-changing trajectory prediction is critical for …

To improve the long-term trajectory prediction of the intelligent connected vehicle to the
surrounding vehicles, this paper proposes an interaction-aware network framework based …

High-precision vehicle trajectory prediction can enable autonomous vehicles to provide a
safer and more comfortable trajectory planning and control. Unfortunately, current trajectory …

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 …

A Long short-term memory (LSTM) recurrent neural network (RNN) model was trained on
making vehicle trajectory predictions based on real world driving data at a minimum driving …

Skilled drivers have the driving behavioral characteristic of pre-sighted following, and
similarly intelligent vehicles need accurate prediction of future trajectories. The LSTM (Long …

Vehicle trajectory prediction is essential for the operation safety and control efficiency of
automated driving. Prevailing studies predict car following and lane change processes in a …

There should be a complex driving environment formed by manned and unmanned vehicles
with highly uncertain and dynamic interaction when autonomous vehicles enter actual traffic …

It is a widely accepted view that considering the memory effects of historical information
(driving operations) is beneficial for vehicle trajectory prediction models to improve …

Predicting the future trajectory of a vehicle in a dynamic scene is not a simple problem
because the future trajectory of a vehicle is not only influenced by its historical trajectory but …