Vehicle Trajectory Prediction with Lane Stream Attention-Based LSTMs and Road Geometry Linearization.

It is essential for autonomous vehicles at level 3 or higher to have the ability to predict the trajectories of surrounding vehicles to safely and effectively plan and drive along trajectories in complex traffic situations. However, predicting the future behavior of vehicles is a challenging issue because traffic vehicles each have different drivers with different driving tendencies and intentions and they interact with each other. This paper presents a Long Short-Term Memory (LSTM) encoder-decoder model that utilizes an attention mechanism that focuses on certain information to predict vehicles' trajectories.