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

Dongyeon Yu,Honggyu Lee,Sung-Ho Hwang,Taehoon Kim

doi:10.3390/s21238152

Abstract

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. The proposed model was trained using the Highway Drone (HighD) dataset, which is a high-precision, large-scale traffic dataset. We also compared this model to previous studies. Our model effectively predicted future trajectories by using an attention mechanism to manage the importance of the driving flow of the target and adjacent vehicles and the target vehicle’s dynamics in each driving situation. Furthermore, this study presents a method of linearizing the road geometry such that the trajectory prediction model can be used in a variety of road environments. We verified that the road geometry linearization mechanism can improve the trajectory prediction model’s performance on various road environments in a virtual test-driving simulator constructed based on actual road data.

Highlights

Intelligent vehicles, including partially automated vehicles that are equipped with Adaptive Cruise Control (ACC), require the ability to drive strategically according to the flow of traffic while simultaneously ensuring safety
This study presented an Lane Stream (LS) attention-based Long Short-Term Memory (LSTM) encoder–decoder model and a road shape linearization method for predicting the future trajectory of surrounding vehicles
The proposed attention mechanism could implement the driver pattern by selectively focusing on adjacent lanes and the target vehicle to predict the future trajectories of vehicles

Summary

Introduction

Intelligent vehicles, including partially automated vehicles that are equipped with Adaptive Cruise Control (ACC), require the ability to drive strategically according to the flow of traffic while simultaneously ensuring safety. The classic trajectory prediction method that is generally employed uses a Bayesian filtering technique such as a Kalman filter in the vehicle motion model [7,8,9] These methods use simple models to ensure quick computation speed and are good at predicting the near future; they show poor performance regarding long-term predictions that reflect the nonlinear movements of vehicles. To address these limitations, more elaborate models such as the Gaussian mixture model [10] and Dynamic Bayesian Network (DBN) [11]

Results

Discussion

Conclusion